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Sample records for dynamic network modules

  1. Identify Dynamic Network Modules with Temporal and Spatial Constraints

    Energy Technology Data Exchange (ETDEWEB)

    Jin, R; McCallen, S; Liu, C; Almaas, E; Zhou, X J

    2007-09-24

    Despite the rapid accumulation of systems-level biological data, understanding the dynamic nature of cellular activity remains a difficult task. The reason is that most biological data are static, or only correspond to snapshots of cellular activity. In this study, we explicitly attempt to detangle the temporal complexity of biological networks by using compilations of time-series gene expression profiling data.We define a dynamic network module to be a set of proteins satisfying two conditions: (1) they form a connected component in the protein-protein interaction (PPI) network; and (2) their expression profiles form certain structures in the temporal domain. We develop the first efficient mining algorithm to discover dynamic modules in a temporal network, as well as frequently occurring dynamic modules across many temporal networks. Using yeast as a model system, we demonstrate that the majority of the identified dynamic modules are functionally homogeneous. Additionally, many of them provide insight into the sequential ordering of molecular events in cellular systems. We further demonstrate that identifying frequent dynamic network modules can significantly increase the signal to noise separation, despite the fact that most dynamic network modules are highly condition-specific. Finally, we note that the applicability of our algorithm is not limited to the study of PPI systems, instead it is generally applicable to the combination of any type of network and time-series data.

  2. Dismissing Attachment Characteristics Dynamically Modulate Brain Networks Subserving Social Aversion.

    Science.gov (United States)

    Krause, Anna Linda; Borchardt, Viola; Li, Meng; van Tol, Marie-José; Demenescu, Liliana Ramona; Strauss, Bernhard; Kirchmann, Helmut; Buchheim, Anna; Metzger, Coraline D; Nolte, Tobias; Walter, Martin

    2016-01-01

    Attachment patterns influence actions, thoughts and feeling through a person's "inner working model". Speech charged with attachment-dependent content was proposed to modulate the activation of cognitive-emotional schemata in listeners. We performed a 7 Tesla rest-task-rest functional magnetic resonance imaging (fMRI)-experiment, presenting auditory narratives prototypical of dismissing attachment representations to investigate their effect on 23 healthy males. We then examined effects of participants' attachment style and childhood trauma on brain state changes using seed-based functional connectivity (FC) analyses, and finally tested whether subjective differences in responsivity to narratives could be predicted by baseline network states. In comparison to a baseline state, we observed increased FC in a previously described "social aversion network" including dorsal anterior cingulated cortex (dACC) and left anterior middle temporal gyrus (aMTG) specifically after exposure to insecure-dismissing attachment narratives. Increased dACC-seeded FC within the social aversion network was positively related to the participants' avoidant attachment style and presence of a history of childhood trauma. Anxious attachment style on the other hand was positively correlated with FC between the dACC and a region outside of the "social aversion network", namely the dorsolateral prefrontal cortex, which suggests decreased network segregation as a function of anxious attachment. Finally, the extent of subjective experience of friendliness towards the dismissing narrative was predicted by low baseline FC-values between hippocampus and inferior parietal lobule (IPL). Taken together, our study demonstrates an activation of networks related to social aversion in terms of increased connectivity after listening to insecure-dismissing attachment narratives. A causal interrelation of brain state changes and subsequent changes in social reactivity was further supported by our observation of

  3. Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy

    Directory of Open Access Journals (Sweden)

    Oyang Yen-Jen

    2010-10-01

    Full Text Available Abstract Background Molecular networks represent the backbone of molecular activity within cells and provide opportunities for understanding the mechanism of diseases. While protein-protein interaction data constitute static network maps, integration of condition-specific co-expression information provides clues to the dynamic features of these networks. Dilated cardiomyopathy is a leading cause of heart failure. Although previous studies have identified putative biomarkers or therapeutic targets for heart failure, the underlying molecular mechanism of dilated cardiomyopathy remains unclear. Results We developed a network-based comparative analysis approach that integrates protein-protein interactions with gene expression profiles and biological function annotations to reveal dynamic functional modules under different biological states. We found that hub proteins in condition-specific co-expressed protein interaction networks tended to be differentially expressed between biological states. Applying this method to a cohort of heart failure patients, we identified two functional modules that significantly emerged from the interaction networks. The dynamics of these modules between normal and disease states further suggest a potential molecular model of dilated cardiomyopathy. Conclusions We propose a novel framework to analyze the interaction networks in different biological states. It successfully reveals network modules closely related to heart failure; more importantly, these network dynamics provide new insights into the cause of dilated cardiomyopathy. The revealed molecular modules might be used as potential drug targets and provide new directions for heart failure therapy.

  4. Dismissing attachment characteristics dynamically modulate brain networks subserving social aversion.

    Directory of Open Access Journals (Sweden)

    Anna Linda eKrause

    2016-03-01

    Full Text Available Attachment patterns influence actions, thoughts and feeling through a person’s ‘Inner Working Model’. Speech charged with attachment-dependent content was proposed to modulate the activation of cognitive-emotional schemata in listeners. We performed a 7 Tesla rest-task-rest fMRI-experiment, presenting auditory narratives prototypical of dismissing attachment representations to investigate their effect on 23 healthy males. We then examined effects of participants’ attachment style and childhood trauma on brain state changes using seed-based functional connectivity (FC analyses, and finally tested whether subjective differences in responsivity to narratives could be predicted by baseline network states. In comparison to a baseline state, we observed increased FC in a previously described ‘social aversion network’ including dorsal anterior cingulated cortex (dACC and left anterior middle temporal gyrus (aMTG specifically after exposure to insecure-dismissing attachment narratives. Increased dACC-seeded FC within the social aversion network was positively related to the participants’ avoidant attachment style and presence of a history of childhood trauma. Anxious attachment style on the other hand was positively correlated with FC between the dACC and a region outside of the ‘social aversion network’, namely the dorsolateral prefrontal cortex, which suggests decreased network segregation as a function of anxious attachment. Finally, the extent of subjective experience of friendliness towards the dismissing narrative was predicted by low baseline FC-values between hippocampus and inferior parietal lobule. Taken together, our study demonstrates an activation of networks related to social aversion in terms of increased connectivity after listening to insecure-dismissing attachment narratives. A causal interrelation of brain state changes and subsequent changes in social reactivity was further supported by our observation of direct

  5. Dismissing Attachment Characteristics Dynamically Modulate Brain Networks Subserving Social Aversion

    NARCIS (Netherlands)

    Krause, Anna Linda; Borchardt, Viola; Li, Meng; van Tol, Marie-Jose; Demenescu, Liliana Ramona; Strauss, Bernhard; Kirchmanny, Helmut; Buchheim, Anna; Metzger, Coraline D.; Nolte, Tobias; Walter, Martin

    2016-01-01

    Attachment patterns influence actions, thoughts and feeling through a person's "inner working model". Speech charged with attachment-dependent content was proposed to modulate the activation of cognitive-emotional schemata in listeners. We performed a 7 Tesla rest-task-rest functional magnetic

  6. Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics.

    Directory of Open Access Journals (Sweden)

    István A Kovács

    Full Text Available BACKGROUND: Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. METHODOLOGY/PRINCIPAL FINDINGS: Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1 determine persvasively overlapping modules with high resolution; (2 uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3 allow the determination of key network nodes and (4 help to predict network dynamics. CONCLUSIONS/SIGNIFICANCE: The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction.

  7. Modulation of dynamic modes by interplay between positive and negative feedback loops in gene regulatory networks

    Science.gov (United States)

    Wang, Liu-Suo; Li, Ning-Xi; Chen, Jing-Jia; Zhang, Xiao-Peng; Liu, Feng; Wang, Wei

    2018-04-01

    A positive and a negative feedback loop can induce bistability and oscillation, respectively, in biological networks. Nevertheless, they are frequently interlinked to perform more elaborate functions in many gene regulatory networks. Coupled positive and negative feedback loops may exhibit either oscillation or bistability depending on the intensity of the stimulus in some particular networks. It is less understood how the transition between the two dynamic modes is modulated by the positive and negative feedback loops. We developed an abstract model of such systems, largely based on the core p53 pathway, to explore the mechanism for the transformation of dynamic behaviors. Our results show that enhancing the positive feedback may promote or suppress oscillations depending on the strength of both feedback loops. We found that the system oscillates with low amplitudes in response to a moderate stimulus and switches to the on state upon a strong stimulus. When the positive feedback is activated much later than the negative one in response to a strong stimulus, the system exhibits long-term oscillations before switching to the on state. We explain this intriguing phenomenon using quasistatic approximation. Moreover, early switching to the on state may occur when the system starts from a steady state in the absence of stimuli. The interplay between the positive and negative feedback plays a key role in the transitions between oscillation and bistability. Of note, our conclusions should be applicable only to some specific gene regulatory networks, especially the p53 network, in which both oscillation and bistability exist in response to a certain type of stimulus. Our work also underscores the significance of transient dynamics in determining cellular outcome.

  8. Externally induced frontoparietal synchronization modulates network dynamics and enhances working memory performance.

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    Violante, Ines R; Li, Lucia M; Carmichael, David W; Lorenz, Romy; Leech, Robert; Hampshire, Adam; Rothwell, John C; Sharp, David J

    2017-03-14

    Cognitive functions such as working memory (WM) are emergent properties of large-scale network interactions. Synchronisation of oscillatory activity might contribute to WM by enabling the coordination of long-range processes. However, causal evidence for the way oscillatory activity shapes network dynamics and behavior in humans is limited. Here we applied transcranial alternating current stimulation (tACS) to exogenously modulate oscillatory activity in a right frontoparietal network that supports WM. Externally induced synchronization improved performance when cognitive demands were high. Simultaneously collected fMRI data reveals tACS effects dependent on the relative phase of the stimulation and the internal cognitive processing state. Specifically, synchronous tACS during the verbal WM task increased parietal activity, which correlated with behavioral performance. Furthermore, functional connectivity results indicate that the relative phase of frontoparietal stimulation influences information flow within the WM network. Overall, our findings demonstrate a link between behavioral performance in a demanding WM task and large-scale brain synchronization.

  9. Physiological modules for generating discrete and rhythmic movements: action identification by a dynamic recurrent neural network.

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    Bengoetxea, Ana; Leurs, Françoise; Hoellinger, Thomas; Cebolla, Ana M; Dan, Bernard; McIntyre, Joseph; Cheron, Guy

    2014-01-01

    In this study we employed a dynamic recurrent neural network (DRNN) in a novel fashion to reveal characteristics of control modules underlying the generation of muscle activations when drawing figures with the outstretched arm. We asked healthy human subjects to perform four different figure-eight movements in each of two workspaces (frontal plane and sagittal plane). We then trained a DRNN to predict the movement of the wrist from information in the EMG signals from seven different muscles. We trained different instances of the same network on a single movement direction, on all four movement directions in a single movement plane, or on all eight possible movement patterns and looked at the ability of the DRNN to generalize and predict movements for trials that were not included in the training set. Within a single movement plane, a DRNN trained on one movement direction was not able to predict movements of the hand for trials in the other three directions, but a DRNN trained simultaneously on all four movement directions could generalize across movement directions within the same plane. Similarly, the DRNN was able to reproduce the kinematics of the hand for both movement planes, but only if it was trained on examples performed in each one. As we will discuss, these results indicate that there are important dynamical constraints on the mapping of EMG to hand movement that depend on both the time sequence of the movement and on the anatomical constraints of the musculoskeletal system. In a second step, we injected EMG signals constructed from different synergies derived by the PCA in order to identify the mechanical significance of each of these components. From these results, one can surmise that discrete-rhythmic movements may be constructed from three different fundamental modules, one regulating the co-activation of all muscles over the time span of the movement and two others elliciting patterns of reciprocal activation operating in orthogonal directions.

  10. Hippocampal Ripple Oscillations and Inhibition-First Network Models: Frequency Dynamics and Response to GABA Modulators.

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    Donoso, José R; Schmitz, Dietmar; Maier, Nikolaus; Kempter, Richard

    2018-03-21

    Hippocampal ripples are involved in memory consolidation, but the mechanisms underlying their generation remain unclear. Models relying on interneuron networks in the CA1 region disagree on the predominant source of excitation to interneurons: either "direct," via the Schaffer collaterals that provide feedforward input from CA3 to CA1, or "indirect," via the local pyramidal cells in CA1, which are embedded in a recurrent excitatory-inhibitory network. Here, we used physiologically constrained computational models of basket-cell networks to investigate how they respond to different conditions of transient, noisy excitation. We found that direct excitation of interneurons could evoke ripples (140-220 Hz) that exhibited intraripple frequency accommodation and were frequency-insensitive to GABA modulators, as previously shown in in vitro experiments. In addition, the indirect excitation of the basket-cell network enabled the expression of intraripple frequency accommodation in the fast-gamma range (90-140 Hz), as in vivo In our model, intraripple frequency accommodation results from a hysteresis phenomenon in which the frequency responds differentially to the rising and descending phases of the transient excitation. Such a phenomenon predicts a maximum oscillation frequency occurring several milliseconds before the peak of excitation. We confirmed this prediction for ripples in brain slices from male mice. These results suggest that ripple and fast-gamma episodes are produced by the same interneuron network that is recruited via different excitatory input pathways, which could be supported by the previously reported intralaminar connectivity bias between basket cells and functionally distinct subpopulations of pyramidal cells in CA1. Together, our findings unify competing inhibition-first models of rhythm generation in the hippocampus. SIGNIFICANCE STATEMENT The hippocampus is a part of the brain of humans and other mammals that is critical for the acquisition and

  11. An ELMO2-RhoG-ILK network modulates microtubule dynamics.

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    Jackson, Bradley C; Ivanova, Iordanka A; Dagnino, Lina

    2015-07-15

    ELMO2 belongs to a family of scaffold proteins involved in phagocytosis and cell motility. ELMO2 can simultaneously bind integrin-linked kinase (ILK) and RhoG, forming tripartite ERI complexes. These complexes are involved in promoting β1 integrin-dependent directional migration in undifferentiated epidermal keratinocytes. ELMO2 and ILK have also separately been implicated in microtubule regulation at integrin-containing focal adhesions. During differentiation, epidermal keratinocytes cease to express integrins, but ERI complexes persist. Here we show an integrin-independent role of ERI complexes in modulation of microtubule dynamics in differentiated keratinocytes. Depletion of ERI complexes by inactivating the Ilk gene in these cells reduces microtubule growth and increases the frequency of catastrophe. Reciprocally, exogenous expression of ELMO2 or RhoG stabilizes microtubules, but only if ILK is also present. Mechanistically, activation of Rac1 downstream from ERI complexes mediates their effects on microtubule stability. In this pathway, Rac1 serves as a hub to modulate microtubule dynamics through two different routes: 1) phosphorylation and inactivation of the microtubule-destabilizing protein stathmin and 2) phosphorylation and inactivation of GSK-3β, which leads to the activation of CRMP2, promoting microtubule growth. At the cellular level, the absence of ERI species impairs Ca(2+)-mediated formation of adherens junctions, critical to maintaining mechanical integrity in the epidermis. Our findings support a key role for ERI species in integrin-independent stabilization of the microtubule network in differentiated keratinocytes. © 2015 Jackson et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

  12. Dynamics of neuromodulatory feedback determines frequency modulation in a reduced respiratory network: a computational study.

    Science.gov (United States)

    Toporikova, Natalia; Butera, Robert J

    2013-02-01

    Neuromodulators, such as amines and neuropeptides, alter the activity of neurons and neuronal networks. In this work, we investigate how neuromodulators, which activate G(q)-protein second messenger systems, can modulate the bursting frequency of neurons in a critical portion of the respiratory neural network, the pre-Bötzinger complex (preBötC). These neurons are a vital part of the ponto-medullary neuronal network, which generates a stable respiratory rhythm whose frequency is regulated by neuromodulator release from the nearby Raphe nucleus. Using a simulated 50-cell network of excitatory preBötC neurons with a heterogeneous distribution of persistent sodium conductance and Ca(2+), we determined conditions for frequency modulation in such a network by simulating interaction between Raphe and preBötC nuclei. We found that the positive feedback between the Raphe excitability and preBötC activity induces frequency modulation in the preBötC neurons. In addition, the frequency of the respiratory rhythm can be regulated via phasic release of excitatory neuromodulators from the Raphe nucleus. We predict that the application of a G(q) antagonist will eliminate this frequency modulation by the Raphe and keep the network frequency constant and low. In contrast, application of a G(q) agonist will result in a high frequency for all levels of Raphe stimulation. Our modeling results also suggest that high [K(+)] requirement in respiratory brain slice experiments may serve as a compensatory mechanism for low neuromodulatory tone. Copyright © 2012 Elsevier B.V. All rights reserved.

  13. Directed network modules

    International Nuclear Information System (INIS)

    Palla, Gergely; Farkas, Illes J; Pollner, Peter; Derenyi, Imre; Vicsek, Tamas

    2007-01-01

    A search technique locating network modules, i.e. internally densely connected groups of nodes in directed networks is introduced by extending the clique percolation method originally proposed for undirected networks. After giving a suitable definition for directed modules we investigate their percolation transition in the Erdos-Renyi graph both analytically and numerically. We also analyse four real-world directed networks, including Google's own web-pages, an email network, a word association graph and the transcriptional regulatory network of the yeast Saccharomyces cerevisiae. The obtained directed modules are validated by additional information available for the nodes. We find that directed modules of real-world graphs inherently overlap and the investigated networks can be classified into two major groups in terms of the overlaps between the modules. Accordingly, in the word-association network and Google's web-pages, overlaps are likely to contain in-hubs, whereas the modules in the email and transcriptional regulatory network tend to overlap via out-hubs

  14. Weighted network modules

    International Nuclear Information System (INIS)

    Farkas, Illes; Abel, Daniel; Palla, Gergely; Vicsek, Tamas

    2007-01-01

    The inclusion of link weights into the analysis of network properties allows a deeper insight into the (often overlapping) modular structure of real-world webs. We introduce a clustering algorithm clique percolation method with weights (CPMw) for weighted networks based on the concept of percolating k-cliques with high enough intensity. The algorithm allows overlaps between the modules. First, we give detailed analytical and numerical results about the critical point of weighted k-clique percolation on (weighted) Erdos-Renyi graphs. Then, for a scientist collaboration web and a stock correlation graph we compute three-link weight correlations and with the CPMw the weighted modules. After reshuffling link weights in both networks and computing the same quantities for the randomized control graphs as well, we show that groups of three or more strong links prefer to cluster together in both original graphs

  15. Asynchronous networks: modularization of dynamics theorem

    Science.gov (United States)

    Bick, Christian; Field, Michael

    2017-02-01

    Building on the first part of this paper, we develop the theory of functional asynchronous networks. We show that a large class of functional asynchronous networks can be (uniquely) represented as feedforward networks connecting events or dynamical modules. For these networks we can give a complete description of the network function in terms of the function of the events comprising the network: the modularization of dynamics theorem. We give examples to illustrate the main results.

  16. Functional Module Analysis for Gene Coexpression Networks with Network Integration.

    Science.gov (United States)

    Zhang, Shuqin; Zhao, Hongyu; Ng, Michael K

    2015-01-01

    Network has been a general tool for studying the complex interactions between different genes, proteins, and other small molecules. Module as a fundamental property of many biological networks has been widely studied and many computational methods have been proposed to identify the modules in an individual network. However, in many cases, a single network is insufficient for module analysis due to the noise in the data or the tuning of parameters when building the biological network. The availability of a large amount of biological networks makes network integration study possible. By integrating such networks, more informative modules for some specific disease can be derived from the networks constructed from different tissues, and consistent factors for different diseases can be inferred. In this paper, we have developed an effective method for module identification from multiple networks under different conditions. The problem is formulated as an optimization model, which combines the module identification in each individual network and alignment of the modules from different networks together. An approximation algorithm based on eigenvector computation is proposed. Our method outperforms the existing methods, especially when the underlying modules in multiple networks are different in simulation studies. We also applied our method to two groups of gene coexpression networks for humans, which include one for three different cancers, and one for three tissues from the morbidly obese patients. We identified 13 modules with three complete subgraphs, and 11 modules with two complete subgraphs, respectively. The modules were validated through Gene Ontology enrichment and KEGG pathway enrichment analysis. We also showed that the main functions of most modules for the corresponding disease have been addressed by other researchers, which may provide the theoretical basis for further studying the modules experimentally.

  17. Is my network module preserved and reproducible?

    Directory of Open Access Journals (Sweden)

    Peter Langfelder

    2011-01-01

    Full Text Available In many applications, one is interested in determining which of the properties of a network module change across conditions. For example, to validate the existence of a module, it is desirable to show that it is reproducible (or preserved in an independent test network. Here we study several types of network preservation statistics that do not require a module assignment in the test network. We distinguish network preservation statistics by the type of the underlying network. Some preservation statistics are defined for a general network (defined by an adjacency matrix while others are only defined for a correlation network (constructed on the basis of pairwise correlations between numeric variables. Our applications show that the correlation structure facilitates the definition of particularly powerful module preservation statistics. We illustrate that evaluating module preservation is in general different from evaluating cluster preservation. We find that it is advantageous to aggregate multiple preservation statistics into summary preservation statistics. We illustrate the use of these methods in six gene co-expression network applications including 1 preservation of cholesterol biosynthesis pathway in mouse tissues, 2 comparison of human and chimpanzee brain networks, 3 preservation of selected KEGG pathways between human and chimpanzee brain networks, 4 sex differences in human cortical networks, 5 sex differences in mouse liver networks. While we find no evidence for sex specific modules in human cortical networks, we find that several human cortical modules are less preserved in chimpanzees. In particular, apoptosis genes are differentially co-expressed between humans and chimpanzees. Our simulation studies and applications show that module preservation statistics are useful for studying differences between the modular structure of networks. Data, R software and accompanying tutorials can be downloaded from the following webpage: http

  18. Dense module enumeration in biological networks

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    Tsuda, Koji; Georgii, Elisabeth

    2009-12-01

    Analysis of large networks is a central topic in various research fields including biology, sociology, and web mining. Detection of dense modules (a.k.a. clusters) is an important step to analyze the networks. Though numerous methods have been proposed to this aim, they often lack mathematical rigorousness. Namely, there is no guarantee that all dense modules are detected. Here, we present a novel reverse-search-based method for enumerating all dense modules. Furthermore, constraints from additional data sources such as gene expression profiles or customer profiles can be integrated, so that we can systematically detect dense modules with interesting profiles. We report successful applications in human protein interaction network analyses.

  19. Dense module enumeration in biological networks

    International Nuclear Information System (INIS)

    Tsuda, Koji; Georgii, Elisabeth

    2009-01-01

    Analysis of large networks is a central topic in various research fields including biology, sociology, and web mining. Detection of dense modules (a.k.a. clusters) is an important step to analyze the networks. Though numerous methods have been proposed to this aim, they often lack mathematical rigorousness. Namely, there is no guarantee that all dense modules are detected. Here, we present a novel reverse-search-based method for enumerating all dense modules. Furthermore, constraints from additional data sources such as gene expression profiles or customer profiles can be integrated, so that we can systematically detect dense modules with interesting profiles. We report successful applications in human protein interaction network analyses.

  20. Solar dynamic power module design

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    Secunde, Richard R.; Labus, Thomas L.; Lovely, Ronald G.

    1989-01-01

    Studies have shown that the use of solar dynamic (SD) power for the growth areas of the Space Station Freedom program will result in life cycle cost savings when compared to power supplied by photovoltaic sources. In the SD power module, a concentrator collects and focuses solar energy into a heat receiver which has integral thermal energy storage. A Power Conversion Unit (PCU) based on the closed Brayton cycle removes thermal energy from the receiver and converts that energy to electrical energy. Since the closed Brayton cycle is a single phase gas cycle, the conversion hardware (heat exchangers, turbine, compressor, etc.) can be designed for operation in low earth orbit, and tested with confidence in test facilities on earth before launch into space. The concentrator subassemblies will be aligned and the receiver/PCU/radiator combination completely assembled and charged with gas and cooling liquid on earth before launch to, and assembly on, orbit.

  1. Studying Dynamics in Business Networks

    DEFF Research Database (Denmark)

    Andersen, Poul Houman; Anderson, Helen; Havila, Virpi

    1998-01-01

    This paper develops a theory on network dynamics using the concepts of role and position from sociological theory. Moreover, the theory is further tested using case studies from Denmark and Finland......This paper develops a theory on network dynamics using the concepts of role and position from sociological theory. Moreover, the theory is further tested using case studies from Denmark and Finland...

  2. Dynamic training algorithm for dynamic neural networks

    International Nuclear Information System (INIS)

    Tan, Y.; Van Cauwenberghe, A.; Liu, Z.

    1996-01-01

    The widely used backpropagation algorithm for training neural networks based on the gradient descent has a significant drawback of slow convergence. A Gauss-Newton method based recursive least squares (RLS) type algorithm with dynamic error backpropagation is presented to speed-up the learning procedure of neural networks with local recurrent terms. Finally, simulation examples concerning the applications of the RLS type algorithm to identification of nonlinear processes using a local recurrent neural network are also included in this paper

  3. Network-dependent modulation of brain activity during sleep.

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    Watanabe, Takamitsu; Kan, Shigeyuki; Koike, Takahiko; Misaki, Masaya; Konishi, Seiki; Miyauchi, Satoru; Miyahsita, Yasushi; Masuda, Naoki

    2014-09-01

    Brain activity dynamically changes even during sleep. A line of neuroimaging studies has reported changes in functional connectivity and regional activity across different sleep stages such as slow-wave sleep (SWS) and rapid-eye-movement (REM) sleep. However, it remains unclear whether and how the large-scale network activity of human brains changes within a given sleep stage. Here, we investigated modulation of network activity within sleep stages by applying the pairwise maximum entropy model to brain activity obtained by functional magnetic resonance imaging from sleeping healthy subjects. We found that the brain activity of individual brain regions and functional interactions between pairs of regions significantly increased in the default-mode network during SWS and decreased during REM sleep. In contrast, the network activity of the fronto-parietal and sensory-motor networks showed the opposite pattern. Furthermore, in the three networks, the amount of the activity changes throughout REM sleep was negatively correlated with that throughout SWS. The present findings suggest that the brain activity is dynamically modulated even in a sleep stage and that the pattern of modulation depends on the type of the large-scale brain networks. Copyright © 2014 Elsevier Inc. All rights reserved.

  4. Dynamic ad hoc networks

    CERN Document Server

    Rashvand, Habib

    2013-01-01

    Motivated by the exciting new application paradigm of using amalgamated technologies of the Internet and wireless, the next generation communication networks (also called 'ubiquitous', 'complex' and 'unstructured' networking) are changing the way we develop and apply our future systems and services at home and on local, national and global scales. Whatever the interconnection - a WiMAX enabled networked mobile vehicle, MEMS or nanotechnology enabled distributed sensor systems, Vehicular Ad hoc Networking (VANET) or Mobile Ad hoc Networking (MANET) - all can be classified under new networking s

  5. Default network modulation and large-scale network interactivity in healthy young and old adults.

    Science.gov (United States)

    Spreng, R Nathan; Schacter, Daniel L

    2012-11-01

    We investigated age-related changes in default, attention, and control network activity and their interactions in young and old adults. Brain activity during autobiographical and visuospatial planning was assessed using multivariate analysis and with intrinsic connectivity networks as regions of interest. In both groups, autobiographical planning engaged the default network while visuospatial planning engaged the attention network, consistent with a competition between the domains of internalized and externalized cognition. The control network was engaged for both planning tasks. In young subjects, the control network coupled with the default network during autobiographical planning and with the attention network during visuospatial planning. In old subjects, default-to-control network coupling was observed during both planning tasks, and old adults failed to deactivate the default network during visuospatial planning. This failure is not indicative of default network dysfunction per se, evidenced by default network engagement during autobiographical planning. Rather, a failure to modulate the default network in old adults is indicative of a lower degree of flexible network interactivity and reduced dynamic range of network modulation to changing task demands.

  6. Cognitive Dynamic Optical Networks

    DEFF Research Database (Denmark)

    de Miguel, Ignacio; Duran, Ramon J.; Lorenzo, Ruben M.

    2013-01-01

    Cognitive networks are a promising solution for the control of heterogeneous optical networks. We review their fundamentals as well as a number of applications developed in the framework of the EU FP7 CHRON project.......Cognitive networks are a promising solution for the control of heterogeneous optical networks. We review their fundamentals as well as a number of applications developed in the framework of the EU FP7 CHRON project....

  7. Periodic dynamics in queuing networks

    Energy Technology Data Exchange (ETDEWEB)

    Addabbo, Tommaso [Information Engineering Department, University of Siena, Via Roma 56, 53100 Siena (Italy)], E-mail: addabbo@dii.unisi.it; Kocarev, Ljupco [Macedonian Academy of Sciences and Arts, bul. Krste Misirkov 2, P.O. Box 428, 1000 Skopje, Republic of Macedonia (Macedonia, The Former Yugoslav Republic of)], E-mail: lkocarev@ucsd.edu

    2009-08-30

    This paper deals with state-dependent open Markovian (or exponential) queuing networks, for which arrival and service rates, as well as routing probabilities, may depend on the queue lengths. For a network of this kind, following Mandelbaum and Pats, we provide a formal definition of its associated fluid model, and we focus on the relationships which may occur between the network stochastic dynamics and the deterministic dynamics of its corresponding fluid model, particularly focusing on queuing networks whose fluid models have global periodic attractors.

  8. Cognitive Dynamic Optical Networks

    DEFF Research Database (Denmark)

    de Miguel, Ignacio; Duran, Ramon J.; Jimenez, Tamara

    2013-01-01

    The use of cognition is a promising element for the control of heterogeneous optical networks. Not only are cognitive networks able to sense current network conditions and act according to them, but they also take into account the knowledge acquired through past experiences; that is, they include...... learning with the aim of improving performance. In this paper, we review the fundamentals of cognitive networks and focus on their application to the optical networking area. In particular, a number of cognitive network architectures proposed so far, as well as their associated supporting technologies......, are reviewed. Moreover, several applications, mainly developed in the framework of the EU FP7 Cognitive Heterogeneous Reconfigurable Optical Network (CHRON) project, are also described....

  9. Entropy of dynamical social networks

    Science.gov (United States)

    Zhao, Kun; Karsai, Marton; Bianconi, Ginestra

    2012-02-01

    Dynamical social networks are evolving rapidly and are highly adaptive. Characterizing the information encoded in social networks is essential to gain insight into the structure, evolution, adaptability and dynamics. Recently entropy measures have been used to quantify the information in email correspondence, static networks and mobility patterns. Nevertheless, we still lack methods to quantify the information encoded in time-varying dynamical social networks. In this talk we present a model to quantify the entropy of dynamical social networks and use this model to analyze the data of phone-call communication. We show evidence that the entropy of the phone-call interaction network changes according to circadian rhythms. Moreover we show that social networks are extremely adaptive and are modified by the use of technologies such as mobile phone communication. Indeed the statistics of duration of phone-call is described by a Weibull distribution and is significantly different from the distribution of duration of face-to-face interactions in a conference. Finally we investigate how much the entropy of dynamical social networks changes in realistic models of phone-call or face-to face interactions characterizing in this way different type human social behavior.

  10. Multi-equilibrium property of metabolic networks: SSI module

    Directory of Open Access Journals (Sweden)

    Chen Luonan

    2011-06-01

    Full Text Available Abstract Background Revealing the multi-equilibrium property of a metabolic network is a fundamental and important topic in systems biology. Due to the complexity of the metabolic network, it is generally a difficult task to study the problem as a whole from both analytical and numerical viewpoint. On the other hand, the structure-oriented modularization idea is a good choice to overcome such a difficulty, i.e. decomposing the network into several basic building blocks and then studying the whole network through investigating the dynamical characteristics of the basic building blocks and their interactions. Single substrate and single product with inhibition (SSI metabolic module is one type of the basic building blocks of metabolic networks, and its multi-equilibrium property has important influence on that of the whole metabolic networks. Results In this paper, we describe what the SSI metabolic module is, characterize the rates of the metabolic reactions by Hill kinetics and give a unified model for SSI modules by using a set of nonlinear ordinary differential equations with multi-variables. Specifically, a sufficient and necessary condition is first given to describe the injectivity of a class of nonlinear systems, and then, the sufficient condition is used to study the multi-equilibrium property of SSI modules. As a main theoretical result, for the SSI modules in which each reaction has no more than one inhibitor, a sufficient condition is derived to rule out multiple equilibria, i.e. the Jacobian matrix of its rate function is nonsingular everywhere. Conclusions In summary, we describe SSI modules and give a general modeling framework based on Hill kinetics, and provide a sufficient condition for ruling out multiple equilibria of a key type of SSI module.

  11. Nonlinear Dynamics on Interconnected Networks

    Science.gov (United States)

    Arenas, Alex; De Domenico, Manlio

    2016-06-01

    Networks of dynamical interacting units can represent many complex systems, from the human brain to transportation systems and societies. The study of these complex networks, when accounting for different types of interactions has become a subject of interest in the last few years, especially because its representational power in the description of users' interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.) [1], or in representing different transportation modes in urban networks [2,3]. The general name coined for these networks is multilayer networks, where each layer accounts for a type of interaction (see Fig. 1).

  12. Robust adaptive synchronization of general dynamical networks ...

    Indian Academy of Sciences (India)

    Robust adaptive synchronization; dynamical network; multiple delays; multiple uncertainties. ... Networks such as neural networks, communication transmission networks, social rela- tionship networks etc. ..... a very good effect. Pramana – J.

  13. Dynamic behaviors in directed networks

    International Nuclear Information System (INIS)

    Park, Sung Min; Kim, Beom Jun

    2006-01-01

    Motivated by the abundance of directed synaptic couplings in a real biological neuronal network, we investigate the synchronization behavior of the Hodgkin-Huxley model in a directed network. We start from the standard model of the Watts-Strogatz undirected network and then change undirected edges to directed arcs with a given probability, still preserving the connectivity of the network. A generalized clustering coefficient for directed networks is defined and used to investigate the interplay between the synchronization behavior and underlying structural properties of directed networks. We observe that the directedness of complex networks plays an important role in emerging dynamical behaviors, which is also confirmed by a numerical study of the sociological game theoretic voter model on directed networks

  14. Dynamic Sensor Networks

    National Research Council Canada - National Science Library

    Schott, Brian

    2004-01-01

    ...: Declarative Languages and Execution Environment includes topographical soldier interface and a sensor network simulation environment for algorithm development, deployment planning, and operational support. Finally, Task 3...

  15. Dynamic and interacting complex networks

    Science.gov (United States)

    Dickison, Mark E.

    This thesis employs methods of statistical mechanics and numerical simulations to study some aspects of dynamic and interacting complex networks. The mapping of various social and physical phenomena to complex networks has been a rich field in the past few decades. Subjects as broad as petroleum engineering, scientific collaborations, and the structure of the internet have all been analyzed in a network physics context, with useful and universal results. In the first chapter we introduce basic concepts in networks, including the two types of network configurations that are studied and the statistical physics and epidemiological models that form the framework of the network research, as well as covering various previously-derived results in network theory that are used in the work in the following chapters. In the second chapter we introduce a model for dynamic networks, where the links or the strengths of the links change over time. We solve the model by mapping dynamic networks to the problem of directed percolation, where the direction corresponds to the time evolution of the network. We show that the dynamic network undergoes a percolation phase transition at a critical concentration pc, that decreases with the rate r at which the network links are changed. The behavior near criticality is universal and independent of r. We find that for dynamic random networks fundamental laws are changed: i) The size of the giant component at criticality scales with the network size N for all values of r, rather than as N2/3 in static network, ii) In the presence of a broad distribution of disorder, the optimal path length between two nodes in a dynamic network scales as N1/2, compared to N1/3 in a static network. The third chapter consists of a study of the effect of quarantine on the propagation of epidemics on an adaptive network of social contacts. For this purpose, we analyze the susceptible-infected-recovered model in the presence of quarantine, where susceptible

  16. Complex networks: Dynamics and security

    Indian Academy of Sciences (India)

    This paper presents a perspective in the study of complex networks by focusing on how dynamics may affect network security under attacks. ... Department of Mathematics and Statistics, Arizona State University, Tempe, Arizona 85287, USA; Institute of Mathematics and Computer Science, University of Sao Paulo, Brazil ...

  17. Caffeine Modulates Attention Network Function

    Science.gov (United States)

    Brunye, Tad T.; Mahoney, Caroline R.; Lieberman, Harris R.; Taylor, Holly A.

    2010-01-01

    The present work investigated the effects of caffeine (0 mg, 100 mg, 200 mg, 400 mg) on a flanker task designed to test Posner's three visual attention network functions: alerting, orienting, and executive control [Posner, M. I. (2004). "Cognitive neuroscience of attention". New York, NY: Guilford Press]. In a placebo-controlled, double-blind…

  18. Dynamic capabilities and network benefits

    Directory of Open Access Journals (Sweden)

    Helge Svare

    2017-01-01

    Full Text Available The number of publicly funded initiatives to establish or strengthen networks and clusters, in order to enhance innovation, has been increasing. Returns on such investments vary, and the aim of this study is to explore to what extent the variation in benefits for firms participating in networks or clusters can be explained by their dynamic capabilities (DC. Based on survey data from five Norwegian networks, the results suggest that firms with higher DC are more successful in harvesting the potential benefits of being member of a network.

  19. Decoding network dynamics in cancer

    DEFF Research Database (Denmark)

    Linding, Rune

    2014-01-01

    Biological systems are composed of highly dynamic and interconnected molecular networks that drive biological decision processes. The goal of network biology is to describe, quantify and predict the information flow and functional behaviour of living systems in a formal language and with an accur......Biological systems are composed of highly dynamic and interconnected molecular networks that drive biological decision processes. The goal of network biology is to describe, quantify and predict the information flow and functional behaviour of living systems in a formal language...... and with an accuracy that parallels our characterisation of other physical systems such as Jumbo-jets. Decades of targeted molecular and biological studies have led to numerous pathway models of developmental and disease related processes. However, so far no global models have been derived from pathways, capable...

  20. Dynamics of associating networks

    Science.gov (United States)

    Tang, Shengchang; Habicht, Axel; Wang, Muzhou; Li, Shuaili; Seiffert, Sebastian; Olsen, Bradley

    Associating polymers offer important technological solutions to renewable and self-healing materials, conducting electrolytes for energy storage and transport, and vehicles for cell and protein deliveries. The interplay between polymer topologies and association chemistries warrants new interesting physics from associating networks, yet poses significant challenges to study these systems over a wide range of time and length scales. In a series of studies, we explored self-diffusion mechanisms of associating polymers above the percolation threshold, by combining experimental measurements using forced Rayleigh scattering and analytical insights from a two-state model. Despite the differences in molecular structures, a universal super-diffusion phenomenon is observed when diffusion of molecular species is hindered by dissociation kinetics. The molecular dissociation rate can be used to renormalize shear rheology data, which yields an unprecedented time-temperature-concentration superposition. The obtained shear rheology master curves provide experimental evidence of the relaxation hierarchy in associating networks.

  1. The behaviour of basic autocatalytic signalling modules in isolation and embedded in networks

    Energy Technology Data Exchange (ETDEWEB)

    Krishnan, J. [Department of Chemical Engineering, Centre for Process Systems Engineering, Institute for Systems and Synthetic Biology, Imperial College London, London SW7 2AZ (United Kingdom); Mois, Kristina; Suwanmajo, Thapanar [Department of Chemical Engineering, Centre for Process Systems Engineering, Imperial College London, London SW7 2AZ (United Kingdom)

    2014-11-07

    In this paper, we examine the behaviour of basic autocatalytic feedback modules involving a species catalyzing its own production, either directly or indirectly. We first perform a systematic study of the autocatalytic feedback module in isolation, examining the effect of different factors, showing how this module is capable of exhibiting monostable threshold and bistable switch-like behaviour. We then study the behaviour of this module embedded in different kinds of basic networks including (essentially) irreversible cycles, open and closed reversible chains, and networks with additional feedback. We study the behaviour of the networks deterministically and also stochastically, using simulations, analytical work, and bifurcation analysis. We find that (i) there are significant differences between the behaviour of this module in isolation and in a network: thresholds may be altered or destroyed and bistability may be destroyed or even induced, even when the ambient network is simple. The global characteristics and topology of this network and the position of the module in the ambient network can play important and unexpected roles. (ii) There can be important differences between the deterministic and stochastic dynamics of the module embedded in networks, which may be accentuated by the ambient network. This provides new insights into the functioning of such enzymatic modules individually and as part of networks, with relevance to other enzymatic signalling modules as well.

  2. Network Dynamics of Innovation Processes

    Science.gov (United States)

    Iacopini, Iacopo; Milojević, Staša; Latora, Vito

    2018-01-01

    We introduce a model for the emergence of innovations, in which cognitive processes are described as random walks on the network of links among ideas or concepts, and an innovation corresponds to the first visit of a node. The transition matrix of the random walk depends on the network weights, while in turn the weight of an edge is reinforced by the passage of a walker. The presence of the network naturally accounts for the mechanism of the "adjacent possible," and the model reproduces both the rate at which novelties emerge and the correlations among them observed empirically. We show this by using synthetic networks and by studying real data sets on the growth of knowledge in different scientific disciplines. Edge-reinforced random walks on complex topologies offer a new modeling framework for the dynamics of correlated novelties and are another example of coevolution of processes and networks.

  3. Solving Dynamic Battlespace Movement Problems Using Dynamic Distributed Computer Networks

    National Research Council Canada - National Science Library

    Bradford, Robert

    2000-01-01

    .... The thesis designs a system using this architecture that invokes operations research network optimization algorithms to solve problems involving movement of people and equipment over dynamic road networks...

  4. Competitive Dynamics on Complex Networks

    Science.gov (United States)

    Zhao, Jiuhua; Liu, Qipeng; Wang, Xiaofan

    2014-07-01

    We consider a dynamical network model in which two competitors have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. The state of each normal agent converges to a steady value which is a convex combination of the competitors' states, and is independent of the initial states of agents. This implies that the competition result is fully determined by the network structure and positions of competitors in the network. We compute an Influence Matrix (IM) in which each element characterizing the influence of an agent on another agent in the network. We use the IM to predict the bias of each normal agent and thus predict which competitor will win. Furthermore, we compare the IM criterion with seven node centrality measures to predict the winner. We find that the competitor with higher Katz Centrality in an undirected network or higher PageRank in a directed network is most likely to be the winner. These findings may shed new light on the role of network structure in competition and to what extent could competitors adjust network structure so as to win the competition.

  5. Antagonistic Phenomena in Network Dynamics

    Science.gov (United States)

    Motter, Adilson E.; Timme, Marc

    2018-03-01

    Recent research on the network modeling of complex systems has led to a convenient representation of numerous natural, social, and engineered systems that are now recognized as networks of interacting parts. Such systems can exhibit a wealth of phenomena that not only cannot be anticipated from merely examining their parts, as per the textbook definition of complexity, but also challenge intuition even when considered in the context of what is now known in network science. Here, we review the recent literature on two major classes of such phenomena that have far-reaching implications: (a) antagonistic responses to changes of states or parameters and (b) coexistence of seemingly incongruous behaviors or properties - both deriving from the collective and inherently decentralized nature of the dynamics. They include effects as diverse as negative compressibility in engineered materials, rescue interactions in biological networks, negative resistance in fluid networks, and the Braess paradox occurring across transport and supply networks. They also include remote synchronization, chimera states, and the converse of symmetry breaking in brain, power-grid, and oscillator networks as well as remote control in biological and bioinspired systems. By offering a unified view of these various scenarios, we suggest that they are representative of a yet broader class of unprecedented network phenomena that ought to be revealed and explained by future research.

  6. Energy Efficiency Analysis for Dynamic Routing in Optical Transport Networks

    DEFF Research Database (Denmark)

    Vizcaíno, Jorge López; Ye, Yabin; Tafur Monroy, Idelfonso

    2012-01-01

    The energy efficiency in telecommunication networks is gaining more relevance as the Internet traffic is growing. The introduction of OFDM and dynamic operation opens new horizons in the operation of optical networks, improving the network flexibility and its efficiency. In this paper, we compare...... the performance in terms of energy efficiency of a flexible-grid OFDM-based solution with a fixed-grid WDM network in a dynamic scenario with time-varying connections. We highlight the benefits that the bandwidth elasticity and the flexibility of selecting different modulation formats can offer compared...

  7. Dynamic Intelligent Feedback Scheduling in Networked Control Systems

    Directory of Open Access Journals (Sweden)

    Hui-ying Chen

    2013-01-01

    Full Text Available For the networked control system with limited bandwidth and flexible workload, a dynamic intelligent feedback scheduling strategy is proposed. Firstly, a monitor is used to acquire the current available network bandwidth. Then, the new available bandwidth in the next interval is predicted by using LS_SVM approach. At the same time, the dynamic performance indices of all control loops are obtained with a two-dimensional fuzzy logic modulator. Finally, the predicted network bandwidth is dynamically allocated by the bandwidth manager and the priority allocator in terms of the loops' dynamic performance indices. Simulation results show that the sampling periods and priorities of control loops are adjusted timely according to the network workload condition and the dynamic performance of control loops, which make the system running in the optimal state all the time.

  8. Notational usage modulates attention networks in binumerates

    Directory of Open Access Journals (Sweden)

    Atesh eKoul

    2014-05-01

    Full Text Available Multicultural environments require learning multiple number notations wherein some are encountered more frequently than others. This leads to differences in exposure and consequently differences in usage between notations. We find that differential notational usage imposes a significant neurocognitive load on number processing. Despite simultaneous acquisition, forty-two adult binumerate populations, familiar with two positional writing systems namely Hindu Nagari digits and Hindu Arabic digits, reported significantly lower preference and usage for Nagari as compared to Arabic. Twenty-four participants showed significantly increased reaction times and reduced accuracy while performing magnitude comparison tasks in Nagari with respect to Arabic. Functional magnetic resonance imaging revealed that processing Nagari elicited significantly greater activity in number processing and attention networks. A direct subtraction of networks for Nagari and Arabic notations revealed a neural circuit comprising of bilateral intra-parietal sulcus, inferior and mid frontal gyri, fusiform gyrus and the anterior cingulate cortex (FDR p<0.005. Additionally, whole brain correlation analysis showed that activity in the left inferior parietal region was modulated by task performance in Nagari. We attribute the increased activation in Ng to increased task difficulty due to infrequent exposure and usage. Our results reiterate the role of the left intra-parietal sulcus in modulating performance in numeric tasks and highlight that of the attention network for monitoring symbolic notation mode in binumerates.

  9. Modulation for emergent networks: serotonin and dopamine.

    Science.gov (United States)

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

    2013-05-01

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

  10. Anomaly Detection in Dynamic Networks

    Energy Technology Data Exchange (ETDEWEB)

    Turcotte, Melissa [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2014-10-14

    Anomaly detection in dynamic communication networks has many important security applications. These networks can be extremely large and so detecting any changes in their structure can be computationally challenging; hence, computationally fast, parallelisable methods for monitoring the network are paramount. For this reason the methods presented here use independent node and edge based models to detect locally anomalous substructures within communication networks. As a first stage, the aim is to detect changes in the data streams arising from node or edge communications. Throughout the thesis simple, conjugate Bayesian models for counting processes are used to model these data streams. A second stage of analysis can then be performed on a much reduced subset of the network comprising nodes and edges which have been identified as potentially anomalous in the first stage. The first method assumes communications in a network arise from an inhomogeneous Poisson process with piecewise constant intensity. Anomaly detection is then treated as a changepoint problem on the intensities. The changepoint model is extended to incorporate seasonal behavior inherent in communication networks. This seasonal behavior is also viewed as a changepoint problem acting on a piecewise constant Poisson process. In a static time frame, inference is made on this extended model via a Gibbs sampling strategy. In a sequential time frame, where the data arrive as a stream, a novel, fast Sequential Monte Carlo (SMC) algorithm is introduced to sample from the sequence of posterior distributions of the change points over time. A second method is considered for monitoring communications in a large scale computer network. The usage patterns in these types of networks are very bursty in nature and don’t fit a Poisson process model. For tractable inference, discrete time models are considered, where the data are aggregated into discrete time periods and probability models are fitted to the

  11. A network dynamics approach to chemical reaction networks

    NARCIS (Netherlands)

    van der Schaft, Abraham; Rao, S.; Jayawardhana, B.

    2016-01-01

    A treatment of chemical reaction network theory is given from the perspective of nonlinear network dynamics, in particular of consensus dynamics. By starting from the complex-balanced assumption the reaction dynamics governed by mass action kinetics can be rewritten into a form which allows for a

  12. Hierarchical feedback modules and reaction hubs in cell signaling networks.

    Science.gov (United States)

    Xu, Jianfeng; Lan, Yueheng

    2015-01-01

    Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks.

  13. Hierarchical feedback modules and reaction hubs in cell signaling networks.

    Directory of Open Access Journals (Sweden)

    Jianfeng Xu

    Full Text Available Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks.

  14. Hierarchical Feedback Modules and Reaction Hubs in Cell Signaling Networks

    Science.gov (United States)

    Xu, Jianfeng; Lan, Yueheng

    2015-01-01

    Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks. PMID:25951347

  15. Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks.

    Science.gov (United States)

    Schmidt, Christoph; Piper, Diana; Pester, Britta; Mierau, Andreas; Witte, Herbert

    2018-05-01

    Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration. We introduce a general computational framework for extracting consensus partitions from defined time windows in sequences of weighted directed edge-complete networks and show how the temporal reorganization of the module structure can be tracked and visualized. Part of the framework is a new approach for computing edge weight thresholds for individual networks based on multiobjective optimization of module structure quality criteria as well as an approach for matching modules across time steps. By testing our framework using synthetic network sequences and applying it to brain functional networks computed from electroencephalographic recordings of healthy subjects that were exposed to a major balance perturbation, we demonstrate the framework's potential for gaining meaningful insights into dynamic brain function in the form of evolving network modules. The precise chronology of the neural processing inferred with our framework and its interpretation helps to improve the currently incomplete understanding of the cortical contribution for the compensation of such balance perturbations.

  16. Load-induced modulation of signal transduction networks.

    Science.gov (United States)

    Jiang, Peng; Ventura, Alejandra C; Sontag, Eduardo D; Merajver, Sofia D; Ninfa, Alexander J; Del Vecchio, Domitilla

    2011-10-11

    Biological signal transduction networks are commonly viewed as circuits that pass along information--in the process amplifying signals, enhancing sensitivity, or performing other signal-processing tasks--to transcriptional and other components. Here, we report on a "reverse-causality" phenomenon, which we call load-induced modulation. Through a combination of analytical and experimental tools, we discovered that signaling was modulated, in a surprising way, by downstream targets that receive the signal and, in doing so, apply what in physics is called a load. Specifically, we found that non-intuitive changes in response dynamics occurred for a covalent modification cycle when load was present. Loading altered the response time of a system, depending on whether the activity of one of the enzymes was maximal and the other was operating at its minimal rate or whether both enzymes were operating at submaximal rates. These two conditions, which we call "limit regime" and "intermediate regime," were associated with increased or decreased response times, respectively. The bandwidth, the range of frequency in which the system can process information, decreased in the presence of load, suggesting that downstream targets participate in establishing a balance between noise-filtering capabilities and a circuit's ability to process high-frequency stimulation. Nodes in a signaling network are not independent relay devices, but rather are modulated by their downstream targets.

  17. Attractor dynamics in local neuronal networks

    Directory of Open Access Journals (Sweden)

    Jean-Philippe eThivierge

    2014-03-01

    Full Text Available Patterns of synaptic connectivity in various regions of the brain are characterized by the presence of synaptic motifs, defined as unidirectional and bidirectional synaptic contacts that follow a particular configuration and link together small groups of neurons. Recent computational work proposes that a relay network (two populations communicating via a third, relay population of neurons can generate precise patterns of neural synchronization. Here, we employ two distinct models of neuronal dynamics and show that simulated neural circuits designed in this way are caught in a global attractor of activity that prevents neurons from modulating their response on the basis of incoming stimuli. To circumvent the emergence of a fixed global attractor, we propose a mechanism of selective gain inhibition that promotes flexible responses to external stimuli. We suggest that local neuronal circuits may employ this mechanism to generate precise patterns of neural synchronization whose transient nature delimits the occurrence of a brief stimulus.

  18. Tourism-planning network knowledge dynamics

    DEFF Research Database (Denmark)

    Dredge, Dianne

    2014-01-01

    This chapter explores the characteristics and functions of tourism networks as a first step in understanding how networks facilitate and reproduce knowledge. A framework to progress understandings of knowledge dynamics in tourism networks is presented that includes four key dimensions: context......, network agents, network boundaries and network resources. A case study of the development of the Next Generation Tourism Handbook (Queensland, Australia), a policy initiative that sought to bring tourism and land use planning knowledge closer together is presented. The case study illustrates...... that the tourism policy and land use planning networks operate in very different spheres and that context, network agents, network boundaries and network resources have a significant influence not only on knowledge dynamics but also on the capacity of network agents to overcome barriers to learning and to innovate....

  19. Traffic Dynamics on Complex Networks: A Survey

    Directory of Open Access Journals (Sweden)

    Shengyong Chen

    2012-01-01

    Full Text Available Traffic dynamics on complex networks are intriguing in recent years due to their practical implications in real communication networks. In this survey, we give a brief review of studies on traffic routing dynamics on complex networks. Strategies for improving transport efficiency, including designing efficient routing strategies and making appropriate adjustments to the underlying network structure, are introduced in this survey. Finally, a few open problems are discussed in this survey.

  20. Airborne Network Optimization with Dynamic Network Update

    Science.gov (United States)

    2015-03-26

    source si and a target ti . For each commodity (si, ki) the commodity specifies a non- negative demand di [5]. The objective of the multi-commodity...queue predictions, and network con- gestion [15]. The implementation of the DRQC uses the Kalman filter to predict the state of the network and optimize

  1. FMFinder: A Functional Module Detector for PPI Networks

    Directory of Open Access Journals (Sweden)

    M. Modi

    2017-10-01

    Full Text Available Bioinformatics is an integrated area of data mining, statistics and computational biology. Protein-Protein Interaction (PPI network is the most important biological process in living beings. In this network a protein module interacts with another module and so on, forming a large network of proteins. The same set of proteins which takes part in the organic courses of biological actions is detected through the Function Module Detection method. Clustering process when applied in PPI networks is made of proteins which are part of a larger communication network. As a result of this, we can define the limits for module detection as well as clarify the construction of a PPI network. For understating the bio-mechanism of various living beings, a detailed study of FMFinder detection by clustering process is called for.

  2. Learning dynamic Bayesian networks with mixed variables

    DEFF Research Database (Denmark)

    Bøttcher, Susanne Gammelgaard

    This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learned...

  3. Temporal fidelity in dynamic social networks

    DEFF Research Database (Denmark)

    Stopczynski, Arkadiusz; Sapiezynski, Piotr; Pentland, Alex ‘Sandy’

    2015-01-01

    of the network dynamics can be used to inform the process of measuring social networks. The details of measurement are of particular importance when considering dynamic processes where minute-to-minute details are important, because collection of physical proximity interactions with high temporal resolution...

  4. Revealing networks from dynamics: an introduction

    International Nuclear Information System (INIS)

    Timme, Marc; Casadiego, Jose

    2014-01-01

    What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein and gene regulatory networks as well as neural circuits in biology and electric power grids or wireless sensor networks in engineering. Moreover, we briefly mention some standard ways of inferring effective or functional connectivity. (topical review)

  5. Local Dynamics in Trained Recurrent Neural Networks.

    Science.gov (United States)

    Rivkind, Alexander; Barak, Omri

    2017-06-23

    Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

  6. Local Dynamics in Trained Recurrent Neural Networks

    Science.gov (United States)

    Rivkind, Alexander; Barak, Omri

    2017-06-01

    Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

  7. Using Network Dynamical Influence to Drive Consensus

    Science.gov (United States)

    Punzo, Giuliano; Young, George F.; MacDonald, Malcolm; Leonard, Naomi E.

    2016-05-01

    Consensus and decision-making are often analysed in the context of networks, with many studies focusing attention on ranking the nodes of a network depending on their relative importance to information routing. Dynamical influence ranks the nodes with respect to their ability to influence the evolution of the associated network dynamical system. In this study it is shown that dynamical influence not only ranks the nodes, but also provides a naturally optimised distribution of effort to steer a network from one state to another. An example is provided where the “steering” refers to the physical change in velocity of self-propelled agents interacting through a network. Distinct from other works on this subject, this study looks at directed and hence more general graphs. The findings are presented with a theoretical angle, without targeting particular applications or networked systems; however, the framework and results offer parallels with biological flocks and swarms and opportunities for design of technological networks.

  8. Dynamic channel assignment scheme for multi-radio wireless mesh networks

    CSIR Research Space (South Africa)

    Kareem, TR

    2008-09-01

    Full Text Available This paper investigates the challenges involve in designing a dynamic channel assignment (DCA) scheme for wireless mesh networks, particularly for multi-radio systems. It motivates the need for fast switching and process coordination modules...

  9. Inferring network topology from complex dynamics

    International Nuclear Information System (INIS)

    Shandilya, Srinivas Gorur; Timme, Marc

    2011-01-01

    Inferring the network topology from dynamical observations is a fundamental problem pervading research on complex systems. Here, we present a simple, direct method for inferring the structural connection topology of a network, given an observation of one collective dynamical trajectory. The general theoretical framework is applicable to arbitrary network dynamical systems described by ordinary differential equations. No interference (external driving) is required and the type of dynamics is hardly restricted in any way. In particular, the observed dynamics may be arbitrarily complex; stationary, invariant or transient; synchronous or asynchronous and chaotic or periodic. Presupposing a knowledge of the functional form of the dynamical units and of the coupling functions between them, we present an analytical solution to the inverse problem of finding the network topology from observing a time series of state variables only. Robust reconstruction is achieved in any sufficiently long generic observation of the system. We extend our method to simultaneously reconstructing both the entire network topology and all parameters appearing linear in the system's equations of motion. Reconstruction of network topology and system parameters is viable even in the presence of external noise that distorts the original dynamics substantially. The method provides a conceptually new step towards reconstructing a variety of real-world networks, including gene and protein interaction networks and neuronal circuits.

  10. Irrelevant stimulus processing in ADHD: catecholamine dynamics and attentional networks

    Directory of Open Access Journals (Sweden)

    Francisco eAboitiz

    2014-03-01

    Full Text Available A cardinal symptom of Attenion Deficit and Hyperactivity Disorder (ADHD is a general distractibility where children and adults shift their attentional focus to stimuli that are irrelevant to the ongoing behavior. This has been attributed to a deficit in dopaminergic signaling in cortico-striatal networks that regulate goal-directed behavior. Furthermore, recent imaging evidence points to an impairment of large scale, antagonistic brain networks that normally contribute to attentional engagement and disengagement, such as the task-positive networks and the Default Mode Network (DMN. Related networks are the ventral attentional network (VAN involved in attentional shifting, and the salience network (SN related to task expectancy. Here we discuss the tonic-phasic dynamics of catecholaminergic signaling in the brain, and attempt to provide a link between this and the activities of the large-scale cortical networks that regulate behavior. More specifically, we propose that a disbalance of tonic catecholamine levels during task performance produce an emphasis of phasic signaling and increased excitability of the VAN, yielding distractibility symptoms. Likewise, immaturity of the SN may relate to abnormal tonic signaling and an incapacity to build up a proper executive system during task performance. We discuss different lines of evidence including pharmacology, brain imaging and electrophysiology, that are consistent with our proposal. Finally, restoring the pharmacodynamics of catecholaminergic signaling seems crucial to alleviate ADHD symptoms; however, the possibility is open to explore cognitive rehabilitation strategies to top-down modulate network dynamics compensating the pharmacological deficits.

  11. Convergent dynamics for multistable delayed neural networks

    International Nuclear Information System (INIS)

    Shih, Chih-Wen; Tseng, Jui-Pin

    2008-01-01

    This investigation aims at developing a methodology to establish convergence of dynamics for delayed neural network systems with multiple stable equilibria. The present approach is general and can be applied to several network models. We take the Hopfield-type neural networks with both instantaneous and delayed feedbacks to illustrate the idea. We shall construct the complete dynamical scenario which comprises exactly 2 n stable equilibria and exactly (3 n − 2 n ) unstable equilibria for the n-neuron network. In addition, it is shown that every solution of the system converges to one of the equilibria as time tends to infinity. The approach is based on employing the geometrical structure of the network system. Positively invariant sets and componentwise dynamical properties are derived under the geometrical configuration. An iteration scheme is subsequently designed to confirm the convergence of dynamics for the system. Two examples with numerical simulations are arranged to illustrate the present theory

  12. Information governance in dynamic networked business process management

    NARCIS (Netherlands)

    Rasouli, M.; Eshuis, H.; Grefen, P.W.P.J.; Trienekens, J.J.M.; Kusters, R.J.

    2016-01-01

    Competition in today’s globalized markets forces organizations to collaborate within dynamic business networks to provide mass-customized integrated solutions for customers. The collaboration within dynamic business networks necessitates forming dynamic networked business processes (DNBPs).

  13. Pinning Synchronization of Switched Complex Dynamical Networks

    Directory of Open Access Journals (Sweden)

    Liming Du

    2015-01-01

    Full Text Available Network topology and node dynamics play a key role in forming synchronization of complex networks. Unfortunately there is no effective synchronization criterion for pinning synchronization of complex dynamical networks with switching topology. In this paper, pinning synchronization of complex dynamical networks with switching topology is studied. Two basic problems are considered: one is pinning synchronization of switched complex networks under arbitrary switching; the other is pinning synchronization of switched complex networks by design of switching when synchronization cannot achieved by using any individual connection topology alone. For the two problems, common Lyapunov function method and single Lyapunov function method are used respectively, some global synchronization criteria are proposed and the designed switching law is given. Finally, simulation results verify the validity of the results.

  14. Action Potential Modulation of Neural Spin Networks Suggests Possible Role of Spin

    CERN Document Server

    Hu, H P

    2004-01-01

    In this paper we show that nuclear spin networks in neural membranes are modulated by action potentials through J-coupling, dipolar coupling and chemical shielding tensors and perturbed by microscopically strong and fluctuating internal magnetic fields produced largely by paramagnetic oxygen. We suggest that these spin networks could be involved in brain functions since said modulation inputs information carried by the neural spike trains into them, said perturbation activates various dynamics within them and the combination of the two likely produce stochastic resonance thus synchronizing said dynamics to the neural firings. Although quantum coherence is desirable and may indeed exist, it is not required for these spin networks to serve as the subatomic components for the conventional neural networks.

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

    Science.gov (United States)

    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).

  16. A mathematical programming approach for sequential clustering of dynamic networks

    Science.gov (United States)

    Silva, Jonathan C.; Bennett, Laura; Papageorgiou, Lazaros G.; Tsoka, Sophia

    2016-02-01

    A common analysis performed on dynamic networks is community structure detection, a challenging problem that aims to track the temporal evolution of network modules. An emerging area in this field is evolutionary clustering, where the community structure of a network snapshot is identified by taking into account both its current state as well as previous time points. Based on this concept, we have developed a mixed integer non-linear programming (MINLP) model, SeqMod, that sequentially clusters each snapshot of a dynamic network. The modularity metric is used to determine the quality of community structure of the current snapshot and the historical cost is accounted for by optimising the number of node pairs co-clustered at the previous time point that remain so in the current snapshot partition. Our method is tested on social networks of interactions among high school students, college students and members of the Brazilian Congress. We show that, for an adequate parameter setting, our algorithm detects the classes that these students belong more accurately than partitioning each time step individually or by partitioning the aggregated snapshots. Our method also detects drastic discontinuities in interaction patterns across network snapshots. Finally, we present comparative results with similar community detection methods for time-dependent networks from the literature. Overall, we illustrate the applicability of mathematical programming as a flexible, adaptable and systematic approach for these community detection problems. Contribution to the Topical Issue "Temporal Network Theory and Applications", edited by Petter Holme.

  17. Fundamental structures of dynamic social networks

    DEFF Research Database (Denmark)

    Sekara, Vedran; Stopczynski, Arkadiusz; Jørgensen, Sune Lehmann

    2016-01-01

    Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships...... and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection......, and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals...

  18. Evolutionary dynamics of complex communications networks

    CERN Document Server

    Karyotis, Vasileios; Papavassiliou, Symeon

    2013-01-01

    Until recently, most network design techniques employed a bottom-up approach with lower protocol layer mechanisms affecting the development of higher ones. This approach, however, has not yielded fascinating results in the case of wireless distributed networks. Addressing the emerging aspects of modern network analysis and design, Evolutionary Dynamics of Complex Communications Networks introduces and develops a top-bottom approach where elements of the higher layer can be exploited in modifying the lowest physical topology-closing the network design loop in an evolutionary fashion similar to

  19. Role of network dynamics in shaping spike timing reliability

    International Nuclear Information System (INIS)

    Bazhenov, Maxim; Rulkov, Nikolai F.; Fellous, Jean-Marc; Timofeev, Igor

    2005-01-01

    We study the reliability of cortical neuron responses to periodically modulated synaptic stimuli. Simple map-based models of two different types of cortical neurons are constructed to replicate the intrinsic resonances of reliability found in experimental data and to explore the effects of those resonance properties on collective behavior in a cortical network model containing excitatory and inhibitory cells. We show that network interactions can enhance the frequency range of reliable responses and that the latter can be controlled by the strength of synaptic connections. The underlying dynamical mechanisms of reliability enhancement are discussed

  20. Dynamics on Networks of Manifolds

    Science.gov (United States)

    DeVille, Lee; Lerman, Eugene

    2015-03-01

    We propose a precise definition of a continuous time dynamical system made up of interacting open subsystems. The interconnections of subsystems are coded by directed graphs. We prove that the appropriate maps of graphs called graph fibrations give rise to maps of dynamical systems. Consequently surjective graph fibrations give rise to invariant subsystems and injective graph fibrations give rise to projections of dynamical systems.

  1. How complex a dynamical network can be?

    International Nuclear Information System (INIS)

    Baptista, M.S.; Kakmeni, F. Moukam; Del Magno, Gianluigi; Hussein, M.S.

    2011-01-01

    Positive Lyapunov exponents measure the asymptotic exponential divergence of nearby trajectories of a dynamical system. Not only they quantify how chaotic a dynamical system is, but since their sum is an upper bound for the rate of information production, they also provide a convenient way to quantify the complexity of a dynamical network. We conjecture based on numerical evidences that for a large class of dynamical networks composed by equal nodes, the sum of the positive Lyapunov exponents is bounded by the sum of all the positive Lyapunov exponents of both the synchronization manifold and its transversal directions, the last quantity being in principle easier to compute than the latter. As applications of our conjecture we: (i) show that a dynamical network composed of equal nodes and whose nodes are fully linearly connected produces more information than similar networks but whose nodes are connected with any other possible connecting topology; (ii) show how one can calculate upper bounds for the information production of realistic networks whose nodes have parameter mismatches, randomly chosen; (iii) discuss how to predict the behavior of a large dynamical network by knowing the information provided by a system composed of only two coupled nodes.

  2. Dynamical Adaptation in Terrorist Cells/Networks

    DEFF Research Database (Denmark)

    Hussain, Dil Muhammad Akbar; Ahmed, Zaki

    2010-01-01

    Typical terrorist cells/networks have dynamical structure as they evolve or adapt to changes which may occur due to capturing or killing of a member of the cell/network. Analytical measures in graph theory like degree centrality, betweenness and closeness centralities are very common and have long...

  3. The Social Dynamics of Innovation Networks

    NARCIS (Netherlands)

    Rutten, Roel; Benneworth, Paul Stephen; Irawati, Dessy; Boekema, Frans

    2014-01-01

    The social dynamics of innovation networks captures the important role of trust, social capital, institutions and norms and values in the creation of knowledge in innovation networks. In doing so, this book connects to a long-standing debate on the socio-spatial context of innovation in economic

  4. Dynamic Frequency Control in Power Networks

    OpenAIRE

    Zhao, Changhong; Mallada Garcia, Enrique; Low, Steven H.

    2016-01-01

    Node controllers in power distribution networks in accordance with embodiments of the invention enable dynamic frequency control. One embodiment includes a node controller comprising a network interface a processor; and a memory containing a frequency control application; and a plurality of node operating parameters describing the operating parameters of a node, where the node is selected from a group consisting of at least one generator node in a power distribution network wherein the proces...

  5. Identification of unstable network modules reveals disease modules associated with the progression of Alzheimer's disease.

    Directory of Open Access Journals (Sweden)

    Masataka Kikuchi

    Full Text Available Alzheimer's disease (AD, the most common cause of dementia, is associated with aging, and it leads to neuron death. Deposits of amyloid β and aberrantly phosphorylated tau protein are known as pathological hallmarks of AD, but the underlying mechanisms have not yet been revealed. A high-throughput gene expression analysis previously showed that differentially expressed genes accompanying the progression of AD were more down-regulated than up-regulated in the later stages of AD. This suggested that the molecular networks and their constituent modules collapsed along with AD progression. In this study, by using gene expression profiles and protein interaction networks (PINs, we identified the PINs expressed in three brain regions: the entorhinal cortex (EC, hippocampus (HIP and superior frontal gyrus (SFG. Dividing the expressed PINs into modules, we examined the stability of the modules with AD progression and with normal aging. We found that in the AD modules, the constituent proteins, interactions and cellular functions were not maintained between consecutive stages through all brain regions. Interestingly, the modules were collapsed with AD progression, specifically in the EC region. By identifying the modules that were affected by AD pathology, we found the transcriptional regulation-associated modules that interact with the proteasome-associated module via UCHL5 hub protein, which is a deubiquitinating enzyme. Considering PINs as a system made of network modules, we found that the modules relevant to the transcriptional regulation are disrupted in the EC region, which affects the ubiquitin-proteasome system.

  6. Network Physiology: How Organ Systems Dynamically Interact.

    Science.gov (United States)

    Bartsch, Ronny P; Liu, Kang K L; Bashan, Amir; Ivanov, Plamen Ch

    2015-01-01

    We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems.

  7. Network Physiology: How Organ Systems Dynamically Interact

    Science.gov (United States)

    Bartsch, Ronny P.; Liu, Kang K. L.; Bashan, Amir; Ivanov, Plamen Ch.

    2015-01-01

    We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems. PMID:26555073

  8. Silicon nanowire networks for multi-stage thermoelectric modules

    International Nuclear Information System (INIS)

    Norris, Kate J.; Garrett, Matthew P.; Zhang, Junce; Coleman, Elane; Tompa, Gary S.; Kobayashi, Nobuhiko P.

    2015-01-01

    Highlights: • Fabricated flexible single, double, and quadruple stacked Si thermoelectric modules. • Measured an enhanced power production of 27%, showing vertical stacking is scalable. • Vertically scalable thermoelectric module design of semiconducting nanowires. • Design can utilize either p or n-type semiconductors, both types are not required. • ΔT increases with thickness therefore power/area can increase as modules are stacked. - Abstract: We present the fabrication and characterization of single, double, and quadruple stacked flexible silicon nanowire network based thermoelectric modules. From double to quadruple stacked modules, power production increased 27%, demonstrating that stacking multiple nanowire thermoelectric devices in series is a scalable method to generate power by supplying larger temperature gradient. We present a vertically scalable multi-stage thermoelectric module design using semiconducting nanowires, eliminating the need for both n-type and p-type semiconductors for modules

  9. Incremental Centrality Algorithms for Dynamic Network Analysis

    Science.gov (United States)

    2013-08-01

    literature.   7.1.3 Small World Networks In 1998, Watts and Strogatz introduced a model that starts with a regular lattice (ring) of n nodes and...and S. Strogatz , "Collective Dynamics of ‘Small-World’ Networks," Nature, vol. 393, pp. 440-442, 1998. [13] T. Opsahl, "Structure and Evolution of...34On Random Graphs," Publicationes Mathematicae, vol. 6, 1959. [167] D.J. Watts and S.H. Strogatz , "Collective Dynamics of ‘Small-World’ Networks

  10. A method for identifying hierarchical sub-networks / modules and weighting network links based on their similarity in sub-network / module affiliation

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2016-06-01

    Full Text Available Some networks, including biological networks, consist of hierarchical sub-networks / modules. Based on my previous study, in present study a method for both identifying hierarchical sub-networks / modules and weighting network links is proposed. It is based on the cluster analysis in which between-node similarity in sets of adjacency nodes is used. Two matrices, linkWeightMat and linkClusterIDs, are achieved by using the algorithm. Two links with both the same weight in linkWeightMat and the same cluster ID in linkClusterIDs belong to the same sub-network / module. Two links with the same weight in linkWeightMat but different cluster IDs in linkClusterIDs belong to two sub-networks / modules at the same hirarchical level. However, a link with an unique cluster ID in linkClusterIDs does not belong to any sub-networks / modules. A sub-network / module of the greater weight is the more connected sub-network / modules. Matlab codes of the algorithm are presented.

  11. Functional modules by relating protein interaction networks and gene expression.

    Science.gov (United States)

    Tornow, Sabine; Mewes, H W

    2003-11-01

    Genes and proteins are organized on the basis of their particular mutual relations or according to their interactions in cellular and genetic networks. These include metabolic or signaling pathways and protein interaction, regulatory or co-expression networks. Integrating the information from the different types of networks may lead to the notion of a functional network and functional modules. To find these modules, we propose a new technique which is based on collective, multi-body correlations in a genetic network. We calculated the correlation strength of a group of genes (e.g. in the co-expression network) which were identified as members of a module in a different network (e.g. in the protein interaction network) and estimated the probability that this correlation strength was found by chance. Groups of genes with a significant correlation strength in different networks have a high probability that they perform the same function. Here, we propose evaluating the multi-body correlations by applying the superparamagnetic approach. We compare our method to the presently applied mean Pearson correlations and show that our method is more sensitive in revealing functional relationships.

  12. A network dynamics approach to chemical reaction networks

    Science.gov (United States)

    van der Schaft, A. J.; Rao, S.; Jayawardhana, B.

    2016-04-01

    A treatment of a chemical reaction network theory is given from the perspective of nonlinear network dynamics, in particular of consensus dynamics. By starting from the complex-balanced assumption, the reaction dynamics governed by mass action kinetics can be rewritten into a form which allows for a very simple derivation of a number of key results in the chemical reaction network theory, and which directly relates to the thermodynamics and port-Hamiltonian formulation of the system. Central in this formulation is the definition of a balanced Laplacian matrix on the graph of chemical complexes together with a resulting fundamental inequality. This immediately leads to the characterisation of the set of equilibria and their stability. Furthermore, the assumption of complex balancedness is revisited from the point of view of Kirchhoff's matrix tree theorem. Both the form of the dynamics and the deduced behaviour are very similar to consensus dynamics, and provide additional perspectives to the latter. Finally, using the classical idea of extending the graph of chemical complexes by a 'zero' complex, a complete steady-state stability analysis of mass action kinetics reaction networks with constant inflows and mass action kinetics outflows is given, and a unified framework is provided for structure-preserving model reduction of this important class of open reaction networks.

  13. Conflict and convention in dynamic networks.

    Science.gov (United States)

    Foley, Michael; Forber, Patrick; Smead, Rory; Riedl, Christoph

    2018-03-01

    An important way to resolve games of conflict (snowdrift, hawk-dove, chicken) involves adopting a convention: a correlated equilibrium that avoids any conflict between aggressive strategies. Dynamic networks allow individuals to resolve conflict via their network connections rather than changing their strategy. Exploring how behavioural strategies coevolve with social networks reveals new dynamics that can help explain the origins and robustness of conventions. Here, we model the emergence of conventions as correlated equilibria in dynamic networks. Our results show that networks have the tendency to break the symmetry between the two conventional solutions in a strongly biased way. Rather than the correlated equilibrium associated with ownership norms (play aggressive at home, not away), we usually see the opposite host-guest norm (play aggressive away, not at home) evolve on dynamic networks, a phenomenon common to human interaction. We also show that learning to avoid conflict can produce realistic network structures in a way different than preferential attachment models. © 2017 The Author(s).

  14. Markovian dynamics on complex reaction networks

    Energy Technology Data Exchange (ETDEWEB)

    Goutsias, J., E-mail: goutsias@jhu.edu; Jenkinson, G., E-mail: jenkinson@jhu.edu

    2013-08-10

    Complex networks, comprised of individual elements that interact with each other through reaction channels, are ubiquitous across many scientific and engineering disciplines. Examples include biochemical, pharmacokinetic, epidemiological, ecological, social, neural, and multi-agent networks. A common approach to modeling such networks is by a master equation that governs the dynamic evolution of the joint probability mass function of the underlying population process and naturally leads to Markovian dynamics for such process. Due however to the nonlinear nature of most reactions and the large size of the underlying state-spaces, computation and analysis of the resulting stochastic population dynamics is a difficult task. This review article provides a coherent and comprehensive coverage of recently developed approaches and methods to tackle this problem. After reviewing a general framework for modeling Markovian reaction networks and giving specific examples, the authors present numerical and computational techniques capable of evaluating or approximating the solution of the master equation, discuss a recently developed approach for studying the stationary behavior of Markovian reaction networks using a potential energy landscape perspective, and provide an introduction to the emerging theory of thermodynamic analysis of such networks. Three representative problems of opinion formation, transcription regulation, and neural network dynamics are used as illustrative examples.

  15. Markovian dynamics on complex reaction networks

    International Nuclear Information System (INIS)

    Goutsias, J.; Jenkinson, G.

    2013-01-01

    Complex networks, comprised of individual elements that interact with each other through reaction channels, are ubiquitous across many scientific and engineering disciplines. Examples include biochemical, pharmacokinetic, epidemiological, ecological, social, neural, and multi-agent networks. A common approach to modeling such networks is by a master equation that governs the dynamic evolution of the joint probability mass function of the underlying population process and naturally leads to Markovian dynamics for such process. Due however to the nonlinear nature of most reactions and the large size of the underlying state-spaces, computation and analysis of the resulting stochastic population dynamics is a difficult task. This review article provides a coherent and comprehensive coverage of recently developed approaches and methods to tackle this problem. After reviewing a general framework for modeling Markovian reaction networks and giving specific examples, the authors present numerical and computational techniques capable of evaluating or approximating the solution of the master equation, discuss a recently developed approach for studying the stationary behavior of Markovian reaction networks using a potential energy landscape perspective, and provide an introduction to the emerging theory of thermodynamic analysis of such networks. Three representative problems of opinion formation, transcription regulation, and neural network dynamics are used as illustrative examples

  16. Learning State Space Dynamics in Recurrent Networks

    Science.gov (United States)

    Simard, Patrice Yvon

    Fully recurrent (asymmetrical) networks can be used to learn temporal trajectories. The network is unfolded in time, and backpropagation is used to train the weights. The presence of recurrent connections creates internal states in the system which vary as a function of time. The resulting dynamics can provide interesting additional computing power but learning is made more difficult by the existence of internal memories. This study first exhibits the properties of recurrent networks in terms of convergence when the internal states of the system are unknown. A new energy functional is provided to change the weights of the units in order to the control the stability of the fixed points of the network's dynamics. The power of the resultant algorithm is illustrated with the simulation of a content addressable memory. Next, the more general case of time trajectories on a recurrent network is studied. An application is proposed in which trajectories are generated to draw letters as a function of an input. In another application of recurrent systems, a neural network certain temporal properties observed in human callosally sectioned brains. Finally the proposed algorithm for stabilizing dynamics around fixed points is extended to one for stabilizing dynamics around time trajectories. Its effects are illustrated on a network which generates Lisajous curves.

  17. Dynamic Protection of Optical Networks

    DEFF Research Database (Denmark)

    Ruepp, Sarah Renée

    2008-01-01

    This thesis deals with making optical networks resilient to failures. The recovery performance of path, segment and span restoration is evaluated in a network with limited wavelength conversion capability using both standard and enhanced wavelength assignment schemes. The enhanced wavelength...... stubs at the failure adjacent nodes. Both modifcations have a positive influence on the recovery percentage. The recovery enhancements are applicable in both single and multi-domain network environments. Stub release, where the still working parts of a failure affected connection are released prior...... of the modularity of capacity units is investigated for resilient network design. Different span upgrading strategies and algorithms for finding restoration paths are evaluated. Furthermore, the capacity effciency of constraining restoration requests for the same destination node to the same restoration path...

  18. Cognitive radio networks dynamic resource allocation schemes

    CERN Document Server

    Wang, Shaowei

    2014-01-01

    This SpringerBrief presents a survey of dynamic resource allocation schemes in Cognitive Radio (CR) Systems, focusing on the spectral-efficiency and energy-efficiency in wireless networks. It also introduces a variety of dynamic resource allocation schemes for CR networks and provides a concise introduction of the landscape of CR technology. The author covers in detail the dynamic resource allocation problem for the motivations and challenges in CR systems. The Spectral- and Energy-Efficient resource allocation schemes are comprehensively investigated, including new insights into the trade-off

  19. Competing dynamic phases of active polymer networks

    Science.gov (United States)

    Freedman, Simon; Banerjee, Shiladitya; Dinner, Aaron R.

    Recent experiments on in-vitro reconstituted assemblies of F-actin, myosin-II motors, and cross-linking proteins show that tuning local network properties can changes the fundamental biomechanical behavior of the system. For example, by varying cross-linker density and actin bundle rigidity, one can switch between contractile networks useful for reshaping cells, polarity sorted networks ideal for directed molecular transport, and frustrated networks with robust structural properties. To efficiently investigate the dynamic phases of actomyosin networks, we developed a coarse grained non-equilibrium molecular dynamics simulation of model semiflexible filaments, molecular motors, and cross-linkers with phenomenologically defined interactions. The simulation's accuracy was verified by benchmarking the mechanical properties of its individual components and collective behavior against experimental results at the molecular and network scales. By adjusting the model's parameters, we can reproduce the qualitative phases observed in experiment and predict the protein characteristics where phase crossovers could occur in collective network dynamics. Our model provides a framework for understanding cells' multiple uses of actomyosin networks and their applicability in materials research. Supported by the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program.

  20. Dynamics of High-Resolution Networks

    DEFF Research Database (Denmark)

    Sekara, Vedran

    the unprecedented amounts of information collected by mobile phones to gain detailed insight into the dynamics of social systems. This dissertation presents an unparalleled data collection campaign, collecting highly detailed traces for approximately 1000 people over the course of multiple years. The availability...... are we all affected by an ever changing network structure? Answering these questions will enrich our understanding of ourselves, our organizations, and our societies. Yet, mapping the dynamics of social networks has traditionally been an arduous undertaking. Today, however, it is possible to use...... of such dynamic maps allows us to probe the underlying social network and understand how individuals interact and form lasting friendships. More importantly, these highly detailed dynamic maps provide us new perspectives at traditional problems and allow us to quantify and predict human life....

  1. Control theory of digitally networked dynamic systems

    CERN Document Server

    Lunze, Jan

    2013-01-01

    The book gives an introduction to networked control systems and describes new modeling paradigms, analysis methods for event-driven, digitally networked systems, and design methods for distributed estimation and control. Networked model predictive control is developed as a means to tolerate time delays and packet loss brought about by the communication network. In event-based control the traditional periodic sampling is replaced by state-dependent triggering schemes. Novel methods for multi-agent systems ensure complete or clustered synchrony of agents with identical or with individual dynamic

  2. Resolution enhancement in neural networks with dynamical synapses

    Directory of Open Access Journals (Sweden)

    C. C. Alan Fung

    2013-06-01

    Full Text Available Conventionally, information is represented by spike rates in the neural system. Here, we consider the ability of temporally modulated activities in neuronal networks to carry information extra to spike rates. These temporal modulations, commonly known as population spikes, are due to the presence of synaptic depression in a neuronal network model. We discuss its relevance to an experiment on transparent motions in macaque monkeys by Treue et al. in 2000. They found that if the moving directions of objects are too close, the firing rate profile will be very similar to that with one direction. As the difference in the moving directions of objects is large enough, the neuronal system would respond in such a way that the network enhances the resolution in the moving directions of the objects. In this paper, we propose that this behavior can be reproduced by neural networks with dynamical synapses when there are multiple external inputs. We will demonstrate how resolution enhancement can be achieved, and discuss the conditions under which temporally modulated activities are able to enhance information processing performances in general.

  3. Phase resetting reveals network dynamics underlying a bacterial cell cycle.

    Science.gov (United States)

    Lin, Yihan; Li, Ying; Crosson, Sean; Dinner, Aaron R; Scherer, Norbert F

    2012-01-01

    Genomic and proteomic methods yield networks of biological regulatory interactions but do not provide direct insight into how those interactions are organized into functional modules, or how information flows from one module to another. In this work we introduce an approach that provides this complementary information and apply it to the bacterium Caulobacter crescentus, a paradigm for cell-cycle control. Operationally, we use an inducible promoter to express the essential transcriptional regulatory gene ctrA in a periodic, pulsed fashion. This chemical perturbation causes the population of cells to divide synchronously, and we use the resulting advance or delay of the division times of single cells to construct a phase resetting curve. We find that delay is strongly favored over advance. This finding is surprising since it does not follow from the temporal expression profile of CtrA and, in turn, simulations of existing network models. We propose a phenomenological model that suggests that the cell-cycle network comprises two distinct functional modules that oscillate autonomously and couple in a highly asymmetric fashion. These features collectively provide a new mechanism for tight temporal control of the cell cycle in C. crescentus. We discuss how the procedure can serve as the basis for a general approach for probing network dynamics, which we term chemical perturbation spectroscopy (CPS).

  4. Oscillations during observations: Dynamic oscillatory networks serving visuospatial attention.

    Science.gov (United States)

    Wiesman, Alex I; Heinrichs-Graham, Elizabeth; Proskovec, Amy L; McDermott, Timothy J; Wilson, Tony W

    2017-10-01

    The dynamic allocation of neural resources to discrete features within a visual scene enables us to react quickly and accurately to salient environmental circumstances. A network of bilateral cortical regions is known to subserve such visuospatial attention functions; however the oscillatory and functional connectivity dynamics of information coding within this network are not fully understood. Particularly, the coding of information within prototypical attention-network hubs and the subsecond functional connections formed between these hubs have not been adequately characterized. Herein, we use the precise temporal resolution of magnetoencephalography (MEG) to define spectrally specific functional nodes and connections that underlie the deployment of attention in visual space. Twenty-three healthy young adults completed a visuospatial discrimination task designed to elicit multispectral activity in visual cortex during MEG, and the resulting data were preprocessed and reconstructed in the time-frequency domain. Oscillatory responses were projected to the cortical surface using a beamformer, and time series were extracted from peak voxels to examine their temporal evolution. Dynamic functional connectivity was then computed between nodes within each frequency band of interest. We find that visual attention network nodes are defined functionally by oscillatory frequency, that the allocation of attention to the visual space dynamically modulates functional connectivity between these regions on a millisecond timescale, and that these modulations significantly correlate with performance on a spatial discrimination task. We conclude that functional hubs underlying visuospatial attention are segregated not only anatomically but also by oscillatory frequency, and importantly that these oscillatory signatures promote dynamic communication between these hubs. Hum Brain Mapp 38:5128-5140, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  5. Network monitoring module of BES III system environment

    International Nuclear Information System (INIS)

    Song Liwen; Zhao Jingwei; Zhang Bingyun

    2002-01-01

    In order to meet the needs of the complicated network architecture of BES III (Beijing Spectrometer III) and make sure normal online running in the future, it is necessary to develop a multi-platforms Network Monitoring Tool which can help system administrator monitor and manage BES III network. The author provides a module that can monitor not only the traffic of switch-router's ports but also the performance status of key devices in the network environment, meanwhile it can also give warning to manager and submit the related reports. the great sense, the theory basis, the implementing method and the graph in formation of this tool will be discussed

  6. Traffic Dynamics of Computer Networks

    Science.gov (United States)

    Fekete, Attila

    2008-10-01

    Two important aspects of the Internet, namely the properties of its topology and the characteristics of its data traffic, have attracted growing attention of the physics community. My thesis has considered problems of both aspects. First I studied the stochastic behavior of TCP, the primary algorithm governing traffic in the current Internet, in an elementary network scenario consisting of a standalone infinite-sized buffer and an access link. The effect of the fast recovery and fast retransmission (FR/FR) algorithms is also considered. I showed that my model can be extended further to involve the effect of link propagation delay, characteristic of WAN. I continued my thesis with the investigation of finite-sized semi-bottleneck buffers, where packets can be dropped not only at the link, but also at the buffer. I demonstrated that the behavior of the system depends only on a certain combination of the parameters. Moreover, an analytic formula was derived that gives the ratio of packet loss rate at the buffer to the total packet loss rate. This formula makes it possible to treat buffer-losses as if they were link-losses. Finally, I studied computer networks from a structural perspective. I demonstrated through fluid simulations that the distribution of resources, specifically the link bandwidth, has a serious impact on the global performance of the network. Then I analyzed the distribution of edge betweenness in a growing scale-free tree under the condition that a local property, the in-degree of the "younger" node of an arbitrary edge, is known in order to find an optimum distribution of link capacity. The derived formula is exact even for finite-sized networks. I also calculated the conditional expectation of edge betweenness, rescaled for infinite networks.

  7. The dynamics of transmission and the dynamics of networks.

    Science.gov (United States)

    Farine, Damien

    2017-05-01

    A toy example depicted here highlighting the results of a study in this issue of the Journal of Animal Ecology that investigates the impact of network dynamics on potential disease outbreaks. Infections (stars) that spread by contact only (left) reduce the predicted outbreak size compared to situations where individuals can become infected by moving through areas that previously contained infected individuals (right). This is potentially important in species where individuals, or in this case groups, have overlapping ranges (as depicted on the top right). Incorporating network dynamics that maintain information about the ordering of contacts (central blocks; including the ordering of spatial overlap as noted by the arrows that highlight the blue group arriving after the red group in top-right of the figure) is important for capturing how a disease might not have the opportunity to spread to all individuals. By contrast, a static or 'average' network (lower blocks) does not capture any of these dynamics. Interestingly, although static networks generally predict larger outbreak sizes, the authors find that in cases when transmission probability is low, this prediction can switch as a result of changes in the estimated intensity of contacts among individuals. [Colour figure can be viewed at wileyonlinelibrary.com]. Springer, A., Kappeler, P.M. & Nunn, C.L. (2017) Dynamic vs. static social networks in models of parasite transmission: Predicting Cryptosporidium spread in wild lemurs. Journal of Animal Ecology, 86, 419-433. The spread of disease or information through networks can be affected by several factors. Whether and how these factors are accounted for can fundamentally change the predicted impact of a spreading epidemic. Springer, Kappeler & Nunn () investigate the role of different modes of transmission and network dynamics on the predicted size of a disease outbreak across several groups of Verreaux's sifakas, a group-living species of lemur. While some factors

  8. Dynamics-based centrality for directed networks.

    Science.gov (United States)

    Masuda, Naoki; Kori, Hiroshi

    2010-11-01

    Determining the relative importance of nodes in directed networks is important in, for example, ranking websites, publications, and sports teams, and for understanding signal flows in systems biology. A prevailing centrality measure in this respect is the PageRank. In this work, we focus on another class of centrality derived from the Laplacian of the network. We extend the Laplacian-based centrality, which has mainly been applied to strongly connected networks, to the case of general directed networks such that we can quantitatively compare arbitrary nodes. Toward this end, we adopt the idea used in the PageRank to introduce global connectivity between all the pairs of nodes with a certain strength. Numerical simulations are carried out on some networks. We also offer interpretations of the Laplacian-based centrality for general directed networks in terms of various dynamical and structural properties of networks. Importantly, the Laplacian-based centrality defined as the stationary density of the continuous-time random walk with random jumps is shown to be equivalent to the absorption probability of the random walk with sinks at each node but without random jumps. Similarly, the proposed centrality represents the importance of nodes in dynamics on the original network supplied with sinks but not with random jumps.

  9. Critical dynamics in associative memory networks

    Directory of Open Access Journals (Sweden)

    Maximilian eUhlig

    2013-07-01

    Full Text Available Critical behavior in neural networks is characterized by scale-free avalanche size distributions and can be explained by self-regulatory mechanisms. Theoretical and experimental evidence indicates that information storage capacity reaches its maximum in the critical regime. We study the effect of structural connectivity formed by Hebbian learning on the criticality of network dynamics. The network endowed with Hebbian learning only does not allow for simultaneous information storage and criticality. However, the critical regime is can be stabilized by short-term synaptic dynamics in the form of synaptic depression and facilitation or, alternatively, by homeostatic adaptation of the synaptic weights. We show that a heterogeneous distribution of maximal synaptic strengths does not preclude criticality if the Hebbian learning is alternated with periods of critical dynamics recovery. We discuss the relevance of these findings for the flexibility of memory in aging and with respect to the recent theory of synaptic plasticity.

  10. Agent-based modeling and network dynamics

    CERN Document Server

    Namatame, Akira

    2016-01-01

    The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the gi...

  11. Altered brain network modules induce helplessness in major depressive disorder.

    Science.gov (United States)

    Peng, Daihui; Shi, Feng; Shen, Ting; Peng, Ziwen; Zhang, Chen; Liu, Xiaohua; Qiu, Meihui; Liu, Jun; Jiang, Kaida; Fang, Yiru; Shen, Dinggang

    2014-10-01

    The abnormal brain functional connectivity (FC) has been assumed to be a pathophysiological aspect of major depressive disorder (MDD). However, it is poorly understood, regarding the underlying patterns of global FC network and their relationships with the clinical characteristics of MDD. Resting-state functional magnetic resonance imaging data were acquired from 16 first episode, medication-naïve MDD patients and 16 healthy control subjects. The global FC network was constructed using 90 brain regions. The global topological patterns, e.g., small-worldness and modularity, and their relationships with depressive characteristics were investigated. Furthermore, the participant coefficient and module degree of MDD patients were measured to reflect the regional roles in module network, and the impairment of FC was examined by network based statistic. Small-world property was not altered in MDD. However, MDD patients exhibited 5 atypically reorganized modules compared to the controls. A positive relationship was also found among MDD patients between the intra-module I and helplessness factor evaluated via the Hamilton Depression Scale. Specifically, eight regions exhibited the abnormal participant coefficient or module degree, e.g., left superior orbital frontal cortex and right amygdala. The decreased FC was identified among the sub-network of 24 brain regions, e.g., frontal cortex, supplementary motor area, amygdala, thalamus, and hippocampus. The limited size of MDD samples precluded meaningful study of distinct clinical characteristics in relation to aberrant FC. The results revealed altered patterns of brain module network at the global level in MDD patients, which might contribute to the feelings of helplessness. Copyright © 2014 Elsevier B.V. All rights reserved.

  12. Hydrogen application dynamics and networks

    Energy Technology Data Exchange (ETDEWEB)

    Schmidt, E. [Air Liquide Large Industries, Champigny-sur-Marne (France)

    2010-12-30

    The Chemical Industry consumes large volumes of hydrogen as raw material for the manufacture of numerous products (e.g. polyamides and polyurethanes account for 60% of hydrogen demand). The hydrogen demand was in the recent past and will continue to be driven by the polyurethane family. China will host about 60% of new hydrogen needs over the period 2010-2015 becoming the first hydrogen market next year and reaching 25% of market share by 2015 (vs. only 4% in 2001). Air Liquide supplies large volumes of Hydrogen (and other Industrial Gases) to customers by on-site plants and through pipeline networks which offer significant benefits such as higher safety, reliability and flexibility of supply. Thanks to its long term strategy and heavy investment in large units and pipeline networks, Air Liquide is the Industrial Gas leader in most of the world class Petrochemical basins (Rotterdam, Antwerp, US Gulf Coast, Yosu, Caojing,..) (orig.)

  13. Modulation of neuronal network activity with ghrelin

    NARCIS (Netherlands)

    Stoyanova, Irina; Rutten, Wim; le Feber, Jakob

    2012-01-01

    Ghrelin is a neuropeptide regulating multiple physiological processes, including high brain functions such as learning and memory formation. However, the effect of ghrelin on network activity patterns and developments has not been studied yet. Therefore, we used dissociated cortical neurons plated

  14. Information Dynamics as Foundation for Network Management

    Science.gov (United States)

    2014-12-04

    developed to adapt to channel dynamics in a mobile network environment. We devise a low- complexity online scheduling algorithm integrated with the...has been accepted for the Journal on Network and Systems Management in 2014. - RINC programmable platform for Infrastructure -as-a-Service public... backend servers. Rather than implementing load balancing in dedicated appliances, commodity SDN switches can perform this function. We design

  15. Modulation aware cluster size optimisation in wireless sensor networks

    Science.gov (United States)

    Sriram Naik, M.; Kumar, Vinay

    2017-07-01

    Wireless sensor networks (WSNs) play a great role because of their numerous advantages to the mankind. The main challenge with WSNs is the energy efficiency. In this paper, we have focused on the energy minimisation with the help of cluster size optimisation along with consideration of modulation effect when the nodes are not able to communicate using baseband communication technique. Cluster size optimisations is important technique to improve the performance of WSNs. It provides improvement in energy efficiency, network scalability, network lifetime and latency. We have proposed analytical expression for cluster size optimisation using traditional sensing model of nodes for square sensing field with consideration of modulation effects. Energy minimisation can be achieved by changing the modulation schemes such as BPSK, 16-QAM, QPSK, 64-QAM, etc., so we are considering the effect of different modulation techniques in the cluster formation. The nodes in the sensing fields are random and uniformly deployed. It is also observed that placement of base station at centre of scenario enables very less number of modulation schemes to work in energy efficient manner but when base station placed at the corner of the sensing field, it enable large number of modulation schemes to work in energy efficient manner.

  16. Module detection in complex networks using integer optimisation

    Directory of Open Access Journals (Sweden)

    Tsoka Sophia

    2010-11-01

    Full Text Available Abstract Background The detection of modules or community structure is widely used to reveal the underlying properties of complex networks in biology, as well as physical and social sciences. Since the adoption of modularity as a measure of network topological properties, several methodologies for the discovery of community structure based on modularity maximisation have been developed. However, satisfactory partitions of large graphs with modest computational resources are particularly challenging due to the NP-hard nature of the related optimisation problem. Furthermore, it has been suggested that optimising the modularity metric can reach a resolution limit whereby the algorithm fails to detect smaller communities than a specific size in large networks. Results We present a novel solution approach to identify community structure in large complex networks and address resolution limitations in module detection. The proposed algorithm employs modularity to express network community structure and it is based on mixed integer optimisation models. The solution procedure is extended through an iterative procedure to diminish effects that tend to agglomerate smaller modules (resolution limitations. Conclusions A comprehensive comparative analysis of methodologies for module detection based on modularity maximisation shows that our approach outperforms previously reported methods. Furthermore, in contrast to previous reports, we propose a strategy to handle resolution limitations in modularity maximisation. Overall, we illustrate ways to improve existing methodologies for community structure identification so as to increase its efficiency and applicability.

  17. Complex networks under dynamic repair model

    Science.gov (United States)

    Chaoqi, Fu; Ying, Wang; Kun, Zhao; Yangjun, Gao

    2018-01-01

    Invulnerability is not the only factor of importance when considering complex networks' security. It is also critical to have an effective and reasonable repair strategy. Existing research on network repair is confined to the static model. The dynamic model makes better use of the redundant capacity of repaired nodes and repairs the damaged network more efficiently than the static model; however, the dynamic repair model is complex and polytropic. In this paper, we construct a dynamic repair model and systematically describe the energy-transfer relationships between nodes in the repair process of the failure network. Nodes are divided into three types, corresponding to three structures. We find that the strong coupling structure is responsible for secondary failure of the repaired nodes and propose an algorithm that can select the most suitable targets (nodes or links) to repair the failure network with minimal cost. Two types of repair strategies are identified, with different effects under the two energy-transfer rules. The research results enable a more flexible approach to network repair.

  18. perception of communication network fraud dynamics by network ...

    African Journals Online (AJOL)

    ES Obe

    work fraud dynamics by network administrators and stakeholders. In considering ... cyber crime within the last two years. How- ever, two-thirds of the ... ˆ increased exposure to unpredictable fi- nancial losses ... The intentions of the customers are reflected ..... 'There is a 95% confidence that the differ- ence between the ...

  19. An abstract machine model of dynamic module replacement

    OpenAIRE

    Walton, Chris; Kırlı, Dilsun; Gilmore, Stephen

    2000-01-01

    In this paper we define an abstract machine model for the mλ typed intermediate language. This abstract machine is used to give a formal description of the operation of run-time module replacement for the programming language Dynamic ML. The essential technical device which we employ for module replacement is a modification of two-space copying garbage collection. We show how the operation of module replacement could be applied to other garbage-collected languages such as Java.

  20. Discrete dynamic modeling of cellular signaling networks.

    Science.gov (United States)

    Albert, Réka; Wang, Rui-Sheng

    2009-01-01

    Understanding signal transduction in cellular systems is a central issue in systems biology. Numerous experiments from different laboratories generate an abundance of individual components and causal interactions mediating environmental and developmental signals. However, for many signal transduction systems there is insufficient information on the overall structure and the molecular mechanisms involved in the signaling network. Moreover, lack of kinetic and temporal information makes it difficult to construct quantitative models of signal transduction pathways. Discrete dynamic modeling, combined with network analysis, provides an effective way to integrate fragmentary knowledge of regulatory interactions into a predictive mathematical model which is able to describe the time evolution of the system without the requirement for kinetic parameters. This chapter introduces the fundamental concepts of discrete dynamic modeling, particularly focusing on Boolean dynamic models. We describe this method step-by-step in the context of cellular signaling networks. Several variants of Boolean dynamic models including threshold Boolean networks and piecewise linear systems are also covered, followed by two examples of successful application of discrete dynamic modeling in cell biology.

  1. Implementation of intensity modulation with dynamic multileaf collimation

    Energy Technology Data Exchange (ETDEWEB)

    Wong, J W; Yu, C; Jaffray, D [William Beaumont Hospital, Royal Oak, MI (United States)

    1995-12-01

    The computer-controlled multileaf collimator (MLC) marks one of the most important advances in radiation therapy. The device efficiently replaces manual blocking to shape fields and can be used to modulate beam intensity. The results of a research programme at William Beaumont Hospital, aimed at bringing dynamic intensity modulation into clinical use, are discussed.

  2. Optical vector network analyzer based on double-sideband modulation.

    Science.gov (United States)

    Jun, Wen; Wang, Ling; Yang, Chengwu; Li, Ming; Zhu, Ning Hua; Guo, Jinjin; Xiong, Liangming; Li, Wei

    2017-11-01

    We report an optical vector network analyzer (OVNA) based on double-sideband (DSB) modulation using a dual-parallel Mach-Zehnder modulator. The device under test (DUT) is measured twice with different modulation schemes. By post-processing the measurement results, the response of the DUT can be obtained accurately. Since DSB modulation is used in our approach, the measurement range is doubled compared with conventional single-sideband (SSB) modulation-based OVNA. Moreover, the measurement accuracy is improved by eliminating the even-order sidebands. The key advantage of the proposed scheme is that the measurement of a DUT with bandpass response can also be simply realized, which is a big challenge for the SSB-based OVNA. The proposed method is theoretically and experimentally demonstrated.

  3. Spreading dynamics in complex networks

    Science.gov (United States)

    Pei, Sen; Makse, Hernán A.

    2013-12-01

    Searching for influential spreaders in complex networks is an issue of great significance for applications across various domains, ranging from epidemic control, innovation diffusion, viral marketing, and social movement to idea propagation. In this paper, we first display some of the most important theoretical models that describe spreading processes, and then discuss the problem of locating both the individual and multiple influential spreaders respectively. Recent approaches in these two topics are presented. For the identification of privileged single spreaders, we summarize several widely used centralities, such as degree, betweenness centrality, PageRank, k-shell, etc. We investigate the empirical diffusion data in a large scale online social community—LiveJournal. With this extensive dataset, we find that various measures can convey very distinct information of nodes. Of all the users in the LiveJournal social network, only a small fraction of them are involved in spreading. For the spreading processes in LiveJournal, while degree can locate nodes participating in information diffusion with higher probability, k-shell is more effective in finding nodes with a large influence. Our results should provide useful information for designing efficient spreading strategies in reality.

  4. Spreading dynamics in complex networks

    International Nuclear Information System (INIS)

    Pei, Sen; Makse, Hernán A

    2013-01-01

    Searching for influential spreaders in complex networks is an issue of great significance for applications across various domains, ranging from epidemic control, innovation diffusion, viral marketing, and social movement to idea propagation. In this paper, we first display some of the most important theoretical models that describe spreading processes, and then discuss the problem of locating both the individual and multiple influential spreaders respectively. Recent approaches in these two topics are presented. For the identification of privileged single spreaders, we summarize several widely used centralities, such as degree, betweenness centrality, PageRank, k-shell, etc. We investigate the empirical diffusion data in a large scale online social community—LiveJournal. With this extensive dataset, we find that various measures can convey very distinct information of nodes. Of all the users in the LiveJournal social network, only a small fraction of them are involved in spreading. For the spreading processes in LiveJournal, while degree can locate nodes participating in information diffusion with higher probability, k-shell is more effective in finding nodes with a large influence. Our results should provide useful information for designing efficient spreading strategies in reality. (paper)

  5. Remote synchronization reveals network symmetries and functional modules.

    Science.gov (United States)

    Nicosia, Vincenzo; Valencia, Miguel; Chavez, Mario; Díaz-Guilera, Albert; Latora, Vito

    2013-04-26

    We study a Kuramoto model in which the oscillators are associated with the nodes of a complex network and the interactions include a phase frustration, thus preventing full synchronization. The system organizes into a regime of remote synchronization where pairs of nodes with the same network symmetry are fully synchronized, despite their distance on the graph. We provide analytical arguments to explain this result, and we show how the frustration parameter affects the distribution of phases. An application to brain networks suggests that anatomical symmetry plays a role in neural synchronization by determining correlated functional modules across distant locations.

  6. Synchronization of coupled chaotic dynamics on networks

    Indian Academy of Sciences (India)

    We review some recent work on the synchronization of coupled dynamical systems on a variety of networks. When nodes show synchronized behaviour, two interesting phenomena can be observed. First, there are some nodes of the floating type that show intermittent behaviour between getting attached to some clusters ...

  7. Modular networks with hierarchical organization: The dynamical ...

    Indian Academy of Sciences (India)

    Most of the complex systems seen in real life also have associated dynamics [10], and the ... another example, this time a hierarchical structure, viz., the Cayley tree with b ..... natural constraints operating on networks in real life, such as the ...

  8. Dynamical networks with topological self-organization

    Science.gov (United States)

    Zak, M.

    2001-01-01

    Coupled evolution of state and topology of dynamical networks is introduced. Due to the well organized tensor structure, the governing equations are presented in a canonical form, and required attractors as well as their basins can be easily implanted and controlled.

  9. Dynamics of nephron-vascular network

    DEFF Research Database (Denmark)

    Postnov, Dmitry; Postnov, D E; Marsh, D J

    2012-01-01

    The paper presents a modeling study of the spatial dynamics of a nephro-vascular network consisting of individual nephrons connected via a tree-like vascular branching structure. We focus on the effects of nonlinear mechanisms that are responsible for the formation of synchronous patterns in order...

  10. Discerning connectivity from dynamics in climate networks

    Czech Academy of Sciences Publication Activity Database

    Paluš, Milan; Hartman, David; Hlinka, Jaroslav; Vejmelka, Martin

    2011-01-01

    Roč. 18, č. 5 (2011), s. 751-763 ISSN 1023-5809 R&D Projects: GA ČR GCP103/11/J068 Institutional research plan: CEZ:AV0Z10300504 Keywords : complex networks * climate dynamics * connectivity * North Atlantic Oscillation * solar activity Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.597, year: 2011

  11. Hardware Module for the Security Enhancement of Optical Telecom Network Equipment

    International Nuclear Information System (INIS)

    Nadeem; Ali, M.

    2015-01-01

    The telecommunication equipment physical security threats have increased not only in Pakistan but also anywhere in the world and hence, reducing the revenue. This new challenging and alarming situation is created for the telecom network provider. The main focus of this paper is to provide a low cost economical design for reducing the theft of the costly telecommunication equipment like optical network units (ONU). This system is based on instant messaging on the mobile in the event of theft through GSM modem. The proposed security module is dynamic, flexible and can also be integrated in the existing networks and separately having its own independent low power consumption source. The module will continuously work successfully under different scenarios such as completely isolated from other devices by power break down or by fibre cut. (author)

  12. Methylphenidate Modulates Functional Network Connectivity to Enhance Attention

    Science.gov (United States)

    Zhang, Sheng; Hsu, Wei-Ting; Scheinost, Dustin; Finn, Emily S.; Shen, Xilin; Constable, R. Todd; Li, Chiang-Shan R.; Chun, Marvin M.

    2016-01-01

    Recent work has demonstrated that human whole-brain functional connectivity patterns measured with fMRI contain information about cognitive abilities, including sustained attention. To derive behavioral predictions from connectivity patterns, our group developed a connectome-based predictive modeling (CPM) approach (Finn et al., 2015; Rosenberg et al., 2016). Previously using CPM, we defined a high-attention network, comprising connections positively correlated with performance on a sustained attention task, and a low-attention network, comprising connections negatively correlated with performance. Validating the networks as generalizable biomarkers of attention, models based on network strength at rest predicted attention-deficit/hyperactivity disorder (ADHD) symptoms in an independent group of individuals (Rosenberg et al., 2016). To investigate whether these networks play a causal role in attention, here we examined their strength in healthy adults given methylphenidate (Ritalin), a common ADHD treatment, compared with unmedicated controls. As predicted, individuals given methylphenidate showed patterns of connectivity associated with better sustained attention: higher high-attention and lower low-attention network strength than controls. There was significant overlap between the high-attention network and a network with greater strength in the methylphenidate group, and between the low-attention network and a network with greater strength in the control group. Network strength also predicted behavior on a stop-signal task, such that participants with higher go response rates showed higher high-attention and lower low-attention network strength. These results suggest that methylphenidate acts by modulating functional brain networks related to sustained attention, and that changing whole-brain connectivity patterns may help improve attention. SIGNIFICANCE STATEMENT Recent work identified a promising neuromarker of sustained attention based on whole

  13. Asymmetric Modulation Gains in Network Coded Relay Networks

    DEFF Research Database (Denmark)

    Roetter, Daniel Enrique Lucani; Fitzek, Frank

    2015-01-01

    Wireless relays have usually been considered in two ways. On the one hand, a physical layer approach focused on per-packet reliability and involving the relay on each packet transmission. On the other, recent approaches have relied on the judicious activation of the relay at the network level to ...

  14. Dynamical Networks Characterization of Space Weather Events

    Science.gov (United States)

    Orr, L.; Chapman, S. C.; Dods, J.; Gjerloev, J. W.

    2017-12-01

    Space weather can cause disturbances to satellite systems, impacting navigation technology and telecommunications; it can cause power loss and aviation disruption. A central aspect of the earth's magnetospheric response to space weather events are large scale and rapid changes in ionospheric current patterns. Space weather is highly dynamic and there are still many controversies about how the current system evolves in time. The recent SuperMAG initiative, collates ground-based vector magnetic field time series from over 200 magnetometers with 1-minute temporal resolution. In principle this combined dataset is an ideal candidate for quantification using dynamical networks. Network properties and parameters allow us to characterize the time dynamics of the full spatiotemporal pattern of the ionospheric current system. However, applying network methodologies to physical data presents new challenges. We establish whether a given pair of magnetometers are connected in the network by calculating their canonical cross correlation. The magnetometers are connected if their cross correlation exceeds a threshold. In our physical time series this threshold needs to be both station specific, as it varies with (non-linear) individual station sensitivity and location, and able to vary with season, which affects ground conductivity. Additionally, the earth rotates and therefore the ground stations move significantly on the timescales of geomagnetic disturbances. The magnetometers are non-uniformly spatially distributed. We will present new methodology which addresses these problems and in particular achieves dynamic normalization of the physical time series in order to form the network. Correlated disturbances across the magnetometers capture transient currents. Once the dynamical network has been obtained [1][2] from the full magnetometer data set it can be used to directly identify detailed inferred transient ionospheric current patterns and track their dynamics. We will show

  15. Dynamical systems on networks a tutorial

    CERN Document Server

    Porter, Mason A

    2016-01-01

    This volume is a tutorial for the study of dynamical systems on networks. It discusses both methodology and models, including spreading models for social and biological contagions. The authors focus especially on “simple” situations that are analytically tractable, because they are insightful and provide useful springboards for the study of more complicated scenarios. This tutorial, which also includes key pointers to the literature, should be helpful for junior and senior undergraduate students, graduate students, and researchers from mathematics, physics, and engineering who seek to study dynamical systems on networks but who may not have prior experience with graph theory or networks. Mason A. Porter is Professor of Nonlinear and Complex Systems at the Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, UK. He is also a member of the CABDyN Complexity Centre and a Tutorial Fellow of Somerville College. James P. Gleeson is Professor of Industrial and Appli...

  16. Power Aware Dynamic Provisioning of HPC Networks

    Energy Technology Data Exchange (ETDEWEB)

    Groves, Taylor [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Grant, Ryan [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-10-01

    Future exascale systems are under increased pressure to find power savings. The network, while it consumes a considerable amount of power is often left out of the picture when discussing total system power. Even when network power is being considered, the references are frequently a decade or older and rely on models that lack validation on modern inter- connects. In this work we explore how dynamic mechanisms of an Infiniband network save power and at what granularity we can engage these features. We explore this within the context of the host controller adapter (HCA) on the node and for the fabric, i.e. switches, using three different mechanisms of dynamic link width, frequency and disabling of links for QLogic and Mellanox systems. Our results show that while there is some potential for modest power savings, real world systems need to improved responsiveness to adjustments in order to fully leverage these savings. This page intentionally left blank.

  17. Individual heterogeneity generating explosive system network dynamics.

    Science.gov (United States)

    Manrique, Pedro D; Johnson, Neil F

    2018-03-01

    Individual heterogeneity is a key characteristic of many real-world systems, from organisms to humans. However, its role in determining the system's collective dynamics is not well understood. Here we study how individual heterogeneity impacts the system network dynamics by comparing linking mechanisms that favor similar or dissimilar individuals. We find that this heterogeneity-based evolution drives an unconventional form of explosive network behavior, and it dictates how a polarized population moves toward consensus. Our model shows good agreement with data from both biological and social science domains. We conclude that individual heterogeneity likely plays a key role in the collective development of real-world networks and communities, and it cannot be ignored.

  18. Individual heterogeneity generating explosive system network dynamics

    Science.gov (United States)

    Manrique, Pedro D.; Johnson, Neil F.

    2018-03-01

    Individual heterogeneity is a key characteristic of many real-world systems, from organisms to humans. However, its role in determining the system's collective dynamics is not well understood. Here we study how individual heterogeneity impacts the system network dynamics by comparing linking mechanisms that favor similar or dissimilar individuals. We find that this heterogeneity-based evolution drives an unconventional form of explosive network behavior, and it dictates how a polarized population moves toward consensus. Our model shows good agreement with data from both biological and social science domains. We conclude that individual heterogeneity likely plays a key role in the collective development of real-world networks and communities, and it cannot be ignored.

  19. Force Factor Modulation in Electro Dynamic Loudspeakers

    DEFF Research Database (Denmark)

    Risbo, Lars; Agerkvist, Finn T.; Tinggaard, Carsten

    2016-01-01

    The relationship between the non-linear phenomenon of ’reluctance force’ and the position dependency of the voice coil inductance was established in 1949 by Cunningham, who called it ’magnetic attraction force’. This paper revisits Cunningham’s analysis and expands it into a generalised form...... that includes the frequency dependency and applies to coils with non-inductive (lossy) blocked impedance. The paper also demonstrates that Cunningham’s force can be explained physically as a modulation of the force factor which again is directly linked to modulation of the flux of the coil. A verification based...... on both experiments and simulations is presented along discussions of the impact of force factor modulation for various motor topologies. Finally, it is shown that the popular L2R2 coil impedance model does not correctly predict the force unless the new analysis is applied....

  20. Effects of Neuromodulation on Excitatory-Inhibitory Neural Network Dynamics Depend on Network Connectivity Structure

    Science.gov (United States)

    Rich, Scott; Zochowski, Michal; Booth, Victoria

    2018-01-01

    Acetylcholine (ACh), one of the brain's most potent neuromodulators, can affect intrinsic neuron properties through blockade of an M-type potassium current. The effect of ACh on excitatory and inhibitory cells with this potassium channel modulates their membrane excitability, which in turn affects their tendency to synchronize in networks. Here, we study the resulting changes in dynamics in networks with inter-connected excitatory and inhibitory populations (E-I networks), which are ubiquitous in the brain. Utilizing biophysical models of E-I networks, we analyze how the network connectivity structure in terms of synaptic connectivity alters the influence of ACh on the generation of synchronous excitatory bursting. We investigate networks containing all combinations of excitatory and inhibitory cells with high (Type I properties) or low (Type II properties) modulatory tone. To vary network connectivity structure, we focus on the effects of the strengths of inter-connections between excitatory and inhibitory cells (E-I synapses and I-E synapses), and the strengths of intra-connections among excitatory cells (E-E synapses) and among inhibitory cells (I-I synapses). We show that the presence of ACh may or may not affect the generation of network synchrony depending on the network connectivity. Specifically, strong network inter-connectivity induces synchronous excitatory bursting regardless of the cellular propensity for synchronization, which aligns with predictions of the PING model. However, when a network's intra-connectivity dominates its inter-connectivity, the propensity for synchrony of either inhibitory or excitatory cells can determine the generation of network-wide bursting.

  1. Adaptive-network models of collective dynamics

    Science.gov (United States)

    Zschaler, G.

    2012-09-01

    Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom. In this work, which is essentially my PhD thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system's collective and long-term behaviour by applying tools from dynamical systems theory. We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge

  2. Bilingual Contexts Modulate the Inhibitory Control Network

    Directory of Open Access Journals (Sweden)

    Jing Yang

    2018-03-01

    Full Text Available The present functional magnetic resonance imaging (fMRI study investigated influences of language contexts on inhibitory control and the underlying neural processes. Thirty Cantonese–Mandarin–English trilingual speakers, who were highly proficient in Cantonese (L1 and Mandarin (L2, and moderately proficient in English (L3, performed a picture-naming task in three dual-language contexts (L1-L2, L2-L3, and L1-L3. After each of the three naming tasks, participants performed a flanker task, measuring contextual effects on the inhibitory control system. Behavioral results showed a typical flanker effect in the L2-L3 and L1-L3 condition, but not in the L1-L2 condition, which indicates contextual facilitation on inhibitory control performance by the L1-L2 context. Whole brain analysis of the fMRI data acquired during the flanker tasks showed more neural activations in the right prefrontal cortex and subcortical areas in the L2-L3 and L1-L3 condition on one hand as compared to the L1-L2 condition on the other hand, suggesting greater involvement of the cognitive control areas when participants were performing the flanker task in L2-L3 and L1-L3 contexts. Effective connectivity analyses displayed a cortical-subcortical-cerebellar circuitry for inhibitory control in the trilinguals. However, contrary to the right-lateralized network in the L1-L2 condition, functional networks for inhibitory control in the L2-L3 and L1-L3 condition are less integrated and more left-lateralized. These findings provide a novel perspective for investigating the interaction between bilingualism (multilingualism and inhibitory control by demonstrating instant behavioral effects and neural plasticity as a function of changes in global language contexts.

  3. Innovation networking between stability and political dynamics

    DEFF Research Database (Denmark)

    Koch, Christian

    2004-01-01

    of the contribution is to challenge and transcend these notions and develop an understanding of innovation networks as an interplay between stable and dynamic elements, where political processes in innovation are much more than a disruptive and even a counterproductive feature. It reviews the growing number...... of studies that highlight the political aspect of innovation. The paper reports on a study of innovation processes conducted within the EU—TSER-programme and a study made under the banner of management of technology. Intensive field studies in two constellations of enterprises were carried out. One......This contribution views innovation as a social activity of building networks, using software product development in multicompany alliances and networks as example. Innovation networks are frequently understood as quite stable arrangements characterised by high trust among the participants. The aim...

  4. Vascular dynamics aid a coupled neurovascular network learn sparse independent features: A computational model

    Directory of Open Access Journals (Sweden)

    Ryan Thomas Philips

    2016-02-01

    Full Text Available Cerebral vascular dynamics are generally thought to be controlled by neural activity in a unidirectional fashion. However, both computational modeling and experimental evidence point to the feedback effects of vascular dynamics on neural activity. Vascular feedback in the form of glucose and oxygen controls neuronal ATP, either directly or via the agency of astrocytes, which in turn modulates neural firing. Recently, a detailed model of the neuron-astrocyte-vessel system has shown how vasomotion can modulate neural firing. Similarly, arguing from known cerebrovascular physiology, an approach known as `hemoneural hypothesis' postulates functional modulation of neural activity by vascular feedback. To instantiate this perspective, we present a computational model in which a network of `vascular units' supplies energy to a neural network. The complex dynamics of the vascular network, modeled by a network of oscillators, turns neurons ON and OFF randomly. The informational consequence of such dynamics is explored in the context of an auto-encoder network. In the proposed model, each vascular unit supplies energy to a subset of hidden neurons of an autoencoder network, which constitutes its `projective field'. Neurons that receive adequate energy in a given trial have reduced threshold, and thus are prone to fire. Dynamics of the vascular network are governed by changes in the reconstruction error of the auto-encoder network, interpreted as the neuronal demand. Vascular feedback causes random inactivation of a subset of hidden neurons in every trial. We observe that, under conditions of desynchronized vascular dynamics, the output reconstruction error is low and the feature vectors learnt are sparse and independent. Our earlier modeling study highlighted the link between desynchronized vascular dynamics and efficient energy delivery in skeletal muscle. We now show that desynchronized vascular dynamics leads to efficient training in an auto

  5. Nonparametric inference of network structure and dynamics

    Science.gov (United States)

    Peixoto, Tiago P.

    The network structure of complex systems determine their function and serve as evidence for the evolutionary mechanisms that lie behind them. Despite considerable effort in recent years, it remains an open challenge to formulate general descriptions of the large-scale structure of network systems, and how to reliably extract such information from data. Although many approaches have been proposed, few methods attempt to gauge the statistical significance of the uncovered structures, and hence the majority cannot reliably separate actual structure from stochastic fluctuations. Due to the sheer size and high-dimensionality of many networks, this represents a major limitation that prevents meaningful interpretations of the results obtained with such nonstatistical methods. In this talk, I will show how these issues can be tackled in a principled and efficient fashion by formulating appropriate generative models of network structure that can have their parameters inferred from data. By employing a Bayesian description of such models, the inference can be performed in a nonparametric fashion, that does not require any a priori knowledge or ad hoc assumptions about the data. I will show how this approach can be used to perform model comparison, and how hierarchical models yield the most appropriate trade-off between model complexity and quality of fit based on the statistical evidence present in the data. I will also show how this general approach can be elegantly extended to networks with edge attributes, that are embedded in latent spaces, and that change in time. The latter is obtained via a fully dynamic generative network model, based on arbitrary-order Markov chains, that can also be inferred in a nonparametric fashion. Throughout the talk I will illustrate the application of the methods with many empirical networks such as the internet at the autonomous systems level, the global airport network, the network of actors and films, social networks, citations among

  6. Volunteerism: Social Network Dynamics and Education

    Science.gov (United States)

    Ajrouch, Kristine J.; Antonucci, Toni C.; Webster, Noah J.

    2016-01-01

    Objectives . We examine how changes in social networks influence volunteerism through bridging (diversity) and bonding (spending time) mechanisms. We further investigate whether social network change substitutes or amplifies the effects of education on volunteerism. Methods . Data (n = 543) are drawn from a two-wave survey of Social Relations and Health over the Life Course (SRHLC). Zero-inflated negative binomial regressions were conducted to test competing hypotheses about how changes in social network characteristics alone and in conjunction with education level predict likelihood and frequency of volunteering. Results . Changes in social networks were associated with volunteerism: as the proportion of family members decreased and the average number of network members living within a one-hour drive increased over time, participants reported higher odds of volunteering. The substitution hypothesis was supported: social networks that exhibited more geographic proximity and greater contact frequency over-time compensated for lower levels of education to predict volunteering more hours. Discussion . The dynamic role of social networks and the ways in which they may work through bridging and bonding to influence both likelihood and frequency of volunteering are discussed. The potential benefits of volunteerism in light of longer life expectancies and smaller families are also considered. PMID:25512570

  7. Modulation of neuronal dynamic range using two different adaptation mechanisms

    Directory of Open Access Journals (Sweden)

    Lei Wang

    2017-01-01

    Full Text Available The capability of neurons to discriminate between intensity of external stimulus is measured by its dynamic range. A larger dynamic range indicates a greater probability of neuronal survival. In this study, the potential roles of adaptation mechanisms (ion currents in modulating neuronal dynamic range were numerically investigated. Based on the adaptive exponential integrate-and-fire model, which includes two different adaptation mechanisms, i.e. subthreshold and suprathreshold (spike-triggered adaptation, our results reveal that the two adaptation mechanisms exhibit rather different roles in regulating neuronal dynamic range. Specifically, subthreshold adaptation acts as a negative factor that observably decreases the neuronal dynamic range, while suprathreshold adaptation has little influence on the neuronal dynamic range. Moreover, when stochastic noise was introduced into the adaptation mechanisms, the dynamic range was apparently enhanced, regardless of what state the neuron was in, e.g. adaptive or non-adaptive. Our model results suggested that the neuronal dynamic range can be differentially modulated by different adaptation mechanisms. Additionally, noise was a non-ignorable factor, which could effectively modulate the neuronal dynamic range.

  8. Advances in dynamic network modeling in complex transportation systems

    CERN Document Server

    Ukkusuri, Satish V

    2013-01-01

    This book focuses on the latest in dynamic network modeling, including route guidance and traffic control in transportation systems and other complex infrastructure networks. Covers dynamic traffic assignment, flow modeling, mobile sensor deployment and more.

  9. Dynamic social networks based on movement

    Science.gov (United States)

    Scharf, Henry; Hooten, Mevin B.; Fosdick, Bailey K.; Johnson, Devin S.; London, Joshua M.; Durban, John W.

    2016-01-01

    Network modeling techniques provide a means for quantifying social structure in populations of individuals. Data used to define social connectivity are often expensive to collect and based on case-specific, ad hoc criteria. Moreover, in applications involving animal social networks, collection of these data is often opportunistic and can be invasive. Frequently, the social network of interest for a given population is closely related to the way individuals move. Thus, telemetry data, which are minimally invasive and relatively inexpensive to collect, present an alternative source of information. We develop a framework for using telemetry data to infer social relationships among animals. To achieve this, we propose a Bayesian hierarchical model with an underlying dynamic social network controlling movement of individuals via two mechanisms: an attractive effect and an aligning effect. We demonstrate the model and its ability to accurately identify complex social behavior in simulation, and apply our model to telemetry data arising from killer whales. Using auxiliary information about the study population, we investigate model validity and find the inferred dynamic social network is consistent with killer whale ecology and expert knowledge.

  10. Modeling Insurgent Network Structure and Dynamics

    Science.gov (United States)

    Gabbay, Michael; Thirkill-Mackelprang, Ashley

    2010-03-01

    We present a methodology for mapping insurgent network structure based on their public rhetoric. Indicators of cooperative links between insurgent groups at both the leadership and rank-and-file levels are used, such as joint policy statements or joint operations claims. In addition, a targeting policy measure is constructed on the basis of insurgent targeting claims. Network diagrams which integrate these measures of insurgent cooperation and ideology are generated for different periods of the Iraqi and Afghan insurgencies. The network diagrams exhibit meaningful changes which track the evolution of the strategic environment faced by insurgent groups. Correlations between targeting policy and network structure indicate that insurgent targeting claims are aimed at establishing a group identity among the spectrum of rank-and-file insurgency supporters. A dynamical systems model of insurgent alliance formation and factionalism is presented which evolves the relationship between insurgent group dyads as a function of their ideological differences and their current relationships. The ability of the model to qualitatively and quantitatively capture insurgent network dynamics observed in the data is discussed.

  11. Dynamic motifs in socio-economic networks

    Science.gov (United States)

    Zhang, Xin; Shao, Shuai; Stanley, H. Eugene; Havlin, Shlomo

    2014-12-01

    Socio-economic networks are of central importance in economic life. We develop a method of identifying and studying motifs in socio-economic networks by focusing on “dynamic motifs,” i.e., evolutionary connection patterns that, because of “node acquaintances” in the network, occur much more frequently than random patterns. We examine two evolving bi-partite networks: i) the world-wide commercial ship chartering market and ii) the ship build-to-order market. We find similar dynamic motifs in both bipartite networks, even though they describe different economic activities. We also find that “influence” and “persistence” are strong factors in the interaction behavior of organizations. When two companies are doing business with the same customer, it is highly probable that another customer who currently only has business relationship with one of these two companies, will become customer of the second in the future. This is the effect of influence. Persistence means that companies with close business ties to customers tend to maintain their relationships over a long period of time.

  12. Mean field methods for cortical network dynamics

    DEFF Research Database (Denmark)

    Hertz, J.; Lerchner, Alexander; Ahmadi, M.

    2004-01-01

    We review the use of mean field theory for describing the dynamics of dense, randomly connected cortical circuits. For a simple network of excitatory and inhibitory leaky integrate- and-fire neurons, we can show how the firing irregularity, as measured by the Fano factor, increases...... with the strength of the synapses in the network and with the value to which the membrane potential is reset after a spike. Generalizing the model to include conductance-based synapses gives insight into the connection between the firing statistics and the high- conductance state observed experimentally in visual...

  13. MONOMIALS AND BASIN CYLINDERS FOR NETWORK DYNAMICS.

    Science.gov (United States)

    Austin, Daniel; Dinwoodie, Ian H

    We describe methods to identify cylinder sets inside a basin of attraction for Boolean dynamics of biological networks. Such sets are used for designing regulatory interventions that make the system evolve towards a chosen attractor, for example initiating apoptosis in a cancer cell. We describe two algebraic methods for identifying cylinders inside a basin of attraction, one based on the Groebner fan that finds monomials that define cylinders and the other on primary decomposition. Both methods are applied to current examples of gene networks.

  14. Pramipexole Modulates Interregional Connectivity Within the Sensorimotor Network.

    Science.gov (United States)

    Ye, Zheng; Hammer, Anke; Münte, Thomas F

    2017-05-01

    Pramipexole is widely prescribed to treat Parkinson's disease but has been reported to cause impulse control disorders such as pathological gambling. Recent neurocomputational models suggested that D2 agonists may distort functional connections between the striatum and the motor cortex, resulting in impaired reinforcement learning and pathological gambling. To examine how D2 agonists modulate the striatal-motor connectivity, we carried out a pharmacological resting-state functional magnetic resonance imaging study with a double-blind randomized within-subject crossover design. We analyzed the medication-induced changes of network connectivity and topology with two approaches, an independent component analysis (ICA) and a graph theoretical analysis (GTA). The ICA identified the sensorimotor network (SMN) as well as other classical resting-state networks. Within the SMN, the connectivity between the right caudate nucleus and other cortical regions was weaker under pramipexole than under placebo. The GTA measured the topological properties of the whole-brain network at global and regional levels. Both the whole-brain network under placebo and that under pramipexole were identified as small-world networks. The two whole-brain networks were similar in global efficiency, clustering coefficient, small-world index, and modularity. However, the degree of the right caudate nucleus decreased under pramipexole mainly due to the loss of the connectivity with the supplementary motor area, paracentral lobule, and precentral and postcentral gyrus of the SMN. The two network analyses consistently revealed that pramipexole weakened the functional connectivity between the caudate nucleus and the SMN regions.

  15. Dynamic Trust Management for Mobile Networks and Its Applications

    Science.gov (United States)

    Bao, Fenye

    2013-01-01

    Trust management in mobile networks is challenging due to dynamically changing network environments and the lack of a centralized trusted authority. In this dissertation research, we "design" and "validate" a class of dynamic trust management protocols for mobile networks, and demonstrate the utility of dynamic trust management…

  16. High Dimensional Modulation and MIMO Techniques for Access Networks

    DEFF Research Database (Denmark)

    Binti Othman, Maisara

    Exploration of advanced modulation formats and multiplexing techniques for next generation optical access networks are of interest as promising solutions for delivering multiple services to end-users. This thesis addresses this from two different angles: high dimensionality carrierless...... the capacity per wavelength of the femto-cell network. Bit rate up to 1.59 Gbps with fiber-wireless transmission over 1 m air distance is demonstrated. The results presented in this thesis demonstrate the feasibility of high dimensionality CAP in increasing the number of dimensions and their potentially......) optical access network. 2 X 2 MIMO RoF employing orthogonal frequency division multiplexing (OFDM) with 5.6 GHz RoF signaling over all-vertical cavity surface emitting lasers (VCSEL) WDM passive optical networks (PONs). We have employed polarization division multiplexing (PDM) to further increase...

  17. Network dynamics: The World Wide Web

    Science.gov (United States)

    Adamic, Lada Ariana

    Despite its rapidly growing and dynamic nature, the Web displays a number of strong regularities which can be understood by drawing on methods of statistical physics. This thesis finds power-law distributions in website sizes, traffic, and links, and more importantly, develops a stochastic theory which explains them. Power-law link distributions are shown to lead to network characteristics which are especially suitable for scalable localized search. It is also demonstrated that the Web is a "small world": to reach one site from any other takes an average of only 4 hops, while most related sites cluster together. Additional dynamical properties of the Web graph are extracted from diffusion processes.

  18. Functional asynchronous networks: Factorization of dynamics and function

    Directory of Open Access Journals (Sweden)

    Bick Christian

    2016-01-01

    Full Text Available In this note we describe the theory of functional asynchronous networks and one of the main results, the Modularization of Dynamics Theorem, which for a large class of functional asynchronous networks gives a factorization of dynamics in terms of constituent subnetworks. For these networks we can give a complete description of the network function in terms of the function of the events comprising the network and thereby answer a question originally raised by Alon in the context of biological networks.

  19. Activating and inhibiting connections in biological network dynamics

    Directory of Open Access Journals (Sweden)

    Knight Rob

    2008-12-01

    Full Text Available Abstract Background Many studies of biochemical networks have analyzed network topology. Such work has suggested that specific types of network wiring may increase network robustness and therefore confer a selective advantage. However, knowledge of network topology does not allow one to predict network dynamical behavior – for example, whether deleting a protein from a signaling network would maintain the network's dynamical behavior, or induce oscillations or chaos. Results Here we report that the balance between activating and inhibiting connections is important in determining whether network dynamics reach steady state or oscillate. We use a simple dynamical model of a network of interacting genes or proteins. Using the model, we study random networks, networks selected for robust dynamics, and examples of biological network topologies. The fraction of activating connections influences whether the network dynamics reach steady state or oscillate. Conclusion The activating fraction may predispose a network to oscillate or reach steady state, and neutral evolution or selection of this parameter may affect the behavior of biological networks. This principle may unify the dynamics of a wide range of cellular networks. Reviewers Reviewed by Sergei Maslov, Eugene Koonin, and Yu (Brandon Xia (nominated by Mark Gerstein. For the full reviews, please go to the Reviewers' comments section.

  20. System crash as dynamics of complex networks.

    Science.gov (United States)

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

    2016-10-18

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

  1. Organization of excitable dynamics in hierarchical biological networks.

    Directory of Open Access Journals (Sweden)

    Mark Müller-Linow

    Full Text Available This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation. The results demonstrate that two principal topological aspects of hierarchical networks, node centrality and network modularity, correlate with the network activity patterns at different levels of spontaneous network activation. The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes. These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks.

  2. Dynamics of domain wall networks with junctions

    International Nuclear Information System (INIS)

    Avelino, P. P.; Oliveira, J. C. R. E.; Martins, C. J. A. P.; Menezes, J.; Menezes, R.

    2008-01-01

    We use a combination of analytic tools and an extensive set of the largest and most accurate three-dimensional field theory numerical simulations to study the dynamics of domain wall networks with junctions. We build upon our previous work and consider a class of models which, in the limit of large number N of coupled scalar fields, approaches the so-called ''ideal'' model (in terms of its potential to lead to network frustration). We consider values of N between N=2 and N=20, and a range of cosmological epochs, and we also compare this class of models with other toy models used in the past. In all cases we find compelling evidence for a gradual approach to scaling, strongly supporting our no-frustration conjecture. We also discuss the various possible types of junctions (including cases where there is a hierarchy of them) and their roles in the dynamics of the network. Finally, we provide a cosmological Zel'dovich-type bound on the energy scale of this kind of defect network: it must be lower than 10 keV.

  3. Validating module network learning algorithms using simulated data.

    Science.gov (United States)

    Michoel, Tom; Maere, Steven; Bonnet, Eric; Joshi, Anagha; Saeys, Yvan; Van den Bulcke, Tim; Van Leemput, Koenraad; van Remortel, Piet; Kuiper, Martin; Marchal, Kathleen; Van de Peer, Yves

    2007-05-03

    In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Despite the demonstrated success of such algorithms in uncovering biologically relevant regulatory relations, further developments in the area are hampered by a lack of tools to compare the performance of alternative module network learning strategies. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance. Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators. We show that data simulators such as SynTReN are very well suited for the purpose of developing, testing and improving module network

  4. Dynamics of the ethanolamine glycerophospholipid remodeling network.

    Directory of Open Access Journals (Sweden)

    Lu Zhang

    Full Text Available Acyl chain remodeling in lipids is a critical biochemical process that plays a central role in disease. However, remodeling remains poorly understood, despite massive increases in lipidomic data. In this work, we determine the dynamic network of ethanolamine glycerophospholipid (PE remodeling, using data from pulse-chase experiments and a novel bioinformatic network inference approach. The model uses a set of ordinary differential equations based on the assumptions that (1 sn1 and sn2 acyl positions are independently remodeled; (2 remodeling reaction rates are constant over time; and (3 acyl donor concentrations are constant. We use a novel fast and accurate two-step algorithm to automatically infer model parameters and their values. This is the first such method applicable to dynamic phospholipid lipidomic data. Our inference procedure closely fits experimental measurements and shows strong cross-validation across six independent experiments with distinct deuterium-labeled PE precursors, demonstrating the validity of our assumptions. In contrast, fits of randomized data or fits using random model parameters are worse. A key outcome is that we are able to robustly distinguish deacylation and reacylation kinetics of individual acyl chain types at the sn1 and sn2 positions, explaining the established prevalence of saturated and unsaturated chains in the respective positions. The present study thus demonstrates that dynamic acyl chain remodeling processes can be reliably determined from dynamic lipidomic data.

  5. Network structure shapes spontaneous functional connectivity dynamics.

    Science.gov (United States)

    Shen, Kelly; Hutchison, R Matthew; Bezgin, Gleb; Everling, Stefan; McIntosh, Anthony R

    2015-04-08

    The structural organization of the brain constrains the range of interactions between different regions and shapes ongoing information processing. Therefore, it is expected that large-scale dynamic functional connectivity (FC) patterns, a surrogate measure of coordination between brain regions, will be closely tied to the fiber pathways that form the underlying structural network. Here, we empirically examined the influence of network structure on FC dynamics by comparing resting-state FC (rsFC) obtained using BOLD-fMRI in macaques (Macaca fascicularis) to structural connectivity derived from macaque axonal tract tracing studies. Consistent with predictions from simulation studies, the correspondence between rsFC and structural connectivity increased as the sample duration increased. Regions with reciprocal structural connections showed the most stable rsFC across time. The data suggest that the transient nature of FC is in part dependent on direct underlying structural connections, but also that dynamic coordination can occur via polysynaptic pathways. Temporal stability was found to be dependent on structural topology, with functional connections within the rich-club core exhibiting the greatest stability over time. We discuss these findings in light of highly variable functional hubs. The results further elucidate how large-scale dynamic functional coordination exists within a fixed structural architecture. Copyright © 2015 the authors 0270-6474/15/355579-10$15.00/0.

  6. Pro-cognitive drug effects modulate functional brain network organization

    Science.gov (United States)

    Giessing, Carsten; Thiel, Christiane M.

    2012-01-01

    Previous studies document that cholinergic and noradrenergic drugs improve attention, memory and cognitive control in healthy subjects and patients with neuropsychiatric disorders. In humans neural mechanisms of cholinergic and noradrenergic modulation have mainly been analyzed by investigating drug-induced changes of task-related neural activity measured with functional magnetic resonance imaging (fMRI). Endogenous neural activity has often been neglected. Further, although drugs affect the coupling between neurons, only a few human studies have explicitly addressed how drugs modulate the functional connectome, i.e., the functional neural interactions within the brain. These studies have mainly focused on synchronization or correlation of brain activations. Recently, there are some drug studies using graph theory and other new mathematical approaches to model the brain as a complex network of interconnected processing nodes. Using such measures it is possible to detect not only focal, but also subtle, widely distributed drug effects on functional network topology. Most important, graph theoretical measures also quantify whether drug-induced changes in topology or network organization facilitate or hinder information processing. Several studies could show that functional brain integration is highly correlated with behavioral performance suggesting that cholinergic and noradrenergic drugs which improve measures of cognitive performance should increase functional network integration. The purpose of this paper is to show that graph theory provides a mathematical tool to develop theory-driven biomarkers of pro-cognitive drug effects, and also to discuss how these approaches can contribute to the understanding of the role of cholinergic and noradrenergic modulation in the human brain. Finally we discuss the “global workspace” theory as a theoretical framework of pro-cognitive drug effects and argue that pro-cognitive effects of cholinergic and noradrenergic drugs

  7. Implementation of quantum key distribution network simulation module in the network simulator NS-3

    Science.gov (United States)

    Mehic, Miralem; Maurhart, Oliver; Rass, Stefan; Voznak, Miroslav

    2017-10-01

    As the research in quantum key distribution (QKD) technology grows larger and becomes more complex, the need for highly accurate and scalable simulation technologies becomes important to assess the practical feasibility and foresee difficulties in the practical implementation of theoretical achievements. Due to the specificity of the QKD link which requires optical and Internet connection between the network nodes, to deploy a complete testbed containing multiple network hosts and links to validate and verify a certain network algorithm or protocol would be very costly. Network simulators in these circumstances save vast amounts of money and time in accomplishing such a task. The simulation environment offers the creation of complex network topologies, a high degree of control and repeatable experiments, which in turn allows researchers to conduct experiments and confirm their results. In this paper, we described the design of the QKD network simulation module which was developed in the network simulator of version 3 (NS-3). The module supports simulation of the QKD network in an overlay mode or in a single TCP/IP mode. Therefore, it can be used to simulate other network technologies regardless of QKD.

  8. Structural network heterogeneities and network dynamics: a possible dynamical mechanism for hippocampal memory reactivation.

    Science.gov (United States)

    Jablonski, Piotr; Poe, Gina; Zochowski, Michal

    2007-03-01

    The hippocampus has the capacity for reactivating recently acquired memories and it is hypothesized that one of the functions of sleep reactivation is the facilitation of consolidation of novel memory traces. The dynamic and network processes underlying such a reactivation remain, however, unknown. We show that such a reactivation characterized by local, self-sustained activity of a network region may be an inherent property of the recurrent excitatory-inhibitory network with a heterogeneous structure. The entry into the reactivation phase is mediated through a physiologically feasible regulation of global excitability and external input sources, while the reactivated component of the network is formed through induced network heterogeneities during learning. We show that structural changes needed for robust reactivation of a given network region are well within known physiological parameters.

  9. From cognitive networks to seizures: Stimulus evoked dynamics in a coupled cortical network

    Science.gov (United States)

    Lee, Jaejin; Ermentrout, Bard; Bodner, Mark

    2013-12-01

    Epilepsy is one of the most common neuropathologies worldwide. Seizures arising in epilepsy or in seizure disorders are characterized generally by uncontrolled spread of excitation and electrical activity to a limited region or even over the entire cortex. While it is generally accepted that abnormal excessive firing and synchronization of neuron populations lead to seizures, little is known about the precise mechanisms underlying human epileptic seizures, the mechanisms of transitions from normal to paroxysmal activity, or about how seizures spread. Further complication arises in that seizures do not occur with a single type of dynamics but as many different phenotypes and genotypes with a range of patterns, synchronous oscillations, and time courses. The concept of preventing, terminating, or modulating seizures and/or paroxysmal activity through stimulation of brain has also received considerable attention. The ability of such stimulation to prevent or modulate such pathological activity may depend on identifiable parameters. In this work, firing rate networks with inhibitory and excitatory populations were modeled. Network parameters were chosen to model normal working memory behaviors. Two different models of cognitive activity were developed. The first model consists of a single network corresponding to a local area of the brain. The second incorporates two networks connected through sparser recurrent excitatory connectivity with transmission delays ranging from approximately 3 ms within local populations to 15 ms between populations residing in different cortical areas. The effect of excitatory stimulation to activate working memory behavior through selective persistent activation of populations is examined in the models, and the conditions and transition mechanisms through which that selective activation breaks down producing spreading paroxysmal activity and seizure states are characterized. Specifically, we determine critical parameters and architectural

  10. Discrete Opinion Dynamics on Online Social Networks

    Science.gov (United States)

    Hu, Yan-Li; Bai, Liang; Zhang, Wei-Ming

    2013-01-01

    This paper focuses on the dynamics of binary opinions {+1, -1} on online social networks consisting of heterogeneous actors. In our model, actors update their opinions under the interplay of social influence and self- affirmation, which leads to rich dynamical behaviors on online social networks. We find that the opinion leading to the consensus features an advantage of the initially weighted fraction based on actors' strength over the other, instead of the population. For the role of specific actors, the consensus converges towards the opinion that a small fraction of high-strength actors hold, and individual diversity of self-affirmation slows down the ordering process of consensus. These indicate that high-strength actors play an essential role in opinion formation with strong social influence as well as high persistence. Further investigations show that the initial fraction of high-strength actors to dominate the evolution depends on the heterogeneity of the strength distribution, and less high-strength actors are needed in the case of a smaller exponent of power-law distribution of actors' strength. Our study provides deep insights into the role of social influence and self-affirmation on opinion formation on online social networks.

  11. Discrete Opinion Dynamics on Online Social Networks

    International Nuclear Information System (INIS)

    Hu Yan-Li; Bai Liang; Zhang Wei-Ming

    2013-01-01

    This paper focuses on the dynamics of binary opinions {+1, −1} on online social networks consisting of heterogeneous actors. In our model, actors update their opinions under the interplay of social influence and self- affirmation, which leads to rich dynamical behaviors on online social networks. We find that the opinion leading to the consensus features an advantage of the initially weighted fraction based on actors' strength over the other, instead of the population. For the role of specific actors, the consensus converges towards the opinion that a small fraction of high-strength actors hold, and individual diversity of self-affirmation slows down the ordering process of consensus. These indicate that high-strength actors play an essential role in opinion formation with strong social influence as well as high persistence. Further investigations show that the initial fraction of high-strength actors to dominate the evolution depends on the heterogeneity of the strength distribution, and less high-strength actors are needed in the case of a smaller exponent of power-law distribution of actors' strength. Our study provides deep insights into the role of social influence and self-affirmation on opinion formation on online social networks. (general)

  12. Dynamical investigation of modulated Kepler RR Lyrae stars

    Directory of Open Access Journals (Sweden)

    Plachy E.

    2015-01-01

    Full Text Available We performed a non-linear dynamical analysis on the Blazhko modulation for the first time. Our results suggest that the detection of chaotic nature behind the phenomenon is limited by the instrumental effects and the data processing problems of the Kepler pipeline concerning high-amplitude variable stars.

  13. Physical Proximity and Spreading in Dynamic Social Networks

    OpenAIRE

    Stopczynski, Arkadiusz; Pentland, Alex Sandy; Lehmann, Sune

    2015-01-01

    Most infectious diseases spread on a dynamic network of human interactions. Recent studies of social dynamics have provided evidence that spreading patterns may depend strongly on detailed micro-dynamics of the social system. We have recorded every single interaction within a large population, mapping out---for the first time at scale---the complete proximity network for a densely-connected system. Here we show the striking impact of interaction-distance on the network structure and dynamics ...

  14. Pushing the network harder `Dynamic Ratings`

    Energy Technology Data Exchange (ETDEWEB)

    Liondas, V.; Howatt, C.; Norrie, P. [Prospect Electricity, Blacktown, NSW (Australia)

    1995-12-31

    The demand for electricity in the area serviced by Prospect Electricity, is increasing, necessitating an increase in power transfer through the distribution system. Satisfying this demand generally requires more electrical infrastructure, but this is becoming less feasible due to economic constraints and environmental considerations. This paper discusses an approach to the dynamic (or real time) rating of different network elements. Dynamic rating is taken to mean that rating which is determined essentially in real time using known temperature constraints for the relevant elements, together with the prevailing ambient or environmental conditions. The purpose of dynamic rating is to achieve greater system utilization, thus allowing significant economic benefits, particularly from deferment of capital expenditure and greater operational flexibility. A number of technologies are being developed to do this for overhead lines, underground cables and transformers. The dynamic rating of cables has proved to be the most intractable part of the dynamic rating project. Work done to date, however, using finite element techniques together with the proposals to further develop point and distributed temperature sensing using fibre optic methods gives some confidence to the future success of this development. (author). 2 tabs., 4 figs., 4 refs.

  15. Opinion dynamics on an adaptive random network

    Science.gov (United States)

    Benczik, I. J.; Benczik, S. Z.; Schmittmann, B.; Zia, R. K. P.

    2009-04-01

    We revisit the classical model for voter dynamics in a two-party system with two basic modifications. In contrast to the original voter model studied in regular lattices, we implement the opinion formation process in a random network of agents in which interactions are no longer restricted by geographical distance. In addition, we incorporate the rapidly changing nature of the interpersonal relations in the model. At each time step, agents can update their relationships. This update is determined by their own opinion, and by their preference to make connections with individuals sharing the same opinion, or rather with opponents. In this way, the network is built in an adaptive manner, in the sense that its structure is correlated and evolves with the dynamics of the agents. The simplicity of the model allows us to examine several issues analytically. We establish criteria to determine whether consensus or polarization will be the outcome of the dynamics and on what time scales these states will be reached. In finite systems consensus is typical, while in infinite systems a disordered metastable state can emerge and persist for infinitely long time before consensus is reached.

  16. Collective Dynamics in Physical and Social Networks

    Science.gov (United States)

    Isakov, Alexander

    We study four systems where individual units come together to display a range of collective behavior. First, we consider a physical system of phase oscillators on a network that expands the Kuramoto model to include oscillator-network interactions and the presence of noise: using a Hebbian-like learning rule, oscillators that synchronize in turn strengthen their connections to each other. We find that the average degree of connectivity strongly affects rates of flipping between aligned and anti-aligned states, and that this result persists to the case of complex networks. Turning to a fully multi-player, multi-strategy evolutionary dynamics model of cooperating bacteria that change who they give resources to and take resources from, we find several regimes that give rise to high levels of collective structure in the resulting networks. In this setting, we also explore the conditions in which an intervention that affects cooperation itself (e.g. "seeding the network with defectors") can lead to wiping out an infection. We find a non-monotonic connection between the percent of disabled cooperation and cure rate, suggesting that in some regimes a limited perturbation can lead to total population collapse. At a larger scale, we study how the locomotor system recovers after amputation in fruit flies. Through experiment and a theoretical model of multi-legged motion controlled by neural oscillators, we find that proprioception plays a role in the ability of flies to control leg forces appropriately to recover from a large initial turning bias induced by the injury. Finally, at the human scale, we consider a social network in a traditional society in Africa to understand how social ties lead to group formation for collective action (stealth raids). We identify critical and distinct roles for both leadership (important for catalyzing a group) and friendship (important for final composition). We conclude with prospects for future work.

  17. Dynamics of neural networks with continuous attractors

    Science.gov (United States)

    Fung, C. C. Alan; Wong, K. Y. Michael; Wu, Si

    2008-10-01

    We investigate the dynamics of continuous attractor neural networks (CANNs). Due to the translational invariance of their neuronal interactions, CANNs can hold a continuous family of stationary states. We systematically explore how their neutral stability facilitates the tracking performance of a CANN, which is believed to have wide applications in brain functions. We develop a perturbative approach that utilizes the dominant movement of the network stationary states in the state space. We quantify the distortions of the bump shape during tracking, and study their effects on the tracking performance. Results are obtained on the maximum speed for a moving stimulus to be trackable, and the reaction time to catch up an abrupt change in stimulus.

  18. Multinephron dynamics on the renal vascular network

    DEFF Research Database (Denmark)

    Marsh, Donald J; Wexler, Anthony S; Brazhe, Alexey

    2012-01-01

    Tubuloglomerular feedback (TGF) and the myogenic mechanism combine in each nephron to regulate blood flow and glomerular filtration rate. Both mechanisms are non-linear, generate self-sustained oscillations, and interact as their signals converge on arteriolar smooth muscle, forming a regulatory...... clusters. In-phase synchronization predominated among nephrons separated by 1 or 3 vascular nodes, and anti-phase synchronization for 5 or 7 nodes of separation. Nephron dynamics were irregular and contained low frequency fluctuations. Results are consistent with simultaneous blood flow measurements...... of both mechanisms in the regulatory ensemble, to examine the effects of network structure on nephron synchronization. Symmetry, as a property of a network, facilitates synchronization. Nephrons received blood from a symmetric electrically conductive vascular tree. Symmetry was created by using identical...

  19. A class of convergent neural network dynamics

    Science.gov (United States)

    Fiedler, Bernold; Gedeon, Tomáš

    1998-01-01

    We consider a class of systems of differential equations in Rn which exhibits convergent dynamics. We find a Lyapunov function and show that every bounded trajectory converges to the set of equilibria. Our result generalizes the results of Cohen and Grossberg (1983) for convergent neural networks. It replaces the symmetry assumption on the matrix of weights by the assumption on the structure of the connections in the neural network. We prove the convergence result also for a large class of Lotka-Volterra systems. These are naturally defined on the closed positive orthant. We show that there are no heteroclinic cycles on the boundary of the positive orthant for the systems in this class.

  20. Exponential Synchronization of Uncertain Complex Dynamical Networks with Delay Coupling

    International Nuclear Information System (INIS)

    Wang Lifu; Kong Zhi; Jing Yuanwei

    2010-01-01

    This paper studies the global exponential synchronization of uncertain complex delayed dynamical networks. The network model considered is general dynamical delay networks with unknown network structure and unknown coupling functions but bounded. Novel delay-dependent linear controllers are designed via the Lyapunov stability theory. Especially, it is shown that the controlled networks are globally exponentially synchronized with a given convergence rate. An example of typical dynamical network of this class, having the Lorenz system at each node, has been used to demonstrate and verify the novel design proposed. And, the numerical simulation results show the effectiveness of proposed synchronization approaches. (general)

  1. Content Dynamics Over the Network Cloud

    Science.gov (United States)

    2015-11-04

    AFRL-AFOSR-CL-TR-2015-0003 Content dynamics over the network cloud Fernando Paganini UNIVERSIDAD ORT URUGUAY CUAREIM 1451 MONTEVIDEO, 11100 UY 11/04...approved for public release. FINAL PERFORMANCE REPORT: 7-15-2012 to 7-14-2015 AFOSR GRANT NUMBER: FA9550-12-1-0398 PI: Fernando Paganini Universidad ORT...349-362, Apr 2014. 7. M. Zubeldía, “From resource allocation to neighbor selection in peer-to-peer networks”, MS Thesis, Universidad ORT Uruguay

  2. Modulation of attentional networks by food-related disinhibition.

    Science.gov (United States)

    Hege, Maike A; Stingl, Krunoslav T; Veit, Ralf; Preissl, Hubert

    2017-07-01

    The risk of weight gain is especially related to disinhibition, which indicates the responsiveness to external food stimuli with associated disruptions in eating control. We adapted a food-related version of the attention network task and used functional magnetic resonance imaging to study the effects of disinhibition on attentional networks in 19 normal-weight participants. High disinhibition scores were associated with a rapid reorienting response to food pictures after invalid cueing and with an enhanced alerting effect of a warning cue signalizing the upcoming appearance of a food picture. Imaging data revealed activation of a right-lateralized ventral attention network during reorienting. The faster the reorienting and the higher the disinhibition score, the less activation of this network was observed. The alerting contrast showed activation in visual, temporo-parietal and anterior sites. These modulations of attentional networks by food-related disinhibition might be related to an attentional bias to energy dense and palatable food and increased intake of food in disinhibited individuals. Copyright © 2017 Elsevier Inc. All rights reserved.

  3. Space station common module network topology and hardware development

    Science.gov (United States)

    Anderson, P.; Braunagel, L.; Chwirka, S.; Fishman, M.; Freeman, K.; Eason, D.; Landis, D.; Lech, L.; Martin, J.; Mccorkle, J.

    1990-01-01

    Conceptual space station common module power management and distribution (SSM/PMAD) network layouts and detailed network evaluations were developed. Individual pieces of hardware to be developed for the SSM/PMAD test bed were identified. A technology assessment was developed to identify pieces of equipment requiring development effort. Equipment lists were developed from the previously selected network schematics. Additionally, functional requirements for the network equipment as well as other requirements which affected the suitability of specific items for use on the Space Station Program were identified. Assembly requirements were derived based on the SSM/PMAD developed requirements and on the selected SSM/PMAD network concepts. Basic requirements and simplified design block diagrams are included. DC remote power controllers were successfully integrated into the DC Marshall Space Flight Center breadboard. Two DC remote power controller (RPC) boards experienced mechanical failure of UES 706 stud-mounted diodes during mechanical installation of the boards into the system. These broken diodes caused input to output shorting of the RPC's. The UES 706 diodes were replaced on these RPC's which eliminated the problem. The DC RPC's as existing in the present breadboard configuration do not provide ground fault protection because the RPC was designed to only switch the hot side current. If ground fault protection were to be implemented, it would be necessary to design the system so the RPC switched both the hot and the return sides of power.

  4. Reliable dynamics in Boolean and continuous networks

    International Nuclear Information System (INIS)

    Ackermann, Eva; Drossel, Barbara; Peixoto, Tiago P

    2012-01-01

    We investigate the dynamical behavior of a model of robust gene regulatory networks which possess ‘entirely reliable’ trajectories. In a Boolean representation, these trajectories are characterized by being insensitive to the order in which the nodes are updated, i.e. they always go through the same sequence of states. The Boolean model for gene activity is compared with a continuous description in terms of differential equations for the concentrations of mRNA and proteins. We found that entirely reliable Boolean trajectories can be reproduced perfectly in the continuous model when realistic Hill coefficients are used. We investigate to what extent this high correspondence between Boolean and continuous trajectories depends on the extent of reliability of the Boolean trajectories, and we identify simple criteria that enable the faithful reproduction of the Boolean dynamics in the continuous description. (paper)

  5. Development of Active External Network Topology Module for Floodlight SDN Controller

    Directory of Open Access Journals (Sweden)

    A. A. Noskov

    2015-01-01

    Full Text Available Traditional network architecture is inflexible and complicated. This observation has led to a paradigm shift towards software-defined networking (SDN, where network management level is separated from data forwarding level. This change was made possible by control plane transfer from the switching equipment to software modules that run on a dedicated server, called the controller (or network operating system, or network applications, that work with this controller. Methods of representation, storage and communication interfaces with network topology elements are the most important aspects of network operating systems available to SDN user because performance of some key controller modules is heavily dependent on internal representation of the network topology. Notably, firewall and routing modules are examples of such modules. This article describes the methods used for presentation and storage of network topologies, as well as interface to the corresponding Floodlight modules. An alternative algorithm has been suggested and developed for message exchange conveying network topology alterations between the controller and network applications. Proposed algorithm makes implementation of module alerting based on subscription to the relevant events. API for interaction between controller and network applications has been developed. This algorithm and API formed the base for Topology Tracker module capable to inform network applications about the changes that had occurred in the network topology and also stores compact representation of the network to speed up the interaction process.

  6. Dynamic burstiness of word-occurrence and network modularity in textbook systems

    Science.gov (United States)

    Cui, Xue-Mei; Yoon, Chang No; Youn, Hyejin; Lee, Sang Hoon; Jung, Jean S.; Han, Seung Kee

    2017-12-01

    We show that the dynamic burstiness of word occurrence in textbook systems is attributed to the modularity of the word association networks. At first, a measure of dynamic burstiness is introduced to quantify burstiness of word occurrence in a textbook. The advantage of this measure is that the dynamic burstiness is decomposable into two contributions: one coming from the inter-event variance and the other from the memory effects. Comparing network structures of physics textbook systems with those of surrogate random textbooks without the memory or variance effects are absent, we show that the network modularity increases systematically with the dynamic burstiness. The intra-connectivity of individual word representing the strength of a tie with which a node is bound to a module accordingly increases with the dynamic burstiness, suggesting individual words with high burstiness are strongly bound to one module. Based on the frequency and dynamic burstiness, physics terminology is classified into four categories: fundamental words, topical words, special words, and common words. In addition, we test the correlation between the dynamic burstiness of word occurrence and network modularity using a two-state model of burst generation.

  7. Revealing Pathway Dynamics in Heart Diseases by Analyzing Multiple Differential Networks.

    Directory of Open Access Journals (Sweden)

    Xiaoke Ma

    2015-06-01

    Full Text Available Development of heart diseases is driven by dynamic changes in both the activity and connectivity of gene pathways. Understanding these dynamic events is critical for understanding pathogenic mechanisms and development of effective treatment. Currently, there is a lack of computational methods that enable analysis of multiple gene networks, each of which exhibits differential activity compared to the network of the baseline/healthy condition. We describe the iMDM algorithm to identify both unique and shared gene modules across multiple differential co-expression networks, termed M-DMs (multiple differential modules. We applied iMDM to a time-course RNA-Seq dataset generated using a murine heart failure model generated on two genotypes. We showed that iMDM achieves higher accuracy in inferring gene modules compared to using single or multiple co-expression networks. We found that condition-specific M-DMs exhibit differential activities, mediate different biological processes, and are enriched for genes with known cardiovascular phenotypes. By analyzing M-DMs that are present in multiple conditions, we revealed dynamic changes in pathway activity and connectivity across heart failure conditions. We further showed that module dynamics were correlated with the dynamics of disease phenotypes during the development of heart failure. Thus, pathway dynamics is a powerful measure for understanding pathogenesis. iMDM provides a principled way to dissect the dynamics of gene pathways and its relationship to the dynamics of disease phenotype. With the exponential growth of omics data, our method can aid in generating systems-level insights into disease progression.

  8. Imaging complex nutrient dynamics in mycelial networks.

    Science.gov (United States)

    Fricker, M D; Lee, J A; Bebber, D P; Tlalka, M; Hynes, J; Darrah, P R; Watkinson, S C; Boddy, L

    2008-08-01

    techniques shows that as the colony forms, it self-organizes into well demarcated domains that are identifiable by differences in the phase relationship of the pulses. On the centimetre to metre scale, we have begun to use techniques borrowed from graph theory to characterize the development and dynamics of the network, and used these abstracted network models to predict the transport characteristics, resilience, and cost of the network.

  9. Stochastic dynamics of genetic broadcasting networks

    Science.gov (United States)

    Potoyan, Davit A.; Wolynes, Peter G.

    2017-11-01

    The complex genetic programs of eukaryotic cells are often regulated by key transcription factors occupying or clearing out of a large number of genomic locations. Orchestrating the residence times of these factors is therefore important for the well organized functioning of a large network. The classic models of genetic switches sidestep this timing issue by assuming the binding of transcription factors to be governed entirely by thermodynamic protein-DNA affinities. Here we show that relying on passive thermodynamics and random release times can lead to a "time-scale crisis" for master genes that broadcast their signals to a large number of binding sites. We demonstrate that this time-scale crisis for clearance in a large broadcasting network can be resolved by actively regulating residence times through molecular stripping. We illustrate these ideas by studying a model of the stochastic dynamics of the genetic network of the central eukaryotic master regulator NFκ B which broadcasts its signals to many downstream genes that regulate immune response, apoptosis, etc.

  10. Tau can switch microtubule network organizations: from random networks to dynamic and stable bundles.

    Science.gov (United States)

    Prezel, Elea; Elie, Auréliane; Delaroche, Julie; Stoppin-Mellet, Virginie; Bosc, Christophe; Serre, Laurence; Fourest-Lieuvin, Anne; Andrieux, Annie; Vantard, Marylin; Arnal, Isabelle

    2018-01-15

    In neurons, microtubule networks alternate between single filaments and bundled arrays under the influence of effectors controlling their dynamics and organization. Tau is a microtubule bundler that stabilizes microtubules by stimulating growth and inhibiting shrinkage. The mechanisms by which tau organizes microtubule networks remain poorly understood. Here, we studied the self-organization of microtubules growing in the presence of tau isoforms and mutants. The results show that tau's ability to induce stable microtubule bundles requires two hexapeptides located in its microtubule-binding domain and is modulated by its projection domain. Site-specific pseudophosphorylation of tau promotes distinct microtubule organizations: stable single microtubules, stable bundles, or dynamic bundles. Disease-related tau mutations increase the formation of highly dynamic bundles. Finally, cryo-electron microscopy experiments indicate that tau and its variants similarly change the microtubule lattice structure by increasing both the protofilament number and lattice defects. Overall, our results uncover novel phosphodependent mechanisms governing tau's ability to trigger microtubule organization and reveal that disease-related modifications of tau promote specific microtubule organizations that may have a deleterious impact during neurodegeneration. © 2018 Prezel, Elie, et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

  11. Quantifying the dynamics of coupled networks of switches and oscillators.

    Directory of Open Access Journals (Sweden)

    Matthew R Francis

    Full Text Available Complex network dynamics have been analyzed with models of systems of coupled switches or systems of coupled oscillators. However, many complex systems are composed of components with diverse dynamics whose interactions drive the system's evolution. We, therefore, introduce a new modeling framework that describes the dynamics of networks composed of both oscillators and switches. Both oscillator synchronization and switch stability are preserved in these heterogeneous, coupled networks. Furthermore, this model recapitulates the qualitative dynamics for the yeast cell cycle consistent with the hypothesized dynamics resulting from decomposition of the regulatory network into dynamic motifs. Introducing feedback into the cell-cycle network induces qualitative dynamics analogous to limitless replicative potential that is a hallmark of cancer. As a result, the proposed model of switch and oscillator coupling provides the ability to incorporate mechanisms that underlie the synchronized stimulus response ubiquitous in biochemical systems.

  12. Agent Based Modeling on Organizational Dynamics of Terrorist Network

    OpenAIRE

    Bo Li; Duoyong Sun; Renqi Zhu; Ze Li

    2015-01-01

    Modeling organizational dynamics of terrorist network is a critical issue in computational analysis of terrorism research. The first step for effective counterterrorism and strategic intervention is to investigate how the terrorists operate with the relational network and what affects the performance. In this paper, we investigate the organizational dynamics by employing a computational experimentation methodology. The hierarchical cellular network model and the organizational dynamics model ...

  13. Sync in Complex Dynamical Networks: Stability, Evolution, Control, and Application

    OpenAIRE

    Li, Xiang

    2005-01-01

    In the past few years, the discoveries of small-world and scale-free properties of many natural and artificial complex networks have stimulated significant advances in better understanding the relationship between the topology and the collective dynamics of complex networks. This paper reports recent progresses in the literature of synchronization of complex dynamical networks including stability criteria, network synchronizability and uniform synchronous criticality in different topologies, ...

  14. Google matrix, dynamical attractors, and Ulam networks.

    Science.gov (United States)

    Shepelyansky, D L; Zhirov, O V

    2010-03-01

    We study the properties of the Google matrix generated by a coarse-grained Perron-Frobenius operator of the Chirikov typical map with dissipation. The finite-size matrix approximant of this operator is constructed by the Ulam method. This method applied to the simple dynamical model generates directed Ulam networks with approximate scale-free scaling and characteristics being in certain features similar to those of the world wide web with approximate scale-free degree distributions as well as two characteristics similar to the web: a power-law decay in PageRank that mirrors the decay of PageRank on the world wide web and a sensitivity to the value alpha in PageRank. The simple dynamical attractors play here the role of popular websites with a strong concentration of PageRank. A variation in the Google parameter alpha or other parameters of the dynamical map can drive the PageRank of the Google matrix to a delocalized phase with a strange attractor where the Google search becomes inefficient.

  15. Complex Dynamical Network Control for Trajectory Tracking Using Delayed Recurrent Neural Networks

    Directory of Open Access Journals (Sweden)

    Jose P. Perez

    2014-01-01

    Full Text Available In this paper, the problem of trajectory tracking is studied. Based on the V-stability and Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a complex dynamical network is obtained. To illustrate the analytic results, we present a tracking simulation of a dynamical network with each node being just one Lorenz’s dynamical system and three identical Chen’s dynamical systems.

  16. Interestingness-Driven Diffusion Process Summarization in Dynamic Networks

    DEFF Research Database (Denmark)

    Qu, Qiang; Liu, Siyuan; Jensen, Christian S.

    2014-01-01

    The widespread use of social networks enables the rapid diffusion of information, e.g., news, among users in very large communities. It is a substantial challenge to be able to observe and understand such diffusion processes, which may be modeled as networks that are both large and dynamic. A key...... tool in this regard is data summarization. However, few existing studies aim to summarize graphs/networks for dynamics. Dynamic networks raise new challenges not found in static settings, including time sensitivity and the needs for online interestingness evaluation and summary traceability, which...... render existing techniques inapplicable. We study the topic of dynamic network summarization: how to summarize dynamic networks with millions of nodes by only capturing the few most interesting nodes or edges over time, and we address the problem by finding interestingness-driven diffusion processes...

  17. Self-organization of complex networks as a dynamical system.

    Science.gov (United States)

    Aoki, Takaaki; Yawata, Koichiro; Aoyagi, Toshio

    2015-01-01

    To understand the dynamics of real-world networks, we investigate a mathematical model of the interplay between the dynamics of random walkers on a weighted network and the link weights driven by a resource carried by the walkers. Our numerical studies reveal that, under suitable conditions, the co-evolving dynamics lead to the emergence of stationary power-law distributions of the resource and link weights, while the resource quantity at each node ceaselessly changes with time. We analyze the network organization as a deterministic dynamical system and find that the system exhibits multistability, with numerous fixed points, limit cycles, and chaotic states. The chaotic behavior of the system leads to the continual changes in the microscopic network dynamics in the absence of any external random noises. We conclude that the intrinsic interplay between the states of the nodes and network reformation constitutes a major factor in the vicissitudes of real-world networks.

  18. Magnetoencephalography from signals to dynamic cortical networks

    CERN Document Server

    Aine, Cheryl

    2014-01-01

    "Magnetoencephalography (MEG) provides a time-accurate view into human brain function. The concerted action of neurons generates minute magnetic fields that can be detected---totally noninvasively---by sensitive multichannel magnetometers. The obtained millisecond accuracycomplements information obtained by other modern brain-imaging tools. Accurate timing is quintessential in normal brain function, often distorted in brain disorders. The noninvasiveness and time-sensitivityof MEG are great assets to developmental studies, as well. This multiauthored book covers an ambitiously wide range of MEG research from introductory to advanced level, from sensors to signals, and from focal sources to the dynamics of cortical networks. Written by active practioners of this multidisciplinary field, the book contains tutorials for newcomers and chapters of new challenging methods and emerging technologies to advanced MEG users. The reader will obtain a firm grasp of the possibilities of MEG in the study of audition, vision...

  19. Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach.

    Science.gov (United States)

    Li, Jun; Zhao, Patrick X

    2016-01-01

    Identification of functional modules/sub-networks in large-scale biological networks is one of the important research challenges in current bioinformatics and systems biology. Approaches have been developed to identify functional modules in single-class biological networks; however, methods for systematically and interactively mining multiple classes of heterogeneous biological networks are lacking. In this paper, we present a novel algorithm (called mPageRank) that utilizes the Multiplex PageRank approach to mine functional modules from two classes of biological networks. We demonstrate the capabilities of our approach by successfully mining functional biological modules through integrating expression-based gene-gene association networks and protein-protein interaction networks. We first compared the performance of our method with that of other methods using simulated data. We then applied our method to identify the cell division cycle related functional module and plant signaling defense-related functional module in the model plant Arabidopsis thaliana. Our results demonstrated that the mPageRank method is effective for mining sub-networks in both expression-based gene-gene association networks and protein-protein interaction networks, and has the potential to be adapted for the discovery of functional modules/sub-networks in other heterogeneous biological networks. The mPageRank executable program, source code, the datasets and results of the presented two case studies are publicly and freely available at http://plantgrn.noble.org/MPageRank/.

  20. Dynamics of subway networks based on vehicles operation timetable

    Science.gov (United States)

    Xiao, Xue-mei; Jia, Li-min; Wang, Yan-hui

    2017-05-01

    In this paper, a subway network is represented as a dynamic, directed and weighted graph, in which vertices represent subway stations and weights of edges represent the number of vehicles passing through the edges by considering vehicles operation timetable. Meanwhile the definitions of static and dynamic metrics which can represent vertices' and edges' local and global attributes are proposed. Based on the model and metrics, standard deviation is further introduced to study the dynamic properties (heterogeneity and vulnerability) of subway networks. Through a detailed analysis of the Beijing subway network, we conclude that with the existing network structure, the heterogeneity and vulnerability of the Beijing subway network varies over time when the vehicle operation timetable is taken into consideration, and the distribution of edge weights affects the performance of the network. In other words, although the vehicles operation timetable is restrained by the physical structure of the network, it determines the performances and properties of the Beijing subway network.

  1. The stochastic network dynamics underlying perceptual discrimination

    Directory of Open Access Journals (Sweden)

    Genis Prat-Ortega

    2015-04-01

    Full Text Available The brain is able to interpret streams of high-dimensional ambiguous information and yield coherent percepts. The mechanisms governing sensory integration have been extensively characterized using time-varying visual stimuli (Britten et al. 1996; Roitman and Shadlen 2002, but some of the basic principles regarding the network dynamics underlying this process remain largely unknown. We captured the basic features of a neural integrator using three canonical one-dimensional models: (1 the Drift Diffusion Model (DDM, (2 the Perfect Integrator (PI which is a particular case of the DDM where the bounds are set to infinity and (3 the double-well potential (DW which captures the dynamics of the attractor networks (Wang 2002; Roxin and Ledberg 2008. Although these models has been widely studied (Bogacz et al. 2006; Roxin and Ledberg 2008; Gold and Shadlen 2002, it has been difficult to experimentally discriminate among them because most of the observables measured are only quantitatively different among these models (e.g. psychometric curves. Here we aim to find experimentally measurable quantities that can yield qualitatively different behaviors depending on the nature of the underlying network dynamics. We examined the categorization dynamics of these models in response to fluctuating stimuli of different duration (T. On each time step, stimuli are drawn from a Gaussian distribution N(μ, σ and the two stimulus categories are defined by μ > 0 and μ < 0. Psychometric curves can therefore be obtained by quantifying the probability of the integrator to yield one category versus μ . We find however that varying σ can reveal more clearly the differences among the different integrators. In the small σ regime, both the DW and the DDM perform transient integration and exhibit a decaying stimulus reverse correlation kernel revealing a primacy effect (Nienborg and Cumming 2009; Wimmer et al. 2015 . In the large σ regime, the integration in the DDM

  2. The Dynamics of Initiative in Communication Networks.

    Directory of Open Access Journals (Sweden)

    Anders Mollgaard

    Full Text Available Human social interaction is often intermittent. Two acquainted persons can have extended periods without social interaction punctuated by periods of repeated interaction. In this case, the repeated interaction can be characterized by a seed initiative by either of the persons and a number of follow-up interactions. The tendency to initiate social interaction plays an important role in the formation of social networks and is in general not symmetric between persons. In this paper, we study the dynamics of initiative by analysing and modeling a detailed call and text message network sampled from a group of 700 individuals. We show that in an average relationship between two individuals, one part is almost twice as likely to initiate communication compared to the other part. The asymmetry has social consequences and ultimately might lead to the discontinuation of a relationship. We explain the observed asymmetry by a positive feedback mechanism where individuals already taking initiative are more likely to take initiative in the future. In general, people with many initiatives receive attention from a broader spectrum of friends than people with few initiatives. Lastly, we compare the likelihood of taking initiative with the basic personality traits of the five factor model.

  3. Choice Shift in Opinion Network Dynamics

    Science.gov (United States)

    Gabbay, Michael

    Choice shift is a phenomenon associated with small group dynamics whereby group discussion causes group members to shift their opinions in a more extreme direction so that the mean post-discussion opinion exceeds the mean pre-discussion opinion. Also known as group polarization, choice shift is a robust experimental phenomenon and has been well-studied within social psychology. In opinion network models, shifts toward extremism are typically produced by the presence of stubborn agents at the extremes of the opinion axis, whose opinions are much more resistant to change than moderate agents. However, we present a model in which choice shift can arise without the assumption of stubborn agents; the model evolves member opinions and uncertainties using coupled nonlinear differential equations. In addition, we briefly describe the results of a recent experiment conducted involving online group discussion concerning the outcome of National Football League games are described. The model predictions concerning the effects of network structure, disagreement level, and team choice (favorite or underdog) are in accord with the experimental results. This research was funded by the Office of Naval Research and the Defense Threat Reduction Agency.

  4. Filtering in Hybrid Dynamic Bayesian Networks

    Science.gov (United States)

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

    2000-01-01

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

  5. SDN-Enabled Dynamic Feedback Control and Sensing in Agile Optical Networks

    Science.gov (United States)

    Lin, Likun

    Fiber optic networks are no longer just pipelines for transporting data in the long haul backbone. Exponential growth in traffic in metro-regional areas has pushed higher capacity fiber toward the edge of the network, and highly dynamic patterns of heterogeneous traffic have emerged that are often bursty, severely stressing the historical "fat and dumb pipe" static optical network, which would need to be massively over-provisioned to deal with these loads. What is required is a more intelligent network with a span of control over the optical as well as electrical transport mechanisms which enables handling of service requests in a fast and efficient way that guarantees quality of service (QoS) while optimizing capacity efficiency. An "agile" optical network is a reconfigurable optical network comprised of high speed intelligent control system fed by real-time in situ network sensing. It provides fast response in the control and switching of optical signals in response to changing traffic demands and network conditions. This agile control of optical signals is enabled by pushing switching decisions downward in the network stack to the physical layer. Implementing such agility is challenging due to the response dynamics and interactions of signals in the physical layer. Control schemes must deal with issues such as dynamic power equalization, EDFA transients and cascaded noise effects, impairments due to self-phase modulation and dispersion, and channel-to-channel cross talk. If these issues are not properly predicted and mitigated, attempts at dynamic control can drive the optical network into an unstable state. In order to enable high speed actuation of signal modulators and switches, the network controller must be able to make decisions based on predictive models. In this thesis, we consider how to take advantage of Software Defined Networking (SDN) capabilities for network reconfiguration, combined with embedded models that access updates from deployed network

  6. Automatic modulation format recognition for the next generation optical communication networks using artificial neural networks

    Science.gov (United States)

    Guesmi, Latifa; Hraghi, Abir; Menif, Mourad

    2015-03-01

    A new technique for Automatic Modulation Format Recognition (AMFR) in next generation optical communication networks is presented. This technique uses the Artificial Neural Network (ANN) in conjunction with the features of Linear Optical Sampling (LOS) of the detected signal at high bit rates using direct detection or coherent detection. The use of LOS method for this purpose mainly driven by the increase of bit rates which enables the measurement of eye diagrams. The efficiency of this technique is demonstrated under different transmission impairments such as chromatic dispersion (CD) in the range of -500 to 500 ps/nm, differential group delay (DGD) in the range of 0-15 ps and the optical signal tonoise ratio (OSNR) in the range of 10-30 dB. The results of numerical simulation for various modulation formats demonstrate successful recognition from a known bit rates with a higher estimation accuracy, which exceeds 99.8%.

  7. Disentangling the co-structure of multilayer interaction networks: degree distribution and module composition in two-layer bipartite networks.

    Science.gov (United States)

    Astegiano, Julia; Altermatt, Florian; Massol, François

    2017-11-13

    Species establish different interactions (e.g. antagonistic, mutualistic) with multiple species, forming multilayer ecological networks. Disentangling network co-structure in multilayer networks is crucial to predict how biodiversity loss may affect the persistence of multispecies assemblages. Existing methods to analyse multilayer networks often fail to consider network co-structure. We present a new method to evaluate the modular co-structure of multilayer networks through the assessment of species degree co-distribution and network module composition. We focus on modular structure because of its high prevalence among ecological networks. We apply our method to two Lepidoptera-plant networks, one describing caterpillar-plant herbivory interactions and one representing adult Lepidoptera nectaring on flowers, thereby possibly pollinating them. More than 50% of the species established either herbivory or visitation interactions, but not both. These species were over-represented among plants and lepidopterans, and were present in most modules in both networks. Similarity in module composition between networks was high but not different from random expectations. Our method clearly delineates the importance of interpreting multilayer module composition similarity in the light of the constraints imposed by network structure to predict the potential indirect effects of species loss through interconnected modular networks.

  8. Sparse dynamical Boltzmann machine for reconstructing complex networks with binary dynamics

    Science.gov (United States)

    Chen, Yu-Zhong; Lai, Ying-Cheng

    2018-03-01

    Revealing the structure and dynamics of complex networked systems from observed data is a problem of current interest. Is it possible to develop a completely data-driven framework to decipher the network structure and different types of dynamical processes on complex networks? We develop a model named sparse dynamical Boltzmann machine (SDBM) as a structural estimator for complex networks that host binary dynamical processes. The SDBM attains its topology according to that of the original system and is capable of simulating the original binary dynamical process. We develop a fully automated method based on compressive sensing and a clustering algorithm to construct the SDBM. We demonstrate, for a variety of representative dynamical processes on model and real world complex networks, that the equivalent SDBM can recover the network structure of the original system and simulates its dynamical behavior with high precision.

  9. Random and systematic beam modulator errors in dynamic intensity modulated radiotherapy

    International Nuclear Information System (INIS)

    Parsai, Homayon; Cho, Paul S; Phillips, Mark H; Giansiracusa, Robert S; Axen, David

    2003-01-01

    This paper reports on the dosimetric effects of random and systematic modulator errors in delivery of dynamic intensity modulated beams. A sliding-widow type delivery that utilizes a combination of multileaf collimators (MLCs) and backup diaphragms was examined. Gaussian functions with standard deviations ranging from 0.5 to 1.5 mm were used to simulate random positioning errors. A clinical example involving a clival meningioma was chosen with optic chiasm and brain stem as limiting critical structures in the vicinity of the tumour. Dose calculations for different modulator fluctuations were performed, and a quantitative analysis was carried out based on cumulative and differential dose volume histograms for the gross target volume and surrounding critical structures. The study indicated that random modulator errors have a strong tendency to reduce minimum target dose and homogeneity. Furthermore, it was shown that random perturbation of both MLCs and backup diaphragms in the order of σ = 1 mm can lead to 5% errors in prescribed dose. In comparison, when MLCs or backup diaphragms alone was perturbed, the system was more robust and modulator errors of at least σ = 1.5 mm were required to cause dose discrepancies greater than 5%. For systematic perturbation, even errors in the order of ±0.5 mm were shown to result in significant dosimetric deviations

  10. Modelling flow dynamics in water distribution networks using ...

    African Journals Online (AJOL)

    One such approach is the Artificial Neural Networks (ANNs) technique. The advantage of ANNs is that they are robust and can be used to model complex linear and non-linear systems without making implicit assumptions. ANNs can be trained to forecast flow dynamics in a water distribution network. Such flow dynamics ...

  11. Quetiapine modulates functional connectivity in brain aggression networks.

    Science.gov (United States)

    Klasen, Martin; Zvyagintsev, Mikhail; Schwenzer, Michael; Mathiak, Krystyna A; Sarkheil, Pegah; Weber, René; Mathiak, Klaus

    2013-07-15

    Aggressive behavior is associated with dysfunctions in an affective regulation network encompassing amygdala and prefrontal areas such as orbitofrontal (OFC), anterior cingulate (ACC), and dorsolateral prefrontal cortex (DLPFC). In particular, prefrontal regions have been postulated to control amygdala activity by inhibitory projections, and this process may be disrupted in aggressive individuals. The atypical antipsychotic quetiapine successfully attenuates aggressive behavior in various disorders; the underlying neural processes, however, are unknown. A strengthened functional coupling in the prefrontal-amygdala system may account for these anti-aggressive effects. An inhibition of this network has been reported for virtual aggression in violent video games as well. However, there have been so far no in-vivo observations of pharmacological influences on corticolimbic projections during human aggressive behavior. In a double-blind, placebo-controlled study, quetiapine and placebo were administered for three successive days prior to an fMRI experiment. In this experiment, functional brain connectivity was assessed during virtual aggressive behavior in a violent video game and an aggression-free control task in a non-violent modification. Quetiapine increased the functional connectivity of ACC and DLPFC with the amygdala during virtual aggression, whereas OFC-amygdala coupling was attenuated. These effects were observed neither for placebo nor for the non-violent control. These results demonstrate for the first time a pharmacological modification of aggression-related human brain networks in a naturalistic setting. The violence-specific modulation of prefrontal-amygdala networks appears to control aggressive behavior and provides a neurobiological model for the anti-aggressive effects of quetiapine. Copyright © 2013 Elsevier Inc. All rights reserved.

  12. Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer.

    Science.gov (United States)

    Besserve, Michel; Lowe, Scott C; Logothetis, Nikos K; Schölkopf, Bernhard; Panzeri, Stefano

    2015-01-01

    Distributed neural processing likely entails the capability of networks to reconfigure dynamically the directionality and strength of their functional connections. Yet, the neural mechanisms that may allow such dynamic routing of the information flow are not yet fully understood. We investigated the role of gamma band (50-80 Hz) oscillations in transient modulations of communication among neural populations by using measures of direction-specific causal information transfer. We found that the local phase of gamma-band rhythmic activity exerted a stimulus-modulated and spatially-asymmetric directed effect on the firing rate of spatially separated populations within the primary visual cortex. The relationships between gamma phases at different sites (phase shifts) could be described as a stimulus-modulated gamma-band wave propagating along the spatial directions with the largest information transfer. We observed transient stimulus-related changes in the spatial configuration of phases (compatible with changes in direction of gamma wave propagation) accompanied by a relative increase of the amount of information flowing along the instantaneous direction of the gamma wave. These effects were specific to the gamma-band and suggest that the time-varying relationships between gamma phases at different locations mark, and possibly causally mediate, the dynamic reconfiguration of functional connections.

  13. Cluster-based single-sink wireless sensor networks and passive optical network converged network incorporating sideband modulation schemes

    Science.gov (United States)

    Kumar, Love; Sharma, Vishal; Singh, Amarpal

    2018-02-01

    Wireless sensor networks have tremendous applications, such as civil, military, and environmental monitoring. In most of the applications, sensor data are required to be propagated over the internet/core networks, which result in backhaul setback. Subsequently, there is a necessity to backhaul the sensed information of such networks together with prolonging of the transmission link. Passive optical network (PON) is next-generation access technology emerging as a potential candidate for convergence of the sensed data to the core system. Earlier, the work with single-optical line terminal-PON was demonstrated and investigated merely analytically. This work is an attempt to demonstrate a practical model of a bidirectional single-sink wireless sensor network-PON converged network in which the collected data from cluster heads are transmitted over PON networks. Further, modeled converged structure has been investigated under the influence of double, single, and tandem sideband modulation schemes incorporating a corresponding phase-delay to the sensor data entities that have been overlooked in the past. The outcome illustrates the successful fusion of the sensor data entities over PON with acceptable bit error rate and signal to noise ratio serving as a potential development in the sphere of such converged networks. It has also been revealed that the data entities treated with tandem side band modulation scheme help in improving the performance of the converged structure. Additionally, analysis for uplink transmission reported with queue theory in terms of time cycle, average time delay, data packet generation, and bandwidth utilization. An analytical analysis of proposed converged network shows that average time delay for data packet transmission is less as compared with time cycle delay.

  14. Anger Modulates Influence Hierarchies Within and Between Emotional Reactivity and Regulation Networks

    Science.gov (United States)

    Jacob, Yael; Gilam, Gadi; Lin, Tamar; Raz, Gal; Hendler, Talma

    2018-01-01

    Emotion regulation is hypothesized to be mediated by the interactions between emotional reactivity and regulation networks during the dynamic unfolding of the emotional episode. Yet, it remains unclear how to delineate the effective relationships between these networks. In this study, we examined the aforementioned networks’ information flow hierarchy during viewing of an anger provoking movie excerpt. Anger regulation is particularly essential for averting individuals from aggression and violence, thus improving prosocial behavior. Using subjective ratings of anger intensity we differentiated between low and high anger periods of the film. We then applied the Dependency Network Analysis (DEPNA), a newly developed graph theory method to quantify networks’ node importance during the two anger periods. The DEPNA analysis revealed that the impact of the ventromedial prefrontal cortex (vmPFC) was higher in the high anger condition, particularly within the regulation network and on the connections between the reactivity and regulation networks. We further showed that higher levels of vmPFC impact on the regulation network were associated with lower subjective anger intensity during the high-anger cinematic period, and lower trait anger levels. Supporting and replicating previous findings, these results emphasize the previously acknowledged central role of vmPFC in modulating negative affect. We further show that the impact of the vmPFC relies on its correlational influence on the connectivity between reactivity and regulation networks. More importantly, the hierarchy network analysis revealed a link between connectivity patterns of the vmPFC and individual differences in anger reactivity and trait, suggesting its potential therapeutic role. PMID:29681803

  15. Dynamic Evolution Model Based on Social Network Services

    Science.gov (United States)

    Xiong, Xi; Gou, Zhi-Jian; Zhang, Shi-Bin; Zhao, Wen

    2013-11-01

    Based on the analysis of evolutionary characteristics of public opinion in social networking services (SNS), in the paper we propose a dynamic evolution model, in which opinions are coupled with topology. This model shows the clustering phenomenon of opinions in dynamic network evolution. The simulation results show that the model can fit the data from a social network site. The dynamic evolution of networks accelerates the opinion, separation and aggregation. The scale and the number of clusters are influenced by confidence limit and rewiring probability. Dynamic changes of the topology reduce the number of isolated nodes, while the increased confidence limit allows nodes to communicate more sufficiently. The two effects make the distribution of opinion more neutral. The dynamic evolution of networks generates central clusters with high connectivity and high betweenness, which make it difficult to control public opinions in SNS.

  16. Pramipexole modulates the neural network of reward anticipation.

    Science.gov (United States)

    Ye, Zheng; Hammer, Anke; Camara, Estela; Münte, Thomas F

    2011-05-01

    Pramipexole is widely prescribed to treat Parkinson's disease. It has been found to cause impulse control disorders such as pathological gambling. To examine how pramipexole modulates the network of reward anticipation, we carried out a pharmacological functional magnetic resonance imaging study with a double-blind, within-subject design. During the anticipation of monetary rewards, pramipexole increased the activity of the nucleus accumbens (NAcc), enhanced the interaction between the NAcc and the anterior insula, but weakened the interaction between the NAcc and the prefrontal cortex. These results suggest that pramipexole may exaggerate incentive and affective responses to possible rewards, but reduce the top-down control of impulses, leading to an increase in impulsive behaviors. This imbalance between the prefrontal-striatum connectivity and the insula-striatum connectivity may represent the neural mechanism of pathological gambling caused by pramipexole. Copyright © 2010 Wiley-Liss, Inc.

  17. Spinal cellular and network properties modulate pain perception

    Directory of Open Access Journals (Sweden)

    Darbon Pascal

    2016-01-01

    Previously, it has been shown that high levels of plasma glucocorticoids give rise to analgesia. However to our knowledge nothing has been reported regarding a direct non genomic modulation of neuronal spinal activity by peripheral CORT. In the present study, we used combined in vivo and in vitro electrophysiology approaches, associated with the measure of nociceptive mechanical sensitivity and plasma corticosterone level measurement to assess the impact of circulating CORT on rat nociception. We showed that CORT plasma level elevation produced analgesia via the reduction of nociceptive fiber mediated spinal responses. CORT is spinally reduced in the neuroactive metabolite THDOC that specifically enhances lamina II GABAergic synaptic transmission. The main consequence is a reduction of lamina II network excitability reflecting a selective decrease in processing of nociceptive inputs. The depressed neuronal activity at the spinal level then in turn leads to a weaker nociceptive message transmission to supraspinal structures and hence to an alleviation of pain.

  18. Dynamic Mobile IP routers in ad hoc networks

    NARCIS (Netherlands)

    Kock, B.A.; Schmidt, J.R.

    2005-01-01

    This paper describes a concept combining mobile IP and ad hoc routing to create a robust mobile network. In this network all nodes are mobile and globally and locally reachable under the same IP address. Essential for implementing this network are the dynamic mobile IP routers. They act as gateways

  19. Gender, Friendship Networks, and Delinquency: A Dynamic Network Approach.

    Science.gov (United States)

    Haynie, Dana L; Doogan, Nathan J; Soller, Brian

    2014-11-01

    Researchers have examined selection and influence processes in shaping delinquency similarity among friends, but little is known about the role of gender in moderating these relationships. Our objective is to examine differences between adolescent boys and girls regarding delinquency-based selection and influence processes. Using longitudinal network data from adolescents attending two large schools in AddHealth ( N = 1,857) and stochastic actor-oriented models, we evaluate whether girls are influenced to a greater degree by friends' violence or delinquency than boys (influence hypothesis) and whether girls are more likely to select friends based on violent or delinquent behavior than boys (selection hypothesis). The results indicate that girls are more likely than boys to be influenced by their friends' involvement in violence. Although a similar pattern emerges for nonviolent delinquency, the gender differences are not significant. Some evidence shows that boys are influenced toward increasing their violence or delinquency when exposed to more delinquent or violent friends but are immune to reducing their violence or delinquency when associating with less violent or delinquent friends. In terms of selection dynamics, although both boys and girls have a tendency to select friends based on friends' behavior, girls have a stronger tendency to do so, suggesting that among girls, friends' involvement in violence or delinquency is an especially decisive factor for determining friendship ties.

  20. Gender, Friendship Networks, and Delinquency: A Dynamic Network Approach**

    Science.gov (United States)

    Haynie, Dana L.; Doogan, Nathan J.; Soller, Brian

    2014-01-01

    Researchers have examined selection and influence processes in shaping delinquency similarity among friends, but little is known about the role of gender in moderating these relationships. Our objective is to examine differences between adolescent boys and girls regarding delinquency-based selection and influence processes. Using longitudinal network data from adolescents attending two large schools in AddHealth (N = 1,857) and stochastic actor-oriented models, we evaluate whether girls are influenced to a greater degree by friends' violence or delinquency than boys (influence hypothesis) and whether girls are more likely to select friends based on violent or delinquent behavior than boys (selection hypothesis). The results indicate that girls are more likely than boys to be influenced by their friends' involvement in violence. Although a similar pattern emerges for nonviolent delinquency, the gender differences are not significant. Some evidence shows that boys are influenced toward increasing their violence or delinquency when exposed to more delinquent or violent friends but are immune to reducing their violence or delinquency when associating with less violent or delinquent friends. In terms of selection dynamics, although both boys and girls have a tendency to select friends based on friends' behavior, girls have a stronger tendency to do so, suggesting that among girls, friends' involvement in violence or delinquency is an especially decisive factor for determining friendship ties. PMID:26097241

  1. Topology Identification of General Dynamical Network with Distributed Time Delays

    International Nuclear Information System (INIS)

    Zhao-Yan, Wu; Xin-Chu, Fu

    2009-01-01

    General dynamical networks with distributed time delays are studied. The topology of the networks are viewed as unknown parameters, which need to be identified. Some auxiliary systems (also called the network estimators) are designed to achieve this goal. Both linear feedback control and adaptive strategy are applied in designing these network estimators. Based on linear matrix inequalities and the Lyapunov function method, the sufficient condition for the achievement of topology identification is obtained. This method can also better monitor the switching topology of dynamical networks. Illustrative examples are provided to show the effectiveness of this method. (general)

  2. Dynamic defense and network randomization for computer systems

    Science.gov (United States)

    Chavez, Adrian R.; Stout, William M. S.; Hamlet, Jason R.; Lee, Erik James; Martin, Mitchell Tyler

    2018-05-29

    The various technologies presented herein relate to determining a network attack is taking place, and further to adjust one or more network parameters such that the network becomes dynamically configured. A plurality of machine learning algorithms are configured to recognize an active attack pattern. Notification of the attack can be generated, and knowledge gained from the detected attack pattern can be utilized to improve the knowledge of the algorithms to detect a subsequent attack vector(s). Further, network settings and application communications can be dynamically randomized, wherein artificial diversity converts control systems into moving targets that help mitigate the early reconnaissance stages of an attack. An attack(s) based upon a known static address(es) of a critical infrastructure network device(s) can be mitigated by the dynamic randomization. Network parameters that can be randomized include IP addresses, application port numbers, paths data packets navigate through the network, application randomization, etc.

  3. Arresting Strategy Based on Dynamic Criminal Networks Changing over Time

    Directory of Open Access Journals (Sweden)

    Junqing Yuan

    2013-01-01

    Full Text Available We investigate a sequence of dynamic criminal networks on a time series based on the dynamic network analysis (DNA. According to the change of networks’ structure, networks’ variation trend is analyzed to forecast its future structure. Finally, an optimal arresting time and priority list are designed based on our analysis. Better results can be expected than that based on social network analysis (SNA.

  4. Dynamic Aperture Extrapolation in Presence of Tune Modulation

    CERN Document Server

    Giovannozzi, Massimo; Todesco, Ezio

    1998-01-01

    In hadron colliders, such as the Large Hadron Collider (LHC) to be built at CERN, the long-term stability of the single-particle motion is mostly determined by the field-shape quality of the superconducting magnets. The mechanism of particle loss may be largely enhanced by modulation of betatron tunes, induced either by synchro-betatron coupling (via the residual uncorrected chromaticity), or by unavoidable power supply ripple. This harmful effect is investigated in a simple dynamical system model, the Henon map with modulated linear frequencies. Then, a realistic accelerator model describing the injection optics of the LHC lattice is analyzed. Orbital data obtained with long-term tracking simulations ($10^5$-$10^7$ turns) are post-processed to obtain the dynamic aperture. It turns out that the dynamic aperture can be interpolated using a simple mpirical formula, and it decays proportionally to a power of the inverse logarithm of the number of turns. Furthermore, the extrapolation of tracking data at $10^5$ t...

  5. On modulated complex non-linear dynamical systems

    International Nuclear Information System (INIS)

    Mahmoud, G.M.; Mohamed, A.A.; Rauh, A.

    1999-01-01

    This paper is concerned with the development of an approximate analytical method to investigate periodic solutions and their stability in the case of modulated non-linear dynamical systems whose equation of motion is describe. Such differential equations appear, for example, in problems of colliding particle beams in high-energy accelerators or one-mass systems with two or more degrees of freedom, e.g. rotors. The significance of periodic solutions lies on the fact that all non-periodic responses, if convergent, would approach to periodic solutions at the steady-state conditions. The example shows a good agreement between numerical and analytical results for small values of ε. The effect of the periodic modulation on the stability of the 2π-periodic solutions is discussed

  6. Differential network analysis reveals genetic effects on catalepsy modules.

    Directory of Open Access Journals (Sweden)

    Ovidiu D Iancu

    Full Text Available We performed short-term bi-directional selective breeding for haloperidol-induced catalepsy, starting from three mouse populations of increasingly complex genetic structure: an F2 intercross, a heterogeneous stock (HS formed by crossing four inbred strains (HS4 and a heterogeneous stock (HS-CC formed from the inbred strain founders of the Collaborative Cross (CC. All three selections were successful, with large differences in haloperidol response emerging within three generations. Using a custom differential network analysis procedure, we found that gene coexpression patterns changed significantly; importantly, a number of these changes were concordant across genetic backgrounds. In contrast, absolute gene-expression changes were modest and not concordant across genetic backgrounds, in spite of the large and similar phenotypic differences. By inferring strain contributions from the parental lines, we are able to identify significant differences in allelic content between the selected lines concurrent with large changes in transcript connectivity. Importantly, this observation implies that genetic polymorphisms can affect transcript and module connectivity without large changes in absolute expression levels. We conclude that, in this case, selective breeding acts at the subnetwork level, with the same modules but not the same transcripts affected across the three selections.

  7. OFDM with Index Modulation for Asynchronous mMTC Networks.

    Science.gov (United States)

    Doğan, Seda; Tusha, Armed; Arslan, Hüseyin

    2018-04-21

    One of the critical missions for next-generation wireless communication systems is to fulfill the high demand for massive Machine-Type Communications (mMTC). In mMTC systems, a sporadic transmission is performed between machine users and base station (BS). Lack of coordination between the users and BS in time destroys orthogonality between the subcarriers, and causes inter-carrier interference (ICI). Therefore, providing services to asynchronous massive machine users is a major challenge for Orthogonal Frequency Division Multiplexing (OFDM). In this study, OFDM with index modulation (OFDM-IM) is proposed as an eligible solution to alleviate ICI caused by asynchronous transmission in uncoordinated mMTC networks. In OFDM-IM, data transmission is performed not only by modulated subcarriers but also by the indices of active subcarriers. Unlike classical OFDM, fractional subcarrier activation leads to less ICI in OFDM-IM technology. A novel subcarrier mapping scheme (SMS) named as Inner Subcarrier Activation is proposed to further alleviate adjacent user interference in asynchronous OFDM-IM-based systems. ISA reduces inter-user interference since it gives more activation priority to inner subcarriers compared with the existing SMS-s. The superiority of the proposed SMS is shown through both theoretical analysis and computer-based simulations in comparison to existing mapping schemes for asynchronous systems.

  8. Major component analysis of dynamic networks of physiologic organ interactions

    International Nuclear Information System (INIS)

    Liu, Kang K L; Ma, Qianli D Y; Ivanov, Plamen Ch; Bartsch, Ronny P

    2015-01-01

    The human organism is a complex network of interconnected organ systems, where the behavior of one system affects the dynamics of other systems. Identifying and quantifying dynamical networks of diverse physiologic systems under varied conditions is a challenge due to the complexity in the output dynamics of the individual systems and the transient and nonlinear characteristics of their coupling. We introduce a novel computational method based on the concept of time delay stability and major component analysis to investigate how organ systems interact as a network to coordinate their functions. We analyze a large database of continuously recorded multi-channel physiologic signals from healthy young subjects during night-time sleep. We identify a network of dynamic interactions between key physiologic systems in the human organism. Further, we find that each physiologic state is characterized by a distinct network structure with different relative contribution from individual organ systems to the global network dynamics. Specifically, we observe a gradual decrease in the strength of coupling of heart and respiration to the rest of the network with transition from wake to deep sleep, and in contrast, an increased relative contribution to network dynamics from chin and leg muscle tone and eye movement, demonstrating a robust association between network topology and physiologic function. (paper)

  9. Transport on river networks: A dynamical approach

    OpenAIRE

    Zaliapin, I; Foufoula-Georgiou, E; Ghil, M

    2017-01-01

    This study is motivated by problems related to environmental transport on river networks. We establish statistical properties of a flow along a directed branching network and suggest its compact parameterization. The downstream network transport is treated as a particular case of nearest-neighbor hierarchical aggregation with respect to the metric induced by the branching structure of the river network. We describe the static geometric structure of a drainage network by a tree, referred to as...

  10. Mathematical model for spreading dynamics of social network worms

    International Nuclear Information System (INIS)

    Sun, Xin; Liu, Yan-Heng; Han, Jia-Wei; Liu, Xue-Jie; Li, Bin; Li, Jin

    2012-01-01

    In this paper, a mathematical model for social network worm spreading is presented from the viewpoint of social engineering. This model consists of two submodels. Firstly, a human behavior model based on game theory is suggested for modeling and predicting the expected behaviors of a network user encountering malicious messages. The game situation models the actions of a user under the condition that the system may be infected at the time of opening a malicious message. Secondly, a social network accessing model is proposed to characterize the dynamics of network users, by which the number of online susceptible users can be determined at each time step. Several simulation experiments are carried out on artificial social networks. The results show that (1) the proposed mathematical model can well describe the spreading dynamics of social network worms; (2) weighted network topology greatly affects the spread of worms; (3) worms spread even faster on hybrid social networks

  11. Bistable responses in bacterial genetic networks: Designs and dynamical consequences

    Science.gov (United States)

    Tiwari, Abhinav; Ray, J. Christian J.; Narula, Jatin; Igoshin, Oleg A.

    2011-01-01

    A key property of living cells is their ability to react to stimuli with specific biochemical responses. These responses can be understood through the dynamics of underlying biochemical and genetic networks. Evolutionary design principles have been well studied in networks that display graded responses, with a continuous relationship between input signal and system output. Alternatively, biochemical networks can exhibit bistable responses so that over a range of signals the network possesses two stable steady states. In this review, we discuss several conceptual examples illustrating network designs that can result in a bistable response of the biochemical network. Next, we examine manifestations of these designs in bacterial master-regulatory genetic circuits. In particular, we discuss mechanisms and dynamic consequences of bistability in three circuits: two-component systems, sigma-factor networks, and a multistep phosphorelay. Analyzing these examples allows us to expand our knowledge of evolutionary design principles for networks with bistable responses. PMID:21385588

  12. Epidemic dynamics and endemic states in complex networks

    OpenAIRE

    Pastor-Satorras, Romualdo; Vespignani, Alessandro

    2001-01-01

    We study by analytical methods and large scale simulations a dynamical model for the spreading of epidemics in complex networks. In networks with exponentially bounded connectivity we recover the usual epidemic behavior with a threshold defining a critical point below which the infection prevalence is null. On the contrary, on a wide range of scale-free networks we observe the absence of an epidemic threshold and its associated critical behavior. This implies that scale-free networks are pron...

  13. Network dynamics and its relationships to topology and coupling structure in excitable complex networks

    International Nuclear Information System (INIS)

    Zhang Li-Sheng; Mi Yuan-Yuan; Gu Wei-Feng; Hu Gang

    2014-01-01

    All dynamic complex networks have two important aspects, pattern dynamics and network topology. Discovering different types of pattern dynamics and exploring how these dynamics depend on network topologies are tasks of both great theoretical importance and broad practical significance. In this paper we study the oscillatory behaviors of excitable complex networks (ECNs) and find some interesting dynamic behaviors of ECNs in oscillatory probability, the multiplicity of oscillatory attractors, period distribution, and different types of oscillatory patterns (e.g., periodic, quasiperiodic, and chaotic). In these aspects, we further explore strikingly sharp differences among network dynamics induced by different topologies (random or scale-free topologies) and different interaction structures (symmetric or asymmetric couplings). The mechanisms behind these differences are explained physically. (interdisciplinary physics and related areas of science and technology)

  14. Opinion competition dynamics on multiplex networks

    Science.gov (United States)

    Amato, R.; Kouvaris, N. E.; San Miguel, M.; Díaz-Guilera, A.

    2017-12-01

    Multilayer and multiplex networks represent a good proxy for the description of social phenomena where social structure is important and can have different origins. Here, we propose a model of opinion competition where individuals are organized according to two different structures in two layers. Agents exchange opinions according to the Abrams-Strogatz model in each layer separately and opinions can be copied across layers by the same individual. In each layer a different opinion is dominant, so each layer has a different absorbing state. Consensus in one opinion is not the only possible stable solution because of the interaction between the two layers. A new mean field solution has been found where both opinions coexist. In a finite system there is a long transient time for the dynamical coexistence of both opinions. However, the system ends in a consensus state due to finite size effects. We analyze sparse topologies in the two layers and the existence of positive correlations between them, which enables the coexistence of inter-layer groups of agents sharing the same opinion.

  15. Opinion dynamics in activity-driven networks

    Science.gov (United States)

    Li, Dandan; Han, Dun; Ma, Jing; Sun, Mei; Tian, Lixin; Khouw, Timothy; Stanley, H. Eugene

    2017-10-01

    Social interaction between individuals constantly affects the development of their personal opinions. Previous models such as the Deffuant model and the Hegselmann-Krause (HK) model have assumed that individuals only update their opinions after interacting with neighbors whose opinions are similar to their own. However, people are capable of communicating widely with all of their neighbors to gather their ideas and opinions, even if they encounter a number of opposing attitudes. We propose a model in which agents listen to the opinions of all their neighbors. Continuous opinion dynamics are investigated in activity-driven networks with a tolerance threshold. We study how the initial opinion distribution, tolerance threshold, opinion-updating speed, and activity rate affect the evolution of opinion. We find that when the initial fraction of positive opinion is small, all opinions become negative by the end of the simulation. As the initial fraction of positive opinions rises above a certain value —about 0.45— the final fraction of positive opinions sharply increases and eventually equals 1. Increased tolerance threshold δ is found to lead to a more varied final opinion distribution. We also find that if the negative opinion has an initial advantage, the final fraction of negative opinion increases and reaches its peak as the updating speed λ approaches 0.5. Finally we show that the lower the activity rate of individuals, the greater the fluctuation range of their opinions.

  16. Dynamic modulation of phosphoprotein expression in ovarian cancer xenograft models

    International Nuclear Information System (INIS)

    Koussounadis, Antonis; Langdon, Simon P.; Um, Inhwa; Kay, Charlene; Francis, Kyle E.; Harrison, David J.; Smith, V. Anne

    2016-01-01

    The dynamic changes that occur in protein expression after treatment of a cancer in vivo are poorly described. In this study we measure the effect of chemotherapy over time on the expression of a panel of proteins in ovarian cancer xenograft models. The objective was to identify phosphoprotein and other protein changes indicative of pathway activation that might link with drug response. Two xenograft models, platinum-responsive OV1002 and platinum-unresponsive HOX424, were used. Treatments were carboplatin and carboplatin-paclitaxel. Expression of 49 proteins over 14 days post treatment was measured by quantitative immunofluorescence and analysed by AQUA. Carboplatin treatment in the platinum-sensitive OV1002 model triggered up-regulation of cell cycle, mTOR and DDR pathways, while at late time points WNT, invasion, EMT and MAPK pathways were modulated. Estrogen receptor-alpha (ESR1) and ERBB pathways were down-regulated early, within 24 h from treatment administration. Combined carboplatin-paclitaxel treatment triggered a more extensive response in the OV1002 model modulating expression of 23 of 49 proteins. Therefore the cell cycle and DDR pathways showed similar or more pronounced changes than with carboplatin alone. In addition to expression of pS6 and pERK increasing, components of the AKT pathway were modulated with pAKT increasing while its regulator PTEN was down-regulated early. WNT signaling, EMT and invasion markers were modulated at later time points. Additional pathways were also observed with the NFκB and JAK/STAT pathways being up-regulated. ESR1 was down-regulated as was HER4, while further protein members of the ERBB pathway were upregulated late. By contrast, in the carboplatin-unresponsive HOX 424 xenograft, carboplatin only modulated expression of MLH1 while carboplatin-paclitaxel treatment modulated ESR1 and pMET. Thirteen proteins were modulated by carboplatin and a more robust set of changes by carboplatin-paclitaxel. Early changes included

  17. Clustering promotes switching dynamics in networks of noisy neurons

    Science.gov (United States)

    Franović, Igor; Klinshov, Vladimir

    2018-02-01

    Macroscopic variability is an emergent property of neural networks, typically manifested in spontaneous switching between the episodes of elevated neuronal activity and the quiescent episodes. We investigate the conditions that facilitate switching dynamics, focusing on the interplay between the different sources of noise and heterogeneity of the network topology. We consider clustered networks of rate-based neurons subjected to external and intrinsic noise and derive an effective model where the network dynamics is described by a set of coupled second-order stochastic mean-field systems representing each of the clusters. The model provides an insight into the different contributions to effective macroscopic noise and qualitatively indicates the parameter domains where switching dynamics may occur. By analyzing the mean-field model in the thermodynamic limit, we demonstrate that clustering promotes multistability, which gives rise to switching dynamics in a considerably wider parameter region compared to the case of a non-clustered network with sparse random connection topology.

  18. Synthesis of recurrent neural networks for dynamical system simulation.

    Science.gov (United States)

    Trischler, Adam P; D'Eleuterio, Gabriele M T

    2016-08-01

    We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Efficient Neural Network Modeling for Flight and Space Dynamics Simulation

    Directory of Open Access Journals (Sweden)

    Ayman Hamdy Kassem

    2011-01-01

    Full Text Available This paper represents an efficient technique for neural network modeling of flight and space dynamics simulation. The technique will free the neural network designer from guessing the size and structure for the required neural network model and will help to minimize the number of neurons. For linear flight/space dynamics systems, the technique can find the network weights and biases directly by solving a system of linear equations without the need for training. Nonlinear flight dynamic systems can be easily modeled by training its linearized models keeping the same network structure. The training is fast, as it uses the linear system knowledge to speed up the training process. The technique is tested on different flight/space dynamic models and showed promising results.

  20. Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees

    Directory of Open Access Journals (Sweden)

    Chen Xiaoyu

    2007-12-01

    Full Text Available Abstract Background In vertebrates, a large part of gene transcriptional regulation is operated by cis-regulatory modules. These modules are believed to be regulating much of the tissue-specificity of gene expression. Results We develop a Bayesian network approach for identifying cis-regulatory modules likely to regulate tissue-specific expression. The network integrates predicted transcription factor binding site information, transcription factor expression data, and target gene expression data. At its core is a regression tree modeling the effect of combinations of transcription factors bound to a module. A new unsupervised EM-like algorithm is developed to learn the parameters of the network, including the regression tree structure. Conclusion Our approach is shown to accurately identify known human liver and erythroid-specific modules. When applied to the prediction of tissue-specific modules in 10 different tissues, the network predicts a number of important transcription factor combinations whose concerted binding is associated to specific expression.

  1. Complex systems and networks dynamics, controls and applications

    CERN Document Server

    Yu, Xinghuo; Chen, Guanrong; Yu, Wenwu

    2016-01-01

    This elementary book provides some state-of-the-art research results on broad disciplinary sciences on complex networks. It presents an in-depth study with detailed description of dynamics, controls and applications of complex networks. The contents of this book can be summarized as follows. First, the dynamics of complex networks, for example, the cluster dynamic analysis by using kernel spectral methods, community detection algorithms in bipartite networks, epidemiological modeling with demographics and epidemic spreading on multi-layer networks, are studied. Second, the controls of complex networks are investigated including topics like distributed finite-time cooperative control of multi-agent systems by applying homogenous-degree and Lyapunov methods, composite finite-time containment control for disturbed second-order multi-agent systems, fractional-order observer design of multi-agent systems, chaos control and anticontrol of complex systems via Parrondos game and many more. Third, the applications of ...

  2. Controlling the dynamics of multi-state neural networks

    International Nuclear Information System (INIS)

    Jin, Tao; Zhao, Hong

    2008-01-01

    In this paper, we first analyze the distribution of local fields (DLF) which is induced by the memory patterns in the Q-Ising model. It is found that the structure of the DLF is closely correlated with the network dynamics and the system performance. However, the design rule adopted in the Q-Ising model, like the other rules adopted for multi-state neural networks with associative memories, cannot be applied to directly control the DLF for a given set of memory patterns, and thus cannot be applied to further study the relationships between the structure of the DLF and the dynamics of the network. We then extend a design rule, which was presented recently for designing binary-state neural networks, to make it suitable for designing general multi-state neural networks. This rule is able to control the structure of the DLF as expected. We show that controlling the DLF not only can affect the dynamic behaviors of the multi-state neural networks for a given set of memory patterns, but also can improve the storage capacity. With the change of the DLF, the network shows very rich dynamic behaviors, such as the 'chaos phase', the 'memory phase', and the 'mixture phase'. These dynamic behaviors are also observed in the binary-state neural networks; therefore, our results imply that they may be the universal behaviors of feedback neural networks

  3. Local community detection as pattern restoration by attractor dynamics of recurrent neural networks.

    Science.gov (United States)

    Okamoto, Hiroshi

    2016-08-01

    Densely connected parts in networks are referred to as "communities". Community structure is a hallmark of a variety of real-world networks. Individual communities in networks form functional modules of complex systems described by networks. Therefore, finding communities in networks is essential to approaching and understanding complex systems described by networks. In fact, network science has made a great deal of effort to develop effective and efficient methods for detecting communities in networks. Here we put forward a type of community detection, which has been little examined so far but will be practically useful. Suppose that we are given a set of source nodes that includes some (but not all) of "true" members of a particular community; suppose also that the set includes some nodes that are not the members of this community (i.e., "false" members of the community). We propose to detect the community from this "imperfect" and "inaccurate" set of source nodes using attractor dynamics of recurrent neural networks. Community detection by the proposed method can be viewed as restoration of the original pattern from a deteriorated pattern, which is analogous to cue-triggered recall of short-term memory in the brain. We demonstrate the effectiveness of the proposed method using synthetic networks and real social networks for which correct communities are known. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

    Science.gov (United States)

    Sadeh, Sadra; Rotter, Stefan

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Sadra Sadeh

    2015-01-01

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

  6. Spiking, Bursting, and Population Dynamics in a Network of Growth Transform Neurons.

    Science.gov (United States)

    Gangopadhyay, Ahana; Chakrabartty, Shantanu

    2017-04-27

    This paper investigates the dynamical properties of a network of neurons, each of which implements an asynchronous mapping based on polynomial growth transforms. In the first part of this paper, we present a geometric approach for visualizing the dynamics of the network where each of the neurons traverses a trajectory in a dual optimization space, whereas the network itself traverses a trajectory in an equivalent primal optimization space. We show that as the network learns to solve basic classification tasks, different choices of primal-dual mapping produce unique but interpretable neural dynamics like noise shaping, spiking, and bursting. While the proposed framework is general enough, in this paper, we demonstrate its use for designing support vector machines (SVMs) that exhibit noise-shaping properties similar to those of ΣΔ modulators, and for designing SVMs that learn to encode information using spikes and bursts. It is demonstrated that the emergent switching, spiking, and burst dynamics produced by each neuron encodes its respective margin of separation from a classification hyperplane whose parameters are encoded by the network population dynamics. We believe that the proposed growth transform neuron model and the underlying geometric framework could serve as an important tool to connect well-established machine learning algorithms like SVMs to neuromorphic principles like spiking, bursting, population encoding, and noise shaping.

  7. Multicompartment Drug Release System for Dynamic Modulation of Tissue Responses.

    Science.gov (United States)

    Morris, Aaron H; Mahal, Rajwant S; Udell, Jillian; Wu, Michelle; Kyriakides, Themis R

    2017-10-01

    Pharmacological modulation of responses to injury is complicated by the need to deliver multiple drugs with spatiotemporal resolution. Here, a novel controlled delivery system containing three separate compartments with each releasing its contents over different timescales is fabricated. Core-shell electrospun fibers create two of the compartments in the system, while electrosprayed spheres create the third. Utility is demonstrated by targeting the foreign body response to implants because it is a dynamic process resulting in implant failure. Sequential delivery of a drug targeting nuclear factor-κB (NF-κB) and an antifibrotic is characterized in in vitro experiments. Specifically, macrophage fusion and p65 nuclear translocation in the presence of releasate or with macrophages cultured on the surfaces of the constructs are evaluated. In addition, releasate from pirfenidone scaffolds is shown to reduce transforming growth factor-β (TGF-β)-induced pSMAD3 nuclear localization in fibroblasts. In vivo, drug eluting constructs successfully mitigate macrophage fusion at one week and fibrotic encapsulation in a dose-dependent manner at four weeks, demonstrating effective release of both drugs over different timescales. Future studies can employ this system to improve and prolong implant lifetimes, or load it with other drugs to modulate other dynamic processes. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  8. A User Driven Dynamic Circuit Network Implementation

    Energy Technology Data Exchange (ETDEWEB)

    Guok, Chin; Robertson, David; Chaniotakis, Evangelos; Thompson, Mary; Johnston, William; Tierney, Brian

    2008-10-01

    The requirements for network predictability are becoming increasingly critical to the DoE science community where resources are widely distributed and collaborations are world-wide. To accommodate these emerging requirements, the Energy Sciences Network has established a Science Data Network to provide user driven guaranteed bandwidth allocations. In this paper we outline the design, implementation, and secure coordinated use of such a network, as well as some lessons learned.

  9. Network rewiring dynamics with convergence towards a star network.

    Science.gov (United States)

    Whigham, P A; Dick, G; Parry, M

    2016-10-01

    Network rewiring as a method for producing a range of structures was first introduced in 1998 by Watts & Strogatz ( Nature 393 , 440-442. (doi:10.1038/30918)). This approach allowed a transition from regular through small-world to a random network. The subsequent interest in scale-free networks motivated a number of methods for developing rewiring approaches that converged to scale-free networks. This paper presents a rewiring algorithm (RtoS) for undirected, non-degenerate, fixed size networks that transitions from regular, through small-world and scale-free to star-like networks. Applications of the approach to models for the spread of infectious disease and fixation time for a simple genetics model are used to demonstrate the efficacy and application of the approach.

  10. Traffic Policing in Dynamic Military Networks Using Software Defined Networking

    OpenAIRE

    Skappel, Hans Fredrik

    2016-01-01

    This thesis looks at how Software Defined Networking (SDN) can be used to provide traffic engineering and to police traffic in an Operational Military Network (OMN). SDN is a concept where the control plane is separated from the forwarding plane, and the control plane is capable of controlling forwarding plane elements located on multiple network nodes using the OpenFlow protocol. Specifically, we have discussed the problems in OMNs, and possible SDN approaches to mitigate the challenges. Bas...

  11. Using chemistry and microfluidics to understand the spatial dynamics of complex biological networks.

    Science.gov (United States)

    Kastrup, Christian J; Runyon, Matthew K; Lucchetta, Elena M; Price, Jessica M; Ismagilov, Rustem F

    2008-04-01

    Understanding the spatial dynamics of biochemical networks is both fundamentally important for understanding life at the systems level and also has practical implications for medicine, engineering, biology, and chemistry. Studies at the level of individual reactions provide essential information about the function, interactions, and localization of individual molecular species and reactions in a network. However, analyzing the spatial dynamics of complex biochemical networks at this level is difficult. Biochemical networks are nonequilibrium systems containing dozens to hundreds of reactions with nonlinear and time-dependent interactions, and these interactions are influenced by diffusion, flow, and the relative values of state-dependent kinetic parameters. To achieve an overall understanding of the spatial dynamics of a network and the global mechanisms that drive its function, networks must be analyzed as a whole, where all of the components and influential parameters of a network are simultaneously considered. Here, we describe chemical concepts and microfluidic tools developed for network-level investigations of the spatial dynamics of these networks. Modular approaches can be used to simplify these networks by separating them into modules, and simple experimental or computational models can be created by replacing each module with a single reaction. Microfluidics can be used to implement these models as well as to analyze and perturb the complex network itself with spatial control on the micrometer scale. We also describe the application of these network-level approaches to elucidate the mechanisms governing the spatial dynamics of two networkshemostasis (blood clotting) and early patterning of the Drosophila embryo. To investigate the dynamics of the complex network of hemostasis, we simplified the network by using a modular mechanism and created a chemical model based on this mechanism by using microfluidics. Then, we used the mechanism and the model to

  12. Design and implementation of dynamic hybrid Honeypot network

    Science.gov (United States)

    Qiao, Peili; Hu, Shan-Shan; Zhai, Ji-Qiang

    2013-05-01

    The method of constructing a dynamic and self-adaptive virtual network is suggested to puzzle adversaries, delay and divert attacks, exhaust attacker resources and collect attacking information. The concepts of Honeypot and Honeyd, which is the frame of virtual Honeypot are introduced. The techniques of network scanning including active fingerprint recognition are analyzed. Dynamic virtual network system is designed and implemented. A virtual network similar to real network topology is built according to the collected messages from real environments in this system. By doing this, the system can perplex the attackers when Hackers attack and can further analyze and research the attacks. The tests to this system prove that this design can successfully simulate real network environment and can be used in network security analysis.

  13. Dynamics of epidemic diseases on a growing adaptive network.

    Science.gov (United States)

    Demirel, Güven; Barter, Edmund; Gross, Thilo

    2017-02-10

    The study of epidemics on static networks has revealed important effects on disease prevalence of network topological features such as the variance of the degree distribution, i.e. the distribution of the number of neighbors of nodes, and the maximum degree. Here, we analyze an adaptive network where the degree distribution is not independent of epidemics but is shaped through disease-induced dynamics and mortality in a complex interplay. We study the dynamics of a network that grows according to a preferential attachment rule, while nodes are simultaneously removed from the network due to disease-induced mortality. We investigate the prevalence of the disease using individual-based simulations and a heterogeneous node approximation. Our results suggest that in this system in the thermodynamic limit no epidemic thresholds exist, while the interplay between network growth and epidemic spreading leads to exponential networks for any finite rate of infectiousness when the disease persists.

  14. Synchronization of complex delayed dynamical networks with nonlinearly coupled nodes

    International Nuclear Information System (INIS)

    Liu Tao; Zhao Jun; Hill, David J.

    2009-01-01

    In this paper, we study the global synchronization of nonlinearly coupled complex delayed dynamical networks with both directed and undirected graphs. Via Lyapunov-Krasovskii stability theory and the network topology, we investigate the global synchronization of such networks. Under the assumption that coupling coefficients are known, a family of delay-independent decentralized nonlinear feedback controllers are designed to globally synchronize the networks. When coupling coefficients are unavailable, an adaptive mechanism is introduced to synthesize a family of delay-independent decentralized adaptive controllers which guarantee the global synchronization of the uncertain networks. Two numerical examples of directed and undirected delayed dynamical network are given, respectively, using the Lorenz system as the nodes of the networks, which demonstrate the effectiveness of proposed results.

  15. Perception of Communication Network Fraud Dynamics by Network ...

    African Journals Online (AJOL)

    In considering the implications of the varied nature of the potential targets, the paper identifies the view to develop effective intelligence analysis methodologies for network fraud detection and prevention by network administrators and stakeholders. The paper further notes that organizations are fighting an increasingly ...

  16. Modeling reveals bistability and low-pass filtering in the network module determining blood stem cell fate.

    Directory of Open Access Journals (Sweden)

    Jatin Narula

    2010-05-01

    Full Text Available Combinatorial regulation of gene expression is ubiquitous in eukaryotes with multiple inputs converging on regulatory control elements. The dynamic properties of these elements determine the functionality of genetic networks regulating differentiation and development. Here we propose a method to quantitatively characterize the regulatory output of distant enhancers with a biophysical approach that recursively determines free energies of protein-protein and protein-DNA interactions from experimental analysis of transcriptional reporter libraries. We apply this method to model the Scl-Gata2-Fli1 triad-a network module important for cell fate specification of hematopoietic stem cells. We show that this triad module is inherently bistable with irreversible transitions in response to physiologically relevant signals such as Notch, Bmp4 and Gata1 and we use the model to predict the sensitivity of the network to mutations. We also show that the triad acts as a low-pass filter by switching between steady states only in response to signals that persist for longer than a minimum duration threshold. We have found that the auto-regulation loops connecting the slow-degrading Scl to Gata2 and Fli1 are crucial for this low-pass filtering property. Taken together our analysis not only reveals new insights into hematopoietic stem cell regulatory network functionality but also provides a novel and widely applicable strategy to incorporate experimental measurements into dynamical network models.

  17. A core eating network and its modulations underlie diverse eating phenomena

    NARCIS (Netherlands)

    Chen, Jing|info:eu-repo/dai/nl/411887548; Papies, Esther K.|info:eu-repo/dai/nl/304832766; Barsalou, Lawrence W.

    2016-01-01

    We propose that a core eating network and its modulations account for much of what is currently known about the neural activity underlying a wide range of eating phenomena in humans (excluding homeostasis and related phenomena). The core eating network is closely adapted from a network that Kaye,

  18. Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks.

    Science.gov (United States)

    Onaga, Tomokatsu; Gleeson, James P; Masuda, Naoki

    2017-09-08

    Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node's concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations.

  19. Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks

    Science.gov (United States)

    Onaga, Tomokatsu; Gleeson, James P.; Masuda, Naoki

    2017-09-01

    Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node's concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations.

  20. Dynamical community structure of populations evolving on genotype networks

    International Nuclear Information System (INIS)

    Capitán, José A.; Aguirre, Jacobo; Manrubia, Susanna

    2015-01-01

    Neutral evolutionary dynamics of replicators occurs on large and heterogeneous networks of genotypes. These networks, formed by all genotypes that yield the same phenotype, have a complex architecture that conditions the molecular composition of populations and their movements on genome spaces. Here we consider as an example the case of populations evolving on RNA secondary structure neutral networks and study the community structure of the network revealed through dynamical properties of the population at equilibrium and during adaptive transients. We unveil a rich hierarchical community structure that, eventually, can be traced back to the non-trivial relationship between RNA secondary structure and sequence composition. We demonstrate that usual measures of modularity that only take into account the static, topological structure of networks, cannot identify the community structure disclosed by population dynamics

  1. Modeling of carrier dynamics in quantum-well electroabsorption modulators

    DEFF Research Database (Denmark)

    Højfeldt, Sune; Mørk, Jesper

    2002-01-01

    We present a comprehensive drift-diffusion-type electroabsorption modulator (EAM) model. The model allows us to investigate both steady-state properties and to follow the sweep-out of carriers after pulsed optical excitation. Furthermore, it allows for the investigation of the influence that vari...... in the field near each well affect the escape of carriers from that well. Finally, we look at the influence that the separate-confinement heterostructure barriers have on the carrier sweep-out....... that various design parameters have on the device properties, in particular how they affect the carrier dynamics and the corresponding field dynamics. A number of different types of results are presented. We calculate absorption spectra and steady-state field screening due to carrier pile-up at the separate......-confinement heterobarriers. We then move on to look at carrier sweep-out upon short-pulse optical excitation. For a structure with one well, we analyze how the well position affects the carrier sweep-out and the absorption recovery. We calculate the field dynamics in a multiquantum-well structure and discuss how the changes...

  2. Spontaneous formation of dynamical groups in an adaptive networked system

    International Nuclear Information System (INIS)

    Li Menghui; Guan Shuguang; Lai, C-H

    2010-01-01

    In this work, we investigate a model of an adaptive networked dynamical system, where the coupling strengths among phase oscillators coevolve with the phase states. It is shown that in this model the oscillators can spontaneously differentiate into two dynamical groups after a long time evolution. Within each group, the oscillators have similar phases, while oscillators in different groups have approximately opposite phases. The network gradually converts from the initial random structure with a uniform distribution of connection strengths into a modular structure that is characterized by strong intra-connections and weak inter-connections. Furthermore, the connection strengths follow a power-law distribution, which is a natural consequence of the coevolution of the network and the dynamics. Interestingly, it is found that if the inter-connections are weaker than a certain threshold, the two dynamical groups will almost decouple and evolve independently. These results are helpful in further understanding the empirical observations in many social and biological networks.

  3. Structure-based control of complex networks with nonlinear dynamics.

    Science.gov (United States)

    Zañudo, Jorge Gomez Tejeda; Yang, Gang; Albert, Réka

    2017-07-11

    What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances.

  4. Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism

    DEFF Research Database (Denmark)

    Buescher, Joerg Martin; Liebermeister, Wolfram; Jules, Matthieu

    2012-01-01

    Adaptation of cells to environmental changes requires dynamic interactions between metabolic and regulatory networks, but studies typically address only one or a few layers of regulation. For nutritional shifts between two preferred carbon sources of Bacillus subtilis, we combined statistical...

  5. Wavelength converter placement in optical networks with dynamic traffic

    DEFF Research Database (Denmark)

    Buron, Jakob Due; Ruepp, Sarah Renée; Wessing, Henrik

    2008-01-01

    We evaluate the connection provisioning performance of GMPLS-controlled wavelength routed networks under dynamic traffic load and using three different wavelength converter placement heuristics. Results show that a simple uniform placement heuristic matches the performance of complex heuristics...

  6. Epidemic dynamics and endemic states in complex networks

    Science.gov (United States)

    Pastor-Satorras, Romualdo; Vespignani, Alessandro

    2001-06-01

    We study by analytical methods and large scale simulations a dynamical model for the spreading of epidemics in complex networks. In networks with exponentially bounded connectivity we recover the usual epidemic behavior with a threshold defining a critical point below that the infection prevalence is null. On the contrary, on a wide range of scale-free networks we observe the absence of an epidemic threshold and its associated critical behavior. This implies that scale-free networks are prone to the spreading and the persistence of infections whatever spreading rate the epidemic agents might possess. These results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks.

  7. Epidemic dynamics and endemic states in complex networks

    International Nuclear Information System (INIS)

    Pastor-Satorras, Romualdo; Vespignani, Alessandro

    2001-01-01

    We study by analytical methods and large scale simulations a dynamical model for the spreading of epidemics in complex networks. In networks with exponentially bounded connectivity we recover the usual epidemic behavior with a threshold defining a critical point below that the infection prevalence is null. On the contrary, on a wide range of scale-free networks we observe the absence of an epidemic threshold and its associated critical behavior. This implies that scale-free networks are prone to the spreading and the persistence of infections whatever spreading rate the epidemic agents might possess. These results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks

  8. Characterization of Static/Dynamic Topological Routing For Grid Networks

    DEFF Research Database (Denmark)

    Gutierrez Lopez, Jose Manuel; Cuevas, Ruben; Riaz, M. Tahir

    2009-01-01

    Grid or 2D Mesh structures are becoming one of the most attractive network topologies to study. They can be used in many different fields raging from future broadband networks to multiprocessors structures. In addition, the high requirements of future services and applications demand more flexible...... and adaptive networks. Topological routing in grid networks is a simple and efficient alternative to traditional routing techniques, e.g. routing tables, and the paper extends this kind of routing providing a "Dynamic" attribute. This new property attempts to improve the overall network performance for future...

  9. Dynamical Encoding by Networks of Competing Neuron Groups: Winnerless Competition

    International Nuclear Information System (INIS)

    Rabinovich, M.; Volkovskii, A.; Lecanda, P.; Huerta, R.; Abarbanel, H. D. I.; Laurent, G.

    2001-01-01

    Following studies of olfactory processing in insects and fish, we investigate neural networks whose dynamics in phase space is represented by orbits near the heteroclinic connections between saddle regions (fixed points or limit cycles). These networks encode input information as trajectories along the heteroclinic connections. If there are N neurons in the network, the capacity is approximately e(N-1) ! , i.e., much larger than that of most traditional network structures. We show that a small winnerless competition network composed of FitzHugh-Nagumo spiking neurons efficiently transforms input information into a spatiotemporal output

  10. The Graph Laplacian and the Dynamics of Complex Networks

    Energy Technology Data Exchange (ETDEWEB)

    Thulasidasan, Sunil [Los Alamos National Laboratory

    2012-06-11

    In this talk, we explore the structure of networks from a spectral graph-theoretic perspective by analyzing the properties of the Laplacian matrix associated with the graph induced by a network. We will see how the eigenvalues of the graph Laplacian relate to the underlying network structure and dynamics and provides insight into a phenomenon frequently observed in real world networks - the emergence of collective behavior from purely local interactions seen in the coordinated motion of animals and phase transitions in biological networks, to name a few.

  11. Network-state modulation of power-law frequency-scaling in visual cortical neurons.

    Science.gov (United States)

    El Boustani, Sami; Marre, Olivier; Béhuret, Sébastien; Baudot, Pierre; Yger, Pierre; Bal, Thierry; Destexhe, Alain; Frégnac, Yves

    2009-09-01

    Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of V(m) activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the V(m) reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the "effective" connectivity responsible for the dynamical signature of the population signals measured

  12. Network-state modulation of power-law frequency-scaling in visual cortical neurons.

    Directory of Open Access Journals (Sweden)

    Sami El Boustani

    2009-09-01

    Full Text Available Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of V(m activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the V(m reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the "effective" connectivity responsible for the dynamical signature of the population

  13. Dynamic reorganization of human resting-state networks during visuospatial attention.

    Science.gov (United States)

    Spadone, Sara; Della Penna, Stefania; Sestieri, Carlo; Betti, Viviana; Tosoni, Annalisa; Perrucci, Mauro Gianni; Romani, Gian Luca; Corbetta, Maurizio

    2015-06-30

    Fundamental problems in neuroscience today are understanding how patterns of ongoing spontaneous activity are modified by task performance and whether/how these intrinsic patterns influence task-evoked activation and behavior. We examined these questions by comparing instantaneous functional connectivity (IFC) and directed functional connectivity (DFC) changes in two networks that are strongly correlated and segregated at rest: the visual (VIS) network and the dorsal attention network (DAN). We measured how IFC and DFC during a visuospatial attention task, which requires dynamic selective rerouting of visual information across hemispheres, changed with respect to rest. During the attention task, the two networks remained relatively segregated, and their general pattern of within-network correlation was maintained. However, attention induced a decrease of correlation in the VIS network and an increase of the DAN→VIS IFC and DFC, especially in a top-down direction. In contrast, within the DAN, IFC was not modified by attention, whereas DFC was enhanced. Importantly, IFC modulations were behaviorally relevant. We conclude that a stable backbone of within-network functional connectivity topography remains in place when transitioning between resting wakefulness and attention selection. However, relative decrease of correlation of ongoing "idling" activity in visual cortex and synchronization between frontoparietal and visual cortex were behaviorally relevant, indicating that modulations of resting activity patterns are important for task performance. Higher order resting connectivity in the DAN was relatively unaffected during attention, potentially indicating a role for simultaneous ongoing activity as a "prior" for attention selection.

  14. Dynamic and modular gene regulatory networks drive the development of gametogenesis.

    Science.gov (United States)

    Che, Dongxue; Wang, Yang; Bai, Weiyang; Li, Leijie; Liu, Guiyou; Zhang, Liangcai; Zuo, Yongchun; Tao, Shiheng; Hua, Jinlian; Liao, Mingzhi

    2017-07-01

    Gametogenesis is a complex process, which includes mitosis and meiosis and results in the production of ovum and sperm. The development of gametogenesis is dynamic and needs many different genes to work synergistically, but it is lack of global perspective research about this process. In this study, we detected the dynamic process of gametogenesis from the perspective of systems biology based on protein-protein interaction networks (PPINs) and functional analysis. Results showed that gametogenesis genes have strong synergistic effects in PPINs within and between different phases during the development. Addition to the synergistic effects on molecular networks, gametogenesis genes showed functional consistency within and between different phases, which provides the further evidence about the dynamic process during the development of gametogenesis. At last, we detected and provided the core molecular modules of different phases about gametogenesis. The gametogenesis genes and related modules can be obtained from our Web site Gametogenesis Molecule Online (GMO, http://gametsonline.nwsuaflmz.com/index.php), which is freely accessible. GMO may be helpful for the reference and application of these genes and modules in the future identification of key genes about gametogenesis. Summary, this work provided a computational perspective and frame to the analysis of the gametogenesis dynamics and modularity in both human and mouse. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  15. Recovery time after localized perturbations in complex dynamical networks

    Science.gov (United States)

    Mitra, Chiranjit; Kittel, Tim; Choudhary, Anshul; Kurths, Jürgen; Donner, Reik V.

    2017-10-01

    Maintaining the synchronous motion of dynamical systems interacting on complex networks is often critical to their functionality. However, real-world networked dynamical systems operating synchronously are prone to random perturbations driving the system to arbitrary states within the corresponding basin of attraction, thereby leading to epochs of desynchronized dynamics with a priori unknown durations. Thus, it is highly relevant to have an estimate of the duration of such transient phases before the system returns to synchrony, following a random perturbation to the dynamical state of any particular node of the network. We address this issue here by proposing the framework of single-node recovery time (SNRT) which provides an estimate of the relative time scales underlying the transient dynamics of the nodes of a network during its restoration to synchrony. We utilize this in differentiating the particularly slow nodes of the network from the relatively fast nodes, thus identifying the critical nodes which when perturbed lead to significantly enlarged recovery time of the system before resuming synchronized operation. Further, we reveal explicit relationships between the SNRT values of a network, and its global relaxation time when starting all the nodes from random initial conditions. Earlier work on relaxation time generally focused on investigating its dependence on macroscopic topological properties of the respective network. However, we employ the proposed concept for deducing microscopic relationships between topological features of nodes and their respective SNRT values. The framework of SNRT is further extended to a measure of resilience of the different nodes of a networked dynamical system. We demonstrate the potential of SNRT in networks of Rössler oscillators on paradigmatic topologies and a model of the power grid of the United Kingdom with second-order Kuramoto-type nodal dynamics illustrating the conceivable practical applicability of the proposed

  16. Recovery time after localized perturbations in complex dynamical networks

    International Nuclear Information System (INIS)

    Mitra, Chiranjit; Kittel, Tim; Kurths, Jürgen; Donner, Reik V; Choudhary, Anshul

    2017-01-01

    Maintaining the synchronous motion of dynamical systems interacting on complex networks is often critical to their functionality. However, real-world networked dynamical systems operating synchronously are prone to random perturbations driving the system to arbitrary states within the corresponding basin of attraction, thereby leading to epochs of desynchronized dynamics with a priori unknown durations. Thus, it is highly relevant to have an estimate of the duration of such transient phases before the system returns to synchrony, following a random perturbation to the dynamical state of any particular node of the network. We address this issue here by proposing the framework of single-node recovery time (SNRT) which provides an estimate of the relative time scales underlying the transient dynamics of the nodes of a network during its restoration to synchrony. We utilize this in differentiating the particularly slow nodes of the network from the relatively fast nodes, thus identifying the critical nodes which when perturbed lead to significantly enlarged recovery time of the system before resuming synchronized operation. Further, we reveal explicit relationships between the SNRT values of a network, and its global relaxation time when starting all the nodes from random initial conditions. Earlier work on relaxation time generally focused on investigating its dependence on macroscopic topological properties of the respective network. However, we employ the proposed concept for deducing microscopic relationships between topological features of nodes and their respective SNRT values. The framework of SNRT is further extended to a measure of resilience of the different nodes of a networked dynamical system. We demonstrate the potential of SNRT in networks of Rössler oscillators on paradigmatic topologies and a model of the power grid of the United Kingdom with second-order Kuramoto-type nodal dynamics illustrating the conceivable practical applicability of the proposed

  17. Improving functional modules discovery by enriching interaction networks with gene profiles

    KAUST Repository

    Salem, Saeed

    2013-05-01

    Recent advances in proteomic and transcriptomic technologies resulted in the accumulation of vast amount of high-throughput data that span multiple biological processes and characteristics in different organisms. Much of the data come in the form of interaction networks and mRNA expression arrays. An important task in systems biology is functional modules discovery where the goal is to uncover well-connected sub-networks (modules). These discovered modules help to unravel the underlying mechanisms of the observed biological processes. While most of the existing module discovery methods use only the interaction data, in this work we propose, CLARM, which discovers biological modules by incorporating gene profiles data with protein-protein interaction networks. We demonstrate the effectiveness of CLARM on Yeast and Human interaction datasets, and gene expression and molecular function profiles. Experiments on these real datasets show that the CLARM approach is competitive to well established functional module discovery methods.

  18. Successive lag synchronization on dynamical networks with communication delay

    International Nuclear Information System (INIS)

    Zhang Xin-Jian; Wei Ai-Ju; Li Ke-Zan

    2016-01-01

    In this paper, successive lag synchronization (SLS) on a dynamical network with communication delay is investigated. In order to achieve SLS on the dynamical network with communication delay, we design linear feedback control and adaptive control, respectively. By using the Lyapunov function method, we obtain some sufficient conditions for global stability of SLS. To verify these results, some numerical examples are further presented. This work may find potential applications in consensus of multi-agent systems. (paper)

  19. Practical synchronization on complex dynamical networks via optimal pinning control

    Science.gov (United States)

    Li, Kezan; Sun, Weigang; Small, Michael; Fu, Xinchu

    2015-07-01

    We consider practical synchronization on complex dynamical networks under linear feedback control designed by optimal control theory. The control goal is to minimize global synchronization error and control strength over a given finite time interval, and synchronization error at terminal time. By utilizing the Pontryagin's minimum principle, and based on a general complex dynamical network, we obtain an optimal system to achieve the control goal. The result is verified by performing some numerical simulations on Star networks, Watts-Strogatz networks, and Barabási-Albert networks. Moreover, by combining optimal control and traditional pinning control, we propose an optimal pinning control strategy which depends on the network's topological structure. Obtained results show that optimal pinning control is very effective for synchronization control in real applications.

  20. Complex networks: when random walk dynamics equals synchronization

    International Nuclear Information System (INIS)

    Kriener, Birgit; Anand, Lishma; Timme, Marc

    2012-01-01

    Synchrony prevalently emerges from the interactions of coupled dynamical units. For simple systems such as networks of phase oscillators, the asymptotic synchronization process is assumed to be equivalent to a Markov process that models standard diffusion or random walks on the same network topology. In this paper, we analytically derive the conditions for such equivalence for networks of pulse-coupled oscillators, which serve as models for neurons and pacemaker cells interacting by exchanging electric pulses or fireflies interacting via light flashes. We find that the pulse synchronization process is less simple, but there are classes of, e.g., network topologies that ensure equivalence. In particular, local dynamical operators are required to be doubly stochastic. These results provide a natural link between stochastic processes and deterministic synchronization on networks. Tools for analyzing diffusion (or, more generally, Markov processes) may now be transferred to pin down features of synchronization in networks of pulse-coupled units such as neural circuits. (paper)

  1. Network statistics of genetically-driven gene co-expression modules in mouse crosses

    Directory of Open Access Journals (Sweden)

    Marie-Pier eScott-Boyer

    2013-12-01

    Full Text Available In biology, networks are used in different contexts as ways to represent relationships between entities, such as for instance interactions between genes, proteins or metabolites. Despite progress in the analysis of such networks and their potential to better understand the collective impact of genes on complex traits, one remaining challenge is to establish the biologic validity of gene co-expression networks and to determine what governs their organization. We used WGCNA to construct and analyze seven gene expression datasets from several tissues of mouse recombinant inbred strains (RIS. For six out of the 7 networks, we found that linkage to module QTLs (mQTLs could be established for 29.3% of gene co-expression modules detected in the several mouse RIS. For about 74.6% of such genetically-linked modules, the mQTL was on the same chromosome as the one contributing most genes to the module, with genes originating from that chromosome showing higher connectivity than other genes in the modules. Such modules (that we considered as genetically-driven had network statistic properties (density, centralization and heterogeneity that set them apart from other modules in the network. Altogether, a sizeable portion of gene co-expression modules detected in mouse RIS panels had genetic determinants as their main organizing principle. In addition to providing a biologic interpretation validation for these modules, these genetic determinants imparted on them particular properties that set them apart from other modules in the network, to the point that they can be predicted to a large extent on the basis of their network statistics.

  2. Network dynamics in the healthy and epileptic developing brain

    Directory of Open Access Journals (Sweden)

    Richard Rosch

    2018-03-01

    Full Text Available Electroencephalography (EEG allows recording of cortical activity at high temporal resolution. EEG recordings can be summarized along different dimensions using network-level quantitative measures, such as channel-to-channel correlation, or band power distributions across channels. These reveal network patterns that unfold over a range of different timescales and can be tracked dynamically. Here we describe the dynamics of network state transitions in EEG recordings of spontaneous brain activity in normally developing infants and infants with severe early infantile epileptic encephalopathies (n = 8, age: 1–8 months. We describe differences in measures of EEG dynamics derived from band power, and correlation-based summaries of network-wide brain activity. We further show that EEGs from different patient groups and controls may be distinguishable on a small set of the novel quantitative measures introduced here, which describe dynamic network state switching. Quantitative measures related to the sharpness of switching from one correlation pattern to another show the largest differences between groups. These findings reveal that the early epileptic encephalopathies are associated with characteristic dynamic features at the network level. Quantitative network-based analyses like the one presented here may in the future inform the clinical use of quantitative EEG for diagnosis.

  3. Network evolution driven by dynamics applied to graph coloring

    International Nuclear Information System (INIS)

    Wu Jian-She; Li Li-Guang; Yu Xin; Jiao Li-Cheng; Wang Xiao-Hua

    2013-01-01

    An evolutionary network driven by dynamics is studied and applied to the graph coloring problem. From an initial structure, both the topology and the coupling weights evolve according to the dynamics. On the other hand, the dynamics of the network are determined by the topology and the coupling weights, so an interesting structure-dynamics co-evolutionary scheme appears. By providing two evolutionary strategies, a network described by the complement of a graph will evolve into several clusters of nodes according to their dynamics. The nodes in each cluster can be assigned the same color and nodes in different clusters assigned different colors. In this way, a co-evolution phenomenon is applied to the graph coloring problem. The proposed scheme is tested on several benchmark graphs for graph coloring

  4. Simulating market dynamics: interactions between consumer psychology and social networks.

    Science.gov (United States)

    Janssen, Marco A; Jager, Wander

    2003-01-01

    Markets can show different types of dynamics, from quiet markets dominated by one or a few products, to markets with continual penetration of new and reintroduced products. In a previous article we explored the dynamics of markets from a psychological perspective using a multi-agent simulation model. The main results indicated that the behavioral rules dominating the artificial consumer's decision making determine the resulting market dynamics, such as fashions, lock-in, and unstable renewal. Results also show the importance of psychological variables like social networks, preferences, and the need for identity to explain the dynamics of markets. In this article we extend this work in two directions. First, we will focus on a more systematic investigation of the effects of different network structures. The previous article was based on Watts and Strogatz's approach, which describes the small-world and clustering characteristics in networks. More recent research demonstrated that many large networks display a scale-free power-law distribution for node connectivity. In terms of market dynamics this may imply that a small proportion of consumers may have an exceptional influence on the consumptive behavior of others (hubs, or early adapters). We show that market dynamics is a self-organized property depending on the interaction between the agents' decision-making process (heuristics), the product characteristics (degree of satisfaction of unit of consumption, visibility), and the structure of interactions between agents (size of network and hubs in a social network).

  5. Dynamic photonic lightpaths in the StarPlane network

    NARCIS (Netherlands)

    Grosso, P.; Marchal, D.; Maassen, J.; Bernier, E.; Xu, L.; de Laat, C.

    2009-01-01

    The StarPlane project enables users to dynamically control network photonic paths. Applications running on the Distributed ASCI Supercomputer (DAS-3) can manipulate wavelengths in the Dutch research and education network SURFnet6. The goal is to achieve fast switching times so that when the

  6. Complete synchronization on multi-layer center dynamical networks

    International Nuclear Information System (INIS)

    Liu Meng; Shao Yingying; Fu Xinchu

    2009-01-01

    In this paper, complete synchronization of three-layer center networks is studied. By using linear stability analysis approach, several different coupling schemes of three-layer center networks with the Logistic map local dynamics are discussed, and the stability conditions for synchronization are illustrated via some examples.

  7. Collaborative Recurrent Neural Networks forDynamic Recommender Systems

    Science.gov (United States)

    2016-11-22

    JMLR: Workshop and Conference Proceedings 63:366–381, 2016 ACML 2016 Collaborative Recurrent Neural Networks for Dynamic Recommender Systems Young...an unprece- dented scale. Although such activity logs are abundantly available, most approaches to recommender systems are based on the rating...Recurrent Neural Network, Recommender System , Neural Language Model, Collaborative Filtering 1. Introduction As ever larger parts of the population

  8. Popularity and Adolescent Friendship Networks : Selection and Influence Dynamics

    NARCIS (Netherlands)

    Dijkstra, Jan Kornelis; Cillessen, Antonius H. N.; Borch, Casey

    This study examined the dynamics of popularity in adolescent friendship networks across 3 years in middle school. Longitudinal social network modeling was used to identify selection and influence in the similarity of popularity among friends. It was argued that lower status adolescents strive to

  9. Popularity and Adolescent Friendship Networks: Selection and Influence Dynamics

    NARCIS (Netherlands)

    Dijkstra, J.K.; Cillessen, A.H.N.; Borch, C.

    2013-01-01

    This study examined the dynamics of popularity in adolescent friendship networks across 3 years in middle school. Longitudinal social network modeling was used to identify selection and influence in the similarity of popularity among friends. It was argued that lower status adolescents strive to

  10. Popularity and Adolescent Friendship Networks: Selection and Influence Dynamics

    Science.gov (United States)

    Dijkstra, Jan Kornelis; Cillessen, Antonius H. N.; Borch, Casey

    2013-01-01

    This study examined the dynamics of popularity in adolescent friendship networks across 3 years in middle school. Longitudinal social network modeling was used to identify selection and influence in the similarity of popularity among friends. It was argued that lower status adolescents strive to enhance their status through befriending higher…

  11. Resumption of dynamism in damaged networks of coupled oscillators

    Science.gov (United States)

    Kundu, Srilena; Majhi, Soumen; Ghosh, Dibakar

    2018-05-01

    Deterioration in dynamical activities may come up naturally or due to environmental influences in a massive portion of biological and physical systems. Such dynamical degradation may have outright effect on the substantive network performance. This requires us to provide some proper prescriptions to overcome undesired circumstances. In this paper, we present a scheme based on external feedback that can efficiently revive dynamism in damaged networks of active and inactive oscillators and thus enhance the network survivability. Both numerical and analytical investigations are performed in order to verify our claim. We also provide a comparative study on the effectiveness of this mechanism for feedbacks to the inactive group or to the active group only. Most importantly, resurrection of dynamical activity is realized even in time-delayed damaged networks, which are considered to be less persistent against deterioration in the form of inactivity in the oscillators. Furthermore, prominence in our approach is substantiated by providing evidence of enhanced network persistence in complex network topologies taking small-world and scale-free architectures, which makes the proposed remedy quite general. Besides the study in the network of Stuart-Landau oscillators, affirmative influence of external feedback has been justified in the network of chaotic Rössler systems as well.

  12. Non-homogeneous dynamic Bayesian networks for continuous data

    NARCIS (Netherlands)

    Grzegorczyk, Marco; Husmeier, Dirk

    Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with non-homogeneous temporal processes. Various approaches to relax the homogeneity assumption have recently been proposed. The present paper presents a combination of a Bayesian network with

  13. Dynamic Network Drivers of Seizure Generation, Propagation and Termination in Human Neocortical Epilepsy

    Science.gov (United States)

    Khambhati, Ankit N.; Davis, Kathryn A.; Oommen, Brian S.; Chen, Stephanie H.; Lucas, Timothy H.; Litt, Brian; Bassett, Danielle S.

    2015-01-01

    The epileptic network is characterized by pathologic, seizure-generating ‘foci’ embedded in a web of structural and functional connections. Clinically, seizure foci are considered optimal targets for surgery. However, poor surgical outcome suggests a complex relationship between foci and the surrounding network that drives seizure dynamics. We developed a novel technique to objectively track seizure states from dynamic functional networks constructed from intracranial recordings. Each dynamical state captures unique patterns of network connections that indicate synchronized and desynchronized hubs of neural populations. Our approach suggests that seizures are generated when synchronous relationships near foci work in tandem with rapidly changing desynchronous relationships from the surrounding epileptic network. As seizures progress, topographical and geometrical changes in network connectivity strengthen and tighten synchronous connectivity near foci—a mechanism that may aid seizure termination. Collectively, our observations implicate distributed cortical structures in seizure generation, propagation and termination, and may have practical significance in determining which circuits to modulate with implantable devices. PMID:26680762

  14. Developing a dynamic control system for mine compressed air networks

    OpenAIRE

    Van Heerden, S.W.; Pelzer, R.; Marais, J.H.

    2014-01-01

    Mines in general, make use of compressed air systems for daily operational activities. Compressed air on mines is traditionally distributed via compressed air ring networks where multiple shafts are supplied with compressed air from an integral system. These compressed air networks make use of a number of compressors feeding the ring from various locations in the network. While these mines have sophisticated control systems to control these compressors, they are not dynamic systems. Compresso...

  15. Kowledge-based dynamic network safety calculations. Wissensbasierte dynamische Netzsicherheitsberechnungen

    Energy Technology Data Exchange (ETDEWEB)

    Kulicke, B [Inst. fuer Hochspannungstechnik und Starkstromanlagen, Berlin (Germany); Schlegel, S [Inst. fuer Hochspannungstechnik und Starkstromanlagen, Berlin (Germany)

    1993-06-28

    An important part of network operation management is the estimation and maintenance of the security of supply. So far the control personnel has only been supported by static network analyses and safety calculations. The authors describe an expert system, which is coupled to a real time simulation program on a transputer basis, for dynamic network safety calculations. They also introduce the system concept and the most important functions of the expert system. (orig.)

  16. Molecular dynamics simulation of amplitude modulation atomic force microscopy

    International Nuclear Information System (INIS)

    Hu, Xiaoli; Martini, Ashlie; Egberts, Philip; Dong, Yalin

    2015-01-01

    Molecular dynamics (MD) simulations were used to model amplitude modulation atomic force microscopy (AM-AFM). In this novel simulation, the model AFM tip responds to both tip–substrate interactions and to a sinusoidal excitation signal. The amplitude and phase shift of the tip oscillation observed in the simulation and their variation with tip–sample distance were found to be consistent with previously reported trends from experiments and theory. These simulation results were also fit to an expression enabling estimation of the energy dissipation, which was found to be smaller than that in a corresponding experiment. The difference was analyzed in terms of the effects of tip size and substrate thickness. Development of this model is the first step toward using MD to gain insight into the atomic-scale phenomena that occur during an AM-AFM measurement. (paper)

  17. Traffic dynamics on coupled spatial networks

    International Nuclear Information System (INIS)

    Du, Wen-Bo; Zhou, Xing-Lian; Chen, Zhen; Cai, Kai-Quan; Cao, Xian-Bin

    2014-01-01

    With the rapid development of modern traffic, various means of transportation systems make it more convenient and diversified for passengers to travel out. In this paper, we establish a two-layered spatial network model where the low-speed lower layer is a regular lattice and the high-speed upper layer is a scale-free network embedded in the lattice. Passengers will travel along the path with the minimal travel time, and they can transfer from one layer to the other, which will induce extra transfer cost. We extensively investigate the traffic process on these coupled spatial networks and focus on the effect of the parameter α, the speed ratio between two networks. It is found that, as α grows, the network capacity of the coupled networks increases in the early stage and then decreases, indicating that cooperation between the coupled networks will induce the highest network capacity at an optimal α. We then provide an explanation for this non-monotonous dependence from a micro-scope point of view. The travel time reliability is also examined. Both in free-flow state and congestion state, the travel time is linearly related to the Euclidean distance. However, the variance of travel time in the congestion state is remarkably larger than that in the free-flow state, namely, people have to set aside more redundant time in an unreliable traffic system

  18. RADYBAN: A tool for reliability analysis of dynamic fault trees through conversion into dynamic Bayesian networks

    International Nuclear Information System (INIS)

    Montani, S.; Portinale, L.; Bobbio, A.; Codetta-Raiteri, D.

    2008-01-01

    In this paper, we present RADYBAN (Reliability Analysis with DYnamic BAyesian Networks), a software tool which allows to analyze a dynamic fault tree relying on its conversion into a dynamic Bayesian network. The tool implements a modular algorithm for automatically translating a dynamic fault tree into the corresponding dynamic Bayesian network and exploits classical algorithms for the inference on dynamic Bayesian networks, in order to compute reliability measures. After having described the basic features of the tool, we show how it operates on a real world example and we compare the unreliability results it generates with those returned by other methodologies, in order to verify the correctness and the consistency of the results obtained

  19. A Neural Network Model to Learn Multiple Tasks under Dynamic Environments

    Science.gov (United States)

    Tsumori, Kenji; Ozawa, Seiichi

    When environments are dynamically changed for agents, the knowledge acquired in an environment might be useless in future. In such dynamic environments, agents should be able to not only acquire new knowledge but also modify old knowledge in learning. However, modifying all knowledge acquired before is not efficient because the knowledge once acquired may be useful again when similar environment reappears and some knowledge can be shared among different environments. To learn efficiently in such environments, we propose a neural network model that consists of the following modules: resource allocating network, long-term & short-term memory, and environment change detector. We evaluate the model under a class of dynamic environments where multiple function approximation tasks are sequentially given. The experimental results demonstrate that the proposed model possesses stable incremental learning, accurate environmental change detection, proper association and recall of old knowledge, and efficient knowledge transfer.

  20. Working Memory Modulation of Frontoparietal Network Connectivity in First-Episode Schizophrenia

    DEFF Research Database (Denmark)

    Nielsen, Jesper Duemose; Madsen, Kristoffer Hougaard; Wang, Zheng

    2017-01-01

    Working memory (WM) impairment is regarded as a core aspect of schizophrenia. However, the neural mechanisms behind this cognitive deficit remain unclear. The connectivity of a frontoparietal network is known to be important for subserving WM. Using functional magnetic resonance imaging, the curr......Working memory (WM) impairment is regarded as a core aspect of schizophrenia. However, the neural mechanisms behind this cognitive deficit remain unclear. The connectivity of a frontoparietal network is known to be important for subserving WM. Using functional magnetic resonance imaging......, the current study investigated whether WM-dependent modulation of effective connectivity in this network is affected in a group of first-episode schizophrenia (FES) patients compared with similarly performing healthy participants during a verbal n-back task. Dynamic causal modeling (DCM) of the coupling...... between regions (left inferior frontal gyrus (IFG), left inferior parietal lobe (IPL), and primary visual area) identified in a psychophysiological interaction (PPI) analysis was performed to characterize effective connectivity during the n-back task. The PPI analysis revealed that the connectivity...

  1. Vaccination intervention on epidemic dynamics in networks

    Science.gov (United States)

    Peng, Xiao-Long; Xu, Xin-Jian; Fu, Xinchu; Zhou, Tao

    2013-02-01

    Vaccination is an important measure available for preventing or reducing the spread of infectious diseases. In this paper, an epidemic model including susceptible, infected, and imperfectly vaccinated compartments is studied on Watts-Strogatz small-world, Barabási-Albert scale-free, and random scale-free networks. The epidemic threshold and prevalence are analyzed. For small-world networks, the effective vaccination intervention is suggested and its influence on the threshold and prevalence is analyzed. For scale-free networks, the threshold is found to be strongly dependent both on the effective vaccination rate and on the connectivity distribution. Moreover, so long as vaccination is effective, it can linearly decrease the epidemic prevalence in small-world networks, whereas for scale-free networks it acts exponentially. These results can help in adopting pragmatic treatment upon diseases in structured populations.

  2. Functional clustering in hippocampal cultures: relating network structure and dynamics

    International Nuclear Information System (INIS)

    Feldt, S; Dzakpasu, R; Olariu, E; Żochowski, M; Wang, J X; Shtrahman, E

    2010-01-01

    In this work we investigate the relationship between gross anatomic structural network properties, neuronal dynamics and the resultant functional structure in dissociated rat hippocampal cultures. Specifically, we studied cultures as they developed under two conditions: the first supporting glial cell growth (high glial group), and the second one inhibiting it (low glial group). We then compared structural network properties and the spatio-temporal activity patterns of the neurons. Differences in dynamics between the two groups could be linked to the impact of the glial network on the neuronal network as the cultures developed. We also implemented a recently developed algorithm called the functional clustering algorithm (FCA) to obtain the resulting functional network structure. We show that this new algorithm is useful for capturing changes in functional network structure as the networks evolve over time. The FCA detects changes in functional structure that are consistent with expected dynamical differences due to the impact of the glial network. Cultures in the high glial group show an increase in global synchronization as the cultures age, while those in the low glial group remain locally synchronized. We additionally use the FCA to quantify the amount of synchronization present in the cultures and show that the total level of synchronization in the high glial group is stronger than in the low glial group. These results indicate an interdependence between the glial and neuronal networks present in dissociated cultures

  3. Discriminating lysosomal membrane protein types using dynamic neural network.

    Science.gov (United States)

    Tripathi, Vijay; Gupta, Dwijendra Kumar

    2014-01-01

    This work presents a dynamic artificial neural network methodology, which classifies the proteins into their classes from their sequences alone: the lysosomal membrane protein classes and the various other membranes protein classes. In this paper, neural networks-based lysosomal-associated membrane protein type prediction system is proposed. Different protein sequence representations are fused to extract the features of a protein sequence, which includes seven feature sets; amino acid (AA) composition, sequence length, hydrophobic group, electronic group, sum of hydrophobicity, R-group, and dipeptide composition. To reduce the dimensionality of the large feature vector, we applied the principal component analysis. The probabilistic neural network, generalized regression neural network, and Elman regression neural network (RNN) are used as classifiers and compared with layer recurrent network (LRN), a dynamic network. The dynamic networks have memory, i.e. its output depends not only on the input but the previous outputs also. Thus, the accuracy of LRN classifier among all other artificial neural networks comes out to be the highest. The overall accuracy of jackknife cross-validation is 93.2% for the data-set. These predicted results suggest that the method can be effectively applied to discriminate lysosomal associated membrane proteins from other membrane proteins (Type-I, Outer membrane proteins, GPI-Anchored) and Globular proteins, and it also indicates that the protein sequence representation can better reflect the core feature of membrane proteins than the classical AA composition.

  4. Learning related modulation of functional retrieval networks in man.

    Science.gov (United States)

    Petersson, K M; Sandblom, J; Gisselgård, J; Ingvar, M

    2001-07-01

    The medial temporal lobe has been implicated in studies of episodic memory tasks involving spatio-temporal context and object-location conjunctions. We have previously demonstrated that an increased level of practice in a free-recall task parallels a decrease in the functional activity of several brain regions, including the medial temporal lobe, the prefrontal, the anterior cingulate, the anterior insular, and the posterior parietal cortices, that in concert demonstrate a move from elaborate controlled processing towards a higher degree of automaticity. Here we report data from two experiments that extend these initial observations. We used a similar experimental approach but probed for effects of retrieval paradigms and stimulus material. In the first experiment we investigated practice related changes during recognition of object-location conjunctions and in the second during free-recall of pseudo-words. Learning in a neural network is a dynamic consequence of information processing and network plasticity. The present and previous PET results indicate that practice can induce a learning related functional restructuring of information processing. Different adaptive processes likely subserve the functional re-organisation observed. These may in part be related to different demands for attentional and working memory processing. It appears that the role(s) of the prefrontal cortex and the medial temporal lobe in memory retrieval are complex, perhaps reflecting several different interacting processes or cognitive components. We suggest that an integrative interactive perspective on the role of the prefrontal and medial temporal lobe is necessary for an understanding of the processing significance of these regions in learning and memory. It appears necessary to develop elaborated and explicit computational models for prefrontal and medial temporal functions in order to derive detailed empirical predictions, and in combination with an efficient use and development of

  5. Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks.

    Science.gov (United States)

    Pu, Yi-Fei; Yi, Zhang; Zhou, Ji-Liu

    2017-10-01

    This paper mainly discusses a novel conceptual framework: fractional Hopfield neural networks (FHNN). As is commonly known, fractional calculus has been incorporated into artificial neural networks, mainly because of its long-term memory and nonlocality. Some researchers have made interesting attempts at fractional neural networks and gained competitive advantages over integer-order neural networks. Therefore, it is naturally makes one ponder how to generalize the first-order Hopfield neural networks to the fractional-order ones, and how to implement FHNN by means of fractional calculus. We propose to introduce a novel mathematical method: fractional calculus to implement FHNN. First, we implement fractor in the form of an analog circuit. Second, we implement FHNN by utilizing fractor and the fractional steepest descent approach, construct its Lyapunov function, and further analyze its attractors. Third, we perform experiments to analyze the stability and convergence of FHNN, and further discuss its applications to the defense against chip cloning attacks for anticounterfeiting. The main contribution of our work is to propose FHNN in the form of an analog circuit by utilizing a fractor and the fractional steepest descent approach, construct its Lyapunov function, prove its Lyapunov stability, analyze its attractors, and apply FHNN to the defense against chip cloning attacks for anticounterfeiting. A significant advantage of FHNN is that its attractors essentially relate to the neuron's fractional order. FHNN possesses the fractional-order-stability and fractional-order-sensitivity characteristics.

  6. Maritime piracy situation modelling with dynamic Bayesian networks

    CSIR Research Space (South Africa)

    Dabrowski, James M

    2015-05-01

    Full Text Available A generative model for modelling maritime vessel behaviour is proposed. The model is a novel variant of the dynamic Bayesian network (DBN). The proposed DBN is in the form of a switching linear dynamic system (SLDS) that has been extended into a...

  7. Stochastic Online Learning in Dynamic Networks under Unknown Models

    Science.gov (United States)

    2016-08-02

    The key is to develop online learning strategies at each individual node. Specifically, through local information exchange with its neighbors, each...infinitely repeated game with incomplete information and developed a dynamic pricing strategy referred to as Competitive and Cooperative Demand Learning...Stochastic Online Learning in Dynamic Networks under Unknown Models This research aims to develop fundamental theories and practical algorithms for

  8. The Dynamics of network and dyad level supply management

    DEFF Research Database (Denmark)

    Ellegaard, Chris

    -supplier relation and its immediate network context, are presented. In analysing the data, the dynamic interdependency between management of one level and management of the other, will be demonstrated. The analysis reveals a need for an alternating approach to supply management, which takes the dynamic complexity...

  9. Classification of networks of automata by dynamical mean field theory

    International Nuclear Information System (INIS)

    Burda, Z.; Jurkiewicz, J.; Flyvbjerg, H.

    1990-01-01

    Dynamical mean field theory is used to classify the 2 24 =65,536 different networks of binary automata on a square lattice with nearest neighbour interactions. Application of mean field theory gives 700 different mean field classes, which fall in seven classes of different asymptotic dynamics characterized by fixed points and two-cycles. (orig.)

  10. Dynamic baseline detection method for power data network service

    Science.gov (United States)

    Chen, Wei

    2017-08-01

    This paper proposes a dynamic baseline Traffic detection Method which is based on the historical traffic data for the Power data network. The method uses Cisco's NetFlow acquisition tool to collect the original historical traffic data from network element at fixed intervals. This method uses three dimensions information including the communication port, time, traffic (number of bytes or number of packets) t. By filtering, removing the deviation value, calculating the dynamic baseline value, comparing the actual value with the baseline value, the method can detect whether the current network traffic is abnormal.

  11. Spatial price dynamics: From complex network perspective

    Science.gov (United States)

    Li, Y. L.; Bi, J. T.; Sun, H. J.

    2008-10-01

    The spatial price problem means that if the supply price plus the transportation cost is less than the demand price, there exists a trade. Thus, after an amount of exchange, the demand price will decrease. This process is continuous until an equilibrium state is obtained. However, how the trade network structure affects this process has received little attention. In this paper, we give a evolving model to describe the levels of spatial price on different complex network structures. The simulation results show that the network with shorter path length is sensitive to the variation of prices.

  12. The architecture of dynamic reservoir in the echo state network

    Science.gov (United States)

    Cui, Hongyan; Liu, Xiang; Li, Lixiang

    2012-09-01

    Echo state network (ESN) has recently attracted increasing interests because of its superior capability in modeling nonlinear dynamic systems. In the conventional echo state network model, its dynamic reservoir (DR) has a random and sparse topology, which is far from the real biological neural networks from both structural and functional perspectives. We hereby propose three novel types of echo state networks with new dynamic reservoir topologies based on complex network theory, i.e., with a small-world topology, a scale-free topology, and a mixture of small-world and scale-free topologies, respectively. We then analyze the relationship between the dynamic reservoir structure and its prediction capability. We utilize two commonly used time series to evaluate the prediction performance of the three proposed echo state networks and compare them to the conventional model. We also use independent and identically distributed time series to analyze the short-term memory and prediction precision of these echo state networks. Furthermore, we study the ratio of scale-free topology and the small-world topology in the mixed-topology network, and examine its influence on the performance of the echo state networks. Our simulation results show that the proposed echo state network models have better prediction capabilities, a wider spectral radius, but retain almost the same short-term memory capacity as compared to the conventional echo state network model. We also find that the smaller the ratio of the scale-free topology over the small-world topology, the better the memory capacities.

  13. Scalable Approaches to Control Network Dynamics: Prospects for City Networks

    Science.gov (United States)

    Motter, Adilson E.; Gray, Kimberly A.

    2014-07-01

    A city is a complex, emergent system and as such can be conveniently represented as a network of interacting components. A fundamental aspect of networks is that the systemic properties can depend as much on the interactions as they depend on the properties of the individual components themselves. Another fundamental aspect is that changes to one component can affect other components, in a process that may cause the entire or a substantial part of the system to change behavior. Over the past 2 decades, much research has been done on the modeling of large and complex networks involved in communication and transportation, disease propagation, and supply chains, as well as emergent phenomena, robustness and optimization in such systems...

  14. Extracting neuronal functional network dynamics via adaptive Granger causality analysis.

    Science.gov (United States)

    Sheikhattar, Alireza; Miran, Sina; Liu, Ji; Fritz, Jonathan B; Shamma, Shihab A; Kanold, Patrick O; Babadi, Behtash

    2018-04-24

    Quantifying the functional relations between the nodes in a network based on local observations is a key challenge in studying complex systems. Most existing time series analysis techniques for this purpose provide static estimates of the network properties, pertain to stationary Gaussian data, or do not take into account the ubiquitous sparsity in the underlying functional networks. When applied to spike recordings from neuronal ensembles undergoing rapid task-dependent dynamics, they thus hinder a precise statistical characterization of the dynamic neuronal functional networks underlying adaptive behavior. We develop a dynamic estimation and inference paradigm for extracting functional neuronal network dynamics in the sense of Granger, by integrating techniques from adaptive filtering, compressed sensing, point process theory, and high-dimensional statistics. We demonstrate the utility of our proposed paradigm through theoretical analysis, algorithm development, and application to synthetic and real data. Application of our techniques to two-photon Ca 2+ imaging experiments from the mouse auditory cortex reveals unique features of the functional neuronal network structures underlying spontaneous activity at unprecedented spatiotemporal resolution. Our analysis of simultaneous recordings from the ferret auditory and prefrontal cortical areas suggests evidence for the role of rapid top-down and bottom-up functional dynamics across these areas involved in robust attentive behavior.

  15. Dynamic properties of epidemic spreading on finite size complex networks

    Science.gov (United States)

    Li, Ying; Liu, Yang; Shan, Xiu-Ming; Ren, Yong; Jiao, Jian; Qiu, Ben

    2005-11-01

    The Internet presents a complex topological structure, on which computer viruses can easily spread. By using theoretical analysis and computer simulation methods, the dynamic process of disease spreading on finite size networks with complex topological structure is investigated. On the finite size networks, the spreading process of SIS (susceptible-infected-susceptible) model is a finite Markov chain with an absorbing state. Two parameters, the survival probability and the conditional infecting probability, are introduced to describe the dynamic properties of disease spreading on finite size networks. Our results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks. Also, knowledge about the dynamic character of virus spreading is helpful for adopting immunity policy.

  16. Congested Link Inference Algorithms in Dynamic Routing IP Network

    Directory of Open Access Journals (Sweden)

    Yu Chen

    2017-01-01

    Full Text Available The performance descending of current congested link inference algorithms is obviously in dynamic routing IP network, such as the most classical algorithm CLINK. To overcome this problem, based on the assumptions of Markov property and time homogeneity, we build a kind of Variable Structure Discrete Dynamic Bayesian (VSDDB network simplified model of dynamic routing IP network. Under the simplified VSDDB model, based on the Bayesian Maximum A Posteriori (BMAP and Rest Bayesian Network Model (RBNM, we proposed an Improved CLINK (ICLINK algorithm. Considering the concurrent phenomenon of multiple link congestion usually happens, we also proposed algorithm CLILRS (Congested Link Inference algorithm based on Lagrangian Relaxation Subgradient to infer the set of congested links. We validated our results by the experiments of analogy, simulation, and actual Internet.

  17. Actin dynamics and the elasticity of cytoskeletal networks

    Directory of Open Access Journals (Sweden)

    2009-09-01

    Full Text Available The structural integrity of a cell depends on its cytoskeleton, which includes an actin network. This network is transient and depends upon the continual polymerization and depolymerization of actin. The degradation of an actin network, and a corresponding reduction in cell stiffness, can indicate the presence of disease. Numerical simulations will be invaluable for understanding the physics of these systems and the correlation between actin dynamics and elasticity. Here we develop a model that is capable of generating actin network structures. In particular, we develop a model of actin dynamics which considers the polymerization, depolymerization, nucleation, severing, and capping of actin filaments. The structures obtained are then fed directly into a mechanical model. This allows us to qualitatively assess the effects of changing various parameters associated with actin dynamics on the elasticity of the material.

  18. Agent Based Modeling on Organizational Dynamics of Terrorist Network

    Directory of Open Access Journals (Sweden)

    Bo Li

    2015-01-01

    Full Text Available Modeling organizational dynamics of terrorist network is a critical issue in computational analysis of terrorism research. The first step for effective counterterrorism and strategic intervention is to investigate how the terrorists operate with the relational network and what affects the performance. In this paper, we investigate the organizational dynamics by employing a computational experimentation methodology. The hierarchical cellular network model and the organizational dynamics model are developed for modeling the hybrid relational structure and complex operational processes, respectively. To intuitively elucidate this method, the agent based modeling is used to simulate the terrorist network and test the performance in diverse scenarios. Based on the experimental results, we show how the changes of operational environments affect the development of terrorist organization in terms of its recovery and capacity to perform future tasks. The potential strategies are also discussed, which can be used to restrain the activities of terrorists.

  19. Dynamic Virtual LANs for Adaptive Network Security

    National Research Council Canada - National Science Library

    Merani, Diego; Berni, Alessandro; Leonard, Michel

    2004-01-01

    The development of Network-Enabled capabilities in support of undersea research requires architectures for the interconnection and data sharing that are flexible, scalable, and built on open standards...

  20. Value network dynamics and industry evolution

    NARCIS (Netherlands)

    Vermeulen, B.

    2012-01-01

    Machines, appliances, and consumption goods are developed and produced in value networks populated by firms ranging from final assemblers, component suppliers, complement providers, the suppliers’ suppliers, all the way upstream to firms that extrude raw material. Evolutionary models of industry

  1. Dynamic Data-Driven UAV Network for Plume Characterization

    Science.gov (United States)

    2016-05-23

    AFRL-AFOSR-VA-TR-2016-0203 Dynamic Data-Driven UAV Network for Plume Characterization Kamran Mohseni UNIVERSITY OF FLORIDA Final Report 05/23/2016...AND SUBTITLE Dynamic Data-Driven UAV Network for Plume Characterization 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-13-1-0090 5c.  PROGRAM ELEMENT...studied a dynamic data driven (DDD) approach to operation of a heterogeneous team of unmanned aerial vehicles ( UAVs ) or micro/miniature aerial

  2. Network Reconstruction of Dynamic Biological Systems

    OpenAIRE

    Asadi, Behrang

    2013-01-01

    Inference of network topology from experimental data is a central endeavor in biology, since knowledge of the underlying signaling mechanisms a requirement for understanding biological phenomena. As one of the most important tools in bioinformatics area, development of methods to reconstruct biological networks has attracted remarkable attention in the current decade. Integration of different data types can lead to remarkable improvements in our ability to identify the connectivity of differe...

  3. Connectivity, topology and dynamics in climate networks

    Czech Academy of Sciences Publication Activity Database

    Paluš, Milan; Hartman, David; Hlinka, Jaroslav; Vejmelka, Martin

    2012-01-01

    Roč. 14, - (2012), s. 8397 ISSN 1607-7962. [European Geosciences Union General Assembly 2012. 22.04.2012-27.04.2012, Vienna] R&D Projects: GA ČR GCP103/11/J068 Institutional support: RVO:67985807 Keywords : complex networks * climate network * connectivity * entropy rate * El Nino Southern Oscillation * North Atlantic Oscillation Subject RIV: BB - Applied Statistics, Operational Research

  4. Dynamic Aggregation Protocol for Wireless Sensor Networks

    OpenAIRE

    Mounir Said , Adel; William Ibrahim , Ashraf; Soua , Ahmed; Afifi , Hossam

    2013-01-01

    International audience; Sensor networks suffer from limited capabilities such as bandwidth, low processing power, and memory size. There is therefore a need for protocols that deliver sensor data in an energy-efficient way to the sink. One of those techniques, it gathers sensors' data in a small size packet suitable for transmission. In this paper, we propose a new Effective Data Aggregation Protocol (DAP) to reduce the energy consumption in Wireless Sensor Networks (WSNs), which prolongs the...

  5. A 3D Lumped Thermal Network Model for Long-term Load Profiles Analysis in High Power IGBT Modules

    DEFF Research Database (Denmark)

    Bahman, Amir Sajjad; Ma, Ke; Ghimire, Pramod

    2016-01-01

    )-based simulation is another method which is often used to analyze the steady-state thermal distribution of IGBT modules, but it is not possible to be used for long-term analysis of load profiles of power converter, which is needed for reliability assessments and better thermal design. This paper proposes a novel...... enables both accurate and fast temperature estimation of high power IGBT modules in the real loading conditions of the converter; meanwhile the critical details of the thermal dynamics and thermal distribution are also maintained. The proposed thermal model is verified by both FEM simulation......The conventional RC lumped thermal networks are widely used to estimate the temperature of power devices, but they are lack of accuracy in addressing detailed thermal behaviors/couplings in different locations and layers of the high power IGBT modules. On the other hand, Finite Element (FE...

  6. Analysing Stagecoach Network Problem Using Dynamic ...

    African Journals Online (AJOL)

    In this paper we present a recursive dynamic programming algorithm for solving the stagecoach problem. The algorithm is computationally more efficient than the first method as it obtains its minimum total cost using the suboptimal policies of the different stages without computing the cost of all the routes. By the dynamic ...

  7. Damping Effects of Drogue Parachutes on Orion Crew Module Dynamics

    Science.gov (United States)

    Aubuchon, Vanessa V.; Owens, D. Bruce

    2016-01-01

    Because simulations of the Orion Crew Module (CM) dynamics with drogue parachutes deployed were under-predicting the amount of damping seen in free-flight tests, an attach-point damping model was applied to the Orion system. A key hypothesis in this model is that the drogue parachutes' net load vector aligns with the CM drogue attachment point velocity vector. This assumption seems reasonable and has historically produced good results, but has never been experimentally verified. The wake of the CM influences the drogue parachutes, which makes performance predictions of the parachutes difficult. Many of these effects are not currently modeled in the simulations. A forced oscillation test of the CM with parachutes was conducted in the NASA LaRC 20-Ft Vertical Spin Tunnel (VST) to gather additional data to validate and refine the attach-point damping model. A second loads balance was added to the original Orion VST model to measure the drogue parachute loads independently of the CM. The objective of the test was to identify the contribution of the drogues to CM damping and provide additional information to quantify wake effects and the interactions between the CM and parachutes. The drogue parachute force vector was shown to be highly dependent on the CM wake characteristics. Based on these wind tunnel test data, the attach-point damping model was determined to be a sufficient approximation of the parachute dynamics in relationship to the CM dynamics for preliminary entry vehicle system design. More wake effects should be included to better model the system.

  8. Identifying and tracking dynamic processes in social networks

    Science.gov (United States)

    Chung, Wayne; Savell, Robert; Schütt, Jan-Peter; Cybenko, George

    2006-05-01

    The detection and tracking of embedded malicious subnets in an active social network can be computationally daunting due to the quantity of transactional data generated in the natural interaction of large numbers of actors comprising a network. In addition, detection of illicit behavior may be further complicated by evasive strategies designed to camouflage the activities of the covert subnet. In this work, we move beyond traditional static methods of social network analysis to develop a set of dynamic process models which encode various modes of behavior in active social networks. These models will serve as the basis for a new application of the Process Query System (PQS) to the identification and tracking of covert dynamic processes in social networks. We present a preliminary result from application of our technique in a real-world data stream-- the Enron email corpus.

  9. A Scalable Distribution Network Risk Evaluation Framework via Symbolic Dynamics

    Science.gov (United States)

    Yuan, Kai; Liu, Jian; Liu, Kaipei; Tan, Tianyuan

    2015-01-01

    Background Evaluations of electric power distribution network risks must address the problems of incomplete information and changing dynamics. A risk evaluation framework should be adaptable to a specific situation and an evolving understanding of risk. Methods This study investigates the use of symbolic dynamics to abstract raw data. After introducing symbolic dynamics operators, Kolmogorov-Sinai entropy and Kullback-Leibler relative entropy are used to quantitatively evaluate relationships between risk sub-factors and main factors. For layered risk indicators, where the factors are categorized into four main factors – device, structure, load and special operation – a merging algorithm using operators to calculate the risk factors is discussed. Finally, an example from the Sanya Power Company is given to demonstrate the feasibility of the proposed method. Conclusion Distribution networks are exposed and can be affected by many things. The topology and the operating mode of a distribution network are dynamic, so the faults and their consequences are probabilistic. PMID:25789859

  10. Cytoskeleton dynamics: Fluctuations within the network

    International Nuclear Information System (INIS)

    Bursac, Predrag; Fabry, Ben; Trepat, Xavier; Lenormand, Guillaume; Butler, James P.; Wang, Ning; Fredberg, Jeffrey J.; An, Steven S.

    2007-01-01

    Out-of-equilibrium systems, such as the dynamics of a living cytoskeleton (CSK), are inherently noisy with fluctuations arising from the stochastic nature of the underlying biochemical and molecular events. Recently, such fluctuations within the cell were characterized by observing spontaneous nano-scale motions of an RGD-coated microbead bound to the cell surface [Bursac et al., Nat. Mater. 4 (2005) 557-561]. While these reported anomalous bead motions represent a molecular level reorganization (remodeling) of microstructures in contact with the bead, a precise nature of these cytoskeletal constituents and forces that drive their remodeling dynamics are largely unclear. Here, we focused upon spontaneous motions of an RGD-coated bead and, in particular, assessed to what extent these motions are attributable to (i) bulk cell movement (cell crawling), (ii) dynamics of focal adhesions, (iii) dynamics of lipid membrane, and/or (iv) dynamics of the underlying actin CSK driven by myosin motors

  11. State-dependent intrinsic predictability of cortical network dynamics.

    Directory of Open Access Journals (Sweden)

    Leila Fakhraei

    Full Text Available The information encoded in cortical circuit dynamics is fleeting, changing from moment to moment as new input arrives and ongoing intracortical interactions progress. A combination of deterministic and stochastic biophysical mechanisms governs how cortical dynamics at one moment evolve from cortical dynamics in recently preceding moments. Such temporal continuity of cortical dynamics is fundamental to many aspects of cortex function but is not well understood. Here we study temporal continuity by attempting to predict cortical population dynamics (multisite local field potential based on its own recent history in somatosensory cortex of anesthetized rats and in a computational network-level model. We found that the intrinsic predictability of cortical dynamics was dependent on multiple factors including cortical state, synaptic inhibition, and how far into the future the prediction extends. By pharmacologically tuning synaptic inhibition, we obtained a continuum of cortical states with asynchronous population activity at one extreme and stronger, spatially extended synchrony at the other extreme. Intermediate between these extremes we observed evidence for a special regime of population dynamics called criticality. Predictability of the near future (10-100 ms increased as the cortical state was tuned from asynchronous to synchronous. Predictability of the more distant future (>1 s was generally poor, but, surprisingly, was higher for asynchronous states compared to synchronous states. These experimental results were confirmed in a computational network model of spiking excitatory and inhibitory neurons. Our findings demonstrate that determinism and predictability of network dynamics depend on cortical state and the time-scale of the dynamics.

  12. RM-SORN: a reward-modulated self-organizing recurrent neural network.

    Science.gov (United States)

    Aswolinskiy, Witali; Pipa, Gordon

    2015-01-01

    Neural plasticity plays an important role in learning and memory. Reward-modulation of plasticity offers an explanation for the ability of the brain to adapt its neural activity to achieve a rewarded goal. Here, we define a neural network model that learns through the interaction of Intrinsic Plasticity (IP) and reward-modulated Spike-Timing-Dependent Plasticity (STDP). IP enables the network to explore possible output sequences and STDP, modulated by reward, reinforces the creation of the rewarded output sequences. The model is tested on tasks for prediction, recall, non-linear computation, pattern recognition, and sequence generation. It achieves performance comparable to networks trained with supervised learning, while using simple, biologically motivated plasticity rules, and rewarding strategies. The results confirm the importance of investigating the interaction of several plasticity rules in the context of reward-modulated learning and whether reward-modulated self-organization can explain the amazing capabilities of the brain.

  13. Tourist activated networks: Implications for dynamic packaging systems in tourism

    DEFF Research Database (Denmark)

    Zach, Florian; Gretzel, Ulrike; Fesenmaier, Daniel R.

    2008-01-01

    This paper discusses tourist activated networks as a concept to inform technological applications supporting dynamic bundling and en-route recommendations. Empirical data was collected from travellers who visited a regional destination in the US and then analyzed with respect to its network...... structure. The results indicate that the tourist activated network for the destination is rather sparse and that there are clearly differences in core and peripheral nodes. The findings illustrate the structure of a tourist activated network and provide implications for technology design and tourism...

  14. A fuzzy network module extraction technique for gene expression data

    Indian Academy of Sciences (India)

    2014-05-01

    expression network from the distance matrix. The distance matrix is .... mental process, cellular component assembly involved in ..... the molecules are present in the network. User can ... hsa05213:Endometrial cancer. 24. 0.07.

  15. Module type plant system dynamics analysis code (MSG-COPD). Code manual

    International Nuclear Information System (INIS)

    Sakai, Takaaki

    2002-11-01

    MSG-COPD is a module type plant system dynamics analysis code which involves a multi-dimensional thermal-hydraulics calculation module to analyze pool type of fast breeder reactors. Explanations of each module and the methods for the input data are described in this code manual. (author)

  16. Advanced Modulation Formats in Cognitive Optical Networks: EU project CHRON Demonstration

    DEFF Research Database (Denmark)

    Borkowski, Robert; Caballero Jambrina, Antonio; Klonidis, Dimitris

    2014-01-01

    We demonstrate real-time path establishment and switching of coherent modulation formats (QPSK, 16QAM) within an optical network driven by cognitive algorithms. Cognition aims at autonomous configuration optimization to satisfy quality of transmission requirements.......We demonstrate real-time path establishment and switching of coherent modulation formats (QPSK, 16QAM) within an optical network driven by cognitive algorithms. Cognition aims at autonomous configuration optimization to satisfy quality of transmission requirements....

  17. Subthalamic stimulation modulates cortical motor network activity and synchronization in Parkinson’s disease

    Science.gov (United States)

    Klotz, Rosa; Govindan, Rathinaswamy B.; Scholten, Marlieke; Naros, Georgios; Ramos-Murguialday, Ander; Bunjes, Friedemann; Meisner, Christoph; Plewnia, Christian; Krüger, Rejko

    2015-01-01

    Dynamic modulations of large-scale network activity and synchronization are inherent to a broad spectrum of cognitive processes and are disturbed in neuropsychiatric conditions including Parkinson’s disease. Here, we set out to address the motor network activity and synchronization in Parkinson’s disease and its modulation with subthalamic stimulation. To this end, 20 patients with idiopathic Parkinson’s disease with subthalamic nucleus stimulation were analysed on externally cued right hand finger movements with 1.5-s interstimulus interval. Simultaneous recordings were obtained from electromyography on antagonistic muscles (right flexor digitorum and extensor digitorum) together with 64-channel electroencephalography. Time-frequency event-related spectral perturbations were assessed to determine cortical and muscular activity. Next, cross-spectra in the time-frequency domain were analysed to explore the cortico-cortical synchronization. The time-frequency modulations enabled us to select a time-frequency range relevant for motor processing. On these time-frequency windows, we developed an extension of the phase synchronization index to quantify the global cortico-cortical synchronization and to obtain topographic differentiations of distinct electrode sites with respect to their contributions to the global phase synchronization index. The spectral measures were used to predict clinical and reaction time outcome using regression analysis. We found that movement-related desynchronization of cortical activity in the upper alpha and beta range was significantly facilitated with ‘stimulation on’ compared to ‘stimulation off’ on electrodes over the bilateral parietal, sensorimotor, premotor, supplementary-motor, and prefrontal areas, including the bilateral inferior prefrontal areas. These spectral modulations enabled us to predict both clinical and reaction time improvement from subthalamic stimulation. With ‘stimulation on’, interhemispheric cortico

  18. Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis

    Science.gov (United States)

    Li, Yuanyuan; Jin, Suoqin; Lei, Lei; Pan, Zishu; Zou, Xiufen

    2015-03-01

    The early diagnosis and investigation of the pathogenic mechanisms of complex diseases are the most challenging problems in the fields of biology and medicine. Network-based systems biology is an important technique for the study of complex diseases. The present study constructed dynamic protein-protein interaction (PPI) networks to identify dynamical network biomarkers (DNBs) and analyze the underlying mechanisms of complex diseases from a systems level. We developed a model-based framework for the construction of a series of time-sequenced networks by integrating high-throughput gene expression data into PPI data. By combining the dynamic networks and molecular modules, we identified significant DNBs for four complex diseases, including influenza caused by either H3N2 or H1N1, acute lung injury and type 2 diabetes mellitus, which can serve as warning signals for disease deterioration. Function and pathway analyses revealed that the identified DNBs were significantly enriched during key events in early disease development. Correlation and information flow analyses revealed that DNBs effectively discriminated between different disease processes and that dysfunctional regulation and disproportional information flow may contribute to the increased disease severity. This study provides a general paradigm for revealing the deterioration mechanisms of complex diseases and offers new insights into their early diagnoses.

  19. Verification and Validation of the New Dynamic Mooring Modules Available in FAST v8

    DEFF Research Database (Denmark)

    Wendt, Fabian F.; Andersen, Morten Thøtt; Robertson, Amy N.

    2016-01-01

    The open-source, aero-hydro-servo-elastic wind turbine simulation software FAST v8 (created by the National Renewable Energy Laboratory) was recently coupled to two newly developed mooring dynamics modules: MoorDyn and FEAMooring. MoorDyn is a lumped-mass-based mooring dynamics module developed b...

  20. Rumor Diffusion in an Interests-Based Dynamic Social Network

    Directory of Open Access Journals (Sweden)

    Mingsheng Tang

    2013-01-01

    Full Text Available To research rumor diffusion in social friend network, based on interests, a dynamic friend network is proposed, which has the characteristics of clustering and community, and a diffusion model is also proposed. With this friend network and rumor diffusion model, based on the zombie-city model, some simulation experiments to analyze the characteristics of rumor diffusion in social friend networks have been conducted. The results show some interesting observations: (1 positive information may evolve to become a rumor through the diffusion process that people may modify the information by word of mouth; (2 with the same average degree, a random social network has a smaller clustering coefficient and is more beneficial for rumor diffusion than the dynamic friend network; (3 a rumor is spread more widely in a social network with a smaller global clustering coefficient than in a social network with a larger global clustering coefficient; and (4 a network with a smaller clustering coefficient has a larger efficiency.

  1. Employing Deceptive Dynamic Network Topology Through Software-Defined Networking

    Science.gov (United States)

    2014-03-01

    actions. From [64] . . . . . 37 xi THIS PAGE INTENTIONALLY LEFT BLANK xii List of Acronyms and Abbreviations ACL Access Control List API Application...can be extremely useful in topology mapping through various latency-based geolocation methods [35], [36], [37]. PING 1 7 2 . 2 0 . 5 . 2 ( 1 7 2 . 2 0...defined northbound Applica- tion Programming Interfaces ( APIs ). Figure 3.1: Software-Defined Network Architecture. From [8] 29 3.3 SDN OpenFlow

  2. Ecological modules and roles of species in heathland plant-insect flower visitor networks

    DEFF Research Database (Denmark)

    Dupont, Yoko; Olesen, Jens Mogens

    2009-01-01

    1.  Co-existing plants and flower-visiting animals often form complex interaction networks. A long-standing question in ecology and evolutionary biology is how to detect nonrandom subsets (compartments, blocks, modules) of strongly interacting species within such networks. Here we use a network...... analytical approach to (i) detect modularity in pollination networks, (ii) investigate species composition of modules, and (iii) assess the stability of modules across sites. 2.  Interactions between entomophilous plants and their flower-visitors were recorded throughout the flowering season at three...... heathland sites in Denmark, separated by ≥ 10 km. Among sites, plant communities were similar, but composition of flower-visiting insect faunas differed. Visitation frequencies of visitor species were recorded as a measure of insect abundance. 3.  Qualitative (presence-absence) interaction networks were...

  3. Recruitment dynamics in adaptive social networks

    International Nuclear Information System (INIS)

    Shkarayev, Maxim S; Shaw, Leah B; Schwartz, Ira B

    2013-01-01

    We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean-field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime). (paper)

  4. Recruitment dynamics in adaptive social networks

    Science.gov (United States)

    Shkarayev, Maxim S.; Schwartz, Ira B.; Shaw, Leah B.

    2013-06-01

    We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean-field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime).

  5. Dynamic artificial neural networks with affective systems.

    Directory of Open Access Journals (Sweden)

    Catherine D Schuman

    Full Text Available Artificial neural networks (ANNs are processors that are trained to perform particular tasks. We couple a computational ANN with a simulated affective system in order to explore the interaction between the two. In particular, we design a simple affective system that adjusts the threshold values in the neurons of our ANN. The aim of this paper is to demonstrate that this simple affective system can control the firing rate of the ensemble of neurons in the ANN, as well as to explore the coupling between the affective system and the processes of long term potentiation (LTP and long term depression (LTD, and the effect of the parameters of the affective system on its performance. We apply our networks with affective systems to a simple pole balancing example and briefly discuss the effect of affective systems on network performance.

  6. Cortical electrophysiological network dynamics of feedback learning

    NARCIS (Netherlands)

    Cohen, M.X.; Wilmes, K.A.; van de Vijver, I.

    2011-01-01

    Understanding the neurophysiological mechanisms of learning is important for both fundamental and clinical neuroscience. We present a neurophysiologically inspired framework for understanding cortical mechanisms of feedback-guided learning. This framework is based on dynamic changes in systems-level

  7. The topology and dynamics of complex networks

    Science.gov (United States)

    Dezso, Zoltan

    We start with a brief introduction about the topological properties of real networks. Most real networks are scale-free, being characterized by a power-law degree distribution. The scale-free nature of real networks leads to unexpected properties such as the vanishing epidemic threshold. Traditional methods aiming to reduce the spreading rate of viruses cannot succeed on eradicating the epidemic on a scale-free network. We demonstrate that policies that discriminate between the nodes, curing mostly the highly connected nodes, can restore a finite epidemic threshold and potentially eradicate the virus. We find that the more biased a policy is towards the hubs, the more chance it has to bring the epidemic threshold above the virus' spreading rate. We continue by studying a large Web portal as a model system for a rapidly evolving network. We find that the visitation pattern of a news document decays as a power law, in contrast with the exponential prediction provided by simple models of site visitation. This is rooted in the inhomogeneous nature of the browsing pattern characterizing individual users: the time interval between consecutive visits by the same user to the site follows a power law distribution, in contrast with the exponential expected for Poisson processes. We show that the exponent characterizing the individual user's browsing patterns determines the power-law decay in a document's visitation. Finally, we turn our attention to biological networks and demonstrate quantitatively that protein complexes in the yeast, Saccharomyces cerevisiae, are comprised of a core in which subunits are highly coexpressed, display the same deletion phenotype (essential or non-essential) and share identical functional classification and cellular localization. The results allow us to define the deletion phenotype and cellular task of most known complexes, and to identify with high confidence the biochemical role of hundreds of proteins with yet unassigned functionality.

  8. Reliability-based Dynamic Network Design with Stochastic Networks

    NARCIS (Netherlands)

    Li, H.

    2009-01-01

    Transportation systems are stochastic and dynamic systems. The road capacities and the travel demand are fluctuating from time to time within a day and at the same time from day to day. For road users, the travel time and travel costs experienced over time and space are stochastic, thus desire

  9. Predictive coding of dynamical variables in balanced spiking networks.

    Science.gov (United States)

    Boerlin, Martin; Machens, Christian K; Denève, Sophie

    2013-01-01

    Two observations about the cortex have puzzled neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks that represent information efficiently in their spikes. We illustrate this insight with spiking networks that represent dynamical variables. Our approach is based on two assumptions: We assume that information about dynamical variables can be read out linearly from neural spike trains, and we assume that neurons only fire a spike if that improves the representation of the dynamical variables. Based on these assumptions, we derive a network of leaky integrate-and-fire neurons that is able to implement arbitrary linear dynamical systems. We show that the membrane voltage of the neurons is equivalent to a prediction error about a common population-level signal. Among other things, our approach allows us to construct an integrator network of spiking neurons that is robust against many perturbations. Most importantly, neural variability in our networks cannot be equated to noise. Despite exhibiting the same single unit properties as widely used population code models (e.g. tuning curves, Poisson distributed spike trains), balanced networks are orders of magnitudes more reliable. Our approach suggests that spikes do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly underestimated.

  10. Salience network dynamics underlying successful resistance of temptation

    Science.gov (United States)

    Nomi, Jason S; Calhoun, Vince D; Stelzel, Christine; Paschke, Lena M; Gaschler, Robert; Goschke, Thomas; Walter, Henrik; Uddin, Lucina Q

    2017-01-01

    Abstract Self-control and the ability to resist temptation are critical for successful completion of long-term goals. Contemporary models in cognitive neuroscience emphasize the primary role of prefrontal cognitive control networks in aligning behavior with such goals. Here, we use gaze pattern analysis and dynamic functional connectivity fMRI data to explore how individual differences in the ability to resist temptation are related to intrinsic brain dynamics of the cognitive control and salience networks. Behaviorally, individuals exhibit greater gaze distance from target location (e.g. higher distractibility) during presentation of tempting erotic images compared with neutral images. Individuals whose intrinsic dynamic functional connectivity patterns gravitate toward configurations in which salience detection systems are less strongly coupled with visual systems resist tempting distractors more effectively. The ability to resist tempting distractors was not significantly related to intrinsic dynamics of the cognitive control network. These results suggest that susceptibility to temptation is governed in part by individual differences in salience network dynamics and provide novel evidence for involvement of brain systems outside canonical cognitive control networks in contributing to individual differences in self-control. PMID:29048582

  11. Dynamic hydro-climatic networks in pristine and regulated rivers

    Science.gov (United States)

    Botter, G.; Basso, S.; Lazzaro, G.; Doulatyari, B.; Biswal, B.; Schirmer, M.; Rinaldo, A.

    2014-12-01

    Flow patterns observed at-a-station are the dynamical byproduct of a cascade of processes involving different compartments of the hydro-climatic network (e.g., climate, rainfall, soil, vegetation) that regulates the transformation of rainfall into streamflows. In complex branching rivers, flow regimes result from the heterogeneous arrangement around the stream network of multiple hydrologic cascades that simultaneously occur within distinct contributing areas. As such, flow regimes are seen as the integrated output of a complex "network of networks", which can be properly characterized by its degree of temporal variability and spatial heterogeneity. Hydrologic networks that generate river flow regimes are dynamic in nature. In pristine rivers, the time-variance naturally emerges at multiple timescales from climate variability (namely, seasonality and inter-annual fluctuations), implying that the magnitude (and the features) of the water flow between two nodes may be highly variable across different seasons and years. Conversely, the spatial distribution of river flow regimes within pristine rivers involves scale-dependent transport features, as well as regional climatic and soil use gradients, which in small and meso-scale catchments (A guarantee quite uniform flow regimes and high spatial correlations. Human-impacted rivers, instead, constitute hybrid networks where observed spatio-temporal patterns are dominated by anthropogenic shifts, such as landscape alterations and river regulation. In regulated rivers, the magnitude and the features of water flows from node to node may change significantly through time due to damming and withdrawals. However, regulation may impact river regimes in a spatially heterogeneous manner (e.g. in localized river reaches), with a significant decrease of spatial correlations and network connectivity. Provided that the spatial and temporal dynamics of flow regimes in complex rivers may strongly impact important biotic processes

  12. How does the antagonism between capping and anti-capping proteins affect actin network dynamics?

    International Nuclear Information System (INIS)

    Hu Longhua; Papoian, Garegin A

    2011-01-01

    Actin-based cell motility is essential to many biological processes. We built a simplified, three-dimensional computational model and subsequently performed stochastic simulations to study the growth dynamics of lamellipodia-like branched networks. In this work, we shed light on the antagonism between capping and anti-capping proteins in regulating actin dynamics in the filamentous network. We discuss detailed mechanisms by which capping and anti-capping proteins affect the protrusion speed of the actin network and the rate of nucleation of filaments. We computed a phase diagram showing the regimes of motility enhancement and inhibition by these proteins. Our work shows that the effects of capping and anti-capping proteins are mainly transmitted by modulation of the filamentous network density and local availability of monomeric actin. We discovered that the combination of the capping/anti-capping regulatory network with nucleation-promoting proteins introduces robustness and redundancy in cell motility machinery, allowing the cell to easily achieve maximal protrusion speeds under a broader set of conditions. Finally, we discuss distributions of filament lengths under various conditions and speculate on their potential implication for the emergence of filopodia from the lamellipodial network.

  13. Cell fate reprogramming by control of intracellular network dynamics

    Science.gov (United States)

    Zanudo, Jorge G. T.; Albert, Reka

    Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell's fate, such as disease therapeutics and stem cell reprogramming. Although the topic of controlling the dynamics of a system has a long history in control theory, most of this work is not directly applicable to intracellular networks. Here we present a network control method that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our control method takes advantage of certain function-dependent network components and their relation to steady states in order to identify control targets, which are guaranteed to drive any initial state to the target state with 100% effectiveness and need to be applied only transiently for the system to reach and stay in the desired state. We illustrate our method's potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments. This work was supported by NSF Grant PHY 1205840.

  14. Impact of constrained rewiring on network structure and node dynamics

    Science.gov (United States)

    Rattana, P.; Berthouze, L.; Kiss, I. Z.

    2014-11-01

    In this paper, we study an adaptive spatial network. We consider a susceptible-infected-susceptible (SIS) epidemic on the network, with a link or contact rewiring process constrained by spatial proximity. In particular, we assume that susceptible nodes break links with infected nodes independently of distance and reconnect at random to susceptible nodes available within a given radius. By systematically manipulating this radius we investigate the impact of rewiring on the structure of the network and characteristics of the epidemic. We adopt a step-by-step approach whereby we first study the impact of rewiring on the network structure in the absence of an epidemic, then with nodes assigned a disease status but without disease dynamics, and finally running network and epidemic dynamics simultaneously. In the case of no labeling and no epidemic dynamics, we provide both analytic and semianalytic formulas for the value of clustering achieved in the network. Our results also show that the rewiring radius and the network's initial structure have a pronounced effect on the endemic equilibrium, with increasingly large rewiring radiuses yielding smaller disease prevalence.

  15. Robust adaptive synchronization of general dynamical networks ...

    Indian Academy of Sciences (India)

    Home; Journals; Pramana – Journal of Physics; Volume 86; Issue 6. Robust ... A robust adaptive synchronization scheme for these general complex networks with multiple delays and uncertainties is established and raised by employing the robust adaptive control principle and the Lyapunov stability theory. We choose ...

  16. Dynamic Optical Networks for Future Internet Environments

    Science.gov (United States)

    Matera, Francesco

    2014-05-01

    This article reports an overview on the evolution of the optical network scenario taking into account the exponential growth of connected devices, big data, and cloud computing that is driving a concrete transformation impacting the information and communication technology world. This hyper-connected scenario is deeply affecting relationships between individuals, enterprises, citizens, and public administrations, fostering innovative use cases in practically any environment and market, and introducing new opportunities and new challenges. The successful realization of this hyper-connected scenario depends on different elements of the ecosystem. In particular, it builds on connectivity and functionalities allowed by converged next-generation networks and their capacity to support and integrate with the Internet of Things, machine-to-machine, and cloud computing. This article aims at providing some hints of this scenario to contribute to analyze impacts on optical system and network issues and requirements. In particular, the role of the software-defined network is investigated by taking into account all scenarios regarding data centers, cloud computing, and machine-to-machine and trying to illustrate all the advantages that could be introduced by advanced optical communications.

  17. Dynamical graph theory networks techniques for the analysis of sparse connectivity networks in dementia

    Science.gov (United States)

    Tahmassebi, Amirhessam; Pinker-Domenig, Katja; Wengert, Georg; Lobbes, Marc; Stadlbauer, Andreas; Romero, Francisco J.; Morales, Diego P.; Castillo, Encarnacion; Garcia, Antonio; Botella, Guillermo; Meyer-Bäse, Anke

    2017-05-01

    Graph network models in dementia have become an important computational technique in neuroscience to study fundamental organizational principles of brain structure and function of neurodegenerative diseases such as dementia. The graph connectivity is reflected in the connectome, the complete set of structural and functional connections of the graph network, which is mostly based on simple Pearson correlation links. In contrast to simple Pearson correlation networks, the partial correlations (PC) only identify direct correlations while indirect associations are eliminated. In addition to this, the state-of-the-art techniques in brain research are based on static graph theory, which is unable to capture the dynamic behavior of the brain connectivity, as it alters with disease evolution. We propose a new research avenue in neuroimaging connectomics based on combining dynamic graph network theory and modeling strategies at different time scales. We present the theoretical framework for area aggregation and time-scale modeling in brain networks as they pertain to disease evolution in dementia. This novel paradigm is extremely powerful, since we can derive both static parameters pertaining to node and area parameters, as well as dynamic parameters, such as system's eigenvalues. By implementing and analyzing dynamically both disease driven PC-networks and regular concentration networks, we reveal differences in the structure of these network that play an important role in the temporal evolution of this disease. The described research is key to advance biomedical research on novel disease prediction trajectories and dementia therapies.

  18. Supporting Dynamic Adaptive Streaming over HTTP in Wireless Meshed Networks using Random Linear Network Coding

    DEFF Research Database (Denmark)

    Hundebøll, Martin; Pedersen, Morten Videbæk; Roetter, Daniel Enrique Lucani

    2014-01-01

    This work studies the potential and impact of the FRANC network coding protocol for delivering high quality Dynamic Adaptive Streaming over HTTP (DASH) in wireless networks. Although DASH aims to tailor the video quality rate based on the available throughput to the destination, it relies...

  19. Dynamic Network Security Control Using Software Defined Networking

    Science.gov (United States)

    2016-03-24

    technologies such as Open vSwitch (OVS) [26], OpenFlow [22], Cisco Nexus 5000V [27], and IBM 5000V [28]. 2.2.4 OpenFlow. The Open Networking Foundation...companies with more than 64 OpenFlow products on the market . Since 2009, ONF released four major revisions to OpenFlow and the latest proposed...2015 from http://www.openvswitch.org, 2014. 27. Cisco Systems. Cisco Nexus 5000 Series Architecture. Retrieved 9 Oc- tober, 2015 from http

  20. Exploring the evolution of node neighborhoods in Dynamic Networks

    Science.gov (United States)

    Orman, Günce Keziban; Labatut, Vincent; Naskali, Ahmet Teoman

    2017-09-01

    Dynamic Networks are a popular way of modeling and studying the behavior of evolving systems. However, their analysis constitutes a relatively recent subfield of Network Science, and the number of available tools is consequently much smaller than for static networks. In this work, we propose a method specifically designed to take advantage of the longitudinal nature of dynamic networks. It characterizes each individual node by studying the evolution of its direct neighborhood, based on the assumption that the way this neighborhood changes reflects the role and position of the node in the whole network. For this purpose, we define the concept of neighborhood event, which corresponds to the various transformations such groups of nodes can undergo, and describe an algorithm for detecting such events. We demonstrate the interest of our method on three real-world networks: DBLP, LastFM and Enron. We apply frequent pattern mining to extract meaningful information from temporal sequences of neighborhood events. This results in the identification of behavioral trends emerging in the whole network, as well as the individual characterization of specific nodes. We also perform a cluster analysis, which reveals that, in all three networks, one can distinguish two types of nodes exhibiting different behaviors: a very small group of active nodes, whose neighborhood undergo diverse and frequent events, and a very large group of stable nodes.

  1. Distributed dynamic simulations of networked control and building performance applications.

    Science.gov (United States)

    Yahiaoui, Azzedine

    2018-02-01

    The use of computer-based automation and control systems for smart sustainable buildings, often so-called Automated Buildings (ABs), has become an effective way to automatically control, optimize, and supervise a wide range of building performance applications over a network while achieving the minimum energy consumption possible, and in doing so generally refers to Building Automation and Control Systems (BACS) architecture. Instead of costly and time-consuming experiments, this paper focuses on using distributed dynamic simulations to analyze the real-time performance of network-based building control systems in ABs and improve the functions of the BACS technology. The paper also presents the development and design of a distributed dynamic simulation environment with the capability of representing the BACS architecture in simulation by run-time coupling two or more different software tools over a network. The application and capability of this new dynamic simulation environment are demonstrated by an experimental design in this paper.

  2. Development of a diagnostic system for Klystron modulators using a neural network

    International Nuclear Information System (INIS)

    Mutoh, M.; Oonuma, T.; Shibasaki, Y.; Abe, I.; Nakahara, K.

    1992-01-01

    The diagnostic system for klystron modulators using a neural network has been developed. Large changes in the voltage and current of the main circuit in a klystron modulator were observed just several ten milli-seconds before the modulator experienced trouble. These changes formed a peculiar pattern that depended on the parts with problems. Diagnosis was possible by means of pattern recognition. The recognition test of patterns using a neutral network has shown good results. This system, which is built in a linac control system, is presently being operated so as to collect new trouble patterns and to carry out tests for practical use. (author)

  3. Robustness of pinning a general complex dynamical network

    International Nuclear Information System (INIS)

    Wang Lei; Sun Youxian

    2010-01-01

    This Letter studies the robustness problem of pinning a general complex dynamical network toward an assigned synchronous evolution. Several synchronization criteria are presented to guarantee the convergence of the pinning process locally and globally by construction of Lyapunov functions. In particular, if a pinning strategy has been designed for synchronization of a given complex dynamical network, then no matter what uncertainties occur among the pinned nodes, synchronization can still be guaranteed through the pinning. The analytical results show that pinning control has a certain robustness against perturbations on network architecture: adding, deleting and changing the weights of edges. Numerical simulations illustrated by scale-free complex networks verify the theoretical results above-acquired.

  4. Dynamic Relaying in 3GPP LTE-Advanced Networks

    Directory of Open Access Journals (Sweden)

    Van Phan Vinh

    2009-01-01

    Full Text Available Relaying is one of the proposed technologies for LTE-Advanced networks. In order to enable a flexible and reliable relaying support, the currently adopted architectural structure of LTE networks has to be modified. In this paper, we extend the LTE architecture to enable dynamic relaying, while maintaining backward compatibility with LTE Release 8 user equipments, and without limiting the flexibility and reliability expected from relaying. With dynamic relaying, relays can be associated with base stations on a need basis rather than in a fixed manner which is based only on initial radio planning. Proposals are also given on how to further improve a relay enhanced LTE network by enabling multiple interfaces between the relay nodes and their controlling base stations, which can possibly be based on technologies different from LTE, so that load balancing can be realized. This load balancing can be either between different base stations or even between different networks.

  5. Node-Dependence-Based Dynamic Incentive Algorithm in Opportunistic Networks

    Directory of Open Access Journals (Sweden)

    Ruiyun Yu

    2014-01-01

    Full Text Available Opportunistic networks lack end-to-end paths between source nodes and destination nodes, so the communications are mainly carried out by the “store-carry-forward” strategy. Selfish behaviors of rejecting packet relay requests will severely worsen the network performance. Incentive is an efficient way to reduce selfish behaviors and hence improves the reliability and robustness of the networks. In this paper, we propose the node-dependence-based dynamic gaming incentive (NDI algorithm, which exploits the dynamic repeated gaming to motivate nodes relaying packets for other nodes. The NDI algorithm presents a mechanism of tolerating selfish behaviors of nodes. Reward and punishment methods are also designed based on the node dependence degree. Simulation results show that the NDI algorithm is effective in increasing the delivery ratio and decreasing average latency when there are a lot of selfish nodes in the opportunistic networks.

  6. Triadic closure dynamics drives scaling laws in social multiplex networks

    International Nuclear Information System (INIS)

    Klimek, Peter; Thurner, Stefan

    2013-01-01

    Social networks exhibit scaling laws for several structural characteristics, such as degree distribution, scaling of the attachment kernel and clustering coefficients as a function of node degree. A detailed understanding if and how these scaling laws are inter-related is missing so far, let alone whether they can be understood through a common, dynamical principle. We propose a simple model for stationary network formation and show that the three mentioned scaling relations follow as natural consequences of triadic closure. The validity of the model is tested on multiplex data from a well-studied massive multiplayer online game. We find that the three scaling exponents observed in the multiplex data for the friendship, communication and trading networks can simultaneously be explained by the model. These results suggest that triadic closure could be identified as one of the fundamental dynamical principles in social multiplex network formation. (paper)

  7. Dynamic thermo-hydraulic model of district cooling networks

    International Nuclear Information System (INIS)

    Oppelt, Thomas; Urbaneck, Thorsten; Gross, Ulrich; Platzer, Bernd

    2016-01-01

    Highlights: • A dynamic thermo-hydraulic model for district cooling networks is presented. • The thermal modelling is based on water segment tracking (Lagrangian approach). • Thus, numerical errors and balance inaccuracies are avoided. • Verification and validation studies proved the reliability of the model. - Abstract: In the present paper, the dynamic thermo-hydraulic model ISENA is presented which can be applied for answering different questions occurring in design and operation of district cooling networks—e.g. related to economic and energy efficiency. The network model consists of a quasistatic hydraulic model and a transient thermal model based on tracking water segments through the whole network (Lagrangian method). Applying this approach, numerical errors and balance inaccuracies can be avoided which leads to a higher quality of results compared to other network models. Verification and validation calculations are presented in order to show that ISENA provides reliable results and is suitable for practical application.

  8. Enabling dynamic network analysis through visualization in TVNViewer

    Directory of Open Access Journals (Sweden)

    Curtis Ross E

    2012-08-01

    Full Text Available Abstract Background Many biological processes are context-dependent or temporally specific. As a result, relationships between molecular constituents evolve across time and environments. While cutting-edge machine learning techniques can recover these networks, exploring and interpreting the rewiring behavior is challenging. Information visualization shines in this type of exploratory analysis, motivating the development ofTVNViewer (http://sailing.cs.cmu.edu/tvnviewer, a visualization tool for dynamic network analysis. Results In this paper, we demonstrate visualization techniques for dynamic network analysis by using TVNViewer to analyze yeast cell cycle and breast cancer progression datasets. Conclusions TVNViewer is a powerful new visualization tool for the analysis of biological networks that change across time or space.

  9. Enabling dynamic network analysis through visualization in TVNViewer

    Science.gov (United States)

    2012-01-01

    Background Many biological processes are context-dependent or temporally specific. As a result, relationships between molecular constituents evolve across time and environments. While cutting-edge machine learning techniques can recover these networks, exploring and interpreting the rewiring behavior is challenging. Information visualization shines in this type of exploratory analysis, motivating the development ofTVNViewer (http://sailing.cs.cmu.edu/tvnviewer), a visualization tool for dynamic network analysis. Results In this paper, we demonstrate visualization techniques for dynamic network analysis by using TVNViewer to analyze yeast cell cycle and breast cancer progression datasets. Conclusions TVNViewer is a powerful new visualization tool for the analysis of biological networks that change across time or space. PMID:22897913

  10. Modeling genome-wide dynamic regulatory network in mouse lungs with influenza infection using high-dimensional ordinary differential equations.

    Science.gov (United States)

    Wu, Shuang; Liu, Zhi-Ping; Qiu, Xing; Wu, Hulin

    2014-01-01

    The immune response to viral infection is regulated by an intricate network of many genes and their products. The reverse engineering of gene regulatory networks (GRNs) using mathematical models from time course gene expression data collected after influenza infection is key to our understanding of the mechanisms involved in controlling influenza infection within a host. A five-step pipeline: detection of temporally differentially expressed genes, clustering genes into co-expressed modules, identification of network structure, parameter estimate refinement, and functional enrichment analysis, is developed for reconstructing high-dimensional dynamic GRNs from genome-wide time course gene expression data. Applying the pipeline to the time course gene expression data from influenza-infected mouse lungs, we have identified 20 distinct temporal expression patterns in the differentially expressed genes and constructed a module-based dynamic network using a linear ODE model. Both intra-module and inter-module annotations and regulatory relationships of our inferred network show some interesting findings and are highly consistent with existing knowledge about the immune response in mice after influenza infection. The proposed method is a computationally efficient, data-driven pipeline bridging experimental data, mathematical modeling, and statistical analysis. The application to the influenza infection data elucidates the potentials of our pipeline in providing valuable insights into systematic modeling of complicated biological processes.

  11. Dynamics of macro- and microscopic neural networks

    DEFF Research Database (Denmark)

    Mikkelsen, Kaare

    2014-01-01

    that the method continues to find use, of which examples are presented. In the second part of the thesis, numerical simulations of networks of neurons are described. To simplify the analysis, a relatively simpled neuron model - Leaky Integrate and Fire - is chosen. The strengths of the connections between...... shown that the syncronizing effect of the plasticity disappears when the strengths of the connections are frozen in time. Subsequently, the so-called ``Sisyphus'' mechanism is discussed, which is shown to cause slow fluctuations in the both the network synchronization and the strengths...... in groups of high internal syncronization. It is demonstrated that these states are exceedingly robust towards additive noise. Studies of simulations such as these are important, in that simple models grant us an efficient way to investigat...

  12. An Efficient Dynamic Trust Evaluation Model for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Zhengwang Ye

    2017-01-01

    Full Text Available Trust evaluation is an effective method to detect malicious nodes and ensure security in wireless sensor networks (WSNs. In this paper, an efficient dynamic trust evaluation model (DTEM for WSNs is proposed, which implements accurate, efficient, and dynamic trust evaluation by dynamically adjusting the weights of direct trust and indirect trust and the parameters of the update mechanism. To achieve accurate trust evaluation, the direct trust is calculated considering multitrust including communication trust, data trust, and energy trust with the punishment factor and regulating function. The indirect trust is evaluated conditionally by the trusted recommendations from a third party. Moreover, the integrated trust is measured by assigning dynamic weights for direct trust and indirect trust and combining them. Finally, we propose an update mechanism by a sliding window based on induced ordered weighted averaging operator to enhance flexibility. We can dynamically adapt the parameters and the interactive history windows number according to the actual needs of the network to realize dynamic update of direct trust value. Simulation results indicate that the proposed dynamic trust model is an efficient dynamic and attack-resistant trust evaluation model. Compared with existing approaches, the proposed dynamic trust model performs better in defending multiple malicious attacks.

  13. LOGISTIC NETWORK REGRESSION FOR SCALABLE ANALYSIS OF NETWORKS WITH JOINT EDGE/VERTEX DYNAMICS.

    Science.gov (United States)

    Almquist, Zack W; Butts, Carter T

    2014-08-01

    Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach.

  14. Dynamics in a delayed-neural network

    International Nuclear Information System (INIS)

    Yuan Yuan

    2007-01-01

    In this paper, we consider a neural network of four identical neurons with time-delayed connections. Some parameter regions are given for global, local stability and synchronization using the theory of functional differential equations. The root distributions in the corresponding characteristic transcendental equation are analyzed, Pitchfork bifurcation, Hopf and equivariant Hopf bifurcations are investigated by revealing the center manifolds and normal forms. Numerical simulations are shown the agreements with the theoretical results

  15. Dynamic queuing transmission model for dynamic network loading

    DEFF Research Database (Denmark)

    Raovic, Nevena; Nielsen, Otto Anker; Prato, Carlo Giacomo

    2017-01-01

    and allowing for the representation of multiple vehicle classes, queue spillbacks and shock waves. The model assumes that a link is split into a moving part plus a queuing part, and p that traffic dynamics are given by a triangular fundamental diagram. A case-study is investigated and the DQTM is compared...

  16. Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks

    Science.gov (United States)

    Pyle, Ryan; Rosenbaum, Robert

    2017-01-01

    Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

  17. Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks.

    Science.gov (United States)

    Pyle, Ryan; Rosenbaum, Robert

    2017-01-06

    Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

  18. SEWER NETWORK DISCHARGE OPTIMIZATION USING THE DYNAMIC PROGRAMMING

    Directory of Open Access Journals (Sweden)

    Viorel MINZU

    2015-12-01

    Full Text Available It is necessary to adopt an optimal control that allows an efficient usage of the existing sewer networks, in order to avoid the building of new retention facilities. The main objective of the control action is to minimize the overflow volume of a sewer network. This paper proposes a method to apply a solution obtained by discrete dynamic programming through a realistic closed loop system.

  19. Stochastic population dynamic models as probability networks

    Science.gov (United States)

    M.E. and D.C. Lee. Borsuk

    2009-01-01

    The dynamics of a population and its response to environmental change depend on the balance of birth, death and age-at-maturity, and there have been many attempts to mathematically model populations based on these characteristics. Historically, most of these models were deterministic, meaning that the results were strictly determined by the equations of the model and...

  20. Catalysis and communication in dynamic molecular networks

    NARCIS (Netherlands)

    Fanlo Virgos, Hugo

    2015-01-01

    The interactions of a Dynamic Combinatorial Library (DCL) of molecules with specific targets leads to composition changes of the library which can reveal potential guests and / or catalysts. In this thesis some chemical systems have been proposed to achieve a certain level of molecular complexity

  1. Semantic integration to identify overlapping functional modules in protein interaction networks

    Directory of Open Access Journals (Sweden)

    Ramanathan Murali

    2007-07-01

    Full Text Available Abstract Background The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms. Results We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches. Conclusion The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.

  2. Complex and unexpected dynamics in simple genetic regulatory networks

    Science.gov (United States)

    Borg, Yanika; Ullner, Ekkehard; Alagha, Afnan; Alsaedi, Ahmed; Nesbeth, Darren; Zaikin, Alexey

    2014-03-01

    One aim of synthetic biology is to construct increasingly complex genetic networks from interconnected simpler ones to address challenges in medicine and biotechnology. However, as systems increase in size and complexity, emergent properties lead to unexpected and complex dynamics due to nonlinear and nonequilibrium properties from component interactions. We focus on four different studies of biological systems which exhibit complex and unexpected dynamics. Using simple synthetic genetic networks, small and large populations of phase-coupled quorum sensing repressilators, Goodwin oscillators, and bistable switches, we review how coupled and stochastic components can result in clustering, chaos, noise-induced coherence and speed-dependent decision making. A system of repressilators exhibits oscillations, limit cycles, steady states or chaos depending on the nature and strength of the coupling mechanism. In large repressilator networks, rich dynamics can also be exhibited, such as clustering and chaos. In populations of Goodwin oscillators, noise can induce coherent oscillations. In bistable systems, the speed with which incoming external signals reach steady state can bias the network towards particular attractors. These studies showcase the range of dynamical behavior that simple synthetic genetic networks can exhibit. In addition, they demonstrate the ability of mathematical modeling to analyze nonlinearity and inhomogeneity within these systems.

  3. Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer.

    Science.gov (United States)

    Chiu, Yu-Chiao; Wang, Li-Ju; Hsiao, Tzu-Hung; Chuang, Eric Y; Chen, Yidong

    2017-10-03

    With the advances in high-throughput gene profiling technologies, a large volume of gene interaction maps has been constructed. A higher-level layer of gene-gene interaction, namely modulate gene interaction, is composed of gene pairs of which interaction strengths are modulated by (i.e., dependent on) the expression level of a key modulator gene. Systematic investigations into the modulation by estrogen receptor (ER), the best-known modulator gene, have revealed the functional and prognostic significance in breast cancer. However, a genome-wide identification of key modulator genes that may further unveil the landscape of modulated gene interaction is still lacking. We proposed a systematic workflow to screen for key modulators based on genome-wide gene expression profiles. We designed four modularity parameters to measure the ability of a putative modulator to perturb gene interaction networks. Applying the method to a dataset of 286 breast tumors, we comprehensively characterized the modularity parameters and identified a total of 973 key modulator genes. The modularity of these modulators was verified in three independent breast cancer datasets. ESR1, the encoding gene of ER, appeared in the list, and abundant novel modulators were illuminated. For instance, a prognostic predictor of breast cancer, SFRP1, was found the second modulator. Functional annotation analysis of the 973 modulators revealed involvements in ER-related cellular processes as well as immune- and tumor-associated functions. Here we present, as far as we know, the first comprehensive analysis of key modulator genes on a genome-wide scale. The validity of filtering parameters as well as the conservativity of modulators among cohorts were corroborated. Our data bring new insights into the modulated layer of gene-gene interaction and provide candidates for further biological investigations.

  4. Flexible establishment of functional brain networks supports attentional modulation of unconscious cognition.

    Science.gov (United States)

    Ulrich, Martin; Adams, Sarah C; Kiefer, Markus

    2014-11-01

    In classical theories of attention, unconscious automatic processes are thought to be independent of higher-level attentional influences. Here, we propose that unconscious processing depends on attentional enhancement of task-congruent processing pathways implemented by a dynamic modulation of the functional communication between brain regions. Using functional magnetic resonance imaging, we tested our model with a subliminally primed lexical decision task preceded by an induction task preparing either a semantic or a perceptual task set. Subliminal semantic priming was significantly greater after semantic compared to perceptual induction in ventral occipito-temporal (vOT) and inferior frontal cortex, brain areas known to be involved in semantic processing. The functional connectivity pattern of vOT varied depending on the induction task and successfully predicted the magnitude of behavioral and neural priming. Together, these findings support the proposal that dynamic establishment of functional networks by task sets is an important mechanism in the attentional control of unconscious processing. © 2014 Wiley Periodicals, Inc.

  5. Deciphering the imprint of topology on nonlinear dynamical network stability

    International Nuclear Information System (INIS)

    Nitzbon, J; Schultz, P; Heitzig, J; Kurths, J; Hellmann, F

    2017-01-01

    Coupled oscillator networks show complex interrelations between topological characteristics of the network and the nonlinear stability of single nodes with respect to large but realistic perturbations. We extend previous results on these relations by incorporating sampling-based measures of the transient behaviour of the system, its survivability, as well as its asymptotic behaviour, its basin stability. By combining basin stability and survivability we uncover novel, previously unknown asymptotic states with solitary, desynchronized oscillators which are rotating with a frequency different from their natural one. They occur almost exclusively after perturbations at nodes with specific topological properties. More generally we confirm and significantly refine the results on the distinguished role tree-shaped appendices play for nonlinear stability. We find a topological classification scheme for nodes located in such appendices, that exactly separates them according to their stability properties, thus establishing a strong link between topology and dynamics. Hence, the results can be used for the identification of vulnerable nodes in power grids or other coupled oscillator networks. From this classification we can derive general design principles for resilient power grids. We find that striving for homogeneous network topologies facilitates a better performance in terms of nonlinear dynamical network stability. While the employed second-order Kuramoto-like model is parametrised to be representative for power grids, we expect these insights to transfer to other critical infrastructure systems or complex network dynamics appearing in various other fields. (paper)

  6. Generalized master equations for non-Poisson dynamics on networks.

    Science.gov (United States)

    Hoffmann, Till; Porter, Mason A; Lambiotte, Renaud

    2012-10-01

    The traditional way of studying temporal networks is to aggregate the dynamics of the edges to create a static weighted network. This implicitly assumes that the edges are governed by Poisson processes, which is not typically the case in empirical temporal networks. Accordingly, we examine the effects of non-Poisson inter-event statistics on the dynamics of edges, and we apply the concept of a generalized master equation to the study of continuous-time random walks on networks. We show that this equation reduces to the standard rate equations when the underlying process is Poissonian and that its stationary solution is determined by an effective transition matrix whose leading eigenvector is easy to calculate. We conduct numerical simulations and also derive analytical results for the stationary solution under the assumption that all edges have the same waiting-time distribution. We discuss the implications of our work for dynamical processes on temporal networks and for the construction of network diagnostics that take into account their nontrivial stochastic nature.

  7. Network module detection: Affinity search technique with the multi-node topological overlap measure.

    Science.gov (United States)

    Li, Ai; Horvath, Steve

    2009-07-20

    Many clustering procedures only allow the user to input a pairwise dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP) where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high multi-node topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis. We adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST), which is a generalized version of the Cluster Affinity Search Technique (CAST). MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes). We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering. Our flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/MTOM/

  8. Applications of flow-networks to opinion-dynamics

    Science.gov (United States)

    Tupikina, Liubov; Kurths, Jürgen

    2015-04-01

    Networks were successfully applied to describe complex systems, such as brain, climate, processes in society. Recently a socio-physical problem of opinion-dynamics was studied using network techniques. We present the toy-model of opinion-formation based on the physical model of advection-diffusion. We consider spreading of the opinion on the fixed subject, assuming that opinion on society is binary: if person has opinion then the state of the node in the society-network equals 1, if the person doesn't have opinion state of the node equals 0. Opinion can be spread from one person to another if they know each other, or in the network-terminology, if the nodes are connected. We include into the system governed by advection-diffusion equation the external field to model such effects as for instance influence from media. The assumptions for our model can be formulated as the following: 1.the node-states are influenced by the network structure in such a way, that opinion can be spread only between adjacent nodes (the advective term of the opinion-dynamics), 2.the network evolution can have two scenarios: -network topology is not changing with time; -additional links can appear or disappear each time-step with fixed probability which requires adaptive networks properties. Considering these assumptions for our system we obtain the system of equations describing our model-dynamics which corresponds well to other socio-physics models, for instance, the model of the social cohesion and the famous voter-model. We investigate the behavior of the suggested model studying "waiting time" of the system, time to get to the stable state, stability of the model regimes for different values of model parameters and network topology.

  9. Higher-Order Synaptic Interactions Coordinate Dynamics in Recurrent Networks.

    Directory of Open Access Journals (Sweden)

    Brendan Chambers

    2016-08-01

    Full Text Available Linking synaptic connectivity to dynamics is key to understanding information processing in neocortex. Circuit dynamics emerge from complex interactions of interconnected neurons, necessitating that links between connectivity and dynamics be evaluated at the network level. Here we map propagating activity in large neuronal ensembles from mouse neocortex and compare it to a recurrent network model, where connectivity can be precisely measured and manipulated. We find that a dynamical feature dominates statistical descriptions of propagating activity for both neocortex and the model: convergent clusters comprised of fan-in triangle motifs, where two input neurons are themselves connected. Fan-in triangles coordinate the timing of presynaptic inputs during ongoing activity to effectively generate postsynaptic spiking. As a result, paradoxically, fan-in triangles dominate the statistics of spike propagation even in randomly connected recurrent networks. Interplay between higher-order synaptic connectivity and the integrative properties of neurons constrains the structure of network dynamics and shapes the routing of information in neocortex.

  10. Coupled disease-behavior dynamics on complex networks: A review

    Science.gov (United States)

    Wang, Zhen; Andrews, Michael A.; Wu, Zhi-Xi; Wang, Lin; Bauch, Chris T.

    2015-12-01

    It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.

  11. Dynamic network expansion, contraction, and connectivity in the river corridor of mountain stream network

    Science.gov (United States)

    Ward, A. S.; Schmadel, N.; Wondzell, S. M.

    2017-12-01

    River networks are broadly recognized to expand and contract in response to hydrologic forcing. Additionally, the individual controls on river corridor dynamics of hydrologic forcing and geologic setting are well recognized. However, we currently lack tools to integrate our understanding of process dynamics in the river corridor and make predictions at the scale of river networks. In this study, we develop a perceptual model of the river corridor in mountain river networks, translate this into a reduced-complexity mechanistic model, and implement the model in a well-studied headwater catchment. We found that the river network was most sensitive to hydrologic dynamics under the lowest discharges (Qgauge managers of water resources who need to estimate connectivity and flow initiation location along the river corridor over broad, unstudied catchments.

  12. Phonological experience modulates voice discrimination: Evidence from functional brain networks analysis.

    Science.gov (United States)

    Hu, Xueping; Wang, Xiangpeng; Gu, Yan; Luo, Pei; Yin, Shouhang; Wang, Lijun; Fu, Chao; Qiao, Lei; Du, Yi; Chen, Antao

    2017-10-01

    Numerous behavioral studies have found a modulation effect of phonological experience on voice discrimination. However, the neural substrates underpinning this phenomenon are poorly understood. Here we manipulated language familiarity to test the hypothesis that phonological experience affects voice discrimination via mediating the engagement of multiple perceptual and cognitive resources. The results showed that during voice discrimination, the activation of several prefrontal regions was modulated by language familiarity. More importantly, the same effect was observed concerning the functional connectivity from the fronto-parietal network to the voice-identity network (VIN), and from the default mode network to the VIN. Our findings indicate that phonological experience could bias the recruitment of cognitive control and information retrieval/comparison processes during voice discrimination. Therefore, the study unravels the neural substrates subserving the modulation effect of phonological experience on voice discrimination, and provides new insights into studying voice discrimination from the perspective of network interactions. Copyright © 2017. Published by Elsevier Inc.

  13. Verification and Validation of the New Dynamic Mooring Modules Available in FAST v8: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Wendt, Fabian; Robertson, Amy; Jonkman, Jason; Andersen, Morten T.

    2016-08-01

    The open-source aero-hydro-servo-elastic wind turbine simulation software, FAST v8, was recently coupled to two newly developed mooring dynamics modules: MoorDyn and FEAMooring. MoorDyn is a lumped-mass-based mooring dynamics module developed by the University of Maine, and FEAMooring is a finite-element-based mooring dynamics module developed by Texas A&M University. This paper summarizes the work performed to verify and validate these modules against other mooring models and measured test data to assess their reliability and accuracy. The quality of the fairlead load predictions by the open-source mooring modules MoorDyn and FEAMooring appear to be largely equivalent to what is predicted by the commercial tool OrcaFlex. Both mooring dynamic model predictions agree well with the experimental data, considering the given limitations in the accuracy of the platform hydrodynamic load calculation and the quality of the measurement data.

  14. Synchronization in Complex Networks of Nonlinear Dynamical Systems

    CERN Document Server

    Wu, Chai Wah

    2007-01-01

    This book brings together two emerging research areas: synchronization in coupled nonlinear systems and complex networks, and study conditions under which a complex network of dynamical systems synchronizes. While there are many texts that study synchronization in chaotic systems or properties of complex networks, there are few texts that consider the intersection of these two very active and interdisciplinary research areas. The main theme of this book is that synchronization conditions can be related to graph theoretical properties of the underlying coupling topology. The book introduces ide

  15. Phase multistability in a dynamical small world network

    Energy Technology Data Exchange (ETDEWEB)

    Shabunin, A. V., E-mail: shabuninav@info.sgu.ru [Radiophysics and Nonlinear Dynamics Department, Saratov State University, Saratov (Russian Federation)

    2015-01-15

    The effect of phase multistability is explored in a small world network of periodic oscillators with diffusive couplings. The structure of the network represents a ring with additional non-local links, which spontaneously arise and vanish between arbitrary nodes. The dynamics of random couplings is modeled by “birth” and “death” stochastic processes by means of the cellular automate approach. The evolution of the network under gradual increasing of the number of random couplings goes through stages of phases fluctuations and spatial cluster formation. Finally, in the presence of non-local couplings the phase multistability “dies” and only the in-phase regime survives.

  16. On the dynamics of a gene regulatory network

    International Nuclear Information System (INIS)

    Grammaticos, B; Carstea, A S; Ramani, A

    2006-01-01

    We examine the dynamics of a network of genes focusing on a periodic chain of genes, of arbitrary length. We show that within a given class of sigmoids representing the equilibrium probability of the binding of the RNA polymerase to the core promoter, the system possesses a single stable fixed point. By slightly modifying the sigmoid, introducing 'stiffer' forms, we show that it is possible to find network configurations exhibiting bistable behaviour. Our results do not depend crucially on the length of the chain considered: calculations with finite chains lead to similar results. However, a realistic study of regulatory genetic networks would require the consideration of more complex topologies and interactions

  17. Complex Dynamics of Delay-Coupled Neural Networks

    Science.gov (United States)

    Mao, Xiaochen

    2016-09-01

    This paper reveals the complicated dynamics of a delay-coupled system that consists of a pair of sub-networks and multiple bidirectional couplings. Time delays are introduced into the internal connections and network-couplings, respectively. The stability and instability of the coupled network are discussed. The sufficient conditions for the existence of oscillations are given. Case studies of numerical simulations are given to validate the analytical results. Interesting and complicated neuronal activities are observed numerically, such as rest states, periodic oscillations, multiple switches of rest states and oscillations, and the coexistence of different types of oscillations.

  18. An Improved Dynamic Programming Decomposition Approach for Network Revenue Management

    OpenAIRE

    Dan Zhang

    2011-01-01

    We consider a nonlinear nonseparable functional approximation to the value function of a dynamic programming formulation for the network revenue management (RM) problem with customer choice. We propose a simultaneous dynamic programming approach to solve the resulting problem, which is a nonlinear optimization problem with nonlinear constraints. We show that our approximation leads to a tighter upper bound on optimal expected revenue than some known bounds in the literature. Our approach can ...

  19. Dynamical Interplay between Awareness and Epidemic Spreading in Multiplex Networks

    Science.gov (United States)

    Granell, Clara; Gómez, Sergio; Arenas, Alex

    2013-09-01

    We present the analysis of the interrelation between two processes accounting for the spreading of an epidemic, and the information awareness to prevent its infection, on top of multiplex networks. This scenario is representative of an epidemic process spreading on a network of persistent real contacts, and a cyclic information awareness process diffusing in the network of virtual social contacts between the same individuals. The topology corresponds to a multiplex network where two diffusive processes are interacting affecting each other. The analysis using a microscopic Markov chain approach reveals the phase diagram of the incidence of the epidemics and allows us to capture the evolution of the epidemic threshold depending on the topological structure of the multiplex and the interrelation with the awareness process. Interestingly, the critical point for the onset of the epidemics has a critical value (metacritical point) defined by the awareness dynamics and the topology of the virtual network, from which the onset increases and the epidemics incidence decreases.

  20. Dynamical interplay between awareness and epidemic spreading in multiplex networks.

    Science.gov (United States)

    Granell, Clara; Gómez, Sergio; Arenas, Alex

    2013-09-20

    We present the analysis of the interrelation between two processes accounting for the spreading of an epidemic, and the information awareness to prevent its infection, on top of multiplex networks. This scenario is representative of an epidemic process spreading on a network of persistent real contacts, and a cyclic information awareness process diffusing in the network of virtual social contacts between the same individuals. The topology corresponds to a multiplex network where two diffusive processes are interacting affecting each other. The analysis using a microscopic Markov chain approach reveals the phase diagram of the incidence of the epidemics and allows us to capture the evolution of the epidemic threshold depending on the topological structure of the multiplex and the interrelation with the awareness process. Interestingly, the critical point for the onset of the epidemics has a critical value (metacritical point) defined by the awareness dynamics and the topology of the virtual network, from which the onset increases and the epidemics incidence decreases.

  1. A paradox for traffic dynamics in complex networks with ATIS

    International Nuclear Information System (INIS)

    Zheng Jianfeng; Gao Ziyou

    2008-01-01

    In this work, we study the statistical properties of traffic (e.g., vehicles) dynamics in complex networks, by introducing advanced transportation information systems (ATIS). The ATIS can provide the information of traffic flow pattern throughout the network and have an obvious effect on path routing strategy for such vehicles equipped with ATIS. The ATIS can be described by the understanding of link cost functions. Different indices such as efficiency and system total cost are discussed in depth. It is found that, for random networks (scale-free networks), the efficiency is effectively improved (decreased) if ATIS is properly equipped; however the system total cost is largely increased (decreased). It indicates that there exists a paradox between the efficiency and system total cost in complex networks. Furthermore, we report the simulation results by considering different kinds of link cost functions, and the paradox is recovered. Finally, we extend our traffic model, and also find the existence of the paradox

  2. Controllability of Weighted and Directed Networks with Nonidentical Node Dynamics

    Directory of Open Access Journals (Sweden)

    Linying Xiang

    2013-01-01

    Full Text Available The concept of controllability from control theory is applied to weighted and directed networks with heterogenous linear or linearized node dynamics subject to exogenous inputs, where the nodes are grouped into leaders and followers. Under this framework, the controllability of the controlled network can be decomposed into two independent problems: the controllability of the isolated leader subsystem and the controllability of the extended follower subsystem. Some necessary and/or sufficient conditions for the controllability of the leader-follower network are derived based on matrix theory and graph theory. In particular, it is shown that a single-leader network is controllable if it is a directed path or cycle, but it is uncontrollable for a complete digraph or a star digraph in general. Furthermore, some approaches to improving the controllability of a heterogenous network are presented. Some simulation examples are given for illustration and verification.

  3. A network-based dynamical ranking system for competitive sports

    Science.gov (United States)

    Motegi, Shun; Masuda, Naoki

    2012-12-01

    From the viewpoint of networks, a ranking system for players or teams in sports is equivalent to a centrality measure for sports networks, whereby a directed link represents the result of a single game. Previously proposed network-based ranking systems are derived from static networks, i.e., aggregation of the results of games over time. However, the score of a player (or team) fluctuates over time. Defeating a renowned player in the peak performance is intuitively more rewarding than defeating the same player in other periods. To account for this factor, we propose a dynamic variant of such a network-based ranking system and apply it to professional men's tennis data. We derive a set of linear online update equations for the score of each player. The proposed ranking system predicts the outcome of the future games with a higher accuracy than the static counterparts.

  4. Dynamics of functional failures and recovery in complex road networks

    Science.gov (United States)

    Zhan, Xianyuan; Ukkusuri, Satish V.; Rao, P. Suresh C.

    2017-11-01

    We propose a new framework for modeling the evolution of functional failures and recoveries in complex networks, with traffic congestion on road networks as the case study. Differently from conventional approaches, we transform the evolution of functional states into an equivalent dynamic structural process: dual-vertex splitting and coalescing embedded within the original network structure. The proposed model successfully explains traffic congestion and recovery patterns at the city scale based on high-resolution data from two megacities. Numerical analysis shows that certain network structural attributes can amplify or suppress cascading functional failures. Our approach represents a new general framework to model functional failures and recoveries in flow-based networks and allows understanding of the interplay between structure and function for flow-induced failure propagation and recovery.

  5. Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning.

    Science.gov (United States)

    Jarvers, Christian; Brosch, Tobias; Brechmann, André; Woldeit, Marie L; Schulz, Andreas L; Ohl, Frank W; Lommerzheim, Marcel; Neumann, Heiko

    2016-01-01

    Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e., tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals) show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN), which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that large sudden

  6. Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results

    Directory of Open Access Journals (Sweden)

    R.A.T.M. Ranasinghe

    2017-04-01

    Full Text Available Rolling dynamic compaction (RDC, which involves the towing of a noncircular module, is now widespread and accepted among many other soil compaction methods. However, to date, there is no accurate method for reliable prediction of the densification of soil and the extent of ground improvement by means of RDC. This study presents the application of artificial neural networks (ANNs for a priori prediction of the effectiveness of RDC. The models are trained with in situ dynamic cone penetration (DCP test data obtained from previous civil projects associated with the 4-sided impact roller. The predictions from the ANN models are in good agreement with the measured field data, as indicated by the model correlation coefficient of approximately 0.8. It is concluded that the ANN models developed in this study can be successfully employed to provide more accurate prediction of the performance of the RDC on a range of soil types.

  7. Network Physics anounces first product to provide business-level management of the most complex and dynamic networks

    CERN Multimedia

    2003-01-01

    Network Physics, provider of business-level, traffic flow-based network management solutions, today announced the introduction of the Network Physics NP/BizFlow-1000. With the NP/BizFlow-1000, Fortune 1000 companies with complex and dynamic networks can analyze the flows that link business groups, critical applications, and network software and hardware (1 page).

  8. Role of density modulation in the spatially resolved dynamics of strongly confined liquids.

    Science.gov (United States)

    Saw, Shibu; Dasgupta, Chandan

    2016-08-07

    Confinement by walls usually produces a strong modulation in the density of dense liquids near the walls. Using molecular dynamics simulations, we examine the effects of the density modulation on the spatially resolved dynamics of a liquid confined between two parallel walls, using a resolution of a fraction of the interparticle distance in the liquid. The local dynamics is quantified by the relaxation time associated with the temporal autocorrelation function of the local density. We find that this local relaxation time varies in phase with the density modulation. The amplitude of the spatial modulation of the relaxation time can be quite large, depending on the characteristics of the wall and thermodynamic parameters of the liquid. To disentangle the effects of confinement and density modulation on the spatially resolved dynamics, we compare the dynamics of a confined liquid with that of an unconfined one in which a similar density modulation is induced by an external potential. We find several differences indicating that density modulation alone cannot account for all the features seen in the spatially resolved dynamics of confined liquids. We also examine how the dynamics near a wall depends on the separation between the two walls and show that the features seen in our simulations persist in the limit of large wall separation.

  9. Network Unfolding Map by Vertex-Edge Dynamics Modeling.

    Science.gov (United States)

    Verri, Filipe Alves Neto; Urio, Paulo Roberto; Zhao, Liang

    2018-02-01

    The emergence of collective dynamics in neural networks is a mechanism of the animal and human brain for information processing. In this paper, we develop a computational technique using distributed processing elements in a complex network, which are called particles, to solve semisupervised learning problems. Three actions govern the particles' dynamics: generation, walking, and absorption. Labeled vertices generate new particles that compete against rival particles for edge domination. Active particles randomly walk in the network until they are absorbed by either a rival vertex or an edge currently dominated by rival particles. The result from the model evolution consists of sets of edges arranged by the label dominance. Each set tends to form a connected subnetwork to represent a data class. Although the intrinsic dynamics of the model is a stochastic one, we prove that there exists a deterministic version with largely reduced computational complexity; specifically, with linear growth. Furthermore, the edge domination process corresponds to an unfolding map in such way that edges "stretch" and "shrink" according to the vertex-edge dynamics. Consequently, the unfolding effect summarizes the relevant relationships between vertices and the uncovered data classes. The proposed model captures important details of connectivity patterns over the vertex-edge dynamics evolution, in contrast to the previous approaches, which focused on only vertex or only edge dynamics. Computer simulations reveal that the new model can identify nonlinear features in both real and artificial data, including boundaries between distinct classes and overlapping structures of data.

  10. Selective attention to phonology dynamically modulates initial encoding of auditory words within the left hemisphere.

    Science.gov (United States)

    Yoncheva, Yuliya; Maurer, Urs; Zevin, Jason D; McCandliss, Bruce D

    2014-08-15

    Selective attention to phonology, i.e., the ability to attend to sub-syllabic units within spoken words, is a critical precursor to literacy acquisition. Recent functional magnetic resonance imaging evidence has demonstrated that a left-lateralized network of frontal, temporal, and posterior language regions, including the visual word form area, supports this skill. The current event-related potential (ERP) study investigated the temporal dynamics of selective attention to phonology during spoken word perception. We tested the hypothesis that selective attention to phonology dynamically modulates stimulus encoding by recruiting left-lateralized processes specifically while the information critical for performance is unfolding. Selective attention to phonology was captured by manipulating listening goals: skilled adult readers attended to either rhyme or melody within auditory stimulus pairs. Each pair superimposed rhyming and melodic information ensuring identical sensory stimulation. Selective attention to phonology produced distinct early and late topographic ERP effects during stimulus encoding. Data-driven source localization analyses revealed that selective attention to phonology led to significantly greater recruitment of left-lateralized posterior and extensive temporal regions, which was notably concurrent with the rhyme-relevant information within the word. Furthermore, selective attention effects were specific to auditory stimulus encoding and not observed in response to cues, arguing against the notion that they reflect sustained task setting. Collectively, these results demonstrate that selective attention to phonology dynamically engages a left-lateralized network during the critical time-period of perception for achieving phonological analysis goals. These findings suggest a key role for selective attention in on-line phonological computations. Furthermore, these findings motivate future research on the role that neural mechanisms of attention may

  11. Selective attention to phonology dynamically modulates initial encoding of auditory words within the left hemisphere

    Science.gov (United States)

    Yoncheva; Maurer, Urs; Zevin, Jason; McCandliss, Bruce

    2015-01-01

    Selective attention to phonology, i.e., the ability to attend to sub-syllabic units within spoken words, is a critical precursor to literacy acquisition. Recent functional magnetic resonance imaging evidence has demonstrated that a left-lateralized network of frontal, temporal, and posterior language regions, including the visual word form area, supports this skill. The current event-related potential (ERP) study investigated the temporal dynamics of selective attention to phonology during spoken word perception. We tested the hypothesis that selective atten tion to phonology dynamically modulates stimulus encoding by recruiting left-lateralized processes specifically while the information critical for performance is unfolding. Selective attention to phonology was captured by ma nipulating listening goals: skilled adult readers attended to either rhyme or melody within auditory stimulus pairs. Each pair superimposed rhyming and melodic information ensuring identical sensory stimulation. Selective attention to phonology produced distinct early and late topographic ERP effects during stimulus encoding. Data- driven source localization analyses revealed that selective attention to phonology led to significantly greater re cruitment of left-lateralized posterior and extensive temporal regions, which was notably concurrent with the rhyme-relevant information within the word. Furthermore, selective attention effects were specific to auditory stimulus encoding and not observed in response to cues, arguing against the notion that they reflect sustained task setting. Collectively, these results demonstrate that selective attention to phonology dynamically engages a left-lateralized network during the critical time-period of perception for achieving phonological analysis goals. These findings support the key role of selective attention to phonology in the development of literacy and motivate future research on the neural bases of the interaction between phonological

  12. Complex human mobility dynamics on a network

    International Nuclear Information System (INIS)

    Szell, M.

    2010-01-01

    Massive multiplayer online games provide a fascinating new way of observing hundreds of thousands of simultaneously interacting individuals engaged in virtual socio-economic activities. We have compiled a data set consisting of practically all actions of all players over a period of four years from an online game played by over 350,000 people. The universe of this online world is a lattice-like network on which players move in order to interact with other players. We focus on the mobility of human players on this network over a time-period of 500 days. We take a number of mobility measurements and compare them with measures of simulated random walkers on the same topology. Mobility of players is sub-diffusive - the mean squared displacement follows a power law with exponent 0.4 - and significantly deviates from mobility patterns of random walkers. Mean first passage times and transition counts relate via a power-law with slope -1/3. We compare our results with studies where human mobility was measured via mobile phone data and find striking similarities. (author)

  13. Popularity and Novelty Dynamics in Evolving Networks.

    Science.gov (United States)

    Abbas, Khushnood; Shang, Mingsheng; Abbasi, Alireza; Luo, Xin; Xu, Jian Jun; Zhang, Yu-Xia

    2018-04-20

    Network science plays a big role in the representation of real-world phenomena such as user-item bipartite networks presented in e-commerce or social media platforms. It provides researchers with tools and techniques to solve complex real-world problems. Identifying and predicting future popularity and importance of items in e-commerce or social media platform is a challenging task. Some items gain popularity repeatedly over time while some become popular and novel only once. This work aims to identify the key-factors: popularity and novelty. To do so, we consider two types of novelty predictions: items appearing in the popular ranking list for the first time; and items which were not in the popular list in the past time window, but might have been popular before the recent past time window. In order to identify the popular items, a careful consideration of macro-level analysis is needed. In this work we propose a model, which exploits item level information over a span of time to rank the importance of the item. We considered ageing or decay effect along with the recent link-gain of the items. We test our proposed model on four various real-world datasets using four information retrieval based metrics.

  14. Evidence for dynamic network regulation of Drosophila photoreceptor function from mutants lacking the neurotransmitter histamine

    Directory of Open Access Journals (Sweden)

    An eDau

    2016-03-01

    Full Text Available Synaptic feedback from interneurons to photoreceptors can help to optimize visual information flow by balancing its allocation on retinal pathways under changing light conditions. But little is known about how this critical network operation is regulated dynamically. Here, we investigate this question by comparing signaling properties and performance of wild-type Drosophila R1-R6 photoreceptors to those of the hdcJK910 mutant, which lacks the neurotransmitter histamine and therefore cannot transmit information to interneurons. Recordings show that hdcJK910 photoreceptors sample similar amounts of information from naturalistic stimulation to wild-type photoreceptors, but this information is packaged in smaller responses, especially under bright illumination. Analyses reveal how these altered dynamics primarily resulted from network overload that affected hdcJK910 photoreceptors in two ways. First, the missing inhibitory histamine input to interneurons almost certainly depolarized them irrevocably, which in turn increased their excitatory feedback to hdcJK910 R1-R6s. This tonic excitation depolarized the photoreceptors to artificially high potentials, reducing their operational range. Second, rescuing histamine input to interneurons in hdcJK910 mutant also restored their normal phasic feedback modulation to R1-R6s, causing photoreceptor output to accentuate dynamic intensity differences at bright illumination, similar to the wild-type. These results provide mechanistic explanations of how synaptic feedback connections optimize information packaging in photoreceptor output and novel insight into the operation and design of dynamic network regulation of sensory neurons.

  15. Formation and Dynamics of Waves in a Cortical Model of Cholinergic Modulation.

    Directory of Open Access Journals (Sweden)

    James P Roach

    2015-08-01

    Full Text Available Acetylcholine (ACh is a regulator of neural excitability and one of the neurochemical substrates of sleep. Amongst the cellular effects induced by cholinergic modulation are a reduction in spike-frequency adaptation (SFA and a shift in the phase response curve (PRC. We demonstrate in a biophysical model how changes in neural excitability and network structure interact to create three distinct functional regimes: localized asynchronous, traveling asynchronous, and traveling synchronous. Our results qualitatively match those observed experimentally. Cortical activity during slow wave sleep (SWS differs from that during REM sleep or waking states. During SWS there are traveling patterns of activity in the cortex; in other states stationary patterns occur. Our model is a network composed of Hodgkin-Huxley type neurons with a M-current regulated by ACh. Regulation of ACh level can account for dynamical changes between functional regimes. Reduction of the magnitude of this current recreates the reduction in SFA the shift from a type 2 to a type 1 PRC observed in the presence of ACh. When SFA is minimal (in waking or REM sleep state, high ACh patterns of activity are localized and easily pinned by network inhomogeneities. When SFA is present (decreasing ACh, traveling waves of activity naturally arise. A further decrease in ACh leads to a high degree of synchrony within traveling waves. We also show that the level of ACh determines how sensitive network activity is to synaptic heterogeneity. These regimes may have a profound functional significance as stationary patterns may play a role in the proper encoding of external input as memory and traveling waves could lead to synaptic regularization, giving unique insights into the role and significance of ACh in determining patterns of cortical activity and functional differences arising from the patterns.

  16. Fragility in dynamic networks: application to neural networks in the epileptic cortex.

    Science.gov (United States)

    Sritharan, Duluxan; Sarma, Sridevi V

    2014-10-01

    Epilepsy is a network phenomenon characterized by atypical activity at the neuronal and population levels during seizures, including tonic spiking, increased heterogeneity in spiking rates, and synchronization. The etiology of epilepsy is unclear, but a common theme among proposed mechanisms is that structural connectivity between neurons is altered. It is hypothesized that epilepsy arises not from random changes in connectivity, but from specific structural changes to the most fragile nodes or neurons in the network. In this letter, the minimum energy perturbation on functional connectivity required to destabilize linear networks is derived. Perturbation results are then applied to a probabilistic nonlinear neural network model that operates at a stable fixed point. That is, if a small stimulus is applied to the network, the activation probabilities of each neuron respond transiently but eventually recover to their baseline values. When the perturbed network is destabilized, the activation probabilities shift to larger or smaller values or oscillate when a small stimulus is applied. Finally, the structural modifications to the neural network that achieve the functional perturbation are derived. Simulations of the unperturbed and perturbed networks qualitatively reflect neuronal activity observed in epilepsy patients, suggesting that the changes in network dynamics due to destabilizing perturbations, including the emergence of an unstable manifold or a stable limit cycle, may be indicative of neuronal or population dynamics during seizure. That is, the epileptic cortex is always on the brink of instability and minute changes in the synaptic weights associated with the most fragile node can suddenly destabilize the network to cause seizures. Finally, the theory developed here and its interpretation of epileptic networks enables the design of a straightforward feedback controller that first detects when the network has destabilized and then applies linear state

  17. Firing rate dynamics in recurrent spiking neural networks with intrinsic and network heterogeneity.

    Science.gov (United States)

    Ly, Cheng

    2015-12-01

    Heterogeneity of neural attributes has recently gained a lot of attention and is increasing recognized as a crucial feature in neural processing. Despite its importance, this physiological feature has traditionally been neglected in theoretical studies of cortical neural networks. Thus, there is still a lot unknown about the consequences of cellular and circuit heterogeneity in spiking neural networks. In particular, combining network or synaptic heterogeneity and intrinsic heterogeneity has yet to be considered systematically despite the fact that both are known to exist and likely have significant roles in neural network dynamics. In a canonical recurrent spiking neural network model, we study how these two forms of heterogeneity lead to different distributions of excitatory firing rates. To analytically characterize how these types of heterogeneities affect the network, we employ a dimension reduction method that relies on a combination of Monte Carlo simulations and probability density function equations. We find that the relationship between intrinsic and network heterogeneity has a strong effect on the overall level of heterogeneity of the firing rates. Specifically, this relationship can lead to amplification or attenuation of firing rate heterogeneity, and these effects depend on whether the recurrent network is firing asynchronously or rhythmically firing. These observations are captured with the aforementioned reduction method, and furthermore simpler analytic descriptions based on this dimension reduction method are developed. The final analytic descriptions provide compact and descriptive formulas for how the relationship between intrinsic and network heterogeneity determines the firing rate heterogeneity dynamics in various settings.

  18. Phase dynamics of a Josephson junction ladder driven by modulated currents

    International Nuclear Information System (INIS)

    Kawaguchi, T.

    2011-01-01

    Phase dynamics of disordered Josephson junction ladders (JJLs) driven by external currents which are spatially and temporally modulated is studied using a numerical simulation based on a random field XY model. This model is considered theoretically as an effective model of JJLs with structural disorder in a magnetic field. The spatiotemporal modulation of external currents causes peculiar dynamical effects of phases in the system under certain conditions, such as the directed motion of phases and the mode-locking in the absence of dc currents. We clarify the details of effects of the spatiotemporal modulation on the phase dynamics.

  19. Linux containers networking: performance and scalability of kernel modules

    NARCIS (Netherlands)

    Claassen, J.; Koning, R.; Grosso, P.; Oktug Badonnel, S.; Ulema, M.; Cavdar, C.; Zambenedetti Granville, L.; dos Santos, C.R.P.

    2016-01-01

    Linux container virtualisation is gaining momentum as lightweight technology to support cloud and distributed computing. Applications relying on container architectures might at times rely on inter-container communication, and container networking solutions are emerging to address this need.

  20. Chain networking revealed by molecular dynamics simulation

    Science.gov (United States)

    Zheng, Yexin; Tsige, Mesfin; Wang, Shi-Qing

    Based on Kremer-Grest model for entangled polymer melts, we demonstrate how the response of a polymer glass depends critically on the chain length. After quenching two melts of very different chain lengths (350 beads per chain and 30 beads per chain) into deeply glassy states, we subject them to uniaxial extension. Our MD simulations show that the glass of long chains undergoes stable necking after yielding whereas the system of short chains is unable to neck and breaks up after strain localization. During ductile extension of the polymer glass made of long chain significant chain tension builds up in the load-bearing strands (LBSs). Further analysis is expected to reveal evidence of activation of the primary structure during post-yield extension. These results lend support to the recent molecular model 1 and are the simulations to demonstrate the role of chain networking. This work is supported, in part, by a NSF Grant (DMR-EAGER-1444859)

  1. Network dynamics of human face perception.

    Directory of Open Access Journals (Sweden)

    Cihan Mehmet Kadipasaoglu

    Full Text Available Prevailing theories suggests that cortical regions responsible for face perception operate in a serial, feed-forward fashion. Here, we utilize invasive human electrophysiology to evaluate serial models of face-processing via measurements of cortical activation, functional connectivity, and cortico-cortical evoked potentials. We find that task-dependent changes in functional connectivity between face-selective regions in the inferior occipital (f-IOG and fusiform gyrus (f-FG are bidirectional, not feed-forward, and emerge following feed-forward input from early visual cortex (EVC to both of these regions. Cortico-cortical evoked potentials similarly reveal independent signal propagations between EVC and both f-IOG and f-FG. These findings are incompatible with serial models, and support a parallel, distributed network underpinning face perception in humans.

  2. Filtering in hybrid dynamic Bayesian networks

    DEFF Research Database (Denmark)

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

    2004-01-01

    for inference. We extend the experiment and perform approximate inference using The Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). Furthermore, we combine these techniques in a 'non-strict' Rao-Blackwellisation framework and apply it to the watertank system. We show that UKF and UKF in a PF...... framework outperform the generic PF, EKF and EKF in a PF framework with respect to accuracy and robustness in terms of estimation RMSE (root-mean-square error). Especially we demonstrate the superiority of UKF in a PF framework when our beliefs of how data was generated are wrong. We also show...... that the choice of network structure is very important for the performance of the generic PF and the EKF algorithms, but not for the UKF algorithms. Furthermore, we investigate the influence of data noise in the watertank simulation. Theory and implementation is based on the theory presented in (v.d. Merwe et al...

  3. Age structure and cooperation in coevolutionary games on dynamic network

    Science.gov (United States)

    Qin, Zilong; Hu, Zhenhua; Zhou, Xiaoping; Yi, Jingzhang

    2015-04-01

    Our proposed model imitates the growth of a population and describes the age structure and the level of cooperation in games on dynamic network with continuous changes of structure and topology. The removal of nodes and links caused by age-dependent attack, together with the nodes addition standing for the newborns of population, badly ruins Matthew effect in this coevolutionary process. Though the network is generated by growth and preferential attachment, it degenerates into random network and it is no longer heterogeneous. When the removal of nodes and links is equal to the addition of nodes and links, the size of dynamic network is maintained in steady-state, so is the low level of cooperation. Severe structure variation, homogeneous topology and continuous invasion of new defection jointly make dynamic network unsuitable for the survival of cooperator even when the probability with which the newborn players initially adopt the strategy cooperation is high, while things change slightly when the connections of newborn players are restricted. Fortunately, moderate interactions in a generation trigger an optimal recovering process to encourage cooperation. The model developed in this paper outlines an explanation of the cohesion changes in the development process of an organization. Some suggestions for cooperative behavior improvement are given in the end.

  4. State-dependent, bidirectional modulation of neural network activity by endocannabinoids.

    Science.gov (United States)

    Piet, Richard; Garenne, André; Farrugia, Fanny; Le Masson, Gwendal; Marsicano, Giovanni; Chavis, Pascale; Manzoni, Olivier J

    2011-11-16

    The endocannabinoid (eCB) system and the cannabinoid CB1 receptor (CB1R) play key roles in the modulation of brain functions. Although actions of eCBs and CB1Rs are well described at the synaptic level, little is known of their modulation of neural activity at the network level. Using microelectrode arrays, we have examined the role of CB1R activation in the modulation of the electrical activity of rat and mice cortical neural networks in vitro. We find that exogenous activation of CB1Rs expressed on glutamatergic neurons decreases the spontaneous activity of cortical neural networks. Moreover, we observe that the net effect of the CB1R antagonist AM251 inversely correlates with the initial level of activity in the network: blocking CB1Rs increases network activity when basal network activity is low, whereas it depresses spontaneous activity when its initial level is high. Our results reveal a complex role of CB1Rs in shaping spontaneous network activity, and suggest that the outcome of endogenous neuromodulation on network function might be state dependent.

  5. Adaptive spectrum decision framework for heterogeneous dynamic spectrum access networks

    CSIR Research Space (South Africa)

    Masonta, M

    2015-09-01

    Full Text Available Spectrum decision is the ability of a cognitive radio (CR) system to select the best available spectrum band to satisfy dynamic spectrum access network (DSAN) users¿ quality of service (QoS) requirements without causing harmful interference...

  6. Network evolution induced by the dynamical rules of two populations

    International Nuclear Information System (INIS)

    Platini, Thierry; Zia, R K P

    2010-01-01

    We study the dynamical properties of a finite dynamical network composed of two interacting populations, namely extrovert (a) and introvert (b). In our model, each group is characterized by its size (N a and N b ) and preferred degree (κ a and κ b a ). The network dynamics is governed by the competing microscopic rules of each population that consist of the creation and destruction of links. Starting from an unconnected network, we give a detailed analysis of the mean field approach which is compared to Monte Carlo simulation data. The time evolution of the restricted degrees (k bb ) and (k ab ) presents three time regimes and a non-monotonic behavior well captured by our theory. Surprisingly, when the population sizes are equal N a = N b , the ratio of the restricted degree θ 0 = (k ab )/(k bb ) appears to be an integer in the asymptotic limits of the three time regimes. For early times (defined by t 1 = κ b ) the total number of links presents a linear evolution, where the two populations are indistinguishable and where θ 0 = 1. Interestingly, in the intermediate time regime (defined for t 1 2 ∝κ a and for which θ 0 = 5), the system reaches a transient stationary state, where the number of contacts among introverts remains constant while the number of connections increases linearly in the extrovert population. Finally, due to the competing dynamics, the network presents a frustrated stationary state characterized by a ratio θ 0 = 3

  7. Gossips and prejudices: ergodic randomized dynamics in social networks

    NARCIS (Netherlands)

    Frasca, Paolo; Ravazzi, Chiara; Tempo, Roberto; Ishii, Hideaki

    In this paper we study a new model of opinion dynamics in social networks, which has two main features. First, agents asynchronously interact in pairs, and these pairs are chosen according to a random process: following recent literature, we refer to this communication model as “gossiping‿. Second,

  8. Dynamic state estimation for distribution networks with renewable energy integration

    NARCIS (Netherlands)

    Nguyen, P.H.; Venayagamoorthy, G.K.; Kling, W.L.; Ribeiro, P.F.

    2013-01-01

    The massive integration of variable and unpredictable Renewable Energy Sources (RES) and new types of load consumptions increases the dynamic and uncertain nature of the electricity grid. Emerging interests have focused on improving the monitoring capabilities of network operators so that they can

  9. Adaptive dynamic capacity borrowing in road-covering mobile networks

    NARCIS (Netherlands)

    Ule, A.; Boucherie, Richardus J.; Li, W.; Pan, Y.

    2006-01-01

    This paper introduces adaptive dynamic capacity borrowing strategies for wireless networks covering a road. In a F/TDMA-based model, road traffic prediction models are used to characterise the movement of hot spots, such as traffic jams, and subsequently to predict the teletraffic load offered to

  10. Dynamic Adaptive Neural Network Arrays: A Neuromorphic Architecture

    Energy Technology Data Exchange (ETDEWEB)

    Disney, Adam [University of Tennessee (UT); Reynolds, John [University of Tennessee (UT)

    2015-01-01

    Dynamic Adaptive Neural Network Array (DANNA) is a neuromorphic hardware implementation. It differs from most other neuromorphic projects in that it allows for programmability of structure, and it is trained or designed using evolutionary optimization. This paper describes the DANNA structure, how DANNA is trained using evolutionary optimization, and an application of DANNA to a very simple classification task.

  11. Network evolution induced by the dynamical rules of two populations

    Science.gov (United States)

    Platini, Thierry; Zia, R. K. P.

    2010-10-01

    We study the dynamical properties of a finite dynamical network composed of two interacting populations, namely extrovert (a) and introvert (b). In our model, each group is characterized by its size (Na and Nb) and preferred degree (κa and \\kappa_b\\ll \\kappa_a ). The network dynamics is governed by the competing microscopic rules of each population that consist of the creation and destruction of links. Starting from an unconnected network, we give a detailed analysis of the mean field approach which is compared to Monte Carlo simulation data. The time evolution of the restricted degrees langkbbrang and langkabrang presents three time regimes and a non-monotonic behavior well captured by our theory. Surprisingly, when the population sizes are equal Na = Nb, the ratio of the restricted degree θ0 = langkabrang/langkbbrang appears to be an integer in the asymptotic limits of the three time regimes. For early times (defined by t introverts remains constant while the number of connections increases linearly in the extrovert population. Finally, due to the competing dynamics, the network presents a frustrated stationary state characterized by a ratio θ0 = 3.

  12. RD2: Resilient Dynamic Desynchronization for TDMA over Lossy Networks

    DEFF Research Database (Denmark)

    Hinterhofer, Thomas; Schwefel, Hans-Peter; Tomic, Slobodanka

    2012-01-01

    We present a distributed TDMA negotiation approach for single-hop ad-hoc network communication. It is distributed, resilient to arbitrary transient packet loss and defines a non-overlapping TDMA schedule without the need of global time synchronization. A participating node can dynamically request...

  13. Dynamics of user networks in on-line electronic auctions

    Czech Academy of Sciences Publication Activity Database

    Slanina, František

    2014-01-01

    Roč. 17, č. 1 (2014), "1450002-1"-"1450002-14" ISSN 0219-5259 R&D Projects: GA MŠk OC09078 Institutional support: RVO:68378271 Keywords : networks * random graphs * dynamics Subject RIV: BE - Theoretical Physics Impact factor: 0.968, year: 2014

  14. Efficient and accurate Greedy Search Methods for mining functional modules in protein interaction networks.

    Science.gov (United States)

    He, Jieyue; Li, Chaojun; Ye, Baoliu; Zhong, Wei

    2012-06-25

    Most computational algorithms mainly focus on detecting highly connected subgraphs in PPI networks as protein complexes but ignore their inherent organization. Furthermore, many of these algorithms are computationally expensive. However, recent analysis indicates that experimentally detected protein complexes generally contain Core/attachment structures. In this paper, a Greedy Search Method based on Core-Attachment structure (GSM-CA) is proposed. The GSM-CA method detects densely connected regions in large protein-protein interaction networks based on the edge weight and two criteria for determining core nodes and attachment nodes. The GSM-CA method improves the prediction accuracy compared to other similar module detection approaches, however it is computationally expensive. Many module detection approaches are based on the traditional hierarchical methods, which is also computationally inefficient because the hierarchical tree structure produced by these approaches cannot provide adequate information to identify whether a network belongs to a module structure or not. In order to speed up the computational process, the Greedy Search Method based on Fast Clustering (GSM-FC) is proposed in this work. The edge weight based GSM-FC method uses a greedy procedure to traverse all edges just once to separate the network into the suitable set of modules. The proposed methods are applied to the protein interaction network of S. cerevisiae. Experimental results indicate that many significant functional modules are detected, most of which match the known complexes. Results also demonstrate that the GSM-FC algorithm is faster and more accurate as compared to other competing algorithms. Based on the new edge weight definition, the proposed algorithm takes advantages of the greedy search procedure to separate the network into the suitable set of modules. Experimental analysis shows that the identified modules are statistically significant. The algorithm can reduce the

  15. Hierarchical surface code for network quantum computing with modules of arbitrary size

    Science.gov (United States)

    Li, Ying; Benjamin, Simon C.

    2016-10-01

    The network paradigm for quantum computing involves interconnecting many modules to form a scalable machine. Typically it is assumed that the links between modules are prone to noise while operations within modules have a significantly higher fidelity. To optimize fault tolerance in such architectures we introduce a hierarchical generalization of the surface code: a small "patch" of the code exists within each module and constitutes a single effective qubit of the logic-level surface code. Errors primarily occur in a two-dimensional subspace, i.e., patch perimeters extruded over time, and the resulting noise threshold for intermodule links can exceed ˜10 % even in the absence of purification. Increasing the number of qubits within each module decreases the number of qubits necessary for encoding a logical qubit. But this advantage is relatively modest, and broadly speaking, a "fine-grained" network of small modules containing only about eight qubits is competitive in total qubit count versus a "course" network with modules containing many hundreds of qubits.

  16. Robustness and Vulnerability of Networks with Dynamical Dependency Groups.

    Science.gov (United States)

    Bai, Ya-Nan; Huang, Ning; Wang, Lei; Wu, Zhi-Xi

    2016-11-28

    The dependency property and self-recovery of failure nodes both have great effects on the robustness of networks during the cascading process. Existing investigations focused mainly on the failure mechanism of static dependency groups without considering the time-dependency of interdependent nodes and the recovery mechanism in reality. In this study, we present an evolving network model consisting of failure mechanisms and a recovery mechanism to explore network robustness, where the dependency relations among nodes vary over time. Based on generating function techniques, we provide an analytical framework for random networks with arbitrary degree distribution. In particular, we theoretically find that an abrupt percolation transition exists corresponding to the dynamical dependency groups for a wide range of topologies after initial random removal. Moreover, when the abrupt transition point is above the failure threshold of dependency groups, the evolving network with the larger dependency groups is more vulnerable; when below it, the larger dependency groups make the network more robust. Numerical simulations employing the Erdős-Rényi network and Barabási-Albert scale free network are performed to validate our theoretical results.

  17. Dynamic Session-Key Generation for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Chen Chin-Ling

    2008-01-01

    Full Text Available Abstract Recently, wireless sensor networks have been used extensively in different domains. For example, if the wireless sensor node of a wireless sensor network is distributed in an insecure area, a secret key must be used to protect the transmission between the sensor nodes. Most of the existing methods consist of preselecting keys from a key pool and forming a key chain. Then, the sensor nodes make use of the key chain to encrypt the data. However, while the secret key is being transmitted, it can easily be exposed during transmission. We propose a dynamic key management protocol, which can improve the security of the key juxtaposed to existing methods. Additionally, the dynamic update of the key can lower the probability of the key to being guessed correctly. In addition, with the new protocol, attacks on the wireless sensor network can be avoided.

  18. Dynamic Relaying in 3GPP LTE-Advanced Networks

    DEFF Research Database (Denmark)

    Teyeb, Oumer Mohammed; Van Phan, Vinh; Redana, Simone

    2009-01-01

    Relaying is one of the proposed technologies for LTE-Advanced networks. In order to enable a flexible and reliable relaying support, the currently adopted architectural structure of LTE networks has to be modified. In this paper, we extend the LTE architecture to enable dynamic relaying, while...... maintaining backward compatibility with LTE Release 8 user equipments, and without limiting the flexibility and reliability expected from relaying.With dynamic relaying, relays can be associated with base stations on a need basis rather than in a fixed manner which is based only on initial radio planning....... Proposals are also given on how to further improve a relay enhanced LTE network by enabling multiple interfaces between the relay nodes and their controlling base stations, which can possibly be based on technologies different from LTE, so that load balancing can be realized. This load balancing can...

  19. Dynamic Session-Key Generation for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Cheng-Ta Li

    2008-09-01

    Full Text Available Recently, wireless sensor networks have been used extensively in different domains. For example, if the wireless sensor node of a wireless sensor network is distributed in an insecure area, a secret key must be used to protect the transmission between the sensor nodes. Most of the existing methods consist of preselecting m keys from a key pool and forming a key chain. Then, the sensor nodes make use of the key chain to encrypt the data. However, while the secret key is being transmitted, it can easily be exposed during transmission. We propose a dynamic key management protocol, which can improve the security of the key juxtaposed to existing methods. Additionally, the dynamic update of the key can lower the probability of the key to being guessed correctly. In addition, with the new protocol, attacks on the wireless sensor network can be avoided.

  20. Dynamic Business Networks: A Headache for Sustainable Systems Interoperability

    Science.gov (United States)

    Agostinho, Carlos; Jardim-Goncalves, Ricardo

    Collaborative networked environments emerged with the spread of the internet, contributing to overcome past communication barriers, and identifying interoperability as an essential property. When achieved seamlessly, efficiency is increased in the entire product life cycle. Nowadays, most organizations try to attain interoperability by establishing peer-to-peer mappings with the different partners, or in optimized networks, by using international standard models as the core for information exchange. In current industrial practice, mappings are only defined once, and the morphisms that represent them, are hardcoded in the enterprise systems. This solution has been effective for static environments, where enterprise and product models are valid for decades. However, with an increasingly complex and dynamic global market, models change frequently to answer new customer requirements. This paper draws concepts from the complex systems science and proposes a framework for sustainable systems interoperability in dynamic networks, enabling different organizations to evolve at their own rate.

  1. Nonlinear identification of process dynamics using neural networks

    International Nuclear Information System (INIS)

    Parlos, A.G.; Atiya, A.F.; Chong, K.T.

    1992-01-01

    In this paper the nonlinear identification of process dynamics encountered in nuclear power plant components is addressed, in an input-output sense, using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the model structure to be identified. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard backpropagation learning algorithm is modified, and it is used for the supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The response of representative steam generator is predicted using a neural network, and it is compared to the response obtained from a sophisticated computer model based on first principles. The transient responses compare well, although further research is warranted to determine the predictive capabilities of these networks during more severe operational transients and accident scenarios

  2. How mutation alters the evolutionary dynamics of cooperation on networks

    Science.gov (United States)

    Ichinose, Genki; Satotani, Yoshiki; Sayama, Hiroki

    2018-05-01

    Cooperation is ubiquitous at every level of living organisms. It is known that spatial (network) structure is a viable mechanism for cooperation to evolve. A recently proposed numerical metric, average gradient of selection (AGoS), a useful tool for interpreting and visualizing evolutionary dynamics on networks, allows simulation results to be visualized on a one-dimensional phase space. However, stochastic mutation of strategies was not considered in the analysis of AGoS. Here we extend AGoS so that it can analyze the evolution of cooperation where mutation may alter strategies of individuals on networks. We show that our extended AGoS correctly visualizes the final states of cooperation with mutation in the individual-based simulations. Our analyses revealed that mutation always has a negative effect on the evolution of cooperation regardless of the payoff functions, fraction of cooperators, and network structures. Moreover, we found that scale-free networks are the most vulnerable to mutation and thus the dynamics of cooperation are altered from bistability to coexistence on those networks, undergoing an imperfect pitchfork bifurcation.

  3. Asymmetrical Modulation for Uplink Communication in Cooperative Networks

    DEFF Research Database (Denmark)

    Zhang, Qi; Fitzek, Frank H.P.; Iversen, Villy Bæk

    2008-01-01

    -range links with neighboring mobile devices to form cooperative clusters. So far the physical communication over cellular links and over short-range links are separated in time or in frequency. Beyond this state of the art, we exploit a method, referred to as asymmetrical modulation, where a mobile device...

  4. Design of multi-phase dynamic chemical networks

    Science.gov (United States)

    Chen, Chenrui; Tan, Junjun; Hsieh, Ming-Chien; Pan, Ting; Goodwin, Jay T.; Mehta, Anil K.; Grover, Martha A.; Lynn, David G.

    2017-08-01

    Template-directed polymerization reactions enable the accurate storage and processing of nature's biopolymer information. This mutualistic relationship of nucleic acids and proteins, a network known as life's central dogma, is now marvellously complex, and the progressive steps necessary for creating the initial sequence and chain-length-specific polymer templates are lost to time. Here we design and construct dynamic polymerization networks that exploit metastable prion cross-β phases. Mixed-phase environments have been used for constructing synthetic polymers, but these dynamic phases emerge naturally from the growing peptide oligomers and create environments suitable both to nucleate assembly and select for ordered templates. The resulting templates direct the amplification of a phase containing only chain-length-specific peptide-like oligomers. Such multi-phase biopolymer dynamics reveal pathways for the emergence, self-selection and amplification of chain-length- and possibly sequence-specific biopolymers.

  5. Spreading dynamics on complex networks: a general stochastic approach.

    Science.gov (United States)

    Noël, Pierre-André; Allard, Antoine; Hébert-Dufresne, Laurent; Marceau, Vincent; Dubé, Louis J

    2014-12-01

    Dynamics on networks is considered from the perspective of Markov stochastic processes. We partially describe the state of the system through network motifs and infer any missing data using the available information. This versatile approach is especially well adapted for modelling spreading processes and/or population dynamics. In particular, the generality of our framework and the fact that its assumptions are explicitly stated suggests that it could be used as a common ground for comparing existing epidemics models too complex for direct comparison, such as agent-based computer simulations. We provide many examples for the special cases of susceptible-infectious-susceptible and susceptible-infectious-removed dynamics (e.g., epidemics propagation) and we observe multiple situations where accurate results may be obtained at low computational cost. Our perspective reveals a subtle balance between the complex requirements of a realistic model and its basic assumptions.

  6. Asymmetrically interacting spreading dynamics on complex layered networks.

    Science.gov (United States)

    Wang, Wei; Tang, Ming; Yang, Hui; Younghae Do; Lai, Ying-Cheng; Lee, GyuWon

    2014-05-29

    The spread of disease through a physical-contact network and the spread of information about the disease on a communication network are two intimately related dynamical processes. We investigate the asymmetrical interplay between the two types of spreading dynamics, each occurring on its own layer, by focusing on the two fundamental quantities underlying any spreading process: epidemic threshold and the final infection ratio. We find that an epidemic outbreak on the contact layer can induce an outbreak on the communication layer, and information spreading can effectively raise the epidemic threshold. When structural correlation exists between the two layers, the information threshold remains unchanged but the epidemic threshold can be enhanced, making the contact layer more resilient to epidemic outbreak. We develop a physical theory to understand the intricate interplay between the two types of spreading dynamics.

  7. Opinion Dynamics on Complex Networks with Communities

    International Nuclear Information System (INIS)

    Ru, Wang; Li-Ping, Chi

    2008-01-01

    The Ising or Potts models of ferromagnetism have been widely used to describe locally interacting social or economic systems. We consider a related model, introduced by Sznajd to describe the evolution of consensus in the scale-free networks with the tunable strength (noted by Q) of community structure. In the Sznajd model, the opinion or state of any spins can only be changed by the influence of neighbouring pairs of similar connection spins. Such pairs can polarize their neighbours. Using asynchronous updating, it is found that the smaller the community strength Q, the larger the slope of the exponential relaxation time distribution. Then the effect of the initial up- spin concentration p as a function of the final all up probability E is investigated by taking different initialization strategies, the random node-chosen initialization strategy has no difference under different community strengths, while the strategies of community node-chosen initialization and hub node-chosen initialization are different in final probability under different Q, and the latter one is more effective in reaching final state

  8. Threshold Learning Dynamics in Social Networks

    Science.gov (United States)

    González-Avella, Juan Carlos; Eguíluz, Victor M.; Marsili, Matteo; Vega-Redondo, Fernado; San Miguel, Maxi

    2011-01-01

    Social learning is defined as the ability of a population to aggregate information, a process which must crucially depend on the mechanisms of social interaction. Consumers choosing which product to buy, or voters deciding which option to take with respect to an important issue, typically confront external signals to the information gathered from their contacts. Economic models typically predict that correct social learning occurs in large populations unless some individuals display unbounded influence. We challenge this conclusion by showing that an intuitive threshold process of individual adjustment does not always lead to such social learning. We find, specifically, that three generic regimes exist separated by sharp discontinuous transitions. And only in one of them, where the threshold is within a suitable intermediate range, the population learns the correct information. In the other two, where the threshold is either too high or too low, the system either freezes or enters into persistent flux, respectively. These regimes are generally observed in different social networks (both complex or regular), but limited interaction is found to promote correct learning by enlarging the parameter region where it occurs. PMID:21637714

  9. Large-scale network dynamics of beta-band oscillations underlie auditory perceptual decision-making

    Directory of Open Access Journals (Sweden)

    Mohsen Alavash

    2017-06-01

    Full Text Available Perceptual decisions vary in the speed at which we make them. Evidence suggests that translating sensory information into perceptual decisions relies on distributed interacting neural populations, with decision speed hinging on power modulations of the neural oscillations. Yet the dependence of perceptual decisions on the large-scale network organization of coupled neural oscillations has remained elusive. We measured magnetoencephalographic signals in human listeners who judged acoustic stimuli composed of carefully titrated clouds of tone sweeps. These stimuli were used in two task contexts, in which the participants judged the overall pitch or direction of the tone sweeps. We traced the large-scale network dynamics of the source-projected neural oscillations on a trial-by-trial basis using power-envelope correlations and graph-theoretical network discovery. In both tasks, faster decisions were predicted by higher segregation and lower integration of coupled beta-band (∼16–28 Hz oscillations. We also uncovered the brain network states that promoted faster decisions in either lower-order auditory or higher-order control brain areas. Specifically, decision speed in judging the tone sweep direction critically relied on the nodal network configurations of anterior temporal, cingulate, and middle frontal cortices. Our findings suggest that global network communication during perceptual decision-making is implemented in the human brain by large-scale couplings between beta-band neural oscillations. The speed at which we make perceptual decisions varies. This translation of sensory information into perceptual decisions hinges on dynamic changes in neural oscillatory activity. However, the large-scale neural-network embodiment supporting perceptual decision-making is unclear. We addressed this question by experimenting two auditory perceptual decision-making situations. Using graph-theoretical network discovery, we traced the large-scale network

  10. Network Signaling Channel for Improving ZigBee Performance in Dynamic Cluster-Tree Networks

    Directory of Open Access Journals (Sweden)

    D. Hämäläinen

    2008-03-01

    Full Text Available ZigBee is one of the most potential standardized technologies for wireless sensor networks (WSNs. Yet, sufficient energy-efficiency for the lowest power WSNs is achieved only in rather static networks. This severely limits the applicability of ZigBee in outdoor and mobile applications, where operation environment is harsh and link failures are common. This paper proposes a network channel beaconing (NCB algorithm for improving ZigBee performance in dynamic cluster-tree networks. NCB reduces the energy consumption of passive scans by dedicating one frequency channel for network beacon transmissions and by energy optimizing their transmission rate. According to an energy analysis, the power consumption of network maintenance operations reduces by 70%–76% in dynamic networks. In static networks, energy overhead is negligible. Moreover, the service time for data routing increases up to 37%. The performance of NCB is validated by ns-2 simulations. NCB can be implemented as an extension on MAC and NWK layers and it is fully compatible with ZigBee.

  11. The Dynamics of Protest Recruitment through an Online Network

    Science.gov (United States)

    González-Bailón, Sandra; Borge-Holthoefer, Javier; Rivero, Alejandro; Moreno, Yamir

    2011-12-01

    The recent wave of mobilizations in the Arab world and across Western countries has generated much discussion on how digital media is connected to the diffusion of protests. We examine that connection using data from the surge of mobilizations that took place in Spain in May 2011. We study recruitment patterns in the Twitter network and find evidence of social influence and complex contagion. We identify the network position of early participants (i.e. the leaders of the recruitment process) and of the users who acted as seeds of message cascades (i.e. the spreaders of information). We find that early participants cannot be characterized by a typical topological position but spreaders tend to be more central in the network. These findings shed light on the connection between online networks, social contagion, and collective dynamics, and offer an empirical test to the recruitment mechanisms theorized in formal models of collective action.

  12. Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

    Science.gov (United States)

    Jorgensen, Charles C.

    1997-01-01

    A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

  13. Cooperative Dynamics in Lattice-Embedded Scale-Free Networks

    International Nuclear Information System (INIS)

    Shang Lihui; Zhang Mingji; Yang Yanqing

    2009-01-01

    We investigate cooperative behaviors of lattice-embedded scale-free networking agents in the prisoner's dilemma game model by employing two initial strategy distribution mechanisms, which are specific distribution to the most connected sites (hubs) and random distribution. Our study indicates that the game dynamics crucially depends on the underlying spatial network structure with different strategy distribution mechanism. The cooperators' specific distribution contributes to an enhanced level of cooperation in the system compared with random one, and cooperation is robust to cooperators' specific distribution but fragile to defectors' specific distribution. Especially, unlike the specific case, increasing heterogeneity of network does not always favor the emergence of cooperation under random mechanism. Furthermore, we study the geographical effects and find that the graphically constrained network structure tends to improve the evolution of cooperation in random case and in specific one for a large temptation to defect.

  14. Submodularity in dynamics and control of networked systems

    CERN Document Server

    Clark, Andrew; Bushnell, Linda; Poovendran, Radha

    2016-01-01

    This book presents a framework for the control of networked systems utilizing submodular optimization techniques. The main focus is on selecting input nodes for the control of networked systems, an inherently discrete optimization problem with applications in power system stability, social influence dynamics, and the control of vehicle formations. The first part of the book is devoted to background information on submodular functions, matroids, and submodular optimization, and presents algorithms for distributed submodular optimization that are scalable to large networked systems. In turn, the second part develops a unifying submodular optimization approach to controlling networked systems based on multiple performance and controllability criteria. Techniques are introduced for selecting input nodes to ensure smooth convergence, synchronization, and robustness to environmental and adversarial noise. Submodular optimization is the first unifying approach towards guaranteeing both performance and controllabilit...

  15. Perception of similarity: a model for social network dynamics

    International Nuclear Information System (INIS)

    Javarone, Marco Alberto; Armano, Giuliano

    2013-01-01

    Some properties of social networks (e.g., the mixing patterns and the community structure) appear deeply influenced by the individual perception of people. In this work we map behaviors by considering similarity and popularity of people, also assuming that each person has his/her proper perception and interpretation of similarity. Although investigated in different ways (depending on the specific scientific framework), from a computational perspective similarity is typically calculated as a distance measure. In accordance with this view, to represent social network dynamics we developed an agent-based model on top of a hyperbolic space on which individual distance measures are calculated. Simulations, performed in accordance with the proposed model, generate small-world networks that exhibit a community structure. We deem this model to be valuable for analyzing the relevant properties of real social networks. (paper)

  16. Characterisation of PV CIS module by artificial neural networks. A comparative study with other methods

    International Nuclear Information System (INIS)

    Almonacid, F.; Rus, C.; Hontoria, L.; Munoz, F.J.

    2010-01-01

    The presence of PV modules made with new technologies and materials is increasing in PV market, in special Thin Film Solar Modules (TFSM). They are ready to make a substantial contribution to the world's electricity generation. Although Si wafer-based cells account for the most of increase, technologies of thin film have been those of the major growth in last three years. During 2007 they grew 133%. On the other hand, manufacturers provide ratings for PV modules for conditions referred to as Standard Test Conditions (STC). However, these conditions rarely occur outdoors, so the usefulness and applicability of the indoors characterisation in standard test conditions of PV modules is a controversial issue. Therefore, to carry out a correct photovoltaic engineering, a suitable characterisation of PV module electrical behaviour is necessary. The IDEA Research Group from Jaen University has developed a method based on artificial neural networks (ANNs) to electrical characterisation of PV modules. An ANN was able to generate V-I curves of si-crystalline PV modules for any irradiance and module cell temperature. The results show that the proposed ANN introduces a good accurate prediction for si-crystalline PV modules performance when compared with the measured values. Now, this method is going to be applied for electrical characterisation of PV CIS modules. Finally, a comparative study with other methods, of electrical characterisation, is done. (author)

  17. Design of Simulink module for dynamic reactivity simulation of marine reactor automatic control rod

    International Nuclear Information System (INIS)

    Chen Zhiyun; Luo Lei; Chen Wenzhen; Gui Xuewen

    2010-01-01

    The power of marine reactor varies frequently and acutely, which induces the frequent and acute adjustment of the automatic control rod. According to the characteristics of marine reactor and the problem of improper control rod reactivity insertion in previous literatures, the Simulink module for dynamic reactivity simulation of automatic control rod was designed and adopted as a sub-module of Simulink program for the fast calculation of the physical and thermal parameters of marine reactor. A typical dynamic process of the marine reactor was used as the benchmark, which indicates that the designed Simulink module is capable of the dynamic simulation of automatic control rod position and reactivity, and is adequate to the fast calculation of physic and thermal parameters. The Simulink module is of significant meaning to the simulation of the dynamic process of marine reactor and the fast calculation of the operating parameters. (authors)

  18. Observation Platform for Dynamic Biomedical and Biotechnology Experiments using the ISS Light Microscopy Module, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed "Observation platform for dynamic biomedical and biotechnology experiments using the ISS Light Microscopy Module" consists of a platen sized to fit the...

  19. Inferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data.

    Science.gov (United States)

    Gong, Wuming; Koyano-Nakagawa, Naoko; Li, Tongbin; Garry, Daniel J

    2015-03-07

    Decoding the temporal control of gene expression patterns is key to the understanding of the complex mechanisms that govern developmental decisions during heart development. High-throughput methods have been employed to systematically study the dynamic and coordinated nature of cardiac differentiation at the global level with multiple dimensions. Therefore, there is a pressing need to develop a systems approach to integrate these data from individual studies and infer the dynamic regulatory networks in an unbiased fashion. We developed a two-step strategy to integrate data from (1) temporal RNA-seq, (2) temporal histone modification ChIP-seq, (3) transcription factor (TF) ChIP-seq and (4) gene perturbation experiments to reconstruct the dynamic network during heart development. First, we trained a logistic regression model to predict the probability (LR score) of any base being bound by 543 TFs with known positional weight matrices. Second, four dimensions of data were combined using a time-varying dynamic Bayesian network model to infer the dynamic networks at four developmental stages in the mouse [mouse embryonic stem cells (ESCs), mesoderm (MES), cardiac progenitors (CP) and cardiomyocytes (CM)]. Our method not only infers the time-varying networks between different stages of heart development, but it also identifies the TF binding sites associated with promoter or enhancers of downstream genes. The LR scores of experimentally verified ESCs and heart enhancers were significantly higher than random regions (p network inference model identified a region with an elevated LR score approximately -9400 bp upstream of the transcriptional start site of Nkx2-5, which overlapped with a previously reported enhancer region (-9435 to -8922 bp). TFs such as Tead1, Gata4, Msx2, and Tgif1 were predicted to bind to this region and participate in the regulation of Nkx2-5 gene expression. Our model also predicted the key regulatory networks for the ESC-MES, MES-CP and CP

  20. Changes in dynamic resting state network connectivity following aphasia therapy.

    Science.gov (United States)

    Duncan, E Susan; Small, Steven L

    2017-10-24

    Resting state magnetic resonance imaging (rsfMRI) permits observation of intrinsic neural networks produced by task-independent correlations in low frequency brain activity. Various resting state networks have been described, with each thought to reflect common engagement in some shared function. There has been limited investigation of the plasticity in these network relationships after stroke or induced by therapy. Twelve individuals with language disorders after stroke (aphasia) were imaged at multiple time points before (baseline) and after an imitation-based aphasia therapy. Language assessment using a narrative production task was performed at the same time points. Group independent component analysis (ICA) was performed on the rsfMRI data to identify resting state networks. A sliding window approach was then applied to assess the dynamic nature of the correlations among these networks. Network correlations during each 30-second window were used to cluster the data into ten states for each window at each time point for each subject. Correlation was performed between changes in time spent in each state and therapeutic gains on the narrative task. The amount of time spent in a single one of the (ten overall) dynamic states was positively associated with behavioral improvement on the narrative task at the 6-week post-therapy maintenance interval, when compared with either baseline or assessment immediately following therapy. This particular state was characterized by minimal correlation among the task-independent resting state networks. Increased functional independence and segregation of resting state networks underlies improvement on a narrative production task following imitation-based aphasia treatment. This has important clinical implications for the targeting of noninvasive brain stimulation in post-stroke remediation.

  1. Dynamics on networks: the role of local dynamics and global networks on the emergence of hypersynchronous neural activity.

    Directory of Open Access Journals (Sweden)

    Helmut Schmidt

    2014-11-01

    Full Text Available Graph theory has evolved into a useful tool for studying complex brain networks inferred from a variety of measures of neural activity, including fMRI, DTI, MEG and EEG. In the study of neurological disorders, recent work has discovered differences in the structure of graphs inferred from patient and control cohorts. However, most of these studies pursue a purely observational approach; identifying correlations between properties of graphs and the cohort which they describe, without consideration of the underlying mechanisms. To move beyond this necessitates the development of computational modeling approaches to appropriately interpret network interactions and the alterations in brain dynamics they permit, which in the field of complexity sciences is known as dynamics on networks. In this study we describe the development and application of this framework using modular networks of Kuramoto oscillators. We use this framework to understand functional networks inferred from resting state EEG recordings of a cohort of 35 adults with heterogeneous idiopathic generalized epilepsies and 40 healthy adult controls. Taking emergent synchrony across the global network as a proxy for seizures, our study finds that the critical strength of coupling required to synchronize the global network is significantly decreased for the epilepsy cohort for functional networks inferred from both theta (3-6 Hz and low-alpha (6-9 Hz bands. We further identify left frontal regions as a potential driver of seizure activity within these networks. We also explore the ability of our method to identify individuals with epilepsy, observing up to 80% predictive power through use of receiver operating characteristic analysis. Collectively these findings demonstrate that a computer model based analysis of routine clinical EEG provides significant additional information beyond standard clinical interpretation, which should ultimately enable a more appropriate mechanistic

  2. Competitive dynamics of lexical innovations in multi-layer networks

    Science.gov (United States)

    Javarone, Marco Alberto

    2014-04-01

    We study the introduction of lexical innovations into a community of language users. Lexical innovations, i.e. new term added to people's vocabulary, plays an important role in the process of language evolution. Nowadays, information is spread through a variety of networks, including, among others, online and offline social networks and the World Wide Web. The entire system, comprising networks of different nature, can be represented as a multi-layer network. In this context, lexical innovations diffusion occurs in a peculiar fashion. In particular, a lexical innovation can undergo three different processes: its original meaning is accepted; its meaning can be changed or misunderstood (e.g. when not properly explained), hence more than one meaning can emerge in the population. Lastly, in the case of a loan word, it can be translated into the population language (i.e. defining a new lexical innovation or using a synonym) or into a dialect spoken by part of the population. Therefore, lexical innovations cannot be considered simply as information. We develop a model for analyzing this scenario using a multi-layer network comprising a social network and a media network. The latter represents the set of all information systems of a society, e.g. television, the World Wide Web and radio. Furthermore, we identify temporal directed edges between the nodes of these two networks. In particular, at each time-step, nodes of the media network can be connected to randomly chosen nodes of the social network and vice versa. In doing so, information spreads through the whole system and people can share a lexical innovation with their neighbors or, in the event they work as reporters, by using media nodes. Lastly, we use the concept of "linguistic sign" to model lexical innovations, showing its fundamental role in the study of these dynamics. Many numerical simulations have been performed to analyze the proposed model and its outcomes.

  3. Unveiling protein functions through the dynamics of the interaction network.

    Directory of Open Access Journals (Sweden)

    Irene Sendiña-Nadal

    Full Text Available Protein interaction networks have become a tool to study biological processes, either for predicting molecular functions or for designing proper new drugs to regulate the main biological interactions. Furthermore, such networks are known to be organized in sub-networks of proteins contributing to the same cellular function. However, the protein function prediction is not accurate and each protein has traditionally been assigned to only one function by the network formalism. By considering the network of the physical interactions between proteins of the yeast together with a manual and single functional classification scheme, we introduce a method able to reveal important information on protein function, at both micro- and macro-scale. In particular, the inspection of the properties of oscillatory dynamics on top of the protein interaction network leads to the identification of misclassification problems in protein function assignments, as well as to unveil correct identification of protein functions. We also demonstrate that our approach can give a network representation of the meta-organization of biological processes by unraveling the interactions between different functional classes.

  4. Epidemic dynamics on a risk-based evolving social network

    Science.gov (United States)

    Antwi, Shadrack; Shaw, Leah

    2013-03-01

    Social network models have been used to study how behavior affects the dynamics of an infection in a population. Motivated by HIV, we consider how a trade-off between benefits and risks of sexual connections determine network structure and disease prevalence. We define a stochastic network model with formation and breaking of links as changes in sexual contacts. Each node has an intrinsic benefit its neighbors derive from connecting to it. Nodes' infection status is not apparent to others, but nodes with more connections (higher degree) are assumed more likely to be infected. The probability to form and break links is determined by a payoff computed from the benefit and degree-dependent risk. The disease is represented by a SI (susceptible-infected) model. We study network and epidemic evolution via Monte Carlo simulation and analytically predict the behavior with a heterogeneous mean field approach. The dependence of network connectivity and infection threshold on parameters is determined, and steady state degree distribution and epidemic levels are obtained. We also study a situation where system-wide infection levels alter perception of risk and cause nodes to adjust their behavior. This is a case of an adaptive network, where node status feeds back to change network geometry.

  5. Exploring Protein Structure and Dynamics through a Project-Oriented Biochemistry Laboratory Module

    Science.gov (United States)

    Lipchock, James M.; Ginther, Patrick S.; Douglas, Bonnie B.; Bird, Kelly E.; Loria, J. Patrick

    2017-01-01

    Here, we present a 10-week project-oriented laboratory module designed to provide a course-based undergraduate research experience in biochemistry that emphasizes the importance of biomolecular structure and dynamics in enzyme function. This module explores the impact of mutagenesis on an important active site loop for a biomedically-relevant…

  6. Seasonal module dynamics of Turbinaria triquetra (Fucales, Phaeophyceae) in the southern Red Sea

    NARCIS (Netherlands)

    Ateweberhan, Mebrahtu; Bruggemann, J. Henrich; Breeman, Anneke M.

    2006-01-01

    Module dynamics in the fucoid alga Turbinaria triquetra (J. Agardh) Kutzing were studied on a shallow reef flat in the southern Red Sea. Seasonal patterns in thallus density and size were determined, and the initiation, growth, reproduction, and shedding of modules were studied using a tagging

  7. Self-modulated dynamics of a relativistic charged particle beam in plasma wake field excitation

    Energy Technology Data Exchange (ETDEWEB)

    Akhter, T.; Fedele, R. [Dipartimento di Fisica ‘Ettore Pancini’, Università di Napoli Federico II and INFN Sezione di Napoli, Napoli (Italy); Nicola, S. De [CNR-SPIN and INFN Sezione di Napoli, Napoli (Italy); Tanjia, F. [Dipartimento di Fisica ‘Ettore Pancini’, Università di Napoli Federico II and INFN Sezione di Napoli, Napoli (Italy); Jovanović, D. [Institute of Physics, University of Belgrade, Belgrade (Serbia); Mannan, A. [Department of Physics, Jahangirnagar University, Savar, Dhaka (Bangladesh)

    2016-09-01

    The self-modulated dynamics of a relativistic charged particle beam is provided within the context of the theory of plasma wake field excitation. The self-consistent description of the beam dynamics is provided by coupling the Vlasov equation with a Poisson-type equation relating the plasma wake potential to the beam density. An analysis of the beam envelope self-modulation is then carried out and the criteria for the occurrence of the instability are discussed thereby.

  8. Surface dynamics in allosteric regulation of protein-protein interactions: modulation of calmodulin functions by Ca2+.

    Directory of Open Access Journals (Sweden)

    Yosef Y Kuttner

    2013-04-01

    Full Text Available Knowledge of the structural basis of protein-protein interactions (PPI is of fundamental importance for understanding the organization and functioning of biological networks and advancing the design of therapeutics which target PPI. Allosteric modulators play an important role in regulating such interactions by binding at site(s orthogonal to the complex interface and altering the protein's propensity for complex formation. In this work, we apply an approach recently developed by us for analyzing protein surfaces based on steered molecular dynamics simulation (SMD to the study of the dynamic properties of functionally distinct conformations of a model protein, calmodulin (CaM, whose ability to interact with target proteins is regulated by the presence of the allosteric modulator Ca(2+. Calmodulin is a regulatory protein that acts as an intracellular Ca(2+ sensor to control a wide variety of cellular processes. We demonstrate that SMD analysis is capable of pinpointing CaM surfaces implicated in the recognition of both the allosteric modulator Ca(2+ and target proteins. Our analysis of changes in the dynamic properties of the CaM backbone elicited by Ca(2+ binding yielded new insights into the molecular mechanism of allosteric regulation of CaM-target interactions.

  9. Applying differential dynamic logic to reconfigurable biological networks.

    Science.gov (United States)

    Figueiredo, Daniel; Martins, Manuel A; Chaves, Madalena

    2017-09-01

    Qualitative and quantitative modeling frameworks are widely used for analysis of biological regulatory networks, the former giving a preliminary overview of the system's global dynamics and the latter providing more detailed solutions. Another approach is to model biological regulatory networks as hybrid systems, i.e., systems which can display both continuous and discrete dynamic behaviors. Actually, the development of synthetic biology has shown that this is a suitable way to think about biological systems, which can often be constructed as networks with discrete controllers, and present hybrid behaviors. In this paper we discuss this approach as a special case of the reconfigurability paradigm, well studied in Computer Science (CS). In CS there are well developed computational tools to reason about hybrid systems. We argue that it is worth applying such tools in a biological context. One interesting tool is differential dynamic logic (dL), which has recently been developed by Platzer and applied to many case-studies. In this paper we discuss some simple examples of biological regulatory networks to illustrate how dL can be used as an alternative, or also as a complement to methods already used. Copyright © 2017 Elsevier Inc. All rights reserved.

  10. Strategic tradeoffs in competitor dynamics on adaptive networks.

    Science.gov (United States)

    Hébert-Dufresne, Laurent; Allard, Antoine; Noël, Pierre-André; Young, Jean-Gabriel; Libby, Eric

    2017-08-08

    Recent empirical work highlights the heterogeneity of social competitions such as political campaigns: proponents of some ideologies seek debate and conversation, others create echo chambers. While symmetric and static network structure is typically used as a substrate to study such competitor dynamics, network structure can instead be interpreted as a signature of the competitor strategies, yielding competition dynamics on adaptive networks. Here we demonstrate that tradeoffs between aggressiveness and defensiveness (i.e., targeting adversaries vs. targeting like-minded individuals) creates paradoxical behaviour such as non-transitive dynamics. And while there is an optimal strategy in a two competitor system, three competitor systems have no such solution; the introduction of extreme strategies can easily affect the outcome of a competition, even if the extreme strategies have no chance of winning. Not only are these results reminiscent of classic paradoxical results from evolutionary game theory, but the structure of social networks created by our model can be mapped to particular forms of payoff matrices. Consequently, social structure can act as a measurable metric for social games which in turn allows us to provide a game theoretical perspective on online political debates.

  11. Analyzing, Modeling, and Simulation for Human Dynamics in Social Network

    Directory of Open Access Journals (Sweden)

    Yunpeng Xiao

    2012-01-01

    Full Text Available This paper studies the human behavior in the top-one social network system in China (Sina Microblog system. By analyzing real-life data at a large scale, we find that the message releasing interval (intermessage time obeys power law distribution both at individual level and at group level. Statistical analysis also reveals that human behavior in social network is mainly driven by four basic elements: social pressure, social identity, social participation, and social relation between individuals. Empirical results present the four elements' impact on the human behavior and the relation between these elements. To further understand the mechanism of such dynamic phenomena, a hybrid human dynamic model which combines “interest” of individual and “interaction” among people is introduced, incorporating the four elements simultaneously. To provide a solid evaluation, we simulate both two-agent and multiagent interactions with real-life social network topology. We achieve the consistent results between empirical studies and the simulations. The model can provide a good understanding of human dynamics in social network.

  12. Mind wandering away from pain dynamically engages antinociceptive and default mode brain networks.

    Science.gov (United States)

    Kucyi, Aaron; Salomons, Tim V; Davis, Karen D

    2013-11-12

    Human minds often wander away from their immediate sensory environment. It remains unknown whether such mind wandering is unsystematic or whether it lawfully relates to an individual's tendency to attend to salient stimuli such as pain and their associated brain structure/function. Studies of pain-cognition interactions typically examine explicit manipulation of attention rather than spontaneous mind wandering. Here we sought to better represent natural fluctuations in pain in daily life, so we assessed behavioral and neural aspects of spontaneous disengagement of attention from pain. We found that an individual's tendency to attend to pain related to the disruptive effect of pain on his or her cognitive task performance. Next, we linked behavioral findings to neural networks with strikingly convergent evidence from functional magnetic resonance imaging during pain coupled with thought probes of mind wandering, dynamic resting state activity fluctuations, and diffusion MRI. We found that (i) pain-induced default mode network (DMN) deactivations were attenuated during mind wandering away from pain; (ii) functional connectivity fluctuations between the DMN and periaqueductal gray (PAG) dynamically tracked spontaneous attention away from pain; and (iii) across individuals, stronger PAG-DMN structural connectivity and more dynamic resting state PAG-DMN functional connectivity were associated with the tendency to mind wander away from pain. These data demonstrate that individual tendencies to mind wander away from pain, in the absence of explicit manipulation, are subserved by functional and structural connectivity within and between default mode and antinociceptive descending modulation networks.

  13. Optical Switching for Dynamic Distribution of Wireless-Over-Fiber Signals in Active Optical Networks

    DEFF Research Database (Denmark)

    Vegas Olmos, Juan José; Rodes, Guillermo; Tafur Monroy, Idelfonso

    2012-01-01

    In this paper, we report on an experimental validation of dynamic distribution of wireless-over-fiber by employing optical switching using semiconductor optical amplifiers; we also provide a channel distribution scheme and a generic topology for such an optical switch. The experiment consists...... of a four wavelength-division-multiplexed channel system operating on a WiMax frequency band and employing an orthogonal-frequency-division-multiplexing modulation at 625 Mbits/s per channel, transmission of the data over 20 km of optical fiber, and active switching in a 1 × 16 active optical switch....... The results show a negligible power penalty on each channel for both the best and the worst case in terms of inter-channel crosstalk. The presented system is highly scalable both in terms of port count and throughput, a desirable feature in highly branched access networks, and is modulation- and frequency...

  14. Network analysis reveals that bacteria and fungi form modules that correlate independently with soil parameters.

    Science.gov (United States)

    de Menezes, Alexandre B; Prendergast-Miller, Miranda T; Richardson, Alan E; Toscas, Peter; Farrell, Mark; Macdonald, Lynne M; Baker, Geoff; Wark, Tim; Thrall, Peter H

    2015-08-01

    Network and multivariate statistical analyses were performed to determine interactions between bacterial and fungal community terminal restriction length polymorphisms as well as soil properties in paired woodland and pasture sites. Canonical correspondence analysis (CCA) revealed that shifts in woodland community composition correlated with soil dissolved organic carbon, while changes in pasture community composition correlated with moisture, nitrogen and phosphorus. Weighted correlation network analysis detected two distinct microbial modules per land use. Bacterial and fungal ribotypes did not group separately, rather all modules comprised of both bacterial and fungal ribotypes. Woodland modules had a similar fungal : bacterial ribotype ratio, while in the pasture, one module was fungal dominated. There was no correspondence between pasture and woodland modules in their ribotype composition. The modules had different relationships to soil variables, and these contrasts were not detected without the use of network analysis. This study demonstrated that fungi and bacteria, components of the soil microbial communities usually treated as separate functional groups as in a CCA approach, were co-correlated and formed distinct associations in these adjacent habitats. Understanding these distinct modular associations may shed more light on their niche space in the soil environment, and allow a more realistic description of soil microbial ecology and function. © 2014 Society for Applied Microbiology and John Wiley & Sons Ltd.

  15. CommWalker: correctly evaluating modules in molecular networks in light of annotation bias.

    Science.gov (United States)

    Luecken, M D; Page, M J T; Crosby, A J; Mason, S; Reinert, G; Deane, C M

    2018-03-15

    Detecting novel functional modules in molecular networks is an important step in biological research. In the absence of gold standard functional modules, functional annotations are often used to verify whether detected modules/communities have biological meaning. However, as we show, the uneven distribution of functional annotations means that such evaluation methods favor communities of well-studied proteins. We propose a novel framework for the evaluation of communities as functional modules. Our proposed framework, CommWalker, takes communities as inputs and evaluates them in their local network environment by performing short random walks. We test CommWalker's ability to overcome annotation bias using input communities from four community detection methods on two protein interaction networks. We find that modules accepted by CommWalker are similarly co-expressed as those accepted by current methods. Crucially, CommWalker performs well not only in well-annotated regions, but also in regions otherwise obscured by poor annotation. CommWalker community prioritization both faithfully captures well-validated communities and identifies functional modules that may correspond to more novel biology. The CommWalker algorithm is freely available at opig.stats.ox.ac.uk/resources or as a docker image on the Docker Hub at hub.docker.com/r/lueckenmd/commwalker/. deane@stats.ox.ac.uk. Supplementary data are available at Bioinformatics online.

  16. Dynamical Response of Networks Under External Perturbations: Exact Results

    Science.gov (United States)

    Chinellato, David D.; Epstein, Irving R.; Braha, Dan; Bar-Yam, Yaneer; de Aguiar, Marcus A. M.

    2015-04-01

    We give exact statistical distributions for the dynamic response of influence networks subjected to external perturbations. We consider networks whose nodes have two internal states labeled 0 and 1. We let nodes be frozen in state 0, in state 1, and the remaining nodes change by adopting the state of a connected node with a fixed probability per time step. The frozen nodes can be interpreted as external perturbations to the subnetwork of free nodes. Analytically extending and to be smaller than 1 enables modeling the case of weak coupling. We solve the dynamical equations exactly for fully connected networks, obtaining the equilibrium distribution, transition probabilities between any two states and the characteristic time to equilibration. Our exact results are excellent approximations for other topologies, including random, regular lattice, scale-free and small world networks, when the numbers of fixed nodes are adjusted to take account of the effect of topology on coupling to the environment. This model can describe a variety of complex systems, from magnetic spins to social networks to population genetics, and was recently applied as a framework for early warning signals for real-world self-organized economic market crises.

  17. The geometry of chaotic dynamics — a complex network perspective

    Science.gov (United States)

    Donner, R. V.; Heitzig, J.; Donges, J. F.; Zou, Y.; Marwan, N.; Kurths, J.

    2011-12-01

    Recently, several complex network approaches to time series analysis have been developed and applied to study a wide range of model systems as well as real-world data, e.g., geophysical or financial time series. Among these techniques, recurrence-based concepts and prominently ɛ-recurrence networks, most faithfully represent the geometrical fine structure of the attractors underlying chaotic (and less interestingly non-chaotic) time series. In this paper we demonstrate that the well known graph theoretical properties local clustering coefficient and global (network) transitivity can meaningfully be exploited to define two new local and two new global measures of dimension in phase space: local upper and lower clustering dimension as well as global upper and lower transitivity dimension. Rigorous analytical as well as numerical results for self-similar sets and simple chaotic model systems suggest that these measures are well-behaved in most non-pathological situations and that they can be estimated reasonably well using ɛ-recurrence networks constructed from relatively short time series. Moreover, we study the relationship between clustering and transitivity dimensions on the one hand, and traditional measures like pointwise dimension or local Lyapunov dimension on the other hand. We also provide further evidence that the local clustering coefficients, or equivalently the local clustering dimensions, are useful for identifying unstable periodic orbits and other dynamically invariant objects from time series. Our results demonstrate that ɛ-recurrence networks exhibit an important link between dynamical systems and graph theory.

  18. Dynamic Resource Allocation in Hybrid Access Femtocell Network

    Directory of Open Access Journals (Sweden)

    Afaz Uddin Ahmed

    2014-01-01

    Full Text Available Intercell interference is one of the most challenging issues in femtocell deployment under the coverage of existing macrocell. Allocation of resources between femtocell and macrocell is essential to counter the effects of interference in dense femtocell networks. Advances in resource management strategies have improved the control mechanism for interference reduction at lower node density, but most of them are ineffective at higher node density. In this paper, a dynamic resource allocation management algorithm (DRAMA for spectrum shared hybrid access OFDMA femtocell network is proposed. To reduce the macro-femto-tier interference and to improve the quality of service, the proposed algorithm features a dynamic resource allocation scheme by controlling them both centrally and locally. The proposed scheme focuses on Femtocell Access Point (FAP owners’ satisfaction and allows maximum utilization of available resources based on congestion in the network. A simulation environment is developed to study the quantitative performance of DRAMA in hybrid access-control femtocell network and compare it to closed and open access mechanisms. The performance analysis shows that higher number of random users gets connected to the FAP without compromising FAP owners’ satisfaction allowing the macrocell to offload a large number of users in a dense heterogeneous network.

  19. Empirical Modeling of the Plasmasphere Dynamics Using Neural Networks

    Science.gov (United States)

    Zhelavskaya, I. S.; Shprits, Y.; Spasojevic, M.

    2017-12-01

    We present a new empirical model for reconstructing the global dynamics of the cold plasma density distribution based only on solar wind data and geomagnetic indices. Utilizing the density database obtained using the NURD (Neural-network-based Upper hybrid Resonance Determination) algorithm for the period of October 1, 2012 - July 1, 2016, in conjunction with solar wind data and geomagnetic indices, we develop a neural network model that is capable of globally reconstructing the dynamics of the cold plasma density distribution for 2 ≤ L ≤ 6 and all local times. We validate and test the model by measuring its performance on independent datasets withheld from the training set and by comparing the model predicted global evolution with global images of He+ distribution in the Earth's plasmasphere from the IMAGE Extreme UltraViolet (EUV) instrument. We identify the parameters that best quantify the plasmasphere dynamics by training and comparing multiple neural networks with different combinations of input parameters (geomagnetic indices, solar wind data, and different durations of their time history). We demonstrate results of both local and global plasma density reconstruction. This study illustrates how global dynamics can be reconstructed from local in-situ observations by using machine learning techniques.

  20. The Limbic-Prefrontal Network Modulated by Electroacupuncture at CV4 and CV12

    Directory of Open Access Journals (Sweden)

    Jiliang Fang

    2012-01-01

    Full Text Available fMRI studies showed that acupuncture could induce hemodynamic changes in brain networks. Many of these studies focused on whether specific acupoints could activate specific brain regions and were often limited to manual acupuncture at acupoints on the limbs. In this fMRI study, we investigated acupuncture's modulation effects on brain functional networks by electroacupuncture (EA at acupoints on the midline of abdomen. Acupoints Guanyuan (CV4 and Zhongwan (CV12 were stimulated in 21 healthy volunteers. The needling sensations, brain activation, and functional connectivity were studied. We found that the limbic-prefrontal functional network was deactivated by EA at CV4 and CV12. More importantly, the local functional connectivity was significantly changed during EA stimulation, and the change persisted during the period after the stimulation. Although minor differences existed, both acupoints similarly modulated the limbic-prefrontal functional network, which is overlapped with the functional circuits associated with emotional and cognitive regulation.

  1. OTDM Networking for Short Range High-Capacity Highly Dynamic Networks

    DEFF Research Database (Denmark)

    Medhin, Ashenafi Kiros

    This PhD thesis aims at investigating the possibility of designing energy-efficient high-capacity (up to Tbit/s) optical network scenarios, leveraging on the effect of collective switching of many bits simultaneously, as is inherent in high bit rate serial optical data signals. The focus...... is on short range highly dynamic networks, catering to data center needs. The investigation concerns optical network scenarios, and experimental implementations of high bit rate serial data packet generation and reception, scalable optical packet labeling, simple optical label extraction and stable ultra...

  2. Network Traffic Monitoring Using Poisson Dynamic Linear Models

    Energy Technology Data Exchange (ETDEWEB)

    Merl, D. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2011-05-09

    In this article, we discuss an approach for network forensics using a class of nonstationary Poisson processes with embedded dynamic linear models. As a modeling strategy, the Poisson DLM (PoDLM) provides a very flexible framework for specifying structured effects that may influence the evolution of the underlying Poisson rate parameter, including diurnal and weekly usage patterns. We develop a novel particle learning algorithm for online smoothing and prediction for the PoDLM, and demonstrate the suitability of the approach to real-time deployment settings via a new application to computer network traffic monitoring.

  3. Fast Distributed Dynamics of Semantic Networks via Social Media

    Directory of Open Access Journals (Sweden)

    Facundo Carrillo

    2015-01-01

    Full Text Available We investigate the dynamics of semantic organization using social media, a collective expression of human thought. We propose a novel, time-dependent semantic similarity measure (TSS, based on the social network Twitter. We show that TSS is consistent with static measures of similarity but provides high temporal resolution for the identification of real-world events and induced changes in the distributed structure of semantic relationships across the entire lexicon. Using TSS, we measured the evolution of a concept and its movement along the semantic neighborhood, driven by specific news/events. Finally, we showed that particular events may trigger a temporary reorganization of elements in the semantic network.

  4. Dynamic Pricing in Electronic Commerce Using Neural Network

    Science.gov (United States)

    Ghose, Tapu Kumar; Tran, Thomas T.

    In this paper, we propose an approach where feed-forward neural network is used for dynamically calculating a competitive price of a product in order to maximize sellers’ revenue. In the approach we considered that along with product price other attributes such as product quality, delivery time, after sales service and seller’s reputation contribute in consumers purchase decision. We showed that once the sellers, by using their limited prior knowledge, set an initial price of a product our model adjusts the price automatically with the help of neural network so that sellers’ revenue is maximized.

  5. Network robustness assessed within a dual connectivity framework: joint dynamics of the Active and Idle Networks.

    Science.gov (United States)

    Tejedor, Alejandro; Longjas, Anthony; Zaliapin, Ilya; Ambroj, Samuel; Foufoula-Georgiou, Efi

    2017-08-17

    Network robustness against attacks has been widely studied in fields as diverse as the Internet, power grids and human societies. But current definition of robustness is only accounting for half of the story: the connectivity of the nodes unaffected by the attack. Here we propose a new framework to assess network robustness, wherein the connectivity of the affected nodes is also taken into consideration, acknowledging that it plays a crucial role in properly evaluating the overall network robustness in terms of its future recovery from the attack. Specifically, we propose a dual perspective approach wherein at any instant in the network evolution under attack, two distinct networks are defined: (i) the Active Network (AN) composed of the unaffected nodes and (ii) the Idle Network (IN) composed of the affected nodes. The proposed robustness metric considers both the efficiency of destroying the AN and that of building-up the IN. We show, via analysis of well-known prototype networks and real world data, that trade-offs between the efficiency of Active and Idle Network dynamics give rise to surprising robustness crossovers and re-rankings, which can have significant implications for decision making.

  6. Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

    Science.gov (United States)

    Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

    2017-10-06

    Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

  7. A smart modules network for real time data acquisition: application to biomedical research.

    Science.gov (United States)

    Logier, R; De jonckheere, J; Dassonneville, A; Chaud, P; Jeanne, M

    2009-01-01

    Healthcare monitoring applications require the measurement and the analysis of multiple physiological data. In the field of biomedical research, these data are issued from different devices involving data centralization and synchronization difficulties. In this paper, we describe a smart hardware modules network for biomedical data real time acquisition. This toolkit, composed of multiple electronic modules, allows users to acquire and transmit all kind of biomedical signals and parameters. These highly efficient hardware modules have been developed and tested especially for biomedical studies and used in a large number of clinical investigations.

  8. Topological Alterations and Symptom-Relevant Modules in the Whole-Brain Structural Network in Semantic Dementia.

    Science.gov (United States)

    Ding, Junhua; Chen, Keliang; Zhang, Weibin; Li, Ming; Chen, Yan; Yang, Qing; Lv, Yingru; Guo, Qihao; Han, Zaizhu

    2017-01-01

    Semantic dementia (SD) is characterized by a selective decline in semantic processing. Although the neuropsychological pattern of this disease has been identified, its topological global alterations and symptom-relevant modules in the whole-brain anatomical network have not been fully elucidated. This study aims to explore the topological alteration of anatomical network in SD and reveal the modules associated with semantic deficits in this disease. We first constructed the whole-brain white-matter networks of 20 healthy controls and 19 patients with SD. Then, the network metrics of graph theory were compared between these two groups. Finally, we separated the network of SD patients into different modules and correlated the structural integrity of each module with the severity of the semantic deficits across patients. The network of the SD patients presented a significantly reduced global efficiency, indicating that the long-distance connections were damaged. The network was divided into the following four distinctive modules: the left temporal/occipital/parietal, frontal, right temporal/occipital, and frontal/parietal modules. The first two modules were associated with the semantic deficits of SD. These findings illustrate the skeleton of the neuroanatomical network of SD patients and highlight the key role of the left temporal/occipital/parietal module and the left frontal module in semantic processing.

  9. Input dependent cell assembly dynamics in a model of the striatal medium spiny neuron network

    Directory of Open Access Journals (Sweden)

    Adam ePonzi

    2012-03-01

    Full Text Available The striatal medium spiny neuron (MSNs network is sparsely connected with fairly weak GABAergic collaterals receiving an excitatory glutamatergic cortical projection. Peri stimulus time histograms (PSTH of MSN population response investigated in various experimental studies display strong firing rate modulations distributed throughout behavioural task epochs. In previous work we have shown by numerical simulation that sparse random networks of inhibitory spiking neurons with characteristics appropriate for UP state MSNs form cell assemblies which fire together coherently in sequences on long behaviourally relevant timescales when the network receives a fixed pattern of constant input excitation. Here we first extend that model to the case where cortical excitation is composed of many independent noisy Poisson processes and demonstrate that cell assembly dynamics is still observed when the input is sufficiently weak. However if cortical excitation strength is increased more regularly firing and completely quiescent cells are found, which depend on the cortical stimulation. Subsequently we further extend previous work to consider what happens when the excitatory input varies as it would in when the animal is engaged in behavior. We investigate how sudden switches in excitation interact with network generated patterned activity. We show that sequences of cell assembly activations can be locked to the excitatory input sequence and delineate the range of parameters where this behaviour is shown. Model cell population PSTH display both stimulus and temporal specificity, with large population firing rate modulations locked to elapsed time from task events. Thus the random network can generate a large diversity of temporally evolving stimulus dependent responses even though the input is fixed between switches. We suggest the MSN network is well suited to the generation of such slow coherent task dependent response

  10. Input dependent cell assembly dynamics in a model of the striatal medium spiny neuron network.

    Science.gov (United States)

    Ponzi, Adam; Wickens, Jeff

    2012-01-01

    The striatal medium spiny neuron (MSN) network is sparsely connected with fairly weak GABAergic collaterals receiving an excitatory glutamatergic cortical projection. Peri-stimulus time histograms (PSTH) of MSN population response investigated in various experimental studies display strong firing rate modulations distributed throughout behavioral task epochs. In previous work we have shown by numerical simulation that sparse random networks of inhibitory spiking neurons with characteristics appropriate for UP state MSNs form cell assemblies which fire together coherently in sequences on long behaviorally relevant timescales when the network receives a fixed pattern of constant input excitation. Here we first extend that model to the case where cortical excitation is composed of many independent noisy Poisson processes and demonstrate that cell assembly dynamics is still observed when the input is sufficiently weak. However if cortical excitation strength is increased more regularly firing and completely quiescent cells are found, which depend on the cortical stimulation. Subsequently we further extend previous work to consider what happens when the excitatory input varies as it would when the animal is engaged in behavior. We investigate how sudden switches in excitation interact with network generated patterned activity. We show that sequences of cell assembly activations can be locked to the excitatory input sequence and outline the range of parameters where this behavior is shown. Model cell population PSTH display both stimulus and temporal specificity, with large population firing rate modulations locked to elapsed time from task events. Thus the random network can generate a large diversity of temporally evolving stimulus dependent responses even though the input is fixed between switches. We suggest the MSN network is well suited to the generation of such slow coherent task dependent response which could be utilized by the animal in behavior.

  11. Networks dynamics in the case of emerging technologies

    Energy Technology Data Exchange (ETDEWEB)

    Rotolo, D

    2016-07-01

    This research in progress aims at increasing our understanding of how collaborative networks form, evolve and are configured in the case of emerging technologies. The architecture of the relationships among the variety of organisational actors involved in the emergence process exerts a significant influence in shaping technological change in certain directions rather than others, especially in the early stage of emergence. As a result, socially optimal or desirable technological trajectories may be ‘opportunistically’ rejected. Our empirical analysis is based on a case-study of an emerging medical technology, namely ‘microneedles’. On the basis of co-authorship data reported in 1,943 publications on the topic from 1990 to 2014, longitudinal collaboration (co-authorship) networks were built at two levels: affiliation and author. We examined the dynamics of co-authorship networks by building on recent methodological advancements in network analysis, i.e. Exponential Random Graph Models (ERGMs). These models enable us to make statistical inferences about on the extent to which a network configuration occurs more than could be expected by chance and to identify which social mechanisms may be shaping the network in certain configurations. The findings of the statistical analyses (currently in progress) combined with the qualitative understanding of the case will increase our understanding of which mechanisms are more likely to drive the network dynamics in the case of emerging technologies. These include evidence of the extent to which the likelihood of forming, maintaining, or terminating ties among actors (authors or affiliations) is affected by actors’ covariates such as types of organisations, diversity/specialisation of the research undertaken, and status. These findings have potential to provide important inputs for policymaking process in the case of emerging technologies. (Author)

  12. Networks dynamics in the case of emerging technologies

    International Nuclear Information System (INIS)

    Rotolo, D

    2016-01-01

    This research in progress aims at increasing our understanding of how collaborative networks form, evolve and are configured in the case of emerging technologies. The architecture of the relationships among the variety of organisational actors involved in the emergence process exerts a significant influence in shaping technological change in certain directions rather than others, especially in the early stage of emergence. As a result, socially optimal or desirable technological trajectories may be ‘opportunistically’ rejected. Our empirical analysis is based on a case-study of an emerging medical technology, namely ‘microneedles’. On the basis of co-authorship data reported in 1,943 publications on the topic from 1990 to 2014, longitudinal collaboration (co-authorship) networks were built at two levels: affiliation and author. We examined the dynamics of co-authorship networks by building on recent methodological advancements in network analysis, i.e. Exponential Random Graph Models (ERGMs). These models enable us to make statistical inferences about on the extent to which a network configuration occurs more than could be expected by chance and to identify which social mechanisms may be shaping the network in certain configurations. The findings of the statistical analyses (currently in progress) combined with the qualitative understanding of the case will increase our understanding of which mechanisms are more likely to drive the network dynamics in the case of emerging technologies. These include evidence of the extent to which the likelihood of forming, maintaining, or terminating ties among actors (authors or affiliations) is affected by actors’ covariates such as types of organisations, diversity/specialisation of the research undertaken, and status. These findings have potential to provide important inputs for policymaking process in the case of emerging technologies. (Author)

  13. Methylphenidate Modulates Functional Network Connectivity to Enhance Attention

    OpenAIRE

    Rosenberg, Monica D.; Zhang, Sheng; Hsu, Wei-Ting; Scheinost, Dustin; Finn, Emily S.; Shen, Xilin; Constable, R. Todd; Li, Chiang-Shan R.; Chun, Marvin M.

    2016-01-01

    Recent work has demonstrated that human whole-brain functional connectivity patterns measured with fMRI contain information about cognitive abilities, including sustained attention. To derive behavioral predictions from connectivity patterns, our group developed a connectome-based predictive modeling (CPM) approach (Finn et al., 2015; Rosenberg et al., 2016). Previously using CPM, we defined a high-attention network, comprising connections positively correlated with performance on a sustained...

  14. Network Structure as a Modulator of Disturbance Impacts in Streams

    Science.gov (United States)

    Warner, S.; Tullos, D. D.

    2017-12-01

    This study examines how river network structure affects the propagation of geomorphic and anthropogenic disturbances through streams. Geomorphic processes such as debris flows can alter channel morphology and modify habitat for aquatic biota. Anthropogenic disturbances such as road construction can interact with the geomorphology and hydrology of forested watersheds to change sediment and water inputs to streams. It was hypothesized that the network structure of streams within forested watersheds would influence the location and magnitude of the impacts of debris flows and road construction on sediment size and channel width. Longitudinal surveys were conducted every 50 meters for 11 kilometers of third-to-fifth order streams in the H.J. Andrews Experimental Forest in the Western Cascade Range of Oregon. Particle counts and channel geometry measurements were collected to characterize the geomorphic impacts of road crossings and debris flows as disturbances. Sediment size distributions and width measurements were plotted against the distance of survey locations through the network to identify variations in longitudinal trends of channel characteristics. Thresholds for the background variation in sediment size and channel width, based on the standard deviations of sample points, were developed for sampled stream segments characterized by location as well as geomorphic and land use history. Survey locations were classified as "disturbed" when they deviated beyond the reference thresholds in expected sediment sizes and channel widths, as well as flow-connected proximity to debris flows and road crossings. River network structure was quantified by drainage density and centrality of nodes upstream of survey locations. Drainage density and node centrality were compared between survey locations with similar channel characteristic classifications. Cluster analysis was used to assess the significance of survey location, proximity of survey location to debris flows and road

  15. Dynamic simulator for nuclear power plants (DSNP): development, verification, and expansion of modules

    International Nuclear Information System (INIS)

    Larson, H.A.; Dean, E.M.; Koenig, J.F.; Gale, J.G.; Lehto, W.K.

    1984-01-01

    The DSNP Simulation Language facilitates whole reactor plant simulation and design. Verification includes DSNP dynamic modeling of Experimental Breeder Reactor No. 2 (EBR-II) plant experiments as well as comparisons with verified simulation programs. Great flexibility is allowed in expanding the DSNP language and accommodate other computer languages. The component modules of DSNP, contained in libraries, are continually updated with new, improved, and verified modules. The modules are used to simulate the dynamic response of LMFBR reactor systems to upset and transient conditions, with special emphasis on investigations of inherent shutdown mechanisms

  16. Pulsewidth modulated DC-to-DC power conversion circuits, dynamics, and control designs

    CERN Document Server

    Choi, Byungcho

    2013-01-01

    This is the definitive reference for anyone involved in pulsewidth modulated DC-to-DC power conversion Pulsewidth Modulated DC-to-DC Power Conversion: Circuits, Dynamics, and Control Designs provides engineers, researchers, and students in the power electronics field with comprehensive and complete guidance to understanding pulsewidth modulated (PWM) DC-to-DC power converters. Presented in three parts, the book addresses the circuitry and operation of PWM DC-to-DC converters and their dynamic characteristics, along with in-depth discussions of control design of PWM DC-to

  17. Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals.

    Science.gov (United States)

    Kim, Junkyeong; Lee, Chaggil; Park, Seunghee

    2017-06-07

    Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since concrete might be susceptible to brittle fracture, it is essential to confirm the strength of concrete at the early-age stage of the curing process to prevent unexpected collapse. To address this issue, this study proposes a novel method to estimate the early-age strength of concrete, by integrating an artificial neural network algorithm with a dynamic response measurement of the concrete material. The dynamic response signals of the concrete, including both electromechanical impedances and guided ultrasonic waves, are obtained from an embedded piezoelectric sensor module. The cross-correlation coefficient of the electromechanical impedance signals and the amplitude of the guided ultrasonic wave signals are selected to quantify the variation in dynamic responses according to the strength of the concrete. Furthermore, an artificial neural network algorithm is used to verify a relationship between the variation in dynamic response signals and concrete strength. The results of an experimental study confirm that the proposed approach can be effectively applied to estimate the strength of concrete material from the early-age stage of the curing process.

  18. Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular interaction networks.

    Directory of Open Access Journals (Sweden)

    Recep Colak

    2010-10-01

    Full Text Available Computational prediction of functionally related groups of genes (functional modules from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when analyzing these two data sources jointly, modules are often reflected by highly interconnected (dense regions in the interaction networks whose participating genes are co-expressed. However, the tractability of the problem had remained unclear and methods by which to exhaustively search for such constellations had not been presented.We provide an algorithmic framework, referred to as Densely Connected Biclustering (DECOB, by which the aforementioned search problem becomes tractable. To benchmark the predictive power inherent to the approach, we computed all co-expressed, dense regions in physical protein and genetic interaction networks from human and yeast. An automatized filtering procedure reduces our output which results in smaller collections of modules, comparable to state-of-the-art approaches. Our results performed favorably in a fair benchmarking competition which adheres to standard criteria. We demonstrate the usefulness of an exhaustive module search, by using the unreduced output to more quickly perform GO term related function prediction tasks. We point out the advantages of our exhaustive output by predicting functional relationships using two examples.We demonstrate that the computation of all densely connected and co-expressed regions in interaction networks is an approach to module discovery of considerable value. Beyond confirming the well settled hypothesis that such co-expressed, densely connected interaction network regions reflect functional modules, we open up novel computational ways to comprehensively analyze the modular organization of an organism based on prevalent and largely

  19. Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular interaction networks.

    Science.gov (United States)

    Colak, Recep; Moser, Flavia; Chu, Jeffrey Shih-Chieh; Schönhuth, Alexander; Chen, Nansheng; Ester, Martin

    2010-10-25

    Computational prediction of functionally related groups of genes (functional modules) from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when analyzing these two data sources jointly, modules are often reflected by highly interconnected (dense) regions in the interaction networks whose participating genes are co-expressed. However, the tractability of the problem had remained unclear and methods by which to exhaustively search for such constellations had not been presented. We provide an algorithmic framework, referred to as Densely Connected Biclustering (DECOB), by which the aforementioned search problem becomes tractable. To benchmark the predictive power inherent to the approach, we computed all co-expressed, dense regions in physical protein and genetic interaction networks from human and yeast. An automatized filtering procedure reduces our output which results in smaller collections of modules, comparable to state-of-the-art approaches. Our results performed favorably in a fair benchmarking competition which adheres to standard criteria. We demonstrate the usefulness of an exhaustive module search, by using the unreduced output to more quickly perform GO term related function prediction tasks. We point out the advantages of our exhaustive output by predicting functional relationships using two examples. We demonstrate that the computation of all densely connected and co-expressed regions in interaction networks is an approach to module discovery of considerable value. Beyond confirming the well settled hypothesis that such co-expressed, densely connected interaction network regions reflect functional modules, we open up novel computational ways to comprehensively analyze the modular organization of an organism based on prevalent and largely available large

  20. Some dynamic resource allocation problems in wireless networks

    Science.gov (United States)

    Berry, Randall

    2001-07-01

    We consider dynamic resource allocation problems that arise in wireless networking. Specifically transmission scheduling problems are studied in cases where a user can dynamically allocate communication resources such as transmission rate and power based on current channel knowledge as well as traffic variations. We assume that arriving data is stored in a transmission buffer, and investigate the trade-off between average transmission power and average buffer delay. A general characterization of this trade-off is given and the behavior of this trade-off in the regime of asymptotically large buffer delays is explored. An extension to a more general utility based quality of service definition is also discussed.