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Sample records for networks mixing temporal

  1. Modeling Temporal Behavior in Large Networks: A Dynamic Mixed-Membership Model

    SciTech Connect

    Rossi, R; Gallagher, B; Neville, J; Henderson, K

    2011-11-11

    Given a large time-evolving network, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the 'roles' of nodes in the graph and how they evolve over time. The proposed dynamic behavioral mixed-membership model (DBMM) is scalable, fully automatic (no user-defined parameters), non-parametric/data-driven (no specific functional form or parameterization), interpretable (identifies explainable patterns), and flexible (applicable to dynamic and streaming networks). Moreover, the interpretable behavioral roles are generalizable, computationally efficient, and natively supports attributes. We applied our model for (a) identifying patterns and trends of nodes and network states based on the temporal behavior, (b) predicting future structural changes, and (c) detecting unusual temporal behavior transitions. We use eight large real-world datasets from different time-evolving settings (dynamic and streaming). In particular, we model the evolving mixed-memberships and the corresponding behavioral transitions of Twitter, Facebook, IP-Traces, Email (University), Internet AS, Enron, Reality, and IMDB. The experiments demonstrate the scalability, flexibility, and effectiveness of our model for identifying interesting patterns, detecting unusual structural transitions, and predicting the future structural changes of the network and individual nodes.

  2. Temporal networks

    NASA Astrophysics Data System (ADS)

    Holme, Petter; Saramäki, Jari

    2012-10-01

    A great variety of systems in nature, society and technology-from the web of sexual contacts to the Internet, from the nervous system to power grids-can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via e-mail, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks. The study of temporal networks is very interdisciplinary in nature. Reflecting this, even the object of study has many names-temporal graphs, evolving graphs, time-varying graphs, time-aggregated graphs, time-stamped graphs, dynamic networks, dynamic graphs, dynamical graphs, and so on. This review covers different fields where temporal graphs are considered

  3. Mixing navigation on networks

    NASA Astrophysics Data System (ADS)

    Zhou, Tao

    2008-05-01

    In this article, we propose a mixing navigation mechanism, which interpolates between random-walk and shortest-path protocol. The navigation efficiency can be remarkably enhanced via a few routers. Some advanced strategies are also designed: For non-geographical scale-free networks, the targeted strategy with a tiny fraction of routers can guarantee an efficient navigation with low and stable delivery time almost independent of network size. For geographical localized networks, the clustering strategy can simultaneously increase efficiency and reduce the communication cost. The present mixing navigation mechanism is of significance especially for information organization of wireless sensor networks and distributed autonomous robotic systems.

  4. Coverage centralities for temporal networks*

    NASA Astrophysics Data System (ADS)

    Takaguchi, Taro; Yano, Yosuke; Yoshida, Yuichi

    2016-02-01

    Structure of real networked systems, such as social relationship, can be modeled as temporal networks in which each edge appears only at the prescribed time. Understanding the structure of temporal networks requires quantifying the importance of a temporal vertex, which is a pair of vertex index and time. In this paper, we define two centrality measures of a temporal vertex based on the fastest temporal paths which use the temporal vertex. The definition is free from parameters and robust against the change in time scale on which we focus. In addition, we can efficiently compute these centrality values for all temporal vertices. Using the two centrality measures, we reveal that distributions of these centrality values of real-world temporal networks are heterogeneous. For various datasets, we also demonstrate that a majority of the highly central temporal vertices are located within a narrow time window around a particular time. In other words, there is a bottleneck time at which most information sent in the temporal network passes through a small number of temporal vertices, which suggests an important role of these temporal vertices in spreading phenomena. Contribution to the Topical Issue "Temporal Network Theory and Applications", edited by Petter Holme.Supplementary material in the form of one pdf file available from the Journal web page at http://dx.doi.org/10.1140/epjb/e2016-60498-7

  5. Helicity in supercritical temporal mixing layers

    NASA Technical Reports Server (NTRS)

    Bellan, J.; Okong'o, N.

    2003-01-01

    Databases of transitional states obtained from Direct Numerical Simulations (DNS) of temporal, supercritical mixing layers for two species systems, 02/H2 and C7Hle/N2, are analyzed to elucidate species-specific turbulence aspects.

  6. Navigability of multiplex temporal network

    NASA Astrophysics Data System (ADS)

    Wang, Yan; Song, Qiao-Zhen

    2017-01-01

    Real world complex systems have multiple levels of relationships and in many cases, they need to be modeled as multiplex networks where the same nodes can interact with each other in different layers, such as social networks. However, social relationships only appear at prescribed times so the temporal structures of edge activations can also affect the dynamical processes located above them. To consider both factors are simultaneously, we introduce multiplex temporal networks and propose three different walk strategies to investigate the concurrent dynamics of random walks and the temporal structure of multiplex networks. Thus, we derive analytical results for the multiplex centrality and coverage function in multiplex temporal networks. By comparing them with the numerical results, we show how the underlying topology of the layers and the walk strategy affect the efficiency when exploring the networks. In particular, the most interesting result is the emergence of a super-diffusion process, where the time scale of the multiplex is faster than that of both layers acting separately.

  7. Effects of temporal fluctuations on mixing

    NASA Astrophysics Data System (ADS)

    Pool, Maria; Dentz, Marco; Post, Vincent E. A.; Simmons, Craig T.

    2016-04-01

    Mixing and dispersion in coastal aquifers are strongly influenced by periodic temporal flow fluctuations on multiple time-scales ranging from days (tides), seasons (pumping and recharge) to glacial cycles (regression and transgressions). Transient forcing effects lead to a complex space- ant time-dependent flow response which induces enhanced spreading and mixing of a dissolved substance. We study effective mixing and solute transport in temporally fluctuating one-dimensional flow for a stable stratification of two fluids of different density. We derive explicit expressions for the concentration distribution and variance to identify the controls and obtain realistic predictions of the coupling between mixing and oscillatory transient flow. We find that the magnitude of transient-driven mixing is mainly controlled by the hydraulic diffusivity, the period and the initial interface location. We also find a spatial dependence of the effective dispersion coefficient which at long times causes the concentration profile to become asymmetric. Sand column experiments under well-controlled laboratory conditions are presented to validate the theoretical effective model defined. The proposed formulation is found to provide very good predictions and correctly reproduces the experimental mixing dynamics.

  8. Mixed deterministic and probabilistic networks.

    PubMed

    Mateescu, Robert; Dechter, Rina

    2008-11-01

    The paper introduces mixed networks, a new graphical model framework for expressing and reasoning with probabilistic and deterministic information. The motivation to develop mixed networks stems from the desire to fully exploit the deterministic information (constraints) that is often present in graphical models. Several concepts and algorithms specific to belief networks and constraint networks are combined, achieving computational efficiency, semantic coherence and user-interface convenience. We define the semantics and graphical representation of mixed networks, and discuss the two main types of algorithms for processing them: inference-based and search-based. A preliminary experimental evaluation shows the benefits of the new model.

  9. Mixed deterministic and probabilistic networks

    PubMed Central

    Dechter, Rina

    2010-01-01

    The paper introduces mixed networks, a new graphical model framework for expressing and reasoning with probabilistic and deterministic information. The motivation to develop mixed networks stems from the desire to fully exploit the deterministic information (constraints) that is often present in graphical models. Several concepts and algorithms specific to belief networks and constraint networks are combined, achieving computational efficiency, semantic coherence and user-interface convenience. We define the semantics and graphical representation of mixed networks, and discuss the two main types of algorithms for processing them: inference-based and search-based. A preliminary experimental evaluation shows the benefits of the new model. PMID:20981243

  10. Temporal motifs in time-dependent networks

    NASA Astrophysics Data System (ADS)

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

    2011-11-01

    Temporal networks are commonly used to represent systems where connections between elements are active only for restricted periods of time, such as telecommunication, neural signal processing, biochemical reaction and human social interaction networks. We introduce the framework of temporal motifs to study the mesoscale topological-temporal structure of temporal networks in which the events of nodes do not overlap in time. Temporal motifs are classes of similar event sequences, where the similarity refers not only to topology but also to the temporal order of the events. We provide a mapping from event sequences to coloured directed graphs that enables an efficient algorithm for identifying temporal motifs. We discuss some aspects of temporal motifs, including causality and null models, and present basic statistics of temporal motifs in a large mobile call network.

  11. Delay synchronization of temporal Boolean networks

    NASA Astrophysics Data System (ADS)

    Wei, Qiang; Xie, Cheng-jun; Liang, Yi; Niu, Yu-jun; Lin, Da

    2016-01-01

    This paper investigates the delay synchronization between two temporal Boolean networks base on semi-tensor product method, which improve complete synchronization. Necessary and sufficient conditions for delay synchronization are drawn base on algebraic expression of temporal Boolean networks. A example is presented to show the effectiveness of theoretical analysis.

  12. Temporal correlation coefficient for directed networks.

    PubMed

    Büttner, Kathrin; Salau, Jennifer; Krieter, Joachim

    2016-01-01

    Previous studies dealing with network theory focused mainly on the static aggregation of edges over specific time window lengths. Thus, most of the dynamic information gets lost. To assess the quality of such a static aggregation the temporal correlation coefficient can be calculated. It measures the overall possibility for an edge to persist between two consecutive snapshots. Up to now, this measure is only defined for undirected networks. Therefore, we introduce the adaption of the temporal correlation coefficient to directed networks. This new methodology enables the distinction between ingoing and outgoing edges. Besides a small example network presenting the single calculation steps, we also calculated the proposed measurements for a real pig trade network to emphasize the importance of considering the edge direction. The farm types at the beginning of the pork supply chain showed clearly higher values for the outgoing temporal correlation coefficient compared to the farm types at the end of the pork supply chain. These farm types showed higher values for the ingoing temporal correlation coefficient. The temporal correlation coefficient is a valuable tool to understand the structural dynamics of these systems, as it assesses the consistency of the edge configuration. The adaption of this measure for directed networks may help to preserve meaningful additional information about the investigated network that might get lost if the edge directions are ignored.

  13. Error and attack vulnerability of temporal networks

    NASA Astrophysics Data System (ADS)

    Trajanovski, S.; Scellato, S.; Leontiadis, I.

    2012-06-01

    The study of real-world communication systems via complex network models has greatly expanded our understanding on how information flows, even in completely decentralized architectures such as mobile wireless networks. Nonetheless, static network models cannot capture the time-varying aspects and, therefore, various temporal metrics have been introduced. In this paper, we investigate the robustness of time-varying networks under various failures and intelligent attacks. We adopt a methodology to evaluate the impact of such events on the network connectivity by employing temporal metrics in order to select and remove nodes based on how critical they are considered for the network. We also define the temporal robustness range, a new metric that quantifies the disruption caused by an attack strategy to a given temporal network. Our results show that in real-world networks, where some nodes are more dominant than others, temporal connectivity is significantly more affected by intelligent attacks than by random failures. Moreover, different intelligent attack strategies have a similar effect on the robustness: even small subsets of highly connected nodes act as a bottleneck in the temporal information flow, becoming critical weak points of the entire system. Additionally, the same nodes are the most important across a range of different importance metrics, expressing the correlation between highly connected nodes and those that trigger most of the changes in the optimal information spreading. Contrarily, we show that in randomly generated networks, where all the nodes have similar properties, random errors and intelligent attacks exhibit similar behavior. These conclusions may help us in design of more robust systems and fault-tolerant network architectures.

  14. Temporal Coding in Realistic Neural Networks

    NASA Astrophysics Data System (ADS)

    Gerasyuta, S. M.; Ivanov, D. V.

    1995-10-01

    The modification of realistic neural network model have been proposed. The model differs from the Hopfield model because of the two characteristic contributions to synaptic efficacious: the short-time contribution which is determined by the chemical reactions in the synapses and the long-time contribution corresponding to the structural changes of synaptic contacts. The approximation solution of the realistic neural network model equations is obtained. This solution allows us to calculate the postsynaptic potential as function of input. Using the approximate solution of realistic neural network model equations the behaviour of postsynaptic potential of realistic neural network as function of time for the different temporal sequences of stimuli is described. The various outputs are obtained for the different temporal sequences of the given stimuli. These properties of the temporal coding can be exploited as a recognition element capable of being selectively tuned to different inputs.

  15. Temporal efficiency evaluation and small-worldness characterization in temporal networks

    NASA Astrophysics Data System (ADS)

    Dai, Zhongxiang; Chen, Yu; Li, Junhua; Fam, Johnson; Bezerianos, Anastasios; Sun, Yu

    2016-09-01

    Numerous real-world systems can be modeled as networks. To date, most network studies have been conducted assuming stationary network characteristics. Many systems, however, undergo topological changes over time. Temporal networks, which incorporate time into conventional network models, are therefore more accurate representations of such dynamic systems. Here, we introduce a novel generalized analytical framework for temporal networks, which enables 1) robust evaluation of the efficiency of temporal information exchange using two new network metrics and 2) quantitative inspection of the temporal small-worldness. Specifically, we define new robust temporal network efficiency measures by incorporating the time dependency of temporal distance. We propose a temporal regular network model, and based on this plus the redefined temporal efficiency metrics and widely used temporal random network models, we introduce a quantitative approach for identifying temporal small-world architectures (featuring high temporal network efficiency both globally and locally). In addition, within this framework, we can uncover network-specific dynamic structures. Applications to brain networks, international trade networks, and social networks reveal prominent temporal small-world properties with distinct dynamic network structures. We believe that the framework can provide further insight into dynamic changes in the network topology of various real-world systems and significantly promote research on temporal networks.

  16. Temporal efficiency evaluation and small-worldness characterization in temporal networks

    PubMed Central

    Dai, Zhongxiang; Chen, Yu; Li, Junhua; Fam, Johnson; Bezerianos, Anastasios; Sun, Yu

    2016-01-01

    Numerous real-world systems can be modeled as networks. To date, most network studies have been conducted assuming stationary network characteristics. Many systems, however, undergo topological changes over time. Temporal networks, which incorporate time into conventional network models, are therefore more accurate representations of such dynamic systems. Here, we introduce a novel generalized analytical framework for temporal networks, which enables 1) robust evaluation of the efficiency of temporal information exchange using two new network metrics and 2) quantitative inspection of the temporal small-worldness. Specifically, we define new robust temporal network efficiency measures by incorporating the time dependency of temporal distance. We propose a temporal regular network model, and based on this plus the redefined temporal efficiency metrics and widely used temporal random network models, we introduce a quantitative approach for identifying temporal small-world architectures (featuring high temporal network efficiency both globally and locally). In addition, within this framework, we can uncover network-specific dynamic structures. Applications to brain networks, international trade networks, and social networks reveal prominent temporal small-world properties with distinct dynamic network structures. We believe that the framework can provide further insight into dynamic changes in the network topology of various real-world systems and significantly promote research on temporal networks. PMID:27682314

  17. Time series analysis of temporal networks

    NASA Astrophysics Data System (ADS)

    Sikdar, Sandipan; Ganguly, Niloy; Mukherjee, Animesh

    2016-01-01

    A common but an important feature of all real-world networks is that they are temporal in nature, i.e., the network structure changes over time. Due to this dynamic nature, it becomes difficult to propose suitable growth models that can explain the various important characteristic properties of these networks. In fact, in many application oriented studies only knowing these properties is sufficient. For instance, if one wishes to launch a targeted attack on a network, this can be done even without the knowledge of the full network structure; rather an estimate of some of the properties is sufficient enough to launch the attack. We, in this paper show that even if the network structure at a future time point is not available one can still manage to estimate its properties. We propose a novel method to map a temporal network to a set of time series instances, analyze them and using a standard forecast model of time series, try to predict the properties of a temporal network at a later time instance. To our aim, we consider eight properties such as number of active nodes, average degree, clustering coefficient etc. and apply our prediction framework on them. We mainly focus on the temporal network of human face-to-face contacts and observe that it represents a stochastic process with memory that can be modeled as Auto-Regressive-Integrated-Moving-Average (ARIMA). We use cross validation techniques to find the percentage accuracy of our predictions. An important observation is that the frequency domain properties of the time series obtained from spectrogram analysis could be used to refine the prediction framework by identifying beforehand the cases where the error in prediction is likely to be high. This leads to an improvement of 7.96% (for error level ≤20%) in prediction accuracy on an average across all datasets. As an application we show how such prediction scheme can be used to launch targeted attacks on temporal networks. Contribution to the Topical Issue

  18. Importance of individual events in temporal networks

    NASA Astrophysics Data System (ADS)

    Takaguchi, Taro; Sato, Nobuo; Yano, Kazuo; Masuda, Naoki

    2012-09-01

    Records of time-stamped social interactions between pairs of individuals (e.g. face-to-face conversations, e-mail exchanges and phone calls) constitute a so-called temporal network. A remarkable difference between temporal networks and conventional static networks is that time-stamped events rather than links are the unit elements generating the collective behavior of nodes. We propose an importance measure for single interaction events. By generalizing the concept of the advance of events proposed by Kossinets et al (2008 Proc. 14th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining p 435), we propose that an event is central when it carries new information about others to the two nodes involved in the event. We find that the proposed measure properly quantifies the importance of events in connecting nodes along time-ordered paths. Because of strong heterogeneity in the importance of events present in real data, a small fraction of highly important events is necessary and sufficient to sustain the connectivity of temporal networks. Nevertheless, in contrast to the behavior of scale-free networks against link removal, this property mainly results from bursty activity patterns and not heterogeneous degree distributions.

  19. Temporal node centrality in complex networks

    NASA Astrophysics Data System (ADS)

    Kim, Hyoungshick; Anderson, Ross

    2012-02-01

    Many networks are dynamic in that their topology changes rapidly—on the same time scale as the communications of interest between network nodes. Examples are the human contact networks involved in the transmission of disease, ad hoc radio networks between moving vehicles, and the transactions between principals in a market. While we have good models of static networks, so far these have been lacking for the dynamic case. In this paper we present a simple but powerful model, the time-ordered graph, which reduces a dynamic network to a static network with directed flows. This enables us to extend network properties such as vertex degree, closeness, and betweenness centrality metrics in a very natural way to the dynamic case. We then demonstrate how our model applies to a number of interesting edge cases, such as where the network connectivity depends on a small number of highly mobile vertices or edges, and show that our centrality definition allows us to track the evolution of connectivity. Finally we apply our model and techniques to two real-world dynamic graphs of human contact networks and then discuss the implication of temporal centrality metrics in the real world.

  20. Network Analysis Using Spatio-Temporal Patterns

    NASA Astrophysics Data System (ADS)

    Miranda, Gisele H. B.; Machicao, Jeaneth; Bruno, Odemir M.

    2016-08-01

    Different network models have been proposed along the last years inspired by real-world topologies. The characterization of these models implies the understanding of the underlying network phenomena, which accounts structural and dynamic properties. Several mathematical tools can be employed to characterize such properties as Cellular Automata (CA), which can be defined as dynamical systems of discrete nature composed by spatially distributed units governed by deterministic rules. In this paper, we proposed a method based on the modeling of one specific CA over distinct network topologies in order to perform the classification of the network model. The proposed methodology consists in the modeling of a binary totalistic CA over a network. The transition function that governs each CA cell is based on the density of living neighbors. Secondly, the distribution of the Shannon entropy is obtained from the evolved spatio-temporal pattern of the referred CA and used as a network descriptor. The experiments were performed using a dataset composed of four different types of networks: random, small-world, scale-free and geographical. We also used cross-validation for training purposes. We evaluated the accuracy of classification as a function of the initial number of living neighbors, and, also, as a function of a threshold parameter related to the density of living neighbors. The results show high accuracy values in distinguishing among the network models which demonstrates the feasibility of the proposed method.

  1. The Temporal Configuration of Airline Networks

    NASA Technical Reports Server (NTRS)

    Burghouwt, Guillaume; deWit, Jaap

    2003-01-01

    The deregulation of US aviation in 1978 resulted in the reconfiguration of airline networks into hub-and-spoke systems, spatially concentrated around a small number of central airports or 'hubs' through which an airline operates a number of daily waves of flights. A hub-and-spoke network requires a concentration of traffic in both space and time. In contrast to the U.S. airlines, European airlines had entered the phase of spatial network concentration long before deregulation. Bilateral negotiation of traffic fights between governments forced European airlines to focus their networks spatially on small number of 'national' airports. In general, these star-shaped networks were not coordinated in time. Transfer opportunities at central airports were mostly created 'by accident'. With the deregulation of the EU air transport market from 1988 on, a second phase of airline network concentration started. European airlines concentrated their networks in time by adopting or intensifying wave-system structures in their flight schedules. Temporal concentration may increase the competitive position of the network in a deregulated market because of certain cost and demand advantages.

  2. Structural Controllability of Temporal Networks with a Single Switching Controller

    PubMed Central

    Yao, Peng; Hou, Bao-Yu; Pan, Yu-Jian; Li, Xiang

    2017-01-01

    Temporal network, whose topology evolves with time, is an important class of complex networks. Temporal trees of a temporal network describe the necessary edges sustaining the network as well as their active time points. By a switching controller which properly selects its location with time, temporal trees are used to improve the controllability of the network. Therefore, more nodes are controlled within the limited time. Several switching strategies to efficiently select the location of the controller are designed, which are verified with synthetic and empirical temporal networks to achieve better control performance. PMID:28107538

  3. Structural Controllability of Temporal Networks with a Single Switching Controller.

    PubMed

    Yao, Peng; Hou, Bao-Yu; Pan, Yu-Jian; Li, Xiang

    2017-01-01

    Temporal network, whose topology evolves with time, is an important class of complex networks. Temporal trees of a temporal network describe the necessary edges sustaining the network as well as their active time points. By a switching controller which properly selects its location with time, temporal trees are used to improve the controllability of the network. Therefore, more nodes are controlled within the limited time. Several switching strategies to efficiently select the location of the controller are designed, which are verified with synthetic and empirical temporal networks to achieve better control performance.

  4. Reconstructing propagation networks with temporal similarity

    PubMed Central

    Liao, Hao; Zeng, An

    2015-01-01

    Node similarity significantly contributes to the growth of real networks. In this paper, based on the observed epidemic spreading results we apply the node similarity metrics to reconstruct the underlying networks hosting the propagation. We find that the reconstruction accuracy of the similarity metrics is strongly influenced by the infection rate of the spreading process. Moreover, there is a range of infection rate in which the reconstruction accuracy of some similarity metrics drops nearly to zero. To improve the similarity-based reconstruction method, we propose a temporal similarity metric which takes into account the time information of the spreading. The reconstruction results are remarkably improved with the new method. PMID:26086198

  5. Reconstructing propagation networks with temporal similarity.

    PubMed

    Liao, Hao; Zeng, An

    2015-06-18

    Node similarity significantly contributes to the growth of real networks. In this paper, based on the observed epidemic spreading results we apply the node similarity metrics to reconstruct the underlying networks hosting the propagation. We find that the reconstruction accuracy of the similarity metrics is strongly influenced by the infection rate of the spreading process. Moreover, there is a range of infection rate in which the reconstruction accuracy of some similarity metrics drops nearly to zero. To improve the similarity-based reconstruction method, we propose a temporal similarity metric which takes into account the time information of the spreading. The reconstruction results are remarkably improved with the new method.

  6. Cyber War Game in Temporal Networks.

    PubMed

    Cho, Jin-Hee; Gao, Jianxi

    2016-01-01

    In a cyber war game where a network is fully distributed and characterized by resource constraints and high dynamics, attackers or defenders often face a situation that may require optimal strategies to win the game with minimum effort. Given the system goal states of attackers and defenders, we study what strategies attackers or defenders can take to reach their respective system goal state (i.e., winning system state) with minimum resource consumption. However, due to the dynamics of a network caused by a node's mobility, failure or its resource depletion over time or action(s), this optimization problem becomes NP-complete. We propose two heuristic strategies in a greedy manner based on a node's two characteristics: resource level and influence based on k-hop reachability. We analyze complexity and optimality of each algorithm compared to optimal solutions for a small-scale static network. Further, we conduct a comprehensive experimental study for a large-scale temporal network to investigate best strategies, given a different environmental setting of network temporality and density. We demonstrate the performance of each strategy under various scenarios of attacker/defender strategies in terms of win probability, resource consumption, and system vulnerability.

  7. Cyber War Game in Temporal Networks

    PubMed Central

    Cho, Jin-Hee; Gao, Jianxi

    2016-01-01

    In a cyber war game where a network is fully distributed and characterized by resource constraints and high dynamics, attackers or defenders often face a situation that may require optimal strategies to win the game with minimum effort. Given the system goal states of attackers and defenders, we study what strategies attackers or defenders can take to reach their respective system goal state (i.e., winning system state) with minimum resource consumption. However, due to the dynamics of a network caused by a node’s mobility, failure or its resource depletion over time or action(s), this optimization problem becomes NP-complete. We propose two heuristic strategies in a greedy manner based on a node’s two characteristics: resource level and influence based on k-hop reachability. We analyze complexity and optimality of each algorithm compared to optimal solutions for a small-scale static network. Further, we conduct a comprehensive experimental study for a large-scale temporal network to investigate best strategies, given a different environmental setting of network temporality and density. We demonstrate the performance of each strategy under various scenarios of attacker/defender strategies in terms of win probability, resource consumption, and system vulnerability. PMID:26859840

  8. Operations automation using temporal dependency networks

    NASA Technical Reports Server (NTRS)

    Cooper, Lynne P.

    1991-01-01

    Precalibration activities for the Deep Space Network are time- and work force-intensive. Significant gains in availability and efficiency could be realized by intelligently incorporating automation techniques. An approach is presented to automation based on the use of Temporal Dependency Networks (TDNs). A TDN represents an activity by breaking it down into its component pieces and formalizing the precedence and other constraints associated with lower level activities. The representations are described which are used to implement a TDN and the underlying system architecture needed to support its use. The commercial applications of this technique are numerous. It has potential for application in any system which requires real-time, system-level control, and accurate monitoring of health, status, and configuration in an asynchronous environment.

  9. Learning oncogenetic networks by reducing to mixed integer linear programming.

    PubMed

    Shahrabi Farahani, Hossein; Lagergren, Jens

    2013-01-01

    Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.

  10. Higher order temporal finite element methods through mixed formalisms.

    PubMed

    Kim, Jinkyu

    2014-01-01

    The extended framework of Hamilton's principle and the mixed convolved action principle provide new rigorous weak variational formalism for a broad range of initial boundary value problems in mathematical physics and mechanics. In this paper, their potential when adopting temporally higher order approximations is investigated. The classical single-degree-of-freedom dynamical systems are primarily considered to validate and to investigate the performance of the numerical algorithms developed from both formulations. For the undamped system, all the algorithms are symplectic and unconditionally stable with respect to the time step. For the damped system, they are shown to be accurate with good convergence characteristics.

  11. Representing operations procedures using temporal dependency networks

    NASA Technical Reports Server (NTRS)

    Fayyad, Kristina E.; Cooper, Lynne P.

    1993-01-01

    DSN Link Monitor & Control (LMC) operations consist primarily of executing procedures to configure, calibrate, test, and operate a communications link between an interplanetary spacecraft and its mission control center. Currently the LMC operators are responsible for integrating procedures into an end-to-end series of steps. The research presented in this paper is investigating new ways of specifying operations procedures that incorporate the insight of operations, engineering, and science personnel to improve mission operations. The paper describes the rationale for using Temporal Dependency Networks (TDN's) to represent the procedures, a description of how the data is acquired, and the knowledge engineering effort required to represent operations procedures. Results of operational tests of this concept, as implemented in the LMC Operator Assistant Prototype (LMCOA), are also presented.

  12. Applying temporal network analysis to the venture capital market

    NASA Astrophysics Data System (ADS)

    Zhang, Xin; Feng, Ling; Zhu, Rongqian; Stanley, H. Eugene

    2015-10-01

    Using complex network theory to study the investment relationships of venture capital firms has produced a number of significant results. However, previous studies have often neglected the temporal properties of those relationships, which in real-world scenarios play a pivotal role. Here we examine the time-evolving dynamics of venture capital investment in China by constructing temporal networks to represent (i) investment relationships between venture capital firms and portfolio companies and (ii) the syndication ties between venture capital investors. The evolution of the networks exhibits rich variations in centrality, connectivity and local topology. We demonstrate that a temporal network approach provides a dynamic and comprehensive analysis of real-world networks.

  13. Spatio-temporal networks: reachability, centrality and robustness

    PubMed Central

    Musolesi, Mirco

    2016-01-01

    Recent advances in spatial and temporal networks have enabled researchers to more-accurately describe many real-world systems such as urban transport networks. In this paper, we study the response of real-world spatio-temporal networks to random error and systematic attack, taking a unified view of their spatial and temporal performance. We propose a model of spatio-temporal paths in time-varying spatially embedded networks which captures the property that, as in many real-world systems, interaction between nodes is non-instantaneous and governed by the space in which they are embedded. Through numerical experiments on three real-world urban transport systems, we study the effect of node failure on a network's topological, temporal and spatial structure. We also demonstrate the broader applicability of this framework to three other classes of network. To identify weaknesses specific to the behaviour of a spatio-temporal system, we introduce centrality measures that evaluate the importance of a node as a structural bridge and its role in supporting spatio-temporally efficient flows through the network. This exposes the complex nature of fragility in a spatio-temporal system, showing that there is a variety of failure modes when a network is subject to systematic attacks. PMID:27429776

  14. Locating the source of spreading in temporal networks

    NASA Astrophysics Data System (ADS)

    Huang, Qiangjuan; Zhao, Chengli; Zhang, Xue; Yi, Dongyun

    2017-02-01

    The topological structure of many real networks changes with time. Thus, locating the sources of a temporal network is a creative and challenging problem, as the enormous size of many real networks makes it unfeasible to observe the state of all nodes. In this paper, we propose an algorithm to solve this problem, named the backward temporal diffusion process. The proposed algorithm calculates the shortest temporal distance to locate the transmission source. We assume that the spreading process can be modeled as a simple diffusion process and by consensus dynamics. To improve the location accuracy, we also adopt four strategies to select which nodes should be observed by ranking their importance in the temporal network. Our paper proposes a highly accurate method for locating the source in temporal networks and is, to the best of our knowledge, a frontier work in this field. Moreover, our framework has important significance for controlling the transmission of diseases or rumors and formulating immediate immunization strategies.

  15. On the viscosity stratification in temporal mixing layer

    NASA Astrophysics Data System (ADS)

    Danaila, Luminita; Taguelmimt, Noureddine; Hadjadj, Abdellah; Turbulence Team

    2015-11-01

    We assess the effects of viscosity variations in low-speed temporally-evolving turbulent mixing layer. The two streams are density-matched, but the slow fluid is Rv times more viscous than the rapid stream. Direct Numerical Simulations (DNS) are performed for several viscosity ratios, Rv varying between 1 and 9. The space-time evolution of Variable-Viscosity Flow (VVF) is compared with that of the Constant-Viscosity Flow (CVF). The velocity fluctuations occur earlier and are more enhanced for VVF. In particular, the kinetic energy peaks earlier and is up to three times larger for VVF than for CVF at the earliest stages of the flow. Over the first stages of the flow, the temporal growth rate of the fluctuations kinetic energy is exponential, in full agreement with linear stability theory. The transport equation for the fluctuations kinetic energy is favourably compared with simulations data. The enhanced kinetic energy for VVF is mainly due to an increased production at the interface between the two fluids, in tight correlation with enlarged values of mean velocity gradient at the inflection point of the mean velocity profile. The transport equations of the one-and two-point kinetic energy show that self-preservation cannot be complete in variable-viscosity flows. ANR is acknowledged for financial support.

  16. Spatio-Temporal Clustering of Monitoring Network

    NASA Astrophysics Data System (ADS)

    Hussain, I.; Pilz, J.

    2009-04-01

    Pakistan has much diversity in seasonal variation of different locations. Some areas are in desserts and remain very hot and waterless, for example coastal areas are situated along the Arabian Sea and have very warm season and a little rainfall. Some areas are covered with mountains, have very low temperature and heavy rainfall; for instance Karakoram ranges. The most important variables that have an impact on the climate are temperature, precipitation, humidity, wind speed and elevation. Furthermore, it is hard to find homogeneous regions in Pakistan with respect to climate variation. Identification of homogeneous regions in Pakistan can be useful in many aspects. It can be helpful for prediction of the climate in the sub-regions and for optimizing the number of monitoring sites. In the earlier literature no one tried to identify homogeneous regions of Pakistan with respect to climate variation. There are only a few papers about spatio-temporal clustering of monitoring network. Steinhaus (1956) presented the well-known K-means clustering method. It can identify a predefined number of clusters by iteratively assigning centriods to clusters based. Castro et al. (1997) developed a genetic heuristic algorithm to solve medoids based clustering. Their method is based on genetic recombination upon random assorting recombination. The suggested method is appropriate for clustering the attributes which have genetic characteristics. Sap and Awan (2005) presented a robust weighted kernel K-means algorithm incorporating spatial constraints for clustering climate data. The proposed algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis by exploring patterns and structures in the data. Soltani and Modarres (2006) used hierarchical and divisive cluster analysis to categorize patterns of rainfall in Iran. They only considered rainfall at twenty-eight monitoring sites and concluded that eight clusters

  17. Dynamic-Sensitive centrality of nodes in temporal networks

    PubMed Central

    Huang, Da-Wen; Yu, Zu-Guo

    2017-01-01

    Locating influential nodes in temporal networks has attracted a lot of attention as data driven and diverse applications. Classic works either looked at analysing static networks or placed too much emphasis on the topological information but rarely highlighted the dynamics. In this paper, we take account the network dynamics and extend the concept of Dynamic-Sensitive centrality to temporal network. According to the empirical results on three real-world temporal networks and a theoretical temporal network for susceptible-infected-recovered (SIR) models, the temporal Dynamic-Sensitive centrality (TDC) is more accurate than both static versions and temporal versions of degree, closeness and betweenness centrality. As an application, we also use TDC to analyse the impact of time-order on spreading dynamics, we find that both topological structure and dynamics contribute the impact on the spreading influence of nodes, and the impact of time-order on spreading influence will be stronger when spreading rate b deviated from the epidemic threshold bc, especially for the temporal scale-free networks. PMID:28150735

  18. Dynamic-Sensitive centrality of nodes in temporal networks

    NASA Astrophysics Data System (ADS)

    Huang, Da-Wen; Yu, Zu-Guo

    2017-02-01

    Locating influential nodes in temporal networks has attracted a lot of attention as data driven and diverse applications. Classic works either looked at analysing static networks or placed too much emphasis on the topological information but rarely highlighted the dynamics. In this paper, we take account the network dynamics and extend the concept of Dynamic-Sensitive centrality to temporal network. According to the empirical results on three real-world temporal networks and a theoretical temporal network for susceptible-infected-recovered (SIR) models, the temporal Dynamic-Sensitive centrality (TDC) is more accurate than both static versions and temporal versions of degree, closeness and betweenness centrality. As an application, we also use TDC to analyse the impact of time-order on spreading dynamics, we find that both topological structure and dynamics contribute the impact on the spreading influence of nodes, and the impact of time-order on spreading influence will be stronger when spreading rate b deviated from the epidemic threshold bc, especially for the temporal scale-free networks.

  19. Lightweight simulation of air traffic control using simple temporal networks

    NASA Technical Reports Server (NTRS)

    Knight, Russell

    2005-01-01

    We provide a formulation of the air traffic control problem and a solver for this problem that makes use of temporal constraint networks and simple geometric reasoning. We provide results showing that this approach is practical for realistic simulated problems.

  20. Identify Dynamic Network Modules with Temporal and Spatial Constraints

    SciTech Connect

    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.

  1. Cyber War Game in Temporal Networks

    DTIC Science & Technology

    2016-02-09

    Research Laboratory, Adelphi, MD 20783, United States of America, 2Center for Complex Network Research and Department of Physics, Northeastern University...Boston, Massachusetts 02115, United States of America * jianxi.gao@gmail.com Abstract In a cyber war game where a network is fully distributed and

  2. Duplication: a Mechanism Producing Disassortative Mixing Networks in Biology

    NASA Astrophysics Data System (ADS)

    Zhao, Dan; Liu, Zeng-Rong; Wang, Jia-Zeng

    2007-10-01

    Assortative/disassortative mixing is an important topological property of a network. A network is called assortative mixing if the nodes in the network tend to connect to their connectivity peers, or disassortative mixing if nodes with low degrees are more likely to connect with high-degree nodes. We have known that biological networks such as protein-protein interaction networks (PPI), gene regulatory networks, and metabolic networks tend to be disassortative. On the other hand, in biological evolution, duplication and divergence are two fundamental processes. In order to make the relationship between the property of disassortative mixing and the two basic biological principles clear and to study the cause of the disassortative mixing property in biological networks, we present a random duplication model and an anti-preference duplication model. Our results show that disassortative mixing networks can be obtained by both kinds of models from uncorrelated initial networks. Moreover, with the growth of the network size, the disassortative mixing property becomes more obvious.

  3. Higher-order aggregate networks in the analysis of temporal networks: path structures and centralities

    NASA Astrophysics Data System (ADS)

    Scholtes, Ingo; Wider, Nicolas; Garas, Antonios

    2016-03-01

    Despite recent advances in the study of temporal networks, the analysis of time-stamped network data is still a fundamental challenge. In particular, recent studies have shown that correlations in the ordering of links crucially alter causal topologies of temporal networks, thus invalidating analyses based on static, time-aggregated representations of time-stamped data. These findings not only highlight an important dimension of complexity in temporal networks, but also call for new network-analytic methods suitable to analyze complex systems with time-varying topologies. Addressing this open challenge, here we introduce a novel framework for the study of path-based centralities in temporal networks. Studying betweenness, closeness and reach centrality, we first show than an application of these measures to time-aggregated, static representations of temporal networks yields misleading results about the actual importance of nodes. To overcome this problem, we define path-based centralities in higher-order aggregate networks, a recently proposed generalization of the commonly used static representation of time-stamped data. Using data on six empirical temporal networks, we show that the resulting higher-order measures better capture the true, temporal centralities of nodes. Our results demonstrate that higher-order aggregate networks constitute a powerful abstraction, with broad perspectives for the design of new, computationally efficient data mining techniques for time-stamped relational data.

  4. Temporal and spatial stability in translation invariant linear resistive networks.

    PubMed

    Solak, M K

    1997-01-01

    Simple algebraic methods are proposed to evaluate the temporal and spatial stability of translation invariant linear resistive networks. Temporal stability is discussed for a finite number of nodes n. The proposed method evaluates stability of a Toeplitz pencil A(n)(a)+muB(n)(b) in terms of parameters a(i ) and b(i). In many cases a simple method allows one to verify positive definition of B(n)(b) in terms of b(i) only.

  5. Temporal representation for gene networks: towards a qualitative temporal data mining.

    PubMed

    Turenne, Nicolas; Schwer, Sylviane R

    2008-01-01

    Processing literature (i.e., text corpora) to capture gene regulation events is not easy and can be driven by the final data representation. We propose to build, manually, an example of temporal representation (whole gene networks for coat formation in Bacillus Subtilis). Our temporal representation is based on a generalised formal language theory (S-languages). We propose an algorithm to link bags of relations with representation, by ordering interactions. In this paper, starting from the network made manually from text data, we show that S-languages are quite relevant to encapsulate gene properties, and infer knowledge across timestamped gene relations found in texts.

  6. Accelerating coordination in temporal networks by engineering the link order

    PubMed Central

    Masuda, Naoki

    2016-01-01

    Social dynamics on a network may be accelerated or decelerated depending on which pairs of individuals in the network communicate early and which pairs do later. The order with which the links in a given network are sequentially used, which we call the link order, may be a strong determinant of dynamical behaviour on networks, potentially adding a new dimension to effects of temporal networks relative to static networks. Here we study the effect of the link order on linear coordination (i.e., synchronisation) dynamics. We show that the coordination speed considerably depends on specific orders of links. In addition, applying each single link for a long time to ensure strong pairwise coordination before moving to a next pair of individuals does not often enhance coordination of the entire network. We also implement a simple greedy algorithm to optimise the link order in favour of fast coordination. PMID:26916093

  7. Imaging structural and functional brain networks in temporal lobe epilepsy

    PubMed Central

    Bernhardt, Boris C.; Hong, SeokJun; Bernasconi, Andrea; Bernasconi, Neda

    2013-01-01

    Early imaging studies in temporal lobe epilepsy (TLE) focused on the search for mesial temporal sclerosis, as its surgical removal results in clinically meaningful improvement in about 70% of patients. Nevertheless, a considerable subgroup of patients continues to suffer from post-operative seizures. Although the reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed brain networks. Recent methodological advances in non-invasive neuroimaging have led to quantify structural and functional networks in vivo. While structural networks can be inferred from diffusion MRI tractography and inter-regional covariance patterns of structural measures such as cortical thickness, functional connectivity is generally computed based on statistical dependencies of neurophysiological time-series, measured through functional MRI or electroencephalographic techniques. This review considers the application of advanced analytical methods in structural and functional connectivity analyses in TLE. We will specifically highlight findings from graph-theoretical analysis that allow assessing the topological organization of brain networks. These studies have provided compelling evidence that TLE is a system disorder with profound alterations in local and distributed networks. In addition, there is emerging evidence for the utility of network properties as clinical diagnostic markers. Nowadays, a network perspective is considered to be essential to the understanding of the development, progression, and management of epilepsy. PMID:24098281

  8. Temporal dynamics of connectivity and epidemic properties of growing networks

    NASA Astrophysics Data System (ADS)

    Fotouhi, Babak; Shirkoohi, Mehrdad Khani

    2016-01-01

    Traditional mathematical models of epidemic disease had for decades conventionally considered static structure for contacts. Recently, an upsurge of theoretical inquiry has strived towards rendering the models more realistic by incorporating the temporal aspects of networks of contacts, societal and online, that are of interest in the study of epidemics (and other similar diffusion processes). However, temporal dynamics have predominantly focused on link fluctuations and nodal activities, and less attention has been paid to the growth of the underlying network. Many real networks grow: Online networks are evidently in constant growth, and societal networks can grow due to migration flux and reproduction. The effect of network growth on the epidemic properties of networks is hitherto unknown, mainly due to the predominant focus of the network growth literature on the so-called steady state. This paper takes a step towards alleviating this gap. We analytically study the degree dynamics of a given arbitrary network that is subject to growth. We use the theoretical findings to predict the epidemic properties of the network as a function of time. We observe that the introduction of new individuals into the network can enhance or diminish its resilience against endemic outbreaks and investigate how this regime shift depends upon the connectivity of newcomers and on how they establish connections to existing nodes. Throughout, theoretical findings are corroborated with Monte Carlo simulations over synthetic and real networks. The results shed light on the effects of network growth on the future epidemic properties of networks and offers insights for devising a priori immunization strategies.

  9. Spatial-Temporal Quantification of Interdependencies Across Infrastructure Networks

    NASA Astrophysics Data System (ADS)

    Chan, Christopher; Dueñas-Osorio, Leonardo

    As infrastructure networks become more complex and intertwined, the relevance of network interdependency research is increasingly evident. Interconnected networks bring about efficiencies during normal operations but also come with risks of cascading failures with disaster events. An adequate understanding of network interdependencies and realistic multi-system modeling capabilities enable the exploration of practical operation strategies and mitigation efforts applicable to existing or future coupled networked systems. This chapter examines recent efforts in quantifying infrastructure network interdependencies through spatial and time-series analyses to reveal the heterogeneity and complexity in their coupling. Furthermore, a combined spatial-temporal methodology is recommended for the future calibration and validation of theoretical and computational models of interdependent networks of networks. An example case study is demonstrated using data derived from the 2010 Chilean Earthquake in the Talcahuano-Concepción region, which highlights the richness in coupling strengths across infrastructure systems, both as a function of time and geographical extent. Insights for design and control of coupled networks are also derivable from joint spatial-temporal analyses of infrastructure interdependence and its evolution.

  10. Roles of mixing patterns in the network reconstruction

    NASA Astrophysics Data System (ADS)

    Guo, Qiang; Liang, Guang; Fu, Jia-Qi; Han, Jing-Ti; Liu, Jian-Guo

    2016-11-01

    Compressive sensing is an effective way to reconstruct the network structure. In this paper, we investigate the effect of the mixing patterns, measured by the assortative coefficient, on the performance of network reconstruction. First, we present a model to generate networks with different assortativity coefficients, then we reconstruct the network structure by using the compressive sensing method. The experimental results show that when the assortativity coefficient r =0.2 , the accuracy of the network reconstruction reaches the maximum value, which suggests that the compressive sensing is more effective for uncovering the links of social networks. Moreover, the accuracy of the network reconstruction will be higher as the network size increases.

  11. Roles of mixing patterns in the network reconstruction.

    PubMed

    Guo, Qiang; Liang, Guang; Fu, Jia-Qi; Han, Jing-Ti; Liu, Jian-Guo

    2016-11-01

    Compressive sensing is an effective way to reconstruct the network structure. In this paper, we investigate the effect of the mixing patterns, measured by the assortative coefficient, on the performance of network reconstruction. First, we present a model to generate networks with different assortativity coefficients, then we reconstruct the network structure by using the compressive sensing method. The experimental results show that when the assortativity coefficient r=0.2, the accuracy of the network reconstruction reaches the maximum value, which suggests that the compressive sensing is more effective for uncovering the links of social networks. Moreover, the accuracy of the network reconstruction will be higher as the network size increases.

  12. Models, Entropy and Information of Temporal Social Networks

    NASA Astrophysics Data System (ADS)

    Zhao, Kun; Karsai, Márton; Bianconi, Ginestra

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

  13. Anterior temporal lobe degeneration produces widespread network-driven dysfunction.

    PubMed

    Guo, Christine C; Gorno-Tempini, Maria Luisa; Gesierich, Benno; Henry, Maya; Trujillo, Andrew; Shany-Ur, Tal; Jovicich, Jorge; Robinson, Simon D; Kramer, Joel H; Rankin, Katherine P; Miller, Bruce L; Seeley, William W

    2013-10-01

    The neural organization of semantic memory remains much debated. A 'distributed-only' view contends that semantic knowledge is represented within spatially distant, modality-selective primary and association cortices. Observations in semantic variant primary progressive aphasia have inspired an alternative model featuring the anterior temporal lobe as an amodal hub that supports semantic knowledge by linking distributed modality-selective regions. Direct evidence has been lacking, however, to support intrinsic functional interactions between an anterior temporal lobe hub and upstream sensory regions in humans. Here, we examined the neural networks supporting semantic knowledge by performing a multimodal brain imaging study in healthy subjects and patients with semantic variant primary progressive aphasia. In healthy subjects, the anterior temporal lobe showed intrinsic connectivity to an array of modality-selective primary and association cortices. Patients showed focal anterior temporal lobe degeneration but also reduced physiological integrity throughout distributed modality-selective regions connected with the anterior temporal lobe in healthy controls. Physiological deficits outside the anterior temporal lobe correlated with scores on semantic tasks and with anterior temporal subregion atrophy, following domain-specific and connectivity-based predictions. The findings provide a neurophysiological basis for the theory that semantic processing is orchestrated through interactions between a critical anterior temporal lobe hub and modality-selective processing nodes.

  14. Dynamics on networks: competition of temporal and topological correlations

    PubMed Central

    Artime, Oriol; Ramasco, José J.; San Miguel, Maxi

    2017-01-01

    Links in many real-world networks activate and deactivate in correspondence to the sporadic interactions between the elements of the system. The activation patterns may be irregular or bursty and play an important role on the dynamics of processes taking place in the network. Information or disease spreading in networks are paradigmatic examples of this situation. Besides burstiness, several correlations may appear in the process of link activation: memory effects imply temporal correlations, but also the existence of communities in the network may mediate the activation patterns of internal an external links. Here we study the competition of topological and temporal correlations in link activation and how they affect the dynamics of systems running on the network. Interestingly, both types of correlations by separate have opposite effects: one (topological) delays the dynamics of processes on the network, while the other (temporal) accelerates it. When they occur together, our results show that the direction and intensity of the final outcome depends on the competition in a non trivial way. PMID:28150708

  15. Dynamics on networks: competition of temporal and topological correlations

    NASA Astrophysics Data System (ADS)

    Artime, Oriol; Ramasco, José J.; San Miguel, Maxi

    2017-02-01

    Links in many real-world networks activate and deactivate in correspondence to the sporadic interactions between the elements of the system. The activation patterns may be irregular or bursty and play an important role on the dynamics of processes taking place in the network. Information or disease spreading in networks are paradigmatic examples of this situation. Besides burstiness, several correlations may appear in the process of link activation: memory effects imply temporal correlations, but also the existence of communities in the network may mediate the activation patterns of internal an external links. Here we study the competition of topological and temporal correlations in link activation and how they affect the dynamics of systems running on the network. Interestingly, both types of correlations by separate have opposite effects: one (topological) delays the dynamics of processes on the network, while the other (temporal) accelerates it. When they occur together, our results show that the direction and intensity of the final outcome depends on the competition in a non trivial way.

  16. Spectral properties of the temporal evolution of brain network structure.

    PubMed

    Wang, Rong; Zhang, Zhen-Zhen; Ma, Jun; Yang, Yong; Lin, Pan; Wu, Ying

    2015-12-01

    The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems.

  17. Spectral properties of the temporal evolution of brain network structure

    NASA Astrophysics Data System (ADS)

    Wang, Rong; Zhang, Zhen-Zhen; Ma, Jun; Yang, Yong; Lin, Pan; Wu, Ying

    2015-12-01

    The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems.

  18. Temporal modulation of collective cell behavior controls vascular network topology.

    PubMed

    Kur, Esther; Kim, Jiha; Tata, Aleksandra; Comin, Cesar H; Harrington, Kyle I; Costa, Luciano da F; Bentley, Katie; Gu, Chenghua

    2016-02-24

    Vascular network density determines the amount of oxygen and nutrients delivered to host tissues, but how the vast diversity of densities is generated is unknown. Reiterations of endothelial-tip-cell selection, sprout extension and anastomosis are the basis for vascular network generation, a process governed by the VEGF/Notch feedback loop. Here, we find that temporal regulation of this feedback loop, a previously unexplored dimension, is the key mechanism to determine vascular density. Iterating between computational modeling and in vivo live imaging, we demonstrate that the rate of tip-cell selection determines the length of linear sprout extension at the expense of branching, dictating network density. We provide the first example of a host tissue-derived signal (Semaphorin3E-Plexin-D1) that accelerates tip cell selection rate, yielding a dense network. We propose that temporal regulation of this critical, iterative aspect of network formation could be a general mechanism, and additional temporal regulators may exist to sculpt vascular topology.

  19. Temporal modulation of collective cell behavior controls vascular network topology

    PubMed Central

    Kur, Esther; Kim, Jiha; Tata, Aleksandra; Comin, Cesar H; Harrington, Kyle I; Costa, Luciano da F; Bentley, Katie; Gu, Chenghua

    2016-01-01

    Vascular network density determines the amount of oxygen and nutrients delivered to host tissues, but how the vast diversity of densities is generated is unknown. Reiterations of endothelial-tip-cell selection, sprout extension and anastomosis are the basis for vascular network generation, a process governed by the VEGF/Notch feedback loop. Here, we find that temporal regulation of this feedback loop, a previously unexplored dimension, is the key mechanism to determine vascular density. Iterating between computational modeling and in vivo live imaging, we demonstrate that the rate of tip-cell selection determines the length of linear sprout extension at the expense of branching, dictating network density. We provide the first example of a host tissue-derived signal (Semaphorin3E-Plexin-D1) that accelerates tip cell selection rate, yielding a dense network. We propose that temporal regulation of this critical, iterative aspect of network formation could be a general mechanism, and additional temporal regulators may exist to sculpt vascular topology. DOI: http://dx.doi.org/10.7554/eLife.13212.001 PMID:26910011

  20. Synchronization in output-coupled temporal Boolean networks

    NASA Astrophysics Data System (ADS)

    Lu, Jianquan; Zhong, Jie; Tang, Yang; Huang, Tingwen; Cao, Jinde; Kurths, Jürgen

    2014-09-01

    This paper presents an analytical study of synchronization in an array of output-coupled temporal Boolean networks. A temporal Boolean network (TBN) is a logical dynamic system developed to model Boolean networks with regulatory delays. Both state delay and output delay are considered, and these two delays are assumed to be different. By referring to the algebraic representations of logical dynamics and using the semi-tensor product of matrices, the output-coupled TBNs are firstly converted into a discrete-time algebraic evolution system, and then the relationship between the states of coupled TBNs and the initial state sequence is obtained. Then, some necessary and sufficient conditions are derived for the synchronization of an array of TBNs with an arbitrary given initial state sequence. Two numerical examples including one epigenetic model are finally given to illustrate the obtained results.

  1. Synchronization in output-coupled temporal Boolean networks

    PubMed Central

    Lu, Jianquan; Zhong, Jie; Tang, Yang; Huang, Tingwen; Cao, Jinde; Kurths, Jürgen

    2014-01-01

    This paper presents an analytical study of synchronization in an array of output-coupled temporal Boolean networks. A temporal Boolean network (TBN) is a logical dynamic system developed to model Boolean networks with regulatory delays. Both state delay and output delay are considered, and these two delays are assumed to be different. By referring to the algebraic representations of logical dynamics and using the semi-tensor product of matrices, the output-coupled TBNs are firstly converted into a discrete-time algebraic evolution system, and then the relationship between the states of coupled TBNs and the initial state sequence is obtained. Then, some necessary and sufficient conditions are derived for the synchronization of an array of TBNs with an arbitrary given initial state sequence. Two numerical examples including one epigenetic model are finally given to illustrate the obtained results. PMID:25189531

  2. Detailed temporal structure of communication networks in groups of songbirds

    PubMed Central

    Clayton, David

    2016-01-01

    Animals in groups often exchange calls, in patterns whose temporal structure may be influenced by contextual factors such as physical location and the social network structure of the group. We introduce a model-based analysis for temporal patterns of animal call timing, originally developed for networks of firing neurons. This has advantages over cross-correlation analysis in that it can correctly handle common-cause confounds and provides a generative model of call patterns with explicit parameters for the influences between individuals. It also has advantages over standard Markovian analysis in that it incorporates detailed temporal interactions which affect timing as well as sequencing of calls. Further, a fitted model can be used to generate novel synthetic call sequences. We apply the method to calls recorded from groups of domesticated zebra finch (Taeniopygia guttata) individuals. We find that the communication network in these groups has stable structure that persists from one day to the next, and that ‘kernels’ reflecting the temporal range of influence have a characteristic structure for a calling individual's effect on itself, its partner and on others in the group. We further find characteristic patterns of influences by call type as well as by individual. PMID:27335223

  3. Detailed temporal structure of communication networks in groups of songbirds.

    PubMed

    Stowell, Dan; Gill, Lisa; Clayton, David

    2016-06-01

    Animals in groups often exchange calls, in patterns whose temporal structure may be influenced by contextual factors such as physical location and the social network structure of the group. We introduce a model-based analysis for temporal patterns of animal call timing, originally developed for networks of firing neurons. This has advantages over cross-correlation analysis in that it can correctly handle common-cause confounds and provides a generative model of call patterns with explicit parameters for the influences between individuals. It also has advantages over standard Markovian analysis in that it incorporates detailed temporal interactions which affect timing as well as sequencing of calls. Further, a fitted model can be used to generate novel synthetic call sequences. We apply the method to calls recorded from groups of domesticated zebra finch (Taeniopygia guttata) individuals. We find that the communication network in these groups has stable structure that persists from one day to the next, and that 'kernels' reflecting the temporal range of influence have a characteristic structure for a calling individual's effect on itself, its partner and on others in the group. We further find characteristic patterns of influences by call type as well as by individual.

  4. Dynamics of history-dependent epidemics in temporal networks

    NASA Astrophysics Data System (ADS)

    Sunny, Albert; Kotnis, Bhushan; Kuri, Joy

    2015-08-01

    The structural properties of temporal networks often influence the dynamical processes that occur on these networks, e.g., bursty interaction patterns have been shown to slow down epidemics. In this paper, we investigate the effect of link lifetimes on the spread of history-dependent epidemics. We formulate an analytically tractable activity-driven temporal network model that explicitly incorporates link lifetimes. For Markovian link lifetimes, we use mean-field analysis for computing the epidemic threshold, while the effect of non-Markovian link lifetimes is studied using simulations. Furthermore, we also study the effect of negative correlation between the number of links spawned by an individual and the lifetimes of those links. Such negative correlations may arise due to the finite cognitive capacity of the individuals. Our investigations reveal that heavy-tailed link lifetimes slow down the epidemic, while negative correlations can reduce epidemic prevalence. We believe that our results help shed light on the role of link lifetimes in modulating diffusion processes on temporal networks.

  5. The Implications of a Mixed Media Network for Information Interchange.

    ERIC Educational Resources Information Center

    Meaney, John W.

    A mixed media network for information interchange is what we are always likely to have. Amid the current permutations of the storage and distribution media we see the emergence of two trends -- toward the common denominators of electronic display on the TV system and of digital processing and control. The economic implications of a mixed network…

  6. Spatial-temporal modeling of malware propagation in networks.

    PubMed

    Chen, Zesheng; Ji, Chuanyi

    2005-09-01

    Network security is an important task of network management. One threat to network security is malware (malicious software) propagation. One type of malware is called topological scanning that spreads based on topology information. The focus of this work is on modeling the spread of topological malwares, which is important for understanding their potential damages, and for developing countermeasures to protect the network infrastructure. Our model is motivated by probabilistic graphs, which have been widely investigated in machine learning. We first use a graphical representation to abstract the propagation of malwares that employ different scanning methods. We then use a spatial-temporal random process to describe the statistical dependence of malware propagation in arbitrary topologies. As the spatial dependence is particularly difficult to characterize, the problem becomes how to use simple (i.e., biased) models to approximate the spatially dependent process. In particular, we propose the independent model and the Markov model as simple approximations. We conduct both theoretical analysis and extensive simulations on large networks using both real measurements and synthesized topologies to test the performance of the proposed models. Our results show that the independent model can capture temporal dependence and detailed topology information and, thus, outperforms the previous models, whereas the Markov model incorporates a certain spatial dependence and, thus, achieves a greater accuracy in characterizing both transient and equilibrium behaviors of malware propagation.

  7. Associative Memory Neural Network with Low Temporal Spiking Rates

    NASA Astrophysics Data System (ADS)

    Amit, Daniel J.; Treves, A.

    1989-10-01

    We describe a modified attractor neural network in which neuronal dynamics takes place on a time scale of the absolute refractory period but the mean temporal firing rate of any neuron in the network is lower by an arbitrary factor that characterizes the strength of the effective inhibition. It operates by encoding information on the excitatory neurons only and assuming the inhibitory neurons to be faster and to inhibit the excitatory ones by an effective postsynaptic potential that is expressed in terms of the activity of the excitatory neurons themselves. Retrieval is identified as a nonergodic behavior of the network whose consecutive states have a significantly enhanced activity rate for the neurons that should be active in a stored pattern and a reduced activity rate for the neurons that are inactive in the memorized pattern. In contrast to the Hopfield model the network operates away from fixed points and under the strong influence of noise. As a consequence, of the neurons that should be active in a pattern, only a small fraction is active in any given time cycle and those are randomly distributed, leading to reduced temporal rates. We argue that this model brings neural network models much closer to biological reality. We present the results of detailed analysis of the model as well as simulations.

  8. Mixing properties of growing networks and Simpson's paradox.

    PubMed

    Capocci, Andrea; Colaiori, Francesca

    2006-08-01

    The mixing properties of networks are usually inferred by comparing the degree of a node with the average degree of its neighbors. This kind of analysis often leads to incorrect conclusions: Assortative patterns may appear reversed by a mechanism known as Simpson's paradox. We prove this fact by analytical calculations and simulations on three classes of growing networks based on preferential attachment and fitness, where the disassortative behavior observed is a spurious effect. Our results give a crucial contribution to the debate about the origin of disassortative mixing, since networks previously classified as disassortative reveal instead assortative behavior to a careful analysis.

  9. Complex earthquake networks: Hierarchical organization and assortative mixing

    NASA Astrophysics Data System (ADS)

    Abe, Sumiyoshi; Suzuki, Norikazu

    2006-08-01

    To characterize the dynamical features of seismicity as a complex phenomenon, the seismic data are mapped to a growing random graph, which is a small-world scale-free network. Here, hierarchical and mixing properties of such a network are studied. The clustering coefficient is found to exhibit asymptotic power-law decay with respect to connectivity, showing hierarchical organization. This structure is supported by not only main shocks but also small shocks, and may have its origin in the combined effect of vertex fitness and deactivation by stress release at faults. The nearest-neighbor average connectivity and the Pearson correlation coefficient are also calculated. It is found that the earthquake network has assortative mixing. This is a main difference of the earthquake network from the Internet with disassortative mixing. Physical implications of these results are discussed.

  10. From blickets to synapses: inferring temporal causal networks by observation.

    PubMed

    Fernando, Chrisantha

    2013-01-01

    How do human infants learn the causal dependencies between events? Evidence suggests that this remarkable feat can be achieved by observation of only a handful of examples. Many computational models have been produced to explain how infants perform causal inference without explicit teaching about statistics or the scientific method. Here, we propose a spiking neuronal network implementation that can be entrained to form a dynamical model of the temporal and causal relationships between events that it observes. The network uses spike-time dependent plasticity, long-term depression, and heterosynaptic competition rules to implement Rescorla-Wagner-like learning. Transmission delays between neurons allow the network to learn a forward model of the temporal relationships between events. Within this framework, biologically realistic synaptic plasticity rules account for well-known behavioral data regarding cognitive causal assumptions such as backwards blocking and screening-off. These models can then be run as emulators for state inference. Furthermore, this mechanism is capable of copying synaptic connectivity patterns between neuronal networks by observing the spontaneous spike activity from the neuronal circuit that is to be copied, and it thereby provides a powerful method for transmission of circuit functionality between brain regions.

  11. Spatio-temporal statistical models for river monitoring networks.

    PubMed

    Clement, L; Thas, O; Vanrolleghem, P A; Ottoy, J P

    2006-01-01

    When introducing new wastewater treatment plants (WWTP), investors and policy makers often want to know if there indeed is a beneficial effect of the installation of a WWTP on the river water quality. Such an effect can be established in time as well as in space. Since both temporal and spatial components affect the output of a monitoring network, their dependence structure has to be modelled. River water quality data typically come from a river monitoring network for which the spatial dependence structure is unidirectional. Thus the traditional spatio-temporal models are not appropriate, as they cannot take advantage of this directional information. In this paper, a state-space model is presented in which the spatial dependence of the state variable is represented by a directed acyclic graph, and the temporal dependence by a first-order autoregressive process. The state-space model is extended with a linear model for the mean to estimate the effect of the activation of a WWTP on the dissolved oxygen concentration downstream.

  12. Temporal-varying failures of nodes in networks

    NASA Astrophysics Data System (ADS)

    Knight, Georgie; Cristadoro, Giampaolo; Altmann, Eduardo G.

    2015-08-01

    We consider networks in which random walkers are removed because of the failure of specific nodes. We interpret the rate of loss as a measure of the importance of nodes, a notion we denote as failure centrality. We show that the degree of the node is not sufficient to determine this measure and that, in a first approximation, the shortest loops through the node have to be taken into account. We propose approximations of the failure centrality which are valid for temporal-varying failures, and we dwell on the possibility of externally changing the relative importance of nodes in a given network by exploiting the interference between the loops of a node and the cycles of the temporal pattern of failures. In the limit of long failure cycles we show analytically that the escape in a node is larger than the one estimated from a stochastic failure with the same failure probability. We test our general formalism in two real-world networks (air-transportation and e-mail users) and show how communities lead to deviations from predictions for failures in hubs.

  13. The multilayer temporal network of public transport in Great Britain

    NASA Astrophysics Data System (ADS)

    Gallotti, Riccardo; Barthelemy, Marc

    2015-01-01

    Despite the widespread availability of information concerning public transport coming from different sources, it is extremely hard to have a complete picture, in particular at a national scale. Here, we integrate timetable data obtained from the United Kingdom open-data program together with timetables of domestic flights, and obtain a comprehensive snapshot of the temporal characteristics of the whole UK public transport system for a week in October 2010. In order to focus on multi-modal aspects of the system, we use a coarse graining procedure and define explicitly the coupling between different transport modes such as connections at airports, ferry docks, rail, metro, coach and bus stations. The resulting weighted, directed, temporal and multilayer network is provided in simple, commonly used formats, ensuring easy access and the possibility of a straightforward use of old or specifically developed methods on this new and extensive dataset.

  14. The multilayer temporal network of public transport in Great Britain

    PubMed Central

    Gallotti, Riccardo; Barthelemy, Marc

    2015-01-01

    Despite the widespread availability of information concerning public transport coming from different sources, it is extremely hard to have a complete picture, in particular at a national scale. Here, we integrate timetable data obtained from the United Kingdom open-data program together with timetables of domestic flights, and obtain a comprehensive snapshot of the temporal characteristics of the whole UK public transport system for a week in October 2010. In order to focus on multi-modal aspects of the system, we use a coarse graining procedure and define explicitly the coupling between different transport modes such as connections at airports, ferry docks, rail, metro, coach and bus stations. The resulting weighted, directed, temporal and multilayer network is provided in simple, commonly used formats, ensuring easy access and the possibility of a straightforward use of old or specifically developed methods on this new and extensive dataset. PMID:25977806

  15. Cortical Networks Involved in Memory for Temporal Order.

    PubMed

    Manelis, Anna; Popov, Vencislav; Paynter, Christopher; Walsh, Matthew; Wheeler, Mark E; Vogt, Keith M; Reder, Lynne M

    2017-03-15

    We examined the neurobiological basis of temporal resetting, an aspect of temporal order memory, using a version of the delayed-match-to-multiple-sample task. While in an fMRI scanner, participants evaluated whether an item was novel or whether it had appeared before or after a reset event that signified the start of a new block of trials. Participants responded "old" to items that were repeated within the current block and "new" to both novel items and items that had last appeared before the reset event (pseudonew items). Medial-temporal, prefrontal, and occipital regions responded to absolute novelty of the stimulus-they differentiated between novel items and previously seen items, but not between old and pseudonew items. Activation for pseudonew items in the frontopolar and parietal regions, in contrast, was intermediate between old and new items. The posterior cingulate cortex extending to precuneus was the only region that showed complete temporal resetting, and its activation reflected whether an item was new or old according to the task instructions regardless of its familiarity. There was also a significant Condition (old/pseudonew) × Familiarity (second/third presentations) interaction effect on behavioral and neural measures. For pseudonew items, greater familiarity decreased response accuracy, increased RTs, increased ACC activation, and increased functional connectivity between ACC and the left frontal pole. The reverse was observed for old items. On the basis of these results, we propose a theoretical framework in which temporal resetting relies on an episodic retrieval network that is modulated by cognitive control and conflict resolution.

  16. Granger causality stock market networks: Temporal proximity and preferential attachment

    NASA Astrophysics Data System (ADS)

    Výrost, Tomáš; Lyócsa, Štefan; Baumöhl, Eduard

    2015-06-01

    The structure of return spillovers is examined by constructing Granger causality networks using daily closing prices of 20 developed markets from 2nd January 2006 to 31st December 2013. The data is properly aligned to take into account non-synchronous trading effects. The study of the resulting networks of over 94 sub-samples revealed three significant findings. First, after the recent financial crisis the impact of the US stock market has declined. Second, spatial probit models confirmed the role of the temporal proximity between market closing times for return spillovers, i.e. the time distance between national stock markets matters. Third, a preferential attachment between stock markets exists, i.e. the probability of the presence of spillover effects between any given two markets increases with their degree of connectedness to others.

  17. Efficient stepwise detection of communities in temporal networks

    NASA Astrophysics Data System (ADS)

    He, Jialin; Chen, Duanbing; Sun, Chongjing; Fu, Yan; Li, Wenjun

    2017-03-01

    In temporal networks, dynamic community detection is composed of two separate stages: (i) community detection at each time step; (ii) community matching across time steps. In the traditional methods, the community matching across time steps is based on nodes, which is time consuming. In this paper, we suggest a simple method which takes advantage of historic community information to detect dynamic communities. After dividing each community at previous time step into a few modules, we cannot only use these modules to detect communities at current time step but also map communities across time steps. Results on synthetic and real networks demonstrate that our method cannot only maintain the quality of communities but also improve the efficiency of community matching significantly.

  18. A mixing evolution model for bidirectional microblog user networks

    NASA Astrophysics Data System (ADS)

    Yuan, Wei-Guo; Liu, Yun

    2015-08-01

    Microblogs have been widely used as a new form of online social networking. Based on the user profile data collected from Sina Weibo, we find that the number of microblog user bidirectional friends approximately corresponds with the lognormal distribution. We then build two microblog user networks with real bidirectional relationships, both of which have not only small-world and scale-free but also some special properties, such as double power-law degree distribution, disassortative network, hierarchical and rich-club structure. Moreover, by detecting the community structures of the two real networks, we find both of their community scales follow an exponential distribution. Based on the empirical analysis, we present a novel evolution network model with mixed connection rules, including lognormal fitness preferential and random attachment, nearest neighbor interconnected in the same community, and global random associations in different communities. The simulation results show that our model is consistent with real network in many topology features.

  19. Temporal Evolution Of Information In Neural Networks With Feedback

    NASA Astrophysics Data System (ADS)

    Giahi Saravani, Aram; Pitkow, Xaq

    2015-03-01

    Recurrent neural networks are pivotal for information processing in the brain. Here we analyze how the information content of a neural population is altered by dynamic feedback of a stimulus estimated from the network activity. We find that the temporal evolution of the Fisher information in the model with feedback is bounded by the Fisher information in a network of pure integrators. The available information in the feedback model saturates with a time constant and to a final level both determined by the match between the estimator weights and the feedback weights. This network then encodes signals specifically from either the beginning or the end of the stimulus presentation, depending on this match. These results are relevant to recent experimental measurements of psychophysical kernels indicating that earlier stimuli have a stronger influence on perceptual discriminations than more recent stimuli. We discuss consequences of this description for choice correlations, a measure of how individual neuronal responses relate to perceptual estimates. McNair Foundation, Baylor College of Medicine, Rice University.

  20. A temporal bottleneck in the language comprehension network.

    PubMed

    Vagharchakian, Laurianne; Dehaene-Lambertz, Ghislaine; Pallier, Christophe; Dehaene, Stanislas

    2012-06-27

    Humans can understand spoken or written sentences presented at extremely fast rates of ∼400 wpm, far exceeding the normal speech rate (∼150 wpm). How does the brain cope with speeded language? And what processing bottlenecks eventually make language incomprehensible above a certain presentation rate? We used time-resolved fMRI to probe the brain responses to spoken and written sentences presented at five compression rates, ranging from intelligible (60-100% of the natural duration) to challenging (40%) and unintelligible (20%). The results show that cortical areas differ sharply in their activation speed and amplitude. In modality-specific sensory areas, activation varies linearly with stimulus duration. However, a large modality-independent left-hemispheric language network, including the inferior frontal gyrus (pars orbitalis and triangularis) and the superior temporal sulcus, shows a remarkably time-invariant response, followed by a sudden collapse for unintelligible stimuli. Finally, linear and nonlinear responses, reflecting a greater effort as compression increases, are seen at various prefrontal and parietal sites. We show that these profiles fit with a simple model according to which the higher stages of language processing operate at a fixed speed and thus impose a temporal bottleneck on sentence comprehension. At presentation rates faster than this internal processing speed, incoming words must be buffered, and intelligibility vanishes when buffer storage and retrieval operations are saturated. Based on their temporal and amplitude profiles, buffer regions can be identified with the left inferior frontal/anterior insula, precentral cortex, and mesial frontal cortex.

  1. An in-depth longitudinal analysis of mixing patterns in a small scientific collaboration network

    SciTech Connect

    Rodriguez, Marko A; Pepe, Alberto

    2009-01-01

    Many investigations of scientific collaboration are based on large-scale statistical analyses of networks constructed from bibliographic repositories. These investigations often rely on a wealth of bibliographic data, but very little or no other information about the individuals in the network, and thus, fail to illustate the broader social and academic landscape in which collaboration takes place. In this article, we perform an in-depth longitudinal analysis of a small-scale network of scientific collaboration (N = 291) constructed from the bibliographic record of a research center involved in the development and application of sensor network technologies. We perform a preliminary analysis of selected structural properties of the network, computing its range, configuration and topology. We then support our preliminary statistical analysis with an in-depth temporal investigation of the assortativity mixing of these node characteristics: academic department, affiliation, position, and country of origin of the individuals in the network. Our qualitative analysis of mixing patterns offers clues as to the nature of the scientific community being modeled in relation to its organizational, disciplinary, institutional, and international arrangements of collaboration.

  2. Temporal Sequence of Hemispheric Network Activation during Semantic Processing: A Functional Network Connectivity Analysis

    ERIC Educational Resources Information Center

    Assaf, Michal; Jagannathan, Kanchana; Calhoun, Vince; Kraut, Michael; Hart, John, Jr.; Pearlson, Godfrey

    2009-01-01

    To explore the temporal sequence of, and the relationship between, the left and right hemispheres (LH and RH) during semantic memory (SM) processing we identified the neural networks involved in the performance of functional MRI semantic object retrieval task (SORT) using group independent component analysis (ICA) in 47 healthy individuals. SORT…

  3. Roles of clustering properties for degree-mixing pattern networks

    NASA Astrophysics Data System (ADS)

    Yu, Pei; Guo, Qiang; Li, Ren-De; Han, Jing-Ti; Liu, Jian-Guo

    The clustering coefficients have been extensively investigated for analyzing the local structural properties of complex networks. In this paper, the clustering coefficients for triangle and square structures, namely C3 and C4, are introduced to measure the local structure properties for different degree-mixing pattern networks. Firstly, a network model with tunable assortative coefficients is introduced. Secondly, the comparison results between the local clustering coefficients C3(k) and C4(k) are reported, one can find that the square structures would increase as the degree k of nodes increasing in disassortative networks. At the same time, the Pearson coefficient p between the clustering coefficients C3(k) and C4(k) is calculated for networks with different assortative coefficients. The Pearson coefficient p changes from ‑0.5 to 0.98 as the assortative coefficient r increasing from ‑0.5 to 0.45, which suggests that the triangle and square structures have the same growth trend in assortative networks whereas the opposite one in disassortative networks. Finally, we analyze the clustering coefficients and for networks with tunable assortative coefficients and find that the clustering coefficient increases from 0.0038 to 0.5952 while the clustering coefficient increases from 0.00039 to 0.005, indicating that the number of cliquishness of the disassortative networks is larger than that of assortative networks.

  4. Understanding Charge Transport in Mixed Networks of Semiconducting Carbon Nanotubes

    PubMed Central

    2016-01-01

    The ability to select and enrich semiconducting single-walled carbon nanotubes (SWNT) with high purity has led to a fast rise of solution-processed nanotube network field-effect transistors (FETs) with high carrier mobilities and on/off current ratios. However, it remains an open question whether it is best to use a network of only one nanotube species (monochiral) or whether a mix of purely semiconducting nanotubes but with different bandgaps is sufficient for high performance FETs. For a range of different polymer-sorted semiconducting SWNT networks, we demonstrate that a very small amount of narrow bandgap nanotubes within a dense network of large bandgap nanotubes can dominate the transport and thus severely limit on-currents and effective carrier mobility. Using gate-voltage-dependent electroluminescence, we spatially and spectrally reveal preferential charge transport that does not depend on nominal network density but on the energy level distribution within the network and carrier density. On the basis of these results, we outline rational guidelines for the use of mixed SWNT networks to obtain high performance FETs while reducing the cost for purification. PMID:26867006

  5. Adaption of the temporal correlation coefficient calculation for temporal networks (applied to a real-world pig trade network).

    PubMed

    Büttner, Kathrin; Salau, Jennifer; Krieter, Joachim

    2016-01-01

    The average topological overlap of two graphs of two consecutive time steps measures the amount of changes in the edge configuration between the two snapshots. This value has to be zero if the edge configuration changes completely and one if the two consecutive graphs are identical. Current methods depend on the number of nodes in the network or on the maximal number of connected nodes in the consecutive time steps. In the first case, this methodology breaks down if there are nodes with no edges. In the second case, it fails if the maximal number of active nodes is larger than the maximal number of connected nodes. In the following, an adaption of the calculation of the temporal correlation coefficient and of the topological overlap of the graph between two consecutive time steps is presented, which shows the expected behaviour mentioned above. The newly proposed adaption uses the maximal number of active nodes, i.e. the number of nodes with at least one edge, for the calculation of the topological overlap. The three methods were compared with the help of vivid example networks to reveal the differences between the proposed notations. Furthermore, these three calculation methods were applied to a real-world network of animal movements in order to detect influences of the network structure on the outcome of the different methods.

  6. The effects of temporal variability of mixed layer depth on primary productivity around Bermuda

    NASA Technical Reports Server (NTRS)

    Bissett, W. Paul; Meyers, Mark B.; Walsh, John J.; Mueller-Karger, Frank E.

    1994-01-01

    Temporal variations in primary production and surface chlorophyll concentrations, as measured by ship and satellite around Bermuda, were simulated with a numerical model. In the upper 450 m of the water column, population dynamics of a size-fractionated phytoplankton community were forced by daily changes of wind, light, grazing stress, and nutrient availability. The temporal variations of production and chlorophyll were driven by changes in nutrient introduction to the euphotic zone due to both high- and low-frequency changes of the mixed layer depth within 32 deg-34 deg N, 62 deg-64 deg W between 1979 and 1984. Results from the model derived from high-frequency (case 1) changes in the mixed layer depth showed variations in primary production and peak chlorophyll concentrations when compared with results from the model derived from low-frequency (case 2) mixed layer depth changes. Incorporation of size-fractionated plankton state variables in the model led to greater seasonal resolution of measured primary production and vertical chlorophyll profiles. The findings of this study highlight the possible inadequacy of estimating primary production in the sea from data of low-frequency temporal resolution and oversimplified biological simulations.

  7. Assortative mixing in functional brain networks during epileptic seizures

    NASA Astrophysics Data System (ADS)

    Bialonski, Stephan; Lehnertz, Klaus

    2013-09-01

    We investigate assortativity of functional brain networks before, during, and after one-hundred epileptic seizures with different anatomical onset locations. We construct binary functional networks from multi-channel electroencephalographic data recorded from 60 epilepsy patients; and from time-resolved estimates of the assortativity coefficient, we conclude that positive degree-degree correlations are inherent to seizure dynamics. While seizures evolve, an increasing assortativity indicates a segregation of the underlying functional network into groups of brain regions that are only sparsely interconnected, if at all. Interestingly, assortativity decreases already prior to seizure end. Together with previous observations of characteristic temporal evolutions of global statistical properties and synchronizability of epileptic brain networks, our findings may help to gain deeper insights into the complicated dynamics underlying generation, propagation, and termination of seizures.

  8. Subregional Mesiotemporal Network Topology Is Altered in Temporal Lobe Epilepsy.

    PubMed

    Bernhardt, Boris C; Bernasconi, Neda; Hong, Seok-Jun; Dery, Sebastian; Bernasconi, Andrea

    2016-07-01

    Temporal lobe epilepsy (TLE) is the most frequent drug-resistant epilepsy in adults and commonly associated with variable degrees of mesiotemporal atrophy on magnetic resonance imaging (MRI). Analyses of inter-regional connectivity have unveiled disruptions in large-scale cortico-cortical networks; little is known about the topological organization of the mesiotemporal lobe, the limbic subnetwork central to the disorder. We generated covariance networks based on high-resolution MRI surface-shape descriptors of the hippocampus, entorhinal cortex, and amygdala in 134 TLE patients and 45 age- and sex-matched controls. Graph-theoretical analysis revealed increased path length and clustering in patients, suggesting a shift toward a more regularized arrangement; findings were reproducible after split-half assessment and across 2 parcellation schemes. Analysis of inter-regional correlations and module participation showed increased within-structure covariance, but decreases between structures, particularly with regards to the hippocampus and amygdala. While higher clustering possibly reflects topological consequences of axonal sprouting, decreases in interstructure covariance may be a consequence of disconnection within limbic circuitry. Preoperative network parameters, specifically the segregation of the ipsilateral hippocampus, predicted long-term seizure freedom after surgery.

  9. Incorporation of varying types of temporal data in a neural network

    NASA Technical Reports Server (NTRS)

    Cohen, M. E.; Hudson, D. L.

    1992-01-01

    Most neural network models do not specifically deal with temporal data. Handling of these variables is complicated by the different uses to which temporal data are put, depending on the application. Even within the same application, temporal variables are often used in a number of different ways. In this paper, types of temporal data are discussed, along with their implications for approximate reasoning. Methods for integrating approximate temporal reasoning into existing neural network structures are presented. These methods are illustrated in a medical application for diagnosis of graft-versus-host disease which requires the use of several types of temporal data.

  10. Mapping the convergent temporal epileptic network in left and right temporal lobe epilepsy.

    PubMed

    Fang, Peng; An, Jie; Zeng, Ling-Li; Shen, Hui; Qiu, Shijun; Hu, Dewen

    2017-02-03

    Left and right mesial temporal lobe epilepsy (mTLE) with hippocampal sclerosis (HS) exhibits similar functional and clinical dysfunctions, such as depressive mood and emotional dysregulation, implying that the left and right mTLE may share a common network substrate. However, the convergent anatomical network disruption between the left and right HS remains largely uncharacterized. This study aimed to investigate whether the left and right mTLE share a similar anatomical network. We examined 43 (22 left, 21 right) mTLE patients with HS and 39 healthy controls using diffusion tensor imaging. Machine learning approaches were applied to extract the abnormal anatomical connectivity patterns in both the left and right mTLE. The left and right mTLE showed that 28 discriminating connections were exactly the same when compared to the controls. The same 28 connections showed high discriminating power in comparisons of the left mTLE versus controls (91.7%) and the right mTLE versus controls (90.0%); however, these connections failed to discriminate the left from the right mTLE. These discriminating connections, which were diminished both in the left and right mTLE, were primarily located in the limbic-frontal network, partially agreeing with the limbic-frontal dysregulation model of depression. These findings suggest that left and right mTLE share a convergent circuit, which may account for the mood and emotional deficits in mTLE and may suggest the neuropathological mechanisms underlying the comorbidity of depression and mTLE.

  11. Temporal variability of diapycnal mixing in the northern South China Sea

    NASA Astrophysics Data System (ADS)

    Sun, Hui; Yang, Qingxuan; Zhao, Wei; Liang, Xinfeng; Tian, Jiwei

    2016-12-01

    Temporal variability of diapycnal mixing over 7 months in the northern South China Sea was examined based on McLane Moored Profiler observations from 850 to 2200 m by employing a finescale parameterization. Intensified diffusivity exceeding the order of 10-3 m2/s in magnitude was found over the first half of October 2014, and from 2 December 2014 to 21 January 2015 (a typical wintertime). Strong internal tides and winds in winter were the likely candidates for the high-level diapycnal mixing in winter. As for the enhanced mixing during October 2014, we suspect the generation of near-bottom near-inertial waves through the interaction of mesoscale eddies and unique bottom topography was the cause.

  12. General asymmetric neutral networks and structure design by genetic algorithms: A learning rule for temporal patterns

    SciTech Connect

    Bornholdt, S.; Graudenz, D.

    1993-07-01

    A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback.

  13. Finding community structures in complex networks using mixed integer optimisation

    NASA Astrophysics Data System (ADS)

    Xu, G.; Tsoka, S.; Papageorgiou, L. G.

    2007-11-01

    The detection of community structure has been used to reveal the relationships between individual objects and their groupings in networks. This paper presents a mathematical programming approach to identify the optimal community structures in complex networks based on the maximisation of a network modularity metric for partitioning a network into modules. The overall problem is formulated as a mixed integer quadratic programming (MIQP) model, which can then be solved to global optimality using standard optimisation software. The solution procedure is further enhanced by developing special symmetry-breaking constraints to eliminate equivalent solutions. It is shown that additional features such as minimum/maximum module size and balancing among modules can easily be incorporated in the model. The applicability of the proposed optimisation-based approach is demonstrated by four examples. Comparative results with other approaches from the literature show that the proposed methodology has superior performance while global optimum is guaranteed.

  14. Bridging Minds: A Mixed Methodology to Assess Networked Flow.

    PubMed

    Galimberti, Carlo; Chirico, Alice; Brivio, Eleonora; Mazzoni, Elvis; Riva, Giuseppe; Milani, Luca; Gaggioli, Andrea

    2015-01-01

    The main goal of this contribution is to present a methodological framework to study Networked Flow, a bio-psycho-social theory of collective creativity applying it on creative processes occurring via a computer network. First, we draw on the definition of Networked Flow to identify the key methodological requirements of this model. Next, we present the rationale of a mixed methodology, which aims at combining qualitative, quantitative and structural analysis of group dynamics to obtain a rich longitudinal dataset. We argue that this integrated strategy holds potential for describing the complex dynamics of creative collaboration, by linking the experiential features of collaborative experience (flow, social presence), with the structural features of collaboration dynamics (network indexes) and the collaboration outcome (the creative product). Finally, we report on our experience with using this methodology in blended collaboration settings (including both face-to-face and virtual meetings), to identify open issues and provide future research directions.

  15. Exploring the structure and function of temporal networks with dynamic graphlets

    PubMed Central

    Hulovatyy, Y.; Chen, H.; Milenković, T.

    2015-01-01

    Motivation: With increasing availability of temporal real-world networks, how to efficiently study these data? One can model a temporal network as a single aggregate static network, or as a series of time-specific snapshots, each being an aggregate static network over the corresponding time window. Then, one can use established methods for static analysis on the resulting aggregate network(s), but losing in the process valuable temporal information either completely, or at the interface between different snapshots, respectively. Here, we develop a novel approach for studying a temporal network more explicitly, by capturing inter-snapshot relationships. Results: We base our methodology on well-established graphlets (subgraphs), which have been proven in numerous contexts in static network research. We develop new theory to allow for graphlet-based analyses of temporal networks. Our new notion of dynamic graphlets is different from existing dynamic network approaches that are based on temporal motifs (statistically significant subgraphs). The latter have limitations: their results depend on the choice of a null network model that is required to evaluate the significance of a subgraph, and choosing a good null model is non-trivial. Our dynamic graphlets overcome the limitations of the temporal motifs. Also, when we aim to characterize the structure and function of an entire temporal network or of individual nodes, our dynamic graphlets outperform the static graphlets. Clearly, accounting for temporal information helps. We apply dynamic graphlets to temporal age-specific molecular network data to deepen our limited knowledge about human aging. Availability and implementation: http://www.nd.edu/∼cone/DG. Contact: tmilenko@nd.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26072480

  16. Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks

    NASA Technical Reports Server (NTRS)

    Cheung, Kar-Ming; Lee, Charles H.

    2012-01-01

    We developed framework and the mathematical formulation for optimizing communication network using mixed integer programming. The design yields a system that is much smaller, in search space size, when compared to the earlier approach. Our constrained network optimization takes into account the dynamics of link performance within the network along with mission and operation requirements. A unique penalty function is introduced to transform the mixed integer programming into the more manageable problem of searching in a continuous space. The constrained optimization problem was proposed to solve in two stages: first using the heuristic Particle Swarming Optimization algorithm to get a good initial starting point, and then feeding the result into the Sequential Quadratic Programming algorithm to achieve the final optimal schedule. We demonstrate the above planning and scheduling methodology with a scenario of 20 spacecraft and 3 ground stations of a Deep Space Network site. Our approach and framework have been simple and flexible so that problems with larger number of constraints and network can be easily adapted and solved.

  17. Temporal prediction of epidemic patterns in community networks

    NASA Astrophysics Data System (ADS)

    Peng, Xiao-Long; Small, Michael; Xu, Xin-Jian; Fu, Xinchu

    2013-11-01

    Most previous studies of epidemic dynamics on complex networks suppose that the disease will eventually stabilize at either a disease-free state or an endemic one. In reality, however, some epidemics always exhibit sporadic and recurrent behaviour in one region because of the invasion from an endemic population elsewhere. In this paper we address this issue and study a susceptible-infected-susceptible epidemiological model on a network consisting of two communities, where the disease is endemic in one community but alternates between outbreaks and extinctions in the other. We provide a detailed characterization of the temporal dynamics of epidemic patterns in the latter community. In particular, we investigate the time duration of both outbreak and extinction, and the time interval between two consecutive inter-community infections, as well as their frequency distributions. Based on the mean-field theory, we theoretically analyse these three timescales and their dependence on the average node degree of each community, the transmission parameters and the number of inter-community links, which are in good agreement with simulations, except when the probability of overlaps between successive outbreaks is too large. These findings aid us in better understanding the bursty nature of disease spreading in a local community, and thereby suggesting effective time-dependent control strategies.

  18. The amygdalo-nigrostriatal network is critical for an optimal temporal performance

    PubMed Central

    Es-seddiqi, Mouna; El Massioui, Nicole; Samson, Nathalie; Brown, Bruce L.

    2016-01-01

    The amygdalo-nigrostriatal (ANS) network plays an essential role in enhanced attention to significant events. Interval timing requires attention to temporal cues. We assessed rats having a disconnected ANS network, due to contralateral lesions of the medial central nucleus of the amygdala (CEm) and dopaminergic afferents to the lateral striatum, as compared to controls (sham and ipsilateral lesions of CEm and dopaminergic afferents to LS) in a temporal bisection task. ANS disconnection induced poorer temporal precision and increased response latencies to a short duration. The present results reveal a role of the ANS network in temporal processing. PMID:26884227

  19. Spatial-temporal modeling for electrical impedance imaging of a mixing process

    NASA Astrophysics Data System (ADS)

    West, R. M.; Meng, S.; Aykroyd, R. G.; Williams, R. A.

    2005-07-01

    The use of electrical tomography techniques for process visualization and investigation is a well-known example of a nonlinear, ill-posed, and underdetermined inverse problem. Hence stable and reliable solution is not possible using measured data alone, but requires regularization through prior information. The rôle of a Bayesian approach is therefore of fundamental importance, and when coupled with Markov chain Monte Carlo (MCMC) sampling, it can provide valuable statistical information about solution behavior and reliability, which is in contrast to most current approaches which provide only a single image reconstruction with unquantified errors. For many applications of dynamic electrical impedance imaging, some degree of both spatial and temporal smoothness is expected. Often temporal smoothness is ignored and only spatial smoothing is used. In the current application, the addition of an aliquot to a mixing vessel, smoothness is not appropriate prior information. Instead an aliquot prior is proposed, parameterized in terms of location, size, and resistivity. This approach leads to data-driven and adaptive smoothing, in contrast to the more usual global smoothing of standard regularization methods. Of further interest is the inclusion of temporal prior information: it is known that the aliquot moves and disperses in a specific manner. With this added temporal information, imaging is improved as are derived process parameters.

  20. A mixed-signal implementation of a polychronous spiking neural network with delay adaptation

    PubMed Central

    Wang, Runchun M.; Hamilton, Tara J.; Tapson, Jonathan C.; van Schaik, André

    2014-01-01

    We present a mixed-signal implementation of a re-configurable polychronous spiking neural network capable of storing and recalling spatio-temporal patterns. The proposed neural network contains one neuron array and one axon array. Spike Timing Dependent Delay Plasticity is used to fine-tune delays and add dynamics to the network. In our mixed-signal implementation, the neurons and axons have been implemented as both analog and digital circuits. The system thus consists of one FPGA, containing the digital neuron array and the digital axon array, and one analog IC containing the analog neuron array and the analog axon array. The system can be easily configured to use different combinations of each. We present and discuss the experimental results of all combinations of the analog and digital axon arrays and the analog and digital neuron arrays. The test results show that the proposed neural network is capable of successfully recalling more than 85% of stored patterns using both analog and digital circuits. PMID:24672422

  1. Spatio-temporal study of non-degenerate two-wave mixing in bacteriorhodopsin films.

    PubMed

    Blaya, Salvador; González, Alejandro; Acebal, Pablo; Carretero, Luis

    2016-10-31

    A spatio-temporal analysis of non-degenerate two-wave mixing in a saturable absorber, such as bacteriorhodopsin (bR) film, is performed. To do this, a theoretical model describing the temporal variation of the intensities is developed by taking into account the dielectric constant as a function of bR population. A good agreement between theory and experimental measurements is obtained. Thus, the dependence of the optical gain and the main dielectric constant parameters are studied at different intensities and frequencies. As a result, the best intensity-frequency zones where higher coupling is reached are proposed, and it is also demonstrated that non-uniform patterns, which evolve over time as a function of frequency difference, can be observed.

  2. Isotopic constraints on water source mixing, network leakage and contamination in an urban groundwater system.

    PubMed

    Grimmeisen, F; Lehmann, M F; Liesch, T; Goeppert, N; Klinger, J; Zopfi, J; Goldscheider, N

    2017-04-01

    Water supply in developing countries is prone to large water losses due to leaky distribution networks and defective sewers, which may affect groundwater quality and quantity in urban areas and result in complex subsurface mixing dynamics. In this study, a multi-stable isotope approach was used to investigate spatiotemporal fluctuations of surface and sub-surface water source partitioning and mixing, and to assess nitrogen (N) contamination in the urban water cycle of As-Salt, Jordan. Water import from the King Abdullah Canal (KAC), mains waters from the network, and wastewater are characterized by distinct isotopic signatures, which allowed us to quantify city effluents into the groundwater. Temporal variations in isotopic signatures of polluted groundwater are explained by seasonally fluctuating inflow, and dilution by water that originates from Lake Tiberias and enters the urban water cycle via the KAC. Isotopic analysis (N and O) and comparison between groundwater nitrate and nitrate from mains water, water imports and wastewater confirmed that septic waste from leaky sewers is the main contributor of nitrate contamination. The nitrate of strongly contaminated groundwater was characterized by highest δ(15)NNO3 values (13.3±1.8‰), whereas lowest δ(15)NNO3 values were measured in unpolluted groundwater (6.9‰). Analogously, nitrate concentration and isotopic ratios were used for source partitioning and qualitatively confirmed δDH2O and δ(18)OH2O-based estimates. Dual water isotope endmember mixing calculations suggest that city effluents from leaky networks and sewers contribute 30-64% to the heavily polluted groundwater. Ternary mixing calculations including also chloride revealed that 5-18% of the polluted groundwater is wastewater. Up to two thirds of the groundwater originates from mains, indicating excessive water loss from the network, and calling for improved water supply management.

  3. Mixed transportation network design under a sustainable development perspective.

    PubMed

    Qin, Jin; Ni, Ling-lin; Shi, Feng

    2013-01-01

    A mixed transportation network design problem considering sustainable development was studied in this paper. Based on the discretization of continuous link-grade decision variables, a bilevel programming model was proposed to describe the problem, in which sustainability factors, including vehicle exhaust emissions, land-use scale, link load, and financial budget, are considered. The objective of the model is to minimize the total amount of resources exploited under the premise of meeting all the construction goals. A heuristic algorithm, which combined the simulated annealing and path-based gradient projection algorithm, was developed to solve the model. The numerical example shows that the transportation network optimized with the method above not only significantly alleviates the congestion on the link, but also reduces vehicle exhaust emissions within the network by up to 41.56%.

  4. Memory and betweenness preference in temporal networks induced from time series

    NASA Astrophysics Data System (ADS)

    Weng, Tongfeng; Zhang, Jie; Small, Michael; Zheng, Rui; Hui, Pan

    2017-02-01

    We construct temporal networks from time series via unfolding the temporal information into an additional topological dimension of the networks. Thus, we are able to introduce memory entropy analysis to unravel the memory effect within the considered signal. We find distinct patterns in the entropy growth rate of the aggregate network at different memory scales for time series with different dynamics ranging from white noise, 1/f noise, autoregressive process, periodic to chaotic dynamics. Interestingly, for a chaotic time series, an exponential scaling emerges in the memory entropy analysis. We demonstrate that the memory exponent can successfully characterize bifurcation phenomenon, and differentiate the human cardiac system in healthy and pathological states. Moreover, we show that the betweenness preference analysis of these temporal networks can further characterize dynamical systems and separate distinct electrocardiogram recordings. Our work explores the memory effect and betweenness preference in temporal networks constructed from time series data, providing a new perspective to understand the underlying dynamical systems.

  5. Memory and betweenness preference in temporal networks induced from time series.

    PubMed

    Weng, Tongfeng; Zhang, Jie; Small, Michael; Zheng, Rui; Hui, Pan

    2017-02-03

    We construct temporal networks from time series via unfolding the temporal information into an additional topological dimension of the networks. Thus, we are able to introduce memory entropy analysis to unravel the memory effect within the considered signal. We find distinct patterns in the entropy growth rate of the aggregate network at different memory scales for time series with different dynamics ranging from white noise, 1/f noise, autoregressive process, periodic to chaotic dynamics. Interestingly, for a chaotic time series, an exponential scaling emerges in the memory entropy analysis. We demonstrate that the memory exponent can successfully characterize bifurcation phenomenon, and differentiate the human cardiac system in healthy and pathological states. Moreover, we show that the betweenness preference analysis of these temporal networks can further characterize dynamical systems and separate distinct electrocardiogram recordings. Our work explores the memory effect and betweenness preference in temporal networks constructed from time series data, providing a new perspective to understand the underlying dynamical systems.

  6. Memory and betweenness preference in temporal networks induced from time series

    PubMed Central

    Weng, Tongfeng; Zhang, Jie; Small, Michael; Zheng, Rui; Hui, Pan

    2017-01-01

    We construct temporal networks from time series via unfolding the temporal information into an additional topological dimension of the networks. Thus, we are able to introduce memory entropy analysis to unravel the memory effect within the considered signal. We find distinct patterns in the entropy growth rate of the aggregate network at different memory scales for time series with different dynamics ranging from white noise, 1/f noise, autoregressive process, periodic to chaotic dynamics. Interestingly, for a chaotic time series, an exponential scaling emerges in the memory entropy analysis. We demonstrate that the memory exponent can successfully characterize bifurcation phenomenon, and differentiate the human cardiac system in healthy and pathological states. Moreover, we show that the betweenness preference analysis of these temporal networks can further characterize dynamical systems and separate distinct electrocardiogram recordings. Our work explores the memory effect and betweenness preference in temporal networks constructed from time series data, providing a new perspective to understand the underlying dynamical systems. PMID:28157194

  7. Temporal and spatial allocation of motor preparation during a mixed-strategy game.

    PubMed

    Mikulić, Areh; Dorris, Michael C

    2008-10-01

    Adopting a mixed response strategy in competitive situations can prevent opponents from exploiting predictable play. What drives stochastic action selection is unclear given that choice patterns suggest that, on average, players are indifferent to available options during mixed-strategy equilibria. To gain insight into this stochastic selection process, we examined how motor preparation was allocated during a mixed-strategy game. If selection processes on each trial reflect a global indifference between options, then there should be no bias in motor preparation (unbiased preparation hypothesis). If, however, differences exist in the desirability of options on each trial then motor preparation should be biased toward the preferred option (biased preparation hypothesis). We tested between these alternatives by examining how saccade preparation was allocated as human subjects competed against an adaptive computer opponent in an oculomotor version of the game "matching pennies." Subjects were free to choose between two visual targets using a saccadic eye movement. Saccade preparation was probed by occasionally flashing a visual distractor at a range of times preceding target presentation. The probability that a distractor would evoke a saccade error, and when it failed to do so, the probability of choosing each of the subsequent targets quantified the temporal and spatial evolution of saccade preparation, respectively. Our results show that saccade preparation became increasingly biased as the time of target presentation approached. Specifically, the spatial locus to which saccade preparation was directed varied from trial to trial, and its time course depended on task timing.

  8. An Algorithm for the Mixed Transportation Network Design Problem.

    PubMed

    Liu, Xinyu; Chen, Qun

    2016-01-01

    This paper proposes an optimization algorithm, the dimension-down iterative algorithm (DDIA), for solving a mixed transportation network design problem (MNDP), which is generally expressed as a mathematical programming with equilibrium constraint (MPEC). The upper level of the MNDP aims to optimize the network performance via both the expansion of the existing links and the addition of new candidate links, whereas the lower level is a traditional Wardrop user equilibrium (UE) problem. The idea of the proposed solution algorithm (DDIA) is to reduce the dimensions of the problem. A group of variables (discrete/continuous) is fixed to optimize another group of variables (continuous/discrete) alternately; then, the problem is transformed into solving a series of CNDPs (continuous network design problems) and DNDPs (discrete network design problems) repeatedly until the problem converges to the optimal solution. The advantage of the proposed algorithm is that its solution process is very simple and easy to apply. Numerical examples show that for the MNDP without budget constraint, the optimal solution can be found within a few iterations with DDIA. For the MNDP with budget constraint, however, the result depends on the selection of initial values, which leads to different optimal solutions (i.e., different local optimal solutions). Some thoughts are given on how to derive meaningful initial values, such as by considering the budgets of new and reconstruction projects separately.

  9. An Algorithm for the Mixed Transportation Network Design Problem

    PubMed Central

    Liu, Xinyu; Chen, Qun

    2016-01-01

    This paper proposes an optimization algorithm, the dimension-down iterative algorithm (DDIA), for solving a mixed transportation network design problem (MNDP), which is generally expressed as a mathematical programming with equilibrium constraint (MPEC). The upper level of the MNDP aims to optimize the network performance via both the expansion of the existing links and the addition of new candidate links, whereas the lower level is a traditional Wardrop user equilibrium (UE) problem. The idea of the proposed solution algorithm (DDIA) is to reduce the dimensions of the problem. A group of variables (discrete/continuous) is fixed to optimize another group of variables (continuous/discrete) alternately; then, the problem is transformed into solving a series of CNDPs (continuous network design problems) and DNDPs (discrete network design problems) repeatedly until the problem converges to the optimal solution. The advantage of the proposed algorithm is that its solution process is very simple and easy to apply. Numerical examples show that for the MNDP without budget constraint, the optimal solution can be found within a few iterations with DDIA. For the MNDP with budget constraint, however, the result depends on the selection of initial values, which leads to different optimal solutions (i.e., different local optimal solutions). Some thoughts are given on how to derive meaningful initial values, such as by considering the budgets of new and reconstruction projects separately. PMID:27626803

  10. Time-dependent degree-degree correlations in epileptic brain networks: from assortative to dissortative mixing.

    PubMed

    Geier, Christian; Lehnertz, Klaus; Bialonski, Stephan

    2015-01-01

    We investigate the long-term evolution of degree-degree correlations (assortativity) in functional brain networks from epilepsy patients. Functional networks are derived from continuous multi-day, multi-channel electroencephalographic data, which capture a wide range of physiological and pathophysiological activities. In contrast to previous studies which all reported functional brain networks to be assortative on average, even in case of various neurological and neurodegenerative disorders, we observe large fluctuations in time-resolved degree-degree correlations ranging from assortative to dissortative mixing. Moreover, in some patients these fluctuations exhibit some periodic temporal structure which can be attributed, to a large extent, to daily rhythms. Relevant aspects of the epileptic process, particularly possible pre-seizure alterations, contribute marginally to the observed long-term fluctuations. Our findings suggest that physiological and pathophysiological activity may modify functional brain networks in a different and process-specific way. We evaluate factors that possibly influence the long-term evolution of degree-degree correlations.

  11. Estimating spatial and temporal components of variation in count data using negative binomial mixed models

    USGS Publications Warehouse

    Irwin, Brian J.; Wagner, Tyler; Bence, James R.; Kepler, Megan V.; Liu, Weihai; Hayes, Daniel B.

    2013-01-01

    Partitioning total variability into its component temporal and spatial sources is a powerful way to better understand time series and elucidate trends. The data available for such analyses of fish and other populations are usually nonnegative integer counts of the number of organisms, often dominated by many low values with few observations of relatively high abundance. These characteristics are not well approximated by the Gaussian distribution. We present a detailed description of a negative binomial mixed-model framework that can be used to model count data and quantify temporal and spatial variability. We applied these models to data from four fishery-independent surveys of Walleyes Sander vitreus across the Great Lakes basin. Specifically, we fitted models to gill-net catches from Wisconsin waters of Lake Superior; Oneida Lake, New York; Saginaw Bay in Lake Huron, Michigan; and Ohio waters of Lake Erie. These long-term monitoring surveys varied in overall sampling intensity, the total catch of Walleyes, and the proportion of zero catches. Parameter estimation included the negative binomial scaling parameter, and we quantified the random effects as the variations among gill-net sampling sites, the variations among sampled years, and site × year interactions. This framework (i.e., the application of a mixed model appropriate for count data in a variance-partitioning context) represents a flexible approach that has implications for monitoring programs (e.g., trend detection) and for examining the potential of individual variance components to serve as response metrics to large-scale anthropogenic perturbations or ecological changes.

  12. Impairment-constrained network design in mixed line rate and flexible-grid optical networks

    NASA Astrophysics Data System (ADS)

    Xie, Weisheng

    Mixed line rate (MLR) and flexible-grid optical networks are two promising network paradigms for next generation optical networks. In MLR optical networks, different optical channels may operate at different line rates and use the same amount of spectrum. In flexible-grid optical networks, besides different line rates, different optical channels can use different amount of spectrum. In both MLR and flexible-grid optical networks, the physical layer impairments will impact the signal reachability and will require regenerator placement to restore the signal quality. Different line rates and modulation formats suffer from different levels of impairments, and thus have different reachabilities. In this dissertation, we study multiple network design problems with impairment constraints for both MLR and flexible-grid optical networks. We first study regenerator site (RS) selection problems in MLR optical networks. Given a network topology, set of requests, and different line rates' reachabilities, the problem is to select the minimum number of nodes in the network as RSs. We divide the topic into two separate research problems depending on whether routing is fixed or flexible. Energy efficiency is an important factor that will impact the operational expenditure of a telecom network. When designing the routing and wavelength assignment approach for a set of connection requests, the placement of regenerators needs to be considered in order to increase energy efficiency. In this work, we study how to place the minimum number of regenerators in MLR optical networks, while satisfying all the requests. Virtual optical network (VON) mapping plays a vital role in optical network virtualization. When mapping VONs, it is necessary to provision backup resources to guarantee survivability. Thus, we consider how to map VONs that can survive single link failures in flexible-grid optical networks. The objective is to minimize network equipment cost, including regenerators. We also

  13. The Iterative Reweighted Mixed-Norm Estimate for Spatio-Temporal MEG/EEG Source Reconstruction.

    PubMed

    Strohmeier, Daniel; Bekhti, Yousra; Haueisen, Jens; Gramfort, Alexandre

    2016-10-01

    Source imaging based on magnetoencephalography (MEG) and electroencephalography (EEG) allows for the non-invasive analysis of brain activity with high temporal and good spatial resolution. As the bioelectromagnetic inverse problem is ill-posed, constraints are required. For the analysis of evoked brain activity, spatial sparsity of the neuronal activation is a common assumption. It is often taken into account using convex constraints based on the l1-norm. The resulting source estimates are however biased in amplitude and often suboptimal in terms of source selection due to high correlations in the forward model. In this work, we demonstrate that an inverse solver based on a block-separable penalty with a Frobenius norm per block and a l0.5-quasinorm over blocks addresses both of these issues. For solving the resulting non-convex optimization problem, we propose the iterative reweighted Mixed Norm Estimate (irMxNE), an optimization scheme based on iterative reweighted convex surrogate optimization problems, which are solved efficiently using a block coordinate descent scheme and an active set strategy. We compare the proposed sparse imaging method to the dSPM and the RAP-MUSIC approach based on two MEG data sets. We provide empirical evidence based on simulations and analysis of MEG data that the proposed method improves on the standard Mixed Norm Estimate (MxNE) in terms of amplitude bias, support recovery, and stability.

  14. Time-Perception Network and Default Mode Network Are Associated with Temporal Prediction in a Periodic Motion Task

    PubMed Central

    Carvalho, Fabiana M.; Chaim, Khallil T.; Sanchez, Tiago A.; de Araujo, Draulio B.

    2016-01-01

    The updating of prospective internal models is necessary to accurately predict future observations. Uncertainty-driven internal model updating has been studied using a variety of perceptual paradigms, and have revealed engagement of frontal and parietal areas. In a distinct literature, studies on temporal expectations have also characterized a time-perception network, which relies on temporal orienting of attention. However, the updating of prospective internal models is highly dependent on temporal attention, since temporal attention must be reoriented according to the current environmental demands. In this study, we used functional magnetic resonance imaging (fMRI) to evaluate to what extend the continuous manipulation of temporal prediction would recruit update-related areas and the time-perception network areas. We developed an exogenous temporal task that combines rhythm cueing and time-to-contact principles to generate implicit temporal expectation. Two patterns of motion were created: periodic (simple harmonic oscillation) and non-periodic (harmonic oscillation with variable acceleration). We found that non-periodic motion engaged the exogenous temporal orienting network, which includes the ventral premotor and inferior parietal cortices, and the cerebellum, as well as the presupplementary motor area, which has previously been implicated in internal model updating, and the motion-sensitive area MT+. Interestingly, we found a right-hemisphere preponderance suggesting the engagement of explicit timing mechanisms. We also show that the periodic motion condition, when compared to the non-periodic motion, activated a particular subset of the default-mode network (DMN) midline areas, including the left dorsomedial prefrontal cortex (DMPFC), anterior cingulate cortex (ACC), and bilateral posterior cingulate cortex/precuneus (PCC/PC). It suggests that the DMN plays a role in processing contextually expected information and supports recent evidence that the DMN may

  15. Aerosol black carbon characteristics over Central India: Temporal variation and its dependence on mixed layer height

    NASA Astrophysics Data System (ADS)

    Kompalli, Sobhan Kumar; Babu, S. Suresh; Moorthy, K. Krishna; Manoj, M. R.; Kumar, N. V. P. Kiran; Shaeb, K. Hareef Baba; Joshi, Ashok Kumar

    2014-10-01

    In a first of its kind study over the Indian region, concurrent and extensive measurements of black carbon (BC) concentration and atmospheric boundary layer parameters are used to quantify the role of atmospheric boundary layer in producing temporal changes in BC. During this study, 18 months (2011-12) data of continuous measurements of BC aerosols, made over a semi-urban location, Nagpur, in Central India are used along with concurrent measurements of vertical profiles of atmospheric thermodynamics, made using weekly ascents of GPS aided Radiosonde for a period of 1 year. From the balloon data, mixed layer heights and ventilation coefficients are estimated, and the monthly and seasonal changes in BC mass concentration are examined in the light of the boundary layer changes. Seasonally, the BC mass concentration was highest (~ 4573 ± 1293 ng m- 3) in winter (December-February), and lowest (~ 1588 ± 897 ng m- 3) in monsoon (June-September), while remained moderate (~ 3137 ± 1446 ng m- 3) in pre-monsoon (March-May), and post-monsoon (~ 3634 ± 813 ng m- 3) (October-November) seasons. During the dry seasons, when the rainfall is scanty or insignificantly small, the seasonal variations in BC concentrations have a strong inverse relationship with mixed layer height and ventilation coefficient. However, the lowest BC concentrations do not occur during the season when the mixed layer height (MLH) is highest or the ventilation coefficient is the highest; rather it occurs when the rainfall is strong (during summer monsoon season) and airmass changes to primarily of marine origin.

  16. Detection and localization of change points in temporal networks with the aid of stochastic block models

    NASA Astrophysics Data System (ADS)

    De Ridder, Simon; Vandermarliere, Benjamin; Ryckebusch, Jan

    2016-11-01

    A framework based on generalized hierarchical random graphs (GHRGs) for the detection of change points in the structure of temporal networks has recently been developed by Peel and Clauset (2015 Proc. 29th AAAI Conf. on Artificial Intelligence). We build on this methodology and extend it to also include the versatile stochastic block models (SBMs) as a parametric family for reconstructing the empirical networks. We use five different techniques for change point detection on prototypical temporal networks, including empirical and synthetic ones. We find that none of the considered methods can consistently outperform the others when it comes to detecting and locating the expected change points in empirical temporal networks. With respect to the precision and the recall of the results of the change points, we find that the method based on a degree-corrected SBM has better recall properties than other dedicated methods, especially for sparse networks and smaller sliding time window widths.

  17. Modelling temporal networks of human face-to-face contacts with public activity and individual reachability

    NASA Astrophysics Data System (ADS)

    Zhang, Yi-Qing; Cui, Jing; Zhang, Shu-Min; Zhang, Qi; Li, Xiang

    2016-02-01

    Modelling temporal networks of human face-to-face contacts is vital both for understanding the spread of airborne pathogens and word-of-mouth spreading of information. Although many efforts have been devoted to model these temporal networks, there are still two important social features, public activity and individual reachability, have been ignored in these models. Here we present a simple model that captures these two features and other typical properties of empirical face-to-face contact networks. The model describes agents which are characterized by an attractiveness to slow down the motion of nearby people, have event-triggered active probability and perform an activity-dependent biased random walk in a square box with periodic boundary. The model quantitatively reproduces two empirical temporal networks of human face-to-face contacts which are testified by their network properties and the epidemic spread dynamics on them.

  18. A study of the temporal robustness of the growing global container-shipping network

    NASA Astrophysics Data System (ADS)

    Wang, Nuo; Wu, Nuan; Dong, Ling-Ling; Yan, Hua-Kun; Wu, Di

    2016-10-01

    Whether they thrive as they grow must be determined for all constantly expanding networks. However, few studies have focused on this important network feature or the development of quantitative analytical methods. Given the formation and growth of the global container-shipping network, we proposed the concept of network temporal robustness and quantitative method. As an example, we collected container liner companies’ data at two time points (2004 and 2014) and built a shipping network with ports as nodes and routes as links. We thus obtained a quantitative value of the temporal robustness. The temporal robustness is a significant network property because, for the first time, we can clearly recognize that the shipping network has become more vulnerable to damage over the last decade: When the node failure scale reached 50% of the entire network, the temporal robustness was approximately ‑0.51% for random errors and ‑12.63% for intentional attacks. The proposed concept and analytical method described in this paper are significant for other network studies.

  19. A study of the temporal robustness of the growing global container-shipping network.

    PubMed

    Wang, Nuo; Wu, Nuan; Dong, Ling-Ling; Yan, Hua-Kun; Wu, Di

    2016-10-07

    Whether they thrive as they grow must be determined for all constantly expanding networks. However, few studies have focused on this important network feature or the development of quantitative analytical methods. Given the formation and growth of the global container-shipping network, we proposed the concept of network temporal robustness and quantitative method. As an example, we collected container liner companies' data at two time points (2004 and 2014) and built a shipping network with ports as nodes and routes as links. We thus obtained a quantitative value of the temporal robustness. The temporal robustness is a significant network property because, for the first time, we can clearly recognize that the shipping network has become more vulnerable to damage over the last decade: When the node failure scale reached 50% of the entire network, the temporal robustness was approximately -0.51% for random errors and -12.63% for intentional attacks. The proposed concept and analytical method described in this paper are significant for other network studies.

  20. A study of the temporal robustness of the growing global container-shipping network

    PubMed Central

    Wang, Nuo; Wu, Nuan; Dong, Ling-ling; Yan, Hua-kun; Wu, Di

    2016-01-01

    Whether they thrive as they grow must be determined for all constantly expanding networks. However, few studies have focused on this important network feature or the development of quantitative analytical methods. Given the formation and growth of the global container-shipping network, we proposed the concept of network temporal robustness and quantitative method. As an example, we collected container liner companies’ data at two time points (2004 and 2014) and built a shipping network with ports as nodes and routes as links. We thus obtained a quantitative value of the temporal robustness. The temporal robustness is a significant network property because, for the first time, we can clearly recognize that the shipping network has become more vulnerable to damage over the last decade: When the node failure scale reached 50% of the entire network, the temporal robustness was approximately −0.51% for random errors and −12.63% for intentional attacks. The proposed concept and analytical method described in this paper are significant for other network studies. PMID:27713549

  1. Statistical mechanics of structural and temporal credit assignment effects on learning in neural networks.

    PubMed

    Saito, Hiroshi; Katahira, Kentaro; Okanoya, Kazuo; Okada, Masato

    2011-05-01

    Neural networks can learn flexible input-output associations by changing their synaptic weights. The representational performance and learning dynamics of neural networks are intensively studied in several fields. Neural networks face the "credit assignment problem" in situations in which only incomplete performance evaluations are available. The credit assignment problem is that a network should assign credit or blame for its behaviors according to the contribution to the network performance. In reinforcement learning, a scalar evaluation signal is delivered to a network. The two main types of credit assignment problems in reinforcement learning are structural and temporal, that is, which parameters of the network (structural) and which past network activities (temporal) are related to an evaluation signal given from an environment. In this study, we apply statistical mechanical analysis to the learning processes in a simple neural network model to clarify the effects of two kinds of credit assignments and their interactions. Our model is based on node perturbation learning with eligibility trace. Node perturbation is a stochastic gradient learning method that can solve structural credit assignment problems by introducing a perturbation into the system output. The eligibility trace preserves the past network activities with a temporal credit to deal with the delay of an instruction signal. We show that both credit assignment effects mutually interact and the optimal time constant of the eligibility trace varies not only for the evaluation delay but also the network size.

  2. Mixed Criticality Scheduling for Industrial Wireless Sensor Networks

    PubMed Central

    Jin, Xi; Xia, Changqing; Xu, Huiting; Wang, Jintao; Zeng, Peng

    2016-01-01

    Wireless sensor networks (WSNs) have been widely used in industrial systems. Their real-time performance and reliability are fundamental to industrial production. Many works have studied the two aspects, but only focus on single criticality WSNs. Mixed criticality requirements exist in many advanced applications in which different data flows have different levels of importance (or criticality). In this paper, first, we propose a scheduling algorithm, which guarantees the real-time performance and reliability requirements of data flows with different levels of criticality. The algorithm supports centralized optimization and adaptive adjustment. It is able to improve both the scheduling performance and flexibility. Then, we provide the schedulability test through rigorous theoretical analysis. We conduct extensive simulations, and the results demonstrate that the proposed scheduling algorithm and analysis significantly outperform existing ones. PMID:27589741

  3. Mixed strategy and coevolution dynamics in social networks

    NASA Astrophysics Data System (ADS)

    Zhong, Weicai; Abbass, Hussein A.; Bender, Axel; Liu, Jing

    2011-01-01

    We investigate coevolution dynamics of both individual strategies and social ties as they adapt within the snowdrift game with mixed strategies. We propose a partner selection mechanism based on the concept of trust. Here trust is considered an instrument for an individual both selecting the right partners and being selected amongst other potential partners. Based on her local views of the system, the focal individual dismisses the link from the partner with the lowest trust and rewires to the partner’s partner with the highest trust. It is shown that such a trust-based partner switching mechanism favors the emergence of cooperators. Furthermore, when the number of an individual’s partners is restricted (which is a metaphor of limited capacities and capabilities of an individual in real environments), surprising assortative mixing patterns are formed in the emerging network and change the network’s degree distribution from a power-law distribution to an asymmetrically U-shaped distribution. This plays a leading role in preventing global avalanches triggered by perturbations acting on the state of the highly connected individuals.

  4. The Amygdalo-Nigrostriatal Network Is Critical for an Optimal Temporal Performance

    ERIC Educational Resources Information Center

    Es-seddiqi, Mouna; El Massioui, Nicole; Samson, Nathalie; Brown, Bruce L.; Doyère, Valérie

    2016-01-01

    The amygdalo-nigrostriatal (ANS) network plays an essential role in enhanced attention to significant events. Interval timing requires attention to temporal cues. We assessed rats having a disconnected ANS network, due to contralateral lesions of the medial central nucleus of the amygdala (CEm) and dopaminergic afferents to the lateral striatum,…

  5. Temporal Reliability and Lateralization of the Resting-State Language Network

    PubMed Central

    Zou, Qihong; Wang, Jue; Gao, Jia-Hong; Niu, Zhendong

    2014-01-01

    The neural processing loop of language is complex but highly associated with Broca's and Wernicke's areas. The left dominance of these two areas was the earliest observation of brain asymmetry. It was demonstrated that the language network and its functional asymmetry during resting state were reproducible across institutions. However, the temporal reliability of resting-state language network and its functional asymmetry are still short of knowledge. In this study, we established a seed-based resting-state functional connectivity analysis of language network with seed regions located at Broca's and Wernicke's areas, and investigated temporal reliability of language network and its functional asymmetry. The language network was found to be temporally reliable in both short- and long-term. In the aspect of functional asymmetry, the Broca's area was found to be left lateralized, while the Wernicke's area is mainly right lateralized. Functional asymmetry of these two areas revealed high short- and long-term reliability as well. In addition, the impact of global signal regression (GSR) on reliability of the resting-state language network was investigated, and our results demonstrated that GSR had negligible effect on the temporal reliability of the resting-state language network. Our study provided methodology basis for future cross-culture and clinical researches of resting-state language network and suggested priority of adopting seed-based functional connectivity for its high reliability. PMID:24475058

  6. Temporal reliability and lateralization of the resting-state language network.

    PubMed

    Zhu, Linlin; Fan, Yang; Zou, Qihong; Wang, Jue; Gao, Jia-Hong; Niu, Zhendong

    2014-01-01

    The neural processing loop of language is complex but highly associated with Broca's and Wernicke's areas. The left dominance of these two areas was the earliest observation of brain asymmetry. It was demonstrated that the language network and its functional asymmetry during resting state were reproducible across institutions. However, the temporal reliability of resting-state language network and its functional asymmetry are still short of knowledge. In this study, we established a seed-based resting-state functional connectivity analysis of language network with seed regions located at Broca's and Wernicke's areas, and investigated temporal reliability of language network and its functional asymmetry. The language network was found to be temporally reliable in both short- and long-term. In the aspect of functional asymmetry, the Broca's area was found to be left lateralized, while the Wernicke's area is mainly right lateralized. Functional asymmetry of these two areas revealed high short- and long-term reliability as well. In addition, the impact of global signal regression (GSR) on reliability of the resting-state language network was investigated, and our results demonstrated that GSR had negligible effect on the temporal reliability of the resting-state language network. Our study provided methodology basis for future cross-culture and clinical researches of resting-state language network and suggested priority of adopting seed-based functional connectivity for its high reliability.

  7. Reconstruction of missing data in social networks based on temporal patterns of interactions

    NASA Astrophysics Data System (ADS)

    Stomakhin, Alexey; Short, Martin B.; Bertozzi, Andrea L.

    2011-11-01

    We discuss a mathematical framework based on a self-exciting point process aimed at analyzing temporal patterns in the series of interaction events between agents in a social network. We then develop a reconstruction model that allows one to predict the unknown participants in a portion of those events. Finally, we apply our results to the Los Angeles gang network.

  8. Research on mixed network architecture collaborative application model

    NASA Astrophysics Data System (ADS)

    Jing, Changfeng; Zhao, Xi'an; Liang, Song

    2009-10-01

    When facing complex requirements of city development, ever-growing spatial data, rapid development of geographical business and increasing business complexity, collaboration between multiple users and departments is needed urgently, however conventional GIS software (such as Client/Server model or Browser/Server model) are not support this well. Collaborative application is one of the good resolutions. Collaborative application has four main problems to resolve: consistency and co-edit conflict, real-time responsiveness, unconstrained operation, spatial data recoverability. In paper, application model called AMCM is put forward based on agent and multi-level cache. AMCM can be used in mixed network structure and supports distributed collaborative. Agent is an autonomous, interactive, initiative and reactive computing entity in a distributed environment. Agent has been used in many fields such as compute science and automation. Agent brings new methods for cooperation and the access for spatial data. Multi-level cache is a part of full data. It reduces the network load and improves the access and handle of spatial data, especially, in editing the spatial data. With agent technology, we make full use of its characteristics of intelligent for managing the cache and cooperative editing that brings a new method for distributed cooperation and improves the efficiency.

  9. Absolute versus temporal anomaly and percent of saturation soil moisture spatial variability for six networks worldwide

    NASA Astrophysics Data System (ADS)

    Brocca, L.; Zucco, G.; Mittelbach, H.; Moramarco, T.; Seneviratne, S. I.

    2014-07-01

    The analysis of the spatial-temporal variability of soil moisture can be carried out considering the absolute (original) soil moisture values or relative values, such as the percent of saturation or temporal anomalies. Over large areas, soil moisture data measured at different sites can be characterized by large differences in their minimum, mean, and maximum absolute values, even though in relative terms their temporal patterns are very similar. In these cases, the analysis considering absolute compared with percent of saturation or temporal anomaly soil moisture values can provide very different results with significant consequences for their use in hydrological applications and climate science. In this study, in situ observations from six soil moisture networks in Italy, Spain, France, Switzerland, Australia, and United States are collected and analyzed to investigate the spatial soil moisture variability over large areas (250-150,000 km2). Specifically, the statistical and temporal stability analyses of soil moisture have been carried out for absolute, temporal anomaly, and percent of saturation values (using two different formulations for temporal anomalies). The results highlight that the spatial variability of the soil moisture dynamic (i.e., temporal anomalies) is significantly lower than that of the absolute soil moisture values. The spatial variance of the time-invariant component (temporal mean of each site) is the predominant contribution to the total spatial variance of absolute soil moisture data. Moreover, half of the networks show a minimum in the spatial variability for intermediate conditions when the temporal anomalies are considered, in contrast with the widely recognized behavior of absolute soil moisture data. The analyses with percent saturation data show qualitatively similar results as those for the temporal anomalies because of the applied normalization which reduces spatial variability induced by differences in mean absolute soil moisture

  10. The Role of Temporal Trends in Growing Networks

    PubMed Central

    Ruppin, Eytan; Shavitt, Yuval

    2016-01-01

    The rich get richer principle, manifested by the Preferential attachment (PA) mechanism, is widely considered one of the major factors in the growth of real-world networks. PA stipulates that popular nodes are bound to be more attractive than less popular nodes; for example, highly cited papers are more likely to garner further citations. However, it overlooks the transient nature of popularity, which is often governed by trends. Here, we show that in a wide range of real-world networks the recent popularity of a node, i.e., the extent by which it accumulated links recently, significantly influences its attractiveness and ability to accumulate further links. We proceed to model this observation with a natural extension to PA, named Trending Preferential Attachment (TPA), in which edges become less influential as they age. TPA quantitatively parametrizes a fundamental network property, namely the network’s tendency to trends. Through TPA, we find that real-world networks tend to be moderately to highly trendy. Networks are characterized by different susceptibilities to trends, which determine their structure to a large extent. Trendy networks display complex structural traits, such as modular community structure and degree-assortativity, occurring regularly in real-world networks. In summary, this work addresses an inherent trait of complex networks, which greatly affects their growth and structure, and develops a unified model to address its interaction with preferential attachment. PMID:27486847

  11. Temporal solar irradiance variability analysis using neural networks

    NASA Astrophysics Data System (ADS)

    Tebabal, Ambelu; Damtie, Baylie; Nigussie, Melessew

    A feed-forward neural network which can account for nonlinear relationship was used to model total solar irradiance (TSI). A single layer feed-forward neural network with Levenberg-marquardt back-propagation algorithm have been implemented for modeling daily total solar irradiance from daily photometric sunspot index, and core-to-wing ratio of Mg II index data. In order to obtain the optimum neural network for TSI modeling, the root mean square error (RMSE) and mean absolute error (MAE) have been taken into account. The modeled and measured TSI have the correlation coefficient of about R=0.97. The neural networks (NNs) model output indicates that reconstructed TSI from solar proxies (photometric sunspot index and Mg II) can explain 94% of the variance of TSI. This modeled TSI using NNs further strengthens the view that surface magnetism indeed plays a dominant role in modulating solar irradiance.

  12. Effect of chemical heat release in a temporally evolving mixing layer

    NASA Technical Reports Server (NTRS)

    Higuera, F. J.; Moser, R. D.

    1994-01-01

    Two-dimensional numerical simulations of a temporally evolving mixing layer with an exothermic infinitely fast diffusion flame between two unmixed reactants have been carried out in the limit of zero Mach number to study the effect of the heat release on the early stages of the evolution of the flow. Attention has been directed to relatively large values of the oxidizer-to-fuel mass stoichiometric ratio typical of hydrocarbon flames, and initial vorticity distributions thicker than the temperature and species distributions have been chosen to mimic the situation at the outlet of a jet. The results show that, during the stages of the evolution covered by the present simulations, enhancement of combustion occurs by local stretching of the flame without much augmentation of its area. The rate of product generation depends strongly on the initial conditions, which suggests the possibility of controlling the combustion by acting on the flow. Rollup and vortex amalgamation still occur in these reacting flows but are very much affected by the production of new vorticity by baroclinic torques. These torques lead to counter rotating vortex pairs around the flame and, more importantly, in thin layers of light fluid that leave the vicinity of the flame when the Kelvin-Helmholtz instability begins to develop. Propelled by the vortex pairs, these layers wind around, split on reaching high pressure regions, and originate new vortex pairs in a process that ends up building large-scale vortices with a vorticity distribution more complex than for a constant density fluid.

  13. Comparative Study of Three High Order Schemes for LES of Temporally Evolving Mixing Layers

    NASA Technical Reports Server (NTRS)

    Yee, Helen M. C.; Sjogreen, Biorn Axel; Hadjadj, C.

    2012-01-01

    Three high order shock-capturing schemes are compared for large eddy simulations (LES) of temporally evolving mixing layers (TML) for different convective Mach numbers (Mc) ranging from the quasi-incompressible regime to highly compressible supersonic regime. The considered high order schemes are fifth-order WENO (WENO5), seventh-order WENO (WENO7) and the associated eighth-order central spatial base scheme with the dissipative portion of WENO7 as a nonlinear post-processing filter step (WENO7fi). This high order nonlinear filter method (H.C. Yee and B. Sjogreen, Proceedings of ICOSAHOM09, June 22-26, 2009, Trondheim, Norway) is designed for accurate and efficient simulations of shock-free compressible turbulence, turbulence with shocklets and turbulence with strong shocks with minimum tuning of scheme parameters. The LES results by WENO7fi using the same scheme parameter agree well with experimental results of Barone et al. (2006), and published direct numerical simulations (DNS) work of Rogers & Moser (1994) and Pantano & Sarkar (2002), whereas results by WENO5 and WENO7 compare poorly with experimental data and DNS computations.

  14. Ultrafast temporal pulse shaping via phase-sensitive three-wave mixing.

    PubMed

    Yin, Y C; French, D; Jovanovic, I

    2010-08-16

    It is well-known that the process of optical parametric amplification (OPA) can be sensitive to the phases of the incident waves. In OPA realized by three-wave mixing, injection of all three waves into the same mode with appropriate phase relationship results in amplification of the signal phase, with an associated deamplification of the signal energy. Prospects for the use of this technique in the temporal domain for shaping ultrashort laser pulses are analyzed using a numerical model. Several representative pulse shaping capabilities of this technique are identified, which can significantly augment the performance of common passive pulse shaping methods operating in the Fourier domain. It is found that the use of phase-sensitive OPA shows a potential for significant compression of approximately 100 fs pulses, steepening of the rise time of ultrashort pulses, and production of pulse doublets and pulse trains. It is also shown that the group velocity mismatch can assist the shaping process. Such pulse shaping capabilities are found to be within reach of this technique in common nonlinear optical crystals pumped by pulses available from compact femtosecond chirped-pulse amplification laser systems.

  15. LES of Temporally Evolving Mixing Layers by an Eighth-Order Filter Scheme

    NASA Technical Reports Server (NTRS)

    Hadjadj, A; Yee, H. C.; Sjogreen, B.

    2011-01-01

    An eighth-order filter method for a wide range of compressible flow speeds (H.C. Yee and B. Sjogreen, Proceedings of ICOSAHOM09, June 22-26, 2009, Trondheim, Norway) are employed for large eddy simulations (LES) of temporally evolving mixing layers (TML) for different convective Mach numbers (Mc) and Reynolds numbers. The high order filter method is designed for accurate and efficient simulations of shock-free compressible turbulence, turbulence with shocklets and turbulence with strong shocks with minimum tuning of scheme parameters. The value of Mc considered is for the TML range from the quasi-incompressible regime to the highly compressible supersonic regime. The three main characteristics of compressible TML (the self similarity property, compressibility effects and the presence of large-scale structure with shocklets for high Mc) are considered for the LES study. The LES results using the same scheme parameters for all studied cases agree well with experimental results of Barone et al. (2006), and published direct numerical simulations (DNS) work of Rogers & Moser (1994) and Pantano & Sarkar (2002).

  16. Mixed care networks of community-dwelling older adults with physical health impairments in the Netherlands.

    PubMed

    Broese van Groenou, Marjolein; Jacobs, Marianne; Zwart-Olde, Ilse; Deeg, Dorly J H

    2016-01-01

    As part of long-term care reforms, home-care organisations in the Netherlands are required to strengthen the linkage between formal and informal caregivers of home-dwelling older adults. Information on the variety in mixed care networks may help home-care organisations to develop network type-dependent strategies to connect with informal caregivers. This study first explores how structural (size, composition) and functional features (contact and task overlap between formal and informal caregivers) contribute to different types of mixed care networks. Second, it examines to what degree these network types are associated with the care recipients' characteristics. Through home-care organisations in Amsterdam, the Netherlands, we selected 74 frail home-dwelling clients who were receiving care in 2011-2012 from both informal and formal caregivers. The care networks of these older adults were identified by listing all persons providing help with five different types of tasks. This resulted in care networks comprising an average of 9.7 caregivers, of whom 67% were formal caregivers. On average, there was contact between caregivers within 34% of the formal-informal dyads, and both caregivers carried out at least one similar type of task in 29% of these dyads. A principal component analysis of size, composition, contact and task overlap showed two distinct network dimensions from which four network types were constructed: a small mixed care network, a small formal network, a large mixed network and a large formal network. Bivariate analyses showed that the care recipients' activities of daily living level, memory problems, social network, perceived control of care and level of mastery differed significantly between these four types. The results imply that different network types require different actions from formal home-care organisations, such as mobilising the social network in small formal networks, decreasing task differentiation in large formal networks and assigning

  17. The structural plasticity of white matter networks following anterior temporal lobe resection

    PubMed Central

    Yogarajah, Mahinda; Focke, Niels K.; Bonelli, Silvia B.; Thompson, Pamela; Vollmar, Christian; McEvoy, Andrew W.; Alexander, Daniel C.; Symms, Mark R.; Koepp, Matthias J.

    2010-01-01

    Anterior temporal lobe resection is an effective treatment for refractory temporal lobe epilepsy. The structural consequences of such surgery in the white matter, and how these relate to language function after surgery remain unknown. We carried out a longitudinal study with diffusion tensor imaging in 26 left and 20 right temporal lobe epilepsy patients before and a mean of 4.5 months after anterior temporal lobe resection. The whole-brain analysis technique tract-based spatial statistics was used to compare pre- and postoperative data in the left and right temporal lobe epilepsy groups separately. We observed widespread, significant, mean 7%, decreases in fractional anisotropy in white matter networks connected to the area of resection, following both left and right temporal lobe resections. However, we also observed a widespread, mean 8%, increase in fractional anisotropy after left anterior temporal lobe resection in the ipsilateral external capsule and posterior limb of the internal capsule, and corona radiata. These findings were confirmed on analysis of the native clusters and hand drawn regions of interest. Postoperative tractography seeded from this area suggests that this cluster is part of the ventro-medial language network. The mean pre- and postoperative fractional anisotropy and parallel diffusivity in this cluster were significantly correlated with postoperative verbal fluency and naming test scores. In addition, the percentage change in parallel diffusivity in this cluster was correlated with the percentage change in verbal fluency after anterior temporal lobe resection, such that the bigger the increase in parallel diffusivity, the smaller the fall in language proficiency after surgery. We suggest that the findings of increased fractional anisotropy in this ventro-medial language network represent structural reorganization in response to the anterior temporal lobe resection, which may damage the more susceptible dorso-lateral language pathway

  18. Habituation-based mechanism for encoding temporal information in artificial neural networks

    NASA Astrophysics Data System (ADS)

    Stiles, Bryan W.; Ghosh, Joydeep

    1995-04-01

    A novel neural network is proposed for the dynamic classification of spatio-temporal signals. The network is designed to classify signals of different durations, taking into account correlations among different signal segments. Such a network is applicable to SONAR and speech signal classification problems, among others. Network parameters are adapted based on the biologically observed habituation mechanism. This allows the storage of contextual information, without a substantial increase in network complexity. Experiments on classification of high dimensional feature vectors obtained from Banzhaf sonograms, demonstrate that the proposed network performs better than time delay neural networks while using a less complex structure. A mathematical justification of the capabilities of the habituation based mechanism is also provided.

  19. Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks.

    PubMed

    Miranda, Gisele Helena Barboni; Machicao, Jeaneth; Bruno, Odemir Martinez

    2016-11-22

    Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability.

  20. Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

    NASA Astrophysics Data System (ADS)

    Miranda, Gisele Helena Barboni; Machicao, Jeaneth; Bruno, Odemir Martinez

    2016-11-01

    Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability.

  1. Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

    PubMed Central

    Miranda, Gisele Helena Barboni; Machicao, Jeaneth; Bruno, Odemir Martinez

    2016-01-01

    Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability. PMID:27874024

  2. Temporal Gillespie Algorithm: Fast Simulation of Contagion Processes on Time-Varying Networks

    PubMed Central

    Vestergaard, Christian L.; Génois, Mathieu

    2015-01-01

    Stochastic simulations are one of the cornerstones of the analysis of dynamical processes on complex networks, and are often the only accessible way to explore their behavior. The development of fast algorithms is paramount to allow large-scale simulations. The Gillespie algorithm can be used for fast simulation of stochastic processes, and variants of it have been applied to simulate dynamical processes on static networks. However, its adaptation to temporal networks remains non-trivial. We here present a temporal Gillespie algorithm that solves this problem. Our method is applicable to general Poisson (constant-rate) processes on temporal networks, stochastically exact, and up to multiple orders of magnitude faster than traditional simulation schemes based on rejection sampling. We also show how it can be extended to simulate non-Markovian processes. The algorithm is easily applicable in practice, and as an illustration we detail how to simulate both Poissonian and non-Markovian models of epidemic spreading. Namely, we provide pseudocode and its implementation in C++ for simulating the paradigmatic Susceptible-Infected-Susceptible and Susceptible-Infected-Recovered models and a Susceptible-Infected-Recovered model with non-constant recovery rates. For empirical networks, the temporal Gillespie algorithm is here typically from 10 to 100 times faster than rejection sampling. PMID:26517860

  3. Towards a temporal network analysis of interactive WiFi users

    NASA Astrophysics Data System (ADS)

    Zhang, Yan; Wang, Lin; Zhang, Yi-Qing; Li, Xiang

    2012-06-01

    Complex networks are used to depict topological features of complex systems. The structure of a network characterizes the interactions among elements of the system, and facilitates the study of many dynamical processes taking place on it. In previous investigations, the topological infrastructure underlying dynamical systems is simplified as a static and invariable skeleton. However, this assumption cannot cover the temporal features of many time-evolution networks, whose components are evolving and mutating. In this letter, utilizing the log data of WiFi users in a Chinese university campus, we infuse the temporal dimension into the construction of dynamical human contact network. By quantitative comparison with the traditional aggregation approach, we find that the temporal contact network differs in many features, e.g., the reachability, the path length distribution. We conclude that the correlation between temporal path length and duration is not only determined by their definitions, but also influenced by the micro-dynamical features of human activities under certain social circumstance as well. The time order of individuals' interaction events plays a critical role in understanding many dynamical processes via human close proximity interactions studied in this letter. Besides, our study also provides a promising measure to identify the potential superspreaders by distinguishing the nodes functioning as the relay hub. The first two authors contributed equally to this paper.

  4. Temporal Gillespie Algorithm: Fast Simulation of Contagion Processes on Time-Varying Networks.

    PubMed

    Vestergaard, Christian L; Génois, Mathieu

    2015-10-01

    Stochastic simulations are one of the cornerstones of the analysis of dynamical processes on complex networks, and are often the only accessible way to explore their behavior. The development of fast algorithms is paramount to allow large-scale simulations. The Gillespie algorithm can be used for fast simulation of stochastic processes, and variants of it have been applied to simulate dynamical processes on static networks. However, its adaptation to temporal networks remains non-trivial. We here present a temporal Gillespie algorithm that solves this problem. Our method is applicable to general Poisson (constant-rate) processes on temporal networks, stochastically exact, and up to multiple orders of magnitude faster than traditional simulation schemes based on rejection sampling. We also show how it can be extended to simulate non-Markovian processes. The algorithm is easily applicable in practice, and as an illustration we detail how to simulate both Poissonian and non-Markovian models of epidemic spreading. Namely, we provide pseudocode and its implementation in C++ for simulating the paradigmatic Susceptible-Infected-Susceptible and Susceptible-Infected-Recovered models and a Susceptible-Infected-Recovered model with non-constant recovery rates. For empirical networks, the temporal Gillespie algorithm is here typically from 10 to 100 times faster than rejection sampling.

  5. Mixed-method Exploration of Social Network Links to Participation

    PubMed Central

    Kreider, Consuelo M.; Bendixen, Roxanna M.; Mann, William C.; Young, Mary Ellen; McCarty, Christopher

    2015-01-01

    The people who regularly interact with an adolescent form that youth's social network, which may impact participation. We investigated the relationship of social networks to participation using personal network analysis and individual interviews. The sample included 36 youth, age 11 – 16 years. Nineteen had diagnoses of learning disability, attention disorder, or high-functioning autism and 17 were typically developing. Network analysis yielded 10 network variables, of which 8 measured network composition and 2 measured network structure, with significant links to at least one measure of participation using the Children's Assessment of Participation and Enjoyment (CAPE). Interviews from youth in the clinical group yielded description of strategies used to negotiate social interactions, as well as processes and reasoning used to remain engaged within social networks. Findings contribute to understanding the ways social networks are linked to youth participation and suggest the potential of social network factors for predicting rehabilitation outcomes. PMID:26594737

  6. Inference of causality in epidemics on temporal contact networks

    NASA Astrophysics Data System (ADS)

    Braunstein, Alfredo; Ingrosso, Alessandro

    2016-06-01

    Investigating into the past history of an epidemic outbreak is a paramount problem in epidemiology. Based on observations about the state of individuals, on the knowledge of the network of contacts and on a mathematical model for the epidemic process, the problem consists in describing some features of the posterior distribution of unobserved past events, such as the source, potential transmissions, and undetected positive cases. Several methods have been proposed for the study of these inference problems on discrete-time, synchronous epidemic models on networks, including naive Bayes, centrality measures, accelerated Monte-Carlo approaches and Belief Propagation. However, most traced real networks consist of short-time contacts on continuous time. A possibility that has been adopted is to discretize time line into identical intervals, a method that becomes more and more precise as the length of the intervals vanishes. Unfortunately, the computational time of the inference methods increase with the number of intervals, turning a sufficiently precise inference procedure often impractical. We show here an extension of the Belief Propagation method that is able to deal with a model of continuous-time events, without resorting to time discretization. We also investigate the effect of time discretization on the quality of the inference.

  7. Activity Changes Induced by Spatio-Temporally Correlated Stimuli in Cultured Cortical Networks

    NASA Astrophysics Data System (ADS)

    Takayama, Yuzo; Moriguchi, Hiroyuki; Jimbo, Yasuhiko

    Activity-dependent plasticity probably plays a key role in learning and memory in biological information processing systems. Though long-term potentiation and depression have been extensively studied in the filed of neuroscience, little is known on the mechanisms for integrating these modifications on network-wide activity changes. In this report, we studied effects of spatio-temporally correlated stimuli on the neuronal network activity. Rat cortical neurons were cultured on substrates with 64 embedded micro-electrodes and the evoked responses were extracellularly recorded and analyzed. We compared spatio-temporal patterns of the responses between before and after repetitive application of correlated stimuli. After the correlated stimuli, the networks showed significantly different responses from those in the initial states. The modified activity reflected structures of the repeatedly applied correlated stimuli. The results suggested that spatiotemporally correlated inputs systematically induced modification of synaptic strengths in neuronal networks, which could serve as an underlying mechanism of associative memory.

  8. Aging and percolation dynamics in a Non-Poissonian temporal network model

    NASA Astrophysics Data System (ADS)

    Moinet, Antoine; Starnini, Michele; Pastor-Satorras, Romualdo

    2016-08-01

    We present an exhaustive mathematical analysis of the recently proposed Non-Poissonian Activity Driven (NoPAD) model [Moinet et al., Phys. Rev. Lett. 114, 108701 (2015), 10.1103/PhysRevLett.114.108701], a temporal network model incorporating the empirically observed bursty nature of social interactions. We focus on the aging effects emerging from the non-Poissonian dynamics of link activation, and on their effects on the topological properties of time-integrated networks, such as the degree distribution. Analytic expressions for the degree distribution of integrated networks as a function of time are derived, exploring both limits of vanishing and strong aging. We also address the percolation process occurring on these temporal networks, by computing the threshold for the emergence of a giant connected component, highlighting the aging dependence. Our analytic predictions are checked by means of extensive numerical simulations of the NoPAD model.

  9. Temporal stability of network centrality in control and default mode networks: Specific associations with externalizing psychopathology in children and adolescents.

    PubMed

    Sato, João Ricardo; Biazoli, Claudinei Eduardo; Salum, Giovanni Abrahão; Gadelha, Ary; Crossley, Nicolas; Satterthwaite, Theodore D; Vieira, Gilson; Zugman, André; Picon, Felipe Almeida; Pan, Pedro Mario; Hoexter, Marcelo Queiroz; Anés, Mauricio; Moura, Luciana Monteiro; Del'aquilla, Marco Antonio Gomes; Amaro, Edson; McGuire, Philip; Lacerda, Acioly L T; Rohde, Luis Augusto; Miguel, Euripedes Constantino; Jackowski, Andrea Parolin; Bressan, Rodrigo Affonseca

    2015-12-01

    Abnormal connectivity patterns have frequently been reported as involved in pathological mental states. However, most studies focus on "static," stationary patterns of connectivity, which may miss crucial biological information. Recent methodological advances have allowed the investigation of dynamic functional connectivity patterns that describe non-stationary properties of brain networks. Here, we introduce a novel graphical measure of dynamic connectivity, called time-varying eigenvector centrality (tv-EVC). In a sample 655 children and adolescents (7-15 years old) from the Brazilian "High Risk Cohort Study for Psychiatric Disorders" who were imaged using resting-state fMRI, we used this measure to investigate age effects in the temporal in control and default-mode networks (CN/DMN). Using support vector regression, we propose a network maturation index based on the temporal stability of tv-EVC. Moreover, we investigated whether the network maturation is associated with the overall presence of behavioral and emotional problems with the Child Behavior Checklist. As hypothesized, we found that the tv-EVC at each node of CN/DMN become more stable with increasing age (P < 0.001 for all nodes). In addition, the maturity index for this particular network is indeed associated with general psychopathology in children assessed by the total score of Child Behavior Checklist (P = 0.027). Moreover, immaturity of the network was mainly correlated with externalizing behavior dimensions. Taken together, these results suggest that changes in functional network dynamics during neurodevelopment may provide unique insights regarding pathophysiology.

  10. Neural network for processing both spatial and temporal data with time based back-propagation

    NASA Technical Reports Server (NTRS)

    Villarreal, James A. (Inventor); Shelton, Robert O. (Inventor)

    1993-01-01

    Neural networks are computing systems modeled after the paradigm of the biological brain. For years, researchers using various forms of neural networks have attempted to model the brain's information processing and decision-making capabilities. Neural network algorithms have impressively demonstrated the capability of modeling spatial information. On the other hand, the application of parallel distributed models to the processing of temporal data has been severely restricted. The invention introduces a novel technique which adds the dimension of time to the well known back-propagation neural network algorithm. In the space-time neural network disclosed herein, the synaptic weights between two artificial neurons (processing elements) are replaced with an adaptable-adjustable filter. Instead of a single synaptic weight, the invention provides a plurality of weights representing not only association, but also temporal dependencies. In this case, the synaptic weights are the coefficients to the adaptable digital filters. Novelty is believed to lie in the disclosure of a processing element and a network of the processing elements which are capable of processing temporal as well as spacial data.

  11. Deciphering the connectivity structure of biological networks using MixNet

    PubMed Central

    Picard, Franck; Miele, Vincent; Daudin, Jean-Jacques; Cottret, Ludovic; Robin, Stéphane

    2009-01-01

    Background As biological networks often show complex topological features, mathematical methods are required to extract meaningful information. Clustering methods are useful in this setting, as they allow the summary of the network's topology into a small number of relevant classes. Different strategies are possible for clustering, and in this article we focus on a model-based strategy that aims at clustering nodes based on their connectivity profiles. Results We present MixNet, the first publicly available computer software that analyzes biological networks using mixture models. We apply this method to various networks such as the E. coli transcriptional regulatory network, the macaque cortex network, a foodweb network and the Buchnera aphidicola metabolic network. This method is also compared with other approaches such as module identification or hierarchical clustering. Conclusion We show how MixNet can be used to extract meaningful biological information, and to give a summary of the networks topology that highlights important biological features. This approach is powerful as MixNet is adaptive to the network under study, and finds structural information without any a priori on the structure that is investigated. This makes MixNet a very powerful tool to summarize and decipher the connectivity structure of biological networks. PMID:19534742

  12. Brain networks of temporal preparation: A multiple regression analysis of neuropsychological data.

    PubMed

    Triviño, Mónica; Correa, Ángel; Lupiáñez, Juan; Funes, María Jesús; Catena, Andrés; He, Xun; Humphreys, Glyn W

    2016-11-15

    There are only a few studies on the brain networks involved in the ability to prepare in time, and most of them followed a correlational rather than a neuropsychological approach. The present neuropsychological study performed multiple regression analysis to address the relationship between both grey and white matter (measured by magnetic resonance imaging in patients with brain lesion) and different effects in temporal preparation (Temporal orienting, Foreperiod and Sequential effects). Two versions of a temporal preparation task were administered to a group of 23 patients with acquired brain injury. In one task, the cue presented (a red versus green square) to inform participants about the time of appearance (early versus late) of a target stimulus was blocked, while in the other task the cue was manipulated on a trial-by-trial basis. The duration of the cue-target time intervals (400 versus 1400ms) was always manipulated within blocks in both tasks. Regression analysis were conducted between either the grey matter lesion size or the white matter tracts disconnection and the three temporal preparation effects separately. The main finding was that each temporal preparation effect was predicted by a different network of structures, depending on cue expectancy. Specifically, the Temporal orienting effect was related to both prefrontal and temporal brain areas. The Foreperiod effect was related to right and left prefrontal structures. Sequential effects were predicted by both parietal cortex and left subcortical structures. These findings show a clear dissociation of brain circuits involved in the different ways to prepare in time, showing for the first time the involvement of temporal areas in the Temporal orienting effect, as well as the parietal cortex in the Sequential effects.

  13. A recurrent fuzzy network for fuzzy temporal sequence processing and gesture recognition.

    PubMed

    Juang, Chia-Feng; Ku, Ksuan-Chun

    2005-08-01

    A fuzzified Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network (FTRFN) for handling fuzzy temporal information is proposed in this paper. The FTRFN extends our previously proposed network, TRFN, to deal with fuzzy temporal signals represented by Gaussian or triangular fuzzy numbers. In the precondition part of FTRFN, matching degrees between input fuzzy variables and fuzzy antecedent sets is performed by similarity measure. In the TSK-type consequence, a linear combination of fuzzy variables is computed, where two sets of combination coefficients, one for the center and the other for the width of each fuzzy number, are used. Derivation of the linear combination results and final network output is based on left-right fuzzy number operation. There are no rules in FTRFN initially; they are constructed online by concurrent structure and parameter learning, where all free parameters in the precondition/consequence of FTRFN are all tunable. FTRFN can be applied on a variety of domains related to fuzzy temporal information processing. In this paper, it has been applied on one-dimensional and two-dimensional fuzzy temporal sequence prediction and CCD-based temporal gesture recognition. The performance of FTRFN is verified from these examples.

  14. Lattice dynamical wavelet neural networks implemented using particle swarm optimization for spatio-temporal system identification.

    PubMed

    Wei, Hua-Liang; Billings, Stephen A; Zhao, Yifan; Guo, Lingzhong

    2009-01-01

    In this brief, by combining an efficient wavelet representation with a coupled map lattice model, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNNs), is introduced for spatio-temporal system identification. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the OPP algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, however, may be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. An example for a real spatio-temporal system identification problem is presented to demonstrate the performance of the proposed new modeling framework.

  15. An adaptive wavelet neural network for spatio-temporal system identification.

    PubMed

    Wei, H L; Billings, S A; Zhao, Y F; Guo, L Z

    2010-12-01

    Starting from the basic concept of coupled map lattices, a new family of adaptive wavelet neural networks (AWNN) is introduced for spatio-temporal system identification, by combining an efficient wavelet representation with a coupled map lattice model. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the orthogonal projection pursuit algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, may however be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. The proposed two-stage hybrid training procedure can generally produce a parsimonious network model, where a ranked list of wavelet neurons, according to the capability of each neuron to represent the total variance in the system output signal is produced. Two spatio-temporal system identification examples are presented to demonstrate the performance of the proposed new modelling framework.

  16. Complex network based techniques to identify extreme events and (sudden) transitions in spatio-temporal systems.

    PubMed

    Marwan, Norbert; Kurths, Jürgen

    2015-09-01

    We present here two promising techniques for the application of the complex network approach to continuous spatio-temporal systems that have been developed in the last decade and show large potential for future application and development of complex systems analysis. First, we discuss the transforming of a time series from such systems to a complex network. The natural approach is to calculate the recurrence matrix and interpret such as the adjacency matrix of an associated complex network, called recurrence network. Using complex network measures, such as transitivity coefficient, we demonstrate that this approach is very efficient for identifying qualitative transitions in observational data, e.g., when analyzing paleoclimate regime transitions. Second, we demonstrate the use of directed spatial networks constructed from spatio-temporal measurements of such systems that can be derived from the synchronized-in-time occurrence of extreme events in different spatial regions. Although there are many possibilities to investigate such spatial networks, we present here the new measure of network divergence and how it can be used to develop a prediction scheme of extreme rainfall events.

  17. Separating temporal and topological effects in walk-based network centrality

    NASA Astrophysics Data System (ADS)

    Colman, Ewan R.; Charlton, Nathaniel

    2016-07-01

    The recently introduced concept of dynamic communicability is a valuable tool for ranking the importance of nodes in a temporal network. Two metrics, broadcast score and receive score, were introduced to measure the centrality of a node with respect to a model of contagion based on time-respecting walks. This article examines the temporal and structural factors influencing these metrics by considering a versatile stochastic temporal network model. We analytically derive formulas to accurately predict the expectation of the broadcast and receive scores when one or more columns in a temporal edge-list are shuffled. These methods are then applied to two publicly available data sets and we quantify how much the centrality of each individual depends on structural or temporal influences. From our analysis, we highlight two practical contributions: a way to control for temporal variation when computing dynamic communicability and the conclusion that the broadcast and receive scores can, under a range of circumstances, be replaced by the row and column sums of the matrix exponential of a weighted adjacency matrix given by the data.

  18. The Semantic Network at Work and Rest: Differential Connectivity of Anterior Temporal Lobe Subregions

    PubMed Central

    Jackson, Rebecca L.; Hoffman, Paul; Pobric, Gorana

    2016-01-01

    The anterior temporal lobe (ATL) makes a critical contribution to semantic cognition. However, the functional connectivity of the ATL and the functional network underlying semantic cognition has not been elucidated. In addition, subregions of the ATL have distinct functional properties and thus the potential differential connectivity between these subregions requires investigation. We explored these aims using both resting-state and active semantic task data in humans in combination with a dual-echo gradient echo planar imaging (EPI) paradigm designed to ensure signal throughout the ATL. In the resting-state analysis, the ventral ATL (vATL) and anterior middle temporal gyrus (MTG) were shown to connect to areas responsible for multimodal semantic cognition, including bilateral ATL, inferior frontal gyrus, medial prefrontal cortex, angular gyrus, posterior MTG, and medial temporal lobes. In contrast, the anterior superior temporal gyrus (STG)/superior temporal sulcus was connected to a distinct set of auditory and language-related areas, including bilateral STG, precentral and postcentral gyri, supplementary motor area, supramarginal gyrus, posterior temporal cortex, and inferior and middle frontal gyri. Complementary analyses of functional connectivity during an active semantic task were performed using a psychophysiological interaction (PPI) analysis. The PPI analysis highlighted the same semantic regions suggesting a core semantic network active during rest and task states. This supports the necessity for semantic cognition in internal processes occurring during rest. The PPI analysis showed additional connectivity of the vATL to regions of occipital and frontal cortex. These areas strongly overlap with regions found to be sensitive to executively demanding, controlled semantic processing. SIGNIFICANCE STATEMENT Previous studies have shown that semantic cognition depends on subregions of the anterior temporal lobe (ATL). However, the network of regions

  19. Network Alterations Supporting Word Retrieval in Patients with Medial Temporal Lobe Epilepsy

    ERIC Educational Resources Information Center

    Protzner, Andrea B.; McAndrews, Mary Pat

    2011-01-01

    Although the hippocampus is not considered a key structure in semantic memory, patients with medial-temporal lobe epilepsy (mTLE) have deficits in semantic access on some word retrieval tasks. We hypothesized that these deficits reflect the negative impact of focal epilepsy on remote cerebral structures. Thus, we expected that the networks that…

  20. Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

    PubMed Central

    Sanz-García, Ancor; Vega-Zelaya, Lorena; Pastor, Jesús; Torres, Cristina V.; Sola, Rafael G.; Ortega, Guillermo J.

    2016-01-01

    Approximately 30% of epilepsy patients are refractory to antiepileptic drugs. In these cases, surgery is the only alternative to eliminate/control seizures. However, a significant minority of patients continues to exhibit post-operative seizures, even in those cases in which the suspected source of seizures has been correctly localized and resected. The protocol presented here combines a clinical procedure routinely employed during the pre-operative evaluation of temporal lobe epilepsy (TLE) patients with a novel technique for network analysis. The method allows for the evaluation of the temporal evolution of mesial network parameters. The bilateral insertion of foramen ovale electrodes (FOE) into the ambient cistern simultaneously records electrocortical activity at several mesial areas in the temporal lobe. Furthermore, network methodology applied to the recorded time series tracks the temporal evolution of the mesial networks both interictally and during the seizures. In this way, the presented protocol offers a unique way to visualize and quantify measures that considers the relationships between several mesial areas instead of a single area. PMID:28060326

  1. Synchronization and information transmission in spatio-temporal networks of deformable units

    NASA Astrophysics Data System (ADS)

    Moukam Kakmeni, F. M.; Baptista, M. S.

    2008-06-01

    We study the relationship between synchronization and the rate with which information is exchanged between nodes in a spatio-temporal network that describes the dynamics of classical particles under a substrate Remoissenet-Peyrard potential. We also show how phase and complete synchronization can be detected in this network. The difficulty in detecting phase synchronization in such a network appears due to the highly non-coherent character of the particle dynamics which unables a proper definition of the phase dynamics. The difficulty in detecting complete synchronization appears due to the spatio character of the potential which results in an asymptotic state highly dependent on the initial state.

  2. Breakdown of long-range temporal dependence in default mode and attention networks during deep sleep

    PubMed Central

    Tagliazucchi, Enzo; von Wegner, Frederic; Morzelewski, Astrid; Brodbeck, Verena; Jahnke, Kolja; Laufs, Helmut

    2013-01-01

    The integration of segregated brain functional modules is a prerequisite for conscious awareness during wakeful rest. Here, we test the hypothesis that temporal integration, measured as long-term memory in the history of neural activity, is another important quality underlying conscious awareness. For this aim, we study the temporal memory of blood oxygen level-dependent signals across the human nonrapid eye movement sleep cycle. Results reveal that this property gradually decreases from wakefulness to deep nonrapid eye movement sleep and that such decreases affect areas identified with default mode and attention networks. Although blood oxygen level-dependent spontaneous fluctuations exhibit nontrivial spatial organization, even during deep sleep, they also display a decreased temporal complexity in specific brain regions. Conversely, this result suggests that long-range temporal dependence might be an attribute of the spontaneous conscious mentation performed during wakeful rest. PMID:24003146

  3. Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks.

    PubMed

    Calhoun, Vince D; Kiehl, Kent A; Pearlson, Godfrey D

    2008-07-01

    Brain regions which exhibit temporally coherent fluctuations, have been increasingly studied using functional magnetic resonance imaging (fMRI). Such networks are often identified in the context of an fMRI scan collected during rest (and thus are called "resting state networks"); however, they are also present during (and modulated by) the performance of a cognitive task. In this article, we will refer to such networks as temporally coherent networks (TCNs). Although there is still some debate over the physiological source of these fluctuations, TCNs are being studied in a variety of ways. Recent studies have examined ways TCNs can be used to identify patterns associated with various brain disorders (e.g. schizophrenia, autism or Alzheimer's disease). Independent component analysis (ICA) is one method being used to identify TCNs. ICA is a data driven approach which is especially useful for decomposing activation during complex cognitive tasks where multiple operations occur simultaneously. In this article we review recent TCN studies with emphasis on those that use ICA. We also present new results showing that TCNs are robust, and can be consistently identified at rest and during performance of a cognitive task in healthy individuals and in patients with schizophrenia. In addition, multiple TCNs show temporal and spatial modulation during the cognitive task versus rest. In summary, TCNs show considerable promise as potential imaging biological markers of brain diseases, though each network needs to be studied in more detail.

  4. Theta Oscillation Reveals the Temporal Involvement of Different Attentional Networks in Contingent Reorienting

    PubMed Central

    Chang, Chi-Fu; Liang, Wei-Kuang; Lai, Chiou-Lian; Hung, Daisy L.; Juan, Chi-Hung

    2016-01-01

    In the visual world, rapidly reorienting to relevant objects outside the focus of attention is vital for survival. This ability from the interaction between goal-directed and stimulus-driven attentional control is termed contingent reorienting. Neuroimaging studies have demonstrated activations of the ventral and dorsal attentional networks (DANs) which exhibit right hemisphere dominance, but the temporal dynamics of the attentional networks still remain unclear. The present study used event-related potential (ERP) to index the locus of spatial attention and Hilbert-Huang transform (HHT) to acquire the time-frequency information during contingent reorienting. The ERP results showed contingent reorienting induced significant N2pc on both hemispheres. In contrast, our time-frequency analysis found further that, unlike the N2pc, theta oscillation during contingent reorienting differed between hemispheres and experimental sessions. The inter-trial coherence (ITC) of the theta oscillation demonstrated that the two sides of the attentional networks became phase-locked to contingent reorienting at different stages. The left attentional networks were associated with contingent reorienting in the first experimental session whereas the bilateral attentional networks play a more important role in this process in the subsequent session. This phase-locked information suggests a dynamic temporal evolution of the involvement of different attentional networks in contingent reorienting and a potential role of the left ventral network in the first session. PMID:27375459

  5. Reconstructing Generalized Logical Networks of Transcriptional Regulation in Mouse Brain from Temporal Gene Expression Data

    SciTech Connect

    Song, Mingzhou; Lewis, Chris K.; Lance, Eric; Chesler, Elissa J; Kirova, Roumyana; Langston, Michael A; Bergeson, Susan

    2009-01-01

    The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from high-throughput transcriptomic data is addressed. A network reconstruction algorithm was developed that uses the statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. Using temporal gene expression data collected from the brains of alcohol-treated mice in an analysis of the molecular response to alcohol, this algorithm identified genes from a major neuronal pathway as putative components of the alcohol response mechanism. Three of these genes have known associations with alcohol in the literature. Several other potentially relevant genes, highlighted and agreeing with independent results from literature mining, may play a role in the response to alcohol. Additional, previously-unknown gene interactions were discovered that, subject to biological verification, may offer new clues in the search for the elusive molecular mechanisms of alcoholism.

  6. A spatio-temporal model of wrinkling in photopolymerised networks

    NASA Astrophysics Data System (ADS)

    Hennessy, Matthew; Vitale, Alessandra; Stavrinou, Paul; Matar, Omar; Cabral, Joao

    2015-03-01

    Photopolymerisation is a common solidification process whereby crosslinked polymer networks are created by illuminating a monomer-rich bath with collimated light. In addition, photopolymerisation is extensively employed industrially and shows exceptional promise for advanced three-dimensional patterning of functional surfaces. Under conditions of strong optical attenuation and limited mass and thermal diffusion, polymerisation occurs in a localised region which propagates from the illuminated surface into the bulk as a travelling wave with a planar wavefront. Under specific conditions that we set out to map, this planar wavefront may become unstable and the surface of the resulting gel can acquire a wrinkled morphology. We believe this instability is mechanical in nature and arises from compressive stresses that are generated during frontal photopolymerization. In this talk, we will present a novel mathematical model that captures both the photopolymerisation with wrinkling processes. We show that by coupling photopolymerisation with wrinkling in a controlled manner, a number of interesting and industrially relevant patterns can be achieved.

  7. Temporal analysis of social networks using three-way DEDICOM.

    SciTech Connect

    Bader, Brett William; Harshman, Richard A. (University of Ontario, London, Ontario, Canada); Kolda, Tamara Gibson

    2006-06-01

    DEDICOM is an algebraic model for analyzing intrinsically asymmetric relationships, such as the balance of trade among nations or the flow of information among organizations or individuals. It provides information on latent components in the data that can be regarded as ''properties'' or ''aspects'' of the objects, and it finds a few patterns that can be combined to describe many relationships among these components. When we apply this technique to adjacency matrices arising from directed graphs, we obtain a smaller graph that gives an idealized description of its patterns. Three-way DEDICOM is a higher-order extension of the model that has certain uniqueness properties. It allows for a third mode of the data, such as time, and permits the analysis of semantic graphs. We present an improved algorithm for computing three-way DEDICOM on sparse data and demonstrate it by applying it to the adjacency tensor of a semantic graph with time-labeled edges. Our application uses the Enron email corpus, from which we construct a semantic graph corresponding to email exchanges among Enron personnel over a series of 44 months. Meaningful patterns are recovered in which the representation of asymmetries adds insight into the social networks at Enron.

  8. Diagnosis of Autism Spectrum Disorders Using Temporally-Distinct Resting-State Functional Connectivity Networks

    PubMed Central

    Wee, Chong-Yaw; Yap, Pew-Thian; Shen, Dinggang

    2016-01-01

    Introduction Resting-state functional magnetic resonance imaging (R-fMRI) is dynamic in nature since neural activities constantly change over the time and are dominated by repeating brief activations and deactivations involving many brain regions. Each region participates in multiple brain functions and is part of various functionally distinct but spatially overlapping networks. Functional connectivity computed as correlations over the entire time series always overlooks inter-region interactions that often occur repeatedly and dynamically in time, limiting its application to disease diagnosis. Aims We develop a novel framework that uses short-time activation patterns of brain connectivity to better detect subtle disease-induced disruptions of brain connectivity. A clustering algorithm is first used to temporally decompose R-fMRI time series into distinct clusters with similar spatial distribution of neural activity based on the assumption that functionally distinct networks should be largely temporally distinct since brain states do not simultaneously coexist in general. A Pearson correlation-based functional connectivity network is then constructed for each cluster to allow for better exploration of spatiotemporal dynamics of individual neural activity. To reduce significant inter-subject variability and to remove possible spurious connections, we use a group-constrained sparse regression model to construct a backbone sparse network for each cluster and use it to weight the corresponding Pearson correlation network. Results The proposed method outperforms the conventional static, temporally-dependent fully-connected correlation-based networks by at least 7% on a publicly available autism dataset. We were able to reproduce similar results using data from other centers. Conclusions By combining the advantages of temporal independence and group-constrained sparse regression, our method improves autism diagnosis. PMID:26821773

  9. Disease Spread through Animal Movements: A Static and Temporal Network Analysis of Pig Trade in Germany

    PubMed Central

    Lentz, Hartmut H. K.; Koher, Andreas; Hövel, Philipp; Gethmann, Jörn; Sauter-Louis, Carola; Selhorst, Thomas; Conraths, Franz J.

    2016-01-01

    Background Animal trade plays an important role for the spread of infectious diseases in livestock populations. The central question of this work is how infectious diseases can potentially spread via trade in such a livestock population. We address this question by analyzing the underlying network of animal movements. In particular, we consider pig trade in Germany, where trade actors (agricultural premises) form a complex network. Methodology The considered pig trade dataset spans several years and is analyzed with respect to its potential to spread infectious diseases. Focusing on measurements of network-topological properties, we avoid the usage of external parameters, since these properties are independent of specific pathogens. They are on the contrary of great importance for understanding any general spreading process on this particular network. We analyze the system using different network models, which include varying amounts of information: (i) static network, (ii) network as a time series of uncorrelated snapshots, (iii) temporal network, where causality is explicitly taken into account. Findings We find that a static network view captures many relevant aspects of the trade system, and premises can be classified into two clearly defined risk classes. Moreover, our results allow for an efficient allocation strategy for intervention measures using centrality measures. Data on trade volume do barely alter the results and is therefore of secondary importance. Although a static network description yields useful results, the temporal resolution of data plays an outstanding role for an in-depth understanding of spreading processes. This applies in particular for an accurate calculation of the maximum outbreak size. PMID:27152712

  10. What does diffusion tensor imaging (DTI) tell us about cognitive networks in temporal lobe epilepsy?

    PubMed Central

    Kucukboyaci, N. Erkut; Puckett, Olivia K.; Lee, Davis; Loi, Richard Q.; Paul, Brianna; McDonald, Carrie R.

    2015-01-01

    Diffusion tensor imaging (DTI) has provided considerable insight into our understanding of epilepsy as a network disorder, revealing subtle alterations in white matter microstructure both proximal and distal to the epileptic focus. These white matter changes have been shown to assist with lateralizing the seizure focus, as well as delineating the location/anatomy of key white matter tracts (i.e., optic radiations) for surgical planning. However, only recently have studies emerged describing the utility of DTI for probing cognitive networks in patients with epilepsy and for examining the structural plasticity within these networks both before and after epilepsy surgery. Here, we review the current literature describing the use of DTI for understanding language and memory networks in patients with temporal lobe epilepsy (TLE), as well as the extant literature on networks associated with executive functioning and global intelligence. Studies of memory and language reveal a complex network of frontotemporal fibers that contribute to naming and fluency performance in TLE, and demonstrate that these networks appear to undergo adaptive changes in response to surgical intervention. Although studies of executive functioning and global intelligence have been less conclusive, there is accumulating evidence that aberrant communication between frontoparietal and medial temporal networks may underlie working memory impairment in TLE. More recently, multimodal imaging studies have provided evidence that disruptions within these white matter networks co-localize with functional changes observed on functional MRI. However, structure-function associations are not entirely coherent and may breakdown in patients with TLE, especially those with a left-sided seizure focus. Although the reasons for discordant findings are unclear, small sample sizes, heterogeneity within patient populations and limitations of the current tensor model may account for contradictory and null findings

  11. Spatial-temporal data model and fractal analysis of transportation network in GIS environment

    NASA Astrophysics Data System (ADS)

    Feng, Yongjiu; Tong, Xiaohua; Li, Yangdong

    2008-10-01

    How to organize transportation data characterized by multi-time, multi-scale, multi-resolution and multi-source is one of the fundamental problems of GIS-T development. A spatial-temporal data model for GIS-T is proposed based on Spatial-temporal- Object Model. Transportation network data is systemically managed using dynamic segmentation technologies. And then a spatial-temporal database is built to integrally store geographical data of multi-time for transportation. Based on the spatial-temporal database, functions of spatial analysis of GIS-T are substantively extended. Fractal module is developed to improve the analyzing in intensity, density, structure and connectivity of transportation network based on the validation and evaluation of topologic relation. Integrated fractal with GIS-T strengthens the functions of spatial analysis and enriches the approaches of data mining and knowledge discovery of transportation network. Finally, the feasibility of the model and methods are tested thorough Guangdong Geographical Information Platform for Highway Project.

  12. Temporal motifs reveal collaboration patterns in online task-oriented networks

    NASA Astrophysics Data System (ADS)

    Xuan, Qi; Fang, Huiting; Fu, Chenbo; Filkov, Vladimir

    2015-05-01

    Real networks feature layers of interactions and complexity. In them, different types of nodes can interact with each other via a variety of events. Examples of this complexity are task-oriented social networks (TOSNs), where teams of people share tasks towards creating a quality artifact, such as academic research papers or software development in commercial or open source environments. Accomplishing those tasks involves both work, e.g., writing the papers or code, and communication, to discuss and coordinate. Taking into account the different types of activities and how they alternate over time can result in much more precise understanding of the TOSNs behaviors and outcomes. That calls for modeling techniques that can accommodate both node and link heterogeneity as well as temporal change. In this paper, we report on methodology for finding temporal motifs in TOSNs, limited to a system of two people and an artifact. We apply the methods to publicly available data of TOSNs from 31 Open Source Software projects. We find that these temporal motifs are enriched in the observed data. When applied to software development outcome, temporal motifs reveal a distinct dependency between collaboration and communication in the code writing process. Moreover, we show that models based on temporal motifs can be used to more precisely relate both individual developer centrality and team cohesion to programmer productivity than models based on aggregated TOSNs.

  13. Temporal Dynamics of the Default Mode Network Characterize Meditation-Induced Alterations in Consciousness.

    PubMed

    Panda, Rajanikant; Bharath, Rose D; Upadhyay, Neeraj; Mangalore, Sandhya; Chennu, Srivas; Rao, Shobini L

    2016-01-01

    Current research suggests that human consciousness is associated with complex, synchronous interactions between multiple cortical networks. In particular, the default mode network (DMN) of the resting brain is thought to be altered by changes in consciousness, including the meditative state. However, it remains unclear how meditation alters the fast and ever-changing dynamics of brain activity within this network. Here we addressed this question using simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to compare the spatial extents and temporal dynamics of the DMN during rest and meditation. Using fMRI, we identified key reductions in the posterior cingulate hub of the DMN, along with increases in right frontal and left temporal areas, in experienced meditators during rest and during meditation, in comparison to healthy controls (HCs). We employed the simultaneously recorded EEG data to identify the topographical microstate corresponding to activation of the DMN. Analysis of the temporal dynamics of this microstate revealed that the average duration and frequency of occurrence of DMN microstate was higher in meditators compared to HCs. Both these temporal parameters increased during meditation, reflecting the state effect of meditation. In particular, we found that the alteration in the duration of the DMN microstate when meditators entered the meditative state correlated negatively with their years of meditation experience. This reflected a trait effect of meditation, highlighting its role in producing durable changes in temporal dynamics of the DMN. Taken together, these findings shed new light on short and long-term consequences of meditation practice on this key brain network.

  14. Temporal Dynamics of the Default Mode Network Characterize Meditation-Induced Alterations in Consciousness

    PubMed Central

    Panda, Rajanikant; Bharath, Rose D.; Upadhyay, Neeraj; Mangalore, Sandhya; Chennu, Srivas; Rao, Shobini L.

    2016-01-01

    Current research suggests that human consciousness is associated with complex, synchronous interactions between multiple cortical networks. In particular, the default mode network (DMN) of the resting brain is thought to be altered by changes in consciousness, including the meditative state. However, it remains unclear how meditation alters the fast and ever-changing dynamics of brain activity within this network. Here we addressed this question using simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to compare the spatial extents and temporal dynamics of the DMN during rest and meditation. Using fMRI, we identified key reductions in the posterior cingulate hub of the DMN, along with increases in right frontal and left temporal areas, in experienced meditators during rest and during meditation, in comparison to healthy controls (HCs). We employed the simultaneously recorded EEG data to identify the topographical microstate corresponding to activation of the DMN. Analysis of the temporal dynamics of this microstate revealed that the average duration and frequency of occurrence of DMN microstate was higher in meditators compared to HCs. Both these temporal parameters increased during meditation, reflecting the state effect of meditation. In particular, we found that the alteration in the duration of the DMN microstate when meditators entered the meditative state correlated negatively with their years of meditation experience. This reflected a trait effect of meditation, highlighting its role in producing durable changes in temporal dynamics of the DMN. Taken together, these findings shed new light on short and long-term consequences of meditation practice on this key brain network. PMID:27499738

  15. Exploiting temporal network structures of human interaction to effectively immunize populations.

    PubMed

    Lee, Sungmin; Rocha, Luis E C; Liljeros, Fredrik; Holme, Petter

    2012-01-01

    Decreasing the number of people who must be vaccinated to immunize a community against an infectious disease could both save resources and decrease outbreak sizes. A key to reaching such a lower threshold of immunization is to find and vaccinate people who, through their behavior, are more likely than average to become infected and to spread the disease further. Fortunately, the very behavior that makes these people important to vaccinate can help us to localize them. Earlier studies have shown that one can use previous contacts to find people that are central in static contact networks. However, real contact patterns are not static. In this paper, we investigate if there is additional information in the temporal contact structure for vaccination protocols to exploit. We answer this affirmative by proposing two immunization methods that exploit temporal correlations and showing that these methods outperform a benchmark static-network protocol in four empirical contact datasets under various epidemic scenarios. Both methods rely only on obtainable, local information, and can be implemented in practice. For the datasets directly related to contact patterns of potential disease spreading (of sexually-transmitted and nosocomial infections respectively), the most efficient protocol is to sample people at random and vaccinate their latest contacts. The network datasets are temporal, which enables us to make more realistic evaluations than earlier studies--we use only information about the past for the purpose of vaccination, and about the future to simulate disease outbreaks. Using analytically tractable models, we identify two temporal structures that explain how the protocols earn their efficiency in the empirical data. This paper is a first step towards real vaccination protocols that exploit temporal-network structure--future work is needed both to characterize the structure of real contact sequences and to devise immunization methods that exploit these.

  16. Temporal changes in the structure of a plant-frugivore network are influenced by bird migration and fruit availability.

    PubMed

    Ramos-Robles, Michelle; Andresen, Ellen; Díaz-Castelazo, Cecilia

    2016-01-01

    Background. Ecological communities are dynamic collections whose composition and structure change over time, making up complex interspecific interaction networks. Mutualistic plant-animal networks can be approached through complex network analysis; these networks are characterized by a nested structure consisting of a core of generalist species, which endows the network with stability and robustness against disturbance. Those mutualistic network structures can vary as a consequence of seasonal fluctuations and food availability, as well as the arrival of new species into the system that might disorder the mutualistic network structure (e.g., a decrease in nested pattern). However, there is no assessment on how the arrival of migratory species into seasonal tropical systems can modify such patterns. Emergent and fine structural temporal patterns are adressed here for the first time for plant-frugivorous bird networks in a highly seasonal tropical environment. Methods. In a plant-frugivorous bird community, we analyzed the temporal turnover of bird species comprising the network core and periphery of ten temporal interaction networks resulting from different bird migration periods. Additionally, we evaluated how fruit abundance and richness, as well as the arrival of migratory birds into the system, explained the temporal changes in network parameters such as network size, connectance, nestedness, specialization, interaction strength asymmetry and niche overlap. The analysis included data from 10 quantitative plant-frugivorous bird networks registered from November 2013 to November 2014. Results. We registered a total of 319 interactions between 42 plant species and 44 frugivorous bird species; only ten bird species were part of the network core. We witnessed a noteworthy turnover of the species comprising the network periphery during migration periods, as opposed to the network core, which did not show significant temporal changes in species composition. Our results

  17. Temporal changes in the structure of a plant-frugivore network are influenced by bird migration and fruit availability

    PubMed Central

    Andresen, Ellen; Díaz-Castelazo, Cecilia

    2016-01-01

    Background. Ecological communities are dynamic collections whose composition and structure change over time, making up complex interspecific interaction networks. Mutualistic plant–animal networks can be approached through complex network analysis; these networks are characterized by a nested structure consisting of a core of generalist species, which endows the network with stability and robustness against disturbance. Those mutualistic network structures can vary as a consequence of seasonal fluctuations and food availability, as well as the arrival of new species into the system that might disorder the mutualistic network structure (e.g., a decrease in nested pattern). However, there is no assessment on how the arrival of migratory species into seasonal tropical systems can modify such patterns. Emergent and fine structural temporal patterns are adressed here for the first time for plant-frugivorous bird networks in a highly seasonal tropical environment. Methods. In a plant-frugivorous bird community, we analyzed the temporal turnover of bird species comprising the network core and periphery of ten temporal interaction networks resulting from different bird migration periods. Additionally, we evaluated how fruit abundance and richness, as well as the arrival of migratory birds into the system, explained the temporal changes in network parameters such as network size, connectance, nestedness, specialization, interaction strength asymmetry and niche overlap. The analysis included data from 10 quantitative plant-frugivorous bird networks registered from November 2013 to November 2014. Results. We registered a total of 319 interactions between 42 plant species and 44 frugivorous bird species; only ten bird species were part of the network core. We witnessed a noteworthy turnover of the species comprising the network periphery during migration periods, as opposed to the network core, which did not show significant temporal changes in species composition. Our

  18. Memory network plasticity after temporal lobe resection: a longitudinal functional imaging study

    PubMed Central

    Sidhu, Meneka K.; Stretton, Jason; Winston, Gavin P.; McEvoy, Andrew W.; Symms, Mark; Thompson, Pamela J.; Koepp, Matthias J.

    2016-01-01

    Anterior temporal lobe resection can control seizures in up to 80% of patients with temporal lobe epilepsy. Memory decrements are the main neurocognitive complication. Preoperative functional reorganization has been described in memory networks, but less is known of postoperative reorganization. We investigated reorganization of memory-encoding networks preoperatively and 3 and 12 months after surgery. We studied 36 patients with unilateral medial temporal lobe epilepsy (19 right) before and 3 and 12 months after anterior temporal lobe resection. Fifteen healthy control subjects were studied at three equivalent time points. All subjects had neuropsychological testing at each of the three time points. A functional magnetic resonance imaging memory-encoding paradigm of words and faces was performed with subsequent out-of-scanner recognition assessments. Changes in activations across the time points in each patient group were compared to changes in the control group in a single flexible factorial analysis. Postoperative change in memory across the time points was correlated with postoperative activations to investigate the efficiency of reorganized networks. Left temporal lobe epilepsy patients showed increased right anterior hippocampal and frontal activation at both 3 and 12 months after surgery relative to preoperatively, for word and face encoding, with a concomitant reduction in left frontal activation 12 months postoperatively. Right anterior hippocampal activation 12 months postoperatively correlated significantly with improved verbal learning in patients with left temporal lobe epilepsy from preoperatively to 12 months postoperatively. Preoperatively, there was significant left posterior hippocampal activation that was sustained 3 months postoperatively at word encoding, and increased at face encoding. For both word and face encoding this was significantly reduced from 3 to 12 months postoperatively. Patients with right temporal lobe epilepsy showed increased

  19. Topological fractal networks introduced by mixed degree distribution

    NASA Astrophysics Data System (ADS)

    Zou, Liuhua; Pei, Wenjiang; Li, Tao; He, Zhenya; Cheung, Yiuming

    2007-07-01

    Several fundamental properties of real complex networks, such as the small-world effect, the scale-free degree distribution, and recently discovered topological fractal structure, have presented the possibility of a unique growth mechanism and allow for uncovering universal origins of collective behaviors. However, highly clustered scale-free network, with power-law degree distribution, or small-world network models, with exponential degree distribution, are not self-similarity. We investigate networks growth mechanism of the branching-deactivated geographical attachment preference that learned from certain empirical evidence of social behaviors. It yields high clustering and spectrums of degree distribution ranging from algebraic to exponential, average shortest path length ranging from linear to logarithmic. We observe that the present networks fit well with small-world graphs and scale-free networks in both limit cases (exponential and algebraic degree distribution, respectively), obviously lacking self-similar property under a length-scale transformation. Interestingly, we find perfect topological fractal structure emerges by a mixture of both algebraic and exponential degree distributions in a wide range of parameter values. The results present a reliable connection among small-world graphs, scale-free networks and topological fractal networks, and promise a natural way to investigate universal origins of collective behaviors.

  20. Synchronization properties of heterogeneous neuronal networks with mixed excitability type.

    PubMed

    Leone, Michael J; Schurter, Brandon N; Letson, Benjamin; Booth, Victoria; Zochowski, Michal; Fink, Christian G

    2015-03-01

    We study the synchronization of neuronal networks with dynamical heterogeneity, showing that network structures with the same propensity for synchronization (as quantified by master stability function analysis) may develop dramatically different synchronization properties when heterogeneity is introduced with respect to neuronal excitability type. Specifically, we investigate networks composed of neurons with different types of phase response curves (PRCs), which characterize how oscillating neurons respond to excitatory perturbations. Neurons exhibiting type 1 PRC respond exclusively with phase advances, while neurons exhibiting type 2 PRC respond with either phase delays or phase advances, depending on when the perturbation occurs. We find that Watts-Strogatz small world networks transition to synchronization gradually as the proportion of type 2 neurons increases, whereas scale-free networks may transition gradually or rapidly, depending upon local correlations between node degree and excitability type. Random placement of type 2 neurons results in gradual transition to synchronization, whereas placement of type 2 neurons as hubs leads to a much more rapid transition, showing that type 2 hub cells easily "hijack" neuronal networks to synchronization. These results underscore the fact that the degree of synchronization observed in neuronal networks is determined by a complex interplay between network structure and the dynamical properties of individual neurons, indicating that efforts to recover structural connectivity from dynamical correlations must in general take both factors into account.

  1. Network-oriented massive spatio-temporal data model and its applications

    NASA Astrophysics Data System (ADS)

    Liu, Renyi; Liu, Nan; Bao, Weizheng; Zhu, Yan

    2006-10-01

    It is foundation and key of developing GIS platforms of new generation to study the network-oriented massive spatial and spatio-temporal data model. But the research has met many difficulties. The paper combines two models of massive spatial data and spatio-temporal data seemed to be independent to study together in theory and technique. On the base of analyzing the limitations of present geographical spatial data model and spatio-temporal data model, a new model with characteristics of new generation's GIS platform, that is, Feature-Oriented Massive Spatio-temporal Object Tree (FOMSOT) with four-tier architectures is presented. The FOMSOT breaks down the constraint of map layer. It can deal with the massive spatio-temporal data better. The dynamic multi-base state with amendment (DMSA), fast index of base state with amendment in section, storage factors of variable granularity (SFVG) are used in FOMSOT which can manage the massive spatio-temporal data in high efficiency. A prototype "LyranMap" of new generation's GIS platform with the theory and technical method of FOMSOT has been realized, and it has been used in some application systems, for example, the land planning system "LandPlanner", land investigation system "LandExplorer" and land cadastral system "LandReGIS". These verify the correctness and effectiveness of the FOMSOT.

  2. A Mixed-Methods Social Networks Study Design for Research on Transnational Families

    ERIC Educational Resources Information Center

    Bernardi, Laura

    2011-01-01

    This paper advocates the adoption of a mixed-methods research design to describe and analyze ego-centered social networks in transnational family research. Drawing on the experience of the "Social Networks Influences on Family Formation" project (2004-2005; see Bernardi, Keim, & von der Lippe, 2007a, 2007b), I show how the combined…

  3. A mixed-methods study of research dissemination across practice-based research networks.

    PubMed

    Lipman, Paula Darby; Lange, Carol J; Cohen, Rachel A; Peterson, Kevin A

    2014-01-01

    Practice-based research networks may be expanding beyond research into rapid learning systems. This mixed-methods study uses Agency for Healthcare Research and Quality registry data to identify networks currently engaged in dissemination of research findings and to select a sample to participate in qualitative semistructured interviews. An adapted Diffusion of Innovations framework was used to organize concepts by characteristics of networks, dissemination activities, and mechanisms for rapid learning. Six regional networks provided detailed information about dissemination strategies, organizational context, role of practice-based research network, member involvement, and practice incentives. Strategies compatible with current practices and learning innovations that generate observable improvements may increase effectiveness of rapid learning approaches.

  4. Assessing the spatial and temporal variability of fAPAR 2-flux estimates in a temperate mixed coniferous forest

    NASA Astrophysics Data System (ADS)

    Putzenlechner, Birgitta; Sanchez-Azofeifa, Arturo; Ludwig, Ralf

    2016-04-01

    The fraction of absorbed photosynthetically active radiation (fAPAR) is recognized as one of the essential climate variables as it characterizes activity and dynamics of the Earth's terrestrial biosphere (GCOS, 2010). By linking photosynthetic active radiation (PAR) to the absorption of plants, fAPAR represents a crucial variable for describing land surface and atmosphere interactions considered in global circulation models as well as in production efficiency models for estimating terrestrial carbon balances. Recent studies report discrepancies between global fAPAR satellite products regarding both absolute values and uncertainty representation, thereby stressing the need for independent ground measurements (D'Odrico et al., 2014; Picket-Heaps et al., 2014; Tao et al., 2015). However, there is a lack of basic information to better understand the spatial and temporal bias of PAR field observations, particularly in forest ecosystems. In theory, it is known that fAPAR estimates are affected by e.g. illumination conditions, leaf area index, leaf color, background brightness, which in turn may lead to considerable bias of field measurements. However, theoretical findings lack validation in the field as well as practical recommendations for field protocols. In this study, the variability of two-flux fAPAR estimates with regards to different illumination conditions (solar zenith angles, diffuse radiation conditions) are investigated. Measurements of PAR are carried out at Graswang environmental monitoring site in Southern Germany within a temperate mixed coniferous forest. A relatively new environmental monitoring technology based on Wireless Sensor Networks (WSN) is applied, allowing for permanent synchronized measurements of transmitted PAR, thereby reducing temporal sampling bias. Transmitted PAR is obtained from 16 photon flux sensors, 1.3 m above the surface. With a reference sensor outside the forest measuring incoming PAR, a two-flux estimate based on the ratio of

  5. The Compact Network RTK Method: An Effective Solution to Reduce GNSS Temporal and Spatial Decorrelation Error

    NASA Astrophysics Data System (ADS)

    Park, Byungwoon; Kee, Changdon

    This paper proposes a method that combines compact real-time kinematic (RTK) and reference station (RS) networking techniques, and shows that this approach can reduce both the temporal and spatial decorrelation error. The compact RTK method compatibility with all the conventional network RTK systems, i.e., Master-Auxiliary Concept (MAC), Virtual Reference Stations (VRS), and Fl40 s and determines position with 68 cm vertical error (95%) in a 100 by 100 km region. Moreover, the Compact Network RTK approach enables network RTK service providers to reduce the data-link bandwidth for correction messages to 5-700 bps (bit/s) down from several thousand bps, currently 9600 bps of GPRS/GSM, without a severe degradation of accuracy.

  6. Spatio-temporal propagation of cascading overload failures in spatially embedded networks.

    PubMed

    Zhao, Jichang; Li, Daqing; Sanhedrai, Hillel; Cohen, Reuven; Havlin, Shlomo

    2016-01-12

    Different from the direct contact in epidemics spread, overload failures propagate through hidden functional dependencies. Many studies focused on the critical conditions and catastrophic consequences of cascading failures. However, to understand the network vulnerability and mitigate the cascading overload failures, the knowledge of how the failures propagate in time and space is essential but still missing. Here we study the spatio-temporal propagation behaviour of cascading overload failures analytically and numerically on spatially embedded networks. The cascading overload failures are found to spread radially from the centre of the initial failure with an approximately constant velocity. The propagation velocity decreases with increasing tolerance, and can be well predicted by our theoretical framework with one single correction for all the tolerance values. This propagation velocity is found similar in various model networks and real network structures. Our findings may help to predict the dynamics of cascading overload failures in realistic systems.

  7. Spatio-temporal propagation of cascading overload failures in spatially embedded networks

    PubMed Central

    Zhao, Jichang; Li, Daqing; Sanhedrai, Hillel; Cohen, Reuven; Havlin, Shlomo

    2016-01-01

    Different from the direct contact in epidemics spread, overload failures propagate through hidden functional dependencies. Many studies focused on the critical conditions and catastrophic consequences of cascading failures. However, to understand the network vulnerability and mitigate the cascading overload failures, the knowledge of how the failures propagate in time and space is essential but still missing. Here we study the spatio-temporal propagation behaviour of cascading overload failures analytically and numerically on spatially embedded networks. The cascading overload failures are found to spread radially from the centre of the initial failure with an approximately constant velocity. The propagation velocity decreases with increasing tolerance, and can be well predicted by our theoretical framework with one single correction for all the tolerance values. This propagation velocity is found similar in various model networks and real network structures. Our findings may help to predict the dynamics of cascading overload failures in realistic systems. PMID:26754065

  8. Effect of node attributes on the temporal dynamics of network structure

    NASA Astrophysics Data System (ADS)

    Momeni, Naghmeh; Fotouhi, Babak

    2017-03-01

    Many natural and social networks evolve in time and their structures are dynamic. In most networks, nodes are heterogeneous, and their roles in the evolution of structure differ. This paper focuses on the role of individual attributes on the temporal dynamics of network structure. We focus on a basic model for growing networks that incorporates node attributes (which we call "quality"), and we focus on the problem of forecasting the structural properties of the network in arbitrary times for an arbitrary initial network. That is, we address the following question: If we are given a certain initial network with given arbitrary structure and known node attributes, then how does the structure change in time as new nodes with given distribution of attributes join the network? We solve the model analytically and obtain the quality-degree joint distribution and degree correlations. We characterize the role of individual attributes in the position of individual nodes in the hierarchy of connections. We confirm the theoretical findings with Monte Carlo simulations.

  9. Using spatio-temporal asymmetry to enhance mixing in chaotic flows: From maps to stirred tanks

    NASA Astrophysics Data System (ADS)

    Alvarez, Mario Moises

    Under laminar flow conditions, chaos is the only route to achieve effective mixing. Indeed, industrially relevant devices such as static mixers, stirred tanks, and roller bottles work because they create chaotic flows. However, they are generally operated and designed in a symmetric fashion (e.g. symmetric construction, periodic operation). Under such circumstances, chaotic and nonchaotic regions always co-exist, often hindering mixing performance. The introduction of asymmetries (in space or time) has been proposed as a means to improve mixing performance by generating globally chaotic systems in which the entire flow domain is subject to the action of exponential stretching and repeated folding, key features of chaotic flows capable of good mixing. Here we compare mixing performance of symmetric and asymmetric mixing flows from the point of view of the properties of the structure that they generate. In particular, we analyze two classes of systems: We use computer simulations to follow the process of elongation and deformation of interfaces as they are advected by time-periodic and aperiodic protocols in an idealized 2-D flow (the sine flow). The distribution of length scales characteristic of the partially mixed structures in this flow is calculated and their statistical properties are investigated. As the main conclusion, we find that the distribution of length scales is universal (independently on the periodic or aperiodic nature of the flow), and predictable (based on stretching calculations) for any globally chaotic flow. Subsequently, mixing structures and flow patterns in stirred tank systems of geometries encountered in engineering practice and operated in the laminar regime are investigated experimentally using UV visualization techniques, Particle Image Velocimetry (PIV) and Planar Laser Induced Fluorescence (p-LIF). It is experimentally demonstrated that concentric stirred tank configurations achieve partial chaos only by virtue of the small

  10. Power-law distributed temporal heterogeneity of human activities promotes cooperation on complex networks

    NASA Astrophysics Data System (ADS)

    Liu, Chao; Li, Rong

    2016-09-01

    An evolutionary prisoner's dilemma game (PDG) with players located on Barabási-Albert scale-free networks is studied. The impact of players' heterogeneous temporal activity pattern on the evolution of cooperation is investigated. To this end, the normal procedure that players update their strategies immediately after a round of game is discarded. Instead, players update strategies according to their assigned reproduction time, which follows a power-law distribution. We find that the temporal heterogeneity of players' activities facilitates the prosperity of cooperation, indicating the important role of hubs in the maintenance of cooperation on scale-free networks. When the reproduction time is assigned to individuals negatively related to their degrees, a fluctuation of the cooperation level with the increase of the exponent β is observed.

  11. Identification of Patient Zero in Static and Temporal Networks: Robustness and Limitations

    NASA Astrophysics Data System (ADS)

    Antulov-Fantulin, Nino; Lančić, Alen; Šmuc, Tomislav; Štefančić, Hrvoje; Šikić, Mile

    2015-06-01

    Detection of patient zero can give new insights to epidemiologists about the nature of first transmissions into a population. In this Letter, we study the statistical inference problem of detecting the source of epidemics from a snapshot of spreading on an arbitrary network structure. By using exact analytic calculations and Monte Carlo estimators, we demonstrate the detectability limits for the susceptible-infected-recovered model, which primarily depend on the spreading process characteristics. Finally, we demonstrate the applicability of the approach in a case of a simulated sexually transmitted infection spreading over an empirical temporal network of sexual interactions.

  12. Nonparametric variable selection and modeling for spatial and temporal regulatory networks.

    PubMed

    Aswani, Anil; Biggin, Mark D; Bickel, Peter; Tomlin, Claire

    2012-01-01

    Because of the increasing diversity of data sets and measurement techniques in biology, a growing spectrum of modeling methods is being developed. It is generally recognized that it is critical to pick the appropriate method to exploit the amount and type of biological data available for a given system. Here, we describe a method for use in situations where temporal data from a network is collected over multiple time points, and in which little prior information is available about the interactions, mathematical structure, and statistical distribution of the network. Our method results in models that we term Nonparametric exterior derivative estimation Ordinary Differential Equation (NODE) model's. We illustrate the method's utility using spatiotemporal gene expression data from Drosophila melanogaster embryos. We demonstrate that the NODE model's use of the temporal characteristics of the network leads to quantifiable improvements in its predictive ability over nontemporal models that only rely on the spatial characteristics of the data. The NODE model provides exploratory visualizations of network behavior and structure, which can identify features that suggest additional experiments. A new extension is also presented that uses the NODE model to generate a comb diagram, a figure that presents a list of possible network structures ranked by plausibility. By being able to quantify a continuum of interaction likelihoods, this helps to direct future experiments.

  13. Stability Analysis of Attractor Neural Network Model of Inferior Temporal Cortex —Relationship between Attractor Stability and Learning Order—

    NASA Astrophysics Data System (ADS)

    Tomoyuki Kimoto,; Tatsuya Uezu,; Masato Okada,

    2010-06-01

    Miyashita found that the long-term memory of visual stimuli is stored in the monkey’s inferior temporal cortex and that the temporal correlation in terms of the learning order of visual stimuli is converted into spatial correlation in terms of the firing rate patterns of the neuron group. To explain Miyashita’s findings, Griniasty et al. [Neural Comput. 5 (1993) 1] and Amit et al. [J. Neurosci. 14 (1994) 6435] proposed the attractor neural network model, and the Amit model has been examined only for the stable state acquired by storing memory patterns in a fixed sequence. In the real world, however, the learning order has statistical continuity but it also has randomness, and the stability of the state changes depending on the statistical properties of learning order when memory patterns are stored randomly. In addition, it is preferable for the stable state to become an appropriate attractor that reflects the relationship between memory patterns by the statistical properties of the learning order. In this study, we examined the dependence of the stable state on the statistical properties of the learning order without modifying the Amit model. The stable state was found to change from the correlated attractor to the Hopfield or Mp attractor, which is the mixed state with all memory patterns when the rate of random learning increases. Furthermore, we found that if the statistical properties of the learning order change, the stable state can change to an appropriate attractor reflecting the relationship between memory patterns.

  14. A Markov model for the temporal dynamics of balanced random networks of finite size

    PubMed Central

    Lagzi, Fereshteh; Rotter, Stefan

    2014-01-01

    The balanced state of recurrent networks of excitatory and inhibitory spiking neurons is characterized by fluctuations of population activity about an attractive fixed point. Numerical simulations show that these dynamics are essentially nonlinear, and the intrinsic noise (self-generated fluctuations) in networks of finite size is state-dependent. Therefore, stochastic differential equations with additive noise of fixed amplitude cannot provide an adequate description of the stochastic dynamics. The noise model should, rather, result from a self-consistent description of the network dynamics. Here, we consider a two-state Markovian neuron model, where spikes correspond to transitions from the active state to the refractory state. Excitatory and inhibitory input to this neuron affects the transition rates between the two states. The corresponding nonlinear dependencies can be identified directly from numerical simulations of networks of leaky integrate-and-fire neurons, discretized at a time resolution in the sub-millisecond range. Deterministic mean-field equations, and a noise component that depends on the dynamic state of the network, are obtained from this model. The resulting stochastic model reflects the behavior observed in numerical simulations quite well, irrespective of the size of the network. In particular, a strong temporal correlation between the two populations, a hallmark of the balanced state in random recurrent networks, are well represented by our model. Numerical simulations of such networks show that a log-normal distribution of short-term spike counts is a property of balanced random networks with fixed in-degree that has not been considered before, and our model shares this statistical property. Furthermore, the reconstruction of the flow from simulated time series suggests that the mean-field dynamics of finite-size networks are essentially of Wilson-Cowan type. We expect that this novel nonlinear stochastic model of the interaction between

  15. Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals?

    PubMed Central

    Chen, Shi; Ilany, Amiyaal; White, Brad J.; Sanderson, Michael W.; Lanzas, Cristina

    2015-01-01

    Animal social network is the key to understand many ecological and epidemiological processes. We used real-time location system (RTLS) to accurately track cattle position, analyze their proximity networks, and tested the hypothesis of temporal stationarity and spatial homogeneity in these networks during different daily time periods and in different areas of the pen. The network structure was analyzed using global network characteristics (network density), subgroup clustering (modularity), triadic property (transitivity), and dyadic interactions (correlation coefficient from a quadratic assignment procedure) at hourly level. We demonstrated substantial spatial-temporal heterogeneity in these networks and potential link between indirect animal-environment contact and direct animal-animal contact. But such heterogeneity diminished if data were collected at lower spatial (aggregated at entire pen level) or temporal (aggregated at daily level) resolution. The network structure (described by the characteristics such as density, modularity, transitivity, etc.) also changed substantially at different time and locations. There were certain time (feeding) and location (hay) that the proximity network structures were more consistent based on the dyadic interaction analysis. These results reveal new insights for animal network structure and spatial-temporal dynamics, provide more accurate descriptions of animal social networks, and allow more accurate modeling of multiple (both direct and indirect) disease transmission pathways. PMID:26107251

  16. Path Network Recovery Using Remote Sensing Data and Geospatial-Temporal Semantic Graphs

    SciTech Connect

    William C. McLendon III; Brost, Randy C.

    2016-05-01

    Remote sensing systems produce large volumes of high-resolution images that are difficult to search. The GeoGraphy (pronounced Geo-Graph-y) framework [2, 20] encodes remote sensing imagery into a geospatial-temporal semantic graph representation to enable high level semantic searches to be performed. Typically scene objects such as buildings and trees tend to be shaped like blocks with few holes, but other shapes generated from path networks tend to have a large number of holes and can span a large geographic region due to their connectedness. For example, we have a dataset covering the city of Philadelphia in which there is a single road network node spanning a 6 mile x 8 mile region. Even a simple question such as "find two houses near the same street" might give unexpected results. More generally, nodes arising from networks of paths (roads, sidewalks, trails, etc.) require additional processing to make them useful for searches in GeoGraphy. We have assigned the term Path Network Recovery to this process. Path Network Recovery is a three-step process involving (1) partitioning the network node into segments, (2) repairing broken path segments interrupted by occlusions or sensor noise, and (3) adding path-aware search semantics into GeoQuestions. This report covers the path network recovery process, how it is used, and some example use cases of the current capabilities.

  17. Large-scale brain networks are distinctly affected in right and left mesial temporal lobe epilepsy.

    PubMed

    de Campos, Brunno Machado; Coan, Ana Carolina; Lin Yasuda, Clarissa; Casseb, Raphael Fernandes; Cendes, Fernando

    2016-09-01

    Mesial temporal lobe epilepsy (MTLE) with hippocampus sclerosis (HS) is associated with functional and structural alterations extending beyond the temporal regions and abnormal pattern of brain resting state networks (RSNs) connectivity. We hypothesized that the interaction of large-scale RSNs is differently affected in patients with right- and left-MTLE with HS compared to controls. We aimed to determine and characterize these alterations through the analysis of 12 RSNs, functionally parceled in 70 regions of interest (ROIs), from resting-state functional-MRIs of 99 subjects (52 controls, 26 right- and 21 left-MTLE patients with HS). Image preprocessing and statistical analysis were performed using UF(2) C-toolbox, which provided ROI-wise results for intranetwork and internetwork connectivity. Intranetwork abnormalities were observed in the dorsal default mode network (DMN) in both groups of patients and in the posterior salience network in right-MTLE. Both groups showed abnormal correlation between the dorsal-DMN and the posterior salience, as well as between the dorsal-DMN and the executive-control network. Patients with left-MTLE also showed reduced correlation between the dorsal-DMN and visuospatial network and increased correlation between bilateral thalamus and the posterior salience network. The ipsilateral hippocampus stood out as a central area of abnormalities. Alterations on left-MTLE expressed a low cluster coefficient, whereas the altered connections on right-MTLE showed low cluster coefficient in the DMN but high in the posterior salience regions. Both right- and left-MTLE patients with HS have widespread abnormal interactions of large-scale brain networks; however, all parameters evaluated indicate that left-MTLE has a more intricate bihemispheric dysfunction compared to right-MTLE. Hum Brain Mapp 37:3137-3152, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

  18. Comparison of mixed and lamellar coculture spatial arrangements for tissue engineering capillary networks in vitro.

    PubMed

    Peters, Erica B; Christoforou, Nicolas; Leong, Kam W; Truskey, George A

    2013-03-01

    Coculture of endothelial cells (ECs) and smooth muscle cells (SMCs) in vitro can yield confluent monolayers or EC networks. Factors influencing this transition are not known. In this study, we examined whether the spatial arrangement of EC-SMC cocultures affected EC migration, network morphology, and angiogenic protein secretion. Human umbilical cord blood-derived ECs (hCB-ECs) were grown in coculture with human aortic SMCs in either a mixed or lamellar spatial geometry and analyzed over a culture period of 12 days. The hCB-ECs cultured on SMCs in a mixed system had higher cell speeds, shorter persistence times, and lower random motility coefficients than ECs in a lamellar system. By day 12 of coculture, mixed systems demonstrated greater anastomoses and capillary loop formation than lamellar systems as evidenced by a higher number of branch points, angle of curvature between branch points, and percentage of imaged area covered by networks. The network morphology was more uniform in the mixed systems than the lamellar systems with fewer EC clusters present after several days in culture. Proliferation of hCB-ECs was higher for mixed cocultures during the first 24 h of coculture, and then declined dramatically suggesting that proliferation only contributed to network formation during the early stages of coculture. Proteome assay results show reduced solution levels, but no change in intracellular levels of angiogenic proteins in lamellar systems compared to mixed systems. These data suggest that mixing ECs and SMCs together favors the formation of EC networks to a greater extent than a lamellar arrangement in which ECs form a cell layer above a confluent, quiescent layer of SMCs.

  19. Dissecting apple tree architecture into genetic, ontogenetic and environmental effects: mixed linear modelling of repeated spatial and temporal measures.

    PubMed

    Segura, Vincent; Cilas, Christian; Costes, Evelyne

    2008-01-01

    The present study aimed to dissect tree architectural plasticity into genetic, ontogenetic and environmental effects over the first 4 yr of growth of an apple (Malus x domestica) F1 progeny by means of mixed linear modelling of repeated data. Traits related to both growth and branching processes were annually assessed on different axes of the trees planted in a staggered-start design. Both spatial repetitions, (i.e. different axis types) and temporal repetitions (i.e. successive ages of trees) were considered in a mixed linear model of repeated data. A significant genotype effect was found for most studied traits and interactions between genotype and year and/or age were also detected. The analysis of repeated temporal measures highlighted that the magnitude of the decrease in primary growth is mainly determined by the first year of growth, and the decrease in bottom diameter increment is concomitant with the first fruiting occurrence. This approach allowed us to distinguish among the traits that were under genetic control, those for which this control is exerted differentially throughout tree life or depending on climatic conditions or an axis type. Mapping quantitative trait loci (QTL) that are specific to these different effects will constitute the next step in the research.

  20. Spatially and temporally continuous LAI datasets based on the mixed pixel decomposition method.

    PubMed

    Zhao, Jianjun; Wang, Yanying; Zhang, Hongyan; Zhang, Zhengxiang; Guo, Xiaoyi; Yu, Shan; Du, Wala

    2016-01-01

    The leaf area index (LAI) is a key biophysical parameter that determines the state of plant growth. A global LAI has been routinely produced by the Moderate Resolution Imaging Spectro-radiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). However, the MODIS and AVHRR LAI products cannot be synchronized with the same spatial and temporal resolution. The LAI features are not discernible when a global LAI product is implemented at the regional scale because it has low resolution and different land cover types. To obtain high spatial and temporal resolution of LAI products, an empirical model based on the pixel scale was developed. The approach to generate a long (multi-decade) time series of a 1-km spatial resolution LAI normally integrates both AVHRR and MODIS datasets for different land cover types. In this paper, a regression-based model for generating a vegetation LAI was developed using the AVHRR Global Inventory Modelling and Mapping Studies Normalized Difference Vegetation Index (NDVI), MODIS LAI and land cover as input data; the model was evaluated by using relevant data from the same period data from 2000 to 2006. The results of this method show a good consistency in LAI values retrieved from the AVHRR NDVI and MODIS LAI. This simple method has no specific-limited data requirements and can provide improved spatial and temporal resolution in a region without ground data.

  1. A diffusion perspective on temporal networks: A case study on a supermarket

    NASA Astrophysics Data System (ADS)

    Deng, Shiguo; Qiu, Lu; Yang, Yue; Yang, Huijie

    2016-01-01

    From a large amount of records, one can extract behavioral characteristics of a social system at different scales. Theoretically, it can help us to know how the global behavior of a social system is formed from individual activities. Practically, it can be used to optimize and even to control the social system. Complicated relationships between the individuals form a network, which evolves with time. The behavior of the system can be accordingly understood in the framework of temporal network. In the present paper, instead of focusing on microscopic structures, we develop a framework to investigate temporal networks from the viewpoint of diffusion process, in which each snapshot network is divided into groups and the ID number of the group a node belongs to is used to measure its state. By this way trajectories of the nodes form an ensemble of realizations of a stochastic process. As an illustration, we investigate the diffusion behavior of a supermarket. One can find that with the increase of time the customers cluster and separate into different groups. Meanwhile, the system evolves in a significant order way, instead of a complete random one.

  2. Investigating the relationship between subjective drug craving and temporal dynamics of the default mode network, executive control network, and salience network in methamphetamine dependents using rsfMRI

    NASA Astrophysics Data System (ADS)

    Soltanian-Zadeh, Somayyeh; Hossein-Zadeh, Gholam-Ali; Shahbabaie, Alireza; Ekhtiari, Hamed

    2016-03-01

    Resting state functional connectivity (rsFC) studies using fMRI provides a great deal of knowledge on the spatiotemporal organization of the brain. The relationships between and within a number of resting state functional networks, namely the default mode network (DMN), salience network (SN) and executive control network (ECN) have been intensely studied in basic and clinical cognitive neuroscience [1]. However, the presumption of spatial and temporal stationarity has mostly restricted the assessment of rsFC [1]. In this study, sliding window correlation analysis and k-means clustering were exploited to examine the temporal dynamics of rsFC of these three networks in 24 abstinent methamphetamine dependents. Afterwards, using canonical correlation analysis (CCA) the possible relationship between the level of self-reported craving and the temporal dynamics was examined. Results indicate that the rsFC transits between 6 discrete "FC states" in the meth dependents. CCA results show that higher levels of craving are associated with higher probability of transiting from state 4 to 6 (positive FC of DMN-ECN getting weak and negative FC of DMN-SN appearing) and staying in state 4 (positive FC of DMN-ECN), lower probability of staying in state 2 (negative FC of DMN-ECN), transiting from state 4 to 2 (change of positive FC of DMN-ECN to negative FC), and transiting from state 3 to 5 (appearance of negative FC of DMN-SN and positive FC of DMN-ECN with the presence of negative FC of SN-ECN). Quantitative measures of temporal dynamics in large-scale brain networks could bring new added values to increase potentials for applications of rsfMRI in addiction medicine.

  3. Capturing complexity: Mixing methods in the analysis of a European tobacco control policy network

    PubMed Central

    Weishaar, Heide; Amos, Amanda; Collin, Jeff

    2015-01-01

    Social network analysis (SNA), a method which can be used to explore networks in various contexts, has received increasing attention. Drawing on the development of European smoke-free policy, this paper explores how a mixed method approach to SNA can be utilised to investigate a complex policy network. Textual data from public documents, consultation submissions and websites were extracted, converted and analysed using plagiarism detection software and quantitative network analysis, and qualitative data from public documents and 35 interviews were thematically analysed. While the quantitative analysis enabled understanding of the network's structure and components, the qualitative analysis provided in-depth information about specific actors' positions, relationships and interactions. The paper establishes that SNA is suited to empirically testing and analysing networks in EU policymaking. It contributes to methodological debates about the antagonism between qualitative and quantitative approaches and demonstrates that qualitative and quantitative network analysis can offer a powerful tool for policy analysis. PMID:26185482

  4. Capturing complexity: Mixing methods in the analysis of a European tobacco control policy network.

    PubMed

    Weishaar, Heide; Amos, Amanda; Collin, Jeff

    Social network analysis (SNA), a method which can be used to explore networks in various contexts, has received increasing attention. Drawing on the development of European smoke-free policy, this paper explores how a mixed method approach to SNA can be utilised to investigate a complex policy network. Textual data from public documents, consultation submissions and websites were extracted, converted and analysed using plagiarism detection software and quantitative network analysis, and qualitative data from public documents and 35 interviews were thematically analysed. While the quantitative analysis enabled understanding of the network's structure and components, the qualitative analysis provided in-depth information about specific actors' positions, relationships and interactions. The paper establishes that SNA is suited to empirically testing and analysing networks in EU policymaking. It contributes to methodological debates about the antagonism between qualitative and quantitative approaches and demonstrates that qualitative and quantitative network analysis can offer a powerful tool for policy analysis.

  5. Modelling and temporal performances evaluation of networked control systems using (max, +) algebra

    NASA Astrophysics Data System (ADS)

    Ammour, R.; Amari, S.

    2015-01-01

    In this paper, we address the problem of temporal performances evaluation of producer/consumer networked control systems. The aim is to develop a formal method for evaluating the response time of this type of control systems. Our approach consists on modelling, using Petri nets classes, the behaviour of the whole architecture including the switches that support multicast communications used by this protocol. (max, +) algebra formalism is then exploited to obtain analytical formulas of the response time and the maximal and minimal bounds. The main novelty is that our approach takes into account all delays experienced at the different stages of networked automation systems. Finally, we show how to apply the obtained results through an example of networked control system.

  6. ViSiBooL-visualization and simulation of Boolean networks with temporal constraints.

    PubMed

    Schwab, Julian; Burkovski, Andre; Siegle, Lea; Müssel, Christoph; Kestler, Hans A

    2016-10-22

    : Mathematical models and their simulation are increasingly used to gain insights into cellular pathways and regulatory networks. Dynamics of regulatory factors can be modeled using Boolean networks (BNs), among others. Text-based representations of models are precise descriptions, but hard to understand and interpret. ViSiBooL aims at providing a graphical way of modeling and simulating networks. By providing visualizations of static and dynamic network properties simultaneously, it is possible to directly observe the effects of changes in the network structure on the behavior. In order to address the challenges of clear design and a user-friendly graphical user interface (GUI), ViSiBooL implements visual representations of BNs. Additionally temporal extensions of the BNs for the modeling of regulatory time delays are incorporated. The GUI of ViSiBooL allows to model, organize, simulate and visualize BNs as well as corresponding simulation results such as attractors. Attractor searches are performed in parallel to the modeling process. Hence, changes in the network behavior are visualized at the same time.

  7. Widespread changes in network activity allow non-invasive detection of mesial temporal lobe seizures.

    PubMed

    Lam, Alice D; Zepeda, Rodrigo; Cole, Andrew J; Cash, Sydney S

    2016-10-01

    Decades of experience with intracranial recordings in patients with epilepsy have demonstrated that seizures can occur in deep cortical regions such as the mesial temporal lobes without showing any obvious signs of seizure activity on scalp electroencephalogram. Predicated on the idea that these seizures are purely focal, currently, the only way to detect these 'scalp-negative seizures' is with intracranial recordings. However, intracranial recordings are only rarely performed in patients with epilepsy, and are almost never performed outside of the context of epilepsy. As such, little is known about scalp-negative seizures and their role in the natural history of epilepsy, their effect on cognitive function, and their association with other neurological diseases. Here, we developed a novel approach to non-invasively identify scalp-negative seizures arising from the mesial temporal lobe based on scalp electroencephalogram network connectivity measures. We identified 25 scalp-negative mesial temporal lobe seizures in 10 patients and obtained control records from an additional 13 patients, all of whom underwent recordings with foramen ovale electrodes and scalp electroencephalogram. Scalp data from these records were used to train a scalp-negative seizure detector, which consisted of a pair of logistic regression classifiers that used scalp electroencephalogram coherence properties as input features. On cross-validation performance, this detector correctly identified scalp-negative seizures in 40% of patients, and correctly identified the side of seizure onset for each seizure detected. In comparison, routine clinical interpretation of these scalp electroencephalograms failed to identify any of the scalp-negative seizures. Among the patients in whom the detector raised seizure alarms, 80% had scalp-negative mesial temporal lobe seizures. The detector had a false alarm rate of only 0.31 per day and a positive predictive value of 75%. Of the 13 control patients, false

  8. Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks.

    PubMed

    Scholtes, Ingo; Wider, Nicolas; Pfitzner, René; Garas, Antonios; Tessone, Claudio J; Schweitzer, Frank

    2014-09-24

    Recent research has highlighted limitations of studying complex systems with time-varying topologies from the perspective of static, time-aggregated networks. Non-Markovian characteristics resulting from the ordering of interactions in temporal networks were identified as one important mechanism that alters causality and affects dynamical processes. So far, an analytical explanation for this phenomenon and for the significant variations observed across different systems is missing. Here we introduce a methodology that allows to analytically predict causality-driven changes of diffusion speed in non-Markovian temporal networks. Validating our predictions in six data sets we show that compared with the time-aggregated network, non-Markovian characteristics can lead to both a slow-down or speed-up of diffusion, which can even outweigh the decelerating effect of community structures in the static topology. Thus, non-Markovian properties of temporal networks constitute an important additional dimension of complexity in time-varying complex systems.

  9. Spatial and temporal variability of the refractivity over Tahiti from a coarse network of GPS stations

    NASA Astrophysics Data System (ADS)

    Serafini, J.; Fadil, A.; Sichoix, L.; Barriot, J.

    2010-12-01

    Slant wet delays (SWD) caused by the presence of water vapor in the atmosphere are routinely obtained from GPS measurements. Powerful tomography techniques have been developed to derive from them the refractivity of the atmosphere, which could be subject to strong spatial and temporal variations, especially over tropical zones. In this poster we model the spatial and temporal variability of the refractivity over the Tahiti Island. In a first study, we model the spatial part of the variability. For this purpose, GPS data spanning a four months period (June-Sept 2010) from a coarse network of nine stations are analyzed using the GAMIT software package. In particular, we found that the SWD variability is more important at the South East of the Island. In a second study we reconstruct the temporal part of the variability. For this purpose, ten years of GPS data from the IGS station THTI, located on the Punaauia suburb of Papeete are processed with respect to the precise point positioning (PPP) mode of the GIPSY-OASIS II software package. The derived SWD allow us to reconstruct, through a regularized inverse process, time series of the ZWD and North / East gradients of the refractivity. We show that the main components of the SWD are relative to semi-diurnal and seasonal terms. Finally, this spatio-temporal model of the refractivity permits us to build a robust estimate of the covariance matrix of the underlying stochastic process.

  10. Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks

    NASA Technical Reports Server (NTRS)

    Lee, Charles H.; Cheung, Kar-Ming

    2012-01-01

    In this paper, we propose to solve the constrained optimization problem in two phases. The first phase uses heuristic methods such as the ant colony method, particle swarming optimization, and genetic algorithm to seek a near optimal solution among a list of feasible initial populations. The final optimal solution can be found by using the solution of the first phase as the initial condition to the SQP algorithm. We demonstrate the above problem formulation and optimization schemes with a large-scale network that includes the DSN ground stations and a number of spacecraft of deep space missions.

  11. Relating Cortical Atrophy in Temporal Lobe Epilepsy with Graph Diffusion-Based Network Models

    PubMed Central

    Abdelnour, Farras; Mueller, Susanne; Raj, Ashish

    2015-01-01

    Mesial temporal lobe epilepsy (TLE) is characterized by stereotyped origination and spread pattern of epileptogenic activity, which is reflected in stereotyped topographic distribution of neuronal atrophy on magnetic resonance imaging (MRI). Both epileptogenic activity and atrophy spread appear to follow white matter connections. We model the networked spread of activity and atrophy in TLE from first principles via two simple first order network diffusion models. Atrophy distribution is modeled as a simple consequence of the propagation of epileptogenic activity in one model, and as a progressive degenerative process in the other. We show that the network models closely reproduce the regional volumetric gray matter atrophy distribution of two epilepsy cohorts: 29 TLE subjects with medial temporal sclerosis (TLE-MTS), and 50 TLE subjects with normal appearance on MRI (TLE-no). Statistical validation at the group level suggests high correlation with measured atrophy (R = 0.586 for TLE-MTS, R = 0.283 for TLE-no). We conclude that atrophy spread model out-performs the hyperactivity spread model. These results pave the way for future clinical application of the proposed model on individual patients, including estimating future spread of atrophy, identification of seizure onset zones and surgical planning. PMID:26513579

  12. Large-Scale Brain Networks of the Human Left Temporal Pole: A Functional Connectivity MRI Study

    PubMed Central

    Pascual, Belen; Masdeu, Joseph C.; Hollenbeck, Mark; Makris, Nikos; Insausti, Ricardo; Ding, Song-Lin; Dickerson, Bradford C.

    2015-01-01

    The most rostral portion of the human temporal cortex, the temporal pole (TP), has been described as “enigmatic” because its functional neuroanatomy remains unclear. Comparative anatomy studies are only partially helpful, because the human TP is larger and cytoarchitectonically more complex than in nonhuman primates. Considered by Brodmann as a single area (BA 38), the human TP has been recently parceled into an array of cytoarchitectonic subfields. In order to clarify the functional connectivity of subregions of the TP, we undertook a study of 172 healthy adults using resting-state functional connectivity MRI. Remarkably, a hierarchical cluster analysis performed to group the seeds into distinct subsystems according to their large-scale functional connectivity grouped 87.5% of the seeds according to the recently described cytoarchitectonic subregions of the TP. Based on large-scale functional connectivity, there appear to be 4 major subregions of the TP: 1) dorsal, with predominant connectivity to auditory/somatosensory and language networks; 2) ventromedial, predominantly connected to visual networks; 3) medial, connected to paralimbic structures; and 4) anterolateral, connected to the default-semantic network. The functional connectivity of the human TP, far more complex than its known anatomic connectivity in monkey, is concordant with its hypothesized role as a cortical convergence zone. PMID:24068551

  13. Multiple Stability of a Sparsely Encoded Attractor Neural Network Model for the Inferior Temporal Cortex

    NASA Astrophysics Data System (ADS)

    Kimoto, Tomoyuki; Uezu, Tatsuya; Okada, Masato

    2008-12-01

    We study a neural network model for the inferior temporal cortex, in terms of finite memory loading and sparse coding. We show that an uncorrelated Hopfield-type attractor and some correlated attractors have multiple stability, and examine the retrieval dynamics for these attractors when the initial state is set to a noise-degraded memory pattern. Then, we show that there is a critical initial overlap: that is, the system converges to the correlated attractor when the noise level is large, and otherwise to the Hopfield-type attractor. Furthermore, we study the time course of the correlation between the correlated attractors in the retrieval dynamics. On the basis of these theoretical results, we resolve the controversy regarding previous physiologic experimental findings regarding neuron properties in the inferior temporal cortex and propose a new experimental paradigm.

  14. Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attention

    PubMed Central

    Shine, James M.; Koyejo, Oluwasanmi; Poldrack, Russell A.

    2016-01-01

    Little is currently known about the coordination of neural activity over longitudinal timescales and how these changes relate to behavior. To investigate this issue, we used resting-state fMRI data from a single individual to identify the presence of two distinct temporal states that fluctuated over the course of 18 mo. These temporal states were associated with distinct patterns of time-resolved blood oxygen level dependent (BOLD) connectivity within individual scanning sessions and also related to significant alterations in global efficiency of brain connectivity as well as differences in self-reported attention. These patterns were replicated in a separate longitudinal dataset, providing additional supportive evidence for the presence of fluctuations in functional network topology over time. Together, our results underscore the importance of longitudinal phenotyping in cognitive neuroscience. PMID:27528672

  15. Dynamic design of ecological monitoring networks for non-Gaussian spatio-temporal data

    USGS Publications Warehouse

    Wikle, C.K.; Royle, J. Andrew

    2005-01-01

    Many ecological processes exhibit spatial structure that changes over time in a coherent, dynamical fashion. This dynamical component is often ignored in the design of spatial monitoring networks. Furthermore, ecological variables related to processes such as habitat are often non-Gaussian (e.g. Poisson or log-normal). We demonstrate that a simulation-based design approach can be used in settings where the data distribution is from a spatio-temporal exponential family. The key random component in the conditional mean function from this distribution is then a spatio-temporal dynamic process. Given the computational burden of estimating the expected utility of various designs in this setting, we utilize an extended Kalman filter approximation to facilitate implementation. The approach is motivated by, and demonstrated on, the problem of selecting sampling locations to estimate July brood counts in the prairie pothole region of the U.S.

  16. Disclosing Sexual Assault Within Social Networks: A Mixed-Method Investigation.

    PubMed

    Dworkin, Emily R; Pittenger, Samantha L; Allen, Nicole E

    2016-03-01

    Most survivors of sexual assault disclose their experiences within their social networks, and these disclosure decisions can have important implications for their entry into formal systems and well-being, but no research has directly examined these networks as a strategy to understand disclosure decisions. Using a mixed-method approach that combined survey data, social network analysis, and interview data, we investigate whom, among potential informal responders in the social networks of college students who have experienced sexual assault, survivors contact regarding their assault, and how survivors narrate the role of networks in their decisions about whom to contact. Quantitative results suggest that characteristics of survivors, their social networks, and members of these networks are associated with disclosure decisions. Using data from social network analysis, we identified that survivors tended to disclose to a smaller proportion of their network when many network members had relationships with each other or when the network had more subgroups. Our qualitative analysis helps to contextualize these findings.

  17. Controllability and Synchronization Analysis of Identical-Hierarchy Mixed-Valued Logical Control Networks.

    PubMed

    Zhong, Jie; Lu, Jianquan; Huang, Tingwen; Ho, Daniel W C

    2016-06-14

    This paper investigates the controllability and synchronization problems for identical-hierarchy mixed-valued logical control networks. The logical network considered is hierarchical, and Boolean network is a special case of logical network. Here, identical-hierarchy means that there are identical number of nodes in each layer of logical network and corresponding nodes have the same dimension for any two layers of logical networks. Meanwhile, in each layer of logical networks, the dimensions of nodes are distinct, and it is called a mixed-valued logical network. First, the controllability problem is investigated and two notions of controllability are presented, i.e., group-controllability and simultaneously-controllability. By resorting to Perron-Frobenius theorem, some necessary and sufficient criteria are obtained to guarantee group-controllability and simultaneously-controllability, respectively. Second, based on the algebraic representation of the studied model, synchronization problems are analytically discussed for two types of controls, i.e., free control sequences and state-output feedback control. Finally, two numerical examples are presented to show the validness of our main results.

  18. ePRISM: A case study in multiple proxy and mixed temporal resolution integration

    USGS Publications Warehouse

    Robinson, Marci M.; Dowsett, Harry J.

    2010-01-01

    As part of the Pliocene Research, Interpretation and Synoptic Mapping (PRISM) Project, we present the ePRISM experiment designed I) to provide climate modelers with a reconstruction of an early Pliocene warm period that was warmer than the PRISM interval (similar to 3.3 to 3.0 Ma), yet still similar in many ways to modern conditions and 2) to provide an example of how best to integrate multiple-proxy sea surface temperature (SST) data from time series with varying degrees of temporal resolution and age control as we begin to build the next generation of PRISM, the PRISM4 reconstruction, spanning a constricted time interval. While it is possible to tie individual SST estimates to a single light (warm) oxygen isotope event, we find that the warm peak average of SST estimates over a narrowed time interval is preferential for paleoclimate reconstruction as it allows for the inclusion of more records of multiple paleotemperature proxies.

  19. Visualizing the flow of evidence in network meta-analysis and characterizing mixed treatment comparisons.

    PubMed

    König, Jochem; Krahn, Ulrike; Binder, Harald

    2013-12-30

    Network meta-analysis techniques allow for pooling evidence from different studies with only partially overlapping designs for getting a broader basis for decision support. The results are network-based effect estimates that take indirect evidence into account for all pairs of treatments. The results critically depend on homogeneity and consistency assumptions, which are sometimes difficult to investigate. To support such evaluation, we propose a display of the flow of evidence and introduce new measures that characterize the structure of a mixed treatment comparison. Specifically, a linear fixed effects model for network meta-analysis is considered, where the network estimates for two treatments are linear combinations of direct effect estimates comparing these or other treatments. The linear coefficients can be seen as the generalization of weights known from classical meta-analysis. We summarize properties of these coefficients and display them as a weighted directed acyclic graph, representing the flow of evidence. Furthermore, measures are introduced that quantify the direct evidence proportion, the mean path length, and the minimal parallelism of mixed treatment comparisons. The graphical display and the measures are illustrated for two published network meta-analyses. In these applications, the proposed methods are seen to render transparent the process of data pooling in mixed treatment comparisons. They can be expected to be more generally useful for guiding and facilitating the validity assessment in network meta-analysis.

  20. Spatial and temporal variations of sediment size on a mixed sand and gravel beach

    NASA Astrophysics Data System (ADS)

    Horn, Diane P.; Walton, Susan M.

    2007-12-01

    Mixed sand and gravel beaches have been the subject of comparatively few studies in the UK. This paper describes the sediment distribution before, during and after a programme of beach nourishment along a section of mixed sand and gravel beach forming part of the Pevensey Bay Coastal Defences, in East Sussex, UK. The beach was recharged in September 2002, and beach profiles were measured along three cross-shore transects from August 2002 to February 2003. Sediment samples were taken along the transects between August and November 2002, and a total of 147 sediment samples were analysed, 40 before nourishment and 107 after nourishment. The majority of the sediment samples were strongly bimodal, with mean sizes varying between a minimum of 0.18 mm (2.48 ϕ) for the sand fraction and a maximum of 27 mm (- 4.74 ϕ) for the gravel. The recharge material was also bimodal but contained more fine sediment than the natural beach material, particularly on the upper beach. The recharge sediment had grain sizes and sorting similar to some of the natural material but lower bimodality parameters than any of the natural sediment. The sediment distributions after recharge contained significantly more fine sediment, particularly on the upper beach. Over time, the beach profile lowered and fine sediment appeared to be selectively transported seawards from the beachface.

  1. Imaging and spatio-temporal analysis of turbulent mixing of hydrothermal water discharging into a river (Breitenbush Hot Springs, Oregon)

    NASA Astrophysics Data System (ADS)

    Andrews, B. J.; Cardenas, M. B.; Bennett, P.

    2009-12-01

    High-frequency (16 Hz), high-resolution (1-2 mm pixels) thermal infrared images show the effects of jet entry conditions on spatial and temporal scales of mixing between a discharging plume of hot spring water (~60 °C) and a small stream (~10 °C) at Breitenbush Hot Springs, Oregon. Images of thermal plumes showing eddy cascades through space and time are analyzed with correlation analyses to obtain timescales and length-scales of mixing. Optical flow velocimetry of the images provides insight to the transient two-dimensional flow fields of the plumes. The 3-inch diameter discharge pipe was positioned such that the jet is at the surface, partially submerged, or at the bottom of the 15-cm deep stream. The three jet entry conditions are hereafter referred to as “shallow”, “middle”, and “deep”. In the shallow and middle positions, the jet is apparent on the stream surface as a hot region extending downstream from the pipe. Turbulent mixing between the jet and the stream occurs along the jet margins, such that the discharge plume broadens and cools downstream. In the deep position, the jet reaches the surface as a broad plume ~7.5 cm downstream from the pipe; the highest measured temperatures are not directly above the pipe mouth, but displaced downstream. Streamwise spatial autocorrelation analysis of the temperature field under shallow and middle entry conditions show correlation length scales of ~30 cm for a transect along the center ~7.5 cm of the jet; the correlation length scale abruptly reduces to <10 cm on either side of the jet. Under deep conditions, the streamwise correlation length scale is ~20 cm along the middle ~10 cm of the plume and ~10 cm on either side of the plume. Temporal autocorrelation analysis of the temperature fields shows quasi-periodicity for all three pipe positions and decrease in frequency with distance from the pipe (shallow and middle) or center of the upwelling plume (deep). Correlation analyses of the velocity fields

  2. Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition.

    PubMed

    Kasabov, Nikola; Dhoble, Kshitij; Nuntalid, Nuttapod; Indiveri, Giacomo

    2013-05-01

    On-line learning and recognition of spatio- and spectro-temporal data (SSTD) is a very challenging task and an important one for the future development of autonomous machine learning systems with broad applications. Models based on spiking neural networks (SNN) have already proved their potential in capturing spatial and temporal data. One class of them, the evolving SNN (eSNN), uses a one-pass rank-order learning mechanism and a strategy to evolve a new spiking neuron and new connections to learn new patterns from incoming data. So far these networks have been mainly used for fast image and speech frame-based recognition. Alternative spike-time learning methods, such as Spike-Timing Dependent Plasticity (STDP) and its variant Spike Driven Synaptic Plasticity (SDSP), can also be used to learn spatio-temporal representations, but they usually require many iterations in an unsupervised or semi-supervised mode of learning. This paper introduces a new class of eSNN, dynamic eSNN, that utilise both rank-order learning and dynamic synapses to learn SSTD in a fast, on-line mode. The paper also introduces a new model called deSNN, that utilises rank-order learning and SDSP spike-time learning in unsupervised, supervised, or semi-supervised modes. The SDSP learning is used to evolve dynamically the network changing connection weights that capture spatio-temporal spike data clusters both during training and during recall. The new deSNN model is first illustrated on simple examples and then applied on two case study applications: (1) moving object recognition using address-event representation (AER) with data collected using a silicon retina device; (2) EEG SSTD recognition for brain-computer interfaces. The deSNN models resulted in a superior performance in terms of accuracy and speed when compared with other SNN models that use either rank-order or STDP learning. The reason is that the deSNN makes use of both the information contained in the order of the first input spikes

  3. Distributed Configuration of Sensor Network for Fault Detection in Spatio-Temporal Systems

    NASA Astrophysics Data System (ADS)

    Patan, Maciej; Kowalów, Damian

    2017-01-01

    The problem of fault detection in spatio-temporal systems is formulated as that of maximizing the power of a parametric hypothesis test verifying the nominal state of the process under consideration. Then, adopting a pairwise communication schemes, a computational procedure is developed for the spatial configuration of the observation locations for sensor network which monitor changes in the underlying parameters of a distributed parameter system. As a result, the problem of planning the percentage of experimental effort spent at given sensor locations can be solved in a fully decentralized fashion. The approach is verified on a numerical example involving sensor selection for a convective diffusion process.

  4. Disrupted topological properties of brain white matter networks in left temporal lobe epilepsy: a diffusion tensor imaging study.

    PubMed

    Xu, Y; Qiu, S; Wang, J; Liu, Z; Zhang, R; Li, S; Cheng, L; Liu, Z; Wang, W; Huang, R

    2014-10-24

    Mesial temporal lobe epilepsy (mTLE) is the most common drug-refractory focal epilepsy in adults. Although previous functional and morphological studies have revealed abnormalities in the brain networks of mTLE, the topological organization of the brain white matter (WM) networks in mTLE patients is still ambiguous. In this study, we constructed brain WM networks for 14 left mTLE patients and 22 age- and gender-matched normal controls using diffusion tensor tractography and estimated the alterations of network properties in the mTLE brain networks using graph theoretical analysis. We found that networks for both the mTLE patients and the controls exhibited prominent small-world properties, suggesting a balanced topology of integration and segregation. However, the brain WM networks of mTLE patients showed a significant increased characteristic path length but significant decreased global efficiency, which indicate a disruption in the organization of the brain WM networks in mTLE patients. Moreover, we found significant between-group differences in the nodal properties in several brain regions, such as the left superior temporal gyrus, left hippocampus, the right occipital and right temporal cortices. The robustness analysis showed that the results were likely to be consistent for the networks constructed with different definitions of node and edge weight. Taken together, our findings may suggest an adverse effect of epileptic seizures on the organization of large-scale brain WM networks in mTLE patients.

  5. Reliable Attention Network Scores and Mutually Inhibited Inter-network Relationships Revealed by Mixed Design and Non-orthogonal Method.

    PubMed

    Wang, Yi-Feng; Jing, Xiu-Juan; Liu, Feng; Li, Mei-Ling; Long, Zhi-Liang; Yan, Jin H; Chen, Hua-Fu

    2015-05-21

    The attention system can be divided into alerting, orienting, and executive control networks. The efficiency and independence of attention networks have been widely tested with the attention network test (ANT) and its revised versions. However, many studies have failed to find effects of attention network scores (ANSs) and inter-network relationships (INRs). Moreover, the low reliability of ANSs can not meet the demands of theoretical and empirical investigations. Two methodological factors (the inter-trial influence in the event-related design and the inter-network interference in orthogonal contrast) may be responsible for the unreliability of ANT. In this study, we combined the mixed design and non-orthogonal method to explore ANSs and directional INRs. With a small number of trials, we obtained reliable and independent ANSs (split-half reliability of alerting: 0.684; orienting: 0.588; and executive control: 0.616), suggesting an individual and specific attention system. Furthermore, mutual inhibition was observed when two networks were operated simultaneously, indicating a differentiated but integrated attention system. Overall, the reliable and individual specific ANSs and mutually inhibited INRs provide novel insight into the understanding of the developmental, physiological and pathological mechanisms of attention networks, and can benefit future experimental and clinical investigations of attention using ANT.

  6. Spatial and temporal variations of microbial community in a mixed plug-flow loop reactor fed with dairy manure.

    PubMed

    Li, Yueh-Fen; Chen, Po-Hsu; Yu, Zhongtang

    2014-07-01

    Mixed plug-flow loop reactor (MPFLR) has been widely adopted by the US dairy farms to convert cattle manure to biogas. However, the microbiome in MPFLR digesters remains unexplored. In this study, the microbiome in a MPFLR digester operated on a mega-dairy farm was examined thrice over a 2 month period. Within 23 days of retention time, 55-70% of total manure solid was digested. Except for a few minor volatile fatty acids (VFAs), total VFA concentration and pH remained similar along the course of the digester and over time. Metagenomic analysis showed that although with some temporal variations, the bacterial community was rather stable spatially in the digester. The methanogenic community was also stable both spatially and temporally in the digester. Among methanogens, genus Methanosaeta dominated in the digester. Quantitative polymerase chain reaction (qPCR) analysis and metagenomic analysis yielded different relative abundance of individual genera of methanogens, especially for Methanobacterium, which was predominant based on qPCR analysis but undetectable by metagenomics. Collectively, the results showed that only small microbial and chemical gradients existed within the digester, and the digestion process occurred similarly throughout the MPFLR digester. The findings of this study may help improve the operation and design of this type of manure digesters.

  7. Spatial and temporal variations of microbial community in a mixed plug-flow loop reactor fed with dairy manure

    PubMed Central

    Li, Yueh-Fen; Chen, Po-Hsu; Yu, Zhongtang

    2014-01-01

    Mixed plug-flow loop reactor (MPFLR) has been widely adopted by the US dairy farms to convert cattle manure to biogas. However, the microbiome in MPFLR digesters remains unexplored. In this study, the microbiome in a MPFLR digester operated on a mega-dairy farm was examined thrice over a 2 month period. Within 23 days of retention time, 55–70% of total manure solid was digested. Except for a few minor volatile fatty acids (VFAs), total VFA concentration and pH remained similar along the course of the digester and over time. Metagenomic analysis showed that although with some temporal variations, the bacterial community was rather stable spatially in the digester. The methanogenic community was also stable both spatially and temporally in the digester. Among methanogens, genus Methanosaeta dominated in the digester. Quantitative polymerase chain reaction (qPCR) analysis and metagenomic analysis yielded different relative abundance of individual genera of methanogens, especially for Methanobacterium, which was predominant based on qPCR analysis but undetectable by metagenomics. Collectively, the results showed that only small microbial and chemical gradients existed within the digester, and the digestion process occurred similarly throughout the MPFLR digester. The findings of this study may help improve the operation and design of this type of manure digesters. PMID:24690147

  8. A network identity authentication protocol of bank account system based on fingerprint identification and mixed encryption

    NASA Astrophysics Data System (ADS)

    Zhu, Lijuan; Liu, Jingao

    2013-07-01

    This paper describes a network identity authentication protocol of bank account system based on fingerprint identification and mixed encryption. This protocol can provide every bank user a safe and effective way to manage his own bank account, and also can effectively prevent the hacker attacks and bank clerk crime, so that it is absolute to guarantee the legitimate rights and interests of bank users.

  9. Using Mixed-Method Design and Network Analysis to Measure Development of Interagency Collaboration

    ERIC Educational Resources Information Center

    Cross, Jennifer Eileen; Dickmann, Ellyn; Newman-Gonchar, Rebecca; Fagan, Jesse Michael

    2009-01-01

    In recent years, there has been increasing attention to the importance of interagency collaboration for improving community well-being, environmental and public health, and educational outcomes. This article uses a mixed-methods approach including network analysis to examine the changes in interagency collaboration in one site funded by the Safe…

  10. Quality assessment of static aggregation compared to the temporal approach based on a pig trade network in Northern Germany.

    PubMed

    Büttner, Kathrin; Salau, Jennifer; Krieter, Joachim

    2016-07-01

    Recent analyses of animal movement networks focused on the static aggregation of trade contacts over different time windows, which neglects the system's temporal variation. In terms of disease spread, ignoring the temporal dynamics can lead to an over- or underestimation of an outbreak's speed and extent. This becomes particularly evident, if the static aggregation allows for the existence of more paths compared to the number of time-respecting paths (i.e. paths in the right chronological order). Therefore, the aim of this study was to reveal differences between static and temporal representations of an animal trade network and to assess the quality of the static aggregation in comparison to the temporal counterpart. Contact data from a pig trade network (2006-2009) of a producer community in Northern Germany were analysed. The results show that a median value of 8.7 % (4.6-14.1%) of the nodes and 3.1% (1.6-5.5%) of the edges were active on a weekly resolution. No fluctuations in the activity patterns were obvious. Furthermore, 50% of the nodes already had one trade contact after approximately six months. For an accumulation window with increasing size (one day each), the accumulation rate, i.e. the relative increase in the number of nodes or edges, stayed relatively constant below 0.07% for the nodes and 0.12 % for the edges. The temporal distances had a much wider distribution than the topological distances. 84% of the temporal distances were smaller than 90 days. The maximum temporal distance was 1000 days, which corresponds to the temporal diameter of the present network. The median temporal correlation coefficient, which measures the probability for an edge to persist across two consecutive time steps, was 0.47, with a maximum value of 0.63 at the accumulation window of 88 days. The causal fidelity measures the fraction of the number of static paths which can also be taken in the temporal network. For the whole observation period relatively high values

  11. Semi-degradable poly(β-amino ester) networks with temporally controlled enhancement of mechanical properties.

    PubMed

    Safranski, David L; Weiss, Daiana; Clark, J Brian; Taylor, W Robert; Gall, Ken

    2014-08-01

    Biodegradable polymers are clinically used in numerous biomedical applications, and classically show a loss of mechanical properties within weeks of implantation. This work demonstrates a new class of semi-degradable polymers that show an increase in mechanical properties through degradation via a controlled shift in a thermal transition. Semi-degradable polymer networks, poly(β-amino ester)-co-methyl methacrylate, were formed from a low glass transition temperature crosslinker, poly(β-amino ester), and high glass transition temperature monomer, methyl methacrylate, which degraded in a manner dependent upon the crosslinker chemical structure. In vitro and in vivo degradation revealed changes in mechanical behavior due to the degradation of the crosslinker from the polymer network. This novel polymer system demonstrates a strategy to temporally control the mechanical behavior of polymers and to enhance the initial performance of smart biomedical devices.

  12. Temporal Evolution and Scaling of Mixing in Two-dimensional Rayleigh-Taylor Turbulence

    NASA Astrophysics Data System (ADS)

    Zhou, Quan; Quan Zhou Team

    2013-11-01

    We report a high-resolution numerical study of two-dimensional (2D) miscible Rayleigh-Taylor (RT) incompressible turbulence with the Boussinesq approximation. We present results from an ensemble of 100 independent realizations performed at unit Prandtl number and small Atwood number with a spatial resolution of 2048 × 8193 grid points and Rayleigh number up to Ra ~1011 . Our main focus is on the temporal evolution and the scaling behavior of global quantities and of small-scale turbulence properties. Our results show that the buoyancy force balances the inertial force at all scales below the integral length scale and thus validate the basic force-balance assumption of the Bolgiano-Obukhov scenario in 2D RT turbulence. It is further found that the Kolmogorov dissipation scale η (t) ~t 1 / 8 , the kinetic-energy dissipation rate ɛu (t) ~t - 1 / 2 , and the thermal dissipation rate ɛθ (t) ~t-1 . All of these scaling properties are in excellent agreement with the theoretical predictions of the Chertkov model [Phys. Rev. Lett. 91, 115001 (2003)]. This work was supported by the Natural Science Foundation of China (NSFC) under Grant Nos. 11222222 and 11161160554 and Innovation Program of Shanghai Municipal Education Commission under Grant No. 13YZ008.

  13. Combining social and genetic networks to study HIV transmission in mixing risk groups

    NASA Astrophysics Data System (ADS)

    Zarrabi, Narges; Prosperi, Mattia C. F.; Belleman, Robbert G.; Di Giambenedetto, Simona; Fabbiani, Massimiliano; De Luca, Andrea; Sloot, Peter M. A.

    2013-09-01

    Reconstruction of HIV transmission networks is important for understanding and preventing the spread of the virus and drug resistant variants. Mixing risk groups is important in network analysis of HIV in order to assess the role of transmission between risk groups in the HIV epidemic. Most of the research focuses on the transmission within HIV risk groups, while transmission between different risk groups has been less studied. We use a proposed filter-reduction method to infer hypothetical transmission networks of HIV by combining data from social and genetic scales. We modified the filtering process in order to include mixing risk groups in the model. For this, we use the information on phylogenetic clusters obtained through phylogenetic analysis. A probability matrix is also defined to specify contact rates between individuals form the same and different risk groups. The method converts the data form each scale into network forms and combines them by overlaying and computing their intersection. We apply this method to reconstruct networks of HIV infected patients in central Italy, including mixing between risk groups. Our results suggests that bisexual behavior among Italian MSM and IDU partnership are relatively important in heterosexual transmission of HIV in central Italy.

  14. Modified Penna bit-string network evolution model for scale-free networks with assortative mixing

    NASA Astrophysics Data System (ADS)

    Kim, Yup; Choi, Woosik; Yook, Soon-Hyung

    2012-02-01

    Motivated by biological aging dynamics, we introduce a network evolution model for social interaction networks. In order to study the effect of social interactions originating from biological and sociological reasons on the topological properties of networks, we introduce the activitydependent rewiring process. From the numerical simulations, we show that the degree distribution of the obtained networks follows a power-law distribution with an exponentially decaying tail, P( k) ˜ ( k + c)- γ exp(- k/k 0). The obtained value of γ is in the range 2 < γ š 3, which is consistent with the values for real social networks. Moreover, we also show that the degree-degree correlation of the network is positive, which is a characteristic of social interaction networks. The possible applications of our model to real systems are also discussed.

  15. The feasibility program with security constraints and network rearrangement costs - Application to mixed /analogue and digital/ transmission networks

    NASA Astrophysics Data System (ADS)

    Serreault, J.-Y.; Minoux, M.

    1980-02-01

    This paper presents a second version of the 'feasibility program' (SECURAD) developed by the CNET. This program enables the routing at minimal costs of several thousand of requirements (expressed in circuits or groups of circuits) on very large mixed (analogue and digital) transmission networks, while respecting given capacities and taking into account security constraints and network-rearrangement costs. Consideration is given to a procedure for determining lower bounds for the cost of the optimal solution by solving a dual problem, and thus checking the quality of the approximate solutions obtained.

  16. Large Eddy Simulation (LES) of Particle-Laden Temporal Mixing Layers

    NASA Technical Reports Server (NTRS)

    Bellan, Josette; Radhakrishnan, Senthilkumaran

    2012-01-01

    High-fidelity models of plume-regolith interaction are difficult to develop because of the widely disparate flow conditions that exist in this process. The gas in the core of a rocket plume can often be modeled as a time-dependent, high-temperature, turbulent, reacting continuum flow. However, due to the vacuum conditions on the lunar surface, the mean molecular path in the outer parts of the plume is too long for the continuum assumption to remain valid. Molecular methods are better suited to model this region of the flow. Finally, granular and multiphase flow models must be employed to describe the dust and debris that are displaced from the surface, as well as how a crater is formed in the regolith. At present, standard commercial CFD (computational fluid dynamics) software is not capable of coupling each of these flow regimes to provide an accurate representation of this flow process, necessitating the development of custom software. This software solves the fluid-flow-governing equations in an Eulerian framework, coupled with the particle transport equations that are solved in a Lagrangian framework. It uses a fourth-order explicit Runge-Kutta scheme for temporal integration, an eighth-order central finite differencing scheme for spatial discretization. The non-linear terms in the governing equations are recast in cubic skew symmetric form to reduce aliasing error. The second derivative viscous terms are computed using eighth-order narrow stencils that provide better diffusion for the highest resolved wave numbers. A fourth-order Lagrange interpolation procedure is used to obtain gas-phase variable values at the particle locations.

  17. Temporally sequenced intelligent block-matching and motion-segmentation using locally coupled networks.

    PubMed

    Zhang, Xiaofu; Minai, A A

    2004-09-01

    Motion-based segmentation is a very important capability for computer vision and video analysis. It depends fundamentally on the system's ability to estimate optic flow using temporally proximate image frames. This is often done using block-matching. However, block-matching is sensitive to the presence of observational noise, which is inevitable in real images. Also, images often include regions of homogeneous intensity, where block-matching is problematic. A better method in this case is to estimate motion at the region level. In the approach described in this paper, we have attempted to address the noise-sensitivity and texture-insufficiency problems using a two-pathway system. The pixel-level pathway is a multilayer pulse-coupled neural network (PCNN)-like locally coupled network used to correct outliers in the block-matching motion estimates and produce improved estimates in regions with sufficient texture. In contrast, the region-level pathway is used to estimate the motion for regions with little intensity variation. In this pathway, a PCNN network first partitions intensity images into homogeneous regions, and a motion vector is then determined for the whole region. The optic flows from both pathways are fused together based on the estimated intensity variation. The fused optic flow is then segmented by a one-layer PCNN network. Results on synthetic and real images are presented to demonstrate that the accuracy of segmentation is improved significantly by taking advantage of the complementary strengths and weaknesses of the two pathways.

  18. Dynamic analysis of traffic time series at different temporal scales: A complex networks approach

    NASA Astrophysics Data System (ADS)

    Tang, Jinjun; Wang, Yinhai; Wang, Hua; Zhang, Shen; Liu, Fang

    2014-07-01

    The analysis of dynamics in traffic flow is an important step to achieve advanced traffic management and control in Intelligent Transportation System (ITS). Complexity and periodicity are definitely two fundamental properties in traffic dynamics. In this study, we first measure the complexity of traffic flow data by Lempel-Ziv algorithm at different temporal scales, and the data are collected from loop detectors on freeway. Second, to obtain more insight into the complexity and periodicity in traffic time series, we then construct complex networks from traffic time series by considering each day as a cycle and each cycle as a single node. The optimal threshold value of complex networks is estimated by the distribution of density and its derivative. In addition, the complex networks are subsequently analyzed in terms of some statistical properties, such as average path length, clustering coefficient, density, average degree and betweenness. Finally, take 2 min aggregation data as example, we use the correlation coefficient matrix, adjacent matrix and closeness to exploit the periodicity of weekdays and weekends in traffic flow data. The findings in this paper indicate that complex network is a practical tool for exploring dynamics in traffic time series.

  19. Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence.

    PubMed

    Zhang, Ran; Peng, Zhen; Wu, Lifeng; Yao, Beibei; Guan, Yong

    2017-03-09

    Intelligent condition monitoring and fault diagnosis by analyzing the sensor data can assure the safety of machinery. Conventional fault diagnosis and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make diagnosis. However, these conventional fault diagnosis approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a fault diagnosis model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, fault diagnosis considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing faults can get 100%. The proposed fault diagnosis approach is effective in recognizing the type of bearing faults.

  20. Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence

    PubMed Central

    Zhang, Ran; Peng, Zhen; Wu, Lifeng; Yao, Beibei; Guan, Yong

    2017-01-01

    Intelligent condition monitoring and fault diagnosis by analyzing the sensor data can assure the safety of machinery. Conventional fault diagnosis and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make diagnosis. However, these conventional fault diagnosis approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a fault diagnosis model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, fault diagnosis considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing faults can get 100%. The proposed fault diagnosis approach is effective in recognizing the type of bearing faults. PMID:28282936

  1. Dynamics of Disagreement: Large-Scale Temporal Network Analysis Reveals Negative Interactions in Online Collaboration

    NASA Astrophysics Data System (ADS)

    Tsvetkova, Milena; García-Gavilanes, Ruth; Yasseri, Taha

    2016-11-01

    Disagreement and conflict are a fact of social life. However, negative interactions are rarely explicitly declared and recorded and this makes them hard for scientists to study. In an attempt to understand the structural and temporal features of negative interactions in the community, we use complex network methods to analyze patterns in the timing and configuration of reverts of article edits to Wikipedia. We investigate how often and how fast pairs of reverts occur compared to a null model in order to control for patterns that are natural to the content production or are due to the internal rules of Wikipedia. Our results suggest that Wikipedia editors systematically revert the same person, revert back their reverter, and come to defend a reverted editor. We further relate these interactions to the status of the involved editors. Even though the individual reverts might not necessarily be negative social interactions, our analysis points to the existence of certain patterns of negative social dynamics within the community of editors. Some of these patterns have not been previously explored and carry implications for the knowledge collection practice conducted on Wikipedia. Our method can be applied to other large-scale temporal collaboration networks to identify the existence of negative social interactions and other social processes.

  2. Dynamics of Disagreement: Large-Scale Temporal Network Analysis Reveals Negative Interactions in Online Collaboration

    PubMed Central

    Tsvetkova, Milena; García-Gavilanes, Ruth; Yasseri, Taha

    2016-01-01

    Disagreement and conflict are a fact of social life. However, negative interactions are rarely explicitly declared and recorded and this makes them hard for scientists to study. In an attempt to understand the structural and temporal features of negative interactions in the community, we use complex network methods to analyze patterns in the timing and configuration of reverts of article edits to Wikipedia. We investigate how often and how fast pairs of reverts occur compared to a null model in order to control for patterns that are natural to the content production or are due to the internal rules of Wikipedia. Our results suggest that Wikipedia editors systematically revert the same person, revert back their reverter, and come to defend a reverted editor. We further relate these interactions to the status of the involved editors. Even though the individual reverts might not necessarily be negative social interactions, our analysis points to the existence of certain patterns of negative social dynamics within the community of editors. Some of these patterns have not been previously explored and carry implications for the knowledge collection practice conducted on Wikipedia. Our method can be applied to other large-scale temporal collaboration networks to identify the existence of negative social interactions and other social processes. PMID:27808267

  3. Ozone deposition in a mixed forest ecosystem - temporal variation and removal processes

    NASA Astrophysics Data System (ADS)

    Pokorska, Olga; Gruening, Carsten; Goded, Ignacio

    2016-04-01

    Forests are a major sink for tropospheric ozone, however the fate of ozone within the forest canopy is still not well understood. In this study we will present results of 3 years ozone flux measurements at eddy covariance flux tower in Ispra (Northern Italy) over mixed forest ecosystem. The main tree species in this ecosystem are Quercus robur (80%), Alnus glutinosa (10%), Populus alba (5%) and Carpinus betulus (3%). The measurements were carried out continuously from January 2013 till December 2015. Flux measurements at the canopy level with the eddy covariance technique were complemented with measurements of meteorological parameters and measurements of VOCs (Volatile Organic Compounds) using PTR-MS (Proton Transfer Reaction - Mass Spectrometry) during an intensive observation period. Continuous measurements produced a big dataset which allowed us to investigate the controls on ozone fluxes and to do multiyear comparisons. Patterns of ozone concentration, ozone fluxes and ozone deposition velocity over forest canopy will be presented in relation to physiological activity of the trees and time of the year. Current research is mainly aimed at better understanding of the contribution of destruction processes within the canopy. Therefore, the collected data were used to calculate the partitioning of total ozone fluxes between stomatal and non-stomatal sinks. We found that the stomatal uptake contributed less that non-stomatal uptake to the total ozone flux during the growing seasons. In particular, non-stomatal ozone removal by reactions with isoprene and other VOCs will be discussed. We will present contribution and change of individual ozone deposition sinks over the years in response to environmental parameters.

  4. Spatial and temporal variability of nitrous oxide emissions in a mixed farming landscape of Denmark

    NASA Astrophysics Data System (ADS)

    Schelde, K.; Cellier, P.; Bertolini, T.; Dalgaard, T.; Weidinger, T.; Theobald, M. R.; Olesen, J. E.

    2012-08-01

    Nitrous oxide (N2O) emissions from agricultural land are variable at the landscape scale due to variability in land use, management, soil type, and topography. A field experiment was carried out in a typical mixed farming landscape in Denmark, to investigate the main drivers of variations in N2O emissions, measured using static chambers. Measurements were made over a period of 20 months, and sampling was intensified during two weeks in spring 2009 when chambers were installed at ten locations or fields to cover different crops and topography and slurry was applied to three of the fields. N2O emissions during spring 2009 were relatively low, with maximum values below 20 ng N m-2 s-1. This applied to all land use types including winter grain crops, grasslands, meadows, and wetlands. Slurry application to wheat fields resulted in short-lived two-fold increases in emissions. The moderate N2O fluxes and their moderate response to slurry application were attributed to dry soil conditions due to the absence of rain during the four previous weeks. Cumulative annual emissions from two arable fields that were both fertilized with mineral fertilizer and manure were large (17 kg N2O-N ha-1 yr-1 and 5.5 kg N2O-N ha-1 yr-1) during the previous year when soil water conditions were favourable for N2O production during the first month following fertilizer application. Our findings confirm the importance of weather conditions as well as nitrogen management on N2O fluxes.

  5. Using the relational event model (REM) to investigate the temporal dynamics of animal social networks.

    PubMed

    Tranmer, Mark; Marcum, Christopher Steven; Morton, F Blake; Croft, Darren P; de Kort, Selvino R

    2015-03-01

    Social dynamics are of fundamental importance in animal societies. Studies on nonhuman animal social systems often aggregate social interaction event data into a single network within a particular time frame. Analysis of the resulting network can provide a useful insight into the overall extent of interaction. However, through aggregation, information is lost about the order in which interactions occurred, and hence the sequences of actions over time. Many research hypotheses relate directly to the sequence of actions, such as the recency or rate of action, rather than to their overall volume or presence. Here, we demonstrate how the temporal structure of social interaction sequences can be quantified from disaggregated event data using the relational event model (REM). We first outline the REM, explaining why it is different from other models for longitudinal data, and how it can be used to model sequences of events unfolding in a network. We then discuss a case study on the European jackdaw, Corvus monedula, in which temporal patterns of persistence and reciprocity of action are of interest, and present and discuss the results of a REM analysis of these data. One of the strengths of a REM analysis is its ability to take into account different ways in which data are collected. Having explained how to take into account the way in which the data were collected for the jackdaw study, we briefly discuss the application of the model to other studies. We provide details of how the models may be fitted in the R statistical software environment and outline some recent extensions to the REM framework.

  6. CGBayesNets: Conditional Gaussian Bayesian Network Learning and Inference with Mixed Discrete and Continuous Data

    PubMed Central

    Weiss, Scott T.

    2014-01-01

    Bayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs) formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal genomic data. We provide four different network learning algorithms, each making a different tradeoff between computational cost and network likelihood. CGBayesNets provides a full suite of functions for model exploration and verification, including cross validation, bootstrapping, and AUC manipulation. We highlight several results obtained previously with CGBayesNets, including predictive models of wood properties from tree genomics, leukemia subtype classification from mixed genomic data, and robust prediction of intensive care unit mortality outcomes from metabolomic profiles. We also provide detailed example analysis on public metabolomic and gene expression datasets. CGBayesNets is implemented in MATLAB and available as MATLAB source code, under an Open Source license and anonymous download at http://www.cgbayesnets.com. PMID:24922310

  7. Impaired cerebral blood flow networks in temporal lobe epilepsy with hippocampal sclerosis: A graph theoretical approach.

    PubMed

    Sone, Daichi; Matsuda, Hiroshi; Ota, Miho; Maikusa, Norihide; Kimura, Yukio; Sumida, Kaoru; Yokoyama, Kota; Imabayashi, Etsuko; Watanabe, Masako; Watanabe, Yutaka; Okazaki, Mitsutoshi; Sato, Noriko

    2016-09-01

    Graph theory is an emerging method to investigate brain networks. Altered cerebral blood flow (CBF) has frequently been reported in temporal lobe epilepsy (TLE), but graph theoretical findings of CBF are poorly understood. Here, we explored graph theoretical networks of CBF in TLE using arterial spin labeling imaging. We recruited patients with TLE and unilateral hippocampal sclerosis (HS) (19 patients with left TLE, and 21 with right TLE) and 20 gender- and age-matched healthy control subjects. We obtained all participants' CBF maps using pseudo-continuous arterial spin labeling and analyzed them using the Graph Analysis Toolbox (GAT) software program. As a result, compared to the controls, the patients with left TLE showed a significantly low clustering coefficient (p=0.024), local efficiency (p=0.001), global efficiency (p=0.010), and high transitivity (p=0.015), whereas the patients with right TLE showed significantly high assortativity (p=0.046) and transitivity (p=0.011). The group with right TLE also had high characteristic path length values (p=0.085), low global efficiency (p=0.078), and low resilience to targeted attack (p=0.101) at a trend level. Lower normalized clustering coefficient (p=0.081) in the left TLE and higher normalized characteristic path length (p=0.089) in the right TLE were found also at a trend level. Both the patients with left and right TLE showed significantly decreased clustering in similar areas, i.e., the cingulate gyri, precuneus, and occipital lobe. Our findings revealed differing left-right network metrics in which an inefficient CBF network in left TLE and vulnerability to irritation in right TLE are suggested. The left-right common finding of regional decreased clustering might reflect impaired default-mode networks in TLE.

  8. Spatio-temporal filtering for determination of common mode error in regional GNSS networks

    NASA Astrophysics Data System (ADS)

    Bogusz, Janusz; Gruszczynski, Maciej; Figurski, Mariusz; Klos, Anna

    2015-04-01

    The spatial correlation between different stations for individual components in the regional GNSS networks seems to be significant. The mismodelling in satellite orbits, the Earth orientation parameters (EOP), largescale atmospheric effects or satellite antenna phase centre corrections can all cause the regionally correlated errors. This kind of GPS time series errors are referred to as common mode errors (CMEs). They are usually estimated with the regional spatial filtering, such as the "stacking". In this paper, we show the stacking approach for the set of ASG-EUPOS permanent stations, assuming that spatial distribution of the CME is uniform over the whole region of Poland (more than 600 km extent). The ASG-EUPOS is a multifunctional precise positioning system based on the reference network designed for Poland. We used a 5- year span time series (2008-2012) of daily solutions in the ITRF2008 from Bernese 5.0 processed by the Military University of Technology EPN Local Analysis Centre (MUT LAC). At the beginning of our analyses concerning spatial dependencies, the correlation coefficients between each pair of the stations in the GNSS network were calculated. This analysis shows that spatio-temporal behaviour of the GPS-derived time series is not purely random, but there is the evident uniform spatial response. In order to quantify the influence of filtering using CME, the norms L1 and L2 were determined. The values of these norms were calculated for the North, East and Up components twice: before performing the filtration and after stacking. The observed reduction of the L1 and L2 norms was up to 30% depending on the dimension of the network. However, the question how to define an optimal size of CME-analysed subnetwork remains unanswered in this research, due to the fact that our network is not extended enough.

  9. Temporal sequence learning in winner-take-all networks of spiking neurons demonstrated in a brain-based device

    PubMed Central

    McKinstry, Jeffrey L.; Edelman, Gerald M.

    2013-01-01

    Animal behavior often involves a temporally ordered sequence of actions learned from experience. Here we describe simulations of interconnected networks of spiking neurons that learn to generate patterns of activity in correct temporal order. The simulation consists of large-scale networks of thousands of excitatory and inhibitory neurons that exhibit short-term synaptic plasticity and spike-timing dependent synaptic plasticity. The neural architecture within each area is arranged to evoke winner-take-all (WTA) patterns of neural activity that persist for tens of milliseconds. In order to generate and switch between consecutive firing patterns in correct temporal order, a reentrant exchange of signals between these areas was necessary. To demonstrate the capacity of this arrangement, we used the simulation to train a brain-based device responding to visual input by autonomously generating temporal sequences of motor actions. PMID:23760804

  10. Spatio-temporal waves and targeted vaccination in recurrent epidemic network models.

    PubMed

    Litvak-Hinenzon, Anna; Stone, Lewi

    2009-09-06

    The success of an infectious disease to invade a population is strongly controlled by the population's specific connectivity structure. Here, a network model is presented as an aid in understanding the role of social behaviour and heterogeneous connectivity in determining the spatio-temporal patterns of disease dynamics. We explore the controversial origins of long-term recurrent oscillations believed to be characteristic of diseases that have a period of temporary immunity after infection. In particular, we focus on sexually transmitted diseases such as syphilis, where this controversy is currently under review. Although temporary immunity plays a key role, it is found that, in realistic small-world networks, the social and sexual behaviour of individuals also has a great influence in generating long-term cycles. The model generates circular waves of infection with unusual spatial dynamics that depend on focal areas that act as pacemakers in the population. Eradication of the disease can be efficiently achieved by eliminating the pacemakers with a targeted vaccination scheme. A simple difference equation model is derived, which captures the infection dynamics of the network model and gives insights into their origins and their eradication through vaccination. Illustrative videos may be found in the electronic supplementary material.

  11. Spatial-temporal assessment and redesign of groundwater quality monitoring network: a case study.

    PubMed

    Owlia, Rashid Reza; Abrishamchi, Ahmad; Tajrishy, Massoud

    2011-01-01

    Assessment of groundwater quality monitoring networks requires methods to determine the potential efficiency and cost-effectiveness of the current monitoring programs. To this end, the concept of entropy has been considered as a promising method in previous studies since it quantitatively measures the information produced by a network. In this study, the measure of transinformation in the discrete entropy theory and the transinformation-distance (T-D) curves, which are used frequently by other researchers, are used to quantify the efficiency of a monitoring network. This paper introduces a new approach to decrease dispersion in results by performing cluster analysis that uses fuzzy equivalence relations. As a result, the sampling (temporal) frequency determination method also recommends the future sampling frequencies for each location based on certain criteria such as direction, magnitude, correlation with neighboring stations, and uncertainty of the concentration trend derived from representative historical concentration data. The proposed methodology is applied to groundwater resources in the Tehran-Karadj aquifer, Tehran, Iran.

  12. Temporal encoding of multispectral satellite imagery for segmentation using pulsed coupled neural networks

    NASA Astrophysics Data System (ADS)

    Tarr, Gregory L.; Carreras, Richard A.; Fender, Janet S.; Clastres, Xavier; Freyss, Laurent; Samuelides, Manuel

    1995-11-01

    Unlike biological vision, most techniques for computer image processing are not robust over large samples of imagery. Natural systems seem unaffected by variation in local illumination and textures which interfere with conventional analysis. While change detection algorithms have been partially successful, many important tasks like extraction of roads and communication lines remain unsolved. The solution to these problems may lie in examining architectures and algorithms used by biological imaging systems. Pulsed oscillatory neural network design, based on biomemetics, seem to solve some of these problems. Pulsed oscillatory neural networks are examined for application to image analysis and segmentation of multispectral imagery from the Satellite Pour l'Observation de la Terre. Using biological systems as a model for image analysis of complex data, a pulsed coupled networks using an integrate and fire mechanism is developed. This architecture, based on layers of pulsed coupled neurons is tested against common image segmentation problems. Using a reset activation pulse similar to that generated by sacatic motor commands, an algorithm is developed which demonstrates the biological vision could be based on adaptive histogram techniques. This architecture is demonstrated to be both biologically plausible and more effective than conventional techniques. Using the pulse time-of-arrival as the information carrier, the image is reduced to a time signal, temporal encoding of imagery, which allows an intelligent filtering based on expectation. This technique is uniquely suited to multispectral/multisensor imagery and other sensor fusion problems.

  13. Bi-Mn mixed metal organic oxide: A novel 3d-6p mixed metal coordination network

    NASA Astrophysics Data System (ADS)

    Shi, Fa-Nian; Rosa Silva, Ana; Bian, Liang

    2015-05-01

    A new terminology of metal organic oxide (MOO) was given a definition as a type of coordination polymers which possess the feature of inorganic connectivity between metals and the direct bonded atoms and show 1D, 2D or 3D inorganic sub-networks. One such compound was shown as an example. A 3d-6p (Mn-Bi. Named MOOMnBi) mixed metals coordination network has been synthesized via hydrothermal method. The new compound with the molecular formula of [MnBi2O(1,3,5-BTC)2]n (1,3,5-BTC stands for benzene-1,3,5-tricarboxylate) was characterized via single crystal X-ray diffraction technique that revealed a very interesting 3-dimensional (3D) framework with Bi4O2(COO)12 clusters which are further connected to Mn(COO)6 fragments into a 2D MOO. The topology study indicates an unprecedented topological type with the net point group of {413.62}{413.68}{416.65}{418.610}{422.614}{43} corresponding to 3,6,7,7,8,9-c hexa-nodal net. MOOMnBi shows catalytic activity in the synthesis of (E)-α,β-unsaturated ketones.

  14. Mixed outer synchronization of coupled complex networks with time-varying coupling delay.

    PubMed

    Wang, Jun-Wei; Ma, Qinghua; Zeng, Li; Abd-Elouahab, Mohammed Salah

    2011-03-01

    In this paper, the problem of outer synchronization between two complex networks with the same topological structure and time-varying coupling delay is investigated. In particular, we introduce a new type of outer synchronization behavior, i.e., mixed outer synchronization (MOS), in which different state variables of the corresponding nodes can evolve into complete synchronization, antisynchronization, and even amplitude death simultaneously for an appropriate choice of the scaling matrix. A novel nonfragile linear state feedback controller is designed to realize the MOS between two networks and proved analytically by using Lyapunov-Krasovskii stability theory. Finally, numerical simulations are provided to demonstrate the feasibility and efficacy of our proposed control approach.

  15. A Regulated Double-Negative Feedback Decodes the Temporal Gradient of Input Stimulation in a Cell Signaling Network

    PubMed Central

    Park, Sang-Min; Shin, Sung-Young; Cho, Kwang-Hyun

    2016-01-01

    Revealing the hidden mechanism of how cells sense and react to environmental signals has been a central question in cell biology. We focused on the rate of increase of stimulation, or temporal gradient, known to cause different responses of cells. We have investigated all possible three-node enzymatic networks and identified a network motif that robustly generates a transient or sustained response by acute or gradual stimulation, respectively. We also found that a regulated double-negative feedback within the motif is essential for the temporal gradient-sensitive switching. Our analysis highlights the essential structure and mechanism enabling cells to properly respond to dynamic environmental changes. PMID:27584002

  16. Cooperation-induced temporal complexity in networks of pulse-coupled units

    NASA Astrophysics Data System (ADS)

    Geneston, Elvis; Grigolini, Paolo

    2012-02-01

    We study a network of stochastic pulse-coupled units generating bursts with the same size distribution as the neuronal avalanches in mature cultured neurons, recently revealed by the experimental observation. We prove that in addition to this form of complexity this model yields a form of phase transition generating also temporal complexity. This means that the distance from two consecutive bursts fits the prescription of a Mittag-Leffler (ML) function renewal theory. There exists a critical value of the cooperation parameter at which this description applies to the whole time regime. By increasing the cooperation parameter the ML theory breaks down and the sequence of bursts tend to become periodic with the same intensity. We make the conjecture that the analysis of this model may shed light into the theoretical foundation of neuronal burst leaders and that the recently discovered principle of complexity management may be conveniently applied to the neuro-physiological processes that are properly described by this model.

  17. Temporal and spatial evolution of brain network topology during the first two years of life.

    PubMed

    Gao, Wei; Gilmore, John H; Giovanello, Kelly S; Smith, Jeffery Keith; Shen, Dinggang; Zhu, Hongtu; Lin, Weili

    2011-01-01

    The mature brain features high wiring efficiency for information transfer. However, the emerging process of such an efficient topology remains elusive. With resting state functional MRI and a large cohort of normal pediatric subjects (n = 147) imaged during a critical time period of brain development, 3 wk- to 2 yr-old, the temporal and spatial evolution of brain network topology is revealed. The brain possesses the small world topology immediately after birth, followed by a remarkable improvement in whole brain wiring efficiency in 1 yr olds and becomes more stable in 2 yr olds. Regional developments of brain wiring efficiency and the evolution of functional hubs suggest differential development trend for primary and higher order cognitive functions during the first two years of life. Simulations of random errors and targeted attacks reveal an age-dependent improvement of resilience. The lower resilience to targeted attack observed in 3 wk old group is likely due to the fact that there are fewer well-established long-distance functional connections at this age whose elimination might have more profound implications in the overall efficiency of information transfer. Overall, our results offer new insights into the temporal and spatial evolution of brain topology during early brain development.

  18. A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation

    NASA Astrophysics Data System (ADS)

    Tapoglou, Evdokia; Karatzas, George P.; Trichakis, Ioannis C.; Varouchakis, Emmanouil A.

    2014-11-01

    Artificial Neural Networks (ANNs) and Kriging have both been used for hydraulic head simulation. In this study, the two methodologies were combined in order to simulate the spatial and temporal distribution of hydraulic head in a study area. In order to achieve that, a fuzzy logic inference system can also be used. Different ANN architectures and variogram models were tested, together with the use or not of a fuzzy logic system. The developed algorithm was implemented and applied for predicting, spatially and temporally, the hydraulic head in an area located in Bavaria, Germany. The performance of the algorithm was evaluated using leave one out cross validation and various performance indicators were derived. The best results were achieved by using ANNs with two hidden layers, with the use of the fuzzy logic system and by utilizing the power-law variogram. The results obtained from this procedure can be characterized as favorable, since the RMSE of the method is in the order of magnitude of 10-2 m. Therefore this method can be used successfully in aquifers where geological characteristics are obscure, but a variety of other, easily accessible data, such as meteorological data can be easily found.

  19. Temporal organization of GABAergic interneurons in the intermediate CA1 hippocampus during network oscillations.

    PubMed

    Forro, Thomas; Valenti, Ornella; Lasztoczi, Balint; Klausberger, Thomas

    2015-05-01

    Travelling theta oscillations and sharp wave-associated ripples (SWRs) provide temporal structures to neural activity in the CA1 hippocampus. The contribution of rhythm-generating GABAergic interneurons to network timing across the septotemporal CA1 axis remains unknown. We recorded the spike-timing of identified parvalbumin (PV)-expressing basket, axo-axonic, oriens-lacunosum moleculare (O-LM) interneurons, and pyramidal cells in the intermediate CA1 (iCA1) of anesthetized rats in relation to simultaneously detected network oscillations in iCA1 and dorsal CA1 (dCA1). Distinct interneuron types were coupled differentially to SWR, and the majority of iCA1 SWR events occurred simultaneously with dCA1 SWR events. In contrast, iCA1 theta oscillations were shifted in time relative to dCA1 theta oscillations. During theta cycles, the highest firing of iCA1 axo-axonic cells was followed by PV-expressing basket cells and subsequently by O-LM together with pyramidal cells, similar to the firing sequence of dCA1 cell types reported previously. However, we observed that this temporal organization of cell types is shifted in time between dCA1 and iCA1, together with the respective shift in theta oscillations. We show that GABAergic activity can be synchronized during SWR but is shifted in time from dCA1 to iCA1 during theta oscillations, highlighting the flexible inhibitory control of excitatory activity across a brain structure.

  20. Social Contact Networks and Mixing among Students in K-12 Schools in Pittsburgh, PA.

    PubMed

    Guclu, Hasan; Read, Jonathan; Vukotich, Charles J; Galloway, David D; Gao, Hongjiang; Rainey, Jeanette J; Uzicanin, Amra; Zimmer, Shanta M; Cummings, Derek A T

    2016-01-01

    Students attending schools play an important role in the transmission of influenza. In this study, we present a social network analysis of contacts among 1,828 students in eight different schools in urban and suburban areas in and near Pittsburgh, Pennsylvania, United States of America, including elementary, elementary-middle, middle, and high schools. We collected social contact information of students who wore wireless sensor devices that regularly recorded other devices if they are within a distance of 3 meters. We analyzed these networks to identify patterns of proximal student interactions in different classes and grades, to describe community structure within the schools, and to assess the impact of the physical environment of schools on proximal contacts. In the elementary and middle schools, we observed a high number of intra-grade and intra-classroom contacts and a relatively low number of inter-grade contacts. However, in high schools, contact networks were well connected and mixed across grades. High modularity of lower grades suggests that assumptions of homogeneous mixing in epidemic models may be inappropriate; whereas lower modularity in high schools suggests that homogenous mixing assumptions may be more acceptable in these settings. The results suggest that interventions targeting subsets of classrooms may work better in elementary schools than high schools. Our work presents quantitative measures of age-specific, school-based contacts that can be used as the basis for constructing models of the transmission of infections in schools.

  1. Social Contact Networks and Mixing among Students in K-12 Schools in Pittsburgh, PA

    PubMed Central

    Guclu, Hasan; Read, Jonathan; Vukotich, Charles J.; Galloway, David D.; Gao, Hongjiang; Rainey, Jeanette J.; Uzicanin, Amra; Zimmer, Shanta M.; Cummings, Derek A. T.

    2016-01-01

    Students attending schools play an important role in the transmission of influenza. In this study, we present a social network analysis of contacts among 1,828 students in eight different schools in urban and suburban areas in and near Pittsburgh, Pennsylvania, United States of America, including elementary, elementary-middle, middle, and high schools. We collected social contact information of students who wore wireless sensor devices that regularly recorded other devices if they are within a distance of 3 meters. We analyzed these networks to identify patterns of proximal student interactions in different classes and grades, to describe community structure within the schools, and to assess the impact of the physical environment of schools on proximal contacts. In the elementary and middle schools, we observed a high number of intra-grade and intra-classroom contacts and a relatively low number of inter-grade contacts. However, in high schools, contact networks were well connected and mixed across grades. High modularity of lower grades suggests that assumptions of homogeneous mixing in epidemic models may be inappropriate; whereas lower modularity in high schools suggests that homogenous mixing assumptions may be more acceptable in these settings. The results suggest that interventions targeting subsets of classrooms may work better in elementary schools than high schools. Our work presents quantitative measures of age-specific, school-based contacts that can be used as the basis for constructing models of the transmission of infections in schools. PMID:26978780

  2. Temporal context in floristic classification

    NASA Astrophysics Data System (ADS)

    Fitzgerald, R. W.; Lees, B. G.

    1996-11-01

    Multi-temporal remote sensing data present a number of significant problems for the statistical and spatial competence of a classifier. Ideally, a classifier of multi-temporal data should be temporally invariant. It must have the capacity to account for the variations in season, growth cycle, radiometric, and atmospheric conditions at any point in time when classifying the land cover. This paper tests two methods of creating a temporally invariant classifier based on the pattern recognition capabilities of a neural network. A suite of twelve multi-temporal datasets spread over 5 yr along with a comprehensive mix of environmental variables are fused into floristic classification images by the neural network. Uncertainties in the classifications are addressed explicitly with a confidence mask generated from the fuzzy membership value's output by the neural network. These confidence masks are used to produce constrained classification images. The overall accuracy percentage achieved from a study site containing highly disturbed undulating terrain averages 60%. The first method of training, sequential learning of temporal context, is tested by an examination of the step-by-step evolution of the sequential training process. This reveals that the sequential classifier may not have learned about time, because time was constant during each network training session. It also suggests that there are optimal times during the annual cycle to train the classifier for particular floristic classes. The second method of training the classifier is randomised exposure to the entire temporal training suite. Time was now a fluctuating input variable during the network training process. This method produced the best spatially accurate results. The performance of this classifier as a temporally invariant classifier is tested amongst four multi-temporal datasets with encouraging results. The classifier consistently achieved an overall accuracy percentage of 60%. The pairwise predicted

  3. Spatio-temporal analysis of brain electrical activity in epilepsy based on cellular nonlinear networks

    NASA Astrophysics Data System (ADS)

    Gollas, Frank; Tetzlaff, Ronald

    2009-05-01

    Epilepsy is the most common chronic disorder of the nervous system. Generally, epileptic seizures appear without foregoing sign or warning. The problem of detecting a possible pre-seizure state in epilepsy from EEG signals has been addressed by many authors over the past decades. Different approaches of time series analysis of brain electrical activity already are providing valuable insights into the underlying complex dynamics. But the main goal the identification of an impending epileptic seizure with a sufficient specificity and reliability, has not been achieved up to now. An algorithm for a reliable, automated prediction of epileptic seizures would enable the realization of implantable seizure warning devices, which could provide valuable information to the patient and time/event specific drug delivery or possibly a direct electrical nerve stimulation. Cellular Nonlinear Networks (CNN) are promising candidates for future seizure warning devices. CNN are characterized by local couplings of comparatively simple dynamical systems. With this property these networks are well suited to be realized as highly parallel, analog computer chips. Today available CNN hardware realizations exhibit a processing speed in the range of TeraOps combined with low power consumption. In this contribution new algorithms based on the spatio-temporal dynamics of CNN are considered in order to analyze intracranial EEG signals and thus taking into account mutual dependencies between neighboring regions of the brain. In an identification procedure Reaction-Diffusion CNN (RD-CNN) are determined for short segments of brain electrical activity, by means of a supervised parameter optimization. RD-CNN are deduced from Reaction-Diffusion Systems, which usually are applied to investigate complex phenomena like nonlinear wave propagation or pattern formation. The Local Activity Theory provides a necessary condition for emergent behavior in RD-CNN. In comparison linear spatio-temporal

  4. Temporal Pattern Recognition: A Network Architecture For Multi-Sensor Fusion

    NASA Astrophysics Data System (ADS)

    Priebe, C. E.; Marchette, D. J.

    1989-03-01

    A self-organizing network architecture for the learning and recognition of temporal patterns is proposed. This multi-layered architecture has as its focal point a layer of multi-dimensional Gaussian classification nodes, and the learning scheme employed is based on standard statistical moving mean and moving covariance calculations. The nodes are implemented in the network architecture by using a Gaussian, rather than sigmoidal, transfer function acting on the input from numerous connections. Each connection is analogous to a separate dimension for the Gaussian function. The learning scheme is a one-pass method, eliminating the need for repetitive presentation of the teaching stimuli. The Gaussian classes developed are representative of the statistics of the teaching data and act as templates in classifying novel inputs. The input layer employs a time-based decay to develop a time-ordered representation of the input stimuli. This temporal pattern recognition architecture is used to perform multi-sensor fusion and scene analysis for ROBART II, an autonomous sentry robot employing heterogeneous and homogeneous binary (on / off) sensors. The system receives sensor packets from ROBART indicating which sensors are active. The packets from various sensors are integrated in the input layer. As time progresses these sensor outputs become ordered, allowing the system to recognize activities which are dependent, not only on the individual events which make up the activity, but also on the order in which these events occur and their relative spacing throughout time. Each Gaussian classification node, representing a learned activity as an ordered sequence of sensor outputs, calculates its activation value independently, based on the activity in the input layer. These Gaussian activation values are then used to determine which, if any, of the learned sequences are present and with what confidence. The classification system is capable of recognizing activities despite missing

  5. Optimal design of mixed-media packet-switching networks - Routing and capacity assignment

    NASA Technical Reports Server (NTRS)

    Huynh, D.; Kuo, F. F.; Kobayashi, H.

    1977-01-01

    This paper considers a mixed-media packet-switched computer communication network which consists of a low-delay terrestrial store-and-forward subnet combined with a low-cost high-bandwidth satellite subnet. We show how to route traffic via ground and/or satellite links by means of static, deterministic procedures and assign capacities to channels subject to a given linear cost such that the network average delay is minimized. Two operational schemes for this network model are investigated: one is a scheme in which the satellite channel is used as a slotted ALOHA channel; the other is a new multiaccess scheme we propose in which whenever a channel collision occurs, retransmission of the involved packets will route through ground links to their destinations. The performance of both schemes is evaluated and compared in terms of cost and average packet delay tradeoffs for some examples. The results offer guidelines for the design and optimal utilization of mixed-media networks.

  6. Hierarchical Organization and Disassortative Mixing of Correlation-Based Weighted Financial Networks

    NASA Astrophysics Data System (ADS)

    Cai, Shi-Min; Zhou, Yan-Bo; Zhou, Tao; Zhou, Pei-Ling

    Correlation-based weighted financial networks are analyzed to present cumulative distribution of strength with a power-law tail, which suggests that a small number of hub-like stocks have greater influence on the whole fluctuation of financial market than others. The relationship between clustering and connectivity of vertices emphasizes hierarchical organization, which has been depicted by minimal span tree in previous work. These results urge us to further study the mixing patter of financial network to understand the tendency for vertices to be connected to vertices that are like (or unlike) them in some way. The measurement of average nearest-neighbor degree running over classes of vertices with degree k shows a descending trend when k increases. This interesting result is first uncovered in our work, and suggests the disassortative mixing of financial network which refers to a bias in favor of connections between dissimilar vertices. All the results in weighted complex network aspect may provide some insights to deeper understand the underlying mechanism of financial market and model the evolution of financial market.

  7. A hybrid dynamic Bayesian network approach for modelling temporal associations of gene expressions for hypertension diagnosis.

    PubMed

    Akutekwe, Arinze; Seker, Huseyin

    2014-01-01

    Computational and machine learning techniques have been applied in identifying biomarkers and constructing predictive models for diagnosis of hypertension. Strategies such as improved classification rules based on decision trees have been proposed. Other techniques such as Fuzzy Expert Systems (FES) and Neuro-Fuzzy Systems (NFS) have recently been applied. However, these methods lack the ability to detect temporal relationships among biomarker genes that will aid better understanding of the mechanism of hypertension disease. In this paper we apply a proposed two-stage bio-network construction approach that combines the power and computational efficiency of classification methods with the well-established predictive ability of Dynamic Bayesian Network. We demonstrate our method using the analysis of male young-onset hypertension microarray dataset. Four key genes were identified by the Least Angle Shrinkage and Selection Operator (LASSO) and three Support Vector Machine Recursive Feature Elimination (SVM-RFE) methods. Results show that cell regulation FOXQ1 may inhibit the expression of focusyltransferase-6 (FUT6) and that ABCG1 ATP-binding cassette sub-family G may also play inhibitory role against NR2E3 nuclear receptor sub-family 2 and CGB2 Chromatin Gonadotrophin.

  8. Bi–Mn mixed metal organic oxide: A novel 3d-6p mixed metal coordination network

    SciTech Connect

    Shi, Fa-Nian; Rosa Silva, Ana; Bian, Liang

    2015-05-15

    A new terminology of metal organic oxide (MOO) was given a definition as a type of coordination polymers which possess the feature of inorganic connectivity between metals and the direct bonded atoms and show 1D, 2D or 3D inorganic sub-networks. One such compound was shown as an example. A 3d-6p (Mn–Bi. Named MOOMnBi) mixed metals coordination network has been synthesized via hydrothermal method. The new compound with the molecular formula of [MnBi{sub 2}O(1,3,5-BTC){sub 2}]{sub n} (1,3,5-BTC stands for benzene-1,3,5-tricarboxylate) was characterized via single crystal X-ray diffraction technique that revealed a very interesting 3-dimensional (3D) framework with Bi{sub 4}O{sub 2}(COO){sub 12} clusters which are further connected to Mn(COO){sub 6} fragments into a 2D MOO. The topology study indicates an unprecedented topological type with the net point group of (4{sup 13}.6{sup 2})(4{sup 13}.6{sup 8})(4{sup 16}.6{sup 5})(4{sup 18}.6{sup 10})(4{sup 22}.6{sup 14})(4{sup 3}) corresponding to 3,6,7,7,8,9-c hexa-nodal net. MOOMnBi shows catalytic activity in the synthesis of (E)-α,β-unsaturated ketones. - Graphical abstract: This metal organic framework (MOF) is the essence of a 2D metal organic oxide (MOO). - Highlights: • New concept of metal organic oxide (MOO) was defined and made difference from metal organic framework. • New MOO of MOOMnBi was synthesized by hydrothermal method. • Crystal structure of MOOMnBi was determined by single crystal X-ray analysis. • The catalytic activity of MOOMnBi was studied showing reusable after 2 cycles.

  9. Understanding spatial and temporal patterning of astrocyte calcium transients via interactions between network transport and extracellular diffusion

    NASA Astrophysics Data System (ADS)

    Shtrahman, E.; Maruyama, D.; Olariu, E.; Fink, C. G.; Zochowski, M.

    2017-02-01

    Astrocytes form interconnected networks in the brain and communicate via calcium signaling. We investigate how modes of coupling between astrocytes influence the spatio-temporal patterns of calcium signaling within astrocyte networks and specifically how these network interactions promote coordination within this group of cells. To investigate these complex phenomena, we study reduced cultured networks of astrocytes and neurons. We image the spatial temporal patterns of astrocyte calcium activity and quantify how perturbing the coupling between astrocytes influences astrocyte activity patterns. To gain insight into the pattern formation observed in these cultured networks, we compare the experimentally observed calcium activity patterns to the patterns produced by a reduced computational model, where we represent astrocytes as simple units that integrate input through two mechanisms: gap junction coupling (network transport) and chemical release (extracellular diffusion). We examine the activity patterns in the simulated astrocyte network and their dependence upon these two coupling mechanisms. We find that gap junctions and extracellular chemical release interact in astrocyte networks to modulate the spatiotemporal patterns of their calcium dynamics. We show agreement between the computational and experimental findings, which suggests that the complex global patterns can be understood as a result of simple local coupling mechanisms.

  10. Identification of Pre-Spike Network in Patients with Mesial Temporal Lobe Epilepsy

    PubMed Central

    Faizo, Nahla L.; Burianová, Hana; Gray, Marcus; Hocking, Julia; Galloway, Graham; Reutens, David

    2014-01-01

    Background: Seizures and interictal spikes in mesial temporal lobe epilepsy (MTLE) affect a network of brain regions rather than a single epileptic focus. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) studies have demonstrated a functional network in which hemodynamic changes are time-locked to spikes. However, whether this reflects the propagation of neuronal activity from a focus, or conversely the activation of a network linked to spike generation remains unknown. The functional connectivity (FC) changes prior to spikes may provide information about the connectivity changes that lead to the generation of spikes. We used EEG-fMRI to investigate FC changes immediately prior to the appearance of interictal spikes on EEG in patients with MTLE. Methods/principal findings: Fifteen patients with MTLE underwent continuous EEG-fMRI during rest. Spikes were identified on EEG and three 10 s epochs were defined relative to spike onset: spike (0–10 s), pre-spike (−10 to 0 s), and rest (−20 to −10 s, with no previous spikes in the preceding 45s). Significant spike-related activation in the hippocampus ipsilateral to the seizure focus was found compared to the pre-spike and rest epochs. The peak voxel within the hippocampus ipsilateral to the seizure focus was used as a seed region for FC analysis in the three conditions. A significant change in FC patterns was observed before the appearance of electrographic spikes. Specifically, there was significant loss of coherence between both hippocampi during the pre-spike period compared to spike and rest states. Conclusion/significance: In keeping with previous findings of abnormal inter-hemispheric hippocampal connectivity in MTLE, our findings specifically link reduced connectivity to the period immediately before spikes. This brief decoupling is consistent with a deficit in mutual (inter-hemispheric) hippocampal inhibition that may predispose to spike generation. PMID

  11. Left fronto-temporal dysconnectivity within the language network in schizophrenia: an fMRI and DTI study.

    PubMed

    Leroux, Elise; Delcroix, Nicolas; Dollfus, Sonia

    2014-09-30

    Schizophrenia is a mental disorder characterized by language disorders. Studies reveal that both a functional dysconnectivity and a disturbance in the integrity of white matter fibers are implicated in the language process in patients with schizophrenia. Here, we investigate the relationship between functional connectivity within a language-comprehension network and anatomical connectivity using fiber tracking in schizophrenia. We hypothesized that patients would present an impaired functional connectivity in the language network due to anatomical dysconnectivity. Participants comprised 20 patients with DSM-IV schizophrenia and 20 healthy controls who were studied with functional magnetic resonance imaging and diffusion tensor imaging. The temporal correlation coefficient and diffusion values between the left frontal and temporal clusters, belonging to the language network, were individually extracted, in order to study the relationships of anatomo-functional connectivity. In patients, functional connectivity was positively correlated with fractional anisotropy, but was negatively correlated with radial diffusivity and/or mean diffusivity, in the left arcuate fasciculus and part of the inferior occipitofrontal fasciculus, determined as the fronto-temporal tracts. Our findings indicate a close relationship between functional and anatomical dysconnectivity in patients with schizophrenia. The disturbance in the integrity of the left fronto-temporal tracts might be one origin of the functional dysconnectivity in the language-comprehension network in schizophrenia.

  12. Consistent Large-Eddy Simulation of a Temporal Mixing Layer Laden with Evaporating Drops. Part 2; A Posteriori Modelling

    NASA Technical Reports Server (NTRS)

    Leboissertier, Anthony; Okong'O, Nora; Bellan, Josette

    2005-01-01

    Large-eddy simulation (LES) is conducted of a three-dimensional temporal mixing layer whose lower stream is initially laden with liquid drops which may evaporate during the simulation. The gas-phase equations are written in an Eulerian frame for two perfect gas species (carrier gas and vapour emanating from the drops), while the liquid-phase equations are written in a Lagrangian frame. The effect of drop evaporation on the gas phase is considered through mass, species, momentum and energy source terms. The drop evolution is modelled using physical drops, or using computational drops to represent the physical drops. Simulations are performed using various LES models previously assessed on a database obtained from direct numerical simulations (DNS). These LES models are for: (i) the subgrid-scale (SGS) fluxes and (ii) the filtered source terms (FSTs) based on computational drops. The LES, which are compared to filtered-and-coarsened (FC) DNS results at the coarser LES grid, are conducted with 64 times fewer grid points than the DNS, and up to 64 times fewer computational than physical drops. It is found that both constant-coefficient and dynamic Smagorinsky SGS-flux models, though numerically stable, are overly dissipative and damp generated small-resolved-scale (SRS) turbulent structures. Although the global growth and mixing predictions of LES using Smagorinsky models are in good agreement with the FC-DNS, the spatial distributions of the drops differ significantly. In contrast, the constant-coefficient scale-similarity model and the dynamic gradient model perform well in predicting most flow features, with the latter model having the advantage of not requiring a priori calibration of the model coefficient. The ability of the dynamic models to determine the model coefficient during LES is found to be essential since the constant-coefficient gradient model, although more accurate than the Smagorinsky model, is not consistently numerically stable despite using DNS

  13. Efficient and Secure Temporal Credential-Based Authenticated Key Agreement Using Extended Chaotic Maps for Wireless Sensor Networks

    PubMed Central

    Lee, Tian-Fu

    2015-01-01

    A secure temporal credential-based authenticated key agreement scheme for Wireless Sensor Networks (WSNs) enables a user, a sensor node and a gateway node to realize mutual authentication using temporal credentials. The user and the sensor node then negotiate a common secret key with the help of the gateway node, and establish a secure and authenticated channel using this common secret key. To increase efficiency, recent temporal credential-based authenticated key agreement schemes for WSNs have been designed to involve few computational operations, such as hash and exclusive-or operations. However, these schemes cannot protect the privacy of users and withstand possible attacks. This work develops a novel temporal credential-based authenticated key agreement scheme for WSNs using extended chaotic maps, in which operations are more efficient than modular exponential computations and scalar multiplications on an elliptic curve. The proposed scheme not only provides higher security and efficiency than related schemes, but also resolves their weaknesses. PMID:26121612

  14. Efficient and Secure Temporal Credential-Based Authenticated Key Agreement Using Extended Chaotic Maps for Wireless Sensor Networks.

    PubMed

    Lee, Tian-Fu

    2015-06-25

    A secure temporal credential-based authenticated key agreement scheme for Wireless Sensor Networks (WSNs) enables a user, a sensor node and a gateway node to realize mutual authentication using temporal credentials. The user and the sensor node then negotiate a common secret key with the help of the gateway node, and establish a secure and authenticated channel using this common secret key. To increase efficiency, recent temporal credential-based authenticated key agreement schemes for WSNs have been designed to involve few computational operations, such as hash and exclusive-or operations. However, these schemes cannot protect the privacy of users and withstand possible attacks. This work develops a novel temporal credential-based authenticated key agreement scheme for WSNs using extended chaotic maps, in which operations are more efficient than modular exponential computations and scalar multiplications on an elliptic curve. The proposed scheme not only provides higher security and efficiency than related schemes, but also resolves their weaknesses.

  15. What Determines the Temporal Changes of Species Degree and Strength in an Oceanic Island Plant-Disperser Network?

    PubMed Central

    González-Castro, Aarón; Yang, Suann; Nogales, Manuel; Carlo, Tomás A.

    2012-01-01

    Network models of frugivory and seed dispersal are usually static. To date, most studies on mutualistic networks assert that interaction properties such as species' degree (k) and strength (s) are strongly influenced by species abundances. We evaluated how species' degree and strength change as a function of temporal variation not only in species abundance, but also in species persistence (i.e., phenology length). In a two-year study, we collected community-wide data on seed dispersal by birds and examined the seasonal dynamics of the above-mentioned interaction properties. Our analyses revealed that species abundance is an important predictor for plant strength within a given sub-network. However, our analyses also reveal that species' degree can often be best explained by the length of fruiting phenology (for plants degree) or by the number of fruiting species (for dispersers degree), which are factors that can be decoupled from the relative abundance of the species participating in the network. Moreover, our results suggest that generalist dispersers (when total study period is considered) act as temporal generalists, with degree constrained by the number of plant species displaying fruits in each span. Along with species identity, our findings underscore the need for a temporal perspective, given that seasonality is an inherent property of many mutualistic networks. PMID:22844470

  16. Rich do not rise early: spatio-temporal patterns in the mobility networks of different socio-economic classes

    NASA Astrophysics Data System (ADS)

    Lotero, Laura; Hurtado, Rafael G.; Floría, Luis Mario; Gómez-Gardeñes, Jesús

    2016-10-01

    We analyse the urban mobility in the cities of Medellín and Manizales (Colombia). Each city is represented by six mobility networks, each one encoding the origin-destination trips performed by a subset of the population corresponding to a particular socio-economic status. The nodes of each network are the different urban locations whereas links account for the existence of a trip between two different areas of the city. We study the main structural properties of these mobility networks by focusing on their spatio-temporal patterns. Our goal is to relate these patterns with the partition into six socio-economic compartments of these two societies. Our results show that spatial and temporal patterns vary across these socio-economic groups. In particular, the two datasets show that as wealth increases the early-morning activity is delayed, the midday peak becomes smoother and the spatial distribution of trips becomes more localized.

  17. Rich do not rise early: spatio-temporal patterns in the mobility networks of different socio-economic classes.

    PubMed

    Lotero, Laura; Hurtado, Rafael G; Floría, Luis Mario; Gómez-Gardeñes, Jesús

    2016-10-01

    We analyse the urban mobility in the cities of Medellín and Manizales (Colombia). Each city is represented by six mobility networks, each one encoding the origin-destination trips performed by a subset of the population corresponding to a particular socio-economic status. The nodes of each network are the different urban locations whereas links account for the existence of a trip between two different areas of the city. We study the main structural properties of these mobility networks by focusing on their spatio-temporal patterns. Our goal is to relate these patterns with the partition into six socio-economic compartments of these two societies. Our results show that spatial and temporal patterns vary across these socio-economic groups. In particular, the two datasets show that as wealth increases the early-morning activity is delayed, the midday peak becomes smoother and the spatial distribution of trips becomes more localized.

  18. Rich do not rise early: spatio-temporal patterns in the mobility networks of different socio-economic classes

    PubMed Central

    Hurtado, Rafael G.; Floría, Luis Mario

    2016-01-01

    We analyse the urban mobility in the cities of Medellín and Manizales (Colombia). Each city is represented by six mobility networks, each one encoding the origin-destination trips performed by a subset of the population corresponding to a particular socio-economic status. The nodes of each network are the different urban locations whereas links account for the existence of a trip between two different areas of the city. We study the main structural properties of these mobility networks by focusing on their spatio-temporal patterns. Our goal is to relate these patterns with the partition into six socio-economic compartments of these two societies. Our results show that spatial and temporal patterns vary across these socio-economic groups. In particular, the two datasets show that as wealth increases the early-morning activity is delayed, the midday peak becomes smoother and the spatial distribution of trips becomes more localized. PMID:27853531

  19. Assessing the performance of the International Monitoring System's infrasound network: Geographical coverage and temporal variabilities

    NASA Astrophysics Data System (ADS)

    Le Pichon, A.; Vergoz, J.; Blanc, E.; Guilbert, J.; Ceranna, L.; Evers, L.; Brachet, N.

    2009-04-01

    A global-scale analysis of detections made at all 36 currently operating International Monitoring System (IMS) infrasound arrays confirms that the primary factor controlling signal detectability is the seasonal variability of the stratospheric zonal wind. At most arrays, ˜80% of the detections in the 0.2- to 2-Hz bandpass are associated with propagation downwind of the dominant stratospheric wind direction. Previous IMS infrasound network performance models neglect the time- and site-dependent effects of both stratospheric meteorological variability and ambient noise models. In this study both effects are incorporated; we compare empirical and improved specifications of the stratospheric wind and include station-dependent wind noise models. Using a deterministic approach, the influence of individual model parameters on the network performance is systematically assessed. At frequencies of interest for detecting atmospheric explosions (0.2-2 Hz), the simulations predict that explosions equivalent to ˜500 t of TNT would be detected by at least two stations at any time of the year. The detection capability is best around January and July when stratospheric winds are strongest, compared to the equinox periods when zonal winds reduce and reverse. The model predicts that temporal fluctuations in the ground-to-stratosphere meteorological variables generate detection threshold variations on daily and seasonal timescales of ˜50 and ˜500 t, respectively. While the strong zonal winds lead to an improvement in detection capability, their highly directional nature leads to an increase in the location uncertainty owing to the decreased azimuthal separation of the detecting stations.

  20. Increased interictal cerebral glucose metabolism in a cortical-subcortical network in drug naive patients with cryptogenic temporal lobe epilepsy.

    PubMed Central

    Franceschi, M; Lucignani, G; Del Sole, A; Grana, C; Bressi, S; Minicucci, F; Messa, C; Canevini, M P; Fazio, F

    1995-01-01

    Positron emission tomography with [18F]-2-fluoro-2-deoxy-D-glucose ([18F]FDG) has been used to assess the pattern of cerebral metabolism in different types of epilepsies. However, PET with [18F]FDG has never been used to evaluate drug naive patients with cryptogenic temporal lobe epilepsy, in whom the mechanism of origin and diffusion of the epileptic discharge may differ from that underlying other epilepsies. In a group of patients with cryptogenic temporal lobe epilepsy, never treated with antiepileptic drugs, evidence has been found of significant interictal glucose hypermetabolism in a bilateral neural network including the temporal lobes, thalami, basal ganglia, and cingular cortices. The metabolism in these areas and frontal lateral cortex enables the correct classification of all patients with temporal lobe epilepsy and controls by discriminant function analysis. Other cortical areas--namely, frontal basal and lateral, temporal mesial, and cerebellar cortices--had bilateral increases of glucose metabolism ranging from 10 to 15% of normal controls, although lacking stringent statistical significance. This metabolic pattern could represent a pathophysiological state of hyperactivity predisposing to epileptic discharge generation or diffusion, or else a network of inhibitory circuits activated to prevent the diffusion of the epileptic discharge. PMID:7561924

  1. Temporal patterns of happiness and information in a global social network: hedonometrics and Twitter.

    PubMed

    Dodds, Peter Sheridan; Harris, Kameron Decker; Kloumann, Isabel M; Bliss, Catherine A; Danforth, Christopher M

    2011-01-01

    Individual happiness is a fundamental societal metric. Normally measured through self-report, happiness has often been indirectly characterized and overshadowed by more readily quantifiable economic indicators such as gross domestic product. Here, we examine expressions made on the online, global microblog and social networking service Twitter, uncovering and explaining temporal variations in happiness and information levels over timescales ranging from hours to years. Our data set comprises over 46 billion words contained in nearly 4.6 billion expressions posted over a 33 month span by over 63 million unique users. In measuring happiness, we construct a tunable, real-time, remote-sensing, and non-invasive, text-based hedonometer. In building our metric, made available with this paper, we conducted a survey to obtain happiness evaluations of over 10,000 individual words, representing a tenfold size improvement over similar existing word sets. Rather than being ad hoc, our word list is chosen solely by frequency of usage, and we show how a highly robust and tunable metric can be constructed and defended.

  2. Synaptic Reorganization of the Perisomatic Inhibitory Network in Hippocampi of Temporal Lobe Epileptic Patients

    PubMed Central

    Wittner, Lucia

    2017-01-01

    GABAergic inhibition and particularly perisomatic inhibition play a crucial role in controlling the firing properties of large principal cell populations. Furthermore, GABAergic network is a key element in the therapy attempting to reduce epileptic activity. Here, we present a review showing the synaptic changes of perisomatic inhibitory neuronal subtypes in the hippocampus of temporal lobe epileptic patients, including parvalbumin- (PV-) containing and cannabinoid Type 1 (CB1) receptor-expressing (and mainly cholecystokinin-positive) perisomatic inhibitory cells, known to control hippocampal synchronies. We have examined the synaptic input of principal cells in the dentate gyrus and Cornu Ammonis region in human control and epileptic hippocampi. Perisomatic inhibitory terminals establishing symmetric synapses were found to be sprouted in the dentate gyrus. Preservation of perisomatic input was found in the Cornu Ammonis 1 and Cornu Ammonis 2 regions, as long as pyramidal cells are present. Higher density of CB1-immunostained terminals was found in the epileptic hippocampus of sclerotic patients, especially in the dentate gyrus. We concluded that both types of (PV- and GABAergic CB1-containing) perisomatic inhibitory cells are mainly preserved or showed sprouting in epileptic samples. The enhanced perisomatic inhibitory signaling may increase principal cell synchronization and contribute to generation of epileptic seizures and interictal spikes. PMID:28116310

  3. A Temporal Credential-Based Mutual Authentication with Multiple-Password Scheme for Wireless Sensor Networks

    PubMed Central

    Zhang, Ruisheng; Liu, Qidong

    2017-01-01

    Wireless sensor networks (WSNs), which consist of a large number of sensor nodes, have become among the most important technologies in numerous fields, such as environmental monitoring, military surveillance, control systems in nuclear reactors, vehicle safety systems, and medical monitoring. The most serious drawback for the widespread application of WSNs is the lack of security. Given the resource limitation of WSNs, traditional security schemes are unsuitable. Approaches toward withstanding related attacks with small overhead have thus recently been studied by many researchers. Numerous studies have focused on the authentication scheme for WSNs, but most of these works cannot achieve the security performance and overhead perfectly. Nam et al. proposed a two-factor authentication scheme with lightweight sensor computation for WSNs. In this paper, we review this scheme, emphasize its drawbacks, and propose a temporal credential-based mutual authentication with a multiple-password scheme for WSNs. Our scheme uses multiple passwords to achieve three-factor security performance and generate a session key between user and sensor nodes. The security analysis phase shows that our scheme can withstand related attacks, including a lost password threat, and the comparison phase shows that our scheme involves a relatively small overhead. In the comparison of the overhead phase, the result indicates that more than 95% of the overhead is composed of communication and not computation overhead. Therefore, the result motivates us to pay further attention to communication overhead than computation overhead in future research. PMID:28135288

  4. Spatio-temporal Remodeling of Functional Membrane Microdomains Organizes the Signaling Networks of a Bacterium

    PubMed Central

    Schneider, Johannes; Klein, Teresa; Mielich-Süss, Benjamin; Koch, Gudrun; Franke, Christian; Kuipers, Oscar P.; Kovács, Ákos T.; Sauer, Markus; Lopez, Daniel

    2015-01-01

    Lipid rafts are membrane microdomains specialized in the regulation of numerous cellular processes related to membrane organization, as diverse as signal transduction, protein sorting, membrane trafficking or pathogen invasion. It has been proposed that this functional diversity would require a heterogeneous population of raft domains with varying compositions. However, a mechanism for such diversification is not known. We recently discovered that bacterial membranes organize their signal transduction pathways in functional membrane microdomains (FMMs) that are structurally and functionally similar to the eukaryotic lipid rafts. In this report, we took advantage of the tractability of the prokaryotic model Bacillus subtilis to provide evidence for the coexistence of two distinct families of FMMs in bacterial membranes, displaying a distinctive distribution of proteins specialized in different biological processes. One family of microdomains harbors the scaffolding flotillin protein FloA that selectively tethers proteins specialized in regulating cell envelope turnover and primary metabolism. A second population of microdomains containing the two scaffolding flotillins, FloA and FloT, arises exclusively at later stages of cell growth and specializes in adaptation of cells to stationary phase. Importantly, the diversification of membrane microdomains does not occur arbitrarily. We discovered that bacterial cells control the spatio-temporal remodeling of microdomains by restricting the activation of FloT expression to stationary phase. This regulation ensures a sequential assembly of functionally specialized membrane microdomains to strategically organize signaling networks at the right time during the lifespan of a bacterium. PMID:25909364

  5. Temporal regulation of EGF signaling networks by the scaffold protein Shc1

    PubMed Central

    Zheng, Yong; Zhang, Cunjie; Croucher, David R.; Soliman, Mohamed A.; St-Denis, Nicole; Pasculescu, Adrian; Taylor, Lorne; Tate, Stephen A.; Hardy, Rod W.; Colwill, Karen; Dai, Anna Yue; Bagshaw, Rick; Dennis, James W.; Gingras, Anne-Claude; Daly, Roger J.; Pawson, Tony

    2016-01-01

    Cell-surface receptors frequently employ scaffold proteins to recruit cytoplasmic targets, but the rationale for this is uncertain. Activated receptor tyrosine kinases, for example, engage scaffolds such as Shc1 that contain phosphotyrosine (pTyr) binding (PTB) domains. Using quantitative mass spectrometry, we find that Shc1 responds to epidermal growth factor (EGF) stimulation through multiple waves of distinct phosphorylation events and protein interactions. Following stimulation, Shc1 rapidly binds a group of proteins that activate pro-mitogenic/survival pathways dependent on recruitment of the Grb2 adaptor to Shc1 pTyr sites. Akt-mediated feedback phosphorylation of Shc1 Ser29 then recruits the Ptpn12 tyrosine phosphatase. This is followed by a sub-network of proteins involved in cytoskeletal reorganization, trafficking and signal termination that binds Shc1 with delayed kinetics, largely through the SgK269 pseudokinase/adaptor protein. Ptpn12 acts as a switch to convert Shc1 from pTyr/Grb2-based signaling to SgK269-mediated pathways that regulate cell invasion and morphogenesis. The Shc1 scaffold therefore directs the temporal flow of signaling information following EGF stimulation. PMID:23846654

  6. Learning invariant object recognition from temporal correlation in a hierarchical network.

    PubMed

    Lessmann, Markus; Würtz, Rolf P

    2014-06-01

    Invariant object recognition, which means the recognition of object categories independent of conditions like viewing angle, scale and illumination, is a task of great interest that humans can fulfill much better than artificial systems. During the last years several basic principles were derived from neurophysiological observations and careful consideration: (1) Developing invariance to possible transformations of the object by learning temporal sequences of visual features that occur during the respective alterations. (2) Learning in a hierarchical structure, so basic level (visual) knowledge can be reused for different kinds of objects. (3) Using feedback to compare predicted input with the current one for choosing an interpretation in the case of ambiguous signals. In this paper we propose a network which implements all of these concepts in a computationally efficient manner which gives very good results on standard object datasets. By dynamically switching off weakly active neurons and pruning weights computation is sped up and thus handling of large databases with several thousands of images and a number of categories in a similar order becomes possible. The involved parameters allow flexible adaptation to the information content of training data and allow tuning to different databases relatively easily. Precondition for successful learning is that training images are presented in an order assuring that images of the same object under similar viewing conditions follow each other. Through an implementation with sparse data structures the system has moderate memory demands and still yields very good recognition rates.

  7. Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter

    PubMed Central

    Dodds, Peter Sheridan; Harris, Kameron Decker; Kloumann, Isabel M.; Bliss, Catherine A.; Danforth, Christopher M.

    2011-01-01

    Individual happiness is a fundamental societal metric. Normally measured through self-report, happiness has often been indirectly characterized and overshadowed by more readily quantifiable economic indicators such as gross domestic product. Here, we examine expressions made on the online, global microblog and social networking service Twitter, uncovering and explaining temporal variations in happiness and information levels over timescales ranging from hours to years. Our data set comprises over 46 billion words contained in nearly 4.6 billion expressions posted over a 33 month span by over 63 million unique users. In measuring happiness, we construct a tunable, real-time, remote-sensing, and non-invasive, text-based hedonometer. In building our metric, made available with this paper, we conducted a survey to obtain happiness evaluations of over 10,000 individual words, representing a tenfold size improvement over similar existing word sets. Rather than being ad hoc, our word list is chosen solely by frequency of usage, and we show how a highly robust and tunable metric can be constructed and defended. PMID:22163266

  8. A Temporal Credential-Based Mutual Authentication with Multiple-Password Scheme for Wireless Sensor Networks.

    PubMed

    Liu, Xin; Zhang, Ruisheng; Liu, Qidong

    2017-01-01

    Wireless sensor networks (WSNs), which consist of a large number of sensor nodes, have become among the most important technologies in numerous fields, such as environmental monitoring, military surveillance, control systems in nuclear reactors, vehicle safety systems, and medical monitoring. The most serious drawback for the widespread application of WSNs is the lack of security. Given the resource limitation of WSNs, traditional security schemes are unsuitable. Approaches toward withstanding related attacks with small overhead have thus recently been studied by many researchers. Numerous studies have focused on the authentication scheme for WSNs, but most of these works cannot achieve the security performance and overhead perfectly. Nam et al. proposed a two-factor authentication scheme with lightweight sensor computation for WSNs. In this paper, we review this scheme, emphasize its drawbacks, and propose a temporal credential-based mutual authentication with a multiple-password scheme for WSNs. Our scheme uses multiple passwords to achieve three-factor security performance and generate a session key between user and sensor nodes. The security analysis phase shows that our scheme can withstand related attacks, including a lost password threat, and the comparison phase shows that our scheme involves a relatively small overhead. In the comparison of the overhead phase, the result indicates that more than 95% of the overhead is composed of communication and not computation overhead. Therefore, the result motivates us to pay further attention to communication overhead than computation overhead in future research.

  9. Selective Attention to Semantic and Syntactic Features Modulates Sentence Processing Networks in Anterior Temporal Cortex

    PubMed Central

    Rogalsky, Corianne

    2009-01-01

    Numerous studies have identified an anterior temporal lobe (ATL) region that responds preferentially to sentence-level stimuli. It is unclear, however, whether this activity reflects a response to syntactic computations or some form of semantic integration. This distinction is difficult to investigate with the stimulus manipulations and anomaly detection paradigms traditionally implemented. The present functional magnetic resonance imaging study addresses this question via a selective attention paradigm. Subjects monitored for occasional semantic anomalies or occasional syntactic errors, thus directing their attention to semantic integration, or syntactic properties of the sentences. The hemodynamic response in the sentence-selective ATL region (defined with a localizer scan) was examined during anomaly/error-free sentences only, to avoid confounds due to error detection. The majority of the sentence-specific region of interest was equally modulated by attention to syntactic or compositional semantic features, whereas a smaller subregion was only modulated by the semantic task. We suggest that the sentence-specific ATL region is sensitive to both syntactic and integrative semantic functions during sentence processing, with a smaller portion of this area preferentially involved in the later. This study also suggests that selective attention paradigms may be effective tools to investigate the functional diversity of networks involved in sentence processing. PMID:18669589

  10. The Structural Plasticity of White Matter Networks Following Anterior Temporal Lobe Resection

    ERIC Educational Resources Information Center

    Yogarajah, Mahinda; Focke, Niels K.; Bonelli, Silvia B.; Thompson, Pamela; Vollmar, Christian; McEvoy, Andrew W.; Alexander, Daniel C.; Symms, Mark R.; Koepp, Matthias J.; Duncan, John S.

    2010-01-01

    Anterior temporal lobe resection is an effective treatment for refractory temporal lobe epilepsy. The structural consequences of such surgery in the white matter, and how these relate to language function after surgery remain unknown. We carried out a longitudinal study with diffusion tensor imaging in 26 left and 20 right temporal lobe epilepsy…

  11. Transient brain activity disentangles fMRI resting-state dynamics in terms of spatially and temporally overlapping networks

    PubMed Central

    Karahanoğlu, Fikret Işik; Van De Ville, Dimitri

    2015-01-01

    Dynamics of resting-state functional magnetic resonance imaging (fMRI) provide a new window onto the organizational principles of brain function. Using state-of-the-art signal processing techniques, we extract innovation-driven co-activation patterns (iCAPs) from resting-state fMRI. The iCAPs' maps are spatially overlapping and their sustained-activity signals temporally overlapping. Decomposing resting-state fMRI using iCAPs reveals the rich spatiotemporal structure of functional components that dynamically assemble known resting-state networks. The temporal overlap between iCAPs is substantial; typically, three to four iCAPs occur simultaneously in combinations that are consistent with their behaviour profiles. In contrast to conventional connectivity analysis, which suggests a negative correlation between fluctuations in the default-mode network (DMN) and task-positive networks, we instead find evidence for two DMN-related iCAPs consisting the posterior cingulate cortex that differentially interact with the attention network. These findings demonstrate how the fMRI resting state can be functionally decomposed into spatially and temporally overlapping building blocks using iCAPs. PMID:26178017

  12. Identification of regulatory structure and kinetic parameters of biochemical networks via mixed-integer dynamic optimization

    PubMed Central

    2013-01-01

    Background Recovering the network topology and associated kinetic parameter values from time-series data are central topics in systems biology. Nevertheless, methods that simultaneously do both are few and lack generality. Results Here, we present a rigorous approach for simultaneously estimating the parameters and regulatory topology of biochemical networks from time-series data. The parameter estimation task is formulated as a mixed-integer dynamic optimization problem with: (i) binary variables, used to model the existence of regulatory interactions and kinetic effects of metabolites in the network processes; and (ii) continuous variables, denoting metabolites concentrations and kinetic parameters values. The approach simultaneously optimizes the Akaike criterion, which captures the trade-off between complexity (measured by the number of parameters), and accuracy of the fitting. This simultaneous optimization mitigates a possible overfitting that could result from addition of spurious regulatory interactions. Conclusion The capabilities of our approach were tested in one benchmark problem. Our algorithm is able to identify a set of plausible network topologies with their associated parameters. PMID:24176044

  13. Temporal and Spatial prediction of groundwater levels using Artificial Neural Networks, Fuzzy logic and Kriging interpolation.

    NASA Astrophysics Data System (ADS)

    Tapoglou, Evdokia; Karatzas, George P.; Trichakis, Ioannis C.; Varouchakis, Emmanouil A.

    2014-05-01

    The purpose of this study is to examine the use of Artificial Neural Networks (ANN) combined with kriging interpolation method, in order to simulate the hydraulic head both spatially and temporally. Initially, ANNs are used for the temporal simulation of the hydraulic head change. The results of the most appropriate ANNs, determined through a fuzzy logic system, are used as an input for the kriging algorithm where the spatial simulation is conducted. The proposed algorithm is tested in an area located across Isar River in Bayern, Germany and covers an area of approximately 7800 km2. The available data extend to a time period from 1/11/2008 to 31/10/2012 (1460 days) and include the hydraulic head at 64 wells, temperature and rainfall at 7 weather stations and surface water elevation at 5 monitoring stations. One feedforward ANN was trained for each of the 64 wells, where hydraulic head data are available, using a backpropagation algorithm. The most appropriate input parameters for each wells' ANN are determined considering their proximity to the measuring station, as well as their statistical characteristics. For the rainfall, the data for two consecutive time lags for best correlated weather station, as well as a third and fourth input from the second best correlated weather station, are used as an input. The surface water monitoring stations with the three best correlations for each well are also used in every case. Finally, the temperature for the best correlated weather station is used. Two different architectures are considered and the one with the best results is used henceforward. The output of the ANNs corresponds to the hydraulic head change per time step. These predictions are used in the kriging interpolation algorithm. However, not all 64 simulated values should be used. The appropriate neighborhood for each prediction point is constructed based not only on the distance between known and prediction points, but also on the training and testing error of

  14. Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence

    PubMed Central

    Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Torralba, Antonio; Oliva, Aude

    2016-01-01

    The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain. PMID:27282108

  15. Recurrent temporal networks and language acquisition-from corticostriatal neurophysiology to reservoir computing.

    PubMed

    Dominey, Peter F

    2013-01-01

    One of the most paradoxical aspects of human language is that it is so unlike any other form of behavior in the animal world, yet at the same time, it has developed in a species that is not far removed from ancestral species that do not possess language. While aspects of non-human primate and avian interaction clearly constitute communication, this communication appears distinct from the rich, combinatorial and abstract quality of human language. So how does the human primate brain allow for language? In an effort to answer this question, a line of research has been developed that attempts to build a language processing capability based in part on the gross neuroanatomy of the corticostriatal system of the human brain. This paper situates this research program in its historical context, that begins with the primate oculomotor system and sensorimotor sequencing, and passes, via recent advances in reservoir computing to provide insight into the open questions, and possible approaches, for future research that attempts to model language processing. One novel and useful idea from this research is that the overlap of cortical projections onto common regions in the striatum allows for adaptive binding of cortical signals from distinct circuits, under the control of dopamine, which has a strong adaptive advantage. A second idea is that recurrent cortical networks with fixed connections can represent arbitrary sequential and temporal structure, which is the basis of the reservoir computing framework. Finally, bringing these notions together, a relatively simple mechanism can be built for learning the grammatical constructions, as the mappings from surface structure of sentences to their meaning. This research suggests that the components of language that link conceptual structure to grammatical structure may be much simpler that has been proposed in other research programs. It also suggests that part of the residual complexity is in the conceptual system itself.

  16. Naturalistic FMRI mapping reveals superior temporal sulcus as the hub for the distributed brain network for social perception.

    PubMed

    Lahnakoski, Juha M; Glerean, Enrico; Salmi, Juha; Jääskeläinen, Iiro P; Sams, Mikko; Hari, Riitta; Nummenmaa, Lauri

    2012-01-01

    Despite the abundant data on brain networks processing static social signals, such as pictures of faces, the neural systems supporting social perception in naturalistic conditions are still poorly understood. Here we delineated brain networks subserving social perception under naturalistic conditions in 19 healthy humans who watched, during 3-T functional magnetic resonance imaging (fMRI), a set of 137 short (approximately 16 s each, total 27 min) audiovisual movie clips depicting pre-selected social signals. Two independent raters estimated how well each clip represented eight social features (faces, human bodies, biological motion, goal-oriented actions, emotion, social interaction, pain, and speech) and six filler features (places, objects, rigid motion, people not in social interaction, non-goal-oriented action, and non-human sounds) lacking social content. These ratings were used as predictors in the fMRI analysis. The posterior superior temporal sulcus (STS) responded to all social features but not to any non-social features, and the anterior STS responded to all social features except bodies and biological motion. We also found four partially segregated, extended networks for processing of specific social signals: (1) a fronto-temporal network responding to multiple social categories, (2) a fronto-parietal network preferentially activated to bodies, motion, and pain, (3) a temporo-amygdalar network responding to faces, social interaction, and speech, and (4) a fronto-insular network responding to pain, emotions, social interactions, and speech. Our results highlight the role of the pSTS in processing multiple aspects of social information, as well as the feasibility and efficiency of fMRI mapping under conditions that resemble the complexity of real life.

  17. Spatial and Temporal Episodic Memory Retrieval Recruit Dissociable Functional Networks in the Human Brain

    ERIC Educational Resources Information Center

    Ekstrom, Arne D.; Bookheimer, Susan Y.

    2007-01-01

    Imaging, electrophysiological studies, and lesion work have shown that the medial temporal lobe (MTL) is important for episodic memory; however, it is unclear whether different MTL regions support the spatial, temporal, and item elements of episodic memory. In this study we used fMRI to examine retrieval performance emphasizing different aspects…

  18. Effect of chromatic-dispersion-induced chirp on the temporal coherence properties of individual beams from spontaneous four-wave mixing

    SciTech Connect

    Ma Xiaoxin; Li Xiaoying; Cui Liang; Guo Xueshi; Yang Lei

    2011-08-15

    Temporal coherence of individual signal or idler beam, determined by the spectral correlation property of photon pairs, is important for realizing quantum interference among independent sources. Based on spontaneous four-wave mixing in optical fibers, we study the effect of chirp on the temporal coherence property by introducing a different amount of chirp into either the pulsed pump or individual signal (idler) beam. The investigation shows that the pump chirp induces additional frequency correlation into photon pairs; the mutual spectral correlation of photon pairs and the coherence of individual beam can be characterized by measuring the intensity correlation function g{sup (2)} of the individual beam. To improve the coherence degree, the pump chirp should be minimized. Moreover, a Hong-Ou-Mandel-type two-photon interference experiment with the signal beams generated in two different fibers illustrates that the chirp of the individual signal (idler) beam does not change the temporal coherence degree, but affects the temporal mode matching. To achieve high visibility among multiple sources, apart from improving the coherence degree, mode matching should be optimized by managing the chirps of individual beams.

  19. Protein domain networks: Scale-free mixing of positive and negative exponents

    NASA Astrophysics Data System (ADS)

    Nacher, J. C.; Hayashida, M.; Akutsu, T.

    2006-07-01

    Many biological studies have been focused on the study of proteins, since proteins are essential for most cell functions. Although proteins are unique, they share certain common properties. For example, well-defined regions within a protein can fold independently from the rest of the protein and have their own function. They are called protein domains, and served as protein building blocks. In this article, we present a theoretical model for studying the protein domain networks, where one node of the network corresponds to one protein and two proteins are connected if they contain the same domain. The resulting distribution of nodes with a given degree, k, shows not only a power-law with negative exponent γ=-1, but it resembles the superposition of two power-law functions, one with a negative exponent and another with a positive exponent β=1. We call this distribution pattern “ scale-free mixing”. To explain the emergence of this superposition of power-laws, we propose a basic model with two main components: (1) mutation and (2) duplication of domains. Precisely, duplication gives rise to complete subgraphs (i.e., cliques) on the network, thus for several values of k a large number of nodes with degree k is produced, which explains the positive power-law branch of the degree distribution. In order to compare our model with experimental data, we generate protein domain networks with data from the UniProt Knowledgebase-Swissprot database for protein sequences and using InterPro, Pfam and Smart for domain databases. Our results indicate that the signal of this scale-free mixing pattern is also observed in the experimental data and it is conserved among organisms as Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana, Drosophila melanogaster, Mus musculus, and Homo sapiens.

  20. A mixed connection recovery strategy for surviving dual link failure in WDM networks

    NASA Astrophysics Data System (ADS)

    Yadav, Dharmendra Singh; Rana, Santosh; Prakash, Shashi

    2013-03-01

    As the size and complexity of a network increases, the probability of a dual link failure also increases. For recovering the dual link failures, two strategies have been presented in past. As per the first strategy, SPP-MAS (Shared Path Protection-Maximum Allowable Sharing), the sharing of backup lightpaths in SPP (Shared Path Protection) has been reduced, and in the second strategy TBPS (Two Backup Path Shared), the reservation of two backup lightpaths for each primary lightpath has been undertaken. The main flaw of these strategies is the requirement of redundant network resources towards the establishment of backup lightpaths, and the occurrence of trap problem after the second link fails. To minimize the redundant backup resources and the trap problem, a mixed connection recovery algorithm namely Adaptive Backup Routing over Reserved Resources (ABRRR) has been proposed. The design of ABRRR takes leverage of both, the pre-planned, and the post-failure connection recovery mechanisms. In ABRRR, the failed connections are re-provisioned adaptively over the pre-allocated backup network resources. Adaptive re-provisioning of the failed connection minimizes the trap problem. Using simulation experiments, we undertake a comparative study of the proposed strategy with the existing strategies (i.e. SPP-MAS and TBPS) under the network parameters of Blocking Probability, Dual Restorability, and Resource Utilization Ratio (RUR). Detailed investigations establish that the use of ABRRR leads to lower Blocking Probability, higher Dual Restorability, and minimized RUR compared to the existing strategies. Results also show that the proposed strategy not only survives more connections but also utilizes fewer numbers of resources compared to the existing strategies.

  1. Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach.

    PubMed

    Yu, Zhaoyuan; Yuan, Linwang; Luo, Wen; Feng, Linyao; Lv, Guonian

    2015-12-30

    Passive infrared (PIR) motion detectors, which can support long-term continuous observation, are widely used for human motion analysis. Extracting all possible trajectories from the PIR sensor networks is important. Because the PIR sensor does not log location and individual information, none of the existing methods can generate all possible human motion trajectories that satisfy various spatio-temporal constraints from the sensor activation log data. In this paper, a geometric algebra (GA)-based approach is developed to generate all possible human trajectories from the PIR sensor network data. Firstly, the representation of the geographical network, sensor activation response sequences and the human motion are represented as algebraic elements using GA. The human motion status of each sensor activation are labeled using the GA-based trajectory tracking. Then, a matrix multiplication approach is developed to dynamically generate the human trajectories according to the sensor activation log and the spatio-temporal constraints. The method is tested with the MERL motion database. Experiments show that our method can flexibly extract the major statistical pattern of the human motion. Compared with direct statistical analysis and tracklet graph method, our method can effectively extract all possible trajectories of the human motion, which makes it more accurate. Our method is also likely to provides a new way to filter other passive sensor log data in sensor networks.

  2. Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach

    PubMed Central

    Yu, Zhaoyuan; Yuan, Linwang; Luo, Wen; Feng, Linyao; Lv, Guonian

    2015-01-01

    Passive infrared (PIR) motion detectors, which can support long-term continuous observation, are widely used for human motion analysis. Extracting all possible trajectories from the PIR sensor networks is important. Because the PIR sensor does not log location and individual information, none of the existing methods can generate all possible human motion trajectories that satisfy various spatio-temporal constraints from the sensor activation log data. In this paper, a geometric algebra (GA)-based approach is developed to generate all possible human trajectories from the PIR sensor network data. Firstly, the representation of the geographical network, sensor activation response sequences and the human motion are represented as algebraic elements using GA. The human motion status of each sensor activation are labeled using the GA-based trajectory tracking. Then, a matrix multiplication approach is developed to dynamically generate the human trajectories according to the sensor activation log and the spatio-temporal constraints. The method is tested with the MERL motion database. Experiments show that our method can flexibly extract the major statistical pattern of the human motion. Compared with direct statistical analysis and tracklet graph method, our method can effectively extract all possible trajectories of the human motion, which makes it more accurate. Our method is also likely to provides a new way to filter other passive sensor log data in sensor networks. PMID:26729123

  3. Untangling spatial and temporal trends in the variability of the Black Sea Cold Intermediate Layer and mixed Layer Depth using the DIVA detrending procedure

    NASA Astrophysics Data System (ADS)

    Capet, A.; Troupin, C.; Carstensen, J.; Grégoire, M.; Beckers, J.-M.

    2014-03-01

    Current spatial interpolation products may be biased by uneven distribution of measurements in time. This manuscript presents a detrending method that recognizes and eliminates this bias. The method estimates temporal trend components in addition to the spatial structure and has been implemented within the Data Interpolating Variational Analysis (DIVA) analysis tool. The assets of this new detrending method are illustrated by producing monthly and annual climatologies of two vertical properties of the Black Sea while recognizing their seasonal and interannual variabilities : the mixed layer depth and the cold content of its cold intermediate layer (CIL). The temporal trends, given as by-products of the method, are used to analyze the seasonal and interannual variability of these variables over the past decades (1955-2011). In particular, the CIL interannual variability is related to the cumulated winter air temperature anomalies, explaining 88 % of its variation.

  4. Spatio-temporal interpolation of soil moisture in 3D+T using automated sensor network data

    NASA Astrophysics Data System (ADS)

    Gasch, C.; Hengl, T.; Magney, T. S.; Brown, D. J.; Gräler, B.

    2014-12-01

    Soil sensor networks provide frequent in situ measurements of dynamic soil properties at fixed locations, producing data in 2- or 3-dimensions and through time (2D+T and 3D+T). Spatio-temporal interpolation of 3D+T point data produces continuous estimates that can then be used for prediction at unsampled times and locations, as input for process models, and can simply aid in visualization of properties through space and time. Regression-kriging with 3D and 2D+T data has successfully been implemented, but currently the field of geostatistics lacks an analytical framework for modeling 3D+T data. Our objective is to develop robust 3D+T models for mapping dynamic soil data that has been collected with high spatial and temporal resolution. For this analysis, we use data collected from a sensor network installed on the R.J. Cook Agronomy Farm (CAF), a 37-ha Long-Term Agro-Ecosystem Research (LTAR) site in Pullman, WA. For five years, the sensors have collected hourly measurements of soil volumetric water content at 42 locations and five depths. The CAF dataset also includes a digital elevation model and derivatives, a soil unit description map, crop rotations, electromagnetic induction surveys, daily meteorological data, and seasonal satellite imagery. The soil-water sensor data, combined with the spatial and temporal covariates, provide an ideal dataset for developing 3D+T models. The presentation will include preliminary results and address main implementation strategies.

  5. Advantages of mixing bioinformatics and visualization approaches for analyzing sRNA-mediated regulatory bacterial networks

    PubMed Central

    Bourqui, Romain; Benchimol, William; Gaspin, Christine; Sirand-Pugnet, Pascal; Uricaru, Raluca; Dutour, Isabelle

    2015-01-01

    The revolution in high-throughput sequencing technologies has enabled the acquisition of gigabytes of RNA sequences in many different conditions and has highlighted an unexpected number of small RNAs (sRNAs) in bacteria. Ongoing exploitation of these data enables numerous applications for investigating bacterial transacting sRNA-mediated regulation networks. Focusing on sRNAs that regulate mRNA translation in trans, recent works have noted several sRNA-based regulatory pathways that are essential for key cellular processes. Although the number of known bacterial sRNAs is increasing, the experimental validation of their interactions with mRNA targets remains challenging and involves expensive and time-consuming experimental strategies. Hence, bioinformatics is crucial for selecting and prioritizing candidates before designing any experimental work. However, current software for target prediction produces a prohibitive number of candidates because of the lack of biological knowledge regarding the rules governing sRNA–mRNA interactions. Therefore, there is a real need to develop new approaches to help biologists focus on the most promising predicted sRNA–mRNA interactions. In this perspective, this review aims at presenting the advantages of mixing bioinformatics and visualization approaches for analyzing predicted sRNA-mediated regulatory bacterial networks. PMID:25477348

  6. Building long-term and high spatio-temporal resolution precipitation and air temperature reanalyses by mixing local observations and global atmospheric reanalyses: the ANATEM model

    NASA Astrophysics Data System (ADS)

    Mathevet, T.; Gailhard, J.; Kuentz, A.; Hingray, B.

    2015-12-01

    Efforts to improve the understanding of past climatic or hydrologic variability have received a great deal of attention in various fields of geosciences such as glaciology, dendrochronology, sedimentology and hydrology. Based on different proxies, each research community produces different kinds of climatic or hydrologic reanalyses at different spatio-temporal scales and resolutions. When considering climate or hydrology, many studies have been devoted to characterising variability, trends or breaks using observed time series representing different regions or climates of the world. However, in hydrology, these studies have usually been limited to short temporal scales (mainly a few decades and more rarely a century) because they require observed time series (which suffer from a limited spatio-temporal density). This paper introduces ANATEM, a method that combines local observations and large-scale climatic information (such as the 20CR Reanalysis) to build long-term probabilistic air temperature and precipitation time series with a high spatio-temporal resolution (1 day and a few km2). ANATEM was tested on the reconstruction of air temperature and precipitation time series of 22 watersheds situated in the Durance River basin, in the French Alps. Based on a multi-criteria and multi-scale diagnosis, the results show that ANATEM improves the performance of classical statistical models - especially concerning spatial homogeneity - while providing an original representation of uncertainties which are conditioned by atmospheric circulation patterns. The ANATEM model has been also evaluated for the regional scale against independent long-term time series and was able to capture regional low-frequency variability over more than a century (1883-2010). Citation: Kuentz, A., Mathevet, T., Gailhard, J., and Hingray, B.: Building long-term and high spatio-temporal resolution precipitation and air temperature reanalyses by mixing local observations and global atmospheric

  7. The small world phenomenon and assortative mixing in Polish corporate board and director networks

    NASA Astrophysics Data System (ADS)

    Sankowska, Anna; Siudak, Dariusz

    2016-02-01

    This paper investigates the corporate board and director networks in the Polish capital market in 2014. We examined real board and director networks in comparison with networks that were randomly constructed. Through empirical analyses, we demonstrated that the real networks have the characteristics of small-world networks. In addition, the networks are assortative and highly clustered, which imposes certain behaviors on them.

  8. Unpacking (In)formal Learning in an Academic Development Programme: A Mixed-Method Social Network Perspective

    ERIC Educational Resources Information Center

    Rienties, Bart; Hosein, Anesa

    2015-01-01

    How and with whom academics develop and maintain formal and informal networks for reflecting on their teaching practice has received limited attention even though academic development (AD) programmes have become an almost ubiquitous feature of higher education. The primary goal of this mixed-method study is to unpack how 114 academics in an AD…

  9. Attitudes toward Using Social Networking Sites in Educational Settings with Underperforming Latino Youth: A Mixed Methods Study

    ERIC Educational Resources Information Center

    Howard, Keith E.; Curwen, Margie Sauceda; Howard, Nicol R.; Colón-Muñiz, Anaida

    2015-01-01

    The researchers examined the online social networking attitudes of underperforming Latino high school students in an alternative education program that uses technology as the prime venue for learning. A sequential explanatory mixed methods study was used to cross-check multiple sources of data explaining students' levels of comfort with utilizing…

  10. Temporal dynamics of a commensal network of cavity-nesting vertebrates: increased diversity during an insect outbreak.

    PubMed

    Cockle, Kristina L; Martin, Kathy

    2015-04-01

    Network analysis offers insight into the structure and function of ecological communities, but little is known about how empirical networks change over time during perturbations. "Nest webs" are commensal networks that link secondary cavity-nesting vertebrates (e.g., bluebirds, ducks, and squirrels, which depend on tree cavities for nesting) with the excavators (e.g., woodpeckers) that produce cavities. In central British Columbia, Canada, Northern Flicker (Colaptes auratus) is considered a keystone excavator, providing most cavities for secondary cavity-nesters. However, roles of species in the network, and overall network architecture, are expected to vary with population fluctuations. Many excavator species increased in abundance in association with a pulse of food (adult and larval beetles) during an outbreak of mountain pine beetle (Dendroctonus ponderosae), which peaked in 2003-2004. We studied nest-web dynamics from 1998 to 2011 to determine how network architecture changed during this resource pulse. Cavity availability increased at the onset of the beetle outbreak and peaked in 2005. During and after the outbreak, secondary cavity-nesters increased their use of cavities made by five species of beetle-eating excavators, and decreased their use of flicker cavities. We found low link turnover, with 74% of links conserved from year to year. Nevertheless, the network increased in evenness and diversity of interactions, and declined slightly in nestedness and niche overlap. These patterns remained evident seven years after the beetle outbreak, suggesting a legacy effect. In contrast to previous snapshot studies of nest webs, our dynamic approach reveals how the role of each cavity producer, and thus quantitative network architecture, can vary over time. The increase in interaction diversity with the beetle outbreak adds to growing evidence that insect outbreaks can increase components of biodiversity in forest ecosystems at various temporal scales. The observed

  11. Sexual mixing patterns among social networks of HIV-positive and HIV-negative Beijing men who have sex with men: a multilevel comparison using roundtable network mapping.

    PubMed

    Ruan, Yuhua; Pan, Stephen W; Chamot, Eric; Qian, Han-Zhu; Li, Dongliang; Li, Qing-Chun; Liang, Hong-Yuan; Spittal, Patricia; Shao, Yiming; Kristensen, Sibylle

    2011-08-01

    Men who have sex with men (MSM) are of immediate concern in China's HIV epidemic. In 2008, approximately 2.5-6.5% of China's eight million MSM were HIV positive, while MSM represented 11% of all new HIV cases. Two factors that will in-part determine HIV-transmission dynamics among MSM, are sexual mixing patterns and the social networks which shape them. Sexual mixing patterns and social networks of Chinese MSM, however, remain poorly understood with little refined data available. One reason is that stigma discourages disclosure of names and identifiers to researchers. Using an alternative network-mapping approach, matched case-control design, and snowball sampling, this pilot study sought to compare characteristics of social networks of HIV-positive and HIV-negative Beijing MSM at the individual, dyad, and network levels. First, HIV-negative MSM controls were matched to HIV-positive MSM cases based on age, education, residency, and ethnicity. Then, each case or control and their MSM social network convened at a specific time and location with study investigators. Venues included health clinics, karaoke clubs, brothels, and community centers. Then, using arbitrarily assigned numbers in lieu of actual names, all participants simultaneously completed self-administered surveys regarding their sexual relationships with other participants of the same social network. These new findings indicate that cross-generational sex (anal or oral sex between men with ≥10 years age difference) was more prevalent among social networks of HIV-positive MSM, and was due to older age structure of the social network, rather than behavioral differences in sex-partner selection. Members of social networks of HIV-positive MSM were also less likely to have ever disclosed their MSM identity to non-MSM. Future studies should partner with MSM advocacy groups to explore behavioral and structural interventions as possible means of reducing the cross-generational sex and sexual identity

  12. Exponential synchronization of discontinuous neural networks with time-varying mixed delays via state feedback and impulsive control.

    PubMed

    Yang, Xinsong; Cao, Jinde; Ho, Daniel W C

    2015-04-01

    This paper investigates drive-response synchronization for a class of neural networks with time-varying discrete and distributed delays (mixed delays) as well as discontinuous activations. Strict mathematical proof shows the global existence of Filippov solutions to neural networks with discontinuous activation functions and the mixed delays. State feedback controller and impulsive controller are designed respectively to guarantee global exponential synchronization of the neural networks. By using Lyapunov function and new analysis techniques, several new synchronization criteria are obtained. Moreover, lower bound on the convergence rate is explicitly estimated when state feedback controller is utilized. Results of this paper are new and some existing ones are extended and improved. Finally, numerical simulations are given to verify the effectiveness of the theoretical results.

  13. Semi-Degradable Poly(β-amino ester) Networks with Temporally-Controlled Enhancement of Mechanical Properties

    PubMed Central

    Safranski, David L.; Weiss, Daiana; Clark, J. Brian; Taylor, W.R.; Gall, Ken

    2014-01-01

    Biodegradable polymers are clinically used in numerous biomedical applications, and classically show a loss in mechanical properties within weeks of implantation. This work demonstrates a new class of semi-degradable polymers that show an increase in mechanical properties through degradation via a controlled shift in a thermal transition. Semi-degradable polymer networks, poly(β-amino ester)-co-methyl methacrylate, were formed from a low glass transition temperature crosslinker, poly(β-amino ester), and high glass transition temperature monomer, methyl methacrylate, which degraded in a manner dependent upon the crosslinker chemical structure. In vitro and in vivo degradation revealed changes in mechanical behavior due to the degradation of the crosslinker from the polymer network. This novel polymer system demonstrates a strategy to temporally control the mechanical behavior of polymers and to enhance the initial performance of smart biomedical devices. PMID:24769113

  14. Experimental characterization of initial conditions and spatio-temporal evolution of a small Atwood number Rayleigh-Taylor mixing layer

    SciTech Connect

    Mueschke, N J; Andrews, M J; Schilling, O

    2005-09-26

    The initial multi-mode interfacial velocity and density perturbations present at the onset of a small Atwood number, incompressible, miscible, Rayleigh-Taylor instability-driven mixing layer have been quantified using a combination of experimental techniques. The streamwise interfacial and spanwise interfacial perturbations were measured using high-resolution thermocouples and planar laser-induced fluorescence (PLIF), respectively. The initial multi-mode streamwise velocity perturbations at the two-fluid density interface were measured using particle-image velocimetry (PIV). It was found that the measured initial conditions describe an initially anisotropic state, in which the perturbations in the streamwise and spanwise directions are independent of one another. The evolution of various fluctuating velocity and density statistics, together with velocity and density variance spectra, were measured using PIV and high-resolution thermocouple data. The evolution of the velocity and density statistics is used to investigate the early-time evolution and the onset of strongly-nonlinear, transitional dynamics within the mixing layer. The early-time evolution of the density and vertical velocity variance spectra indicate that velocity fluctuations are the dominant mechanism driving the instability development. The implications of the present experimental measurements on the initialization of Reynolds-averaged turbulent transport and mixing models and of direct and large-eddy simulations of Rayleigh-Taylor instability-induced turbulence are discussed.

  15. Experimental characterization of initial conditions and spatio-temporal evolution of a small Atwood number Rayleigh-Taylor mixing layer

    SciTech Connect

    Mueschke, N J; Andrews, M J; Schilling, O

    2006-03-24

    The initial multi-mode interfacial velocity and density perturbations present at the onset of a small Atwood number, incompressible, miscible, Rayleigh-Taylor instability-driven mixing layer have been quantified using a combination of experimental techniques. The streamwise interfacial and spanwise interfacial perturbations were measured using high-resolution thermocouples and planar laser-induced fluorescence (PLIF), respectively. The initial multi-mode streamwise velocity perturbations at the two-fluid density interface were measured using particle-image velocimetry (PIV). It was found that the measured initial conditions describe an initially anisotropic state, in which the perturbations in the streamwise and spanwise directions are independent of one another. The evolution of various fluctuating velocity and density statistics, together with velocity and density variance spectra, were measured using PIV and high-resolution thermocouple data. The evolution of the velocity and density statistics is used to investigate the early-time evolution and the onset of strongly-nonlinear, transitional dynamics within the mixing layer. The early-time evolution of the density and vertical velocity variance spectra indicate that velocity fluctuations are the dominant mechanism driving the instability development. The implications of the present experimental measurements on the initialization of Reynolds-averaged turbulent transport and mixing models and of direct and large-eddy simulations of Rayleigh-Taylor instability-induced turbulence are discussed.

  16. Investigating local controls on temporal stability of soil water content using sensor network data and an inverse modeling approach

    NASA Astrophysics Data System (ADS)

    Qu, W.; Bogena, H. R.; Huisman, J. A.; Martinez, G.; Pachepsky, Y. A.; Vereecken, H.

    2013-12-01

    Soil water content is a key variable in the soil, vegetation and atmosphere continuum with high spatial and temporal variability. Temporal stability of soil water content (SWC) has been observed in multiple monitoring studies and the quantification of controls on soil moisture variability and temporal stability presents substantial interest. The objective of this work was to assess the effect of soil hydraulic parameters on the temporal stability. The inverse modeling based on large observed time series SWC with in-situ sensor network was used to estimate the van Genuchten-Mualem (VGM) soil hydraulic parameters in a small grassland catchment located in western Germany. For the inverse modeling, the shuffled complex evaluation (SCE) optimization algorithm was coupled with the HYDRUS 1D code. We considered two cases: without and with prior information about the correlation between VGM parameters. The temporal stability of observed SWC was well pronounced at all observation depths. Both the spatial variability of SWC and the robustness of temporal stability increased with depth. Calibrated models both with and without prior information provided reasonable correspondence between simulated and measured time series of SWC. Furthermore, we found a linear relationship between the mean relative difference (MRD) of SWC and the saturated SWC (θs). Also, the logarithm of saturated hydraulic conductivity (Ks), the VGM parameter n and logarithm of α were strongly correlated with the MRD of saturation degree for the prior information case, but no correlation was found for the non-prior information case except at the 50cm depth. Based on these results we propose that establishing relationships between temporal stability and spatial variability of soil properties presents a promising research avenue for a better understanding of the controls on soil moisture variability. Correlation between Mean Relative Difference of soil water content (or saturation degree) and inversely

  17. Extraction of Temporal Networks from Term Co-Occurrences in Online Textual Sources

    PubMed Central

    Popović, Marko; Štefančić, Hrvoje; Sluban, Borut; Kralj Novak, Petra; Grčar, Miha; Mozetič, Igor; Puliga, Michelangelo; Zlatić, Vinko

    2014-01-01

    A stream of unstructured news can be a valuable source of hidden relations between different entities, such as financial institutions, countries, or persons. We present an approach to continuously collect online news, recognize relevant entities in them, and extract time-varying networks. The nodes of the network are the entities, and the links are their co-occurrences. We present a method to estimate the significance of co-occurrences, and a benchmark model against which their robustness is evaluated. The approach is applied to a large set of financial news, collected over a period of two years. The entities we consider are 50 countries which issue sovereign bonds, and which are insured by Credit Default Swaps (CDS) in turn. We compare the country co-occurrence networks to the CDS networks constructed from the correlations between the CDS. The results show relatively small, but significant overlap between the networks extracted from the news and those from the CDS correlations. PMID:25470498

  18. The Role of Short Term Synaptic Plasticity in Temporal Coding of Neuronal Networks

    ERIC Educational Resources Information Center

    Chandrasekaran, Lakshmi

    2008-01-01

    Short term synaptic plasticity is a phenomenon which is commonly found in the central nervous system. It could contribute to functions of signal processing namely, temporal integration and coincidence detection by modulating the input synaptic strength. This dissertation has two parts. First, we study the effects of short term synaptic plasticity…

  19. Distinct medial temporal networks encode surprise during motivation by reward versus punishment.

    PubMed

    Murty, Vishnu P; LaBar, Kevin S; Adcock, R Alison

    2016-10-01

    Adaptive motivated behavior requires predictive internal representations of the environment, and surprising events are indications for encoding new representations of the environment. The medial temporal lobe memory system, including the hippocampus and surrounding cortex, encodes surprising events and is influenced by motivational state. Because behavior reflects the goals of an individual, we investigated whether motivational valence (i.e., pursuing rewards versus avoiding punishments) also impacts neural and mnemonic encoding of surprising events. During functional magnetic resonance imaging (fMRI), participants encountered perceptually unexpected events either during the pursuit of rewards or avoidance of punishments. Despite similar levels of motivation across groups, reward and punishment facilitated the processing of surprising events in different medial temporal lobe regions. Whereas during reward motivation, perceptual surprises enhanced activation in the hippocampus, during punishment motivation surprises instead enhanced activation in parahippocampal cortex. Further, we found that reward motivation facilitated hippocampal coupling with ventromedial PFC, whereas punishment motivation facilitated parahippocampal cortical coupling with orbitofrontal cortex. Behaviorally, post-scan testing revealed that reward, but not punishment, motivation resulted in greater memory selectivity for surprising events encountered during goal pursuit. Together these findings demonstrate that neuromodulatory systems engaged by anticipation of reward and punishment target separate components of the medial temporal lobe, modulating medial temporal lobe sensitivity and connectivity. Thus, reward and punishment motivation yield distinct neural contexts for learning, with distinct consequences for how surprises are incorporated into predictive mnemonic models of the environment.

  20. Temporal Data-Driven Sleep Scheduling and Spatial Data-Driven Anomaly Detection for Clustered Wireless Sensor Networks

    PubMed Central

    Li, Gang; He, Bin; Huang, Hongwei; Tang, Limin

    2016-01-01

    The spatial–temporal correlation is an important feature of sensor data in wireless sensor networks (WSNs). Most of the existing works based on the spatial–temporal correlation can be divided into two parts: redundancy reduction and anomaly detection. These two parts are pursued separately in existing works. In this work, the combination of temporal data-driven sleep scheduling (TDSS) and spatial data-driven anomaly detection is proposed, where TDSS can reduce data redundancy. The TDSS model is inspired by transmission control protocol (TCP) congestion control. Based on long and linear cluster structure in the tunnel monitoring system, cooperative TDSS and spatial data-driven anomaly detection are then proposed. To realize synchronous acquisition in the same ring for analyzing the situation of every ring, TDSS is implemented in a cooperative way in the cluster. To keep the precision of sensor data, spatial data-driven anomaly detection based on the spatial correlation and Kriging method is realized to generate an anomaly indicator. The experiment results show that cooperative TDSS can realize non-uniform sensing effectively to reduce the energy consumption. In addition, spatial data-driven anomaly detection is quite significant for maintaining and improving the precision of sensor data. PMID:27690035

  1. Temporal Data-Driven Sleep Scheduling and Spatial Data-Driven Anomaly Detection for Clustered Wireless Sensor Networks.

    PubMed

    Li, Gang; He, Bin; Huang, Hongwei; Tang, Limin

    2016-09-28

    The spatial-temporal correlation is an important feature of sensor data in wireless sensor networks (WSNs). Most of the existing works based on the spatial-temporal correlation can be divided into two parts: redundancy reduction and anomaly detection. These two parts are pursued separately in existing works. In this work, the combination of temporal data-driven sleep scheduling (TDSS) and spatial data-driven anomaly detection is proposed, where TDSS can reduce data redundancy. The TDSS model is inspired by transmission control protocol (TCP) congestion control. Based on long and linear cluster structure in the tunnel monitoring system, cooperative TDSS and spatial data-driven anomaly detection are then proposed. To realize synchronous acquisition in the same ring for analyzing the situation of every ring, TDSS is implemented in a cooperative way in the cluster. To keep the precision of sensor data, spatial data-driven anomaly detection based on the spatial correlation and Kriging method is realized to generate an anomaly indicator. The experiment results show that cooperative TDSS can realize non-uniform sensing effectively to reduce the energy consumption. In addition, spatial data-driven anomaly detection is quite significant for maintaining and improving the precision of sensor data.

  2. Distinctive Structural and Effective Connectivity Changes of Semantic Cognition Network across Left and Right Mesial Temporal Lobe Epilepsy Patients

    PubMed Central

    Fan, Xiaotong; Shang, Kun; Wang, Xiaocui; Wang, Peipei; Shan, Yongzhi; Lu, Jie

    2016-01-01

    Occurrence of language impairment in mesial temporal lobe epilepsy (mTLE) patients is common and left mTLE patients always exhibit a primary problem with access to names. To explore different neuropsychological profiles between left and right mTLE patients, the study investigated both structural and effective functional connectivity changes within the semantic cognition network between these two groups and those from normal controls. We found that gray matter atrophy of left mTLE patients was more severe than that of right mTLE patients in the whole brain and especially within the semantic cognition network in their contralateral hemisphere. It suggested that seizure attacks were rather targeted than random for patients with hippocampal sclerosis (HS) in the dominant hemisphere. Functional connectivity analysis during resting state fMRI revealed that subregions of the anterior temporal lobe (ATL) in the left HS patients were no longer effectively connected. Further, we found that, unlike in right HS patients, increased causal linking between ipsilateral regions in the left HS epilepsy patients cannot make up for their decreased contralateral interaction. It suggested that weakened contralateral connection and disrupted effective interaction between subregions of the unitary, transmodal hub of the ATL may be the primary cause of anomia in the left HS patients. PMID:28018680

  3. Impact of numerical method on auto-ignition in a temporally evolving mixing layer at various initial conditions

    NASA Astrophysics Data System (ADS)

    Rosiak, A.; Tyliszczak, A.

    2016-10-01

    Numerical analysis of the auto-ignition of turbulent mixing layer between the cold fuel (hydrogen) and hot oxidizer (air) is presented. The research were performed using an Implicit-Large Eddy Simulation (ILES) method with attention on auto-ignition time, flame kernel localisation and propagation. We focused on an impact of discretization method on auto-ignition scenario and flame development. The results obtained showed that numerical approach plays an important role and to some extent may falsify the results, especially for low oxidiser temperatures.

  4. Development of a selective left-hemispheric fronto-temporal network for processing syntactic complexity in language comprehension

    PubMed Central

    Xiao, Yaqiong; Friederici, Angela D.; Margulies, Daniel S.; Brauer, Jens

    2016-01-01

    The development of language comprehension abilities in childhood is closely related to the maturation of the brain, especially the ability to process syntactically complex sentences. Recent studies proposed that the fronto-temporal connection within left perisylvian regions, supporting the processing of syntactically complex sentences, is still immature at preschool age. In the current study, resting state functional magnetic resonance imaging data were acquired from typically developing 5-year-old children and adults to shed further light on the brain functional development. Children additionally performed a behavioral syntactic comprehension test outside the scanner. The amplitude of low-frequency fluctuations was analyzed in order to identify the functional correlation networks of language-relevant brain regions. Results showed an intrahemispheric correlation between left inferior frontal gyrus (IFG) and left posterior superior temporal sulcus (pSTS) in adults, whereas an interhemispheric correlation between left IFG and its right-hemispheric homolog was predominant in children. Correlation analysis between resting-state functional connectivity and sentence processing performance in 5-year-olds revealed that local connectivity within the left IFG is associated with competence of processing syntactically simple canonical sentences, while long-range connectivity between IFG and pSTS in left hemisphere is associated with competence of processing syntactically relatively more complex non-canonical sentences. The present developmental data suggest that a selective left fronto-temporal connectivity network for processing complex syntax is already in functional connection at the age of 5 years when measured in a non-task situation. The correlational findings provide new insight into the relationship between intrinsic functional connectivity and syntactic language abilities in preschool children. PMID:26352468

  5. Development of a selective left-hemispheric fronto-temporal network for processing syntactic complexity in language comprehension.

    PubMed

    Xiao, Yaqiong; Friederici, Angela D; Margulies, Daniel S; Brauer, Jens

    2016-03-01

    The development of language comprehension abilities in childhood is closely related to the maturation of the brain, especially the ability to process syntactically complex sentences. Recent studies proposed that the fronto-temporal connection within left perisylvian regions, supporting the processing of syntactically complex sentences, is still immature at preschool age. In the current study, resting state functional magnetic resonance imaging data were acquired from typically developing 5-year-old children and adults to shed further light on the brain functional development. Children additionally performed a behavioral syntactic comprehension test outside the scanner. The amplitude of low-frequency fluctuations was analyzed in order to identify the functional correlation networks of language-relevant brain regions. Results showed an intrahemispheric correlation between left inferior frontal gyrus (IFG) and left posterior superior temporal sulcus (pSTS) in adults, whereas an interhemispheric correlation between left IFG and its right-hemispheric homolog was predominant in children. Correlation analysis between resting-state functional connectivity and sentence processing performance in 5-year-olds revealed that local connectivity within the left IFG is associated with competence of processing syntactically simple canonical sentences, while long-range connectivity between IFG and pSTS in left hemisphere is associated with competence of processing syntactically relatively more complex non-canonical sentences. The present developmental data suggest that a selective left fronto-temporal connectivity network for processing complex syntax is already in functional connection at the age of 5 years when measured in a non-task situation. The correlational findings provide new insight into the relationship between intrinsic functional connectivity and syntactic language abilities in preschool children.

  6. Modeling Temporal Variation in Social Network: An Evolutionary Web Graph Approach

    NASA Astrophysics Data System (ADS)

    Mitra, Susanta; Bagchi, Aditya

    A social network is a social structure between actors (individuals, organization or other social entities) and indicates the ways in which they are connected through various social relationships like friendships, kinships, professional, academic etc. Usually, a social network represents a social community, like a club and its members or a city and its citizens etc. or a research group communicating over Internet. In seventies Leinhardt [1] first proposed the idea of representing a social community by a digraph. Later, this idea became popular among other research workers like, network designers, web-service application developers and e-learning modelers. It gave rise to a rapid proliferation of research work in the area of social network analysis. Some of the notable structural properties of a social network are connectedness between actors, reachability between a source and a target actor, reciprocity or pair-wise connection between actors with bi-directional links, centrality of actors or the important actors having high degree or more connections and finally the division of actors into sub-structures or cliques or strongly-connected components. The cycles present in a social network may even be nested [2, 3]. The formal definition of these structural properties will be provided in Sect. 8.2.1. The division of actors into cliques or sub-groups can be a very important factor for understanding a social structure, particularly the degree of cohesiveness in a community. The number, size, and connections among the sub-groups in a network are useful in understanding how the network, as a whole, is likely to behave.

  7. Temporal and spatial characteristics of drainage fracture networks in elastic media with internal fluid generation

    NASA Astrophysics Data System (ADS)

    Dysthe, Dag Kristian; Kobchenko, Maya; Hafver, Andreas; Panahi, Hamed; Jamtveit, Bjørn; Renard, Francois

    2014-05-01

    Escape of internally generated fluids from low permeability elastic solids plays an important role in several natural environments. In geological systems, primary migration of hydrocarbons, dehydration of sediments and hydrated mantle rocks in subduction zones are examples where the existing permeability cannot accommodate transport of generated fluids in low permeability rocks and fluid pressure build-up may alter the permeability by fracturing. Fractures form and propagate in the rock due to internal pressure build-up. We have performed experiments on shales and model materials using X-ray microtomography, 2D imaging and pressure burst recordings to study the spatiotemporal evolution of drainage fracture networks and released fluids. The local growth of fractures due to internal pressure build up has been characterized and modeled. The spatial organization of the fracture networks have been characterized in a novel manner as intermediate between tree networks and hierarchical fracture networks. The dynamics of intermittent fluid release on the network show both periodic, 1/f and 1/f2 dependence of fluid release spectrum. Discrete element, algorithmic and finite element models have been used to reproduce different aspects of the drainage fracture network behavior.

  8. Alterations of the microvascular network in the sclerotic hippocampus of patients with temporal lobe epilepsy.

    PubMed

    Alonso-Nanclares, Lidia; DeFelipe, Javier

    2014-09-01

    Hippocampal sclerosis is the most frequent pathology encountered in resected tissue obtained from patients with temporal lobe epilepsy. The main hallmarks of hippocampal sclerosis are neuronal loss and gliosis. Several authors have proposed that an increase in blood vessel density is a further indicator, based on interpretations from staining of markers related to both blood-brain barrier disruption and the formation of new blood vessels. However, previous studies performed in our laboratory using correlative light and electron microscopy revealed that many of these "blood vessels" are in fact atrophic vascular structures with a reduced or virtually absent lumen and are often filled with processes of reactive astrocytes. Thus, "normal" vasculature within the sclerotic CA1 field is drastically reduced. Since this decrease is consistently observed in the human sclerotic CA1, this feature can be considered another key pathological indicator of hippocampal sclerosis associated with temporal lobe epilepsy.

  9. Spatio-temporal variability of non-regulated disinfection by-products within a drinking water distribution network.

    PubMed

    Mercier Shanks, Catherine; Sérodes, Jean-Baptiste; Rodriguez, Manuel J

    2013-06-01

    The non-regulated disinfection by-products (NrDBP) targeted in this study include four haloacetonitriles (trichloroacetonitrile (TCAN); dichloroacetonitrile (DCAN); bromochloroacetonitrile (BCAN) and dibromoacetonitrile (DBAN)); one halonitromethane (trichloronitromethane, better known under the name chloropicrin (CPK)); and two haloketones (1,1-dichloro-2-propanone (11DCPone) and 1,1,1-trichloro-2-propanone (111TCPone)). This study provides a detailed picture of the spatial and temporal variability of these NrDBP concentrations throughout a drinking water distribution system located in a region with major seasonal climate variations. The results obtained show that the concentrations of the investigated NrDBPs varied significantly according to time and location. The average concentrations of TCAN, DCAN, CKP and 111TCPone were significantly higher in summer. Surprisingly, the average concentrations of 11DCPone were significantly higher in winter. For BCAN and DBAN, the average concentrations observed in winter were higher, but not in a statistically significant way. On the other hand, the four HANs, CPK and 111TCPone generally had spatial profiles involving an increase of the concentrations along the network according to increasing water residence times, whereas 11DCPone overall had a profile where concentrations increased at the beginning of the network, followed by a drop in the concentrations towards the ends of the network. In spite of certain disparities in the individual spatio-temporal variation profiles, strong correlations were generally observed between NrDBPs, and trihalomethanes (THMs) and haloacetic acids (HAAs). Therefore, THMs and HAAs could be good statistical indicators of the presence of NrDBPs in the drinking water of the system under study.

  10. Discrimination of Dynamic Tactile Contact by Temporally Precise Event Sensing in Spiking Neuromorphic Networks

    PubMed Central

    Lee, Wang Wei; Kukreja, Sunil L.; Thakor, Nitish V.

    2017-01-01

    This paper presents a neuromorphic tactile encoding methodology that utilizes a temporally precise event-based representation of sensory signals. We introduce a novel concept where touch signals are characterized as patterns of millisecond precise binary events to denote pressure changes. This approach is amenable to a sparse signal representation and enables the extraction of relevant features from thousands of sensing elements with sub-millisecond temporal precision. We also proposed measures adopted from computational neuroscience to study the information content within the spiking representations of artificial tactile signals. Implemented on a state-of-the-art 4096 element tactile sensor array with 5.2 kHz sampling frequency, we demonstrate the classification of transient impact events while utilizing 20 times less communication bandwidth compared to frame based representations. Spiking sensor responses to a large library of contact conditions were also synthesized using finite element simulations, illustrating an 8-fold improvement in information content and a 4-fold reduction in classification latency when millisecond-precise temporal structures are available. Our research represents a significant advance, demonstrating that a neuromorphic spatiotemporal representation of touch is well suited to rapid identification of critical contact events, making it suitable for dynamic tactile sensing in robotic and prosthetic applications. PMID:28197065

  11. Coding of odors by temporal binding within a model network of the locust antennal lobe

    PubMed Central

    Patel, Mainak J.; Rangan, Aaditya V.; Cai, David

    2013-01-01

    The locust olfactory system interfaces with the external world through antennal receptor neurons (ORNs), which represent odors in a distributed, combinatorial manner. ORN axons bundle together to form the antennal nerve, which relays sensory information centrally to the antennal lobe (AL). Within the AL, an odor generates a dynamically evolving ensemble of active cells, leading to a stimulus-specific temporal progression of neuronal spiking. This experimental observation has led to the hypothesis that an odor is encoded within the AL by a dynamically evolving trajectory of projection neuron (PN) activity that can be decoded piecewise to ascertain odor identity. In order to study information coding within the locust AL, we developed a scaled-down model of the locust AL using Hodgkin–Huxley-type neurons and biologically realistic connectivity parameters and current components. Using our model, we examined correlations in the precise timing of spikes across multiple neurons, and our results suggest an alternative to the dynamic trajectory hypothesis. We propose that the dynamical interplay of fast and slow inhibition within the locust AL induces temporally stable correlations in the spiking activity of an odor-dependent neural subset, giving rise to a temporal binding code that allows rapid stimulus detection by downstream elements. PMID:23630495

  12. Topology of Plant - Flower-Visitor Networks in a Tropical Mountain Forest: Insights on the Role of Altitudinal and Temporal Variation

    PubMed Central

    Cuartas-Hernández, Sandra; Medel, Rodrigo

    2015-01-01

    Understanding the factors determining the spatial and temporal variation of ecological networks is fundamental to the knowledge of their dynamics and functioning. In this study, we evaluate the effect of elevation and time on the structure of plant-flower-visitor networks in a Colombian mountain forest. We examine the level of generalization of plant and animal species and the identity of interactions in 44 bipartite matrices obtained from eight altitudinal levels, from 2200 to 2900 m during eight consecutive months. The contribution of altitude and time to the overall variation in the number of plant (P) and pollinator (A) species, network size (M), number of interactions (I), connectance (C), and nestedness was evaluated. In general, networks were small, showed high connectance values and non-nested patterns of organization. Variation in P, M, I and C was better accounted by time than elevation, seemingly related to temporal variation in precipitation. Most plant and insect species were specialists and the identity of links showed a high turnover over months and at every 100 m elevation. The partition of the whole system into smaller network units allowed us to detect small-scale patterns of interaction that contrasted with patterns commonly described in cumulative networks. The specialized but erratic pattern of network organization observed in this tropical mountain suggests that high connectance coupled with opportunistic attachment may confer robustness to plant-flower-visitor networks occurring at small spatial and temporal units. PMID:26513664

  13. Social Networking Sites as Virtual Communities of Practice: A Mixed Method Study

    ERIC Educational Resources Information Center

    Davis, Lorretta J.

    2010-01-01

    Membership in social networking sites is increasing rapidly. Social networking sites serve many purposes including networking, communication, recruitment, and sharing knowledge. Social networking sites, public or private, may be hosted on applications such as Facebook and LinkedIn. As individuals begin to follow and participate in social…

  14. Global Spatio-Temporal Patterns in Human Migration: A Complex Network Perspective

    PubMed Central

    Davis, Kyle F.; D'Odorico, Paolo; Laio, Francesco; Ridolfi, Luca

    2013-01-01

    Migration is a powerful adaptive strategy for humans to navigate hardship and pursue a better quality of life. As a universal vehicle facilitating exchanges of ideas, culture, money and goods, international migration is a major contributor to globalization. Consisting of countries linked by multiple connections of human movements, global migration constitutes a network. Despite the important role of human migration in connecting various communities in different parts of the world, the topology and behavior of the international migration network and its changes through time remain poorly understood. Here we show that the global human migration network became more interconnected during the latter half of the twentieth century and that migrant destination choice partly reflects colonial and postcolonial histories, language, religion, and distances. From 1960 to 2000 we found a steady increase in network transitivity (i.e. connectivity between nodes connected to the same node), a decrease in average path length and an upward shift in degree distribution, all of which strengthened the ‘small-world’ behavior of the migration network. Furthermore, we found that distinct groups of countries preferentially interact to form migration communities based largely on historical, cultural and economic factors. PMID:23372664

  15. Exotic plant infestation is associated with decreased modularity and increased numbers of connectors in mixed-grass prairie pollination networks

    USGS Publications Warehouse

    Larson, Diane L.; Rabie, Paul A.; Droege, Sam; Larson, Jennifer L.; Haar, Milton

    2016-01-01

    The majority of pollinating insects are generalists whose lifetimes overlap flowering periods of many potentially suitable plant species. Such generality is instrumental in allowing exotic plant species to invade pollination networks. The particulars of how existing networks change in response to an invasive plant over the course of its phenology are not well characterized, but may shed light on the probability of long-term effects on plant-pollinator interactions and the stability of network structure. Here we describe changes in network topology and modular structure of infested and non-infested networks during the flowering season of the generalist non-native flowering plant, Cirsium arvense in mixed-grass prairie at Badlands National Park, South Dakota, USA. Objectives were to compare network-level effects of infestation as they propagate over the season in infested and non-infested (with respect to C. arvense) networks. We characterized plant-pollinator networks on 5 non-infested and 7 infested 1-ha plots during 4 sample periods that collectively covered the length of C. arvense flowering period. Two other abundantly-flowering invasive plants were present during this time: Melilotus officinalis had highly variable floral abundance in both C. arvense-infested and non-infested plots andConvolvulus arvensis, which occurred almost exclusively in infested plots and peaked early in the season. Modularity, including roles of individual species, and network topology were assessed for each sample period as well as in pooled infested and non-infested networks. Differences in modularity and network metrics between infested and non-infested networks were limited to the third and fourth sample periods, during flower senescence of C. arvenseand the other invasive species; generality of pollinators rose concurrently, suggesting rewiring of the network and a lag effect of earlier floral abundance. Modularity was lower and number of connectors higher in infested

  16. Exotic Plant Infestation Is Associated with Decreased Modularity and Increased Numbers of Connectors in Mixed-Grass Prairie Pollination Networks

    PubMed Central

    Larson, Diane L.; Rabie, Paul A.; Droege, Sam; Larson, Jennifer L.; Haar, Milton

    2016-01-01

    The majority of pollinating insects are generalists whose lifetimes overlap flowering periods of many potentially suitable plant species. Such generality is instrumental in allowing exotic plant species to invade pollination networks. The particulars of how existing networks change in response to an invasive plant over the course of its phenology are not well characterized, but may shed light on the probability of long-term effects on plant-pollinator interactions and the stability of network structure. Here we describe changes in network topology and modular structure of infested and non-infested networks during the flowering season of the generalist non-native flowering plant, Cirsium arvense in mixed-grass prairie at Badlands National Park, South Dakota, USA. Objectives were to compare network-level effects of infestation as they propagate over the season in infested and non-infested (with respect to C. arvense) networks. We characterized plant-pollinator networks on 5 non-infested and 7 infested 1-ha plots during 4 sample periods that collectively covered the length of C. arvense flowering period. Two other abundantly-flowering invasive plants were present during this time: Melilotus officinalis had highly variable floral abundance in both C. arvense-infested and non-infested plots and Convolvulus arvensis, which occurred almost exclusively in infested plots and peaked early in the season. Modularity, including roles of individual species, and network topology were assessed for each sample period as well as in pooled infested and non-infested networks. Differences in modularity and network metrics between infested and non-infested networks were limited to the third and fourth sample periods, during flower senescence of C. arvense and the other invasive species; generality of pollinators rose concurrently, suggesting rewiring of the network and a lag effect of earlier floral abundance. Modularity was lower and number of connectors higher in infested networks

  17. High-Speed Planning Method for Cooperative Logistics Networks using Mixed Integer Programming Model and Dummy Load

    NASA Astrophysics Data System (ADS)

    Onoyama, Takashi; Kubota, Sen; Maekawa, Takuya; Komoda, Norihisa

    Adequate response performance is required for the planning of a cooperative logistic network covering multiple enterprises, because this process needs a human expert's evaluation from many aspects. To satisfy this requirement, we propose an accurate model based on mixed integer programming for optimizing cooperative logistics networks where “round transportation” exists together with “depot transportation” including lower limit constraints of loading ratio for round transportation vehicles. Furthermore, to achieve interactive response performance, a dummy load is introduced into the model instead of integer variables. The experimental result shows the proposed method obtains an accurate solution within interactive response time.

  18. Spatio-Temporal Analysis of Micro Economic Activities in Rome Reveals Patterns of Mixed-Use Urban Evolution.

    PubMed

    Fiasconaro, Alessandro; Strano, Emanuele; Nicosia, Vincenzo; Porta, Sergio; Latora, Vito

    2016-01-01

    Understanding urban growth is one with understanding how society evolves to satisfy the needs of its individuals in sharing a common space and adapting to the territory. We propose here a quantitative analysis of the historical development of a large urban area by investigating the spatial distribution and the age of commercial activities in the whole city of Rome. We find that the age of activities of various categories presents a very interesting double exponential trend, with a transition possibly related to the long-term economical effects determined by the oil crisis of the Seventies. The diversification of commercial categories, studied through various measures of entropy, shows, among other interesting features, a saturating behaviour with the density of activities. Moreover, different couples of commercial categories exhibit over the years a tendency to attract in space. Our results demonstrate that the spatio-temporal distribution of commercial activities can provide important insights on the urbanisation processes at work, revealing specific and non trivial socio-economical dynamics, as the presence of crisis periods and expansion trends, and contributing to the characterisation of the maturity of urban areas.

  19. Spatio-Temporal Analysis of Micro Economic Activities in Rome Reveals Patterns of Mixed-Use Urban Evolution

    PubMed Central

    Fiasconaro, Alessandro; Strano, Emanuele; Nicosia, Vincenzo; Porta, Sergio; Latora, Vito

    2016-01-01

    Understanding urban growth is one with understanding how society evolves to satisfy the needs of its individuals in sharing a common space and adapting to the territory. We propose here a quantitative analysis of the historical development of a large urban area by investigating the spatial distribution and the age of commercial activities in the whole city of Rome. We find that the age of activities of various categories presents a very interesting double exponential trend, with a transition possibly related to the long-term economical effects determined by the oil crisis of the Seventies. The diversification of commercial categories, studied through various measures of entropy, shows, among other interesting features, a saturating behaviour with the density of activities. Moreover, different couples of commercial categories exhibit over the years a tendency to attract in space. Our results demonstrate that the spatio-temporal distribution of commercial activities can provide important insights on the urbanisation processes at work, revealing specific and non trivial socio-economical dynamics, as the presence of crisis periods and expansion trends, and contributing to the characterisation of the maturity of urban areas. PMID:26982028

  20. Determination of Spatio-Temporal Characteristics of D-region Electron Density during Annular Solar Eclipse from VLF Network Observations

    NASA Astrophysics Data System (ADS)

    Basak, T.; Hobara, Y.

    2015-12-01

    A major part of the path of the annular solar eclipse of May 20, 2012 (magnitude 0.9439) was over southern Japan. The D-region ionospheric changes associated with that eclipse, led to several degree of observable perturbations of sub-ionospheric very low frequency (VLF) radio signal. The University of Electro-Communications (UEC) operates VLF observation network over Japan. The solar eclipse associated signal changes were recorded in several receiving stations (Rx) simultaneously for the VLF signals coming from NWC/19.8kHz, JJI/22.2kHz, JJY/40.0kHz, NLK/24.8kHz and other VLF transmitters (Tx). These temporal dependences of VLF signal perturbation have been analyzed and the spatio-temporal characteristics of respective sub-ionospheric perturbations has already been studied by earlier workers using 2D-Finite Difference Time Domain method of simulation. In this work, we determine the spatial scale, depth and temporal dependence of lower ionospheric perturbation in consistence with umbral and penumbral motion. We considered the 2-parameter D-region ionospheric model with exponential electron density profile. To model the solar obscuration effect over it, we assumed a generalized space-time dependent 2-dimensional elliptical Gaussian distribution for ionospheric parameters, such as, effective reflection height (h') and sharpness factor (β). The depth (△hmax, △βmax), center of shadow (lato(t), lono(t)) and spatial scale (σlat,lon) of that Gaussian distribution are used as model parameters. In the vicinity of the eclipse zone, we compute the VLF signal perturbations using Long Wave Propagation Capability (LWPC) code for several signal propagation paths. The propagation path characteristics, such as, ground and water conductivity and geomagnetic effect on ionosphere are considered from standard LWPC prescriptions. The model parameters are tuned to set an optimum agreement between our computation and observed positive and negative type of VLF perturbations. Thus

  1. Large Scale Functional Brain Networks Underlying Temporal Integration of Audio-Visual Speech Perception: An EEG Study.

    PubMed

    Kumar, G Vinodh; Halder, Tamesh; Jaiswal, Amit K; Mukherjee, Abhishek; Roy, Dipanjan; Banerjee, Arpan

    2016-01-01

    Observable lip movements of the speaker influence perception of auditory speech. A classical example of this influence is reported by listeners who perceive an illusory (cross-modal) speech sound (McGurk-effect) when presented with incongruent audio-visual (AV) speech stimuli. Recent neuroimaging studies of AV speech perception accentuate the role of frontal, parietal, and the integrative brain sites in the vicinity of the superior temporal sulcus (STS) for multisensory speech perception. However, if and how does the network across the whole brain participates during multisensory perception processing remains an open question. We posit that a large-scale functional connectivity among the neural population situated in distributed brain sites may provide valuable insights involved in processing and fusing of AV speech. Varying the psychophysical parameters in tandem with electroencephalogram (EEG) recordings, we exploited the trial-by-trial perceptual variability of incongruent audio-visual (AV) speech stimuli to identify the characteristics of the large-scale cortical network that facilitates multisensory perception during synchronous and asynchronous AV speech. We evaluated the spectral landscape of EEG signals during multisensory speech perception at varying AV lags. Functional connectivity dynamics for all sensor pairs was computed using the time-frequency global coherence, the vector sum of pairwise coherence changes over time. During synchronous AV speech, we observed enhanced global gamma-band coherence and decreased alpha and beta-band coherence underlying cross-modal (illusory) perception compared to unisensory perception around a temporal window of 300-600 ms following onset of stimuli. During asynchronous speech stimuli, a global broadband coherence was observed during cross-modal perception at earlier times along with pre-stimulus decreases of lower frequency power, e.g., alpha rhythms for positive AV lags and theta rhythms for negative AV lags. Thus, our

  2. Large Scale Functional Brain Networks Underlying Temporal Integration of Audio-Visual Speech Perception: An EEG Study

    PubMed Central

    Kumar, G. Vinodh; Halder, Tamesh; Jaiswal, Amit K.; Mukherjee, Abhishek; Roy, Dipanjan; Banerjee, Arpan

    2016-01-01

    Observable lip movements of the speaker influence perception of auditory speech. A classical example of this influence is reported by listeners who perceive an illusory (cross-modal) speech sound (McGurk-effect) when presented with incongruent audio-visual (AV) speech stimuli. Recent neuroimaging studies of AV speech perception accentuate the role of frontal, parietal, and the integrative brain sites in the vicinity of the superior temporal sulcus (STS) for multisensory speech perception. However, if and how does the network across the whole brain participates during multisensory perception processing remains an open question. We posit that a large-scale functional connectivity among the neural population situated in distributed brain sites may provide valuable insights involved in processing and fusing of AV speech. Varying the psychophysical parameters in tandem with electroencephalogram (EEG) recordings, we exploited the trial-by-trial perceptual variability of incongruent audio-visual (AV) speech stimuli to identify the characteristics of the large-scale cortical network that facilitates multisensory perception during synchronous and asynchronous AV speech. We evaluated the spectral landscape of EEG signals during multisensory speech perception at varying AV lags. Functional connectivity dynamics for all sensor pairs was computed using the time-frequency global coherence, the vector sum of pairwise coherence changes over time. During synchronous AV speech, we observed enhanced global gamma-band coherence and decreased alpha and beta-band coherence underlying cross-modal (illusory) perception compared to unisensory perception around a temporal window of 300–600 ms following onset of stimuli. During asynchronous speech stimuli, a global broadband coherence was observed during cross-modal perception at earlier times along with pre-stimulus decreases of lower frequency power, e.g., alpha rhythms for positive AV lags and theta rhythms for negative AV lags. Thus

  3. Linear eddy mixing based tabulation and artificial neural networks for large eddy simulations of turbulent flames

    SciTech Connect

    Sen, Baris Ali; Menon, Suresh

    2010-01-15

    A large eddy simulation (LES) sub-grid model is developed based on the artificial neural network (ANN) approach to calculate the species instantaneous reaction rates for multi-step, multi-species chemical kinetics mechanisms. The proposed methodology depends on training the ANNs off-line on a thermo-chemical database representative of the actual composition and turbulence (but not the actual geometrical problem) of interest, and later using them to replace the stiff ODE solver (direct integration (DI)) to calculate the reaction rates in the sub-grid. The thermo-chemical database is tabulated with respect to the thermodynamic state vector without any reduction in the number of state variables. The thermo-chemistry is evolved by stand-alone linear eddy mixing (LEM) model simulations under both premixed and non-premixed conditions, where the unsteady interaction of turbulence with chemical kinetics is included as a part of the training database. The proposed methodology is tested in LES and in stand-alone LEM studies of three distinct test cases with different reduced mechanisms and conditions. LES of premixed flame-turbulence-vortex interaction provides direct comparison of the proposed ANN method against DI and ANNs trained on thermo-chemical database created using another type of tabulation method. It is shown that the ANN trained on the LEM database can capture the correct flame physics with accuracy comparable to DI, which cannot be achieved by ANN trained on a laminar premix flame database. A priori evaluation of the ANN generality within and outside its training domain is carried out using stand-alone LEM simulations as well. Results in general are satisfactory, and it is shown that the ANN provides considerable amount of memory saving and speed-up with reasonable and reliable accuracy. The speed-up is strongly affected by the stiffness of the reduced mechanism used for the computations, whereas the memory saving is considerable regardless. (author)

  4. Singular Hopf bifurcations and mixed-mode oscillations in a two-cell inhibitory neural network

    NASA Astrophysics Data System (ADS)

    Curtu, Rodica

    2010-05-01

    Recent studies of a firing rate model for neural competition as observed in binocular rivalry and central pattern generators [R. Curtu, A. Shpiro, N. Rubin, J. Rinzel, Mechanisms for frequency control in neuronal competition models, SIAM J. Appl. Dyn. Syst. 7 (2) (2008) 609-649] showed that the variation of the stimulus strength parameter can lead to rich and interesting dynamics. Several types of behavior were identified such as: fusion, equivalent to a steady state of identical activity levels for both neural units; oscillations due to either an escape or a release mechanism; and a winner-take-all state of bistability. The model consists of two neural populations interacting through reciprocal inhibition, each endowed with a slow negative-feedback process in the form of spike frequency adaptation. In this paper we report the occurrence of another complex oscillatory pattern, the mixed-mode oscillations (MMOs). They exist in the model at the transition between the relaxation oscillator dynamical regime and the winner-take-all regime. The system distinguishes itself from other neuronal models where MMOs were found by the following interesting feature: there is no autocatalysis involved (as in the examples of voltage-gated persistent inward currents and/or intrapopulation recurrent excitation) and therefore the two cells in the network are not intrinsic oscillators; the oscillations are instead a combined result of the mutual inhibition and the adaptation. We prove that the MMOs are due to a singular Hopf bifurcation point situated in close distance to the transition point to the winner-take-all case. We also show that in the vicinity of the singular Hopf other types of bifurcations exist and we construct numerically the corresponding diagrams.

  5. Network Adaptation Improves Temporal Representation of Naturalistic Stimuli in Drosophila Eye: II Mechanisms

    PubMed Central

    Wardill, Trevor J.; O'Kane, Cahir J.; de Polavieja, Gonzalo G.; Juusola, Mikko

    2009-01-01

    Retinal networks must adapt constantly to best present the ever changing visual world to the brain. Here we test the hypothesis that adaptation is a result of different mechanisms at several synaptic connections within the network. In a companion paper (Part I), we showed that adaptation in the photoreceptors (R1–R6) and large monopolar cells (LMC) of the Drosophila eye improves sensitivity to under-represented signals in seconds by enhancing both the amplitude and frequency distribution of LMCs' voltage responses to repeated naturalistic contrast series. In this paper, we show that such adaptation needs both the light-mediated conductance and feedback-mediated synaptic conductance. A faulty feedforward pathway in histamine receptor mutant flies speeds up the LMC output, mimicking extreme light adaptation. A faulty feedback pathway from L2 LMCs to photoreceptors slows down the LMC output, mimicking dark adaptation. These results underline the importance of network adaptation for efficient coding, and as a mechanism for selectively regulating the size and speed of signals in neurons. We suggest that concert action of many different mechanisms and neural connections are responsible for adaptation to visual stimuli. Further, our results demonstrate the need for detailed circuit reconstructions like that of the Drosophila lamina, to understand how networks process information. PMID:19180195

  6. Temporal change of EIA asymmetry revealed by a beacon receiver network in Southeast Asia

    NASA Astrophysics Data System (ADS)

    Watthanasangmechai, Kornyanat; Yamamoto, Mamoru; Saito, Akinori; Maruyama, Takashi; Yokoyama, Tatsuhiro; Nishioka, Michi; Ishii, Mamoru

    2015-05-01

    To reveal the temporal change of the equatorial ionization anomaly (EIA) asymmetry, a multipoint satellite-ground beacon experiment was conducted along the meridional plane of the Thailand-Indonesia sector. The observation includes one station near the magnetic equator and four stations at off-equator latitudes. This is the first EIA asymmetry study with high spatial resolution using GNU Radio Beacon Receiver (GRBR) observations in Southeast Asia. GRBR-total electron contents (TECs) from 97 polar-orbit satellite passes in March 2012 were analyzed in this study. Successive passes captured rapid evolution of EIA asymmetry, especially during geomagnetic disturbances. The penetrating electric fields that occur during geomagnetic disturbed days are not the cause of the asymmetry. Instead, high background TEC associated with an intense electric field empowers the neutral wind to produce severe asymmetry of the EIA. Such rapid evolution of EIA asymmetry was not seen during nighttime, when meridional wind mainly controlled the asymmetric structures. Additional data are necessary to identify the source of the variations, i.e., atmospheric waves. Precisely capturing the locations of the crests and the evolution of the asymmetry enhances understanding of the temporal change of EIA asymmetry at the local scale and leads to a future local modeling for TEC prediction in Southeast Asia.

  7. An Advanced Temporal Credential-Based Security Scheme with Mutual Authentication and Key Agreement for Wireless Sensor Networks

    PubMed Central

    Li, Chun-Ta; Weng, Chi-Yao; Lee, Cheng-Chi

    2013-01-01

    Wireless sensor networks (WSNs) can be quickly and randomly deployed in any harsh and unattended environment and only authorized users are allowed to access reliable sensor nodes in WSNs with the aid of gateways (GWNs). Secure authentication models among the users, the sensor nodes and GWN are important research issues for ensuring communication security and data privacy in WSNs. In 2013, Xue et al. proposed a temporal-credential-based mutual authentication and key agreement scheme for WSNs. However, in this paper, we point out that Xue et al.'s scheme cannot resist stolen-verifier, insider, off-line password guessing, smart card lost problem and many logged-in users' attacks and these security weaknesses make the scheme inapplicable to practical WSN applications. To tackle these problems, we suggest a simple countermeasure to prevent proposed attacks while the other merits of Xue et al.'s authentication scheme are left unchanged. PMID:23887085

  8. An advanced temporal credential-based security scheme with mutual authentication and key agreement for wireless sensor networks.

    PubMed

    Li, Chun-Ta; Weng, Chi-Yao; Lee, Cheng-Chi

    2013-07-24

    Wireless sensor networks (WSNs) can be quickly and randomly deployed in any harsh and unattended environment and only authorized users are allowed to access reliable sensor nodes in WSNs with the aid of gateways (GWNs). Secure authentication models among the users, the sensor nodes and GWN are important research issues for ensuring communication security and data privacy in WSNs. In 2013, Xue et al. proposed a temporal-credential-based mutual authentication and key agreement scheme for WSNs. However, in this paper, we point out that Xue et al.'s scheme cannot resist stolen-verifier, insider, off-line password guessing, smart card lost problem and many logged-in users' attacks and these security weaknesses make the scheme inapplicable to practical WSN applications. To tackle these problems, we suggest a simple countermeasure to prevent proposed attacks while the other merits of Xue et al.'s authentication scheme are left unchanged.

  9. At the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters

    PubMed Central

    Rössert, Christian; Dean, Paul; Porrill, John

    2015-01-01

    Models of the cerebellar microcircuit often assume that input signals from the mossy-fibers are expanded and recoded to provide a foundation from which the Purkinje cells can synthesize output filters to implement specific input-signal transformations. Details of this process are however unclear. While previous work has shown that recurrent granule cell inhibition could in principle generate a wide variety of random outputs suitable for coding signal onsets, the more general application for temporally varying signals has yet to be demonstrated. Here we show for the first time that using a mechanism very similar to reservoir computing enables random neuronal networks in the granule cell layer to provide the necessary signal separation and extension from which Purkinje cells could construct basis filters of various time-constants. The main requirement for this is that the network operates in a state of criticality close to the edge of random chaotic behavior. We further show that the lack of recurrent excitation in the granular layer as commonly required in traditional reservoir networks can be circumvented by considering other inherent granular layer features such as inverted input signals or mGluR2 inhibition of Golgi cells. Other properties that facilitate filter construction are direct mossy fiber excitation of Golgi cells, variability of synaptic weights or input signals and output-feedback via the nucleocortical pathway. Our findings are well supported by previous experimental and theoretical work and will help to bridge the gap between system-level models and detailed models of the granular layer network. PMID:26484859

  10. Time-Dependent Flow Properties of Transient Hydrogels with Temporal Network Junctions

    NASA Astrophysics Data System (ADS)

    Kaneda, Isamu; Koga, Tsuyoshi; Tanaka, Fumihiko

    The nonlinear rheological behavior under startup shear flows in aqueous solutions of telechelic hydrophobically ethoxylated urethane carrying branched alkyl end-chains, 2-decyl-tetradecyl, (referred to as C24-HEUR) was studied by strain-controlled rheomery. Unusual stress upturn (known as strain hardening), followed by the stress overshoot, was observed for 3 wt% aqueous solution above a critical shear rate. The phenomenon is explained by our recent transient network theory in terms of nonlinear chain stretching occurring during the stress build-up. Upon addition of glycerol, the relaxation time was shortened, while the equilibrium modulus increased with the concentration of glycerol. The stress upturn disappeared above a certain value of the glycerol concentration, strongly suggesting that glycerol affects the dynamics of the transient network through the interaction with the micellar junctions.

  11. Synthesis of neural networks for spatio-temporal spike pattern recognition and processing

    PubMed Central

    Tapson, Jonathan C.; Cohen, Greg K.; Afshar, Saeed; Stiefel, Klaus M.; Buskila, Yossi; Wang, Runchun Mark; Hamilton, Tara J.; van Schaik, André

    2013-01-01

    The advent of large scale neural computational platforms has highlighted the lack of algorithms for synthesis of neural structures to perform predefined cognitive tasks. The Neural Engineering Framework (NEF) offers one such synthesis, but it is most effective for a spike rate representation of neural information, and it requires a large number of neurons to implement simple functions. We describe a neural network synthesis method that generates synaptic connectivity for neurons which process time-encoded neural signals, and which makes very sparse use of neurons. The method allows the user to specify—arbitrarily—neuronal characteristics such as axonal and dendritic delays, and synaptic transfer functions, and then solves for the optimal input-output relationship using computed dendritic weights. The method may be used for batch or online learning and has an extremely fast optimization process. We demonstrate its use in generating a network to recognize speech which is sparsely encoded as spike times. PMID:24009550

  12. Synthesis of neural networks for spatio-temporal spike pattern recognition and processing.

    PubMed

    Tapson, Jonathan C; Cohen, Greg K; Afshar, Saeed; Stiefel, Klaus M; Buskila, Yossi; Wang, Runchun Mark; Hamilton, Tara J; van Schaik, André

    2013-01-01

    The advent of large scale neural computational platforms has highlighted the lack of algorithms for synthesis of neural structures to perform predefined cognitive tasks. The Neural Engineering Framework (NEF) offers one such synthesis, but it is most effective for a spike rate representation of neural information, and it requires a large number of neurons to implement simple functions. We describe a neural network synthesis method that generates synaptic connectivity for neurons which process time-encoded neural signals, and which makes very sparse use of neurons. The method allows the user to specify-arbitrarily-neuronal characteristics such as axonal and dendritic delays, and synaptic transfer functions, and then solves for the optimal input-output relationship using computed dendritic weights. The method may be used for batch or online learning and has an extremely fast optimization process. We demonstrate its use in generating a network to recognize speech which is sparsely encoded as spike times.

  13. Spatio-temporal functional data analysis for wireless sensor networks data.

    PubMed

    Lee, D-J; Zhu, Z; Toscas, P

    2015-08-01

    A new methodology is proposed for the analysis, modeling and forecasting of data collected from a wireless sensor network. Our approach is considered in the framework of a functional data analysis paradigm where observed data is represented in a functional form. To reduce dimensionality, functional principal components analysis is applied to highlight important underlying characteristics and find patterns of variations. The principal scores are modeled with tensor product smooths that allow for smoothing over space and time. The model is then used for simultaneous spatial prediction at unsampled locations and to forecast future observations. We consider soil temperature data from a wireless sensor network of 50 sensor nodes in two different land types (grassland and forest) observed during 60 consecutive days in private property close to Yass, New South Wales, Australia.

  14. Spatial and temporal mapping of de novo mutations in schizophrenia to a fetal prefrontal cortical network.

    PubMed

    Gulsuner, Suleyman; Walsh, Tom; Watts, Amanda C; Lee, Ming K; Thornton, Anne M; Casadei, Silvia; Rippey, Caitlin; Shahin, Hashem; Nimgaonkar, Vishwajit L; Go, Rodney C P; Savage, Robert M; Swerdlow, Neal R; Gur, Raquel E; Braff, David L; King, Mary-Claire; McClellan, Jon M

    2013-08-01

    Genes disrupted in schizophrenia may be revealed by de novo mutations in affected persons from otherwise healthy families. Furthermore, during normal brain development, genes are expressed in patterns specific to developmental stage and neuroanatomical structure. We identified de novo mutations in persons with schizophrenia and then mapped the responsible genes onto transcriptome profiles of normal human brain tissues from age 13 weeks gestation to adulthood. In the dorsolateral and ventrolateral prefrontal cortex during fetal development, genes harboring damaging de novo mutations in schizophrenia formed a network significantly enriched for transcriptional coexpression and protein interaction. The 50 genes in the network function in neuronal migration, synaptic transmission, signaling, transcriptional regulation, and transport. These results suggest that disruptions of fetal prefrontal cortical neurogenesis are critical to the pathophysiology of schizophrenia. These results also support the feasibility of integrating genomic and transcriptome analyses to map critical neurodevelopmental processes in time and space in the brain.

  15. Temporal Traffic Dynamics Improve the Connectivity of Ad Hoc Cognitive Radio Networks

    DTIC Science & Technology

    2014-02-12

    of nearby primary users. Using theories and techniques from continuum percolation and ergodicity, we analytically characterize the con- nectivity of...users depends on the transmitting and receiving activities of nearby primary users. Using theories and techniques from continuum percolation and...connectivity of homogeneous net- works (i.e., secondary network only) have been well studied in [6]–[14] and references therein. The theory of continuum 3Since

  16. Network Adaptation Improves Temporal Representation of Naturalistic Stimuli in Drosophila Eye: I Dynamics

    PubMed Central

    Wardill, Trevor J.; O'Kane, Cahir J.; de Polavieja, Gonzalo G.; Juusola, Mikko

    2009-01-01

    Because of the limited processing capacity of eyes, retinal networks must adapt constantly to best present the ever changing visual world to the brain. However, we still know little about how adaptation in retinal networks shapes neural encoding of changing information. To study this question, we recorded voltage responses from photoreceptors (R1–R6) and their output neurons (LMCs) in the Drosophila eye to repeated patterns of contrast values, collected from natural scenes. By analyzing the continuous photoreceptor-to-LMC transformations of these graded-potential neurons, we show that the efficiency of coding is dynamically improved by adaptation. In particular, adaptation enhances both the frequency and amplitude distribution of LMC output by improving sensitivity to under-represented signals within seconds. Moreover, the signal-to-noise ratio of LMC output increases in the same time scale. We suggest that these coding properties can be used to study network adaptation using the genetic tools in Drosophila, as shown in a companion paper (Part II). PMID:19180196

  17. HURON (HUman and Robotic Optimization Network) Multi-Agent Temporal Activity Planner/Scheduler

    NASA Technical Reports Server (NTRS)

    Hua, Hook; Mrozinski, Joseph J.; Elfes, Alberto; Adumitroaie, Virgil; Shelton, Kacie E.; Smith, Jeffrey H.; Lincoln, William P.; Weisbin, Charles R.

    2012-01-01

    HURON solves the problem of how to optimize a plan and schedule for assigning multiple agents to a temporal sequence of actions (e.g., science tasks). Developed as a generic planning and scheduling tool, HURON has been used to optimize space mission surface operations. The tool has also been used to analyze lunar architectures for a variety of surface operational scenarios in order to maximize return on investment and productivity. These scenarios include numerous science activities performed by a diverse set of agents: humans, teleoperated rovers, and autonomous rovers. Once given a set of agents, activities, resources, resource constraints, temporal constraints, and de pendencies, HURON computes an optimal schedule that meets a specified goal (e.g., maximum productivity or minimum time), subject to the constraints. HURON performs planning and scheduling optimization as a graph search in state-space with forward progression. Each node in the graph contains a state instance. Starting with the initial node, a graph is automatically constructed with new successive nodes of each new state to explore. The optimization uses a set of pre-conditions and post-conditions to create the children states. The Python language was adopted to not only enable more agile development, but to also allow the domain experts to easily define their optimization models. A graphical user interface was also developed to facilitate real-time search information feedback and interaction by the operator in the search optimization process. The HURON package has many potential uses in the fields of Operations Research and Management Science where this technology applies to many commercial domains requiring optimization to reduce costs. For example, optimizing a fleet of transportation truck routes, aircraft flight scheduling, and other route-planning scenarios involving multiple agent task optimization would all benefit by using HURON.

  18. Disconnection between the default mode network and medial temporal lobes in post-traumatic amnesia

    PubMed Central

    De Simoni, Sara; Grover, Patrick J.; Jenkins, Peter O.; Honeyfield, Lesley; Quest, Rebecca A.; Ross, Ewan; Scott, Gregory; Wilson, Mark H.; Majewska, Paulina; Waldman, Adam D.; Patel, Maneesh C.

    2016-01-01

    See Bigler (doi:10.1093/aww277) for a scientific commentary on this article. Post-traumatic amnesia is very common immediately after traumatic brain injury. It is characterized by a confused, agitated state and a pronounced inability to encode new memories and sustain attention. Clinically, post-traumatic amnesia is an important predictor of functional outcome. However, despite its prevalence and functional importance, the pathophysiology of post-traumatic amnesia is not understood. Memory processing relies on limbic structures such as the hippocampus, parahippocampus and parts of the cingulate cortex. These structures are connected within an intrinsic connectivity network, the default mode network. Interactions within the default mode network can be assessed using resting state functional magnetic resonance imaging, which can be acquired in confused patients unable to perform tasks in the scanner. Here we used this approach to test the hypothesis that the mnemonic symptoms of post-traumatic amnesia are caused by functional disconnection within the default mode network. We assessed whether the hippocampus and parahippocampus showed evidence of transient disconnection from cortical brain regions involved in memory processing. Nineteen patients with traumatic brain injury were classified into post-traumatic amnesia and traumatic brain injury control groups, based on their performance on a paired associates learning task. Cognitive function was also assessed with a detailed neuropsychological test battery. Functional interactions between brain regions were investigated using resting-state functional magnetic resonance imaging. Together with impairments in associative memory, patients in post-traumatic amnesia demonstrated impairments in information processing speed and spatial working memory. Patients in post-traumatic amnesia showed abnormal functional connectivity between the parahippocampal gyrus and posterior cingulate cortex. The strength of this functional

  19. Disconnection between the default mode network and medial temporal lobes in post-traumatic amnesia.

    PubMed

    De Simoni, Sara; Grover, Patrick J; Jenkins, Peter O; Honeyfield, Lesley; Quest, Rebecca A; Ross, Ewan; Scott, Gregory; Wilson, Mark H; Majewska, Paulina; Waldman, Adam D; Patel, Maneesh C; Sharp, David J

    2016-12-01

    SEE BIGLER DOI101093/AWW277 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Post-traumatic amnesia is very common immediately after traumatic brain injury. It is characterized by a confused, agitated state and a pronounced inability to encode new memories and sustain attention. Clinically, post-traumatic amnesia is an important predictor of functional outcome. However, despite its prevalence and functional importance, the pathophysiology of post-traumatic amnesia is not understood. Memory processing relies on limbic structures such as the hippocampus, parahippocampus and parts of the cingulate cortex. These structures are connected within an intrinsic connectivity network, the default mode network. Interactions within the default mode network can be assessed using resting state functional magnetic resonance imaging, which can be acquired in confused patients unable to perform tasks in the scanner. Here we used this approach to test the hypothesis that the mnemonic symptoms of post-traumatic amnesia are caused by functional disconnection within the default mode network. We assessed whether the hippocampus and parahippocampus showed evidence of transient disconnection from cortical brain regions involved in memory processing. Nineteen patients with traumatic brain injury were classified into post-traumatic amnesia and traumatic brain injury control groups, based on their performance on a paired associates learning task. Cognitive function was also assessed with a detailed neuropsychological test battery. Functional interactions between brain regions were investigated using resting-state functional magnetic resonance imaging. Together with impairments in associative memory, patients in post-traumatic amnesia demonstrated impairments in information processing speed and spatial working memory. Patients in post-traumatic amnesia showed abnormal functional connectivity between the parahippocampal gyrus and posterior cingulate cortex. The strength of this functional

  20. Uncertainty analysis of a combined Artificial Neural Network - Fuzzy logic - Kriging system for spatial and temporal simulation of Hydraulic Head.

    NASA Astrophysics Data System (ADS)

    Tapoglou, Evdokia; Karatzas, George P.; Trichakis, Ioannis C.; Varouchakis, Emmanouil A.

    2015-04-01

    The purpose of this study is to evaluate the uncertainty, using various methodologies, in a combined Artificial Neural Network (ANN) - Fuzzy logic - Kriging system, which can simulate spatially and temporally the hydraulic head in an aquifer. This system uses ANNs for the temporal prediction of hydraulic head in various locations, one ANN for every location with available data, and Kriging for the spatial interpolation of ANN's results. A fuzzy logic is used for the interconnection of these two methodologies. The full description of the initial system and its functionality can be found in Tapoglou et al. (2014). Two methodologies were used for the calculation of uncertainty for the implementation of the algorithm in a study area. First, the uncertainty of Kriging parameters was examined using a Bayesian bootstrap methodology. In this case the variogram is calculated first using the traditional methodology of Ordinary Kriging. Using the parameters derived and the covariance function of the model, the covariance matrix is constructed. A common method for testing a statistical model is the use of artificial data. Normal random numbers generation is the first step in this procedure and by multiplying them by the decomposed covariance matrix, correlated random numbers (sample set) can be calculated. These random values are then fitted into a variogram and the value in an unknown location is estimated using Kriging. The distribution of the simulated values using the Kriging of different correlated random values can be used in order to derive the prediction intervals of the process. In this study 500 variograms were constructed for every time step and prediction point, using the method described above, and their results are presented as the 95th and 5th percentile of the predictions. The second methodology involved the uncertainty of ANNs training. In this case, for all the data points 300 different trainings were implemented having different training datasets each time

  1. Stories in Networks and Networks in Stories: A Tri-Modal Model for Mixed-Methods Social Network Research on Teachers

    ERIC Educational Resources Information Center

    Baker-Doyle, Kira J.

    2015-01-01

    Social network research on teachers and schools has risen exponentially in recent years as an innovative method to reveal the role of social networks in education. However, scholars are still exploring ways to incorporate traditional quantitative methods of Social Network Analysis (SNA) with qualitative approaches to social network research. This…

  2. Using generalized additive mixed models to assess spatial, temporal, and hydrologic controls on bacteria and nitrate in a vulnerable agricultural aquifer

    NASA Astrophysics Data System (ADS)

    Mellor, Andrea F. P.; Cey, Edwin E.

    2015-11-01

    The Abbotsford-Sumas aquifer (ASA) has a history of nitrate contamination from agricultural land use and manure application to soils, yet little is known about its microbial groundwater quality. The goal of this study was to investigate the spatiotemporal distribution of pathogen indicators (Escherichia coli [E. coli] and total coliform [TC]) and nitrate in groundwater, and their potential relation to hydrologic drivers. Sampling of 46 wells over an 11-month period confirmed elevated nitrate concentrations, with more than 50% of samples exceeding 10 mg-N/L. E. coli detections in groundwater were infrequent (4 of 385 total samples) and attributed mainly to surface water-groundwater connections along Fishtrap Creek, which tested positive for E. coli in every sampling event. TC was detected frequently in groundwater (70% of samples) across the ASA. Generalized additive mixed models (GAMMs) yielded valuable insights into relationships between TC or nitrate and a range of spatial, temporal, and hydrologic explanatory variables. Increased TC values over the wetter fall and winter period were most strongly related to groundwater temperatures and levels, while precipitation and well location were weaker (but still significant) predictors. In contrast, the moderate temporal variability in nitrate concentrations was not significantly related to hydrologic forcings. TC was relatively widespread across the ASA and spatial patterns could not be attributed solely to surface water connectivity. Varying nitrate concentrations across the ASA were significantly related to both well location and depth, likely due to spatially variable nitrogen loading and localized geochemical attenuation (i.e., denitrification). Vulnerability of the ASA to bacteria was clearly linked to hydrologic conditions, and was distinct from nitrate, such that a groundwater management strategy specifically for bacterial contaminants is warranted.

  3. Using generalized additive mixed models to assess spatial, temporal, and hydrologic controls on bacteria and nitrate in a vulnerable agricultural aquifer.

    PubMed

    Mellor, Andrea F P; Cey, Edwin E

    2015-11-01

    The Abbotsford-Sumas aquifer (ASA) has a history of nitrate contamination from agricultural land use and manure application to soils, yet little is known about its microbial groundwater quality. The goal of this study was to investigate the spatiotemporal distribution of pathogen indicators (Escherichia coli [E. coli] and total coliform [TC]) and nitrate in groundwater, and their potential relation to hydrologic drivers. Sampling of 46 wells over an 11-month period confirmed elevated nitrate concentrations, with more than 50% of samples exceeding 10 mg-N/L. E. coli detections in groundwater were infrequent (4 of 385 total samples) and attributed mainly to surface water-groundwater connections along Fishtrap Creek, which tested positive for E. coli in every sampling event. TC was detected frequently in groundwater (70% of samples) across the ASA. Generalized additive mixed models (GAMMs) yielded valuable insights into relationships between TC or nitrate and a range of spatial, temporal, and hydrologic explanatory variables. Increased TC values over the wetter fall and winter period were most strongly related to groundwater temperatures and levels, while precipitation and well location were weaker (but still significant) predictors. In contrast, the moderate temporal variability in nitrate concentrations was not significantly related to hydrologic forcings. TC was relatively widespread across the ASA and spatial patterns could not be attributed solely to surface water connectivity. Varying nitrate concentrations across the ASA were significantly related to both well location and depth, likely due to spatially variable nitrogen loading and localized geochemical attenuation (i.e., denitrification). Vulnerability of the ASA to bacteria was clearly linked to hydrologic conditions, and was distinct from nitrate, such that a groundwater management strategy specifically for bacterial contaminants is warranted.

  4. A vascular anatomical network model of the spatio-temporal response to brain activation

    PubMed Central

    Boas, David A.; Jones, Stephanie R.; Devor, Anna; Huppert, Theodore J.; Dale, Anders M.

    2008-01-01

    Neuronal activity-induced changes in vascular tone and oxygen consumption result in a dynamic evolution of blood flow, volume, and oxygenation. Functional neuroimaging techniques, such as functional magnetic resonance imaging, optical imaging, and PET, provide indirect measures of the neural-induced vascular dynamics driving the blood parameters. Models connecting changes in vascular tone and oxygen consumption to observed changes in the blood parameters are needed to guide more quantitative physiological interpretation of these functional neuroimaging modalities. Effective lumped-parameter vascular balloon and Windkessel models have been developed for this purpose, but the lumping of the complex vascular network into a series of arterioles, capillaries, and venules allows only qualitative interpretation. We have therefore developed a parallel vascular anatomical network (VAN) model based on microscopically measurable properties to improve quantitative interpretation of the vascular response. The model, derived from measured physical properties, predicts baseline blood pressure and oxygen saturation distributions and dynamic responses consistent with literature. Furthermore, the VAN model allows investigation of spatial features of the dynamic vascular and oxygen response to neuronal activity. We find that a passive surround negative vascular response (“negative BOLD”) is predicted, but that it underestimates recently observed surround negativity suggesting that additional active surround vasoconstriction is required to explain the experimental data. PMID:18289880

  5. Enhancement of temporal coherence via time-periodic coupling strength in a scale-free network of stochastic Hodgkin-Huxley neurons

    NASA Astrophysics Data System (ADS)

    Yilmaz, Ergin; Baysal, Veli; Ozer, Mahmut

    2015-08-01

    We investigate the effects of time-periodic coupling strength on the temporal coherence or firing regularity of a scale-free network consisting of stochastic Hodgkin-Huxley (H-H) neurons. The temporal coherence exhibits a resonance-like behavior depending on the cell size or the channel noise intensity. The best temporal coherence requires an optimal channel noise intensity, and this coherence can be significantly increased by time-periodic coupling strength when its frequency matches the integer multiples of the intrinsic subthreshold oscillation frequency of H-H neuron. Particularly, we find the multiple-coherence resonance depending on frequency of time-periodic coupling strength at the optimal noise intensity. We also obtain a resonance-like dependence of temporal coherence on the amplitude of time-periodic coupling strength. Additionally, we investigate the effects of average degree on the temporal coherence and find that the temporal coherence exhibits a resonance-like behavior with respect to the network average degree, indicating that the best regularity requires an optimal average degree.

  6. Microsleeps are Associated with Stage-2 Sleep Spindles from Hippocampal-Temporal Network.

    PubMed

    Jonmohamadi, Yaqub; Poudel, Govinda R; Innes, Carrie C R H; Jones, Richard D

    2016-06-01

    Behavioral microsleeps are associated with complete disruption of responsiveness for [Formula: see text][Formula: see text]s to 15[Formula: see text]s. They can result in injury or death, especially in transport and military sectors. In this study, EEGs were obtained from five nonsleep-deprived healthy male subjects performing a 1[Formula: see text]h 2D tracking task. Microsleeps were detected in all subjects. Microsleep-related activities in the EEG were detected, characterized, separated from eye closure-related activity, and, via source-space-independent component analysis and power analysis, the associated sources were localized in the brain. Microsleeps were often, but not always, found to be associated with strong alpha-band spindles originating bilaterally from the anterior temporal gyri and hippocampi. Similarly, theta-related activity was identified as originating bilaterally from the frontal-orbital cortex. The alpha spindles were similar to sleep spindles in terms of frequency, duration, and amplitude-profile, indicating that microsleeps are equivalent to brief instances of Stage-2 sleep.

  7. Temporal condensation and dynamic λ-transition within the complex network: an application to real-life market evolution

    NASA Astrophysics Data System (ADS)

    Wiliński, Mateusz; Szewczak, Bartłomiej; Gubiec, Tomasz; Kutner, Ryszard; Struzik, Zbigniew R.

    2015-02-01

    We fill a void in merging empirical and phenomenological characterisation of the dynamical phase transitions in complex networks by identifying and thoroughly characterising a triple sequence of such transitions on a real-life financial market. We extract and interpret the empirical, numerical, and analytical evidences for the existence of these dynamical phase transitions, by considering the medium size Frankfurt stock exchange (FSE), as a typical example of a financial market. By using the canonical object for the graph theory, i.e. the minimal spanning tree (MST) network, we observe: (i) the (initial) dynamical phase transition from equilibrium to non-equilibrium nucleation phase of the MST network, occurring at some critical time. Coalescence of edges on the FSE's transient leader (defined by its largest degree) is observed within the nucleation phase; (ii) subsequent acceleration of the process of nucleation and the emergence of the condensation phase (the second dynamical phase transition), forming a logarithmically diverging temporal λ-peak of the leader's degree at the second critical time; (iii) the third dynamical fragmentation phase transition (after passing the second critical time), where the λ-peak logarithmically relaxes over three quarters of the year, resulting in a few loosely connected sub-graphs. This λ-peak (comparable to that of the specific heat vs. temperature forming during the equilibrium continuous phase transition from the normal fluid I 4He to the superfluid II 4He) is considered as a prominent result of a non-equilibrium superstar-like superhub or a dragon-king's abrupt evolution over about two and a half year of market evolution. We capture and meticulously characterise a remarkable phenomenon in which a peripheral company becomes progressively promoted to become the dragon-king strongly dominating the complex network over an exceptionally long period of time containing the crash. Detailed analysis of the complete trio of the

  8. Mixing in the Black Sea detected from the temporal and spatial variability of oxygen and sulfide - Argo float observations and numerical modelling

    NASA Astrophysics Data System (ADS)

    Stanev, E. V.; He, Y.; Staneva, J.; Yakushev, E.

    2014-10-01

    The temporal and spatial variability of the upper ocean hydrochemistry in the Black Sea is analysed using data originating from profiling floats with oxygen sensors and carried out with a coupled three-dimensional circulation-biogeochemical model including 24 biochemical state variables. Major focus is on the dynamics of suboxic zone which is the interface separating oxygenated and anoxic waters. The scatter of oxygen data seen when plotted in density coordinates is larger than those for temperature, salinity and passive tracers. This scatter is indicative of vigorous biogeochemical reactions in the suboxic zone, which acts as a boundary layer or internal sink for oxygen. This internal sink affects the mixing patterns of oxygen compared to the ones of conservative tracers. Two different regimes of ventilation of pycnocline were clearly identified: a gyre-dominated (cyclonic) regime in winter and a coastal boundary layer (anticyclonic eddy)-dominated regime in summer. These contrasting states are characterized by very different pathways of oxygen intrusions along the isopycnals and vertical oxygen conveyor belt organized in multiple-layered cells formed in each gyre. The contribution of the three-dimensional modelling to the understanding of the Black Sea hydro-chemistry, and in particular the coast-to-open-sea mixing, is also demonstrated. Evidence is given that the formation of oxic waters and of cold intermediate waters, although triggered by the same physical process, each follow a different evolution. The difference in the depths of the temperature minimum and the oxygen maximum indicates that the variability of oxygen is not only just a response to physical forcing and changes in the surface conditions, but undergoes its own evolution.

  9. Feasibility of using social networking technologies for health research among men who have sex with men: a mixed methods study.

    PubMed

    Young, Sean D; Jaganath, Devan

    2014-01-01

    This study aims to assess the feasibility and acceptability of using social networking as a health research platform among men who have sex with men (MSM). Fifty-five MSM (primarily African American and Latino) were invited to join a "secret" group on the social networking website, Facebook. Peer leaders, trained in health education, posted health-related content to groups. The study and analysis used mixed (qualitative and quantitative) methods. Facebook conversations were thematically analyzed. Latino and African American participants voluntarily used social networking to discuss health-related knowledge and personal topics (exercise, nutrition, mental health, disease prevention, and substance abuse) with other group participants (N=564 excerpts). Although Latinos comprised 60% of the sample and African Americans 25.5%, Latinos contributed 82% of conversations and African Americans contributed only 15% of all conversations. Twenty-four percent of posts from Latinos and 7% of posts from African Americans were related to health topics. Results suggest that Facebook is an acceptable and engaging platform for facilitating and documenting health discussions for mixed methods research among MSM. An understanding of population differences is needed for crafting effective online social health interventions.

  10. Combined diffusion-weighted and functional magnetic resonance imaging reveals a temporal-occipital network involved in auditory-visual object processing

    PubMed Central

    Beer, Anton L.; Plank, Tina; Meyer, Georg; Greenlee, Mark W.

    2013-01-01

    Functional magnetic resonance imaging (MRI) showed that the superior temporal and occipital cortex are involved in multisensory integration. Probabilistic fiber tracking based on diffusion-weighted MRI suggests that multisensory processing is supported by white matter connections between auditory cortex and the temporal and occipital lobe. Here, we present a combined functional MRI and probabilistic fiber tracking study that reveals multisensory processing mechanisms that remained undetected by either technique alone. Ten healthy participants passively observed visually presented lip or body movements, heard speech or body action sounds, or were exposed to a combination of both. Bimodal stimulation engaged a temporal-occipital brain network including the multisensory superior temporal sulcus (msSTS), the lateral superior temporal gyrus (lSTG), and the extrastriate body area (EBA). A region-of-interest (ROI) analysis showed multisensory interactions (e.g., subadditive responses to bimodal compared to unimodal stimuli) in the msSTS, the lSTG, and the EBA region. Moreover, sounds elicited responses in the medial occipital cortex. Probabilistic tracking revealed white matter tracts between the auditory cortex and the medial occipital cortex, the inferior occipital cortex (IOC), and the superior temporal sulcus (STS). However, STS terminations of auditory cortex tracts showed limited overlap with the msSTS region. Instead, msSTS was connected to primary sensory regions via intermediate nodes in the temporal and occipital cortex. Similarly, the lSTG and EBA regions showed limited direct white matter connections but instead were connected via intermediate nodes. Our results suggest that multisensory processing in the STS is mediated by separate brain areas that form a distinct network in the lateral temporal and inferior occipital cortex. PMID:23407860

  11. Atmospheric carbon diooxide mixing ratios from the NOAA Climate Monitoring and Diagnostics Laboratory cooperative flask sampling network, 1967-1993

    SciTech Connect

    Conway, T.J.; Tans, P.P.; BBoden, T.A.

    1996-02-01

    This data report documents monthly atmospheric CO{sub 2} mixing ratios and measurements obtained by analyzing individual flask air samples for the NOAA/CMDL global cooperative flask sampling network. Measurements include land-based sampling sites and shipboard measurements covering 14 latitude bands in the Pacific Ocean and South China Sea. Analysis of the NOAA/CMDL flask CO{sub 2} database shows a long-term increase in atmospheric CO{sub 2} mixing ratios since the late 1960s. This report describes how the samples are collected and analyzed and how the data are processed, defines limitations, and restrictions of the data, describes the contents and format of the data files, and provides tabular listings of the monthly carbon dioxide records.

  12. Regional GPS TEC modeling; Attempted spatial and temporal extrapolation of TEC using neural networks

    NASA Astrophysics Data System (ADS)

    Habarulema, John Bosco; McKinnell, Lee-Anne; Opperman, Ben D. L.

    2011-04-01

    In this paper, the potential extrapolation capabilities and limitations of artificial neural networks (ANNs) are investigated. This is primarily done by generating total electron content (TEC) predictions using the regional southern Africa total electron content prediction (SATECP) model based on the Global Positioning System (GPS) data and ANNs with the aid of multiple inputs intended to enable the software to learn and correlate the relationship between their variations and the target parameter, TEC. TEC values are predicted over regions that were not covered in the model's development, although it is difficult to validate their accuracy in some cases. The SATECP model is also used to forecast hourly TEC variability 1 year ahead in order to assess the forecasting capability of ANNs in generalizing TEC patterns. The developed SATECP model has also been independently validated by ionosonde data and TEC values derived from the adapted University of New Brunswick Ionospheric Mapping Technique (UNB-IMT) over southern Africa. From the comparison of prediction results with actual GPS data, it is observed that ANNs extrapolate relatively well during quiet periods while the accuracy is low during geomagnetically disturbed conditions. However, ANNs correctly identify both positive and negative storm effects observed in GPS TEC data analyzed within the input space.

  13. Single-frequency receivers as master permanent stations in GNSS networks: precision and accuracy of the positioning in mixed networks

    NASA Astrophysics Data System (ADS)

    Dabove, Paolo; Manzino, Ambrogio Maria

    2015-04-01

    The use of GPS/GNSS instruments is a common practice in the world at both a commercial and academic research level. Since last ten years, Continuous Operating Reference Stations (CORSs) networks were born in order to achieve the possibility to extend a precise positioning more than 15 km far from the master station. In this context, the Geomatics Research Group of DIATI at the Politecnico di Torino has carried out several experiments in order to evaluate the achievable precision obtainable with different GNSS receivers (geodetic and mass-market) and antennas if a CORSs network is considered. This work starts from the research above described, in particular focusing the attention on the usefulness of single frequency permanent stations in order to thicken the existing CORSs, especially for monitoring purposes. Two different types of CORSs network are available today in Italy: the first one is the so called "regional network" and the second one is the "national network", where the mean inter-station distances are about 25/30 and 50/70 km respectively. These distances are useful for many applications (e.g. mobile mapping) if geodetic instruments are considered but become less useful if mass-market instruments are used or if the inter-station distance between master and rover increases. In this context, some innovative GNSS networks were developed and tested, analyzing the performance of rover's positioning in terms of quality, accuracy and reliability both in real-time and post-processing approach. The use of single frequency GNSS receivers leads to have some limits, especially due to a limited baseline length, the possibility to obtain a correct fixing of the phase ambiguity for the network and to fix the phase ambiguity correctly also for the rover. These factors play a crucial role in order to reach a positioning with a good level of accuracy (as centimetric o better) in a short time and with an high reliability. The goal of this work is to investigate about the

  14. Mixed H-Infinity and Passive Filtering for Discrete Fuzzy Neural Networks With Stochastic Jumps and Time Delays.

    PubMed

    Shi, Peng; Zhang, Yingqi; Chadli, Mohammed; Agarwal, Ramesh K

    2016-04-01

    In this brief, the problems of the mixed H-infinity and passivity performance analysis and design are investigated for discrete time-delay neural networks with Markovian jump parameters represented by Takagi-Sugeno fuzzy model. The main purpose of this brief is to design a filter to guarantee that the augmented Markovian jump fuzzy neural networks are stable in mean-square sense and satisfy a prescribed passivity performance index by employing the Lyapunov method and the stochastic analysis technique. Applying the matrix decomposition techniques, sufficient conditions are provided for the solvability of the problems, which can be formulated in terms of linear matrix inequalities. A numerical example is also presented to illustrate the effectiveness of the proposed techniques.

  15. Transmission and pass-drop operations of mixed baudrate Nyquist OTDM-WDM signals for all-optical elastic network.

    PubMed

    Tan, Hung Nguyen; Inoue, Takashi; Kurosu, Takayuki; Namiki, Shu

    2013-08-26

    We propose the use of Nyquist OTDM-WDM signal for highly efficient, fully elastic all-optical networks. With the possibility of generation of ultra-coarse yet flexible granular channels, Nyquist OTDM-WDM can eliminate guard-bands in conventional WDM systems, and hence improves the spectral efficiency in network perspective. In this paper, transmission and pass-drop operations of mixed baudrate Nyquist OTDM-WDM channels from 43 Gbaud to dual-polarization 344 Gbaud are successfully demonstrated over 320 km fiber link with four FlexGrid-compatible WSS nodes. A stable clock recovery is also carried out for different baudrate Nyquist OTDMs by optical null-header insertion technique.

  16. Feed-Forward Propagation of Temporal and Rate Information between Cortical Populations during Coherent Activation in Engineered In Vitro Networks

    PubMed Central

    DeMarse, Thomas B.; Pan, Liangbin; Alagapan, Sankaraleengam; Brewer, Gregory J.; Wheeler, Bruce C.

    2016-01-01

    Transient propagation of information across neuronal assembles is thought to underlie many cognitive processes. However, the nature of the neural code that is embedded within these transmissions remains uncertain. Much of our understanding of how information is transmitted among these assemblies has been derived from computational models. While these models have been instrumental in understanding these processes they often make simplifying assumptions about the biophysical properties of neurons that may influence the nature and properties expressed. To address this issue we created an in vitro analog of a feed-forward network composed of two small populations (also referred to as assemblies or layers) of living dissociated rat cortical neurons. The populations were separated by, and communicated through, a microelectromechanical systems (MEMS) device containing a strip of microscale tunnels. Delayed culturing of one population in the first layer followed by the second a few days later induced the unidirectional growth of axons through the microtunnels resulting in a primarily feed-forward communication between these two small neural populations. In this study we systematically manipulated the number of tunnels that connected each layer and hence, the number of axons providing communication between those populations. We then assess the effect of reducing the number of tunnels has upon the properties of between-layer communication capacity and fidelity of neural transmission among spike trains transmitted across and within layers. We show evidence based on Victor-Purpura’s and van Rossum’s spike train similarity metrics supporting the presence of both rate and temporal information embedded within these transmissions whose fidelity increased during communication both between and within layers when the number of tunnels are increased. We also provide evidence reinforcing the role of synchronized activity upon transmission fidelity during the spontaneous

  17. A million-plus neuron model of the hippocampal dentate gyrus: Dependency of spatio-temporal network dynamics on topography.

    PubMed

    Hendrickson, Phillip J; Yu, Gene J; Song, Dong; Berger, Theodore W

    2015-01-01

    This paper describes a million-plus granule cell compartmental model of the rat hippocampal dentate gyrus, including excitatory, perforant path input from the entorhinal cortex, and feedforward and feedback inhibitory input from dentate interneurons. The model includes experimentally determined morphological and biophysical properties of granule cells, together with glutamatergic AMPA-like EPSP and GABAergic GABAA-like IPSP synaptic excitatory and inhibitory inputs, respectively. Each granule cell was composed of approximately 200 compartments having passive and active conductances distributed throughout the somatic and dendritic regions. Modeling excitatory input from the entorhinal cortex was guided by axonal transport studies documenting the topographical organization of projections from subregions of the medial and lateral entorhinal cortex, plus other important details of the distribution of glutamatergic inputs to the dentate gyrus. Results showed that when medial and lateral entorhinal cortical neurons maintained Poisson random firing, dentate granule cells expressed, throughout the million-cell network, a robust, non-random pattern of spiking best described as spatiotemporal "clustering". To identify the network property or properties responsible for generating such firing "clusters", we progressively eliminated from the model key mechanisms such as feedforward and feedback inhibition, intrinsic membrane properties underlying rhythmic burst firing, and/or topographical organization of entorhinal afferents. Findings conclusively identified topographical organization of inputs as the key element responsible for generating a spatio-temporal distribution of clustered firing. These results uncover a functional organization of perforant path afferents to the dentate gyrus not previously recognized: topography-dependent clusters of granule cell activity as "functional units" that organize the processing of entorhinal signals.

  18. A new real-time drinking water network mixing model for the control and command centre of the city of Paris.

    PubMed

    Montiel, F; Coutelan, J; Nguyen, B

    2002-01-01

    The Paris drinking water system has the particularity of being supplied by many different sources: surface water from the Seine and Marne rivers and underground water from sixty-three springs. This diversity of origins of the water and the existence of a gridded network leads the company in charge of the production and the quality of the water in Paris, Sagep, to partly mix the water in the network. Knowing the impact of the mixing on the physical parameters, and consequently on the quality of this mixed water, Sagep had searched for a tool to control the water origins in the network in order to limit extreme changes. A study led Sagep to develop a new kind of real-time water mixing model.

  19. Exponential synchronization of memristive Cohen-Grossberg neural networks with mixed delays.

    PubMed

    Yang, Xinsong; Cao, Jinde; Yu, Wenwu

    2014-06-01

    This paper concerns the problem of global exponential synchronization for a class of memristor-based Cohen-Grossberg neural networks with time-varying discrete delays and unbounded distributed delays. The drive-response set is discussed. A novel controller is designed such that the response (slave) system can be controlled to synchronize with the drive (master) system. Through a nonlinear transformation, we get an alternative system from the considered memristor-based Cohen-Grossberg neural networks. By investigating the global exponential synchronization of the alternative system, we obtain the corresponding synchronization criteria of the considered memristor-based Cohen-Grossberg neural networks. Moreover, the conditions established in this paper are easy to be verified and improve the conditions derived in most of existing papers concerning stability and synchronization for memristor-based neural networks. Numerical simulations are given to show the effectiveness of the theoretical results.

  20. Associative neural network model for the generation of temporal patterns. Theory and application to central pattern generators.

    PubMed Central

    Kleinfeld, D; Sompolinsky, H

    1988-01-01

    Cyclic patterns of motor neuron activity are involved in the production of many rhythmic movements, such as walking, swimming, and scratching. These movements are controlled by neural circuits referred to as central pattern generators (CPGs). Some of these circuits function in the absence of both internal pacemakers and external feedback. We describe an associative neural network model whose dynamic behavior is similar to that of CPGs. The theory predicts the strength of all possible connections between pairs of neurons on the basis of the outputs of the CPG. It also allows the mean operating levels of the neurons to be deduced from the measured synaptic strengths between the pairs of neurons. We apply our theory to the CPG controlling escape swimming in the mollusk Tritonia diomedea. The basic rhythmic behavior is shown to be consistent with a simplified model that approximates neurons as threshold units and slow synaptic responses as elementary time delays. The model we describe may have relevance to other fixed action behaviors, as well as to the learning, recall, and recognition of temporally ordered information. Images FIGURE 2 FIGURE 4 PMID:3233265

  1. NeuCube: a spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data.

    PubMed

    Kasabov, Nikola K

    2014-04-01

    The brain functions as a spatio-temporal information processing machine. Spatio- and spectro-temporal brain data (STBD) are the most commonly collected data for measuring brain response to external stimuli. An enormous amount of such data has been already collected, including brain structural and functional data under different conditions, molecular and genetic data, in an attempt to make a progress in medicine, health, cognitive science, engineering, education, neuro-economics, Brain-Computer Interfaces (BCI), and games. Yet, there is no unifying computational framework to deal with all these types of data in order to better understand this data and the processes that generated it. Standard machine learning techniques only partially succeeded and they were not designed in the first instance to deal with such complex data. Therefore, there is a need for a new paradigm to deal with STBD. This paper reviews some methods of spiking neural networks (SNN) and argues that SNN are suitable for the creation of a unifying computational framework for learning and understanding of various STBD, such as EEG, fMRI, genetic, DTI, MEG, and NIRS, in their integration and interaction. One of the reasons is that SNN use the same computational principle that generates STBD, namely spiking information processing. This paper introduces a new SNN architecture, called NeuCube, for the creation of concrete models to map, learn and understand STBD. A NeuCube model is based on a 3D evolving SNN that is an approximate map of structural and functional areas of interest of the brain related to the modeling STBD. Gene information is included optionally in the form of gene regulatory networks (GRN) if this is relevant to the problem and the data. A NeuCube model learns from STBD and creates connections between clusters of neurons that manifest chains (trajectories) of neuronal activity. Once learning is applied, a NeuCube model can reproduce these trajectories, even if only part of the input

  2. Temporal evolution of brain reorganization under cross-modal training: insights into the functional architecture of encoding and retrieval networks

    NASA Astrophysics Data System (ADS)

    Likova, Lora T.

    2015-03-01

    This study is based on the recent discovery of massive and well-structured cross-modal memory activation generated in the primary visual cortex (V1) of totally blind people as a result of novel training in drawing without any vision (Likova, 2012). This unexpected functional reorganization of primary visual cortex was obtained after undergoing only a week of training by the novel Cognitive-Kinesthetic Method, and was consistent across pilot groups of different categories of visual deprivation: congenitally blind, late-onset blind and blindfolded (Likova, 2014). These findings led us to implicate V1 as the implementation of the theoretical visuo-spatial 'sketchpad' for working memory in the human brain. Since neither the source nor the subsequent 'recipient' of this non-visual memory information in V1 is known, these results raise a number of important questions about the underlying functional organization of the respective encoding and retrieval networks in the brain. To address these questions, an individual totally blind from birth was given a week of Cognitive-Kinesthetic training, accompanied by functional magnetic resonance imaging (fMRI) both before and just after training, and again after a two-month consolidation period. The results revealed a remarkable temporal sequence of training-based response reorganization in both the hippocampal complex and the temporal-lobe object processing hierarchy over the prolonged consolidation period. In particular, a pattern of profound learning-based transformations in the hippocampus was strongly reflected in V1, with the retrieval function showing massive growth as result of the Cognitive-Kinesthetic memory training and consolidation, while the initially strong hippocampal response during tactile exploration and encoding became non-existent. Furthermore, after training, an alternating patch structure in the form of a cascade of discrete ventral regions underwent radical transformations to reach complete functional

  3. Reconstructing Large-Scale Brain Resting-State Networks from High-Resolution EEG: Spatial and Temporal Comparisons with fMRI.

    PubMed

    Yuan, Han; Ding, Lei; Zhu, Min; Zotev, Vadim; Phillips, Raquel; Bodurka, Jerzy

    2016-03-01

    Functional magnetic resonance imaging (fMRI) studies utilizing measures of hemodynamic signal, such as the blood oxygenation level-dependent (BOLD) signal, have discovered that resting-state brain activities are organized into multiple large-scale functional networks, coined as resting-state networks (RSNs). However, an important limitation of the available fMRI studies is that hemodynamic signals only provide an indirect measure of the neuronal activity. In contrast, electroencephalography (EEG) directly measures electrophysiological activity of the brain. However, little is known about the brain-wide organization of such spontaneous neuronal population signals at the resting state. It is not entirely clear if or how the network structure built upon slowly fluctuating hemodynamic signals is represented in terms of fast, dynamic, and spontaneous neuronal activity. In this study, we investigated the electrophysiological representation of RSNs from simultaneously acquired EEG and fMRI data in the resting human brain. We developed a data-driven analysis approach that reconstructed multiple large-scale electrophysiological networks from high-resolution EEG data alone. The networks derived from EEG were then compared with RSNs independently derived from simultaneously acquired fMRI in their spatial structures as well as temporal dynamics. Results reveal spatially and temporally specific electrophysiological correlates for the fMRI-RSNs. Findings suggest that the spontaneous activity of various large-scale cortical networks is reflected in macroscopic EEG potentials.

  4. Distributed Planning in a Mixed-Initiative Environment: Collaborative Technologies for Network Centric Operations

    DTIC Science & Technology

    2008-10-01

    system builds upon distributed blackboards and multi - agent systems to provide automated opportunistic planning capabilities for distributed C2......to capture experiences (successes and/or failures) • Episodic Memory for powerful analogical reasoning • Multi - Agent System for mixed initiative

  5. A Robust Wireless Sensor Network Localization Algorithm in Mixed LOS/NLOS Scenario

    PubMed Central

    Li, Bing; Cui, Wei; Wang, Bin

    2015-01-01

    Localization algorithms based on received signal strength indication (RSSI) are widely used in the field of target localization due to its advantages of convenient application and independent from hardware devices. Unfortunately, the RSSI values are susceptible to fluctuate under the influence of non-line-of-sight (NLOS) in indoor space. Existing algorithms often produce unreliable estimated distances, leading to low accuracy and low effectiveness in indoor target localization. Moreover, these approaches require extra prior knowledge about the propagation model. As such, we focus on the problem of localization in mixed LOS/NLOS scenario and propose a novel localization algorithm: Gaussian mixed model based non-metric Multidimensional (GMDS). In GMDS, the RSSI is estimated using a Gaussian mixed model (GMM). The dissimilarity matrix is built to generate relative coordinates of nodes by a multi-dimensional scaling (MDS) approach. Finally, based on the anchor nodes’ actual coordinates and target’s relative coordinates, the target’s actual coordinates can be computed via coordinate transformation. Our algorithm could perform localization estimation well without being provided with prior knowledge. The experimental verification shows that GMDS effectively reduces NLOS error and is of higher accuracy in indoor mixed LOS/NLOS localization and still remains effective when we extend single NLOS to multiple NLOS. PMID:26389919

  6. An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload.

    PubMed

    Sharma, Richa; Kumar, Vikas; Gaur, Prerna; Mittal, A P

    2016-05-01

    Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller.

  7. A synchronous fiber optic ring local area network for multigigabit/s mixed-traffic communication

    NASA Technical Reports Server (NTRS)

    Bergman, L. A.; Eng, S. T.

    1985-01-01

    A synchronous-ring fiber optic local area network is reported that facilitates the simultaneous transmission of packet and real-time traffic at gigabit/s rates, minimizes the amount of high-speed logic, and simplifies the user interface to the network. The novelty of the technique is based on (1) suspending in transit around the ring's circumference an integral number of data frames and (2) achieving this condition by skewing the frame clock rate a small amount. Rather than use the whole data frame as one packet destined to a specific user, many individual channels are instead time-multiplexed into the data frame. This technique only becomes feasible for local networks as data rates approach the Gbit/s range. This departure from other synchronous rings results in several advantages both in terms of system performance and hardware simplicity.

  8. Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms

    PubMed Central

    Petrovici, Mihai A.; Vogginger, Bernhard; Müller, Paul; Breitwieser, Oliver; Lundqvist, Mikael; Muller, Lyle; Ehrlich, Matthias; Destexhe, Alain; Lansner, Anders; Schüffny, René; Schemmel, Johannes; Meier, Karlheinz

    2014-01-01

    Advancing the size and complexity of neural network models leads to an ever increasing demand for computational resources for their simulation. Neuromorphic devices offer a number of advantages over conventional computing architectures, such as high emulation speed or low power consumption, but this usually comes at the price of reduced configurability and precision. In this article, we investigate the consequences of several such factors that are common to neuromorphic devices, more specifically limited hardware resources, limited parameter configurability and parameter variations due to fixed-pattern noise and trial-to-trial variability. Our final aim is to provide an array of methods for coping with such inevitable distortion mechanisms. As a platform for testing our proposed strategies, we use an executable system specification (ESS) of the BrainScaleS neuromorphic system, which has been designed as a universal emulation back-end for neuroscientific modeling. We address the most essential limitations of this device in detail and study their effects on three prototypical benchmark network models within a well-defined, systematic workflow. For each network model, we start by defining quantifiable functionality measures by which we then assess the effects of typical hardware-specific distortion mechanisms, both in idealized software simulations and on the ESS. For those effects that cause unacceptable deviations from the original network dynamics, we suggest generic compensation mechanisms and demonstrate their effectiveness. Both the suggested workflow and the investigated compensation mechanisms are largely back-end independent and do not require additional hardware configurability beyond the one required to emulate the benchmark networks in the first place. We hereby provide a generic methodological environment for configurable neuromorphic devices that are targeted at emulating large-scale, functional neural networks. PMID:25303102

  9. Characterization and compensation of network-level anomalies in mixed-signal neuromorphic modeling platforms.

    PubMed

    Petrovici, Mihai A; Vogginger, Bernhard; Müller, Paul; Breitwieser, Oliver; Lundqvist, Mikael; Muller, Lyle; Ehrlich, Matthias; Destexhe, Alain; Lansner, Anders; Schüffny, René; Schemmel, Johannes; Meier, Karlheinz

    2014-01-01

    Advancing the size and complexity of neural network models leads to an ever increasing demand for computational resources for their simulation. Neuromorphic devices offer a number of advantages over conventional computing architectures, such as high emulation speed or low power consumption, but this usually comes at the price of reduced configurability and precision. In this article, we investigate the consequences of several such factors that are common to neuromorphic devices, more specifically limited hardware resources, limited parameter configurability and parameter variations due to fixed-pattern noise and trial-to-trial variability. Our final aim is to provide an array of methods for coping with such inevitable distortion mechanisms. As a platform for testing our proposed strategies, we use an executable system specification (ESS) of the BrainScaleS neuromorphic system, which has been designed as a universal emulation back-end for neuroscientific modeling. We address the most essential limitations of this device in detail and study their effects on three prototypical benchmark network models within a well-defined, systematic workflow. For each network model, we start by defining quantifiable functionality measures by which we then assess the effects of typical hardware-specific distortion mechanisms, both in idealized software simulations and on the ESS. For those effects that cause unacceptable deviations from the original network dynamics, we suggest generic compensation mechanisms and demonstrate their effectiveness. Both the suggested workflow and the investigated compensation mechanisms are largely back-end independent and do not require additional hardware configurability beyond the one required to emulate the benchmark networks in the first place. We hereby provide a generic methodological environment for configurable neuromorphic devices that are targeted at emulating large-scale, functional neural networks.

  10. Spatial and temporal patterns of nitrogen isotopic composition of ammonia at U.S. ammonia monitoring network sites

    NASA Astrophysics Data System (ADS)

    Felix, J. David; Elliott, Emily M.; Gay, David A.

    2017-02-01

    Ammonia (NH3) emissions and ammonium (NH4+) deposition can have harmful effects on the environment and human health but remain generally unregulated in the U.S. PM2.5 regulations require that an area not exceed an annual average PM2.5 value of 12 μg/m3 (averaged over three years), and since NH3 is a significant precursor to PM2.5 formation these are the closest indirect regulations of NH3 emissions in the U.S. If the U.S. elects to adopt NH3 emission regulations similar to those applied by the European Union, it will be imperative to first adequately quantify NH3 emission sources and transport, and also understand the factors causing varying emissions from each source. To further investigate NH3 emission sources and transport at a regional scale, NH3 was sampled monthly at a subset of nine Ammonia Monitoring Network (AMoN) sites and analyzed for nitrogen isotopic composition of NH3 (δ15N-NH3). The observed δ15N-NH3 values ranged from -42.4 to +7.1‰ with an average of -15.1 ± 9.7. The observed δ15N-NH3 values reported here provide insight into the spatial and temporal trends of the NH3 sources that contribute to ambient [NH3] in the U.S. In regions where agriculture is prevalent (i.e., U.S. Midwest), low and seasonally variable δ15N-NH3 values are observed and are associated with varying agricultural sources. In comparison, rural nonagricultural areas have higher and more seasonally consistent δ15N-NH3 values associated with a constant "natural" (e.g. soil, vegetation, bi-directional flux, ocean) NH3 source. With regards to temporal variation, the peak in U.S. spring agricultural activity (e.g. fertilizer application, livestock waste volatilization) is accompanied by a decrease in δ15N-NH3 values at a majority of the sites, whereas higher δ15N-NH3 values in other seasons could be due to shifting sources (e.g. coal-fired power plants) and/or fractionation scenarios. Fractionation processes that may mask NH3 source signatures are discussed and require

  11. Temporal properties of network-mediated responses to repetitive stimuli are dependent upon retinal ganglion cell type

    PubMed Central

    Im, Maesoon; Fried, Shelley I.

    2016-01-01

    Objective To provide artificially-elicited vision that is temporally dynamic, retinal prosthetic devices will need to repeatedly stimulate retinal neurons. However, given the diversity of physiological types of retinal ganglion cells (RGCs) as well as the heterogeneity of their responses to electric stimulation, temporal properties of RGC responses have not been adequately investigated. Here, we explored the cell type dependence of network-mediated RGC responses to repetitive electric stimulation at various stimulation rates. Approach We examined responses of ON and OFF types of RGCs in the rabbit retinal explant to five consecutive stimuli with varying inter-stimulus intervals (10–1000 ms). Each stimulus was a 4-ms-long monophasic sinusoidal cathodal current, which was applied epiretinally via a conical electrode. Spiking activity of targeted RGCs was recorded using a cell-attached patch electrode. Main results ON and OFF cells had distinct responses to repetitive stimuli. Consistent with earlier studies, OFF cells always generated reduced responses to subsequent stimuli compared to responses to the first stimulus. In contrast, a new stimulus to ON cells suppressed all pending/ongoing responses from previous stimuli and initiated its own response that was remarkably similar to the response from a single stimulus in isolation. This previously unreported ‘reset’ behavior was observed exclusively and consistently in ON cells. These contrasts between ON and OFF cells created a range of stimulation rates (4–7 Hz) that maximized the ratio of the responses arising in ON vs. OFF cells. Significance Previous clinical testing reported that subjects perceive bright phosphenes (ON responses) and also prefer stimulation rates of 5–7 Hz. Our results suggest that responses of ON cells are weak at high rates of stimulation (> ~7 Hz) due to the reset while responses of OFF cells are strong at low rates (< ~4 Hz) due to reduced desensitization, both reducing the ratio of

  12. Temporal properties of network-mediated responses to repetitive stimuli are dependent upon retinal ganglion cell type

    NASA Astrophysics Data System (ADS)

    Im, Maesoon; Fried, Shelley I.

    2016-04-01

    Objective. To provide artificially-elicited vision that is temporally dynamic, retinal prosthetic devices will need to repeatedly stimulate retinal neurons. However, given the diversity of physiological types of retinal ganglion cells (RGCs) as well as the heterogeneity of their responses to electric stimulation, temporal properties of RGC responses have not been adequately investigated. Here, we explored the cell type dependence of network-mediated RGC responses to repetitive electric stimulation at various stimulation rates. Approach. We examined responses of ON and OFF types of RGCs in the rabbit retinal explant to five consecutive stimuli with varying inter-stimulus intervals (10-1000 ms). Each stimulus was a 4 ms long monophasic sinusoidal cathodal current, which was applied epiretinally via a conical electrode. Spiking activity of targeted RGCs was recorded using a cell-attached patch electrode. Main results. ON and OFF cells had distinct responses to repetitive stimuli. Consistent with earlier studies, OFF cells always generated reduced responses to subsequent stimuli compared to responses to the first stimulus. In contrast, a new stimulus to ON cells suppressed all pending/ongoing responses from previous stimuli and initiated its own response that was remarkably similar to the response from a single stimulus in isolation. This previously unreported ‘reset’ behavior was observed exclusively and consistently in ON cells. These contrasts between ON and OFF cells created a range of stimulation rates (4-7 Hz) that maximized the ratio of the responses arising in ON versus OFF cells. Significance. Previous clinical testing reported that subjects perceive bright phosphenes (ON responses) and also prefer stimulation rates of 5-7 Hz. Our results suggest that responses of ON cells are weak at high rates of stimulation (> ˜7 Hz) due to the reset while responses of OFF cells are strong at low rates (< ˜4 Hz) due to reduced desensitization, both reducing the ratio

  13. Changes in functional connectivity within the fronto-temporal brain network induced by regular and irregular Russian verb production

    PubMed Central

    Kireev, Maxim; Slioussar, Natalia; Korotkov, Alexander D.; Chernigovskaya, Tatiana V.; Medvedev, Svyatoslav V.

    2015-01-01

    Functional connectivity between brain areas involved in the processing of complex language forms remains largely unexplored. Contributing to the debate about neural mechanisms underlying regular and irregular inflectional morphology processing in the mental lexicon, we conducted an fMRI experiment in which participants generated forms from different types of Russian verbs and nouns as well as from nonce stimuli. The data were subjected to a whole brain voxel-wise analysis of context dependent changes in functional connectivity [the so-called psychophysiological interaction (PPI) analysis]. Unlike previously reported subtractive results that reveal functional segregation between brain areas, PPI provides complementary information showing how these areas are functionally integrated in a particular task. To date, PPI evidence on inflectional morphology has been scarce and only available for inflectionally impoverished English verbs in a same-different judgment task. Using PPI here in conjunction with a production task in an inflectionally rich language, we found that functional connectivity between the left inferior frontal gyrus (LIFG) and bilateral superior temporal gyri (STG) was significantly greater for regular real verbs than for irregular ones. Furthermore, we observed a significant positive covariance between the number of mistakes in irregular real verb trials and the increase in functional connectivity between the LIFG and the right anterior cingulate cortex in these trails, as compared to regular ones. Our results therefore allow for dissociation between regularity and processing difficulty effects. These results, on the one hand, shed new light on the functional interplay within the LIFG-bilateral STG language-related network and, on the other hand, call for partial reconsideration of some of the previous findings while stressing the role of functional temporo-frontal connectivity in complex morphological processes. PMID:25741262

  14. Changes in functional connectivity within the fronto-temporal brain network induced by regular and irregular Russian verb production.

    PubMed

    Kireev, Maxim; Slioussar, Natalia; Korotkov, Alexander D; Chernigovskaya, Tatiana V; Medvedev, Svyatoslav V

    2015-01-01

    Functional connectivity between brain areas involved in the processing of complex language forms remains largely unexplored. Contributing to the debate about neural mechanisms underlying regular and irregular inflectional morphology processing in the mental lexicon, we conducted an fMRI experiment in which participants generated forms from different types of Russian verbs and nouns as well as from nonce stimuli. The data were subjected to a whole brain voxel-wise analysis of context dependent changes in functional connectivity [the so-called psychophysiological interaction (PPI) analysis]. Unlike previously reported subtractive results that reveal functional segregation between brain areas, PPI provides complementary information showing how these areas are functionally integrated in a particular task. To date, PPI evidence on inflectional morphology has been scarce and only available for inflectionally impoverished English verbs in a same-different judgment task. Using PPI here in conjunction with a production task in an inflectionally rich language, we found that functional connectivity between the left inferior frontal gyrus (LIFG) and bilateral superior temporal gyri (STG) was significantly greater for regular real verbs than for irregular ones. Furthermore, we observed a significant positive covariance between the number of mistakes in irregular real verb trials and the increase in functional connectivity between the LIFG and the right anterior cingulate cortex in these trails, as compared to regular ones. Our results therefore allow for dissociation between regularity and processing difficulty effects. These results, on the one hand, shed new light on the functional interplay within the LIFG-bilateral STG language-related network and, on the other hand, call for partial reconsideration of some of the previous findings while stressing the role of functional temporo-frontal connectivity in complex morphological processes.

  15. Inorganic nanoparticles for the spatial and temporal control of organic reactions: Applications to radical degradation of biopolymer networks

    NASA Astrophysics Data System (ADS)

    Walker, Joan Marie

    Nanoparticles of gold and iron oxide not only possess remarkable optical and magnetic properties, respectively, but are also capable of influencing their local environment with an astounding degree of precision. Using nanoparticles to direct the reactivity of organic molecules near their surface provides a unique method of spatial and temporal control. Enediynes represent an exceptional class of compounds that are thermally reactive to produce a diradical intermediate via Bergman cycloaromatization. While natural product enediynes are famously cytotoxic, a rich chemistry of synthetic enediynes has developed utilizing creative means to control this reactivity through structure, electronics, metal chelation, and external triggering mechanisms. In a heretofore unexplored arena for Bergman cyclization, we have investigated the reactivity of enediynes in connection with inorganic nanoparticles in which the physical properties of the nanomaterial are directly excited to thermally promote aromatization. As the first example of this methodology, gold nanoparticles conjugated with (Z)-octa-4-en-2,6-diyne-1,8-dithiol were excited with 514 nm laser irradiation. The formation of aromatic and polymeric products was confirmed through Raman spectroscopy and electron microscopy. Water soluble analogues Au-PEG-EDDA and Fe3O4-PEG-EDDA (EDDA = (Z)-octa-4-en-2,6-diyne-1,8-diamine) show similar reactivity under laser irradiation or alternating magnetic field excitation, respectively. Furthermore, we have used these functionalized nanoparticles to attack proteinaceous substrates including fibrin and extracellular matrix proteins, capitalizing on the ability of diradicals to disrupt peptidic bonds. By delivering a locally high payload of reactive molecules and thermal energy to the large biopolymer, network restructuring and collapse is achieved. As a synthetic extension towards multifunctional nanoparticles, noble metal seed-decorated iron oxides have also been prepared and assessed for

  16. Insights into Intrinsic Brain Networks based on Graph Theory and PET in right- compared to left-sided Temporal Lobe Epilepsy

    PubMed Central

    Vanicek, Thomas; Hahn, Andreas; Traub-Weidinger, Tatjana; Hilger, Eva; Spies, Marie; Wadsak, Wolfgang; Lanzenberger, Rupert; Pataraia, Ekaterina; Asenbaum-Nan, Susanne

    2016-01-01

    The human brain exhibits marked hemispheric differences, though it is not fully understood to what extent lateralization of the epileptic focus is relevant. Preoperative [18F]FDG-PET depicts lateralization of seizure focus in patients with temporal lobe epilepsy and reveals dysfunctional metabolic brain connectivity. The aim of the present study was to compare metabolic connectivity, inferred from inter-regional [18F]FDG PET uptake correlations, in right-sided (RTLE; n = 30) and left-sided TLE (LTLE; n = 32) with healthy controls (HC; n = 31) using graph theory based network analysis. Comparing LTLE and RTLE and patient groups separately to HC, we observed higher lobar connectivity weights in RTLE compared to LTLE for connections of the temporal and the parietal lobe of the contralateral hemisphere (CH). Moreover, especially in RTLE compared to LTLE higher local efficiency were found in the temporal cortices and other brain regions of the CH. The results of this investigation implicate altered metabolic networks in patients with TLE specific to the lateralization of seizure focus, and describe compensatory mechanisms especially in the CH of patients with RTLE. We propose that graph theoretical analysis of metabolic connectivity using [18F]FDG-PET offers an important additional modality to explore brain networks. PMID:27349503

  17. Insights into Intrinsic Brain Networks based on Graph Theory and PET in right- compared to left-sided Temporal Lobe Epilepsy.

    PubMed

    Vanicek, Thomas; Hahn, Andreas; Traub-Weidinger, Tatjana; Hilger, Eva; Spies, Marie; Wadsak, Wolfgang; Lanzenberger, Rupert; Pataraia, Ekaterina; Asenbaum-Nan, Susanne

    2016-06-28

    The human brain exhibits marked hemispheric differences, though it is not fully understood to what extent lateralization of the epileptic focus is relevant. Preoperative [(18)F]FDG-PET depicts lateralization of seizure focus in patients with temporal lobe epilepsy and reveals dysfunctional metabolic brain connectivity. The aim of the present study was to compare metabolic connectivity, inferred from inter-regional [(18)F]FDG PET uptake correlations, in right-sided (RTLE; n = 30) and left-sided TLE (LTLE; n = 32) with healthy controls (HC; n = 31) using graph theory based network analysis. Comparing LTLE and RTLE and patient groups separately to HC, we observed higher lobar connectivity weights in RTLE compared to LTLE for connections of the temporal and the parietal lobe of the contralateral hemisphere (CH). Moreover, especially in RTLE compared to LTLE higher local efficiency were found in the temporal cortices and other brain regions of the CH. The results of this investigation implicate altered metabolic networks in patients with TLE specific to the lateralization of seizure focus, and describe compensatory mechanisms especially in the CH of patients with RTLE. We propose that graph theoretical analysis of metabolic connectivity using [(18)F]FDG-PET offers an important additional modality to explore brain networks.

  18. Mixed-scale channel networks including Kingfisher-beak-shaped 3D microfunnels for efficient single particle entrapment

    NASA Astrophysics Data System (ADS)

    Lee, Yunjeong; Lim, Yeongjin; Shin, Heungjoo

    2016-06-01

    Reproducible research results for nanofluidics and their applications require viable fabrication technologies to produce nanochannels integrated with microchannels that can guide fluid flow and analytes into/out of the nanochannels. We present the simple fabrication of mixed-scale polydimethylsiloxane (PDMS) channel networks consisting of nanochannels and microchannels via a single molding process using a monolithic mixed-scale carbon mold. The monolithic carbon mold is fabricated by pyrolyzing a polymer mold patterned by photolithography. During pyrolysis, the polymer mold shrinks by ~90%, which enables nanosized carbon molds to be produced without a complex nanofabrication process. Because of the good adhesion between the polymer mold and the Si substrate, non-uniform volume reduction occurs during pyrolysis resulting in the formation of curved carbon mold side walls. These curved side walls and the relatively low surface energy of the mold provide efficient demolding of the PDMS channel networks. In addition, the trigonal prismatic shape of the polymer is converted into to a Kingfisher-beak-shaped carbon structure due to the non-uniform volume reduction. The transformation of this mold architecture produces a PDMS Kingfisher-beak-shaped 3D microfunnel that connects the microchannel and the nanochannel smoothly. The smooth reduction in the cross-sectional area of the 3D microfunnels enables efficient single microparticle trapping at the nanochannel entrance; this is beneficial for studies of cell transfection.Reproducible research results for nanofluidics and their applications require viable fabrication technologies to produce nanochannels integrated with microchannels that can guide fluid flow and analytes into/out of the nanochannels. We present the simple fabrication of mixed-scale polydimethylsiloxane (PDMS) channel networks consisting of nanochannels and microchannels via a single molding process using a monolithic mixed-scale carbon mold. The monolithic

  19. Synchronization Control for Stochastic Neural Networks with Mixed Time-Varying Delays

    PubMed Central

    Zhu, Qing; Song, Aiguo; Fei, Shumin; Yang, Yuequan; Cao, Zhiqiang

    2014-01-01

    Synchronization control of stochastic neural networks with time-varying discrete and continuous delays has been investigated. A novel control scheme is proposed using the Lyapunov functional method and linear matrix inequality (LMI) approach. Sufficient conditions have been derived to ensure the global asymptotical mean-square stability for the error system, and thus the drive system synchronizes with the response system. Also, the control gain matrix can be obtained. With these effective methods, synchronization can be achieved. Simulation results are presented to show the effectiveness of the theoretical results. PMID:25110747

  20. The geomorphological characteristics of the Mekong River in northern Cambodia: A mixed bedrock-alluvial multi-channel network

    NASA Astrophysics Data System (ADS)

    Meshkova, Liubov V.; Carling, Paul A.

    2012-04-01

    The controls on the development of channel morphology of bedrock-constrained rivers are poorly known. The relative importance of lithological and structural control on channel alignment and character in comparison with the role of hydraulic erosion of the substratum is unclear. In addition, bedrock rivers often have a variable sediment fill and can be described as mixed bedrock-alluvial systems. The Mekong River in northern Cambodia is an anastomosed mixed bedrock-alluvial channel, but little and poorly researched. In this paper information has been gathered from diverse literature sources; digital data sets showing topography, hydrology, geology and land cover; past aerial photographs; and maps. Such data, together with field survey, provide a clear picture of the Mekong River in this region. The channels may be classified into three types: primary, secondary, cross channels. The varying characteristics of these three help towards understanding the evolution of the modern Mekong. Similarly the two-fold classification of the islands reveals the relationship between island inundation characteristics and the annual monsoon flood cycle. The associated riparian vegetation ecotones include a rare and unusual seasonally-inundated forest. Spatial variations in lithology and structure, when combined with maps of river networks, reveals that the channel alignments locally reflect the geological factors that cause the regional topographical gradient. Fault-line constraints on the local slopes of the channel may induce backwater effects and consequent sedimentation patterns (alluvial overprints) or alternatively, steepening of the channels with concomitant reduction in sedimentation. These structural constraints, taken together, lead to the identification of a mixed bedrock-alluvial five-fold geomorphological zonation within the study area. The general absence of paleochannels, and terraces close to the modern river, indicates that the Mekong is laterally stable with a

  1. Testing a model of facilitated reflection on network feedback: a mixed method study on integration of rural mental healthcare services for older people

    PubMed Central

    Fuller, Jeffrey; Oster, Candice; Muir Cochrane, Eimear; Dawson, Suzanne; Lawn, Sharon; Henderson, Julie; O'Kane, Deb; Gerace, Adam; McPhail, Ruth; Sparkes, Deb; Fuller, Michelle; Reed, Richard L

    2015-01-01

    Objective To test a management model of facilitated reflection on network feedback as a means to engage services in problem solving the delivery of integrated primary mental healthcare to older people. Design Participatory mixed methods case study evaluating the impact of a network management model using organisational network feedback (through social network analysis, key informant interviews and policy review). Intervention A model of facilitated network reflection using network theory and methods. Setting A rural community in South Australia. Participants 32 staff from 24 services and 12 senior service managers from mental health, primary care and social care services. Results Health and social care organisations identified that they operated in clustered self-managed networks within sectors, with no overarching purposive older people's mental healthcare network. The model of facilitated reflection revealed service goal and role conflicts. These discussions helped local services to identify as a network, and begin the problem-solving communication and referral links. A Governance Group assisted this process. Barriers to integrated servicing through a network included service funding tied to performance of direct care tasks and the lack of a clear lead network administration organisation. Conclusions A model of facilitated reflection helped organisations to identify as a network, but revealed sensitivity about organisational roles and goals, which demonstrated that conflict should be expected. Networked servicing needed a neutral network administration organisation with cross-sectoral credibility, a mandate and the resources to monitor the network, to deal with conflict, negotiate commitment among the service managers, and provide opportunities for different sectors to meet and problem solve. This requires consistency and sustained intersectoral policies that include strategies and funding to facilitate and maintain health and social care networks in rural

  2. Mixed Algorithms in the Ising Model on Directed BARABÁSI-ALBERT Networks

    NASA Astrophysics Data System (ADS)

    Lima, F. W. S.

    On directed Barabási-Albert networks with two and seven neighbours selected by each added site, the Ising model does not seem to show a spontaneous magnetisation. Instead, the decay time for flipping of the magnetisation follows an Arrhenius law for Metropolis and Glauber algorithms, but for Wolff cluster flipping the magnetisation decays exponentially with time. On these networks the magnetisation behaviour of the Ising model, with Glauber, HeatBath, Metropolis, Wolf or Swendsen-Wang algorithm competing against Kawasaki dynamics, is studied by Monte Carlo simulations. We show that the model exhibits the phenomenon of self-organisation (= stationary equilibrium) defined in Ref. 8 when Kawasaki dynamics is not dominant in its competition with Glauber, HeatBath and Swendsen-Wang algorithms. Only for Wolff cluster flipping the magnetisation, this phenomenon occurs after an exponentially decay of magnetisation with time. The Metropolis results are independent of competition. We also study the same process of competition described above but with Kawasaki dynamics at the same temperature as the other algorithms. The obtained results are similar for Wolff cluster flipping, Metropolis and Swendsen-Wang algorithms but different for HeatBath.

  3. Evaporation over a Heterogeneous Mixed Savanna-Agricultural Catchment using a Distributed Wireless Sensor Network

    NASA Astrophysics Data System (ADS)

    Ceperley, N. C.; Mande, T.; Barrenetxea, G.; Vetterli, M.; Yacouba, H.; Repetti, A.; Parlange, M. B.

    2010-12-01

    Small scale rain fed agriculture is the primary livelihood for a large part of the population of Burkina Faso. Regional climate change means that this population is becoming increasingly vulnerable. Additionally, as natural savanna is converted for agriculture, hydrological systems are observed to become less stable as infiltration is decreased and rapid runoff is increased to the detriment of crop productivity, downstream populations and local water sources. The majority of the Singou River Basin, located in South East Burkina Faso is managed by hunting reserves, geared to maintaining high populations of wild game; however, residents surrounding the protected areas have been forced to intensify agriculture that has resulted in soil degradation as well as increases in the frequency and severity of flooding and droughts. Agroforestry, or planting trees in cultivated fields, has been proposed as a solution to help buffer these negative consequences, however the specific hydrologic behavior of the watershed land cover is unknown. We have installed a distributed sensor network of 17 Sensorscope wireless meteorological stations. These stations are dispersed across cultivated rice and millet fields, natural savanna, fallow fields, and around agroforestry fields. Sensorscope routes data through the network of stations to be delivered by a GPRS connection to a main server. This multi hop network allows data to be gathered over a large area and quickly adapts to changes in station performance. Data are available in real time via a website that can be accessed by a mobile phone. The stations are powered autonomously by small photovoltaic panels. This deployment is the first time that these meteorological stations have been used on the African continent. Initial calibration with measures from 2 eddy covariance stations allows us to calculate the energy balance at each of the Sensorscope stations. Thus, we can observe variation in evaporation over the various land cover in the

  4. Using mixed methods to evaluate the Pediatric Lead Assessment Network Education Training Program (PLANET).

    PubMed

    Polivka, Barbara J; Chaudry, Rosemary V; Sharrock, Timberlee

    2009-03-01

    The Pediatric Lead Assessment Network Education Training Program (PLANET) is a peer-to-peer in-person 1-hr lead poisoning prevention educational program for health professionals. This evaluation was designed to determine the impact of the PLANET program. Evaluation methods included analyzing data from PLANET sign-in sheets, evaluation forms, pre/postknowledge tests, claims data, and focus groups (FGs) and interviews (IVs) with PLANET attendees and nonattendees. Claims data were used to compare blood lead testing rates for physicians attending and those not attending a PLANET program. Over 2,000 health professionals attended the 192 PLANET presentations delivered between June 2001 and December 2006; most were registered nurses or physicians. Written evaluations were overwhelmingly positive. Posttests indicated increased provider knowledge about childhood lead poisoning prevention, and assessment of blood lead testing rates showed higher testing rates for PLANET attendees. FG and IV participants suggesting improvements including using alternative delivery modes.

  5. Facing mixed emotions: Analytic and holistic perception of facial emotion expressions engages separate brain networks.

    PubMed

    Meaux, Emilie; Vuilleumier, Patrik

    2016-11-01

    The ability to decode facial emotions is of primary importance for human social interactions; yet, it is still debated how we analyze faces to determine their expression. Here we compared the processing of emotional face expressions through holistic integration and/or local analysis of visual features, and determined which brain systems mediate these distinct processes. Behavioral, physiological, and brain responses to happy and angry faces were assessed by presenting congruent global configurations of expressions (e.g., happy top+happy bottom), incongruent composite configurations (e.g., angry top+happy bottom), and isolated features (e.g. happy top only). Top and bottom parts were always from the same individual. Twenty-six healthy volunteers were scanned using fMRI while they classified the expression in either the top or the bottom face part but ignored information in the other non-target part. Results indicate that the recognition of happy and anger expressions is neither strictly holistic nor analytic Both routes were involved, but with a different role for analytic and holistic information depending on the emotion type, and different weights of local features between happy and anger expressions. Dissociable neural pathways were engaged depending on emotional face configurations. In particular, regions within the face processing network differed in their sensitivity to holistic expression information, which predominantly activated fusiform, inferior occipital areas and amygdala when internal features were congruent (i.e. template matching), whereas more local analysis of independent features preferentially engaged STS and prefrontal areas (IFG/OFC) in the context of full face configurations, but early visual areas and pulvinar when seen in isolated parts. Collectively, these findings suggest that facial emotion recognition recruits separate, but interactive dorsal and ventral routes within the face processing networks, whose engagement may be shaped by

  6. INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: A Mixed Mechanism of Weighted-Driven and Inner Selection in Networks

    NASA Astrophysics Data System (ADS)

    Zhang, Gui-Qing; Wang, Lin; Chen, Tian-Lun

    2009-05-01

    For most networks, the weight of connection is changing with their attachment and inner affinity. By introducing a mixed mechanism of weighted-driven and inner selection, the model exhibits wide range power-law distributions of node strength and edge weight, and the exponent can be adjusted by not only the parameter δ but also the probability q. Furthermore, we investigate the weighted average shortest distance, clustering coefficient, and the correlation of our network. In addition, the weighted assortativity coefficient which characterizes important information of weighted topological networks has been discussed, but the variation of coefficients is much smaller than the former researches.

  7. Department of Energy Waste Information Network: Hazardous and mixed waste data management

    SciTech Connect

    Fore, C.S.

    1990-01-01

    The Department of Energy (DOE) Waste Information Network (WIN) was developed through the efforts of the DOE Hazardous Waste Remedial Actions Program (HAZWRAP) Support Office (SO) to meet the programmatic information needs of the Director, Office of Environmental Restoration and Waste Management. WIN's key objective is to provide DOE Headquarters (HQ), DOE Operations Offices, and their contractors with an information management tool to support environmental restoration and waste management activities and to promote technology transfer across the DOE complex. WIN has evolved in various stages of growth driven by continued identification of user needs. The current system provides seven key features: technical information systems, bulletin boards, data file transfer, on-line conferencing, formal concurrence system, electronic messaging, and integrated spreadsheet/graphics. WIN is based on Digital Equipment Corporation;s (DEC) VAXcluster platform and is currently supporting nearly 1,000 users. An interactive menu system, DEC's ALL-IN-1 (1), provides easy access to all applications. WIN's many features are designed to provide the DOE waste management community with a repository of information management tools that are accessible, functional, and efficient. The type of tool required depends on the task to be performed, and WIN is equipped to serve many different needs. Each component of the system is evaluated for effectiveness for a particular purpose, ease of use, and quality of operation. The system is fully supported by project managers, systems analysts, and user assistance technicians to ensure subscribers of continued, uninterrupted service. 1 ref.

  8. Molecular tectonics of metal-organic frameworks (MOFs): a rational design strategy for unusual mixed-connected network topologies.

    PubMed

    Du, Miao; Zhang, Zhi-Hui; Tang, Liang-Fu; Wang, Xiu-Guang; Zhao, Xiao-Jun; Batten, Stuart R

    2007-01-01

    To systematically explore the higher-dimensional network structures with mixed connectivity, a series of two-dimensional (2D) and three-dimensional (3D) metal-organic frameworks (MOFs) with unusual (3,6)-connected net topologies are presented. These crystalline materials include [{[Mn(btza)2(H2O)2].2 H2O}n] (1), [{[Zn(btza)2(H2O)2].2 H2O}n] (2), [{[Cu(btza)2].H2O}n] (3), and [{[Cd(btza)2].3 H2O}n] (4), which have been successfully assembled through a predesigned three-connected organic component bis(1,2,4-triazol-1-yl)acetate (btza) with a variety of octahedral metal cores based on the modular synthetic methodology. The topological paradigms shown in this work cover the 2D CdCl2, 3D (4(2).6)2(4(4).6(2).8(7).10(2)), and pyrite (pyr) types. That is, when properly treated with the familiar first-row divalent metal ions, btza may perfectly furnish the coordination spheres for effective connectivity to result in diverse (3,6)-connected nets. Beyond this, a detailed analysis of network topology for all known 3D (3,6)-connected frameworks in both inorganic and inorganic-organic hybrid materials is described. Specific network connectivity of these MOFs indicates that the metal centers represent the most significant and alterable factor in structural assembly, although they show reliable and similar geometries. In this context, the combination of the distinct d10 AgI ion with btza in different solvents affords two isomorphous MOFs [{[Ag(btza)].glycol}n] (5) and [{[Ag(btza)]CH3OH}n] (6) with a binodal 4-connected 3D SrAl2 (sra) topology. The network structures of MOFs 1-3 and 5 turn out to be more complicated and interesting if one considers the hydrogen bonding between the host coordination frameworks and the intercalated solvent molecules. Furthermore, the role of the included solvents in the generation and stabilization of MOFs 1-6 is also investigated.

  9. Repeating Spatial-Temporal Motifs of CA3 Activity Dependent on Engineered Inputs from Dentate Gyrus Neurons in Live Hippocampal Networks

    PubMed Central

    Bhattacharya, Aparajita; Desai, Harsh; DeMarse, Thomas B.; Wheeler, Bruce C.; Brewer, Gregory J.

    2016-01-01

    Anatomical and behavioral studies, and in vivo and slice electrophysiology of the hippocampus suggest specific functions of the dentate gyrus (DG) and the CA3 subregions, but the underlying activity dynamics and repeatability of information processing remains poorly understood. To approach this problem, we engineered separate living networks of the DG and CA3 neurons that develop connections through 51 tunnels for axonal communication. Growing these networks on top of an electrode array enabled us to determine whether the subregion dynamics were separable and repeatable. We found spontaneous development of polarized propagation of 80% of the activity in the native direction from DG to CA3 and different spike and burst dynamics for these subregions. Spatial-temporal differences emerged when the relationships of target CA3 activity were categorized with to the number and timing of inputs from the apposing network. Compared to times of CA3 activity when there was no recorded tunnel input, DG input led to CA3 activity bursts that were 7× more frequent, increased in amplitude and extended in temporal envelope. Logistic regression indicated that a high number of tunnel inputs predict CA3 activity with 90% sensitivity and 70% specificity. Compared to no tunnel input, patterns of >80% tunnel inputs from DG specified different patterns of first-to-fire neurons in the CA3 target well. Clustering dendrograms revealed repeating motifs of three or more patterns at up to 17 sites in CA3 that were importantly associated with specific spatial-temporal patterns of tunnel activity. The number of these motifs recorded in 3 min was significantly higher than shuffled spike activity and not seen above chance in control networks in which CA3 was apposed to CA3 or DG to DG. Together, these results demonstrate spontaneous input-dependent repeatable coding of distributed activity in CA3 networks driven by engineered inputs from DG networks. These functional configurations at measured times

  10. Plasticity of neonatal neuronal networks in very premature infants: Source localization of temporal theta activity, the first endogenous neural biomarker, in temporoparietal areas.

    PubMed

    Routier, L; Mahmoudzadeh, M; Panzani, M; Azizollahi, H; Goudjil, S; Kongolo, G; Wallois, F

    2017-01-23

    Temporal theta slow-wave activity (TTA-SW) in premature infants is a specific signature of the early development of temporal networks, as it is observed at the turning point between non-sensory driven spontaneous local processing and cortical network functioning. The role in development and the precise location of TTA-SW remain unknown. Previous studies have demonstrated that preterms from 28 weeks of gestational age (wGA) are able to discriminate phonemes and voice, supporting the idea of a prior genetic structural or activity-dependent fingerprint that would prepare the auditory network to compute auditory information at the onset of thalamocortical connectivity. They recorded TTA-SW in 26-32 wGA preterms. The rate of TTA-SW in response to click stimuli was evaluated using low-density EEG in 30 preterms. The sources of TTA-SW were localized by high-density EEG using different tissues conductivities, head models and mathematical models. They observed that TTA-SW is not sensory driven. Regardless of age, conductivities, head models and mathematical models, sources of TTA-SW were located adjacent to auditory and temporal junction areas. These sources become situated closer to the surface during development. TTA-SW corresponds to spontaneous transient endogenous activities independent of sensory information at this period which might participate in the implementation of auditory, language, memory, attention and or social cognition convergent and does not simply represent a general interaction between the subplate and the cortical plate. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.

  11. Beta phase synchronization in the frontal-temporal-cerebellar network during auditory-to-motor rhythm learning

    PubMed Central

    Edagawa, Kouki; Kawasaki, Masahiro

    2017-01-01

    Rhythm is an essential element of dancing and music. To investigate the neural mechanisms underlying how rhythm is learned, we recorded electroencephalographic (EEG) data during a rhythm-reproducing task that asked participants to memorize an auditory stimulus and reproduce it via tapping. Based on the behavioral results, we divided the participants into Learning and No-learning groups. EEG analysis showed that error-related negativity (ERN) in the Learning group was larger than in the No-learning group. Time-frequency analysis of the EEG data showed that the beta power in right and left temporal area at the late learning stage was smaller than at the early learning stage in the Learning group. Additionally, the beta power in the temporal and cerebellar areas in the Learning group when learning to reproduce the rhythm were larger than in the No Learning group. Moreover, phase synchronization between frontal and temporal regions and between temporal and cerebellar regions at late stages of learning were larger than at early stages. These results indicate that the frontal-temporal-cerebellar beta neural circuits might be related to auditory-motor rhythm learning. PMID:28225010

  12. Evaluation of flow mixing in an ARID-HV algal raceway using statistics of temporal and spatial distribution of fluid particles

    SciTech Connect

    Xu, Ben; Li, Peiwen; Waller, Peter; Huesemann, Michael H.

    2015-02-27

    This paper analyzes and evaluates the flow mixing in an open channel algal raceway for biofuel production. The flow mixing governs the frequency of how algae cells are exposed to sunlight, due to the fluid movement between the surface and the bottom of the algal raceway, thereby affecting algal growth rate. In this work, we investigated the flow mixing performance in a table-sized model of the High Velocity Algae Raceway Integrated Design (ARID-HV). Various geometries of the raceway channels and dams were considered in both the CFD analysis and experimental flowvisualization. In the CFD simulation, the pathlines of fluid particleswere analyzed to obtain the distribution of the number of times that particles passed across a critical water depth, Dc, defined as a cycle count. In addition, the distribution of the time period fraction that the fluid particles stayed in the zones above and below Dc was recorded. Such information was used to evaluate the flow mixing in the raceway. The CFD evaluation of the flow mixing was validated using experimental flow visualization, which showed a good qualitative agreement with the numerical results. In conclusion, this CFD-based evaluation methodology is recommended for flow field optimization for open channel algal raceways, as well as for other engineering applications in which flow mixing is an important concern.

  13. User's Guide for Mixed-Size Sediment Transport Model for Networks of One-Dimensional Open Channels

    USGS Publications Warehouse

    Bennett, James P.

    2001-01-01

    This user's guide describes a mathematical model for predicting the transport of mixed sizes of sediment by flow in networks of one-dimensional open channels. The simulation package is useful for general sediment routing problems, prediction of erosion and deposition following dam removal, and scour in channels at road embankment crossings or other artificial structures. The model treats input hydrographs as stepwise steady-state, and the flow computation algorithm automatically switches between sub- and supercritical flow as dictated by channel geometry and discharge. A variety of boundary conditions including weirs and rating curves may be applied both external and internal to the flow network. The model may be used to compute flow around islands and through multiple openings in embankments, but the network must be 'simple' in the sense that the flow directions in all channels can be specified before simulation commences. The location and shape of channel banks are user specified, and all bedelevation changes take place between these banks and above a user-specified bedrock elevation. Computation of sediment-transport emphasizes the sand-size range (0.0625-2.0 millimeter) but the user may select any desired range of particle diameters including silt and finer (<0.0625 millimeter). As part of data input, the user may set the original bed-sediment composition of any number of layers of known thickness. The model computes the time evolution of total transport and the size composition of bed- and suspended-load sand through any cross section of interest. It also tracks bed -surface elevation and size composition. The model is written in the FORTRAN programming language for implementation on personal computers using the WINDOWS operating system and, along with certain graphical output display capability, is accessed from a graphical user interface (GUI). The GUI provides a framework for selecting input files and parameters of a number of components of the sediment

  14. Building long-term and high spatio-temporal resolution precipitation and air temperature reanalyses by mixing local observations and global atmospheric reanalyses: the ANATEM method

    NASA Astrophysics Data System (ADS)

    Kuentz, A.; Mathevet, T.; Gailhard, J.; Hingray, B.

    2015-01-01

    Improving the understanding of past climatic or hydrologic variability has received a large attention in different fields of geosciences, such as glaciology, dendrochronology, sedimentology or hydrology. Based on different proxies, each research community produces different kind of climatic or hydrologic reanalyses, at different spatio-temporal scales and resolution. When considering climate or hydrology, numerous studies aim at characterising variability, trends or breaks using observed time-series of different regions or climate of world. However, in hydrology, these studies are usually limited to reduced temporal scale (mainly few decades, seldomly a century) because they are limited to observed time-series, that suffers from a limited spatio-temporal density. This paper introduces a new model, ANATEM, based on a combination of local observations and large scale climatic informations (such as 20CR Reanalysis). This model allow to build long-term air temperature and precipitation time-series, with a high spatio-temporal resolution (daily time-step, few km2). ANATEM was tested on the air temperature and precipitation time-series of 22 watersheds situated on the Durance watershed, in the french Alps. Based on a multi-criteria and multi-scale diagnostic, the results show that ANATEM improves the performances of classical statistical models. ANATEM model have been validated on a regional level, improving spatial homogeneity of performances and on independent long-term time-series, being able to capture the regional low-frequency variabilities over more than a century (1883-2010).

  15. Neural Network Processing of Natural Language: I. Sensitivity to Serial, Temporal, and Abstract Structure of Language in the Infant.

    ERIC Educational Resources Information Center

    Dominey, Peter Ford; Ramus, Franck

    2000-01-01

    Demonstrates how innate representational capabilities for serial and temporal structure of language could arise from a common neural architecture, distinct from that required for the representation of abstract structure, and provides a predictive testable model of the initial computational state of the language learner. (Author/VWL)

  16. Alterations in the functional connectivity of a verbal working memory-related brain network in patients with left temporal lobe epilepsy.

    PubMed

    Huang, Wenli; Huang, Donghong; Chen, Zirong; Ye, Wei; Lv, Zongxia; Diao, Limei; Zheng, Jinou

    2015-08-18

    The aim of this study was to investigate the alterations in a verbal working memory (VWM)-related network in left temporal lobe epilepsy (lTLE) at rest. We evaluated 14 patients with lTLE and 14 control subjects by resting-state functional connectivity (RSFC). The region of interest was defined by the voxel with the highest Z-score during a VWM task according to functional magnetic resonance imaging in 16 healthy volunteers. Our study revealed that the network of RSFC was similar to the task-induced network in the healthy volunteers. Moreover, the patients with lTLE exhibited significantly decreased RSFC in the bilateral middle frontal gyrus, the inferior frontal gyrus and the inferior parietal lobule at rest compared to the control subjects. We found no significant correlation between the mean reaction time of the accurate responses in a 2-back task and the mean z-values within the regions that exhibited significant differences in RSFC at the individual level. The alterations in FCs of VWM-related network in lTLE suggested that epileptiform discharges can damage the brain regions, both local focus and remote areas and that the alterations were not associated with VWM performance.

  17. A Survey on Next-Generation Mixed Line Rate (MLR) and Energy-Driven Wavelength-Division Multiplexed (WDM) Optical Networks

    NASA Astrophysics Data System (ADS)

    Iyer, Sridhar

    2015-06-01

    With the ever-increasing traffic demands, infrastructure of the current 10 Gbps optical network needs to be enhanced. Further, since the energy crisis is gaining increasing concerns, new research topics need to be devised and technological solutions for energy conservation need to be investigated. In all-optical mixed line rate (MLR) network, feasibility of a lightpath is determined by the physical layer impairment (PLI) accumulation. Contrary to PLI-aware routing and wavelength assignment (PLIA-RWA) algorithm applicable for a 10 Gbps wavelength-division multiplexed (WDM) network, a new Routing, Wavelength, Modulation format assignment (RWMFA) algorithm is required for the MLR optical network. With the rapid growth of energy consumption in Information and Communication Technologies (ICT), recently, lot of attention is being devoted toward "green" ICT solutions. This article presents a review of different RWMFA (PLIA-RWA) algorithms for MLR networks, and surveys the most relevant research activities aimed at minimizing energy consumption in optical networks. In essence, this article presents a comprehensive and timely survey on a growing field of research, as it covers most aspects of MLR and energy-driven optical networks. Hence, the author aims at providing a comprehensive reference for the growing base of researchers who will work on MLR and energy-driven optical networks in the upcoming years. Finally, the article also identifies several open problems for future research.

  18. The Contribution of Spatial and Temporal Molecular Networks in the Induction of Long-term Memory and Its Underlying Synaptic Plasticity

    PubMed Central

    Mirisis, Anastasios A.; Alexandrescu, Anamaria; Carew, Thomas J.; Kopec, Ashley M.

    2016-01-01

    The ability to form long-lasting memories is critical to survival and thus is highly conserved across the animal kingdom. By virtue of its complexity, this same ability is vulnerable to disruption by a wide variety of neuronal traumas and pathologies. To identify effective therapies with which to treat memory disorders, it is critical to have a clear understanding of the cellular and molecular mechanisms which subserve normal learning and memory. A significant challenge to achieving this level of understanding is posed by the wide range of distinct temporal and spatial profiles of molecular signaling induced by learning-related stimuli. In this review we propose that a useful framework within which to address this challenge is to view the molecular foundation of long-lasting plasticity as composed of unique spatial and temporal molecular networks that mediate signaling both within neurons (such as via kinase signaling) as well as between neurons (such as via growth factor signaling). We propose that evaluating how cells integrate and interpret these concurrent and interacting molecular networks has the potential to significantly advance our understanding of the mechanisms underlying learning and memory formation. PMID:27819030

  19. Spatio-Temporal Tolerance of Visuo-Tactile Illusions in Artificial Skin by Recurrent Neural Network with Spike-Timing-Dependent Plasticity

    PubMed Central

    Pitti, Alexandre; Pugach, Ganna; Gaussier, Philippe; Shimada, Sotaro

    2017-01-01

    Perceptual illusions across multiple modalities, such as the rubber-hand illusion, show how dynamic the brain is at adapting its body image and at determining what is part of it (the self) and what is not (others). Several research studies showed that redundancy and contingency among sensory signals are essential for perception of the illusion and that a lag of 200–300 ms is the critical limit of the brain to represent one’s own body. In an experimental setup with an artificial skin, we replicate the visuo-tactile illusion within artificial neural networks. Our model is composed of an associative map and a recurrent map of spiking neurons that learn to predict the contingent activity across the visuo-tactile signals. Depending on the temporal delay incidentally added between the visuo-tactile signals or the spatial distance of two distinct stimuli, the two maps detect contingency differently. Spiking neurons organized into complex networks and synchrony detection at different temporal interval can well explain multisensory integration regarding self-body. PMID:28106139

  20. Spatio-temporal organization of dynamics in a two-dimensional periodically driven vortex flow: A Lagrangian flow network perspective

    NASA Astrophysics Data System (ADS)

    Lindner, Michael; Donner, Reik V.

    2017-03-01

    We study the Lagrangian dynamics of passive tracers in a simple model of a driven two-dimensional vortex resembling real-world geophysical flow patterns. Using a discrete approximation of the system's transfer operator, we construct a directed network that describes the exchange of mass between distinct regions of the flow domain. By studying different measures characterizing flow network connectivity at different time-scales, we are able to identify the location of dynamically invariant structures and regions of maximum dispersion. Specifically, our approach allows us to delimit co-existing flow regimes with different dynamics. To validate our findings, we compare several network characteristics to the well-established finite-time Lyapunov exponents and apply a receiver operating characteristic analysis to identify network measures that are particularly useful for unveiling the skeleton of Lagrangian chaos.

  1. Functional paleoclimate networks of North Atlantic terrestrial proxies: A new tool for studying spatio-temporal climate variability within the Arctic 2k framework

    NASA Astrophysics Data System (ADS)

    Franke, Jasper G.; Donner, Reik V.

    2016-04-01

    The increasing availability of high-resolution paleoclimate proxies allows to not only study climate variations in time, but also temporal changes in spatial variability patterns. In this study we use the method of functional paleoclimate network analysis [1] to investigate changes in the statistical similarity patterns among ensembles of high-resolution terrestrial paleoclimate records from Northern Europe. The study region ranging from Southern Finland over Northern Fennoscandia to Iceland is of paramount importance for reconstructions of the climate of the last two millennia within the Arctic 2k framework, and understanding the associated spatial variability of regional paleoclimate is a key question for further regional reconstructions. The analysis reported here is based on an ensemble of 16 paleoclimate proxy records comprising tree ring data from the Scandinavian Peninsula, different lacustrine archives from Southern Finland and one lake sediment record cored on Iceland, having a common interpretation as proxies of (mainly summer) temperatures. Based on the mentioned selection of existing data sets, we construct complex networks capturing the mutual statistical similarity of the variability recorded by different archives furing different episodes in time. These ''functional'' networks are not restricted to capturing linear Pearson correlations, but can also be obtained based on nonlinear characteristics like mutual information. This allows for comparing non-normally distributed time series or data of different origin like tree ring and lake sediment records as considered in this study. Furthermore, the obtained functional paleoclimate networks are used to test if regional (gridded) proxy-based temperature reconstructions preserve the essential spatial correlation patterns of the underlying archives. Temporal changes in the network structure indicate changing dynamics in the regional climate system and enable us to distinguish different episodes with distinct

  2. Building long-term and high spatio-temporal resolution precipitation and air temperature reanalyses by mixing local observations and global atmospheric reanalyses: the ANATEM model

    NASA Astrophysics Data System (ADS)

    Kuentz, A.; Mathevet, T.; Gailhard, J.; Hingray, B.

    2015-06-01

    Efforts to improve the understanding of past climatic or hydrologic variability have received a great deal of attention in various fields of geosciences such as glaciology, dendrochronology, sedimentology and hydrology. Based on different proxies, each research community produces different kinds of climatic or hydrologic reanalyses at different spatio-temporal scales and resolutions. When considering climate or hydrology, many studies have been devoted to characterising variability, trends or breaks using observed time series representing different regions or climates of the world. However, in hydrology, these studies have usually been limited to short temporal scales (mainly a few decades and more rarely a century) because they require observed time series (which suffer from a limited spatio-temporal density). This paper introduces ANATEM, a method that combines local observations and large-scale climatic information (such as the 20CR Reanalysis) to build long-term probabilistic air temperature and precipitation time series with a high spatio-temporal resolution (1 day and a few km2). ANATEM was tested on the reconstruction of air temperature and precipitation time series of 22 watersheds situated in the Durance River basin, in the French Alps. Based on a multi-criteria and multi-scale diagnosis, the results show that ANATEM improves the performance of classical statistical models - especially concerning spatial homogeneity - while providing an original representation of uncertainties which are conditioned by atmospheric circulation patterns. The ANATEM model has been also evaluated for the regional scale against independent long-term time series and was able to capture regional low-frequency variability over more than a century (1883-2010).

  3. Intercomparison of Methods for the Determination of Mixing-Layer Heights Using a New Network of Advanced Ceilometers in Germany

    NASA Astrophysics Data System (ADS)

    Engelbart, D. A. M.; Reichardt, J.; Teschke, G.

    2009-09-01

    The mixing layer height (MLH) is one of the most relevant parameters for modeling and assessing the atmospheric spreading conditions for all kinds of constituents in the boundary layer. For this reason, it has become more and more important to operationally detect and determine MLH with networks of sophisticated observing systems. Such networks can either be used for validation of the output from NWP models, or for the direct assessment of the atmospheric conditions (if the measurements are cost-effective and the results can be proven to be reliable). Typically, MLH is determined from vertical profiles measured with radiosondes, lidar, sodar/RASS, or WPR/RASS. While time resolution of radiosondes is strongly restricted, remote-sensing systems are mostly facing deficits with respect to height coverage or range. Apart from combinations of systems, ceilometers show considerable advantages for monitoring the full daily cycle of the MLH from the surface layer up to more than three kilometers. In 2007, the German Meteorological Service started to install a network of new ceilometers which are being used among other objectives also for MLH detection. In contrast to the previous systems, the new type of ceilometer (JENOPTIK CHM-15K) is based on a diode-pumped Nd:YAG laser and a single-photon-counting detector with considerably higher sensitivity than standard analog-detection systems. Apart from a description of the new type of ceilometers, this contribution focuses on the presentation of a new mathematics-based method for the determination of MLH. In principle, MLH detection is a pattern recognition problem. The basic assumption which is usually made is that the vertical distribution of aerosol can be used as a tracer for finding boundaries. The absolute value of the backscatter is typically not needed since the relevant information seems to be completely coded in the gradient (but possibly of different orders) of the backscatter profile. Currently, two major types of

  4. Unraveling the Drivers of Spatial and Temporal Variability in Biogeochemical Cycling at Aquifer-River Interfaces - The LEVERHULME Hyporheic Zone Research Network

    NASA Astrophysics Data System (ADS)

    Krause, Stefan

    2015-04-01

    While there has been substantial improvement of understanding hyporheic exchange flow and residence time controls on biogeochemical turnover rates, there is little knowledge of the actual drivers of the spatial and temporal variability of interlinked biogeochemical cycles. Previous research has mainly focused on bedform controlled hyporheic exchange and the transformation of surface solutes along a hyporheic flow path but failed to explain observations of spatially and temporally variable nutrient turnover in streambeds with higher structural heterogeneity and autochthonous carbon and nitrogen sources. The "Leverhulme Hyporheic Zone Research Network" has developed an interdisciplinary strategy for investigating the physical controls on hyporheic exchange fluxes and residence time distributions, heat and reactive solute transport along biogeographical and catchment gradients. This strategy combines smart tracer applications with distributed sensor networks in multi-scale nested monitoring schemes and numerical model studies to investigate the interactions between physico-chemical process dynamics and hyporheic microbial, invertebrate and macrophyte ecology. Investigations integrating the process knowledge from mesocosms to artificial channels and stream reaches highlight the impact of small-scale streambed structure on spatial patterns of hyporheic exchange flow, residence time distribution and the development of biogeochemical hotspots. Manipulation studies inhibiting flow through dominant hyporheic exchange flow paths allowed to quantify the functional significance, sensitivity and resilience of biogeochemical, microbial and ecological functioning of identified hyporheic hotspots to environmental change. Further discharge and stage manipulations proved to not only control in-channel macrophyte growth but also temperature patterns and residence time distributions as well as microbial metabolic activity and biogeochemical processing rates, highlighting the potential

  5. Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks

    PubMed Central

    Erfanian Saeedi, Nafise; Blamey, Peter J.; Burkitt, Anthony N.; Grayden, David B.

    2016-01-01

    Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons’ action potentials (spikes) as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy. PMID:27049657

  6. Spatio-Temporal Dynamics of Foraging Networks in the Grass-Cutting Ant Atta bisphaerica Forel, 1908 (Formicidae, Attini)

    PubMed Central

    Lopes, Juliane F. S.; Brugger, Mariana S.; Menezes, Regys B.; Camargo, Roberto S.; Forti, Luiz Carlos; Fourcassié, Vincent

    2016-01-01

    Foraging networks are a key element for ant colonies because they facilitate the flow of resources from the environment to the nest and they allow the sharing of information among individuals. Here we report the results of an 8-month survey, extending from November 2009 to June 2010, of the foraging networks of four mature colonies of Atta bisphaerica, a species of grass-cutting ant which is considered as a pest in Brazil. We found that the distribution of foraging effort was strongly influenced by the landscape features around the nests, in particular by the permanently wet parts of the pasture in which the nests were located. The foraging networks consisted of underground tunnels which opened on average at 21.5m from the nests and of above-ground physical trails that reached on average 4.70m in length. The use of the foraging networks was highly dynamic, with few sections of the networks used for long periods of time. Three different phases, which could be linked to the seasonal change in the local rainfall regime, could be identified in the construction and use of the foraging networks. The first phase corresponded to the beginning of the rainy season and was characterized by a low foraging activity, as well as a low excavation and physical trail construction effort. The second phase, which began in February and extended up to the end of the humid season at the end of March, was characterized by an intense excavation and trail construction effort, resulting in an expansion of the foraging networks. Finally, in the third phase, which corresponded to the beginning of the dry season, the excavation and trail construction effort leveled off or decreased while foraging activity kept increasing. Our hypothesis is that ants could benefit from the underground tunnels and physical trails built during the humid season to maintain their foraging activity at a high level. PMID:26752413

  7. Spatio-Temporal Dynamics of Foraging Networks in the Grass-Cutting Ant Atta bisphaerica Forel, 1908 (Formicidae, Attini).

    PubMed

    Lopes, Juliane F S; Brugger, Mariana S; Menezes, Regys B; Camargo, Roberto S; Forti, Luiz Carlos; Fourcassié, Vincent

    2016-01-01

    Foraging networks are a key element for ant colonies because they facilitate the flow of resources from the environment to the nest and they allow the sharing of information among individuals. Here we report the results of an 8-month survey, extending from November 2009 to June 2010, of the foraging networks of four mature colonies of Atta bisphaerica, a species of grass-cutting ant which is considered as a pest in Brazil. We found that the distribution of foraging effort was strongly influenced by the landscape features around the nests, in particular by the permanently wet parts of the pasture in which the nests were located. The foraging networks consisted of underground tunnels which opened on average at 21.5m from the nests and of above-ground physical trails that reached on average 4.70m in length. The use of the foraging networks was highly dynamic, with few sections of the networks used for long periods of time. Three different phases, which could be linked to the seasonal change in the local rainfall regime, could be identified in the construction and use of the foraging networks. The first phase corresponded to the beginning of the rainy season and was characterized by a low foraging activity, as well as a low excavation and physical trail construction effort. The second phase, which began in February and extended up to the end of the humid season at the end of March, was characterized by an intense excavation and trail construction effort, resulting in an expansion of the foraging networks. Finally, in the third phase, which corresponded to the beginning of the dry season, the excavation and trail construction effort leveled off or decreased while foraging activity kept increasing. Our hypothesis is that ants could benefit from the underground tunnels and physical trails built during the humid season to maintain their foraging activity at a high level.

  8. Racial/Ethnic Differences in Sexual Network Mixing: A Log-Linear Analysis of HIV Status by Partnership and Sexual Behavior Among Most at-Risk MSM.

    PubMed

    Fujimoto, Kayo; Williams, Mark L

    2015-06-01

    Mixing patterns within sexual networks have been shown to have an effect on HIV transmission, both within and across groups. This study examined sexual mixing patterns involving HIV-unknown status and risky sexual behavior conditioned on assortative/dissortative mixing by race/ethnicity. The sample used for this study consisted of drug-using male sex workers and their male sex partners. A log-linear analysis of 257 most at-risk MSM and 3,072 sex partners was conducted. The analysis found two significant patterns. HIV-positive most at-risk Black MSM had a strong tendency to have HIV-unknown Black partners (relative risk, RR = 2.91, p < 0.001) and to engage in risky sexual behavior (RR = 2.22, p < 0.001). White most at-risk MSM with unknown HIV status also had a tendency to engage in risky sexual behavior with Whites (RR = 1.72, p < 0.001). The results suggest that interventions that target the most at-risk MSM and their sex partners should account for specific sexual network mixing patterns by HIV status.

  9. Characterization of flow and mixing regimes within the ileum of the brushtail possum using residence time distribution analysis with simultaneous spatio-temporal mapping

    PubMed Central

    Janssen, P W M; Lentle, R G; Asvarujanon, P; Chambers, P; Stafford, K J; Hemar, Y

    2007-01-01

    We studied the flow and mixing regimes in isolated segments of the terminal ileum of brushtail possums during spontaneous circumferential and longitudinal contractions under conditions that allowed backflow and compared them with those of inactive segments. Residence time distributions (RTDs) were determined by perfusion with two probes of different rheological properties to which an inert dye marker was added. Ileal segment volume and oscillatory flow during the period of RTD determination were derived from spatiotemporal maps. High viscosity guar gum solution generated RTDs characteristic of laminar flow in inactive ileal segments which confirmed that no slip was occurring at the mucosal layer. In active segments, motility and consequent oscillatory flow imparted significant additional axial dispersion to the flow patterns of both probes. Mixing occurred episodically during periods when intestinal volume was reduced and onflow was augmented by peristalsis, which may prevent the establishment of steady state conditions. Marker concentration rose more steeply when active ileal segments were being perfused with a probe of similar viscosity to normal digesta than with low viscosity Earle's/Hepes solution, each being subject to similar levels of oscillatory flow. This indicated that a coarser mixing regime prevailed and that absorption of nutrients from viscous digesta would rely to a greater degree on molecular diffusion. PMID:17495038

  10. Temporal Dynamics and Developmental Maturation of Salience, Default and Central-Executive Network Interactions Revealed by Variational Bayes Hidden Markov Modeling

    PubMed Central

    Chen, Tianwen; Kochalka, John; Padmanabhan, Aarthi; Menon, Vinod

    2016-01-01

    Little is currently known about dynamic brain networks involved in high-level cognition and their ontological basis. Here we develop a novel Variational Bayesian Hidden Markov Model (VB-HMM) to investigate dynamic temporal properties of interactions between salience (SN), default mode (DMN), and central executive (CEN) networks—three brain systems that play a critical role in human cognition. In contrast to conventional models, VB-HMM revealed multiple short-lived states characterized by rapid switching and transient connectivity between SN, CEN, and DMN. Furthermore, the three “static” networks occurred in a segregated state only intermittently. Findings were replicated in two adult cohorts from the Human Connectome Project. VB-HMM further revealed immature dynamic interactions between SN, CEN, and DMN in children, characterized by higher mean lifetimes in individual states, reduced switching probability between states and less differentiated connectivity across states. Our computational techniques provide new insights into human brain network dynamics and its maturation with development. PMID:27959921

  11. Temporal Genetic Modifications after Controlled Cortical Impact—Understanding Traumatic Brain Injury through a Systematic Network Approach

    PubMed Central

    Wong, Yung-Hao; Wu, Chia-Chou; Wu, John Chung-Che; Lai, Hsien-Yong; Chen, Kai-Yun; Jheng, Bo-Ren; Chen, Mien-Cheng; Chang, Tzu-Hao; Chen, Bor-Sen

    2016-01-01

    Traumatic brain injury (TBI) is a primary injury caused by external physical force and also a secondary injury caused by biological processes such as metabolic, cellular, and other molecular events that eventually lead to brain cell death, tissue and nerve damage, and atrophy. It is a common disease process (as opposed to an event) that causes disabilities and high death rates. In order to treat all the repercussions of this injury, treatment becomes increasingly complex and difficult throughout the evolution of a TBI. Using high-throughput microarray data, we developed a systems biology approach to explore potential molecular mechanisms at four time points post-TBI (4, 8, 24, and 72 h), using a controlled cortical impact (CCI) model. We identified 27, 50, 48, and 59 significant proteins as network biomarkers at these four time points, respectively. We present their network structures to illustrate the protein–protein interactions (PPIs). We also identified UBC (Ubiquitin C), SUMO1, CDKN1A (cyclindependent kinase inhibitor 1A), and MYC as the core network biomarkers at the four time points, respectively. Using the functional analytical tool MetaCore™, we explored regulatory mechanisms and biological processes and conducted a statistical analysis of the four networks. The analytical results support some recent findings regarding TBI and provide additional guidance and directions for future research. PMID:26861311

  12. Structural integration of tellurium oxide into mixed-network-former glasses: connectivity distribution in the system NaPO(3)-TeO(2).

    PubMed

    Rinke, Matthias T; Zhang, Long; Eckert, Hellmut

    2007-09-17

    Sodium phosphate tellurite glasses in the system (NaPO(3))(x)(TeO(2))(1-) (x) were prepared and structurally characterized by thermal analysis, vibrational spectroscopy, X-ray photoelectron spectroscopy (XPS) and a variety of complementary solid-state nuclear magnetic resonance (NMR) techniques. Unlike the situation in other mixed-network-former glasses, the interaction between the two network formers tellurium oxide and phosphorus oxide produces no new structural units, and no sharing of the network modifier Na(2)O takes place. The glass structure can be regarded as a network of interlinked metaphosphate-type P(2) tetrahedral and TeO(4/2) antiprismatic units. The combined interpretation of the O 1s XPS data and the (31)P solid-state NMR spectra presents clear quantitative evidence for a nonstatistical connectivity distribution. Rather, the formation of homoatomic P--O--P and Te--O--Te linkages is favored over mixed P--O--Te connectivities. As a consequence of this chemical segregation effect, the spatial sodium distribution is not random, as also indicated by a detailed analysis of (31)P/(23)Na rotational echo double-resonance (REDOR) experiments.

  13. A method for extracting temporal parameters based on hidden Markov models in body sensor networks with inertial sensors.

    PubMed

    Guenterberg, Eric; Yang, Allen Y; Ghasemzadeh, Hassan; Jafari, Roozbeh; Bajcsy, Ruzena; Sastry, S Shankar

    2009-11-01

    Human movement models often divide movements into parts. In walking, the stride can be segmented into four different parts, and in golf and other sports, the swing is divided into sections based on the primary direction of motion. These parts are often divided based on key events, also called temporal parameters. When analyzing a movement, it is important to correctly locate these key events, and so automated techniques are needed. There exist many methods for dividing specific actions using data from specific sensors, but for new sensors or sensing positions, new techniques must be developed. We introduce a generic method for temporal parameter extraction called the hidden Markov event model based on hidden Markov models. Our method constrains the state structure to facilitate precise location of key events. This method can be quickly adapted to new movements and new sensors/sensor placements. Furthermore, it generalizes well to subjects not used for training. A multiobjective optimization technique using genetic algorithms is applied to decrease error and increase cross-subject generalizability. Further, collaborative techniques are explored. We validate this method on a walking dataset by using inertial sensors placed on various locations on a human body. Our technique is designed to be computationally complex for training, but computationally simple at runtime to allow deployment on resource-constrained sensor nodes.

  14. Efficient Streaming Mass Spatio-Temporal Vehicle Data Access in Urban Sensor Networks Based on Apache Storm.

    PubMed

    Zhou, Lianjie; Chen, Nengcheng; Chen, Zeqiang

    2017-04-10

    The efficient data access of streaming vehicle data is the foundation of analyzing, using and mining vehicle data in smart cities, which is an approach to understand traffic environments. However, the number of vehicles in urban cities has grown rapidly, reaching hundreds of thousands in number. Accessing the mass streaming data of vehicles is hard and takes a long time due to limited computation capability and backward modes. We propose an efficient streaming spatio-temporal data access based on Apache Storm (ESDAS) to achieve real-time streaming data access and data cleaning. As a popular streaming data processing tool, Apache Storm can be applied to streaming mass data access and real time data cleaning. By designing the Spout/bolt workflow of topology in ESDAS and by developing the speeding bolt and other bolts, Apache Storm can achieve the prospective aim. In our experiments, Taiyuan BeiDou bus location data is selected as the mass spatio-temporal data source. In the experiments, the data access results with different bolts are shown in map form, and the filtered buses' aggregation forms are different. In terms of performance evaluation, the consumption time in ESDAS for ten thousand records per second for a speeding bolt is approximately 300 milliseconds, and that for MongoDB is approximately 1300 milliseconds. The efficiency of ESDAS is approximately three times higher than that of MongoDB.

  15. MORE: mixed optimization for reverse engineering--an application to modeling biological networks response via sparse systems of nonlinear differential equations.

    PubMed

    Sambo, Francesco; de Oca, Marco A Montes; Di Camillo, Barbara; Toffolo, Gianna; Stützle, Thomas

    2012-01-01

    Reverse engineering is the problem of inferring the structure of a network of interactions between biological variables from a set of observations. In this paper, we propose an optimization algorithm, called MORE, for the reverse engineering of biological networks from time series data. The model inferred by MORE is a sparse system of nonlinear differential equations, complex enough to realistically describe the dynamics of a biological system. MORE tackles separately the discrete component of the problem, the determination of the biological network topology, and the continuous component of the problem, the strength of the interactions. This approach allows us both to enforce system sparsity, by globally constraining the number of edges, and to integrate a priori information about the structure of the underlying interaction network. Experimental results on simulated and real-world networks show that the mixed discrete/continuous optimization approach of MORE significantly outperforms standard continuous optimization and that MORE is competitive with the state of the art in terms of accuracy of the inferred networks.

  16. Generalized semi-analytical solutions to multispecies transport equation coupled with sequential first-order reaction network with spatially or temporally variable transport and decay coefficients

    NASA Astrophysics Data System (ADS)

    Suk, Heejun

    2016-08-01

    This paper presents a semi-analytical procedure for solving coupled the multispecies reactive solute transport equations, with a sequential first-order reaction network on spatially or temporally varying flow velocities and dispersion coefficients involving distinct retardation factors. This proposed approach was developed to overcome the limitation reported by Suk (2013) regarding the identical retardation values for all reactive species, while maintaining the extensive capability of the previous Suk method involving spatially variable or temporally variable coefficients of transport, general initial conditions, and arbitrary temporal variable inlet concentration. The proposed approach sequentially calculates the concentration distributions of each species by employing only the generalized integral transform technique (GITT). Because the proposed solutions for each species' concentration distributions have separable forms in space and time, the solution for subsequent species (daughter species) can be obtained using only the GITT without the decomposition by change-of-variables method imposing the limitation of identical retardation values for all the reactive species by directly substituting solutions for the preceding species (parent species) into the transport equation of subsequent species (daughter species). The proposed solutions were compared with previously published analytical solutions or numerical solutions of the numerical code of the Two-Dimensional Subsurface Flow, Fate and Transport of Microbes and Chemicals (2DFATMIC) in three verification examples. In these examples, the proposed solutions were well matched with previous analytical solutions and the numerical solutions obtained by 2DFATMIC model. A hypothetical single-well push-pull test example and a scale-dependent dispersion example were designed to demonstrate the practical application of the proposed solution to a real field problem.

  17. Temporal profiling of cytokine-induced genes in pancreatic β-cells by meta-analysis and network inference.

    PubMed

    Lopes, Miguel; Kutlu, Burak; Miani, Michela; Bang-Berthelsen, Claus H; Størling, Joachim; Pociot, Flemming; Goodman, Nathan; Hood, Lee; Welsh, Nils; Bontempi, Gianluca; Eizirik, Decio L

    2014-04-01

    Type 1 Diabetes (T1D) is an autoimmune disease where local release of cytokines such as IL-1β and IFN-γ contributes to β-cell apoptosis. To identify relevant genes regulating this process we performed a meta-analysis of 8 datasets of β-cell gene expression after exposure to IL-1β and IFN-γ. Two of these datasets are novel and contain time-series expressions in human islet cells and rat INS-1E cells. Genes were ranked according to their differential expression within and after 24 h from exposure, and characterized by function and prior knowledge in the literature. A regulatory network was then inferred from the human time expression datasets, using a time-series extension of a network inference method. The two most differentially expressed genes previously unknown in T1D literature (RIPK2 and ELF3) were found to modulate cytokine-induced apoptosis. The inferred regulatory network is thus supported by the experimental validation, providing a proof-of-concept for the proposed statistical inference approach.

  18. Spatio-temporal electrical stimuli shape behavior of an embodied cortical network in a goal-directed learning task

    PubMed Central

    Bakkum, Douglas J; Chao, Zenas C; Potter, Steve M

    2008-01-01

    We developed an adaptive training algorithm, whereby an in vitro neocortical network learned to modulate its dynamics and achieve pre-determined activity states within tens of minutes through the application of patterned training stimuli using a multi-electrode array. A priori knowledge of functional connectivity was not necessary. Instead, effective training sequences were continuously discovered and refined based on real-time feedback of performance. The short-term neural dynamics in response to training became engraved in the network, requiring progressively fewer training stimuli to achieve successful behavior in a movement task. After 2 h of training, plasticity remained significantly greater than the baseline for 80 min (p-value <0.01). Interestingly, a given sequence of effective training stimuli did not induce significant plasticity (p-value = 0.82) or desired behavior, when replayed to the network and no longer contingent on feedback. Our results encourage an in vivo investigation of how targeted multi-site artificial stimulation of the brain, contingent on the activity of the body or even of the brain itself could treat neurological disorders by gradually shaping functional connectivity. PMID:18714127

  19. A spatio-temporal screening tool for outlier detection in long term / large scale air quality observation time series and monitoring networks

    NASA Astrophysics Data System (ADS)

    Kracht, Oliver; Reuter, Hannes I.; Gerboles, Michel

    2013-04-01

    We present a consolidated screening tool for the detection of outliers in air quality monitoring data, which considers both attribute values and spatio-temporal relationships. Furthermore, an application example of warnings on abnormal values in time series of PM10 datasets in AirBase is presented. Spatial or temporal outliers in air quality datasets represent stations or individual measurements which differ significantly from other recordings within their spatio-temporal neighbourhood. Such abnormal values can be identified as being extreme compared to their neighbours, even though they do not necessarily require to differ significantly from the statistical distribution of the entire population. The identification of such outliers can be of interest as the basis of data quality control systems when several contributors report their measurements to the collection of larger datasets. Beyond this, it can also provide a simple solution to investigate the accuracy of station classifications. Seen from another viewpoint, it can be used as a tool to detect irregular air pollution emission events (e.g. the influence of fires, wind erosion events, or other accidental situations). The presented procedure for outlier detection was designed based on already existing literature. Specifically, we adapted the "Smooth Spatial Attribute Method" that was first developed for the identification of outlier values in networks of traffic sensors [1]. Since a free and extensible simulation platform was considered important, all codes were prototyped in the R environment which is available under the GNU General Public License [2]. Our algorithms are based on the definition of a neighbourhood for each air quality measurement, corresponding to a spatio-temporal domain limited by time (e.g., +/- 2 days) and distance (e.g., +/- 1 spherical degrees) around the location of ambient air monitoring stations. The objective of the method is that within such a given spatio-temporal domain, in which

  20. The electrical coupling confers to a network of interneurons the ability of transmitting excitatory inputs with high temporal precision.

    PubMed

    Di Garbo, Angelo

    2008-08-15

    The transmission of excitatory inputs by inhibitory networks of different sizes is investigated by means of numerical simulations. The interneurons are coupled by electrical and/or inhibitory synapses and each of them receives an excitatory pulse at a random time. The pulse times are extracted from a Gaussian distribution and each cell model is subject to an independent noisy current. The results described in this paper suggest that the presence of the electrical coupling promotes the transmission of the excitatory synaptic inputs on a time scale of a few milliseconds.

  1. An Efficient Single-Molecule Resolution Method for Simulating Spatio-Temporal Dynamics of Protein Interaction Networks that Involve the Cell Membranes

    NASA Astrophysics Data System (ADS)

    Yogurtcu, Osman N.; Johnson, Margaret E.

    A significant number of the cellular protein interaction networks, such as the receptor mediated signaling and vesicle trafficking pathways, includes membranes as a molecular assembly platform. Computer simulations can provide insight into the dynamics of complex formation and help identify the principles that govern recruitment and assembly on the membranes. Here, we introduce the Free-Propagator Re-weighting (FPR) algorithm, a recently developed method that efficiently simulates the spatio-temporal dynamics of multiprotein complex formation both in the solution and on the membranes. In the FPR, the position of each protein is propagated using the Brownian motion and the reactions between pairs of proteins can occur upon collisions. Depending on the dimensionality of the interaction, the association probabilities are determined by solving the Smoluchowski diffusion equations in 2D or 3D and trajectory reweighting allows us to obtain the exact association rates for all the reactive pairs. Using the FPR, in this presentation, we investigate the interaction dynamics of the receptor mediated endocytic network as a case study and discuss the possible effects of membrane binding and molecular crowding on the formation of complexes. Supported by the NIGMS/NIH under R00GM098371.

  2. Using a combination of fMRI and anterior temporal lobe rTMS to measure intrinsic and induced activation changes across the semantic cognition network

    PubMed Central

    Binney, Richard J.; Lambon Ralph, Matthew A.

    2015-01-01

    By developing and applying a method which combines fMRI and rTMS to explore semantic cognition, we identified both intrinsic (related to automatic changes in task/stimulus-related processing) and induced (i.e., associated with the effect of TMS) activation changes in the core, functionally-coupled network elements. Low-frequency rTMS applied to the human anterior temporal lobe (ATL) induced: (a) a local suppression at the site of stimulation; (b) remote suppression in three other ipsilateral semantic regions; and (c) a compensatory up-regulation in the contralateral ATL. Further examination of activity over time revealed that the compensatory changes appear to be a modulation of intrinsic variations that occur within the unperturbed network. As well as providing insights into the dynamic collaboration between core regions, the ability to observe intrinsic and induced changes in vivo may provide an important opportunity to understand the key mechanisms that underpin recovery of function in neurological patient groups. PMID:25448851

  3. Spatio-temporal variability in movement, age, and growth of mountain whitefish (Prosopium williamsoni) in a river network based upon PIT tagging and otolith chemistry

    USGS Publications Warehouse

    Benjamin, Joseph R.; Wetzel, Lisa A.; Martens, Kyle D.; Larsen, Kimberly; Connolly, Patrick J.

    2013-01-01

    Connectivity of river networks and the movements among habitats can be critical for the life history of many fish species, and understanding of the patterns of movement is central to managing populations, communities, and the landscapes they use. We combined passive integrated transponder tagging over 4 years and strontium isotopes in otoliths to demonstrate that 25% of the mountain whitefish (Prosopium williamsoni) sampled moved between the Methow and Columbia rivers, Washington, USA. Seasonal migrations downstream from the Methow River to the Columbia River to overwinter occurred in autumn and upstream movements in the spring. We observed migration was common during the first year of life, with migrants being larger than nonmigrants. However, growth between migrants and nonmigrants was similar. Water temperature was positively related to the proportion of migrants and negatively related to the timing of migration, but neither was related to discharge. The broad spatio-temporal movements we observed suggest mountain whitefish, and likely other nonanadromous fish, require distant habitats and also suggests that management and conservation strategies to keep connectivity of large river networks are imperative.

  4. Towards a Methodology for Estimating Surface Pollutant Mixing Ratios from High Spatial and Temporal Resolution Retrievals, and its Applicability to High-Resolution Space Based Observations

    NASA Astrophysics Data System (ADS)

    Knepp, T.; Pippin, M.; Crawford, J.; Szykman, J.; Long, R.; Neil, D.; Fishman, J.

    2012-11-01

    A ground-based sun-tracking spectrometer system (Pandora) is used to retrieve high time and spatial resolution total-column nitrogen dioxide. These column observations are compared with data from a surface NOx instrument that employs a photolytic converter. The column data are inverted (via the EDAS-40 model) to yield surface mole fractions (i.e.ppb) that have typically high coefficients of correlation (e.g. R = 0.80) with surface data. Translating these column observations into boundary-layer mole fractions provides a direct NO2 data set that can significantly improve the understanding of emission, chemical transportation, effectiveness of control strategies, and predictive capabilities. Preliminary results regarding the relation of surface and column NO2 were presented. Total-column NO2 was recorded using a Pandora sun-tracking spectrometer system [1]. The Pandora instrument provides high-temporal resolution data, with a retrieval done every ~90s under clear-sky conditions. Surface NO2 was recorded using a Teledyne API 200EU with a photolytic converter.

  5. Community structure analysis of transcriptional networks reveals distinct molecular pathways for early- and late-onset temporal lobe epilepsy with childhood febrile seizures.

    PubMed

    Moreira-Filho, Carlos Alberto; Bando, Silvia Yumi; Bertonha, Fernanda Bernardi; Iamashita, Priscila; Silva, Filipi Nascimento; Costa, Luciano da Fontoura; Silva, Alexandre Valotta; Castro, Luiz Henrique Martins; Wen, Hung-Tzu

    2015-01-01

    Age at epilepsy onset has a broad impact on brain plasticity and epilepsy pathomechanisms. Prolonged febrile seizures in early childhood (FS) constitute an initial precipitating insult (IPI) commonly associated with mesial temporal lobe epilepsy (MTLE). FS-MTLE patients may have early disease onset, i.e. just after the IPI, in early childhood, or late-onset, ranging from mid-adolescence to early adult life. The mechanisms governing early (E) or late (L) disease onset are largely unknown. In order to unveil the molecular pathways underlying E and L subtypes of FS-MTLE we investigated global gene expression in hippocampal CA3 explants of FS-MTLE patients submitted to hippocampectomy. Gene coexpression networks (GCNs) were obtained for the E and L patient groups. A network-based approach for GCN analysis was employed allowing: i) the visualization and analysis of differentially expressed (DE) and complete (CO) - all valid GO annotated transcripts - GCNs for the E and L groups; ii) the study of interactions between all the system's constituents based on community detection and coarse-grained community structure methods. We found that the E-DE communities with strongest connection weights harbor highly connected genes mainly related to neural excitability and febrile seizures, whereas in L-DE communities these genes are not only involved in network excitability but also playing roles in other epilepsy-related processes. Inversely, in E-CO the strongly connected communities are related to compensatory pathways (seizure inhibition, neuronal survival and responses to stress conditions) while in L-CO these communities harbor several genes related to pro-epileptic effects, seizure-related mechanisms and vulnerability to epilepsy. These results fit the concept, based on fMRI and behavioral studies, that early onset epilepsies, although impacting more severely the hippocampus, are associated to compensatory mechanisms, while in late MTLE development the brain is less able to

  6. Community Structure Analysis of Transcriptional Networks Reveals Distinct Molecular Pathways for Early- and Late-Onset Temporal Lobe Epilepsy with Childhood Febrile Seizures

    PubMed Central

    Moreira-Filho, Carlos Alberto; Bando, Silvia Yumi; Bertonha, Fernanda Bernardi; Iamashita, Priscila; Silva, Filipi Nascimento; Costa, Luciano da Fontoura; Silva, Alexandre Valotta; Castro, Luiz Henrique Martins; Wen, Hung-Tzu

    2015-01-01

    Age at epilepsy onset has a broad impact on brain plasticity and epilepsy pathomechanisms. Prolonged febrile seizures in early childhood (FS) constitute an initial precipitating insult (IPI) commonly associated with mesial temporal lobe epilepsy (MTLE). FS-MTLE patients may have early disease onset, i.e. just after the IPI, in early childhood, or late-onset, ranging from mid-adolescence to early adult life. The mechanisms governing early (E) or late (L) disease onset are largely unknown. In order to unveil the molecular pathways underlying E and L subtypes of FS-MTLE we investigated global gene expression in hippocampal CA3 explants of FS-MTLE patients submitted to hippocampectomy. Gene coexpression networks (GCNs) were obtained for the E and L patient groups. A network-based approach for GCN analysis was employed allowing: i) the visualization and analysis of differentially expressed (DE) and complete (CO) - all valid GO annotated transcripts - GCNs for the E and L groups; ii) the study of interactions between all the system’s constituents based on community detection and coarse-grained community structure methods. We found that the E-DE communities with strongest connection weights harbor highly connected genes mainly related to neural excitability and febrile seizures, whereas in L-DE communities these genes are not only involved in network excitability but also playing roles in other epilepsy-related processes. Inversely, in E-CO the strongly connected communities are related to compensatory pathways (seizure inhibition, neuronal survival and responses to stress conditions) while in L-CO these communities harbor several genes related to pro-epileptic effects, seizure-related mechanisms and vulnerability to epilepsy. These results fit the concept, based on fMRI and behavioral studies, that early onset epilepsies, although impacting more severely the hippocampus, are associated to compensatory mechanisms, while in late MTLE development the brain is less able to

  7. Reaction–diffusion systems for spatio-temporal intracellular protein networks: A beginner's guide with two examples

    PubMed Central

    Eliaš, Ján; Clairambault, Jean

    2014-01-01

    Spatio-temporal dynamics of a variety of proteins is, among other things, regulated by post-translational modifications of these proteins. Such modifications can thus influence stability and biochemical activities of the proteins, activity and stability of their upstream targets within specific signalling pathways. Commonly used mathematical tools for such protein–protein (and/or protein-mRNA) interactions in single cells, namely, Michaelis–Menten and Hill kinetics, yielding a system of ordinary differential equations, are extended here into (non-linear) partial differential equations by taking into account a more realistic spatial representation of the environment where these reactions occur. In the modelling framework under consideration, all interactions occur in a cell divided into two compartments, the nucleus and the cytoplasm, connected by the semipermeable nuclear membrane and bounded by the impermeable cell membrane. Passive transport mechanism, modelled by the so-called Kedem–Katchalsky boundary conditions, is used here to represent migration of species throughout the nuclear membrane. Nonlinear systems of partial differential equations are solved by the semi-implicit Rothe method. Examples of two spatial oscillators are shown. Namely, these are the circadian rhythm for concentration of the FRQ protein in Neurospora crassa and oscillatory dynamics observed in the activation and regulation of the p53 protein following DNA damage in mammalian cells. PMID:25210594

  8. The Neocortical Network Representing Associative Memory Reorganizes with Time in a Process Engaging the Anterior Temporal Lobe

    PubMed Central

    Takashima, Atsuko; Oostenveld, Robert; McNaughton, Bruce L.; Fernández, Guillén; Jensen, Ole

    2012-01-01

    During encoding, the distributed neocortical representations of memory components are presumed to be associatively linked by the hippocampus. With time, a reorganization of brain areas supporting memory takes place, which can ultimately result in memories becoming independent of the hippocampus. While it is theorized that with time, the neocortical representations become linked by higher order neocortical association areas, this remains to be experimentally supported. In this study, 24 human participants encoded sets of face–location associations, which they retrieved 1 or 25 h later (“recent” and “remote” conditions, respectively), while their brain activity was recorded using whole-head magnetoencephalography. We investigated changes in the functional interactions between the neocortical representational areas emerging over time. To assess functional interactions, trial-by-trial high gamma (60–140 Hz) power correlations were calculated between the neocortical representational areas relevant to the encoded information, namely the fusiform face area (FFA) and posterior parietal cortex (PPC). With time, both the FFA and the PPC increased their functional interactions with the anterior temporal lobe (ATL). Given that the ATL is involved in semantic representation of paired associates, our results suggest that, already within 25 h after acquiring new memory associations, neocortical functional links are established via higher order semantic association areas. PMID:22139815

  9. Integrating temporal difference methods and self-organizing neural networks for reinforcement learning with delayed evaluative feedback.

    PubMed

    Tan, A H; Lu, N; Xiao, D

    2008-02-01

    This paper presents a neural architecture for learning category nodes encoding mappings across multimodal patterns involving sensory inputs, actions, and rewards. By integrating adaptive resonance theory (ART) and temporal difference (TD) methods, the proposed neural model, called TD fusion architecture for learning, cognition, and navigation (TD-FALCON), enables an autonomous agent to adapt and function in a dynamic environment with immediate as well as delayed evaluative feedback (reinforcement) signals. TD-FALCON learns the value functions of the state-action space estimated through on-policy and off-policy TD learning methods, specifically state-action-reward-state-action (SARSA) and Q-learning. The learned value functions are then used to determine the optimal actions based on an action selection policy. We have developed TD-FALCON systems using various TD learning strategies and compared their performance in terms of task completion, learning speed, as well as time and space efficiency. Experiments based on a minefield navigation task have shown that TD-FALCON systems are able to learn effectively with both immediate and delayed reinforcement and achieve a stable performance in a pace much faster than those of standard gradient-descent-based reinforcement learning systems.

  10. Improving the network management of integrated primary mental healthcare for older people in a rural Australian region: protocol for a mixed methods case study

    PubMed Central

    Fuller, Jeffrey; Oster, Candice; Dawson, Suzanne; O'Kane, Deb; Lawn, Sharon; Henderson, Julie; Gerace, Adam; Reed, Richard; Nosworthy, Ann; Galley, Philip; McPhail, Ruth; Cochrane, Eimear Muir

    2014-01-01

    Introduction An integrated approach to the mental healthcare of older people is advocated across health, aged care and social care sectors. It is not clear, however, how the management of integrated servicing should occur, although interorganisational relations theory suggests a reflective network approach using evaluation feedback. This research will test a network management approach to help regional primary healthcare organisations improve mental health service integration. Methods and analysis This mixed methods case study in rural South Australia will test facilitated reflection within a network of health and social care services to determine if this leads to improved integration. Engagement of services will occur through a governance group and a series of three 1-day service stakeholder workshops. Facilitated reflection and evaluation feedback will use information from a review of health sector and local operational policies, a network survey about current service links, gaps and enablers and interviews with older people and their carers about their help seeking journeys. Quantitative and qualitative analysis will describe the policy enablers and explore the current and ideal links between services. The facilitated reflection will be developed to maximise engagement of senior management in the governance group and the service staff at the operational level in the workshops. Benefit will be assessed through indicators of improved service coordination, collective ownership of service problems, strengthened partnerships, agreed local protocols and the use of feedback for accountability. Ethics, benefits and dissemination Ethics approval will deal with the sensitivities of organisational network research where data anonymity is not preserved. The benefit will be the tested utility of a facilitated reflective process for a network of health and social care services to manage linked primary mental healthcare for older people in a rural region. Dissemination will

  11. Relating the temporal change observed by AIRSAR to surface and canopy properties of mixed conifer and hardwood forests of northern Michigan

    NASA Technical Reports Server (NTRS)

    Dobson, M. Craig; Mcdonald, Kyle; Ulaby, Fawwaz T.; Sharik, Terry

    1991-01-01

    The mixed hardwood and conifer forests of northern Michigan were overflown by a 3-frequency airborne imaging radar in Apr. and Jul. 1990. A set of 10 x 10 km test sites near the University of Michigan Biological Station at Douglas Lake and within the Hiawatha National Forest in the upper peninsula of Michigan contained training stands representing the various forest species typical of forest communities across the ecotone between the coniferous boreal forest and mid-latitude hardwood and coniferous forests. The polarimetric radar data were externally calibrated to allow interdate comparisons. The Apr. flight was prior to bud-break of deciduous species and patchy snowcover was present. The Jul. flights occurred during and 2 days after heavy rain showers, and provide a unique opportunity to examine the differences in radar backscatter attributable to intercepted precipitation. Analyses show that there are significant changes in backscattering between biophysically dissimilar forest stands on any given date and also between dates for a given forest stand. These differences in backscattering can be related to moisture properties of the forest floor and the overlying canopy and also to the quantity and organizational structure of the above-ground biomass.

  12. The use of diagnostic ratios, biomarkers and 3-way Kohonen neural networks to monitor the temporal evolution of oil spills.

    PubMed

    Fernández-Varela, R; Gómez-Carracedo, M P; Ballabio, D; Andrade, J M

    2015-07-15

    Oil spill identification relies usually on a wealth of chromatographic data which requires advanced data treatment (chemometrics). A simple approach based on Kohonen neural networks to handle three-dimensional arrays is presented. A suite of 28 diagnostic ratios was considered to monitor six oils along four months. It was found that some traditional diagnostic ratios were not stable enough. In particular, alkylated PAHs (e.g. 1-methyldibenzothiophene, 4-methylpyrene, 27bbSTER and the TA21 and TA26 triaromatic steroids) seemed less resistant to medium-weathering than biomarkers. One (or two) ratios were found to differentiate each product: 30O, 28ab (and 25nor30ab), C3-dbt/C3-phe, 27Ts, TA26 and 29Ts characterized Ashtart, Brent, Maya, Sahara, IFO and Prestige oils, respectively.

  13. Multi-Temporal Land Use Analysis of AN Ephemeral River Area Using AN Artificial Neural Network Approach on Landsat Imagery

    NASA Astrophysics Data System (ADS)

    Aquilino, M.; Tarantino, E.; Fratino, U.

    2013-01-01

    This paper proposes a change detection analysis method based on multitemporal LANDSAT satellite data, presenting a study performed on the Lama San Giorgio (Bari, Italy) river basin area. Based on its geological and hydrological characteristics, as well as on the number of recent and remote flooding events already occurred, this area seems to be naturally prone to flooding. The historical archive of LANDSAT imagery dating back to the launch of ERTS in 1972 provides a comprehensive and permanent data source for tracking change on the planet‟s land surface. In this study case the imagery acquisition dates of 1987, 2002 and 2011 were selected to cover a time trend of 24 years. Land cover categories were based on classes outlined by the Curve Number method with the aim of characterizing land use according to the level of surface imperviousness. After comparing two land use classification methods, i.e. Maximum Likelihood Classifier (MLC) and Multi-Layer Perceptron (MLP) neural network, the Artificial Neural Networks (ANN) approach was found the best reliable and efficient method in the absence of ground reference data. The ANN approach has a distinct advantage over statistical classification methods in that it is non-parametric and requires little or no a priori knowledge on the distribution model of input data. The results quantify land cover change patterns in the river basin area under study and demonstrate the potential of multitemporal LANDSAT data to provide an accurate and cost-effective means to map and analyse land cover changes over time that can be used as input in land management and policy decision-making.

  14. Detecting hidden spatial and spatio-temporal structures in glasses and complex physical systems by multiresolution network clustering.

    PubMed

    Ronhovde, P; Chakrabarty, S; Hu, D; Sahu, M; Sahu, K K; Kelton, K F; Mauro, N A; Nussinov, Z

    2011-09-01

    We elaborate on a general method that we recently introduced for characterizing the "natural" structures in complex physical systems via multi-scale network analysis. The method is based on "community detection" wherein interacting particles are partitioned into an "ideal gas" of optimally decoupled groups of particles. Specifically, we construct a set of network representations ("replicas") of the physical system based on interatomic potentials and apply a multiscale clustering ("multiresolution community detection") analysis using information-based correlations among the replicas. Replicas may i) be different representations of an identical static system, ii) embody dynamics by considering replicas to be time separated snapshots of the system (with a tunable time separation), or iii) encode general correlations when different replicas correspond to different representations of the entire history of the system as it evolves in space-time. Inputs for our method are the inter-particle potentials or experimentally measured two (or higher order) particle correlations. We apply our method to computer simulations of a binary Kob-Andersen Lennard-Jones system in a mixture ratio of A(80)B(20) , a ternary model system with components "A", "B", and "C" in ratios of A(88)B(7)C(5) (as in Al(88)Y(7)Fe(5) , and to atomic coordinates in a Zr(80)Pt(20) system as gleaned by reverse Monte Carlo analysis of experimentally determined structure factors. We identify the dominant structures (disjoint or overlapping) and general length scales by analyzing extrema of the information theory measures. We speculate on possible links between i) physical transitions or crossovers and ii) changes in structures found by this method as well as phase transitions associated with the computational complexity of the community detection problem. We also briefly consider continuum approaches and discuss rigidity and the shear penetration depth in amorphous systems; this latter length scale increases as