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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. 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

  4. Temporal stability of network partitions.

    PubMed

    Petri, Giovanni; Expert, Paul

    2014-08-01

    We present a method to find the best temporal partition at any time scale and rank the relevance of partitions found at different time scales. This method is based on random walkers coevolving with the network and as such constitutes a generalization of partition stability to the case of temporal networks. We show that, when applied to a toy model and real data sets, temporal stability uncovers structures that are persistent over meaningful time scales as well as important isolated events, making it an effective tool to study both abrupt changes and gradual evolution of a network mesoscopic structures.

  5. Temporal stability of network partitions.

    PubMed

    Petri, Giovanni; Expert, Paul

    2014-08-01

    We present a method to find the best temporal partition at any time scale and rank the relevance of partitions found at different time scales. This method is based on random walkers coevolving with the network and as such constitutes a generalization of partition stability to the case of temporal networks. We show that, when applied to a toy model and real data sets, temporal stability uncovers structures that are persistent over meaningful time scales as well as important isolated events, making it an effective tool to study both abrupt changes and gradual evolution of a network mesoscopic structures. PMID:25215787

  6. 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.

  7. Temporal and structural heterogeneities emerging in adaptive temporal networks

    NASA Astrophysics Data System (ADS)

    Aoki, Takaaki; Rocha, Luis E. C.; Gross, Thilo

    2016-04-01

    We introduce a model of adaptive temporal networks whose evolution is regulated by an interplay between node activity and dynamic exchange of information through links. We study the model by using a master equation approach. Starting from a homogeneous initial configuration, we show that temporal and structural heterogeneities, characteristic of real-world networks, spontaneously emerge. This theoretically tractable model thus contributes to the understanding of the dynamics of human activity and interaction networks.

  8. 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.

  9. Temporal network structures controlling disease spreading.

    PubMed

    Holme, Petter

    2016-08-01

    We investigate disease spreading on eight empirical data sets of human contacts (mostly proximity networks recording who is close to whom, at what time). We compare three levels of representations of these data sets: temporal networks, static networks, and a fully connected topology. We notice that the difference between the static and fully connected networks-with respect to time to extinction and average outbreak size-is smaller than between the temporal and static topologies. This suggests that, for these data sets, temporal structures influence disease spreading more than static-network structures. To explain the details in the differences between the representations, we use 32 network measures. This study concurs that long-time temporal structures, like the turnover of nodes and links, are the most important for the spreading dynamics. PMID:27627315

  10. Temporal network structures controlling disease spreading.

    PubMed

    Holme, Petter

    2016-08-01

    We investigate disease spreading on eight empirical data sets of human contacts (mostly proximity networks recording who is close to whom, at what time). We compare three levels of representations of these data sets: temporal networks, static networks, and a fully connected topology. We notice that the difference between the static and fully connected networks-with respect to time to extinction and average outbreak size-is smaller than between the temporal and static topologies. This suggests that, for these data sets, temporal structures influence disease spreading more than static-network structures. To explain the details in the differences between the representations, we use 32 network measures. This study concurs that long-time temporal structures, like the turnover of nodes and links, are the most important for the spreading dynamics.

  11. Temporal network structures controlling disease spreading

    NASA Astrophysics Data System (ADS)

    Holme, Petter

    2016-08-01

    We investigate disease spreading on eight empirical data sets of human contacts (mostly proximity networks recording who is close to whom, at what time). We compare three levels of representations of these data sets: temporal networks, static networks, and a fully connected topology. We notice that the difference between the static and fully connected networks—with respect to time to extinction and average outbreak size—is smaller than between the temporal and static topologies. This suggests that, for these data sets, temporal structures influence disease spreading more than static-network structures. To explain the details in the differences between the representations, we use 32 network measures. This study concurs that long-time temporal structures, like the turnover of nodes and links, are the most important for the spreading dynamics.

  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. PMID:27516936

  13. Temporal percolation in activity-driven networks

    NASA Astrophysics Data System (ADS)

    Starnini, Michele; Pastor-Satorras, Romualdo

    2014-03-01

    We study the temporal percolation properties of temporal networks by taking as a representative example the recently proposed activity-driven-network model [N. Perra et al., Sci. Rep. 2, 469 (2012), 10.1038/srep00469]. Building upon an analytical framework based on a mapping to hidden variables networks, we provide expressions for the percolation time Tp marking the onset of a giant connected component in the integrated network. In particular, we consider both the generating function formalism, valid for degree-uncorrelated networks, and the general case of networks with degree correlations. We discuss the different limits of the two approaches, indicating the parameter regions where the correlated threshold collapses onto the uncorrelated case. Our analytical predictions are confirmed by numerical simulations of the model. The temporal percolation concept can be fruitfully applied to study epidemic spreading on temporal networks. We show in particular how the susceptible-infected-removed model on an activity-driven network can be mapped to the percolation problem up to a time given by the spreading rate of the epidemic process. This mapping allows us to obtain additional information on this process, not available for previous approaches.

  14. Modern temporal network theory: a colloquium

    NASA Astrophysics Data System (ADS)

    Holme, Petter

    2015-09-01

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

  15. Random walk centrality for temporal networks

    NASA Astrophysics Data System (ADS)

    Rocha, Luis E. C.; Masuda, Naoki

    2014-06-01

    Nodes can be ranked according to their relative importance within a network. Ranking algorithms based on random walks are particularly useful because they connect topological and diffusive properties of the network. Previous methods based on random walks, for example the PageRank, have focused on static structures. However, several realistic networks are indeed dynamic, meaning that their structure changes in time. In this paper, we propose a centrality measure for temporal networks based on random walks under periodic boundary conditions that we call TempoRank. It is known that, in static networks, the stationary density of the random walk is proportional to the degree or the strength of a node. In contrast, we find that, in temporal networks, the stationary density is proportional to the in-strength of the so-called effective network, a weighted and directed network explicitly constructed from the original sequence of transition matrices. The stationary density also depends on the sojourn probability q, which regulates the tendency of the walker to stay in the node, and on the temporal resolution of the data. We apply our method to human interaction networks and show that although it is important for a node to be connected to another node with many random walkers (one of the principles of the PageRank) at the right moment, this effect is negligible in practice when the time order of link activation is included.

  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. Burstiness and Aging in Social Temporal Networks

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

    The presence of burstiness in temporal social networks, revealed by a power-law form of the waiting time distribution of consecutive interactions, is expected to produce aging effects in the corresponding time-integrated network. Here, we propose an analytically tractable model, in which interactions among the agents are ruled by a renewal process, that is able to reproduce this aging behavior. We develop an analytic solution for the topological properties of the integrated network produced by the model, finding that the time translation invariance of the degree distribution is broken. We validate our predictions against numerical simulations, and we check for the presence of aging effects in a empirical temporal network, ruled by bursty social interactions.

  19. Neural network based temporal video segmentation.

    PubMed

    Cao, X; Suganthan, P N

    2002-01-01

    The organization of video information in video databases requires automatic temporal segmentation with minimal user interaction. As neural networks are capable of learning the characteristics of various video segments and clustering them accordingly, in this paper, a neural network based technique is developed to segment the video sequence into shots automatically and with a minimum number of user-defined parameters. We propose to employ growing neural gas (GNG) networks and integrate multiple frame difference features to efficiently detect shot boundaries in the video. Experimental results are presented to illustrate the good performance of the proposed scheme on real video sequences. PMID:12370954

  20. Temporal node centrality in complex networks.

    PubMed

    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.

  1. 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.

  2. Scaling of Reactive Transport in Fracture Networks with Incomplete Mixing: A Metapopulation Network Approach

    NASA Astrophysics Data System (ADS)

    Nicolaides, C.; Cueto-Felgueroso, L.; Juanes, R.

    2011-12-01

    The study of networks as complex systems has revolutionized many disciplines in physics and the social and natural sciences. Recently, the focus of network science has shifted from the analysis of the network topology to the study of the dynamics of processes that take place on them. Here, we adopt a bosonic (metapopulation) network approach to characterize transport and reaction on fracture networks. In a bosonic network, nodes contain populations of particles, which may undertake contact processes within them. This coarse-grained conceptualization permits modeling of nonequilibrium phenomena such as incomplete mixing and kinetic reactions. Particles then move between connected nodes along links, with a rate that reflects the traffic patterns through the network. We generate fracture networks from realistic statistical properties of fracture density, orientation and aperture, and solve potential flow on the network for simple flow configurations. It is well known that the transport of passive particles with complete mixing at the nodes on a fracture network or a scale-free network is anomalous [1,2]. Here, we extend this analysis to account for incomplete mixing at the nodes by considering two types of particles, A and B, which come in contact at the nodes at a rate α (the mixing rate) to produce type-C particles: A+B-> 2C. In the limit of α -> ∞ we recover the instantaneous mixing case. Further, type-C particles decay into stable type-D particles at a rate λ : C→ D. As a result, we capture the interplay among the transport, mixing and reaction time scales on a fracture network. Our analysis demonstrates the strong dependence of global mixing and spreading on incomplete local mixing, and allows us to obtain robust scalings for the spatio-temporal distributions of particles.

  3. 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.

  4. 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.

  5. 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

  6. Reconstructing propagation networks with temporal similarity.

    PubMed

    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

  7. 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. PMID:26859840

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. Effects of viscosity variations in temporal mixing layer

    NASA Astrophysics Data System (ADS)

    Taguelmimt, N.; Danaila, L.; Hadjadj, A.

    2014-08-01

    The objective of the present investigation is to assess the effects of viscosity variations in low-speed temporally-evolving turbulent mixing layer. Direct Numerical Simulations (DNS) are performed for several viscosity ratios, Rv = vhigh/vlow, varying between 1 and 9, whereas the upper and lower streams are of equal density. The space-time evolution of Variable-Viscosity Flow (VVF) is compared with the Constant-Viscosity Flow (CVF), for which Rv = 1. The initial Reynolds number, based on the initial momentum thickness, δθ,0, is Reδθ,0 = 160 for the considered cases. The study focuses on the first stages of the temporal evolution of the mixing-layer. It is shown that in VVF (with respect to CVF): (i) the birth of turbulent fluctuations is accelerated; (ii) large-scale quantities, i.e. mean longitudinal velocity and momentum thickness, are affected by the viscosity variations, thus dispelling the myth that viscosity is a 'small-scale quantity that does not affect the large scales'; (iii) the velocity fluctuations are enhanced for VVF. In particular, the turbulent kinetic energy peaks earlier and is three times larger for VVF than CVF at the earliest stage of the mixing, and (iv) the transport equation for the turbulent kinetic energy is derived and favourably compared with simulations data.

  13. 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. PMID:25210664

  14. 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.

  15. 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.

  16. Mixed reality temporal bone surgical dissector: mechanical design

    PubMed Central

    2014-01-01

    Objective The Development of a Novel Mixed Reality (MR) Simulation. An evolving training environment emphasizes the importance of simulation. Current haptic temporal bone simulators have difficulty representing realistic contact forces and while 3D printed models convincingly represent vibrational properties of bone, they cannot reproduce soft tissue. This paper introduces a mixed reality model, where the effective elements of both simulations are combined; haptic rendering of soft tissue directly interacts with a printed bone model. This paper addresses one aspect in a series of challenges, specifically the mechanical merger of a haptic device with an otic drill. This further necessitates gravity cancelation of the work assembly gripper mechanism. In this system, the haptic end-effector is replaced by a high-speed drill and the virtual contact forces need to be repositioned to the drill tip from the mid wand. Previous publications detail generation of both the requisite printed and haptic simulations. Method Custom software was developed to reposition the haptic interaction point to the drill tip. A custom fitting, to hold the otic drill, was developed and its weight was offset using the haptic device. The robustness of the system to disturbances and its stable performance during drilling were tested. The experiments were performed on a mixed reality model consisting of two drillable rapid-prototyped layers separated by a free-space. Within the free-space, a linear virtual force model is applied to simulate drill contact with soft tissue. Results Testing illustrated the effectiveness of gravity cancellation. Additionally, the system exhibited excellent performance given random inputs and during the drill’s passage between real and virtual components of the model. No issues with registration at model boundaries were encountered. Conclusion These tests provide a proof of concept for the initial stages in the development of a novel mixed-reality temporal bone

  17. 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.

  18. Applications of Temporal Graph Metrics to Real-World Networks

    NASA Astrophysics Data System (ADS)

    Tang, John; Leontiadis, Ilias; Scellato, Salvatore; Nicosia, Vincenzo; Mascolo, Cecilia; Musolesi, Mirco; Latora, Vito

    Real world networks exhibit rich temporal information: friends are added and removed over time in online social networks; the seasons dictate the predator-prey relationship in food webs; and the propagation of a virus depends on the network of human contacts throughout the day. Recent studies have demonstrated that static network analysis is perhaps unsuitable in the study of real world network since static paths ignore time order, which, in turn, results in static shortest paths overestimating available links and underestimating their true corresponding lengths. Temporal extensions to centrality and efficiency metrics based on temporal shortest paths have also been proposed. Firstly, we analyse the roles of key individuals of a corporate network ranked according to temporal centrality within the context of a bankruptcy scandal; secondly, we present how such temporal metrics can be used to study the robustness of temporal networks in presence of random errors and intelligent attacks; thirdly, we study containment schemes for mobile phone malware which can spread via short range radio, similar to biological viruses; finally, we study how the temporal network structure of human interactions can be exploited to effectively immunise human populations. Through these applications we demonstrate that temporal metrics provide a more accurate and effective analysis of real-world networks compared to their static counterparts.

  19. 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

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

    PubMed

    Williams, Matthew J; Musolesi, Mirco

    2016-06-01

    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.

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

    PubMed

    Williams, Matthew J; Musolesi, Mirco

    2016-06-01

    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

  2. Predicting and controlling infectious disease epidemics using temporal networks

    PubMed Central

    Holme, Petter

    2013-01-01

    Infectious diseases can be considered to spread over social networks of people or animals. Mainly owing to the development of data recording and analysis techniques, an increasing amount of social contact data with time stamps has been collected in the last decade. Such temporal data capture the dynamics of social networks on a timescale relevant to epidemic spreading and can potentially lead to better ways to analyze, forecast, and prevent epidemics. However, they also call for extended analysis tools for network epidemiology, which has, to date, mostly viewed networks as static entities. We review recent results of network epidemiology for such temporal network data and discuss future developments. PMID:23513178

  3. 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

  4. 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.

  5. Analytical Computation of the Epidemic Threshold on Temporal Networks

    NASA Astrophysics Data System (ADS)

    Valdano, Eugenio; Ferreri, Luca; Poletto, Chiara; Colizza, Vittoria

    2015-04-01

    The time variation of contacts in a networked system may fundamentally alter the properties of spreading processes and affect the condition for large-scale propagation, as encoded in the epidemic threshold. Despite the great interest in the problem for the physics, applied mathematics, computer science, and epidemiology communities, a full theoretical understanding is still missing and currently limited to the cases where the time-scale separation holds between spreading and network dynamics or to specific temporal network models. We consider a Markov chain description of the susceptible-infectious-susceptible process on an arbitrary temporal network. By adopting a multilayer perspective, we develop a general analytical derivation of the epidemic threshold in terms of the spectral radius of a matrix that encodes both network structure and disease dynamics. The accuracy of the approach is confirmed on a set of temporal models and empirical networks and against numerical results. In addition, we explore how the threshold changes when varying the overall time of observation of the temporal network, so as to provide insights on the optimal time window for data collection of empirical temporal networked systems. Our framework is of both fundamental and practical interest, as it offers novel understanding of the interplay between temporal networks and spreading dynamics.

  6. Structural controllability and controlling centrality of temporal networks.

    PubMed

    Pan, Yujian; Li, Xiang

    2014-01-01

    Temporal networks are such networks where nodes and interactions may appear and disappear at various time scales. With the evidence of ubiquity of temporal networks in our economy, nature and society, it's urgent and significant to focus on its structural controllability as well as the corresponding characteristics, which nowadays is still an untouched topic. We develop graphic tools to study the structural controllability as well as its characteristics, identifying the intrinsic mechanism of the ability of individuals in controlling a dynamic and large-scale temporal network. Classifying temporal trees of a temporal network into different types, we give (both upper and lower) analytical bounds of the controlling centrality, which are verified by numerical simulations of both artificial and empirical temporal networks. We find that the positive relationship between aggregated degree and controlling centrality as well as the scale-free distribution of node's controlling centrality are virtually independent of the time scale and types of datasets, meaning the inherent robustness and heterogeneity of the controlling centrality of nodes within temporal networks. PMID:24747676

  7. Infections on Temporal Networks--A Matrix-Based Approach.

    PubMed

    Koher, Andreas; Lentz, Hartmut H K; Hövel, Philipp; Sokolov, Igor M

    2016-01-01

    We extend the concept of accessibility in temporal networks to model infections with a finite infectious period such as the susceptible-infected-recovered (SIR) model. This approach is entirely based on elementary matrix operations and unifies the disease and network dynamics within one algebraic framework. We demonstrate the potential of this formalism for three examples of networks with high temporal resolution: networks of social contacts, sexual contacts, and livestock-trade. Our investigations provide a new methodological framework that can be used, for instance, to estimate the epidemic threshold, a quantity that determines disease parameters, for which a large-scale outbreak can be expected. PMID:27035128

  8. 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.

  9. Classification of behavior using unsupervised temporal neural networks

    SciTech Connect

    Adair, K.L.; Argo, P.

    1998-03-01

    Adding recurrent connections to unsupervised neural networks used for clustering creates a temporal neural network which clusters a sequence of inputs as they appear over time. The model presented combines the Jordan architecture with the unsupervised learning technique Adaptive Resonance Theory, Fuzzy ART. The combination yields a neural network capable of quickly clustering sequential pattern sequences as the sequences are generated. The applicability of the architecture is illustrated through a facility monitoring problem.

  10. 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.

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

    NASA Astrophysics Data System (ADS)

    Masuda, Naoki

    2016-02-01

    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.

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

    PubMed

    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. PMID:26871086

  13. 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.

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

    PubMed

    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.

  15. 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.

  16. Empirical temporal networks of face-to-face human interactions

    NASA Astrophysics Data System (ADS)

    Barrat, A.; Cattuto, C.; Colizza, V.; Gesualdo, F.; Isella, L.; Pandolfi, E.; Pinton, J.-F.; Ravà, L.; Rizzo, C.; Romano, M.; Stehlé, J.; Tozzi, A. E.; Van den Broeck, W.

    2013-09-01

    The ever increasing adoption of mobile technologies and ubiquitous services allows to sense human behavior at unprecedented level of details and scale. Wearable sensors, in particular, open up a new window on human mobility and proximity in a variety of indoor environments. Here we review stylized facts on the structural and dynamical properties of empirical networks of human face-to-face proximity, measured in three different real-world contexts: an academic conference, a hospital ward, and a museum exhibition. First, we discuss the structure of the aggregated contact networks, that project out the detailed ordering of contact events while preserving temporal heterogeneities in their weights. We show that the structural properties of aggregated networks highlight important differences and unexpected similarities across contexts, and discuss the additional complexity that arises from attributes that are typically associated with nodes in real-world interaction networks, such as role classes in hospitals. We then consider the empirical data at the finest level of detail, i.e., we consider time-dependent networks of face-to-face proximity between individuals. To gain insights on the effects that causal constraints have on spreading processes, we simulate the dynamics of a simple susceptible-infected model over the empirical time-resolved contact data. We show that the spreading pathways for the epidemic process are strongly affected by the temporal structure of the network data, and that the mere knowledge of static aggregated networks leads to erroneous conclusions about the transmission paths on the corresponding dynamical networks.

  17. Learning and representing temporal knowledge in recurrent networks.

    PubMed

    Borges, Rafael V; Garcez, Artur d'Avila; Lamb, Luis C

    2011-12-01

    The effective integration of knowledge representation, reasoning, and learning in a robust computational model is one of the key challenges of computer science and artificial intelligence. In particular, temporal knowledge and models have been fundamental in describing the behavior of computational systems. However, knowledge acquisition of correct descriptions of a system's desired behavior is a complex task. In this paper, we present a novel neural-computation model capable of representing and learning temporal knowledge in recurrent networks. The model works in an integrated fashion. It enables the effective representation of temporal knowledge, the adaptation of temporal models given a set of desirable system properties, and effective learning from examples, which in turn can lead to temporal knowledge extraction from the corresponding trained networks. The model is sound from a theoretical standpoint, but it has also been tested on a case study in the area of model verification and adaptation. The results contained in this paper indicate that model verification and learning can be integrated within the neural computation paradigm, contributing to the development of predictive temporal knowledge-based systems and offering interpretable results that allow system researchers and engineers to improve their models and specifications. The model has been implemented and is available as part of a neural-symbolic computational toolkit.

  18. Statistics of modifier distributions in mixed network glasses.

    PubMed

    Mauro, John C

    2013-03-28

    The constituents of any network glass can be broadly classified as either network formers or network modifiers. Network formers, such as SiO2, Al2O3, B2O3, P2O5, etc., provide the backbone of the glass network and are the primary source of its rigid constraints. Network modifiers play a supporting role, such as charge stabilization of the network formers or alteration of the network topology through rupture of bridging bonds and introduction of floppy modes. The specific role of the modifiers depends on which network formers are present in the glass and the relative free energies of modifier interactions with each type of network former site. This variation of free energy with modifier speciation is responsible for the so-called mixed network former effect, i.e., the nonlinear scaling of property values in glasses having fixed modifier concentration but a varying ratio of network formers. In this paper, a general theoretical framework is presented describing the statistical mechanics of modifier speciation in mixed network glasses. The model provides a natural explanation for the mixed network former effect and also accounts for the impact of thermal history and relaxation on glass network topology. PMID:23556773

  19. 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.

  20. 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. PMID:26723151

  1. 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.

  2. 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

  3. 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

    2016-01-01

    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.

  4. Dissociable networks involved in spatial and temporal order source retrieval.

    PubMed

    Ekstrom, Arne D; Copara, Milagros S; Isham, Eve A; Wang, Wei-chun; Yonelinas, Andrew P

    2011-06-01

    Space and time are important components of our episodic memories. Without this information, we cannot determine the "where and when" of our recent memories, rendering it difficult to disambiguate individual episodes from each other. The neural underpinnings of spatial and temporal order memory in humans remain unclear, in part because of difficulties in disentangling the contributions of these two types of source information. To address this issue, we conducted an experiment in which participants first navigated a virtual city, experiencing unique routes in a specific temporal order and learning about the spatial layout of the city. Spatial and temporal order information were dissociated in our task such that learning one type of information did not facilitate the other behaviorally. This allowed us to then address the extent to which the two types of information involved functionally distinct or overlapping brain areas. During functional magnetic resonance imaging (fMRI), participants retrieved information about the relative distance of stores within the city (spatial task) and the temporal order of stores from each other (temporal task). Comparable hippocampal activity was observed during these two tasks, but greater prefrontal activity was seen during temporal order retrieval whereas greater parahippocampal activity was seen during spatial retrieval. We suggest that while the brain possesses dissociable networks for maintaining and representing spatial layout and temporal order components of episodic memory, this information may converge into a common representation for source memory in areas such as the hippocampus.

  5. 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

  6. 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.

  7. 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. PMID:27335223

  8. 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.

  9. 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. PMID:16252834

  10. 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.

  11. 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…

  12. Degree mixing in multilayer networks impedes the evolution of cooperation

    NASA Astrophysics Data System (ADS)

    Wang, Zhen; Wang, Lin; Perc, Matjaž

    2014-05-01

    Traditionally, the evolution of cooperation has been studied on single, isolated networks. Yet a player, especially in human societies, will typically be a member of many different networks, and those networks will play different roles in the evolutionary process. Multilayer networks are therefore rapidly gaining on popularity as the more apt description of a networked society. With this motivation, we here consider two-layer scale-free networks with all possible combinations of degree mixing, wherein one network layer is used for the accumulation of payoffs and the other is used for strategy updating. We find that breaking the symmetry through assortative mixing in one layer and/or disassortative mixing in the other layer, as well as preserving the symmetry by means of assortative mixing in both layers, impedes the evolution of cooperation. We use degree-dependent distributions of strategies and cluster-size analysis to explain these results, which highlight the importance of hubs and the preservation of symmetry between multilayer networks for the successful resolution of social dilemmas.

  13. Degree mixing in multilayer networks impedes the evolution of cooperation.

    PubMed

    Wang, Zhen; Wang, Lin; Perc, Matjaž

    2014-05-01

    Traditionally, the evolution of cooperation has been studied on single, isolated networks. Yet a player, especially in human societies, will typically be a member of many different networks, and those networks will play different roles in the evolutionary process. Multilayer networks are therefore rapidly gaining on popularity as the more apt description of a networked society. With this motivation, we here consider two-layer scale-free networks with all possible combinations of degree mixing, wherein one network layer is used for the accumulation of payoffs and the other is used for strategy updating. We find that breaking the symmetry through assortative mixing in one layer and/or disassortative mixing in the other layer, as well as preserving the symmetry by means of assortative mixing in both layers, impedes the evolution of cooperation. We use degree-dependent distributions of strategies and cluster-size analysis to explain these results, which highlight the importance of hubs and the preservation of symmetry between multilayer networks for the successful resolution of social dilemmas.

  14. 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.

  15. Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding.

    PubMed

    Gardner, Brian; Grüning, André

    2016-01-01

    Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule's error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism. PMID:27532262

  16. Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding

    PubMed Central

    Gardner, Brian; Grüning, André

    2016-01-01

    Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule’s error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism. PMID:27532262

  17. Complex earthquake networks: Hierarchical organization and assortative mixing

    SciTech Connect

    Abe, Sumiyoshi; Suzuki, Norikazu

    2006-08-15

    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.

  18. Temporal dynamics of a homeostatic pathway controlling neural network activity

    PubMed Central

    Bateup, Helen S.; Denefrio, Cassandra L.; Johnson, Caroline A.; Saulnier, Jessica L.; Sabatini, Bernardo L.

    2013-01-01

    Neurons use a variety of mechanisms to homeostatically regulate neural network activity in order to maintain firing in a bounded range. One such process involves the bi-directional modulation of excitatory synaptic drive in response to chronic changes in network activity. Down-scaling of excitatory synapses in response to high activity requires Arc-dependent endocytosis of glutamate receptors. However, the temporal dynamics and signaling pathways regulating Arc during homeostatic plasticity are not well understood. Here we determine the relative contribution of transcriptional and translational control in the regulation of Arc, the signaling pathways responsible for the activity-dependent production of Arc, and the time course of these signaling events as they relate to the homeostatic adjustment of network activity in hippocampal neurons. We find that an ERK1/2-dependent transcriptional pathway active within 1–2 h of up-regulated network activity induces Arc leading to a restoration of network spiking rates within 12 h. Under basal and low activity conditions, specialized mechanisms are in place to rapidly degrade Arc mRNA and protein such that they have half-lives of less than 1 h. In addition, we find that while mTOR signaling is regulated by network activity on a similar time scale, mTOR-dependent translational control is not a major regulator of Arc production or degradation suggesting that the signaling pathways underlying homeostatic plasticity are distinct from those mediating synapse-specific forms of synaptic depression. PMID:24065881

  19. Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification.

    PubMed

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

    2016-06-01

    In conventional resting-state functional MRI (R-fMRI) analysis, functional connectivity is assumed to be temporally stationary, overlooking neural activities or interactions that may happen within the scan duration. Dynamic changes of neural interactions can be reflected by variations of topology and correlation strength in temporally correlated functional connectivity networks. These connectivity networks may potentially capture subtle yet short neural connectivity disruptions induced by disease pathologies. Accordingly, we are motivated to utilize disrupted temporal network properties for improving control-patient classification performance. Specifically, a sliding window approach is firstly employed to generate a sequence of overlapping R-fMRI sub-series. Based on these sub-series, sliding window correlations, which characterize the neural interactions between brain regions, are then computed to construct a series of temporal networks. Individual estimation of these temporal networks using conventional network construction approaches fails to take into consideration intrinsic temporal smoothness among successive overlapping R-fMRI sub-series. To preserve temporal smoothness of R-fMRI sub-series, we suggest to jointly estimate the temporal networks by maximizing a penalized log likelihood using a fused sparse learning algorithm. This sparse learning algorithm encourages temporally correlated networks to have similar network topology and correlation strengths. We design a disease identification framework based on the estimated temporal networks, and group level network property differences and classification results demonstrate the importance of including temporally dynamic R-fMRI scan information to improve diagnosis accuracy of mild cognitive impairment patients.

  20. Empirical study on structural properties in temporal networks under different time scales

    NASA Astrophysics Data System (ADS)

    Chen, Duanbing

    2015-12-01

    Many network analyzing methods are usually based on static networks. However, temporal networks should be considered so as to investigate real complex systems deeply since some dynamics on these systems cannot be described by static networks accurately. In this paper, four structural properties in temporal networks are empirically studied, including degree, clustering coefficient, adjacent correlation, and connected component. Three real temporal networks with different time scales are analyzed in this paper, including short message, telephone, and router networks. Moreover, structural properties of these temporal networks are compared with that of corresponding static aggregation networks in the whole time window. Some essential differences of structural properties between temporal and static networks are achieved through empirical analysis. Finally, the effect of structural properties on spreading dynamics under different time scales is investigated. Some interesting results such as turning point of structure evolving time scale corresponding to certain spreading dynamics time scale from the point of view of infected scale are achieved.

  1. 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.

  2. Networks of informal caring: a mixed-methods approach.

    PubMed

    Rutherford, Alasdair; Bowes, Alison

    2014-12-01

    Care for older people is a complex phenomenon, and is an area of pressing policy concern. Bringing together literature on care from social gerontology and economics, we report the findings of a mixed-methods project exploring networks of informal caring. Using quantitative data from the British Household Panel Survey (official survey of British households), together with qualitative interviews with older people and informal carers, we describe differences in formal care networks, and the factors and decision-making processes that have contributed to the formation of the networks. A network approach to care permits both quantitative and qualitative study, and the approach can be used to explore many important questions.

  3. 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.

  4. 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.

  5. 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.

  6. 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…

  7. 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.

  8. Studying Participation Networks in Collaboration Using Mixed Methods

    ERIC Educational Resources Information Center

    Martinez, Alejandra; Dimitriadis, Yannis; Gomez-Sanchez, Eduardo; Rubia-Avi, Bartolome; Jorrin-Abellan, Ivan; Marcos, Jose A.

    2006-01-01

    This paper describes the application of a mixed-evaluation method, published elsewhere, to three different learning scenarios. The method defines how to combine social network analysis with qualitative and quantitative analysis in order to study participatory aspects of learning in CSCL contexts. The three case studies include a course-long,…

  9. 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

  10. Assortative Mixing in Close-Packed Spatial Networks

    PubMed Central

    Turgut, Deniz; Atilgan, Ali Rana; Atilgan, Canan

    2010-01-01

    Background In recent years, there is aroused interest in expressing complex systems as networks of interacting nodes. Using descriptors from graph theory, it has been possible to classify many diverse systems derived from social and physical sciences alike. In particular, folded proteins as examples of self-assembled complex molecules have also been investigated intensely using these tools. However, we need to develop additional measures to classify different systems, in order to dissect the underlying hierarchy. Methodology and Principal Findings In this study, a general analytical relation for the dependence of nearest neighbor degree correlations on degree is derived. Dependence of local clustering on degree is shown to be the sole determining factor of assortative versus disassortative mixing in networks. The characteristics of networks constructed from spatial atomic/molecular systems exemplified by self-organized residue networks built from folded protein structures and block copolymers, atomic clusters and well-compressed polymeric melts are studied. Distributions of statistical properties of the networks are presented. For these densely-packed systems, assortative mixing in the network construction is found to apply, and conditions are derived for a simple linear dependence. Conclusions Our analyses (i) reveal patterns that are common to close-packed clusters of atoms/molecules, (ii) identify the type of surface effects prominent in different close-packed systems, and (iii) associate fingerprints that may be used to classify networks with varying types of correlations. PMID:21179578

  11. 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. PMID:26223262

  12. A Temporal Network Version of Watts's Cascade Model

    NASA Astrophysics Data System (ADS)

    Karimi, Fariba; Holme, Petter

    Threshold models of cascades in the social and economical sciences explain the spread of opinion and innovation as an effect of social influence. In threshold cascade models, fads or innovation spread between agents as determined by their interactions to other agents and their personal threshold of resistance. Typically, these models do not account for structure in the timing of interaction between the units. In this work, we extend a model of social cascades by Duncan Watts to temporal interaction networks. In our model, we assume agents are influenced by their friends and acquaintances at certain time into the past. That is, the influence of the past ages and becomes unimportant. Thus, our modified cascade model has an effective time window of influence. We explore two types of thresholds—thresholds to fractions of the neighbors, or absolute numbers. We try our model on six empirical datasets and compare them with null models.

  13. 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.

  14. 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.

  15. Disrupted Ipsilateral Network Connectivity in Temporal Lobe Epilepsy

    PubMed Central

    Vega-Zelaya, Lorena; Pastor, Jesús; de Sola, Rafael G.; Ortega, Guillermo J.

    2015-01-01

    Objective The current practice under which patients with refractory epilepsy are surgically treated is based mainly on the identification of specific cortical areas, mainly the epileptogenic zone, which is believed to be responsible for generation of seizures. A better understanding of the whole epileptic network and its components and properties is required before more effective and less invasive therapies can be developed. The aim of the present study was to partially characterize the evolution of the functional network during the preictal-ictal transition in partial seizures in patients with temporal lobe epilepsy (TLE). Methods Scalp and foramen ovale (FOE) recordings from twenty-two TLE patients were analyzed under the complex network perspective. The density of links, average path length, average clustering coefficient, and modularity were calculated during the preictal and the ictal stages. Both linear–Pearson correlation–and non-linear–phase synchronization–measures were used as proxies of functional connectivity between the electrode locations areas. The transition from one stage to the other was evaluated in the whole network and in the mesial sub-networks. The results were compared with a voltage-dependent measure, namely, the spectral entropy. Results Changes in the global functional network during the transition from the preictal to the ictal stage show, in the linear case, that in sixteen cases (72.7%) the density of the links increased during the seizure, with a decrease in the average path length in fifteen cases (68.1%). There was also a preictal and ictal imbalance in functional connectivity during both stages (77.2% to 86.3%). The SE dropped during the seizure in 95.4% of the cases, but did not show any tendency towards lateralization. When using the nonlinear measure of functional connectivity, the phase synchronization, similar results were obtained. Conclusions In TLE patients, the transition to the ictal stage is accompanied by

  16. Collaboration in sensor network research: an in-depth longitudinal analysis of assortative mixing patterns.

    PubMed

    Pepe, Alberto; Rodriguez, Marko A

    2010-09-01

    Many investigations of scientific collaboration are based on statistical analyses of large 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 illustrate the broader social and academic landscape in which collaboration takes place. In this article, we perform an in-depth longitudinal analysis of a relatively small network of scientific collaboration (N = 291) constructed from the bibliographic record of a research centerin the development and application of wireless and 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 assortative mixing of selected node characteristics, unveiling the researchers' propensity to collaborate preferentially with others with a similar academic profile. 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.

  17. 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.

  18. 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.

  19. Characteristics of the spatio-temporal network of cattle movements in France over a 5-year period.

    PubMed

    Dutta, Bhagat Lal; Ezanno, Pauline; Vergu, Elisabeta

    2014-11-01

    A good knowledge of the specificities of the animal trade network is highly valuable to better control pathogen spread on a large regional to transnational scale. Because of their temporal dynamical nature, studying multi-annual datasets is particularly needed to investigate whether structural patterns are stable over the years. In this study, we analysed the French cattle movement network from 2005 to 2009 for different spatial granularities and temporal windows, with the three-fold objective of exploring temporal variations of the main network characteristics, computing proxies for pathogen spread on this network, which accounts for its time-varying properties and identifying specificities related to the main types of animals and farms (dairy versus beef). Network properties did not qualitatively vary among different temporal and spatial granularities. About 40% of the holdings and 80% of the communes were directly interconnected. The width of the aggregation time window barely impacted normalised distributions of indicators. A period of 8-16 weeks would suffice for robust estimation of their main trends, whereas longer periods would provide more details on tails. The dynamic nature of the network could be seen through the small overlap between consecutive networks with 65% of common active nodes for only 3% of common links over 2005-2009. To control pathogen spread on such a network, by reducing the largest strongly connected component by more than 80%, movements should be prevented from 1 to 5% of the holdings with the highest centrality in the previous year network. The analysis of breed-wise and herd-wise subnetworks, dairy, beef and mixed, reveals similar trends in temporal variation of average indicators and their distributions. The link-based backbones of beef subnetworks seem to be more stable over time than those of other subnetworks. At a regional scale, node reachability accounting for time-respecting paths, as proxy of epidemic burden, is greater for

  20. A spatial-temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images

    NASA Astrophysics Data System (ADS)

    Li, Xiaodong; Ling, Feng; Du, Yun; Feng, Qi; Zhang, Yihang

    2014-07-01

    The mixed pixel problem affects the extraction of land cover information from remotely sensed images. Super-resolution mapping (SRM) can produce land cover maps with a finer spatial resolution than the remotely sensed images, and reduce the mixed pixel problem to some extent. Traditional SRMs solely adopt a single coarse-resolution image as input. Uncertainty always exists in resultant fine-resolution land cover maps, due to the lack of information about detailed land cover spatial patterns. The development of remote sensing technology has enabled the storage of a great amount of fine spatial resolution remotely sensed images. These data can provide fine-resolution land cover spatial information and are promising in reducing the SRM uncertainty. This paper presents a spatial-temporal Hopfield neural network (STHNN) based SRM, by employing both a current coarse-resolution image and a previous fine-resolution land cover map as input. STHNN considers the spatial information, as well as the temporal information of sub-pixel pairs by distinguishing the unchanged, decreased and increased land cover fractions in each coarse-resolution pixel, and uses different rules in labeling these sub-pixels. The proposed STHNN method was tested using synthetic images with different class fraction errors and real Landsat images, by comparing with pixel-based classification method and several popular SRM methods including pixel-swapping algorithm, Hopfield neural network based method and sub-pixel land cover change mapping method. Results show that STHNN outperforms pixel-based classification method, pixel-swapping algorithm and Hopfield neural network based model in most cases. The weight parameters of different STHNN spatial constraints, temporal constraints and fraction constraint have important functions in the STHNN performance. The heterogeneity degree of the previous map and the fraction images errors affect the STHNN accuracy, and can be served as guidances of selecting the

  1. 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%.

  2. Infection propagator approach to compute epidemic thresholds on temporal networks: impact of immunity and of limited temporal resolution

    NASA Astrophysics Data System (ADS)

    Valdano, Eugenio; Poletto, Chiara; Colizza, Vittoria

    2015-12-01

    The epidemic threshold of a spreading process indicates the condition for the occurrence of the wide spreading regime, thus representing a predictor of the network vulnerability to the epidemic. Such threshold depends on the natural history of the disease and on the pattern of contacts of the network with its time variation. Based on the theoretical framework introduced in [E. Valdano, L. Ferreri, C. Poletto, V. Colizza, Phys. Rev. X 5, 21005 (2015)] for a susceptible-infectious-susceptible model, we formulate here an infection propagator approach to compute the epidemic threshold accounting for more realistic effects regarding a varying force of infection per contact, the presence of immunity, and a limited time resolution of the temporal network. We apply the approach to two temporal network models and an empirical dataset of school contacts. We find that permanent or temporary immunity do not affect the estimation of the epidemic threshold through the infection propagator approach. Comparisons with numerical results show the good agreement of the analytical predictions. Aggregating the temporal network rapidly deteriorates the predictions, except for slow diseases once the heterogeneity of the links is preserved. Weight-topology correlations are found to be the critical factor to be preserved to improve accuracy in the prediction.

  3. Quantum-network generation based on four-wave mixing

    NASA Astrophysics Data System (ADS)

    Cai, Yin; Feng, Jingliang; Wang, Hailong; Ferrini, Giulia; Xu, Xinye; Jing, Jietai; Treps, Nicolas

    2015-01-01

    We present a scheme to realize versatile quantum networks by cascading several four-wave mixing (FWM) processes in warm rubidium vapors. FWM is an efficient χ(3 ) nonlinear process, already used as a resource for multimode quantum state generation and which has been proved to be a promising candidate for applications to quantum information processing. We analyze theoretically the multimode output of cascaded FWM systems, derive its independent squeezed modes, and show how, with phase controlled homodyne detection and digital postprocessing, they can be turned into a versatile source of continuous variable cluster states.

  4. Mapping eQTL Networks with Mixed Graphical Markov Models

    PubMed Central

    Tur, Inma; Roverato, Alberto; Castelo, Robert

    2014-01-01

    Expression quantitative trait loci (eQTL) mapping constitutes a challenging problem due to, among other reasons, the high-dimensional multivariate nature of gene-expression traits. Next to the expression heterogeneity produced by confounding factors and other sources of unwanted variation, indirect effects spread throughout genes as a result of genetic, molecular, and environmental perturbations. From a multivariate perspective one would like to adjust for the effect of all of these factors to end up with a network of direct associations connecting the path from genotype to phenotype. In this article we approach this challenge with mixed graphical Markov models, higher-order conditional independences, and q-order correlation graphs. These models show that additive genetic effects propagate through the network as function of gene–gene correlations. Our estimation of the eQTL network underlying a well-studied yeast data set leads to a sparse structure with more direct genetic and regulatory associations that enable a straightforward comparison of the genetic control of gene expression across chromosomes. Interestingly, it also reveals that eQTLs explain most of the expression variability of network hub genes. PMID:25271303

  5. 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.

  6. 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

  7. 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. PMID:27626803

  8. 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.

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

    PubMed

    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

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

    PubMed

    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

  11. 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

  12. 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.

  13. 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.

  14. 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

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

    NASA Astrophysics Data System (ADS)

    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.

  16. Mixed Criticality Scheduling for Industrial Wireless Sensor Networks.

    PubMed

    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

  17. 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

  18. Mixed Criticality Scheduling for Industrial Wireless Sensor Networks.

    PubMed

    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.

  19. The Role of Temporal Trends in Growing Networks.

    PubMed

    Mokryn, Osnat; Wagner, Allon; Blattner, Marcel; 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

  20. 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

  1. 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,…

  2. 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.

  3. 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.

  4. Evaluating the Spatio-Temporal Factors that Structure Network Parameters of Plant-Herbivore Interactions

    PubMed Central

    López-Carretero, Antonio; Díaz-Castelazo, Cecilia; Boege, Karina; Rico-Gray, Víctor

    2014-01-01

    Despite the dynamic nature of ecological interactions, most studies on species networks offer static representations of their structure, constraining our understanding of the ecological mechanisms involved in their spatio-temporal stability. This is the first study to evaluate plant-herbivore interaction networks on a small spatio-temporal scale. Specifically, we simultaneously assessed the effect of host plant availability, habitat complexity and seasonality on the structure of plant-herbivore networks in a coastal tropical ecosystem. Our results revealed that changes in the host plant community resulting from seasonality and habitat structure are reflected not only in the herbivore community, but also in the emergent properties (network parameters) of the plant-herbivore interaction network such as connectance, selectiveness and modularity. Habitat conditions and periods that are most stressful favored the presence of less selective and susceptible herbivore species, resulting in increased connectance within networks. In contrast, the high degree of selectivennes (i.e. interaction specialization) and modularity of the networks under less stressful conditions was promoted by the diversification in resource use by herbivores. By analyzing networks at a small spatio-temporal scale we identified the ecological factors structuring this network such as habitat complexity and seasonality. Our research offers new evidence on the role of abiotic and biotic factors in the variation of the properties of species interaction networks. PMID:25340790

  5. 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

  6. 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.

  7. Temporal properties of dynamic processes on complex networks

    NASA Astrophysics Data System (ADS)

    Turalska, Malgorzata A.

    Many social, biological and technological systems can be viewed as complex networks with a large number of interacting components. However despite recent advancements in network theory, a satisfactory description of dynamic processes arising in such cooperative systems is a subject of ongoing research. In this dissertation the emergence of dynamical complexity in networks of interacting stochastic oscillators is investigated. In particular I demonstrate that networks of two and three state stochastic oscillators present a second-order phase transition with respect to the strength of coupling between individual units. I show that at the critical point fluctuations of the global order parameter are characterized by an inverse-power law distribution and I assess their renewal properties. Additionally, I study the effect that different types of perturbation have on dynamical properties of the model. I discuss the relevance of those observations for the transmission of information between complex systems.

  8. Physical and Temporal Controls on Lower Crustal Melting and Mixing: Mass and Enthalpy Transport in Actively Growing Arcs

    NASA Astrophysics Data System (ADS)

    Dufek, J. D.; Bergantz, G. W.

    2004-12-01

    The growth of continental crust in arc settings, as well as the thermal and compositional character of the crust, is ultimately dictated by the flux of basaltic magma from the mantle and the interaction between crustal and basaltic material. We present a quantitative assessment of the thermal and dynamic response of the lower crust to the intrusion of basaltic dike swarms in a two-dimensional, stochastic computational framework. We will examine the physical and temporal controls on crustal melting, mingling, and mixing as well as some of the major element, trace element, and U-series consequences of these lower crustal interactions. Distinct melting and mixing environments are predicted as a result of the crustal thickness, flux of basalt, and age of the arc system. Shallow crustal (approx. 30 km) environments and arc settings with low fluxes of mantle basalt are likely repositories of isolated pods of mantle and crustal melts in the lower crust, both converging on dacitic to rhyodacitic composition. These may be preferentially rejuvenated in subsequent intrusive episodes. Mature arc systems with thicker crust (approx. 50 km) produce higher crustal and residual basaltic melt fractions reaching approx. .4 for geologically reasonable basalt fluxes. The basaltic to basaltic-andesite composition of both crustal and mantle melts will readily mix as the network of dikes collapses and Reynolds numbers reach 10-4 to 1.0 in the interiors of dikes that have been breached by ascending crustal melts. This may provide one mechanism for MASH-like processes. Residual mineral assemblages of the crust thickened by repeated intrusion are predicted to be garnet pyroxenitic, which are denser than mantle peridotite and also generate convective instabilities where some of the crustal material is lost to the mantle. This reconciles the thinner than predicted crust in regions that have undergone flux of mantle basalt for a prolonged period of time, and helps explain the enrichment of

  9. Giant cell granuloma of the temporal bone in a mixed martial arts fighter.

    PubMed

    Maerki, Jennifer; Riddle, Nicole D; Newman, Jason; Husson, Michael A; Lee, John Y K

    2012-10-01

    Background and Importance Giant cell granuloma (GCG) is a rare, benign, non-neoplastic lesion of the head and neck. More common in the jaw bones, there have been few reports of the lesion arising in the temporal bone. Initially referred to as a "giant cell reparative granuloma," due to the previously accepted notion of its nature in attempting to repair areas of injury, the term "giant cell granuloma" is now more frequently used as this lesion has been found in patients without a history of trauma. In addition, several cases with a destructive nature, in contrast to a reparative one, have been observed. Clinical Presentation We report a case of GCG presenting as a head and neck tumor with dural attachments and extension into the middle cranial fossa in a mixed martial arts fighter. Conclusion Giant cell granulomas are typically treated surgically and have a good prognosis; however, care must be taken when they present in unusual locations. This case supports the theory of trauma and inflammation as risk factors for GCG. PMID:23946929

  10. 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.

  11. 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.

  12. Numerical evidence of mixing in rooms using the free path temporal distribution.

    PubMed

    Billon, Alexis; Embrechts, Jean-Jacques

    2011-09-01

    The ergodic propriety of a room has strong effects on its reverberation. If the room is ergodic, the reverberation can be broken up in two steps: a deterministic process followed by a stochastic one. The late reverberation can be then modeled by a reverberation algorithm instead of more computationally consuming methods. In this study, the free path temporal distribution obtained by ray-tracing is used as an indicator of the room's mixing: the energetic average of the path lengths is computed at each time step. Ergodic rooms are thus characterized by rapidly convergent distributions. The free path value becomes independent of time. On the other hand, path selection mechanism and orbits are observed in non-ergodic rooms. The transition time from the deterministic process to the stochastic one is also studied through the evaluation of the room's time constant. It is shown that its value depends only on the mean free path and the boundaries scattering value. An empirical expression is obtained which agrees well with simulations carried out in a concert hall. This transition time from a deterministic model to a stochastic one can be used to speed up the acoustical predictions and auralizations in ergodic rooms. PMID:21895079

  13. 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).

  14. Effects of temporal correlations on cascades: Threshold models on temporal networks

    NASA Astrophysics Data System (ADS)

    Backlund, Ville-Pekka; Saramäki, Jari; Pan, Raj Kumar

    2014-06-01

    A person's decision to adopt an idea or product is often driven by the decisions of peers, mediated through a network of social ties. A common way of modeling adoption dynamics is to use threshold models, where a node may become an adopter given a high enough rate of contacts with adopted neighbors. We study the dynamics of threshold models that take both the network topology and the timings of contacts into account, using empirical contact sequences as substrates. The models are designed such that adoption is driven by the number of contacts with different adopted neighbors within a chosen time. We find that while some networks support cascades leading to network-level adoption, some do not: the propagation of adoption depends on several factors from the frequency of contacts to burstiness and timing correlations of contact sequences. More specifically, burstiness is seen to suppress cascade sizes when compared to randomized contact timings, while timing correlations between contacts on adjacent links facilitate cascades.

  15. 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

  16. Sequential detection of temporal communities in evolving networks by estrangement confinement

    NASA Astrophysics Data System (ADS)

    Sreenivasan, Sameet; Kawadia, Vikas

    2013-03-01

    Temporal communities are the result of a consistent partitioning of nodes across multiple snapshots of an evolving network, and they provide insights into how dense clusters in a network emerge, combine, split and decay over time. Reliable detection of temporal communities requires finding a good community partition in a given snapshot while simultaneously ensuring that it bears some similarity to the partition(s) found in the previous snapshot(s). This is a particularly difficult task given the extreme sensitivity of community structure yielded by current methods to changes in the network structure. Motivated by the inertia of inter-node relationships, we present a new measure of partition distance called estrangement, and show that constraining estrangement enables the detection of meaningful temporal communities at various degrees of temporal smoothness in diverse real-world datasets. Estrangement confinement consequently provides a principled approach to uncovering temporal communities in evolving networks. (V. Kawadia and S. Sreenivasan, http://arxiv.org/abs/1203.5126) Supported in part by ARL NS-CTA

  17. 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

  18. 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.

  19. Inference of causality in epidemics on temporal contact networks

    PubMed Central

    Braunstein, Alfredo; Ingrosso, Alessandro

    2016-01-01

    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. PMID:27283451

  20. 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.

  1. Temporal dynamics of spontaneous MEG activity in brain networks.

    PubMed

    de Pasquale, Francesco; Della Penna, Stefania; Snyder, Abraham Z; Lewis, Christopher; Mantini, Dante; Marzetti, Laura; Belardinelli, Paolo; Ciancetta, Luca; Pizzella, Vittorio; Romani, Gian Luca; Corbetta, Maurizio

    2010-03-30

    Functional MRI (fMRI) studies have shown that low-frequency (<0.1 Hz) spontaneous fluctuations of the blood oxygenation level dependent (BOLD) signal during restful wakefulness are coherent within distributed large-scale cortical and subcortical networks (resting state networks, RSNs). The neuronal mechanisms underlying RSNs remain poorly understood. Here, we describe magnetoencephalographic correspondents of two well-characterized RSNs: the dorsal attention and the default mode networks. Seed-based correlation mapping was performed using time-dependent MEG power reconstructed at each voxel within the brain. The topography of RSNs computed on the basis of extended (5 min) epochs was similar to that observed with fMRI but confined to the same hemisphere as the seed region. Analyses taking into account the nonstationarity of MEG activity showed transient formation of more complete RSNs, including nodes in the contralateral hemisphere. Spectral analysis indicated that RSNs manifest in MEG as synchronous modulation of band-limited power primarily within the theta, alpha, and beta bands-that is, in frequencies slower than those associated with the local electrophysiological correlates of event-related BOLD responses. PMID:20304792

  2. A linked spatial and temporal model of the chemical and biological status of a large, acid-sensitive river network.

    PubMed

    Evans, Chris D; Cooper, David M; Juggins, Steve; Jenkins, Alan; Norris, Dave

    2006-07-15

    Freshwater sensitivity to acidification varies according to geology, soils and land-use, and consequently it remains difficult to quantify the current extent of acidification, or its biological impacts, based on limited spot samples. The problem is particularly acute for river systems, where the transition from acid to circum-neutral conditions can occur within short distances. This paper links an established point-based long-term acidification model (MAGIC) with a landscape-based mixing model (PEARLS) to simulate spatial and temporal variations in acidification for a 256 km(2) catchment in North Wales. Empirical relationships are used to predict changes in the probability of occurrence of an indicator invertebrate species, Baetis rhodani, across the catchment as a function of changing chemical status. Results suggest that, at present, 27% of the river network has a mean acid neutralising capacity (ANC) below a biologically-relevant threshold of 20 microeq l(-1). At high flows, this proportion increases to 45%. The model suggests that only around 16% of the stream network had a mean ANC < 20 microeq l(-1) in 1850, but that this increased to 42% at the sulphur deposition peak around 1970. By 2050 recovery is predicted, but with some persistence of acid conditions in the most sensitive, peaty headwaters. Stream chemical suitability for Baetis rhodani is also expected to increase in formerly acidified areas, but for overall abundance to remain below that simulated in 1850. The approach of linking plot-scale process-based models to catchment mixing models provides a potential means of predicting the past and future spatial extent of acidification within large, heterogeneous river networks and regions. Further development of ecological response models to include other chemical predictor variables and the effects of acid episodes would allow more realistic simulation of the temporal and spatial dynamics of ecosystem recovery from acidification.

  3. A linked spatial and temporal model of the chemical and biological status of a large, acid-sensitive river network.

    PubMed

    Evans, Chris D; Cooper, David M; Juggins, Steve; Jenkins, Alan; Norris, Dave

    2006-07-15

    Freshwater sensitivity to acidification varies according to geology, soils and land-use, and consequently it remains difficult to quantify the current extent of acidification, or its biological impacts, based on limited spot samples. The problem is particularly acute for river systems, where the transition from acid to circum-neutral conditions can occur within short distances. This paper links an established point-based long-term acidification model (MAGIC) with a landscape-based mixing model (PEARLS) to simulate spatial and temporal variations in acidification for a 256 km(2) catchment in North Wales. Empirical relationships are used to predict changes in the probability of occurrence of an indicator invertebrate species, Baetis rhodani, across the catchment as a function of changing chemical status. Results suggest that, at present, 27% of the river network has a mean acid neutralising capacity (ANC) below a biologically-relevant threshold of 20 microeq l(-1). At high flows, this proportion increases to 45%. The model suggests that only around 16% of the stream network had a mean ANC < 20 microeq l(-1) in 1850, but that this increased to 42% at the sulphur deposition peak around 1970. By 2050 recovery is predicted, but with some persistence of acid conditions in the most sensitive, peaty headwaters. Stream chemical suitability for Baetis rhodani is also expected to increase in formerly acidified areas, but for overall abundance to remain below that simulated in 1850. The approach of linking plot-scale process-based models to catchment mixing models provides a potential means of predicting the past and future spatial extent of acidification within large, heterogeneous river networks and regions. Further development of ecological response models to include other chemical predictor variables and the effects of acid episodes would allow more realistic simulation of the temporal and spatial dynamics of ecosystem recovery from acidification. PMID:16580046

  4. 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.

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

    PubMed

    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)PRLTAO0031-900710.1103/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. PMID:27627326

  6. 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

  7. 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.

  8. 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.

  9. Enhancing the Temporal Complexity of Distributed Brain Networks with Patterned Cerebellar Stimulation

    PubMed Central

    Farzan, Faranak; Pascual-Leone, Alvaro; Schmahmann, Jeremy D.; Halko, Mark

    2016-01-01

    Growing evidence suggests that sensory, motor, cognitive and affective processes map onto specific, distributed neural networks. Cerebellar subregions are part of these networks, but how the cerebellum is involved in this wide range of brain functions remains poorly understood. It is postulated that the cerebellum contributes a basic role in brain functions, helping to shape the complexity of brain temporal dynamics. We therefore hypothesized that stimulating cerebellar nodes integrated in different networks should have the same impact on the temporal complexity of cortical signals. In healthy humans, we applied intermittent theta burst stimulation (iTBS) to the vermis lobule VII or right lateral cerebellar Crus I/II, subregions that prominently couple to the dorsal-attention/fronto-parietal and default-mode networks, respectively. Cerebellar iTBS increased the complexity of brain signals across multiple time scales in a network-specific manner identified through electroencephalography (EEG). We also demonstrated a region-specific shift in power of cortical oscillations towards higher frequencies consistent with the natural frequencies of targeted cortical areas. Our findings provide a novel mechanism and evidence by which the cerebellum contributes to multiple brain functions: specific cerebellar subregions control the temporal dynamics of the networks they are engaged in. PMID:27009405

  10. Enhancing the Temporal Complexity of Distributed Brain Networks with Patterned Cerebellar Stimulation.

    PubMed

    Farzan, Faranak; Pascual-Leone, Alvaro; Schmahmann, Jeremy D; Halko, Mark

    2016-01-01

    Growing evidence suggests that sensory, motor, cognitive and affective processes map onto specific, distributed neural networks. Cerebellar subregions are part of these networks, but how the cerebellum is involved in this wide range of brain functions remains poorly understood. It is postulated that the cerebellum contributes a basic role in brain functions, helping to shape the complexity of brain temporal dynamics. We therefore hypothesized that stimulating cerebellar nodes integrated in different networks should have the same impact on the temporal complexity of cortical signals. In healthy humans, we applied intermittent theta burst stimulation (iTBS) to the vermis lobule VII or right lateral cerebellar Crus I/II, subregions that prominently couple to the dorsal-attention/fronto-parietal and default-mode networks, respectively. Cerebellar iTBS increased the complexity of brain signals across multiple time scales in a network-specific manner identified through electroencephalography (EEG). We also demonstrated a region-specific shift in power of cortical oscillations towards higher frequencies consistent with the natural frequencies of targeted cortical areas. Our findings provide a novel mechanism and evidence by which the cerebellum contributes to multiple brain functions: specific cerebellar subregions control the temporal dynamics of the networks they are engaged in. PMID:27009405

  11. Comparison of a networks-of-zones fluid mixing model for a baffled stirred vessel with three-dimensional electrical resistance tomography

    NASA Astrophysics Data System (ADS)

    Rodgers, T. L.; Siperstein, F. R.; Mann, R.; York, T. A.; Kowalski, A.

    2011-10-01

    Reliable models for the simulation of mixing vessels are important for the understanding of real-life mixing problems. To achieve these models, information about the mixing in the system must be measured to compare with the predicted values. Electrical resistance tomography has the capability to measure spatial and temporal changes within a vessel in three dimensions even in optically inaccessible environments. This paper discusses the creation of a network-of-zones model for the prediction of mixing within a vessel with a Cowles disc-type agitator. Solving of the network-of-zones simplified transport equations for the vessel predicts the concentration distribution of an inert tracer added to the vessel. The change in this distribution with time is calculated and compared with visual inspection of the vessel. The concentration distribution inside the vessel is also measured using electrical resistance tomography and shows good agreement with the predicted values.

  12. 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.

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

    PubMed

    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. PMID:27575154

  14. 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

  15. 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…

  16. A low cost sensor network approach to investigate spatio-temporal patterns of stream temperatures and electrical conductivity

    NASA Astrophysics Data System (ADS)

    Lieder, Ernestine; Weiler, Markus; Blume, Theresa

    2016-04-01

    Understanding water and energy fluxes at the stream and catchment scale remains a challenging task. Within the CAOS-project-framework it is our aim to investigate spatiotemporal patterns of stream temperature and to deduce understanding about the underlying hydrological system. A low cost sensor network was installed in summer 2015 to monitor stream temperature and EC patterns in time and space. 90 HOBO temperature sensors, which were modified to additionally measure EC, were installed at 30 confluences across the Attert catchment (288 km²) in Luxembourg. The design of the sensor network allows for the investigation of three research questions: a) spatial patterns of stream temperatures and EC and their dynamics across the region b) estimation of relative streamflow contributions and their temporal dynamics by using simple mixing models and c) estimation of heat transport. The data will thus provide valuable insight in runoff contributions from different sub-catchments, and a combined analysis with distributed measurements of soil moisture and shallow groundwater will improve our process understanding by linking hillslope scale processes with stream responses. First results indicate that streams in different geologies show distinct temperature and EC patterns throughout the observation period. Differences are also found with respect to temporal dynamics both for longer periods as well as diurnal fluctuations. These differences are likely to be caused by differences in flow paths on the one hand (e.g. amount of groundwater contribution) and exposure to direct radiation on the other hand.

  17. Spatial and temporal heterogeneity explain disease dynamics in a spatially explicit network model.

    PubMed

    Brooks, Christopher P; Antonovics, Janis; Keitt, Timothy H

    2008-08-01

    There is an increasing recognition that individual-level spatial and temporal heterogeneity may play an important role in metapopulation dynamics and persistence. In particular, the patterns of contact within and between aggregates (e.g., demes) at different spatial and temporal scales may reveal important mechanisms governing metapopulation dynamics. Using 7 years of data on the interaction between the anther smut fungus (Microbotryum violaceum) and fire pink (Silene virginica), we show how the application of spatially explicit and implicit network models can be used to make accurate predictions of infection dynamics in spatially structured populations. Explicit consideration of both spatial and temporal organization reveals the role of each in spreading risk for both the host and the pathogen. This work suggests that the application of spatially explicit network models can yield important insights into how heterogeneous structure can promote the persistence of species in natural landscapes. PMID:18662121

  18. A Modularity-Based Method Reveals Mixed Modules from Chemical-Gene Heterogeneous Network

    PubMed Central

    Song, Jianglong; Tang, Shihuan; Liu, Xi; Gao, Yibo; Yang, Hongjun; Lu, Peng

    2015-01-01

    For a multicomponent therapy, molecular network is essential to uncover its specific mode of action from a holistic perspective. The molecular system of a Traditional Chinese Medicine (TCM) formula can be represented by a 2-class heterogeneous network (2-HN), which typically includes chemical similarities, chemical-target interactions and gene interactions. An important premise of uncovering the molecular mechanism is to identify mixed modules from complex chemical-gene heterogeneous network of a TCM formula. We thus proposed a novel method (MixMod) based on mixed modularity to detect accurate mixed modules from 2-HNs. At first, we compared MixMod with Clauset-Newman-Moore algorithm (CNM), Markov Cluster algorithm (MCL), Infomap and Louvain on benchmark 2-HNs with known module structure. Results showed that MixMod was superior to other methods when 2-HNs had promiscuous module structure. Then these methods were tested on a real drug-target network, in which 88 disease clusters were regarded as real modules. MixMod could identify the most accurate mixed modules from the drug-target 2-HN (normalized mutual information 0.62 and classification accuracy 0.4524). In the end, MixMod was applied to the 2-HN of Buchang naoxintong capsule (BNC) and detected 49 mixed modules. By using enrichment analysis, we investigated five mixed modules that contained primary constituents of BNC intestinal absorption liquid. As a matter of fact, the findings of in vitro experiments using BNC intestinal absorption liquid were found to highly accord with previous analysis. Therefore, MixMod is an effective method to detect accurate mixed modules from chemical-gene heterogeneous networks and further uncover the molecular mechanism of multicomponent therapies, especially TCM formulae. PMID:25927435

  19. Temporal updating scheme for probabilistic neural network with application to satellite cloud classification--further results.

    PubMed

    Azimi-Sadjadi, M R; Gao, W; Vonder Haar, T H; Reinke, D

    2001-01-01

    A novel temporal updating approach for probabilistic neural network classifiers was developed by Tian et al. (2000) to account for temporal changes of spectral and temperature features of clouds in the visible and infrared GOES 8 (Geostationary Operational Environmental Satellite) imagery data. In this paper, a new method referred to as moving singular value decomposition (MSVD) is introduced to improve the classification rate of the boundary blocks or blocks containing cloud types with non-uniform texture. The MSVD method is then incorporated into the temporal updating scheme and its effectiveness is demonstrated on several sequences of GOES 8 cloud imagery data. These results indicate that the incorporation of the new MSVD improves the overall performance of the temporal updating process by almost 10%

  20. 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

  1. Impact of spatio-temporal heterogeneities and lateral stirring and mixing on mid-water biotic interactions

    NASA Astrophysics Data System (ADS)

    Martinez, E.; Richards, K. J.

    2010-08-01

    We study the impact of spatial and temporal inhomogeneities in the flux of particles on particle-biology interactions in the mesopelagic zone using a flux-prey-predator model. The mid-water biology is found to affect significantly the carbon flux associated with the sinking particles. Although the annual mean export flux at the bottom of the zone (taken to be at 1000 m depth) is changed at most 25% in the experiments reported here, the timing and amplitude of pulses of the bottom flux are very dependent on the way the flux at the top of the zone (taken to be 100 m depth) is packaged in time and space. The apparent sinking speed, based on the arrival of pulses of the export flux at the bottom of the zone (1000 m), can vary from 5 m day - 1 to 30 m day - 1 . Lateral stirring and mixing also impact the temporal and spatial distributions of the particle flux. A useful metric in determining the impact of stirring and mixing is the "mix-down depth" which combines the effects of the initial patch size, strength of stirring, diffusion and sinking rate. When the mix-down depth is small compared to the depth to which biological interactions are important, then the impact of stirring and mixing is large, producing significant changes to the temporal behavior of the export flux and reducing spatial inhomogeneities. The results have implications for the sampling of the carbon flux associated with sinking particles and the representativeness of point measurements.

  2. 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

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

    PubMed

    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

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

    PubMed

    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.

  5. 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.

  6. 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.

  7. Application of BP Neural Network Based on Genetic Algorithm in Quantitative Analysis of Mixed GAS

    NASA Astrophysics Data System (ADS)

    Chen, Hongyan; Liu, Wenzhen; Qu, Jian; Zhang, Bing; Li, Zhibin

    Aiming at the problem of mixed gas detection in neural network and analysis on the principle of gas detection. Combining BP algorithm of genetic algorithm with hybrid gas sensors, a kind of quantitative analysis system of mixed gas is designed. The local minimum of network learning is the main reason which affects the precision of gas analysis. On the basis of the network study to improve the learning algorithms, the analyses and tests for CO, CO2 and HC compounds were tested. The results showed that the above measures effectively improve and enhance the accuracy of the neural network for gas analysis.

  8. 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

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

    PubMed

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

    2015-04-01

    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

  10. Inferring social network structure in ecological systems from spatio-temporal data streams

    PubMed Central

    Psorakis, Ioannis; Roberts, Stephen J.; Rezek, Iead; Sheldon, Ben C.

    2012-01-01

    We propose a methodology for extracting social network structure from spatio-temporal datasets that describe timestamped occurrences of individuals. Our approach identifies temporal regions of dense agent activity and links are drawn between individuals based on their co-occurrences across these ‘gathering events’. The statistical significance of these connections is then tested against an appropriate null model. Such a framework allows us to exploit the wealth of analytical and computational tools of network analysis in settings where the underlying connectivity pattern between interacting agents (commonly termed the adjacency matrix) is not given a priori. We perform experiments on two large-scale datasets (greater than 106 points) of great tit Parus major wild bird foraging records and illustrate the use of this approach by examining the temporal dynamics of pairing behaviour, a process that was previously very hard to observe. We show that established pair bonds are maintained continuously, whereas new pair bonds form at variable times before breeding, but are characterized by a rapid development of network proximity. The method proposed here is general, and can be applied to any system with information about the temporal co-occurrence of interacting agents. PMID:22696481

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

    PubMed

    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.

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

    PubMed

    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. PMID:26066218

  13. 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.

  14. Mixed-Method Exploration of Social Network Links to Participation.

    PubMed

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

    2015-07-01

    The people who regularly interact with an adolescent form that youth's social network (SN), which may impact participation. We investigated the relationship of SNs to participation using personal network analysis and individual interviews. The sample included 36 youth, aged 11 to 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 I 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 SNs. Findings contribute to understanding the ways SNs are linked to youth participation and suggest the potential of SN factors for predicting rehabilitation outcomes.

  15. 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. PMID:27499738

  16. 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

  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. 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

  19. 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

  20. On the Robustness of In- and Out-Components in a Temporal Network

    PubMed Central

    Konschake, Mario; Lentz, Hartmut H. K.; Conraths, Franz J.; Hövel, Philipp; Selhorst, Thomas

    2013-01-01

    Background Many networks exhibit time-dependent topologies, where an edge only exists during a certain period of time. The first measurements of such networks are very recent so that a profound theoretical understanding is still lacking. In this work, we focus on the propagation properties of infectious diseases in time-dependent networks. In particular, we analyze a dataset containing livestock trade movements. The corresponding networks are known to be a major route for the spread of animal diseases. In this context chronology is crucial. A disease can only spread if the temporal sequence of trade contacts forms a chain of causality. Therefore, the identification of relevant nodes under time-varying network topologies is of great interest for the implementation of counteractions. Methodology/Findings We find that a time-aggregated approach might fail to identify epidemiologically relevant nodes. Hence, we explore the adaptability of the concept of centrality of nodes to temporal networks using a data-driven approach on the example of animal trade. We utilize the size of the in- and out-component of nodes as centrality measures. Both measures are refined to gain full awareness of the time-dependent topology and finite infectious periods. We show that the size of the components exhibit strong temporal heterogeneities. In particular, we find that the size of the components is overestimated in time-aggregated networks. For disease control, however, a risk assessment independent of time and specific disease properties is usually favored. We therefore explore the disease parameter range, in which a time-independent identification of central nodes remains possible. Conclusions We find a ranking of nodes according to their component sizes reasonably stable for a wide range of infectious periods. Samples based on this ranking are robust enough against varying disease parameters and hence are promising tools for disease control. PMID:23405124

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

    PubMed

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

    2016-02-01

    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

  2. Steady state and mean recurrence time for random walks on stochastic temporal networks.

    PubMed

    Speidel, Leo; Lambiotte, Renaud; Aihara, Kazuyuki; Masuda, Naoki

    2015-01-01

    Random walks are basic diffusion processes on networks and have applications in, for example, searching, navigation, ranking, and community detection. Recent recognition of the importance of temporal aspects on networks spurred studies of random walks on temporal networks. Here we theoretically study two types of event-driven random walks on a stochastic temporal network model that produces arbitrary distributions of interevent times. In the so-called active random walk, the interevent time is reinitialized on all links upon each movement of the walker. In the so-called passive random walk, the interevent time is reinitialized only on the link that has been used the last time, and it is a type of correlated random walk. We find that the steady state is always the uniform density for the passive random walk. In contrast, for the active random walk, it increases or decreases with the node's degree depending on the distribution of interevent times. The mean recurrence time of a node is inversely proportional to the degree for both active and passive random walks. Furthermore, the mean recurrence time does or does not depend on the distribution of interevent times for the active and passive random walks, respectively. PMID:25679656

  3. 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.

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

    NASA Astrophysics Data System (ADS)

    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.

  5. 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.

  6. 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 use of network…

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

    PubMed

    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.

  8. 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

  9. Knowledge engineering for temporal dependency networks as operations procedures. [in space communication

    NASA Technical Reports Server (NTRS)

    Fayyad, Kristina E.; Hill, Randall W., Jr.; Wyatt, E. J.

    1993-01-01

    This paper presents a case study of the knowledge engineering process employed to support the Link Monitor and Control Operator Assistant (LMCOA). The LMCOA is a prototype system which automates the configuration, calibration, test, and operation (referred to as precalibration) of the communications, data processing, metric data, antenna, and other equipment used to support space-ground communications with deep space spacecraft in NASA's Deep Space Network (DSN). The primary knowledge base in the LMCOA is the Temporal Dependency Network (TDN), a directed graph which provides a procedural representation of the precalibration operation. The TDN incorporates precedence, temporal, and state constraints and uses several supporting knowledge bases and data bases. The paper provides a brief background on the DSN, and describes the evolution of the TDN and supporting knowledge bases, the process used for knowledge engineering, and an analysis of the successes and problems of the knowledge engineering effort.

  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. Rearranging the world: Neural networks supporting the processing of temporal connectives

    PubMed Central

    Ye, Zheng; Kutas, Marta; George, Marie St.; Sereno, Marty; Münte, Thomas F.

    2014-01-01

    Summary Temporal conjunctions ‘before’ and ‘after’ give us freedom to describe a series of events in different orders. Previous studies suggested ‘before’ sentences in which events were expresses in an order inconsistent with their actual order of occurrence need additional computations, i.e. reversing the order of event mention to reach the actual order of event occurrence. This study found the additional computations may be supported by a neural network connecting the caudate nucleus with the medial prefrontal cortex, middle frontal gyrus (MFG), precuneus and occipital cortex. The connectivity in this network was strongly enhanced for ‘before’ than ‘after’ sentences. Meanwhile, another network was observed to support the memory retrieval, connecting the hippocampus with the MFG via the orbital inferior frontal gyrus and temporal pole. The connectivity pattern of this network was not different between conditions. With the common node MFG, these two networks may communicate in working memory to work together. PMID:22146750

  13. Synchronization and control in time-delayed complex networks and spatio-temporal patterns

    NASA Astrophysics Data System (ADS)

    Banerjee, S.; Kurths, J.; Schöll, E.

    2016-02-01

    This special topics issue is a collection of contributions on the recent developments of control and synchronization in time delayed systems and space time chaos. The various articles report interesting results on time delayed complex networks; fractional order delayed models; dynamics of spatio-temporal patterns; stochastic models etc. Experimental analysis on synchronization, dynamics and control of chaos are also well investigated using Field Programmable Gate Array (FPGA), circuit realizations and chemical reactions.

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

    PubMed

    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. 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

  16. 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

  17. Dissecting spatio-temporal protein networks driving human heart development and related disorders.

    PubMed

    Lage, Kasper; Møllgård, Kjeld; Greenway, Steven; Wakimoto, Hiroko; Gorham, Joshua M; Workman, Christopher T; Bendsen, Eske; Hansen, Niclas T; Rigina, Olga; Roque, Francisco S; Wiese, Cornelia; Christoffels, Vincent M; Roberts, Amy E; Smoot, Leslie B; Pu, William T; Donahoe, Patricia K; Tommerup, Niels; Brunak, Søren; Seidman, Christine E; Seidman, Jonathan G; Larsen, Lars A

    2010-06-22

    Aberrant organ development is associated with a wide spectrum of disorders, from schizophrenia to congenital heart disease, but systems-level insight into the underlying processes is very limited. Using heart morphogenesis as general model for dissecting the functional architecture of organ development, we combined detailed phenotype information from deleterious mutations in 255 genes with high-confidence experimental interactome data, and coupled the results to thorough experimental validation. Hereby, we made the first systematic analysis of spatio-temporal protein networks driving many stages of a developing organ identifying several novel signaling modules. Our results show that organ development relies on surprisingly few, extensively recycled, protein modules that integrate into complex higher-order networks. This design allows the formation of a complicated organ using simple building blocks, and suggests how mutations in the same genes can lead to diverse phenotypes. We observe a striking temporal correlation between organ complexity and the number of discrete functional modules coordinating morphogenesis. Our analysis elucidates the organization and composition of spatio-temporal protein networks that drive the formation of organs, which in the future may lay the foundation of novel approaches in treatments, diagnostics, and regenerative medicine. PMID:20571530

  18. Long-term temporal variation in the organization of an ant–plant network

    PubMed Central

    Díaz-Castelazo, Cecilia; Sánchez-Galván, Ingrid R.; Guimarães, Paulo R.; Raimundo, Rafael L. Galdini; Rico-Gray, Víctor

    2013-01-01

    Background and Aims Functional groups of species interact and coevolve in space and time, forming complex networks of interacting species. A long-term study of temporal variation of an ant–plant network is presented with the aims of: (1) depicting its structural changes over a 20-year period; (2) detailing temporal variation in network topology, as revealed by nestedness and modularity analysis and other parameters (i.e. connectance, niche overlap); and (3) identifying long-term turnover in taxonomic structure (i.e. switches in ant resource use or plant visitor assemblages according to taxa). Methods Fieldwork was carried out at La Mancha, Mexico, and ant–plant interactions were observed between 1989 and 1991, between 1998 and 2000, and between May 2010 and 2011. Occurrences of ants on extrafloral nectaries (EFNs) were recorded. The resulting ant–plant networks were constructed from qualitative presence–absence data determined by a species–species matrix defined by the frequency of occurrence of each pairwise ant–plant interaction. Key Results Network variation across time was stable and a persistent nested structure may have contributed to the maintenance of resilient and species-rich communities. Modularity was lower than expected, especially in the most recent networks, indicating that the community exhibited high overlap among interacting species (e.g. few species were hubs in the more recent network, being partly responsible for the nested pattern). Structurally, the connections created among modules by super-generalists gave cohesion to subsets of species that otherwise would remain unconnected. This may have allowed an increasing cascade-effect of evolutionary events among modules. Mutualistic ant–plant interactions were structured 20 years ago mainly by the subdominant nectarivorous ant species Camponotus planatus and Crematogaster brevispinosa, which monopolized the best extrafloral nectar resources and out-competed other species with broader

  19. Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach.

    PubMed

    Gauvin, Laetitia; Panisson, André; Cattuto, Ciro

    2014-01-01

    The increasing availability of temporal network data is calling for more research on extracting and characterizing mesoscopic structures in temporal networks and on relating such structure to specific functions or properties of the system. An outstanding challenge is the extension of the results achieved for static networks to time-varying networks, where the topological structure of the system and the temporal activity patterns of its components are intertwined. Here we investigate the use of a latent factor decomposition technique, non-negative tensor factorization, to extract the community-activity structure of temporal networks. The method is intrinsically temporal and allows to simultaneously identify communities and to track their activity over time. We represent the time-varying adjacency matrix of a temporal network as a three-way tensor and approximate this tensor as a sum of terms that can be interpreted as communities of nodes with an associated activity time series. We summarize known computational techniques for tensor decomposition and discuss some quality metrics that can be used to tune the complexity of the factorized representation. We subsequently apply tensor factorization to a temporal network for which a ground truth is available for both the community structure and the temporal activity patterns. The data we use describe the social interactions of students in a school, the associations between students and school classes, and the spatio-temporal trajectories of students over time. We show that non-negative tensor factorization is capable of recovering the class structure with high accuracy. In particular, the extracted tensor components can be validated either as known school classes, or in terms of correlated activity patterns, i.e., of spatial and temporal coincidences that are determined by the known school activity schedule.

  20. Copper enhances cellular and network excitabilities, and improves temporal processing in the rat hippocampus.

    PubMed

    Maureira, Carlos; Letelier, Juan Carlos; Alvarez, Osvaldo; Delgado, Ricardo; Vergara, Cecilia

    2015-12-01

    Copper, an ion with many important metabolic functions, has also been proposed to have a role as modulator on neuronal function, mostly based on its effects on voltage- and neurotransmitter-gated conductance as well as on neurological symptoms of patients with altered copper homeostasis. Nevertheless, the mechanisms by which copper exerts its neuromodulatory effects have not been clearly established in a functional neuronal network. Using rat hippocampus slices as a neuronal network model, the effects of copper in the range of 10-100 nm were tested on the intrinsic, synaptic and network properties of the CA1 region. Most of the previously described effects of this cation were in the micromolar range of copper concentrations. The current results indicate that copper is a multifaceted neuromodulator, having effects that may be grouped into two categories: (i) activity enhancement, by modulating synaptic communication and action potential (AP) conductances; and (ii) temporal processing and correlation extraction, by improving reliability and depressing inhibition. Specifically it was found that copper hyperpolarizes AP firing threshold, enhances neuronal and network excitability, modifies CA3-CA1 pathway gain, enhances the frequency of spontaneous synaptic events, decreases inhibitory network activity, and improves AP timing reliability. Moreover, copper chelation by bathocuproine decreases spontaneous network spiking activity. These results allow the proposal that copper affects the network activity from cellular to circuit levels on a moment-by-moment basis, and should be considered a crucial functional component of hippocampal neuronal circuitry.

  1. 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

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. Temporal coding in a silicon network of integrate-and-fire neurons.

    PubMed

    Liu, Shih-Chii; Douglas, Rodney

    2004-09-01

    Spatio-temporal processing of spike trains by neuronal networks depends on a variety of mechanisms distributed across synapses, dendrites, and somata. In natural systems, the spike trains and the processing mechanisms cohere though their common physical instantiation. This coherence is lost when the natural system is encoded for simulation on a general purpose computer. By contrast, analog VLSI circuits are, like neurons, inherently related by their real-time physics, and so, could provide a useful substrate for exploring neuronlike event-based processing. Here, we describe a hybrid analog-digital VLSI chip comprising a set of integrate-and-fire neurons and short-term dynamical synapses that can be configured into simple network architectures with some properties of neocortical neuronal circuits. We show that, despite considerable fabrication variance in the properties of individual neurons, the chip offers a viable substrate for exploring real-time spike-based processing in networks of neurons.

  7. A network of RNA and protein interactions in Fronto Temporal Dementia

    PubMed Central

    Fontana, Francesca; Siva, Kavitha; Denti, Michela A.

    2015-01-01

    Frontotemporal dementia (FTD) is a neurodegenerative disorder characterized by degeneration of the fronto temporal lobes and abnormal protein inclusions. It exhibits a broad clinicopathological spectrum and has been linked to mutations in seven different genes. We will provide a picture, which connects the products of these genes, albeit diverse in nature and function, in a network. Despite the paucity of information available for some of these genes, we believe that RNA processing and post-transcriptional regulation of gene expression might constitute a common theme in the network. Recent studies have unraveled the role of mutations affecting the functions of RNA binding proteins and regulation of microRNAs. This review will combine all the recent findings on genes involved in the pathogenesis of FTD, highlighting the importance of a common network of interactions in order to study and decipher the heterogeneous clinical manifestations associated with FTD. This approach could be helpful for the research of potential therapeutic strategies. PMID:25852467

  8. Most probable paths in temporal weighted networks: An application to ocean transport

    NASA Astrophysics Data System (ADS)

    Ser-Giacomi, Enrico; Vasile, Ruggero; Hernández-García, Emilio; López, Cristóbal

    2015-07-01

    We consider paths in weighted and directed temporal networks, introducing tools to compute sets of paths of high probability. We quantify the relative importance of the most probable path between two nodes with respect to the whole set of paths and to a subset of highly probable paths that incorporate most of the connection probability. These concepts are used to provide alternative definitions of betweenness centrality. We apply our formalism to a transport network describing surface flow in the Mediterranean sea. Despite the full transport dynamics is described by a very large number of paths we find that, for realistic time scales, only a very small subset of high probability paths (or even a single most probable one) is enough to characterize global connectivity properties of the network.

  9. 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.

  10. 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

  11. 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. PMID:27186480

  12. Spatial and temporal patterns of hydrologic connectivity between upland landscapes and stream networks (Invited)

    NASA Astrophysics Data System (ADS)

    McGlynn, B. L.; Nippgen, F.; Jencso, K. G.; Emanuel, R. E.

    2013-12-01

    Congress enacted the Clean Water Act (CWA) 'to restore and maintain the chemical, physical, and biological integrity of the Nation's waters'. A recent Supreme Court decision further described protection for waters with 'a significant nexus to navigable waters" if they are in the same watershed and have an effect on the chemical, physical, or biological integrity of traditional navigable waters or interstate waters that is more than 'speculative or insubstantial.' Evolving interpretation of the CWA and 'significant nexus' (connectivity) requires investigation and understanding of the spatial and temporal patterns of hydrologic connectivity between upland landscapes and stream networks that mediate streamflow magnitude and composition. While hydrologic connectivity is a continuum, strong non-linearities including the shift from unsaturated to saturated flow conditions lead to threshold or transient connectivity behavior and orders of magnitude changes in flow velocities and source water compositions. Here we illustrate the spatial and temporal dynamics of hydrologic connectivity between upland landscapes and stream networks that provide direct and proximate links between streamflow composition and its watershed sources. We suggest that adjacency alone does not determine influence on hydrologic response and streamwater composition and that new understanding and communication of the temporal and spatial dynamics of watershed connectivity are required to address urgent needs at the interface of the CWA, science, and society.

  13. Spatial and temporal patterns of hydrologic connectivity between upland landscapes and stream networks

    NASA Astrophysics Data System (ADS)

    McGlynn, B. L.; Jencso, K. G.; Nippgen, F.; Emanuel, R. E.; Marshall, L. A.; Gooseff, M. N.

    2012-12-01

    Congress enacted the Clean Water Act (CWA) "to restore and maintain the chemical, physical, and biological integrity of the Nation's waters". A recent Supreme Court decision further described protection for waters with "a significant nexus to navigable waters" if they are in the same watershed and have an effect on the chemical, physical, or biological integrity of traditional navigable waters or interstate waters that is more than "speculative or insubstantial." Evolving interpretation of the CWA and "significant nexus" (connectivity) requires investigation and understanding of the spatial and temporal patterns of hydrologic connectivity between upland landscapes and stream networks that mediate streamflow magnitude and composition. While, hydrologic connectivity is a continuum, strong non-linearities including the shift from unsaturated to saturated flow conditions lead to threshold or transient connectivity behavior and orders of magnitude changes in flow velocities. Here we illustrate the spatial and temporal dynamics of hydrologic connectivity between upland landscapes and stream networks that provide direct and proximate links between streamflow composition and its watershed sources. New understanding and communication of the temporal and spatial scales of watershed connectivity are required to address urgent needs at the interface of the CWA, science, and society.

  14. Spatial and temporal patterns of hydrologic connectivity between upland landscapes and stream networks (Invited)

    NASA Astrophysics Data System (ADS)

    Ma, L.; Qi, Z.; Helmers, M. J.; Ahuja, L. R.; Malone, R. W.

    2011-12-01

    Congress enacted the Clean Water Act (CWA) 'to restore and maintain the chemical, physical, and biological integrity of the Nation's waters'. A recent Supreme Court decision further described protection for waters with 'a significant nexus to navigable waters" if they are in the same watershed and have an effect on the chemical, physical, or biological integrity of traditional navigable waters or interstate waters that is more than 'speculative or insubstantial.' Evolving interpretation of the CWA and 'significant nexus' (connectivity) requires investigation and understanding of the spatial and temporal patterns of hydrologic connectivity between upland landscapes and stream networks that mediate streamflow magnitude and composition. While hydrologic connectivity is a continuum, strong non-linearities including the shift from unsaturated to saturated flow conditions lead to threshold or transient connectivity behavior and orders of magnitude changes in flow velocities and source water compositions. Here we illustrate the spatial and temporal dynamics of hydrologic connectivity between upland landscapes and stream networks that provide direct and proximate links between streamflow composition and its watershed sources. We suggest that adjacency alone does not determine influence on hydrologic response and streamwater composition and that new understanding and communication of the temporal and spatial dynamics of watershed connectivity are required to address urgent needs at the interface of the CWA, science, and society.

  15. 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

  16. 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

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

    PubMed

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

    2016-08-30

    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.

  18. 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.

  19. 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.

  20. PAFit: A Statistical Method for Measuring Preferential Attachment in Temporal Complex Networks

    PubMed Central

    Pham, Thong; Sheridan, Paul; Shimodaira, Hidetoshi

    2015-01-01

    Preferential attachment is a stochastic process that has been proposed to explain certain topological features characteristic of complex networks from diverse domains. The systematic investigation of preferential attachment is an important area of research in network science, not only for the theoretical matter of verifying whether this hypothesized process is operative in real-world networks, but also for the practical insights that follow from knowledge of its functional form. Here we describe a maximum likelihood based estimation method for the measurement of preferential attachment in temporal complex networks. We call the method PAFit, and implement it in an R package of the same name. PAFit constitutes an advance over previous methods primarily because we based it on a nonparametric statistical framework that enables attachment kernel estimation free of any assumptions about its functional form. We show this results in PAFit outperforming the popular methods of Jeong and Newman in Monte Carlo simulations. What is more, we found that the application of PAFit to a publically available Flickr social network dataset yielded clear evidence for a deviation of the attachment kernel from the popularly assumed log-linear form. Independent of our main work, we provide a correction to a consequential error in Newman’s original method which had evidently gone unnoticed since its publication over a decade ago. PMID:26378457

  1. Synchronization and rhythm dynamics of a neuronal network consisting of mixed bursting neurons with hybrid synapses

    NASA Astrophysics Data System (ADS)

    Shi, Xia; Xi, Wenqi

    2016-05-01

    In this paper, burst synchronization and rhythm dynamics of a small-world neuronal network consisting of mixed bursting types of neurons coupled via inhibitory-excitatory chemical synapses are explored. Two quantities, the synchronization parameter and average width factor, are used to characterize the synchronization degree and rhythm dynamics of the neuronal network. Numerical results show that the percentage of the inhibitory synapses in the network is the major factor for we get a similarly bell-shaped dependence of synchronization on it, and the decrease of the average width factor of the network. We also find that not only the value of the coupling strength can promote the synchronization degree, but the probability of random edges adding to the small-world network also can. The ratio of the long bursting neurons has little effect on the burst synchronization and rhythm dynamics of the network.

  2. Emergence of disassortative mixing from pruning nodes in growing scale-free networks

    NASA Astrophysics Data System (ADS)

    Wang, Sheng-Jun; Wang, Zhen; Jin, Tao; Boccaletti, Stefano

    2014-12-01

    Disassortative mixing is ubiquitously found in technological and biological networks, while the corresponding interpretation of its origin remains almost virgin. We here give evidence that pruning the largest-degree nodes of a growing scale-free network has the effect of decreasing the degree correlation coefficient in a controllable and tunable way, while keeping both the trait of a power-law degree distribution and the main properties of network's resilience and robustness under failures or attacks. The essence of these observations can be attributed to the fact the deletion of large-degree nodes affects the delicate balance of positive and negative contributions to degree correlation in growing scale-free networks, eventually leading to the emergence of disassortativity. Moreover, these theoretical prediction will get further validation in the empirical networks. We support our claims via numerical results and mathematical analysis, and we propose a generative model for disassortative growing scale-free networks.

  3. Differences in human cortical gene expression match the temporal properties of large-scale functional networks.

    PubMed

    Cioli, Claudia; Abdi, Hervé; Beaton, Derek; Burnod, Yves; Mesmoudi, Salma

    2014-01-01

    We explore the relationships between the cortex functional organization and genetic expression (as provided by the Allen Human Brain Atlas). Previous work suggests that functional cortical networks (resting state and task based) are organized as two large networks (differentiated by their preferred information processing mode) shaped like two rings. The first ring--Visual-Sensorimotor-Auditory (VSA)--comprises visual, auditory, somatosensory, and motor cortices that process real time world interactions. The second ring--Parieto-Temporo-Frontal (PTF)--comprises parietal, temporal, and frontal regions with networks dedicated to cognitive functions, emotions, biological needs, and internally driven rhythms. We found--with correspondence analysis--that the patterns of expression of the 938 genes most differentially expressed across the cortex organized the cortex into two sets of regions that match the two rings. We confirmed this result using discriminant correspondence analysis by showing that the genetic profiles of cortical regions can reliably predict to what ring these regions belong. We found that several of the proteins--coded by genes that most differentiate the rings--were involved in neuronal information processing such as ionic channels and neurotransmitter release. The systematic study of families of genes revealed specific proteins within families preferentially expressed in each ring. The results showed strong congruence between the preferential expression of subsets of genes, temporal properties of the proteins they code, and the preferred processing modes of the rings. Ionic channels and release-related proteins more expressed in the VSA ring favor temporal precision of fast evoked neural transmission (Sodium channels SCNA1, SCNB1 potassium channel KCNA1, calcium channel CACNA2D2, Synaptotagmin SYT2, Complexin CPLX1, Synaptobrevin VAMP1). Conversely, genes expressed in the PTF ring favor slower, sustained, or rhythmic activation (Sodium channels SCNA3

  4. Differences in Human Cortical Gene Expression Match the Temporal Properties of Large-Scale Functional Networks

    PubMed Central

    Cioli, Claudia; Abdi, Hervé; Beaton, Derek; Burnod, Yves; Mesmoudi, Salma

    2014-01-01

    We explore the relationships between the cortex functional organization and genetic expression (as provided by the Allen Human Brain Atlas). Previous work suggests that functional cortical networks (resting state and task based) are organized as two large networks (differentiated by their preferred information processing mode) shaped like two rings. The first ring–Visual-Sensorimotor-Auditory (VSA)–comprises visual, auditory, somatosensory, and motor cortices that process real time world interactions. The second ring–Parieto-Temporo-Frontal (PTF)–comprises parietal, temporal, and frontal regions with networks dedicated to cognitive functions, emotions, biological needs, and internally driven rhythms. We found–with correspondence analysis–that the patterns of expression of the 938 genes most differentially expressed across the cortex organized the cortex into two sets of regions that match the two rings. We confirmed this result using discriminant correspondence analysis by showing that the genetic profiles of cortical regions can reliably predict to what ring these regions belong. We found that several of the proteins–coded by genes that most differentiate the rings–were involved in neuronal information processing such as ionic channels and neurotransmitter release. The systematic study of families of genes revealed specific proteins within families preferentially expressed in each ring. The results showed strong congruence between the preferential expression of subsets of genes, temporal properties of the proteins they code, and the preferred processing modes of the rings. Ionic channels and release-related proteins more expressed in the VSA ring favor temporal precision of fast evoked neural transmission (Sodium channels SCNA1, SCNB1 potassium channel KCNA1, calcium channel CACNA2D2, Synaptotagmin SYT2, Complexin CPLX1, Synaptobrevin VAMP1). Conversely, genes expressed in the PTF ring favor slower, sustained, or rhythmic activation (Sodium

  5. 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

  6. 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.

  7. Networking among young global health researchers through an intensive training approach: a mixed methods exploratory study

    PubMed Central

    2014-01-01

    Background Networks are increasingly regarded as essential in health research aimed at influencing practice and policies. Less research has focused on the role networking can play in researchers’ careers and its broader impacts on capacity strengthening in health research. We used the Canadian Coalition for Global Health Research (CCGHR) annual Summer Institute for New Global Health Researchers (SIs) as an opportunity to explore networking among new global health researchers. Methods A mixed-methods exploratory study was conducted among SI alumni and facilitators who had participated in at least one SI between 2004 and 2010. Alumni and facilitators completed an online short questionnaire, and a subset participated in an in-depth interview. Thematic analysis of the qualitative data was triangulated with quantitative results and CCGHR reports on SIs. Synthesis occurred through the development of a process model relevant to networking through the SIs. Results Through networking at the SIs, participants experienced decreased isolation and strengthened working relationships. Participants accessed new knowledge, opportunities, and resources through networking during the SI. Post-SI, participants reported ongoing contact and collaboration, although most participants desired more opportunities for interaction. They made suggestions for structural supports to networking among new global health researchers. Conclusions Networking at the SI contributed positively to opportunities for individuals, and contributed to the formation of a network of global health researchers. Intentional inclusion of networking in health research capacity strengthening initiatives, with supportive resources and infrastructure could create dynamic, sustainable networks accessible to global health researchers around the world. PMID:24460819

  8. 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. PMID:27217324

  9. Cation Diffusivity and the Mixed Network Former Effect in Borosilicate Glasses.

    PubMed

    Smedskjaer, Morten M; Mauro, John C; Yue, Yuanzheng

    2015-06-11

    Understanding the structural origins of cationic diffusion processes in silicate glasses is important for high-tech applications of silicate glasses. For glasses with more than one network former, transport properties such as diffusivity are often nonlinear functions of the particular distribution of these network formers, a phenomenon known as the mixed network former effect. Here, we investigate the sodium-potassium interdiffusion (D̅Na-K) and the calcium inward diffusion (DCa) in soda lime borosilicate glasses with varying silica/borate ratio but constant modifier content. Indeed, the structural organization of borosilicate glasses results in a pronounced nonlinear composition dependence of D̅Na-K and DCa (i.e., the mixed network former effect). Initial addition of B2O3 to the glass system results in a significant decrease in both diffusivities, whereas the change in diffusivity per mole of added B2O3 decreases with increasing B2O3 concentration. Besides the influences of water content and atomic packing degree, we find that 99% of the composition dependence of log D̅Na-K can be ascribed to the change in concentration of tetrahedral boron groups. This indicates that the formation of BO4/2 groups slows down diffusion processes of alkali and alkaline earth ions. Therefore, the mixed network former effect of the studied glass series is linked with the change of the concentration of tetrahedral boron groups, which is caused by the interactions between the different types of network formers.

  10. 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.

  11. 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.

  12. 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

  13. 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

  14. 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

    2015-01-01

    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.

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

    PubMed Central

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

    2015-01-01

    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. PMID:25997025

  16. 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.

  17. 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…

  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. Robust inference of the context specific structure and temporal dynamics of gene regulatory network

    PubMed Central

    2010-01-01

    Background Response of cells to changing endogenous or exogenous conditions is governed by intricate molecular interactions, or regulatory networks. To lead to appropriate responses, regulatory network should be 1) context-specific, i.e., its constituents and topology depend on the phonotypical and experimental context including tissue types and cell conditions, such as damage, stress, macroenvironments of cell, etc. and 2) time varying, i.e., network elements and their regulatory roles change actively over time to control the endogenous cell states e.g. different stages in a cell cycle. Results A novel network model PathRNet and a reconstruction approach PATTERN are proposed for reconstructing the context specific time varying regulatory networks by integrating microarray gene expression profiles and existing knowledge of pathways and transcription factors. The nodes of the PathRNet are Transcription Factors (TFs) and pathways, and edges represent the regulation between pathways and TFs. The reconstructed PathRNet for Kaposi's sarcoma-associated herpesvirus infection of human endothelial cells reveals the complicated dynamics of the underlying regulatory mechanisms that govern this intricate process. All the related materials including source code are available at http://compgenomics.utsa.edu/tvnet.html. Conclusions The proposed PathRNet provides a system level landscape of the dynamics of gene regulatory circuitry. The inference approach PATTERN enables robust reconstruction of the temporal dynamics of pathway-centric regulatory networks. The proposed approach for the first time provides a dynamic perspective of pathway, TF regulations, and their interaction related to specific endogenous and exogenous conditions. PMID:21143778

  20. 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

  1. 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.

  2. 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.

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

    PubMed Central

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

    2015-01-01

    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. PMID:26190856

  4. 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. PMID:27497065

  5. 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.

  6. 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.

  7. 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.

  8. 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

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

    PubMed Central

    Litvak-Hinenzon, Anna; Stone, Lewi

    2009-01-01

    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. PMID:18957362

  10. Contrasting effects of strong ties on SIR and SIS processes in temporal networks

    NASA Astrophysics Data System (ADS)

    Sun, Kaiyuan; Baronchelli, Andrea; Perra, Nicola

    2015-12-01

    Most real networks are characterized by connectivity patterns that evolve in time following complex, non-Markovian, dynamics. Here we investigate the impact of this ubiquitous feature by studying the Susceptible-Infected-Recovered (SIR) and Susceptible-Infected-Susceptible (SIS) epidemic models on activity driven networks with and without memory (i.e., Markovian and non-Markovian). We find that memory inhibits the spreading process in SIR models by shifting the epidemic threshold to larger values and reducing the final fraction of recovered nodes. On the contrary, in SIS processes memory reduces the epidemic threshold and, for a wide range of disease parameters, increases the fraction of nodes affected by the disease in the endemic state. The heterogeneity in tie strengths, and the frequent repetition of strong ties it entails, allows in fact less virulent SIS-like diseases to survive in tightly connected local clusters that serve as reservoir for the virus. We validate this picture by studying both processes on two real temporal networks.

  11. CGBayesNets: conditional Gaussian Bayesian network learning and inference with mixed discrete and continuous data.

    PubMed

    McGeachie, Michael J; Chang, Hsun-Hsien; Weiss, Scott T

    2014-06-01

    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.

  12. CGBayesNets: conditional Gaussian Bayesian network learning and inference with mixed discrete and continuous data.

    PubMed

    McGeachie, Michael J; Chang, Hsun-Hsien; Weiss, Scott T

    2014-06-01

    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

  13. Microwave Radiometer Networks for Measurement of the Spatio-Temporal Variability of Water Vapor

    NASA Astrophysics Data System (ADS)

    Reising, S. C.; Iturbide-Sanchez, F.; Padmanabhan, S.

    2006-12-01

    Tropospheric water vapor plays a key role in the prediction of convective storm initiation, precipitation and extreme weather events. Conventionally, water vapor profiles are derived from dewpoint and temperature measurements using instrumented weather balloons, including radiosondes. These balloons take approximately one hour to measure from surface to tropopause, and transmitter-sensor packages cannot be reused. Such in-situ measurements provide profiles with very high vertical resolution but with severe limitations in temporal and spatial coverage. Raman lidars use active optical techniques to provide comparable vertical resolution and measurement accuracy to radiosondes. However, these lidars are bulky and expensive, and their operation is limited to clear-sky conditions due to the high optical opacity of clouds. Microwave radiometers provide path-integrated water vapor and liquid water with high temporal resolution during nearly all weather conditions. If multiple frequencies are measured near the water vapor resonance, coarse vertical profiles can be obtained using statistical inversion. Motivated by the need for improved temporal and spatial resolutions, a network of elevation and azimuth scanning radiometers is being developed to provide coordinated volumetric measurements of tropospheric water vapor. To realize this network, two Miniaturized Water Vapor profiling Radiometers (MVWR) have been designed and fabricated at Colorado State University. MWVR is small, light-weight, consumes little power and is highly stable. To reduce the mass, volume, cost and power consumption as compared to traditional waveguide techniques, MWVR was designed based on monolithic microwave integrated-circuit technology developed for the wireless communication and defense industries. It was designed for network operation, in which each radiometer will perform a complete volumetric scan within a few minutes, and overlapping scans from multiple sensors will be combined

  14. Leadership of healthcare commissioning networks in England: a mixed-methods study on clinical commissioning groups

    PubMed Central

    Zachariadis, Markos; Oborn, Eivor; Barrett, Michael; Zollinger-Read, Paul

    2013-01-01

    Objective To explore the relational challenges for general practitioner (GP) leaders setting up new network-centric commissioning organisations in the recent health policy reform in England, we use innovation network theory to identify key network leadership practices that facilitate healthcare innovation. Design Mixed-method, multisite and case study research. Setting Six clinical commissioning groups and local clusters in the East of England area, covering in total 208 GPs and 1 662 000 population. Methods Semistructured interviews with 56 lead GPs, practice managers and staff from the local health authorities (primary care trusts, PCT) as well as various healthcare professionals; 21 observations of clinical commissioning group (CCG) board and executive meetings; electronic survey of 58 CCG board members (these included GPs, practice managers, PCT employees, nurses and patient representatives) and subsequent social network analysis. Main outcome measures Collaborative relationships between CCG board members and stakeholders from their healthcare network; clarifying the role of GPs as network leaders; strengths and areas for development of CCGs. Results Drawing upon innovation network theory provides unique insights of the CCG leaders’ activities in establishing best practices and introducing new clinical pathways. In this context we identified three network leadership roles: managing knowledge flows, managing network coherence and managing network stability. Knowledge sharing and effective collaboration among GPs enable network stability and the alignment of CCG objectives with those of the wider health system (network coherence). Even though activities varied between commissioning groups, collaborative initiatives were common. However, there was significant variation among CCGs around the level of engagement with providers, patients and local authorities. Locality (sub) groups played an important role because they linked commissioning decisions with

  15. A temporal ant colony optimization approach to the shortest path problem in dynamic scale-free networks

    NASA Astrophysics Data System (ADS)

    Yu, Feng; Li, Yanjun; Wu, Tie-Jun

    2010-02-01

    A large number of networks in the real world have a scale-free structure, and the parameters of the networks change stochastically with time. Searching for the shortest paths in a scale-free dynamic and stochastic network is not only necessary for the estimation of the statistical characteristics such as the average shortest path length of the network, but also challenges the traditional concepts related to the “shortest path” of a network and the design of path searching strategies. In this paper, the concept of shortest path is defined on the basis of a scale-free dynamic and stochastic network model, and a temporal ant colony optimization (TACO) algorithm is proposed for searching for the shortest paths in the network. The convergence and the setup for some important parameters of the TACO algorithm are discussed through theoretical analysis and computer simulations, validating the effectiveness of the proposed algorithm.

  16. Correlated Spatio-Temporal Data Collection in Wireless Sensor Networks Based on Low Rank Matrix Approximation and Optimized Node Sampling

    PubMed Central

    Piao, Xinglin; Hu, Yongli; Sun, Yanfeng; Yin, Baocai; Gao, Junbin

    2014-01-01

    The emerging low rank matrix approximation (LRMA) method provides an energy efficient scheme for data collection in wireless sensor networks (WSNs) by randomly sampling a subset of sensor nodes for data sensing. However, the existing LRMA based methods generally underutilize the spatial or temporal correlation of the sensing data, resulting in uneven energy consumption and thus shortening the network lifetime. In this paper, we propose a correlated spatio-temporal data collection method for WSNs based on LRMA. In the proposed method, both the temporal consistence and the spatial correlation of the sensing data are simultaneously integrated under a new LRMA model. Moreover, the network energy consumption issue is considered in the node sampling procedure. We use Gini index to measure both the spatial distribution of the selected nodes and the evenness of the network energy status, then formulate and resolve an optimization problem to achieve optimized node sampling. The proposed method is evaluated on both the simulated and real wireless networks and compared with state-of-the-art methods. The experimental results show the proposed method efficiently reduces the energy consumption of network and prolongs the network lifetime with high data recovery accuracy and good stability. PMID:25490583

  17. 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.

  18. 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

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

    PubMed

    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

  20. Neural network structure for spatio-temporal long-term memory.

    PubMed

    Nguyen, Vu Anh; Starzyk, Janusz A; Goh, Wooi-Boon; Jachyra, Daniel

    2012-06-01

    This paper proposes a neural network structure for spatio-temporal learning and recognition inspired by the long-term memory (LTM) model of the human cortex. Our structure is able to process real-valued and multidimensional sequences. This capability is attained by addressing three critical problems in sequential learning, namely the error tolerance, the significance of sequence elements and memory forgetting. We demonstrate the potential of the framework with a series of synthetic simulations and the Australian sign language (ASL) dataset. Results show that our LTM model is robust to different types of distortions. Second, our LTM model outperforms other sequential processing models in a classification task for the ASL dataset. PMID:24806767

  1. Soil moisture spatial and temporal patterns from a wireless sensor network test bed

    NASA Astrophysics Data System (ADS)

    Villalba, G.; Davis, T. W.; Liang, X.

    2014-12-01

    The dynamics of water movement through vegetated porous media is a complex problem with large variabilities over differing temporal and spatial scales. This study examines a multi-year wireless sensor network (WSN) collecting shallow subsurface (10 and 30 cm) soil moisture content and soil water potential. The study site, located at the Audubon Society of Western Pennsylvania's Beechwood Farms Nature Reserve, is one of the longest running WSNs of its kind. Despite the noisy nature of the collected data (e.g., in comparison to traditional data logger methods), the WSN, consisting of over 50 nodes with more than 100 sensors, provides critical information regarding catchment-scale spatiotemporal patterns of soil moisture and soil water potential within a forested hill-sloped region of southwestern Pennsylvania.

  2. Evaluating complementary networks of restoration plantings for landscape-scale occurrence of temporally dynamic species.

    PubMed

    Ikin, Karen; Tulloch, Ayesha; Gibbons, Philip; Ansell, Dean; Seddon, Julian; Lindenmayer, David

    2016-10-01

    Multibillion dollar investments in land restoration make it critical that conservation goals are achieved cost-effectively. Approaches developed for systematic conservation planning offer opportunities to evaluate landscape-scale, temporally dynamic biodiversity outcomes from restoration and improve on traditional approaches that focus on the most species-rich plantings. We investigated whether it is possible to apply a complementarity-based approach to evaluate the extent to which an existing network of restoration plantings meets representation targets. Using a case study of woodland birds of conservation concern in southeastern Australia, we compared complementarity-based selections of plantings based on temporally dynamic species occurrences with selections based on static species occurrences and selections based on ranking plantings by species richness. The dynamic complementarity approach, which incorporated species occurrences over 5 years, resulted in higher species occurrences and proportion of targets met compared with the static complementarity approach, in which species occurrences were taken at a single point in time. For equivalent cost, the dynamic complementarity approach also always resulted in higher average minimum percent occurrence of species maintained through time and a higher proportion of the bird community meeting representation targets compared with the species-richness approach. Plantings selected under the complementarity approaches represented the full range of planting attributes, whereas those selected under the species-richness approach were larger in size. Our results suggest that future restoration policy should not attempt to achieve all conservation goals within individual plantings, but should instead capitalize on restoration opportunities as they arise to achieve collective value of multiple plantings across the landscape. Networks of restoration plantings with complementary attributes of age, size, vegetation structure, and

  3. High-precision gravity network to monitor temporal variations in gravity across Yucca Mountain, Nevada

    SciTech Connect

    Harris, R.N.; Ponce, D.A.

    1988-12-31

    Repeatable high-precision gravity surveys provide a method of monitoring temporal variations in the gravity field. Fluctuations in the gravity field may indicate water table changes, crustal deformation, or precursors to volcanism and earthquakes. This report describes a high-precision gravity loop which has been established across Yucca Mountain, Nevada in support of the Nevada Nuclear Waste Storage Investigations (NNWSI) program. The purpose of this gravity loop is to monitor temporal variations in gravity across Yucca Mountain in an effort to interpret and predict the stability of the tectonic framework and changes in the subsurface density field. Studies of the tectonic framework which include volcanic hazard seismicity, and faulting studies are in progress. Repeat high-precision gravity surveys are less expensive and can be made more rapidly than a corresponding leveling survey. High-precision gravity surveys are capable of detecting elevation changes of 3 to 5 cm, and thus can be employed as an efficient tool for monitoring vertical crustal movements while supplementing or partially replacing leveling data. The Yucca Mountain gravity network has been tied to absolute gravity measurements established in southern Nevada. These ties provide an absolute datum for comparing repeat occupations of the gravity network, and provide a method of monitoring broad-scale changes in gravity. Absolute gravity measurements were also made at the bottom and top of the Charleston Peak calibration loop in southern Nevada. These absolute gravity measurements provide local control of calibrating gravity meters over the gravity ranges observed at Yucca Mountain. 13 refs., 7 figs., 3 tabs.

  4. MatrixFlow: Temporal Network Visual Analytics to Track Symptom Evolution during Disease Progression

    PubMed Central

    Perer, Adam; Sun, Jimeng

    2012-01-01

    Objective: To develop a visual analytic system to help medical professionals improve disease diagnosis by providing insights for understanding disease progression. Methods: We develop MatrixFlow, a visual analytic system that takes clinical event sequences of patients as input, constructs time-evolving networks and visualizes them as a temporal flow of matrices. MatrixFlow provides several interactive features for analysis: 1) one can sort the events based on the similarity in order to accentuate underlying cluster patterns among those events; 2) one can compare co-occurrence events over time and across cohorts through additional line graph visualization. Results: MatrixFlow is applied to visualize heart failure (HF) symptom events extracted from a large cohort of HF cases and controls (n=50,625), which allows medical experts to reach insights involving temporal patterns and clusters of interest, and compare cohorts in novel ways that may lead to improved disease diagnoses. Conclusions: MatrixFlow is an interactive visual analytic system that allows users to quickly discover patterns in clinical event sequences. By unearthing the patterns hidden within and displaying them to medical experts, users become empowered to make decisions influenced by historical patterns. PMID:23304345

  5. 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.

  6. Neural networks for nonlinear and mixed complementarity problems and their applications.

    PubMed

    Dang, Chuangyin; Leung, Yee; Gao, Xing-Bao; Chen, Kai-zhou

    2004-03-01

    This paper presents two feedback neural networks for solving a nonlinear and mixed complementarity problem. The first feedback neural network is designed to solve the strictly monotone problem. This one has no parameter and possesses a very simple structure for implementation in hardware. Based on a new idea, the second feedback neural network for solving the monotone problem is constructed by using the first one as a subnetwork. This feedback neural network has the least number of state variables. The stability of a solution of the problem is proved. When the problem is strictly monotone, the unique solution is uniformly and asymptotically stable in the large. When the problem has many solutions, it is guaranteed that, for any initial point, the trajectory of the network does converge to an exact solution of the problem. Feasibility and efficiency of the proposed neural networks are supported by simulation experiments. Moreover, the feedback neural network can also be applied to solve general nonlinear convex programming and nonlinear monotone variational inequalities problems with convex constraints.

  7. 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

  8. 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

  9. 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.

  10. 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. PMID:26978780

  11. 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

  12. Temporal evolution of a drainage fracture network into an elastic medium with internal fluid generation

    NASA Astrophysics Data System (ADS)

    Kobchenko, Maya; Hafver, Andreas; Dysthe, Dag Kristian; Renard, Francois

    2013-04-01

    Escape of internally generated fluids from low permeability rocks plays an important role in several geological systems. Primary migration of hydrocarbons, dehydration of sediments and hydrated mantellic rocks in subduction zones in the Earth's crust are geological 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 develop an easy and reproducible analog experiment to simulate fracture formation in low permeability rock during internal fluid/gas production. This work aims to describe the physical mechanism of fracture network growth and temporal evolution of created fractures. A tight elastic gelatin matrix is used as a rock analog. The nucleation, propagation and coalescence of fractures within the solid matrix occurs due to CO2 production by yeast consuming sugar and is followed using optical means. We quantify first how an equilibrium fracture network self-develop, and then how the intermittent fluid transport is controlled by the dynamics of opening and closing of fractures, with a well-defined time frequency.

  13. Dynamic circadian protein-protein interaction networks predict temporal organization of cellular functions.

    PubMed

    Wallach, Thomas; Schellenberg, Katja; Maier, Bert; Kalathur, Ravi Kiran Reddy; Porras, Pablo; Wanker, Erich E; Futschik, Matthias E; Kramer, Achim

    2013-03-01

    Essentially all biological processes depend on protein-protein interactions (PPIs). Timing of such interactions is crucial for regulatory function. Although circadian (~24-hour) clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping (RNAi and overexpression) suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes (signal transduction, cell cycle, etc.) contributing to temporal organization of cellular physiology in an unprecedented manner. PMID:23555304

  14. Mnemonic networks in the hippocampal formation: from spatial maps to temporal and conceptual codes.

    PubMed

    Milivojevic, Branka; Doeller, Christian F

    2013-11-01

    The hippocampal formation has been associated with a wide variety of functions including spatial navigation and planning, memory encoding and retrieval, relational processing, novelty detection, and imagination. These functions are dissimilar in terms of their behavioral consequences and modality of representation. Consequently, theoretical standpoints have focused on explaining the role of the hippocampal formation in terms of either its spatial or nonspatial functions. Contrary to this dichotomy, we propose that it is essential to look beyond these traditional boundaries between mnemonic and spatial functions and focus instead on the processes that these functions have in common. In this framework, we use electrophysiology data from the spatial domain to predict effects on the systems level, both in spatial and nonspatial domains. We initially outline the results of studies that have used findings from spatial navigation in rodents to predict the patterns of brain activity observable in people who are exploring virtual environments. We discuss how certain properties of space-defining neurons enable space to be represented as a mental map of interconnected locations, which are expressed at multiple spatial scales in separate modules in the hippocampal formation. We then suggest that memories are also organized in networks, characterized by mnemonic and temporal hierarchies. We finish by discussing how virtual-reality techniques can be used to create novel lifelike episodes allowing us to look at episodic memory processes while multivariate analysis tools can be used to explore the organizational structure of mnemonic networks.

  15. Cortical and subcortical contributions to sequence retrieval: Schematic coding of temporal context in the neocortical recollection network.

    PubMed

    Hsieh, Liang-Tien; Ranganath, Charan

    2015-11-01

    Episodic memory entails the ability to remember what happened when. Although the available evidence indicates that the hippocampus plays a role in structuring serial order information during retrieval of event sequences, information processed in the hippocampus must be conveyed to other cortical and subcortical areas in order to guide behavior. However, the extent to which other brain regions contribute to the temporal organization of episodic memory remains unclear. Here, we examined multivoxel activity pattern changes during retrieval of learned and random object sequences, focusing on a neocortical "core recollection network" that includes the medial prefrontal cortex, retrosplenial cortex, and angular gyrus, as well as on striatal areas including the caudate nucleus and putamen that have been implicated in processing of sequence information. The results demonstrate that regions of the core recollection network carry information about temporal positions within object sequences, irrespective of object information. This schematic coding of temporal information is in contrast to the putamen, which carried information specific to objects in learned sequences, and the caudate, which carried information about objects, irrespective of sequence context. Our results suggest a role for the cortical recollection network in the representation of temporal structure of events during episodic retrieval, and highlight the possible mechanisms by which the striatal areas may contribute to this process. More broadly, the results indicate that temporal sequence retrieval is a useful paradigm for dissecting the contributions of specific brain regions to episodic memory. PMID:26209802

  16. Cortical and subcortical contributions to sequence retrieval: Schematic coding of temporal context in the neocortical recollection network.

    PubMed

    Hsieh, Liang-Tien; Ranganath, Charan

    2015-11-01

    Episodic memory entails the ability to remember what happened when. Although the available evidence indicates that the hippocampus plays a role in structuring serial order information during retrieval of event sequences, information processed in the hippocampus must be conveyed to other cortical and subcortical areas in order to guide behavior. However, the extent to which other brain regions contribute to the temporal organization of episodic memory remains unclear. Here, we examined multivoxel activity pattern changes during retrieval of learned and random object sequences, focusing on a neocortical "core recollection network" that includes the medial prefrontal cortex, retrosplenial cortex, and angular gyrus, as well as on striatal areas including the caudate nucleus and putamen that have been implicated in processing of sequence information. The results demonstrate that regions of the core recollection network carry information about temporal positions within object sequences, irrespective of object information. This schematic coding of temporal information is in contrast to the putamen, which carried information specific to objects in learned sequences, and the caudate, which carried information about objects, irrespective of sequence context. Our results suggest a role for the cortical recollection network in the representation of temporal structure of events during episodic retrieval, and highlight the possible mechanisms by which the striatal areas may contribute to this process. More broadly, the results indicate that temporal sequence retrieval is a useful paradigm for dissecting the contributions of specific brain regions to episodic memory.

  17. Effect of tunnel excavation on source and mixing of groundwater in a coastal granitoidic fracture network.

    PubMed

    Mathurin, Frédéric A; Åström, Mats E; Laaksoharju, Marcus; Kalinowski, Birgitta E; Tullborg, Eva-Lena

    2012-12-01

    The aim of this study was to assess how the excavation of the Äspö Hard Rock Laboratory tunnel has impacted on sources and mixing of groundwater in fractured crystalline (granitoidic) bedrock. The tunnel is 3600 m long and extends to a depth of 460 m at a coastal site in Boreal Europe. The study builds on a unique data set consisting of 1117 observations on chloride and δ(18)O of groundwater collected from a total of 356 packed-off fractures between 1987 and 2011. On the basis of the values of these two variables in selected source waters, a classification system was developed to relate the groundwater observations to source and postinfiltration mixing phenomena. The results show that the groundwater has multiple sources and a complex history of transport and mixing, and is composed of at least glacial water, marine water, recent meteoric water, and an old saline water. The tunnel excavation has had a large impact on flow, sources, and mixing of the groundwater. Important phenomena include upflow of deep-lying saline water, extensive intrusion of current Baltic Sea water, and substantial temporal variability of chloride and δ(18)O in many fractures.

  18. 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.

  19. Electrical Transport and Network Percolation in Graphene and Boron Nitride Mixed-Platelet Structures.

    PubMed

    Debbarma, Rousan; Behura, Sanjay; Nguyen, Phong; Sreeprasad, T S; Berry, Vikas

    2016-04-01

    Percolating network of mixed 2D nanomaterials (2DNs) can leverage the unique electronic structures of different 2DNs, their interfacial doping, manipulable conduction pathways, and local traps. Here, we report on the percolation mechanism and electro-capacitive transport pathways of mixed-platelet network of hexagonal boron nitride (hBN) and reduced graphene oxide (rGO), two isostructural and isoelectronic 2DNs. The transport mechanism is explained in terms of electron hopping through isolated hBN defect traps between rGO (possibly via electron tunneling/hopping through "funneling" points). With optical bandgaps of 4.57 and 4.08 eV for the hBN-domains and 2.18 eV for the rGO domains, the network of hBN with rGO exhibits Poole-Frenkel emission-based transport with mean hopping gap of 1.12 nm (∼hBN trilayer) and an activation barrier of ∼15 ± 0.7 meV. Further, hBN (1.7 pF) has a 6-fold lower capacitance than 1:1 hBN:rGO, which has a resistance 2 orders of magnitude higher than that of rGO (1.46 MΩ). These carrier transport results can be applied to other multi-2DN networks for development of next-generation functional 2D-devices. PMID:27002378

  20. A Two-Part Mixed-Effects Modeling Framework For Analyzing Whole-Brain Network Data

    PubMed Central

    Simpson, Sean L.; Laurienti, Paul J.

    2015-01-01

    Whole-brain network analyses remain the vanguard in neuroimaging research, coming to prominence within the last decade. Network science approaches have facilitated these analyses and allowed examining the brain as an integrated system. However, statistical methods for modeling and comparing groups of networks have lagged behind. Fusing multivariate statistical approaches with network science presents the best path to develop these methods. Toward this end, we propose a two-part mixed-effects modeling framework that allows modeling both the probability of a connection (presence/absence of an edge) and the strength of a connection if it exists. Models within this framework enable quantifying the relationship between an outcome (e.g., disease status) and connectivity patterns in the brain while reducing spurious correlations through inclusion of confounding covariates. They also enable prediction about an outcome based on connectivity structure and vice versa, simulating networks to gain a better understanding of normal ranges of topological variability, and thresholding networks leveraging group information. Thus, they provide a comprehensive approach to studying system level brain properties to further our understanding of normal and abnormal brain function. PMID:25796135

  1. A two-part mixed-effects modeling framework for analyzing whole-brain network data.

    PubMed

    Simpson, Sean L; Laurienti, Paul J

    2015-06-01

    Whole-brain network analyses remain the vanguard in neuroimaging research, coming to prominence within the last decade. Network science approaches have facilitated these analyses and allowed examining the brain as an integrated system. However, statistical methods for modeling and comparing groups of networks have lagged behind. Fusing multivariate statistical approaches with network science presents the best path to develop these methods. Toward this end, we propose a two-part mixed-effects modeling framework that allows modeling both the probability of a connection (presence/absence of an edge) and the strength of a connection if it exists. Models within this framework enable quantifying the relationship between an outcome (e.g., disease status) and connectivity patterns in the brain while reducing spurious correlations through inclusion of confounding covariates. They also enable prediction about an outcome based on connectivity structure and vice versa, simulating networks to gain a better understanding of normal ranges of topological variability, and thresholding networks leveraging group information. Thus, they provide a comprehensive approach to studying system level brain properties to further our understanding of normal and abnormal brain function. PMID:25796135

  2. Neural networks with dynamical synapses: From mixed-mode oscillations and spindles to chaos

    NASA Astrophysics Data System (ADS)

    Lee, K.; Goltsev, A. V.; Lopes, M. A.; Mendes, J. F. F.

    2013-01-01

    Understanding of short-term synaptic depression (STSD) and other forms of synaptic plasticity is a topical problem in neuroscience. Here we study the role of STSD in the formation of complex patterns of brain rhythms. We use a cortical circuit model of neural networks composed of irregular spiking excitatory and inhibitory neurons having type 1 and 2 excitability and stochastic dynamics. In the model, neurons form a sparsely connected network and their spontaneous activity is driven by random spikes representing synaptic noise. Using simulations and analytical calculations, we found that if the STSD is absent, the neural network shows either asynchronous behavior or regular network oscillations depending on the noise level. In networks with STSD, changing parameters of synaptic plasticity and the noise level, we observed transitions to complex patters of collective activity: mixed-mode and spindle oscillations, bursts of collective activity, and chaotic behavior. Interestingly, these patterns are stable in a certain range of the parameters and separated by critical boundaries. Thus, the parameters of synaptic plasticity can play a role of control parameters or switchers between different network states. However, changes of the parameters caused by a disease may lead to dramatic impairment of ongoing neural activity. We analyze the chaotic neural activity by use of the 0-1 test for chaos (Gottwald, G. & Melbourne, I., 2004) and show that it has a collective nature.

  3. 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.

  4. 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.

  5. 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

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

    PubMed

    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

  7. 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

  8. 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

  9. State estimation for discrete-time Markovian jumping neural networks with mixed mode-dependent delays

    NASA Astrophysics Data System (ADS)

    Liu, Yurong; Wang, Zidong; Liu, Xiaohui

    2008-12-01

    In this Letter, we investigate the state estimation problem for a new class of discrete-time neural networks with Markovian jumping parameters as well as mode-dependent mixed time-delays. The parameters of the discrete-time neural networks are subject to the switching from one mode to another at different times according to a Markov chain, and the mixed time-delays consist of both discrete and distributed delays that are dependent on the Markovian jumping mode. New techniques are developed to deal with the mixed time-delays in the discrete-time setting, and a novel Lyapunov-Krasovskii functional is put forward to reflect the mode-dependent time-delays. Sufficient conditions are established in terms of linear matrix inequalities (LMIs) that guarantee the existence of the state estimators. We show that both the existence conditions and the explicit expression of the desired estimator can be characterized in terms of the solution to an LMI. A numerical example is exploited to show the usefulness of the derived LMI-based conditions.

  10. Identify the diversity of mesoscopic structures in networks: A mixed random walk approach

    NASA Astrophysics Data System (ADS)

    Ma, Yifang; Jiang, Xin; Li, Meng; Shen, Xin; Guo, Quantong; Lei, Yanjun; Zheng, Zhiming

    2013-10-01

    Community or cluster structure, which can provide insight into the natural partitions and inner connections of a network, is a key feature in studying the mesoscopic structure of complex systems. Although numerous methods for community detection have been proposed ever since, there is still a lack of understanding on how to quantify the diversity of pre-divided community structures, or rank the roles of communities in participating in specific dynamic processes. Inspired by the Law of Mass Action in chemical kinetics, we introduce here the community random walk energy (CRWE), which reflects a potential based on the diffusion phase of a mixed random walk process taking place on the network, to identify the configuration of community structures. The difference of CRWE allows us to distinguish the intrinsic topological diversity between individual communities, on condition that all the communities are pre-arranged in the network. We illustrate our method by performing numerical simulations on constructive community networks and a real social network with distinct community structures. As an application, we apply our method to characterize the diversity of human genome communities, which provides a possible use of our method in inferring the genetic similarity between human populations.

  11. Cortical and subcortical contributions to sequence retrieval: schematic coding of temporal context in the neocortical recollection network

    PubMed Central

    Hsieh, Liang-Tien; Ranganath, Charan

    2015-01-01

    Episodic memory entails the ability to remember what happened when. Although the available evidence indicates that the hippocampus plays a role in structuring serial order information during retrieval of event sequences, information processed in the hippocampus must be conveyed to other cortical and subcortical areas in order to guide behavior. However, the extent to which other brain regions contribute to the temporal organization of episodic memory remains unclear. Here, we examined multivoxel activity pattern changes during retrieval of learned and random object sequences, focusing on a neocortical “core recollection network” that includes the medial prefrontal cortex, retrosplenial cortex, and angular gyrus, as well as on striatal areas including the caudate nucleus and putamen that have been implicated in processing of sequence information. The results demonstrate that regions of the core recollection network carry information about temporal positions within object sequences, irrespective of object information. This schematic coding of temporal information is in contrast to the putamen, which carried information specific to objects in learned sequences, and the caudate, which carried information about objects, irrespective of sequence context. Our results suggest a role for the cortical recollection network in the representation of temporal structure of events during episodic retrieval, and highlight the possible mechanisms by which the striatal areas may contribute to this process. More broadly, the results indicate that temporal sequence retrieval is a useful paradigm for dissecting the contributions of specific brain regions to episodic memory. PMID:26209802

  12. Robust passivity analysis for discrete-time recurrent neural networks with mixed delays

    NASA Astrophysics Data System (ADS)

    Huang, Chuan-Kuei; Shu, Yu-Jeng; Chang, Koan-Yuh; Shou, Ho-Nien; Lu, Chien-Yu

    2015-02-01

    This article considers the robust passivity analysis for a class of discrete-time recurrent neural networks (DRNNs) with mixed time-delays and uncertain parameters. The mixed time-delays that consist of both the discrete time-varying and distributed time-delays in a given range are presented, and the uncertain parameters are norm-bounded. The activation functions are assumed to be globally Lipschitz continuous. Based on new bounding technique and appropriate type of Lyapunov functional, a sufficient condition is investigated to guarantee the existence of the desired robust passivity condition for the DRNNs, which can be derived in terms of a family of linear matrix inequality (LMI). Some free-weighting matrices are introduced to reduce the conservatism of the criterion by using the bounding technique. A numerical example is given to illustrate the effectiveness and applicability.

  13. The neural network for chemotaxis to tastants in Caenorhabditis elegans is specialized for temporal differentiation.

    PubMed

    Thiele, Tod R; Faumont, Serge; Lockery, Shawn R

    2009-09-23

    Chemotaxis in Caenorhabditis elegans depends critically on the rate of change of attractant concentration computed as the worm moves through its environment. This computation depends, in turn, on the neuron class ASE, a left-right pair of pair of chemosensory neurons that is functionally asymmetric such that the left neuron is an on-cell, whereas the right neuron is an off-cell. To determine whether this coding strategy is a general feature of chemosensation in C. elegans, we imaged calcium responses in all chemosensory neurons known or in a position to contribute to chemotaxis to tastants in this organism. This survey revealed one new class of on-cells (ADF) and one new class of off-cells (ASH). Thus, the ASE class is unique in having both an on-cell and an off-cell. We also found that the newly characterized on-cells and off-cells promote runs and turns, respectively, mirroring the pattern reported previously for ASEL and ASER. Our results suggest that the C. elegans chemotaxis network is specialized for the temporal differentiation of chemosensory inputs, as required for chemotaxis. PMID:19776276

  14. 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. PMID:24657573

  15. 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

  16. 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.

  17. 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

  18. 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

  19. Ongoing Activity in Temporally Coherent Networks Predicts Intra-Subject Fluctuation of Response Time to Sporadic Executive Control Demands

    PubMed Central

    Nozawa, Takayuki; Sugiura, Motoaki; Yokoyama, Ryoichi; Ihara, Mizuki; Kotozaki, Yuka; Miyauchi, Carlos Makoto; Kanno, Akitake; Kawashima, Ryuta

    2014-01-01

    Can ongoing fMRI BOLD signals predict fluctuations in swiftness of a person’s response to sporadic cognitive demands? This is an important issue because it clarifies whether intrinsic brain dynamics, for which spatio-temporal patterns are expressed as temporally coherent networks (TCNs), have effects not only on sensory or motor processes, but also on cognitive processes. Predictivity has been affirmed, although to a limited extent. Expecting a predictive effect on executive performance for a wider range of TCNs constituting the cingulo-opercular, fronto-parietal, and default mode networks, we conducted an fMRI study using a version of the color–word Stroop task that was specifically designed to put a higher load on executive control, with the aim of making its fluctuations more detectable. We explored the relationships between the fluctuations in ongoing pre-trial activity in TCNs and the task response time (RT). The results revealed the existence of TCNs in which fluctuations in activity several seconds before the onset of the trial predicted RT fluctuations for the subsequent trial. These TCNs were distributed in the cingulo-opercular and fronto-parietal networks, as well as in perceptual and motor networks. Our results suggest that intrinsic brain dynamics in these networks constitute “cognitive readiness,” which plays an active role especially in situations where information for anticipatory attention control is unavailable. Fluctuations in these networks lead to fluctuations in executive control performance. PMID:24901995

  20. Key genes for modulating information flow play a temporal role as breast tumor coexpression networks are dynamically rewired by letrozole

    PubMed Central

    2013-01-01

    Background Genes do not act in isolation but instead as part of complex regulatory networks. To understand how breast tumors adapt to the presence of the drug letrozole, at the molecular level, it is necessary to consider how the expression levels of genes in these networks change relative to one another. Methods Using transcriptomic data generated from sequential tumor biopsy samples, taken at diagnosis, following 10-14 days and following 90 days of letrozole treatment, and a pairwise partial correlation statistic, we build temporal gene coexpression networks. We characterize the structure of each network and identify genes that hold prominent positions for maintaining network integrity and controlling information-flow. Results Letrozole treatment leads to extensive rewiring of the breast tumor coexpression network. Approximately 20% of gene-gene relationships are conserved over time in the presence of letrozole while 80% of relationships are condition dependent. The positions of influence within the networks are transiently held with few genes stably maintaining high centrality scores across the three time points. Conclusions Genes integral for maintaining network integrity and controlling information flow are dynamically changing as the breast tumor coexpression network adapts to perturbation by the drug letrozole. PMID:23819860

  1. 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…

  2. Complex Network Analysis of CA3 Transcriptome Reveals Pathogenic and Compensatory Pathways in Refractory Temporal Lobe Epilepsy

    PubMed Central

    Bando, Silvia Yumi; Silva, Filipi Nascimento; Costa, Luciano da Fontoura; Silva, Alexandre V.; Pimentel-Silva, Luciana R.; Castro, Luiz HM.; Wen, Hung-Tzu; Amaro, Edson; Moreira-Filho, Carlos Alberto

    2013-01-01

    We previously described – studying transcriptional signatures of hippocampal CA3 explants – that febrile (FS) and afebrile (NFS) forms of refractory mesial temporal lobe epilepsy constitute two distinct genomic phenotypes. That network analysis was based on a limited number (hundreds) of differentially expressed genes (DE networks) among a large set of valid transcripts (close to two tens of thousands). Here we developed a methodology for complex network visualization (3D) and analysis that allows the categorization of network nodes according to distinct hierarchical levels of gene-gene connections (node degree) and of interconnection between node neighbors (concentric node degree). Hubs are highly connected nodes, VIPs have low node degree but connect only with hubs, and high-hubs have VIP status and high overall number of connections. Studying the whole set of CA3 valid transcripts we: i) obtained complete transcriptional networks (CO) for FS and NFS phenotypic groups; ii) examined how CO and DE networks are related; iii) characterized genomic and molecular mechanisms underlying FS and NFS phenotypes, identifying potential novel targets for therapeutic interventions. We found that: i) DE hubs and VIPs are evenly distributed inside the CO networks; ii) most DE hubs and VIPs are related to synaptic transmission and neuronal excitability whereas most CO hubs, VIPs and high hubs are related to neuronal differentiation, homeostasis and neuroprotection, indicating compensatory mechanisms. Complex network visualization and analysis is a useful tool for systems biology approaches to multifactorial diseases. Network centrality observed for hubs, VIPs and high hubs of CO networks, is consistent with the network disease model, where a group of nodes whose perturbation leads to a disease phenotype occupies a central position in the network. Conceivably, the chance for exerting therapeutic effects through the modulation of particular genes will be higher if these genes

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

    PubMed

    Karahanoğlu, Fikret Işik; Van De Ville, Dimitri

    2015-07-16

    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.

  4. Combined application of social network and cluster detection analyses for temporal-spatial characterization of animal movements in Salamanca, Spain.

    PubMed

    Martínez-López, B; Perez, A M; Sánchez-Vizcaíno, J M

    2009-09-01

    Social network analysis was used in combination with techniques for detection of temporal-spatial clusters to identify operations at high risk of receiving or dispatching pigs, from January through December 2005, in the Spanish province of Salamanca. The temporal-spatial structure of the network was explicitly analyzed to estimate the statistical significance of observed clusters. Significant (P<0.01) temporal-spatial clusters identified were grouped into two compartments based on the nature and extent of the contacts among operations within the clusters. One of the compartments was identified from January through April, included a high proportion of extensive farms (0.39), and was likely to be related with the production and trade of Iberian pigs. The other compartment encompassed a smaller proportion of extensive farms (0.11: P<0.01), took place from May through December, and was probably related to intensive production systems. Analysis of a sub-section of the network, which was selected based on the administrative division of Spain, yielded to the identification of a different set of clusters, showing that results of social network analysis may be sensitive to the extension of the information used in the analysis. The approach presented here will be useful for the implementation of differential surveillance, prevention, and control strategies at specific times and locations, which will aid in the optimization of human and financial resources.

  5. Stability analysis of Markovian jumping stochastic Cohen-Grossberg neural networks with mixed time delays.

    PubMed

    Zhang, H; Wang, Y

    2008-02-01

    In this letter, the global asymptotical stability analysis problem is considered for a class of Markovian jumping stochastic Cohen-Grossberg neural networks (CGNNs) with mixed delays including discrete delays and distributed delays. An alternative delay-dependent stability analysis result is established based on the linear matrix inequality (LMI) technique, which can easily be checked by utilizing the numerically efficient Matlab LMI toolbox. Neither system transformation nor free-weight matrix via Newton-Leibniz formula is required. Two numerical examples are included to show the effectiveness of the result.

  6. Mixing nonclassical pure states in a linear-optical network almost always generates modal entanglement

    NASA Astrophysics Data System (ADS)

    Jiang, Zhang; Lang, Mattihas; Caves, Carlton; CenterQuantum Information and Control Collaboration

    2014-03-01

    In quantum optics a pure state is considered classical, relative to the statistics of photodetection, if and only if it is a coherent state. A different and newer notion of nonclassicality is based on modal entanglement. One example that relates these two notions is the Hong-Ou-Mandel effect, where modal entanglement is generated by a beamsplitter from the nonclassical photon-number state | 1 > ⊗ | 1 > . This suggests the beamsplitter or, more generally, linear-optical networks as a mediator of the two notions of nonclassicality. We show the following: Given a nonclassical pure product state input to an N-port linear-optical network, the output is almost always mode entangled; the only exception is a product of squeezed states, all with the same squeezing strength, input to a network that does not mix the squeezed and anti-squeezed quadratures. Our work thus gives a necessary and sufficient condition for a linear network to generate modal entanglement from pure product inputs, a result that is of immediate relevance to the boson sampling problem.

  7. Mixing nonclassical pure states in a linear-optical network almost always generates modal entanglement

    NASA Astrophysics Data System (ADS)

    Jiang, Zhang; Lang, Matthias D.; Caves, Carlton M.

    2013-10-01

    In quantum optics a pure state is considered classical, relative to the statistics of photodetection, if and only if it is a coherent state. A different and newer notion of nonclassicality is based on modal entanglement. One example that relates these two notions is the Hong-Ou-Mandel effect, where modal entanglement is generated by a beamsplitter from the nonclassical photon-number state |1>⊗|1>. This suggests that beamsplitters or, more generally, linear-optical networks are mediators of the two notions of nonclassicality. In this Brief Report, we show the following: Given a nonclassical pure-product-state input to an N-port linear-optical network, the output is almost always mode entangled; the only exception is a product of squeezed states, all with the same squeezing strength, input to a network that does not mix the squeezed and antisqueezed quadratures. Our work thus gives a necessary and sufficient condition for a linear network to generate modal entanglement from pure-product inputs, a result that is of immediate relevance to the boson-sampling problem.

  8. Monitoring Spatial and Temporal Variations in Fracture Networks Using Shear-wave Splitting Analysis

    NASA Astrophysics Data System (ADS)

    Kendall, J. M.; Wuestefeld, A.; Verdon, J.; Rutledge, J. T.; Wookey, J.

    2011-12-01

    corridor, which thus increase the overall permeability of the rock mass. Our results show that shear wave splitting analysis can provide a useful tool for monitoring spatial and temporal variations in fracture networks in a range of environments.

  9. 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

  10. 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

  11. fMRI of the face-processing network in the ventral temporal lobe of awake and anesthetized macaques.

    PubMed

    Ku, Shih-Pi; Tolias, Andreas S; Logothetis, Nikos K; Goense, Jozien

    2011-04-28

    The primate brain features specialized areas devoted to processing of faces, which human imaging studies localized in the superior temporal sulcus (STS) and ventral temporal cortex. Studies in macaque monkeys, in contrast, revealed face selectivity predominantly in the STS. While this discrepancy could result from a true species difference, it may simply be the consequence of technical difficulties in obtaining high-quality MR images from the ventral temporal lobe. By using an optimized fMRI protocol we here report face-selective areas in ventral TE, the parahippocampal cortex, the entorhinal cortex, and the hippocampus of awake macaques, in addition to those already known in the STS. Notably, the face-selective activation of these memory-related areas was observed although the animals were passively viewing and it was preserved even under anesthesia. These results point to similarly extensive cortical networks for face processing in humans and monkeys and highlight potential homologs of the human fusiform face area.

  12. 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.

  13. Digital mammography: Mixed feature neural network with spectral entropy decision for detection of microcalcifications

    SciTech Connect

    Zheng, B. |; Qian, W.; Clarke, L.P.

    1996-10-01

    A computationally efficient mixed feature based neural network (MFNN) is proposed for the detection of microcalcification clusters (MCC`s) in digitized mammograms. The MFNN employs features computed in both the spatial and spectral domain and uses spectral entropy as a decision parameter. Backpropagation with Kalman Filtering (KF) is employed to allow more efficient network training as required for evaluation of different features, input images, and related error analysis. A previously reported, wavelet-based image-enhancement method is also employed to enhance microcalcification clusters for improved detection. The relative performance of the MFNN for both the raw and enhanced images is evaluated using a common image database of 30 digitized mammograms, with 20 images containing 21 biopsy proven MCC`s and ten normal cases. The computed sensitivity (true positive (TP) detection rate) was 90.1% with an average low false positive (FP) detection of 0.71 MCCs/image for the enhanced images using a modified k-fold validation error estimation technique. The corresponding computed sensitivity for the raw images was reduced to 81.4% and with 0.59 FP`s MCCs/image. A relative comparison to an earlier neural network (NN) design, using only spatially related features, suggests the importance of the addition of spectral domain features when the raw image data are analyzed.

  14. 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…

  15. 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

  16. 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

    2015-01-01

    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

  17. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2011-08-01

    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(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. 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.

  20. Tweet for health: using an online social network to examine temporal trends in weight loss-related posts.

    PubMed

    Turner-McGrievy, Gabrielle M; Beets, Michael W

    2015-06-01

    Few studies have used social networking sites to track temporal trends in health-related posts, particularly around weight loss. To examine the temporal relationship of Twitter messages about weight loss over 1 year (2012). Temporal trends in #weightloss mentions and #fitness, #diet, and #health tweets which also had the word "weight" in them were examined using three a priori time periods: (1) holidays: pre-winter holidays, holidays, and post-holidays; (2) Season: winter and summer; and (3) New Year's: pre-New Year's and post-New Year's. Regarding #weightloss, there were 145 (95 % CI 79, 211) more posts/day during holidays and 143 (95 % CI 76, 209) more posts/day after holidays as compared to 480 pre-holiday posts/day; 232 (95 % CI 178, 286) more posts/day during the winter versus summer (441 posts/day); there was no difference in posts around New Year's. Examining social networks for trends in health-related posts may aid in timing interventions when individuals are more likely to be discussing weight loss.

  1. Periodicity and dissipativity for memristor-based mixed time-varying delayed neural networks via differential inclusions.

    PubMed

    Duan, Lian; Huang, Lihong

    2014-09-01

    In this paper, we investigate a class of memristor-based neural networks with general mixed delays involving both time-varying delays and distributed delays. By using the Mawhin-like coincidence theorem, together with the differential inclusion theory, M-matrix properties and differential inequality techniques, some novel criteria are established for ensuring the periodicity and dissipativity for the addressed neural networks. Finally, two numerical examples with simulations are presented to demonstrate the effectiveness of the theoretical results.

  2. 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

  3. Tracking people's hands and feet using mixed network AND/OR search.

    PubMed

    Morariu, Vlad I; Harwood, David; Davis, Larry S

    2013-05-01

    We describe a framework that leverages mixed probabilistic and deterministic networks and their AND/OR search space to efficiently find and track the hands and feet of multiple interacting humans in 2D from a single camera view. Our framework detects and tracks multiple people's heads, hands, and feet through partial or full occlusion; requires few constraints (does not require multiple views, high image resolution, knowledge of performed activities, or large training sets); and makes use of constraints and AND/OR Branch-and-Bound with lazy evaluation and carefully computed bounds to efficiently solve the complex network that results from the consideration of interperson occlusion. Our main contributions are: 1) a multiperson part-based formulation that emphasizes extremities and allows for the globally optimal solution to be obtained in each frame, and 2) an efficient and exact optimization scheme that relies on AND/OR Branch-and-Bound, lazy factor evaluation, and factor cost sensitive bound computation. We demonstrate our approach on three datasets: the public single person HumanEva dataset, outdoor sequences where multiple people interact in a group meeting scenario, and outdoor one-on-one basketball videos. The first dataset demonstrates that our framework achieves state-of-the-art performance in the single person setting, while the last two demonstrate robustness in the presence of partial and full occlusion and fast nontrivial motion.

  4. Elucidating the origin of solute spreading in fracture networks: permeability heterogeneity vs fluid mixing at fracture intersections

    NASA Astrophysics Data System (ADS)

    Kang, P. K.; Dentz, M.; Le Borgne, T.; Juanes, R.

    2013-12-01

    Understanding fluid flow and transport in fractured rock is essential in many natural and engineered processes in the geosciences. Despite its broad relevance, our understanding of flow and transport through fractured media still faces significant challenges. One such challenge is the hydrodynamics at fracture-fracture intersections, and their role on macroscopic transport. Here, we study the impact of (local) fluid mixing at fracture intersections on the (global) behavior of transport, as measured by longitudinal and transverse spreading of a tracer plume. We show that both mixing at fracture intersections and permeability heterogeneity in the fracture network can lead to similar global solute spreading, which poses the question of the origin of spreading in fracture networks. To isolate the signature of mixing-induced and heterogeneity-induced plume spreading, we develop a lattice network model with quenched disorder, which leads to spatial correlation in the velocity field through the network. We simulate particle transport under this correlated velocity field with two models of flow at fracture intersections: (1) complete mixing, which assumes that outflow particle density is proportional to outflow flux; and (2) streamtube routing, which assumes that particles follow the trace of streamlines at the intersection, without mixing. While the complete mixing model leads to large spreading, its relative importance decreases as the level of permeability heterogeneity increases (see figure). To unravel the individual contributions of mixing at intersections and network heterogeneity on spreading, we hypothesize that the two mechanisms are fundamentally different from the point of view of their reversibility. Indeed, we show that injection followed by flow-back leads to different universal signatures of transport that can be used to elucidate the origin of spreading. Finally, we develop a stochastic transport model that accounts for velocity correlation and partial

  5. 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

  6. The superficial white matter in temporal lobe epilepsy: a key link between structural and functional network disruptions.

    PubMed

    Liu, Min; Bernhardt, Boris C; Hong, Seok-Jun; Caldairou, Benoit; Bernasconi, Andrea; Bernasconi, Neda

    2016-09-01

    Drug-resistant temporal lobe epilepsy is increasingly recognized as a system-level disorder affecting the structure and function of large-scale grey matter networks. While diffusion magnetic resonance imaging studies have demonstrated deep fibre tract alterations, the superficial white matter immediately below the cortex has so far been neglected despite its proximity to neocortical regions and key role in maintaining cortico-cortical connectivity. Using multi-modal 3 T magnetic resonance imaging, we mapped the topography of superficial white matter diffusion alterations in 61 consecutive temporal lobe epilepsy patients relative to 38 healthy controls and studied the relationship to large-scale structural as well as functional networks. Our approach continuously sampled mean diffusivity and fractional anisotropy along surfaces running 2 mm below the cortex. Multivariate statistics mapped superficial white matter diffusion anomalies in patients relative to controls, while correlation and mediation analyses evaluated their relationship to structural (cortical thickness, mesiotemporal volumetry) and functional parameters (resting state functional magnetic resonance imaging amplitude) and clinical variables. Patients presented with overlapping anomalies in mean diffusivity and anisotropy, particularly in ipsilateral temporo-limbic regions. Diffusion anomalies did not relate to cortical thinning; conversely, they mediated large-scale functional amplitude decreases in patients relative to controls in default mode hub regions (i.e. anterior and posterior midline regions, lateral temporo-parietal cortices), and were themselves mediated by hippocampal atrophy. With respect to clinical variables, we observed more marked diffusion anomalies in patients with a history of febrile convulsions and those with longer disease duration. Similarly, more marked diffusion alterations were associated with seizure-free outcome. Bootstrap analyses indicated high reproducibility of our

  7. The superficial white matter in temporal lobe epilepsy: a key link between structural and functional network disruptions.

    PubMed

    Liu, Min; Bernhardt, Boris C; Hong, Seok-Jun; Caldairou, Benoit; Bernasconi, Andrea; Bernasconi, Neda

    2016-09-01

    Drug-resistant temporal lobe epilepsy is increasingly recognized as a system-level disorder affecting the structure and function of large-scale grey matter networks. While diffusion magnetic resonance imaging studies have demonstrated deep fibre tract alterations, the superficial white matter immediately below the cortex has so far been neglected despite its proximity to neocortical regions and key role in maintaining cortico-cortical connectivity. Using multi-modal 3 T magnetic resonance imaging, we mapped the topography of superficial white matter diffusion alterations in 61 consecutive temporal lobe epilepsy patients relative to 38 healthy controls and studied the relationship to large-scale structural as well as functional networks. Our approach continuously sampled mean diffusivity and fractional anisotropy along surfaces running 2 mm below the cortex. Multivariate statistics mapped superficial white matter diffusion anomalies in patients relative to controls, while correlation and mediation analyses evaluated their relationship to structural (cortical thickness, mesiotemporal volumetry) and functional parameters (resting state functional magnetic resonance imaging amplitude) and clinical variables. Patients presented with overlapping anomalies in mean diffusivity and anisotropy, particularly in ipsilateral temporo-limbic regions. Diffusion anomalies did not relate to cortical thinning; conversely, they mediated large-scale functional amplitude decreases in patients relative to controls in default mode hub regions (i.e. anterior and posterior midline regions, lateral temporo-parietal cortices), and were themselves mediated by hippocampal atrophy. With respect to clinical variables, we observed more marked diffusion anomalies in patients with a history of febrile convulsions and those with longer disease duration. Similarly, more marked diffusion alterations were associated with seizure-free outcome. Bootstrap analyses indicated high reproducibility of our

  8. 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.

  9. 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

  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. 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…

  12. 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…

  13. A network that performs brute-force conversion of a temporal sequence to a spatial pattern: relevance to odor recognition

    PubMed Central

    Sanders, Honi; Kolterman, Brian E.; Shusterman, Roman; Rinberg, Dmitry; Koulakov, Alexei; Lisman, John

    2014-01-01

    A classic problem in neuroscience is how temporal sequences (TSs) can be recognized. This problem is exemplified in the olfactory system, where an odor is defined by the TS of olfactory bulb (OB) output that occurs during a sniff. This sequence is discrete because the output is subdivided by gamma frequency oscillations. Here we propose a new class of “brute-force” solutions to recognition of discrete sequences. We demonstrate a network architecture in which there are a small number of modules, each of which provides a persistent snapshot of what occurs in a different gamma cycle. The collection of these snapshots forms a spatial pattern (SP) that can be recognized by standard attractor-based network mechanisms. We will discuss the implications of this strategy for recognizing odor-specific sequences generated by the OB. PMID:25278870

  14. 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

  15. 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

  16. Temporal evolution of the macropore network and saturated hydraulic conductivity in an arable, clayey topsoil during one growing season

    NASA Astrophysics Data System (ADS)

    Sandin, Maria; Köstel, Johannes; Jarvis, Nicholas; Larsbo, Mats

    2015-04-01

    Soil macropore networks and thus hydraulic properties at and close to saturation vary considerably with time, as a result of the dynamic nature of a diverse range of interacting soil structure-forming and degrading factors such as tillage and traffic events, faunal and plant root activity, swell/shrink arising from wetting and drying cycles, freeze-thaw etc. These properties are nevertheless treated as constants in most hydrological modelling studies. This is mostly justified by a lack of understanding of the processes driving these changes. Temporal variations of saturated and near-saturated hydraulic conductivity have been studied in the field (e.g. by tension disc infiltrometer), but these measurements only indirectly reflect the characteristics of the macropore network. In this study, we used non-destructive X-ray tomography to investigate the temporal changes in the macropore network characteristics occurring in the harrowed layer of a conventionally-tilled agricultural field over one growing season. Undisturbed soil cores (60-70 mm height, 68 mm diameter) were sampled on five different occasions. Changes in the geometric and topological properties of the X-ray imaged macropore system (voxel resolution = 120 µm) were compared with variations in saturated hydraulic conductivity measured on the same samples. Image analysis showed that total porosity, specific surface area, average pore diameter and the connectivity of the pore system in the uppermost 60-70 mm of ploughed and harrowed soil decreased from the first sampling occasion shortly after seedbed preparation and sowing until the middle of the growing season after which it slightly increased again. Separate analysis of the total porosity of the top 5 mm showed a marked decrease between the first two sampling occasions, followed by a gradual increase. Despite these structural changes in the macropore system, saturated hydraulic conductivity was only weakly correlated with macropore network characteristics.

  17. 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

  18. Impacts of cattle grazing on spatio-temporal variability of soil moisture and above-ground live plant biomass in mixed grasslands

    NASA Astrophysics Data System (ADS)

    Virk, Ravinder

    Areas with relatively high spatial heterogeneity generally have more biodiversity than spatially homogeneous areas due to increased potential habitat. Management practices such as controlled grazing also affect the biodiversity in grasslands, but the nature of this impact is not well understood. Therefore this thesis studies the impacts of variation in grazing on soil moisture and biomass heterogeneity. These are not only important in terms of management of protected grasslands, but also for designing an effective grazing system from a livestock management point of view. This research is a part of the cattle grazing experiment underway in Grasslands National Park (GNP) of Canada since 2006, as part of the adaptive management process for restoring ecological integrity of the northern mixed-grass prairie region. An experimental approach using field measurements and remote sensing (Landsat) was combined with modelling (CENTURY) to examine and predict the impacts of grazing intensity on the spatial heterogeneity and patterns of above-ground live plant biomass (ALB) in experimental pastures in a mixed grassland ecosystem. The field-based research quantified the temporal patterns and spatial variability in both soil moisture (SM) and ALB, and the influence of local intra-seasonal weather variability and slope location on the spatio-temporal variability of SM and ALB at field plot scales. Significant impacts of intra-seasonal weather variability, slope position and grazing pressure on SM and ALB across a range of scales (plot and local (within pasture)) were found. Grazing intensity significantly affected the ALB even after controlling for the effect of slope position. Satellite-based analysis extended the scale of interest to full pastures and the surrounding region to assess the effects of grazing intensity on the spatio-temporal pattern of ALB in mixed grasslands. Overall, low to moderate grazing intensity showed increase in ALB heterogeneity whereas no change in ALB

  19. 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.

  20. 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. PMID:26854903

  1. 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.

  2. 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…

  3. Unconstrained handwritten numeral recognition based on radial basis competitive and cooperative networks with spatio-temporal feature representation.

    PubMed

    Lee, S; Pan, J J

    1996-01-01

    This paper presents a new approach to representation and recognition of handwritten numerals. The approach first transforms a two-dimensional (2-D) spatial representation of a numeral into a three-dimensional (3-D) spatio-temporal representation by identifying the tracing sequence based on a set of heuristic rules acting as transformation operators. A multiresolution critical-point segmentation method is then proposed to extract local feature points, at varying degrees of scale and coarseness. A new neural network architecture, referred to as radial-basis competitive and cooperative network (RCCN), is presented especially for handwritten numeral recognition. RCCN is a globally competitive and locally cooperative network with the capability of self-organizing hidden units to progressively achieve desired network performance, and functions as a universal approximator of arbitrary input-output mappings. Three types of RCCNs are explored: input-space RCCN (IRCCN), output-space RCCN (ORCCN), and bidirectional RCCN (BRCCN). Experiments against handwritten zip code numerals acquired by the U.S. Postal Service indicated that the proposed method is robust in terms of variations, deformations, transformations, and corruption, achieving about 97% recognition rate.

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. Coding of odors by temporal binding within a model network of the locust antennal lobe.

    PubMed

    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.

  9. 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

  10. 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.

  11. 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. PMID:23582352

  12. Particle transport and flow modification in planar temporally evolving laminar mixing layers. I. Particle transport under one-way coupling

    NASA Astrophysics Data System (ADS)

    Narayanan, Chidambaram; Lakehal, Djamel

    2006-09-01

    Simulations of two-dimensional, particle-laden mixing layers were performed for particles with Stokes numbers of 0.3, 0.6, 1, and 2 under the assumption of one-way coupling using the Eulerian-Lagrangian method; two-way coupling is addressed in Part II. Analysis of interphase momentum transfer was performed in the Eulerian frame of reference by looking at the balance of fluid-phase mean momentum, mean kinetic energy, modal kinetic energy, and particle-phase mean momentum. The differences in the dominant mechanisms of vertical transport of streamwise momentum between the fluid and particle phases is clearly brought out. In the fluid phase, growth of the mixing layer is due to energy transfer from the mean flow to the unstable Kelvin-Helmholtz modes, and transport of mean momentum by these modes. In contrast, in the particle phase, the primary mechanism of vertical transport of streamwise momentum is convection due to the mean vertical velocity induced by the centrifuging of particles by the spanwise Kelvin-Helmholtz vortices. Although the drag force and the particle-phase modal stress play an important role in the early stages of the evolution of the mixing layer, their role is shown to decrease during the pairing process. After pairing, the particle-phase mean streamwise momentum balance is accounted for by the convection and drag force term. The particle-phase modal stress term is shown to be strongly connected to the fluid phase modal stress with a Stokes-number-dependent time lag in its evolution.

  13. 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

  14. 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

  15. Topology of Plant - Flower-Visitor Networks in a Tropical Mountain Forest: Insights on the Role of Altitudinal and Temporal Variation.

    PubMed

    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.

  16. The stability of impulsive stochastic Cohen-Grossberg neural networks with mixed delays and reaction-diffusion terms.

    PubMed

    Tan, Jie; Li, Chuandong; Huang, Tingwen

    2015-04-01

    The global asymptotic stability of impulsive stochastic Cohen-Grossberg neural networks with mixed delays and reaction-diffusion terms is investigated. Under some suitable assumptions and using Lyapunov-Krasovskii functional method, we apply the linear matrix inequality technique to propose some new sufficient conditions for the global asymptotic stability of the addressed model in the stochastic sense. The mixed time delays comprise both the time-varying and continuously distributed delays. The effectiveness of the theoretical result is illustrated by a numerical example.

  17. Quantifying the effect of temporal resolution on time-varying networks

    PubMed Central

    Ribeiro, Bruno; Perra, Nicola; Baronchelli, Andrea

    2013-01-01

    Time-varying networks describe a wide array of systems whose constituents and interactions evolve over time. They are defined by an ordered stream of interactions between nodes, yet they are often represented in terms of a sequence of static networks, each aggregating all edges and nodes present in a time interval of size Δt. In this work we quantify the impact of an arbitrary Δt on the description of a dynamical process taking place upon a time-varying network. We focus on the elementary random walk, and put forth a simple mathematical framework that well describes the behavior observed on real datasets. The analytical description of the bias introduced by time integrating techniques represents a step forward in the correct characterization of dynamical processes on time-varying graphs. PMID:24141695

  18. Network Disturbance Theory: Spatial and Temporal Organization of Physical Heterogeneity in Rivers

    NASA Astrophysics Data System (ADS)

    Benda, L.; Poff, L.; Miller, D.; Dunne, T.; Reeves, G.; Pess, G.; Pollock, M.

    2003-12-01

    Beginning with the premise that extreme events, or "disturbances" in the parlance of ecologists (i.e., storms, fires, floods, and punctuated erosion and sediment transport), are intrinsic to many landscapes and river systems across the world, we develop new theory on the interaction between disturbances and branching river networks. The interaction of disturbances with network geometry is central to the question of how habitat heterogeneity forms within riverine corridors, a principle underlying the emerging ecosystem concept of "riverscapes". We explore that interaction by examining how tributary confluences interrupt gradual downstream changes in channel morphology leading to locally increased physical heterogeneity. Punctuated erosion during storms and following fires and episodic floods leads to discontinuous inputs of water, sediment, and organic material at confluences that modify channel and valley-floor morphology for tens of meters to kilometers, including substrate sizes, channel hydraulic geometry, floodplain widths, fans, terraces, and log jams. Based on 14 studies that documented these confluence effects at 168 junctions spanning 6 orders of magnitude in drainage area, it appears that the probability of confluence effects increases with increasing size ratio of tributary to mainstem river. This simple scaling relationship indicates that the downstream increase in tributary basin size found in many watersheds results in a downstream increase in the spacing between confluences that have morphological effects, identifying a control on the spatial scale of confluence-related heterogeneity in rivers. Therefore, when a river network is viewed as a population of tributaries and confluences, the extent to which a network interrupts downstream continua of physical and biological processes (and the degree of habitat heterogeneity) should depend on network geometry, basin shape, drainage density, and basin size. For example, oval-shaped basins (containing

  19. 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…

  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 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

  2. Exotic Plant Infestation Is Associated with Decreased Modularity and Increased Numbers of Connectors in Mixed-Grass Prairie Pollination Networks.

    PubMed

    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

  3. Exotic Plant Infestation Is Associated with Decreased Modularity and Increased Numbers of Connectors in Mixed-Grass Prairie Pollination Networks.

    PubMed

    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

  4. 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

  5. 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

  6. Beyond the edge of chaos: Amplification and temporal integration by recurrent networks in the chaotic regime

    NASA Astrophysics Data System (ADS)

    Toyoizumi, T.; Abbott, L. F.

    2011-11-01

    Randomly connected networks of neurons exhibit a transition from fixed-point to chaotic activity as the variance of their synaptic connection strengths is increased. In this study, we analytically evaluate how well a small external input can be reconstructed from a sparse linear readout of network activity. At the transition point, known as the edge of chaos, networks display a number of desirable features, including large gains and integration times. Away from this edge, in the nonchaotic regime that has been the focus of most models and studies, gains and integration times fall off dramatically, which implies that parameters must be fine tuned with considerable precision if high performance is required. Here we show that, near the edge, decoding performance is characterized by a critical exponent that takes a different value on the two sides. As a result, when the network units have an odd saturating nonlinear response function, the falloff in gains and integration times is much slower on the chaotic side of the transition. This means that, under appropriate conditions, good performance can be achieved with less fine tuning beyond the edge, within the chaotic regime.

  7. 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)

  8. 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

  9. 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. PMID:26982028

  10. Spatio-temporal analysis of nitrogen cycling in a mixed coniferous forest of the northern United States

    NASA Astrophysics Data System (ADS)

    Howard, I.; McLauchlan, K. K.

    2015-02-01

    Nitrogen (N) is the limiting nutrient to primary productivity in a variety of temperate forests, and N cycling is undergoing a variety of anthropogenic changes, notably a doubling of Nr on a global scale. Yet, the local scale impacts of 20th century changes to N cycling have been difficult to document in terrestrial ecosystems, especially old-growth forests. To determine the spatial and temporal variability of anthropogenic effects on old-growth forest N dynamics, we measured the composition of stable nitrogen isotopes (δ15N) in wood from living red pine trees (Pinus resinosa) at a~single site in northern Minnesota, USA. A synchronous decline in wood δ15N values began approximately in the 1920s C.E. in 18 individual trees at different topographic positions, indicating a common driver. The decline in wood δ15N values corresponded with declines in sedimentary δ15N recorded in lacustrine sediments of the same catchment. Disturbance regime and species composition began to change at the turn of the 20th century with park establishment, providing a likely mechanism of decline in δ15N values toward present. While other mechanisms are possible, we conclude that the consequences of global-scale alterations to N cycling are not being expressed at a local level in this temperate forest ecosystem.

  11. 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.

  12. Model of cellular and network mechanisms for odor-evoked temporal patterning in the locust antennal lobe.

    PubMed

    Bazhenov, M; Stopfer, M; Rabinovich, M; Abarbanel, H D; Sejnowski, T J; Laurent, G

    2001-05-01

    Locust antennal lobe (AL) projection neurons (PNs) respond to olfactory stimuli with sequences of depolarizing and hyperpolarizing epochs, each lasting hundreds of milliseconds. A computer simulation of an AL network was used to test the hypothesis that slow inhibitory connections between local neurons (LNs) and PNs are responsible for temporal patterning. Activation of slow inhibitory receptors on PNs by the same GABAergic synapses that underlie fast oscillatory synchronization of PNs was sufficient to shape slow response modulations. This slow stimulus- and neuron-specific patterning of AL activity was resistant to blockade of fast inhibition. Fast and slow inhibitory mechanisms at synapses between LNs and PNs can thus form dynamical PN assemblies whose elements synchronize transiently and oscillate collectively, as observed not only in the locust AL, but also in the vertebrate olfactory bulb.

  13. 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

    2013-01-01

    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

  14. 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

  15. 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.

  16. At the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters.

    PubMed

    Rössert, Christian; Dean, Paul; Porrill, John

    2015-10-01

    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.

  17. Temporal entrainment of cognitive functions: musical mnemonics induce brain plasticity and oscillatory synchrony in neural networks underlying memory.

    PubMed

    Thaut, Michael H; Peterson, David A; McIntosh, Gerald C

    2005-12-01

    In a series of experiments, we have begun to investigate the effect of music as a mnemonic device on learning and memory and the underlying plasticity of oscillatory neural networks. We used verbal learning and memory tests (standardized word lists, AVLT) in conjunction with electroencephalographic analysis to determine differences between verbal learning in either a spoken or musical (verbal materials as song lyrics) modality. In healthy adults, learning in both the spoken and music condition was associated with significant increases in oscillatory synchrony across all frequency bands. A significant difference between the spoken and music condition emerged in the cortical topography of the learning-related synchronization. When using EEG measures as predictors during learning for subsequent successful memory recall, significantly increased coherence (phase-locked synchronization) within and between oscillatory brain networks emerged for music in alpha and gamma bands. In a similar study with multiple sclerosis patients, superior learning and memory was shown in the music condition when controlled for word order recall, and subjects were instructed to sing back the word lists. Also, the music condition was associated with a significant power increase in the low-alpha band in bilateral frontal networks, indicating increased neuronal synchronization. Musical learning may access compensatory pathways for memory functions during compromised PFC functions associated with learning and recall. Music learning may also confer a neurophysiological advantage through the stronger synchronization of the neuronal cell assemblies underlying verbal learning and memory. Collectively our data provide evidence that melodic-rhythmic templates as temporal structures in music may drive internal rhythm formation in recurrent cortical networks involved in learning and memory.

  18. Development of a concentration measurement technique for steady state solid-liquid mixing using a neural network

    NASA Astrophysics Data System (ADS)

    Doh, Deog Hee; Yum, Joo Ho; Cho, Gyeong Rae; Kim, Myung Ho; Ryu, Gyong Won; Takei, Masahiro

    2013-10-01

    A measurement technique that can measure the concentration of the solid particles in liquid flow was developed. The measurement system consists of a color camera and three LCD displays. The solid particles were put at the bottom of a cylindrical mixing tank in which JetA1 oil was filled. Transient mixing of the solid particles was performed by rotating a propeller type agitator with three different rotation speed (500, 600, 700 r/min). Mixing state was visualized by the LCD displays and a color camcorder. The color intensity of the glass particles changes with their concentration. The color information was decoded into three principle colors R, G, and B so that, the calibration curve of color-to-concentration was performed using these information. A neural network was used for this calibration. The transient concentration field of the solid particles was quantitatively visualized.

  19. Spatio-temporal functional data analysis for wireless sensor networks data

    PubMed Central

    Lee, D.-J.; Zhu, Z.; Toscas, P.

    2015-01-01

    Summary 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. PMID:26167116

  20. 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

  1. 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

  2. Modeling a Complex Biological Network with Temporal Heterogeneity: Cardiac Myocyte Plasticity as a Case Study

    NASA Astrophysics Data System (ADS)

    Mazloom, Amin R.; Basu, Kalyan; Mandal, Subhrangsu S.; Das, Sajal K.

    Complex biological systems often characterize nonlinear dynamics. Employing traditional deterministic or stochastic approaches to quantify these dynamics either fail to capture their existing deviant effects or lead to combinatorial explosion. In this work we devised a novel approach that projects the biological functions within a pathway to a network of stochastic events that are random in time and space. By applying this approach recursively to the object system we build the event network of the entire system. The dynamics of the system evolves through the execution of the event network by a simulation engine which comprised of a time prioritized event queue. As a case study we utilized the current method and conducted an in-silico experiment on the metabolic plasticity of a cardiac myocyete. We aimed to quantify the down stream effects of insulin signaling that predominantly controls the plasticity in myocardium. Intriguingly, our in-silico results on transcription regulatory effect of insulin showed a good agreement with experimental data. Meanwhile we were able to characterize the flux change across major metabolic pathways over 48 hours of the in-silico experiment. Our simulation performed a remarkable efficiency by conducting 48 hours of simulation-time in less that 2 hours of processor time.

  3. Laser Speckle Flowmetry Method for Measuring Spatial and Temporal Hemodynamic Alterations Throughout Large Microvascular Networks

    PubMed Central

    Meisner, Joshua K.; Sumer, Suna; Murrell, Kelsey P.; Higgins, Timothy J.; Price, Richard J.

    2012-01-01

    Objectives 1) Develop and validate laser speckle flowmetry (LSF) as a quantitative tool for individual microvessel hemodynamics in large networks. 2) Use LSF to determine if structural differences in the dorsal skinfold microcirculation (DSFWC) of C57BL/6 and BALB/c mice impart differential network hemodynamic responses to occlusion. Methods We compared LSF velocity measurements to known/measured velocities in vitro using capillary tube tissue phantoms and in vivo using mouse DSFWCs and cremaster muscles. Hemodynamic changes induced by feed arteriole occlusion were measured using LSF in DSFWCs implanted on C57BL/6 and BALB/c. Results In vitro, we found that the normalized speckle intensity (NSI) versus velocity linear relationship (R2≥0.97) did not vary with diameter or hematocrit and can be shifted to meet an expected operating range. In vivo, DSFWC and cremaster muscle preparations (R2=0.92 and 0.95, respectively) demonstrated similar linear relationships between NSI and centerline velocity. Stratification of arterioles into predicted collateral pathways revealed significant differences between C57BL/6 and BALB/c strains in response to feed arteriole occlusion. Conclusions These data demonstrate the applicability of LSF to intravital microscopy microcirculation preparations for determining both relative and absolute hemodynamics on a network-wide scale while maintaining the resolution of individual microvessels. PMID:22591575

  4. Writer Identification Using Super Paramagnetic Clustering and Spatio Temporal Neural Network

    NASA Astrophysics Data System (ADS)

    Taghavi Sangdehi, Seyyed Ataollah; Faez, Karim

    This paper discusses use of Super Paramagnetic Clustering (SPC) and Spatio Temporal Artificial Neuron in on-line writer identification, on Farsi handwriting. In online cases, speed and automation are advantages of one method on others, therefore we used unsupervised and relatively quick clustering method, which in comparison with conventional approaches, give us better result. Moreover, regardless of various parameters that available from acquisition systems, we only consider to displacement of pen tip at determined direction that lead to quick system due to its quick preprocessing and clustering. Also we use a threshold that remove displacement between disconnected point of a word that lead to a better classification result on on-line Farsi writers.

  5. 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.

  6. Complex networks generated by the Penna bit-string model: Emergence of small-world and assortative mixing

    NASA Astrophysics Data System (ADS)

    Li, Chunguang; Maini, Philip K.

    2005-10-01

    The Penna bit-string model successfully encompasses many phenomena of population evolution, including inheritance, mutation, evolution, and aging. If we consider social interactions among individuals in the Penna model, the population will form a complex network. In this paper, we first modify the Verhulst factor to control only the birth rate, and introduce activity-based preferential reproduction of offspring in the Penna model. The social interactions among individuals are generated by both inheritance and activity-based preferential increase. Then we study the properties of the complex network generated by the modified Penna model. We find that the resulting complex network has a small-world effect and the assortative mixing property.

  7. Duality of rate coding and temporal coding in multilayered feedforward networks.

    PubMed

    Masuda, Naoki; Aihara, Kazuyuki

    2003-01-01

    A functional role for precise spike timing has been proposed as an alternative hypothesis to rate coding. We show in this article that both the synchronous firing code and the population rate code can be used dually in a common framework of a single neural network model. Furthermore, these two coding mechanisms are bridged continuously by several modulatable model parameters, including shared connectivity, feedback strength, membrane leak rate, and neuron heterogeneity. The rates of change of these parameters are closely related to the response time and the timescale of learning. PMID:12590821

  8. 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

  9. Relationships between default-mode network connectivity, medial temporal lobe structure, and age-related memory deficits.

    PubMed

    Ward, Andrew M; Mormino, Elizabeth C; Huijbers, Willem; Schultz, Aaron P; Hedden, Trey; Sperling, Reisa A

    2015-01-01

    Advanced aging negatively impacts memory performance. Brain aging has been associated with shrinkage in medial temporal lobe structures essential for memory--including hippocampus and entorhinal cortex--and with deficits in default-mode network connectivity. Yet, whether and how these imaging markers are relevant to age-related memory deficits remains a topic of debate. Using a sample of 182 older (age 74.6 ± 6.2 years) and 66 young (age 22.2 ± 3.6 years) participants, this study examined relationships among memory performance, hippocampus volume, entorhinal cortex thickness, and default-mode network connectivity across aging. All imaging markers and memory were significantly different between young and older groups. Each imaging marker significantly mediated the relationship between age and memory performance and collectively accounted for most of the variance in age-related memory performance. Within older participants, default-mode connectivity and hippocampus volume were independently associated with memory. Structural equation modeling of cross-sectional data within older participants suggest that entorhinal thinning may occur before reduced default-mode connectivity and hippocampal volume loss, which in turn lead to deficits in memory performance. PMID:25113793

  10. 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…

  11. IEEE 802.21 Assisted Seamless and Energy Efficient Handovers in Mixed Networks

    NASA Astrophysics Data System (ADS)

    Liu, Huaiyu; Maciocco, Christian; Kesavan, Vijay; Low, Andy L. Y.

    Network selection is the decision process for a mobile terminal to handoff between homogeneous or heterogeneous networks. With multiple available networks, the selection process must evaluate factors like network services/conditions, monetary cost, system conditions, user preferences etc. In this paper, we investigate network selection using a cost function and information provided by IEEE 802.21. The cost function provides flexibility to balance different factors in decision making and our research is focused on improving both seamlessness and energy efficiency of handovers. Our solution is evaluated using real WiFi, WiMax, and 3G signal strength traces. The results show that appropriate networks were selected based on selection policies, handovers were triggered at optimal times to increase overall network connectivity as compared to traditional triggering schemes, while at the same time the energy consumption of multi-radio devices for both on-going operations as well as during handovers is optimized.

  12. Differential Code Biases Temporal and Spatial Variation: A case study of permanent network in Egypt

    NASA Astrophysics Data System (ADS)

    Mousa, Ashraf; Abd, Mohamed; Rabah, Moustafa; Mowafi, Mahmoud; Awad, Ahmed

    2016-07-01

    Measurements of Global Positioning Satellite System (GPS) receivers are affected by systematic offsets related to group and phase delays of the signal generation and processing chain. One of the important factors affecting the ionosphere Total Electron Content (TEC) estimation accuracy is the hardware Differential Code Bias (DCBs) inherited in both GPS satellites and receivers. The resulting code and phase biases depend on the transmission frequency and the employed signal modulation. An efficient algorithm using the geometry conditions between satellite and tracking receivers is proposed to determine the receiver differential code bias (DCB) using Egyptian permanent reference stations. This method does not require a traditional single-layer ionosphere model and can be used for estimating DCBs of receivers in a regional network. This paper estimates receiver DCBs for nine receivers located within Egyptian network. The results showed that the estimated mean value of the receiver DCB varied from -28 ns (nanosecond) to 39 ns. It is clear from the results that DCB values for Egyptian sites do not vary much with latitude and longitude, except at ABSM and ASWN. DCB values increase gradually with increasing height.

  13. Temporal and spatial variation of water level in urbanizing plain river network region.

    PubMed

    Xu, Guanglai; Xu, Youpeng; Luo, Xian; Xu, Hongliang; Xu, Xiaohua; Hu, Chunsheng

    2014-01-01

    As one of the most developed regions in China, the plain of East China is undergoing gradually increased flooding under the obvious urbanization process. This paper mainly analyses the trend of water level time series in the region during the past decades, and assesses the temporal and spatial variation of water level and indicators of hydrological alteration. The results show that there is a trend of increasing water level. Bigger slope and higher significant level can be observed in monthly minimum than in monthly maximum water level, in peri-urban than in urban areas. Meanwhile, it is observed that the mean monthly minimum and maximum water level increased in both urban and peri-urban regions, while decreased coefficients of variation (Cv) in urban and increased Cv in peri-urban regions were calculated. Most indicators of hydrologic alteration in urban stations are concentrated to the range of variability approach target, while most indicators are discrete in peri-urban stations. And the degree of hydrologic alteration is higher in peri-urban than in urban regions. PMID:24901612

  14. 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. PMID:27033540

  15. 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

  16. Intraplate seismicity in Canada: Complex network analysis of spatio-temporal recurrences

    NASA Astrophysics Data System (ADS)

    Vasudevan, Kris; Eaton, David; Davidsen, Jörn

    2010-05-01

    Intraplate seismicity in certain regions of Ontario, Quebec, and Nunavut in Canada is a subject of this abstract. The reasons for the occurrence and periodicity of the intraplate earthquakes are not as well understood as the interplate earthquakes. Here, we undertake a complex network analysis with a view to extract information from the physical structure of the network of recurrent seismic events that occur in space and time that can provide insights concerning the causality of seismic events. To this end, we have identified three areas for our study as defined by the following ranges of latitude/longitude values: area 1: 45o-48o/74o-80o; area 2: 42o-45o/76o-81o; area 2: 51o-55o/77o-83o; area 4: 45o-57o/80o-98o; and area 3: 56o-70o/65o-95o. In this work, using a recently proposed definition of 'recurrences' based on record breaking processes (Phys. Rev. E 77, 066107, 2008), we have constructed digraphs of the data extracted from the five areas (http://earthquakescanada.nrcan.gc.ca) with attributes drawn from the location of the events, the time of occurrences and the magnitude of the events. For a quantitative insight into the digraphs of the recurring events in space and time, we have examined the probability distributions of space-interval and time-interval recurrences for different magnitudes of earthquakes, the network properties such as the in-degree as well as the out-degree distributions for different magnitudes, the clustering coefficient, and the degree correlations between a given event and its recurrences. Since there is an uncertainty in spatial locations of earthquakes, we have allowed for uncertainty in recurrences as well to generate a new suite of digraphs for error analysis. Furthermore, to test for the presence of non-trivial spatiotemporal correlations and causal connections, we have carried out a series of Monte-Carlo simulations by reshuffling the spatial locations and magnitudes of the earthquakes without altering the time of occurrences

  17. Two-photon imaging of spatially extended neuronal network dynamics with high temporal resolution

    PubMed Central

    Lillis, Kyle P.; Eng, Alfred; White, John A.; Mertz, Jerome

    2008-01-01

    We describe a simple two-photon fluorescence imaging strategy, called targeted path scanning (TPS), to monitor the dynamics of spatially extended neuronal networks with high spatiotemporal resolution. Our strategy combines the advantages of mirror-based scanning, minimized dead time, ease of implementation, and compatibility with high-resolution low-magnification objectives. To demonstrate the performance of TPS, we monitor the calcium dynamics distributed across an entire juvenile rat hippocampus (>1.5mm), at scan rates of 100Hz, with single cell resolution and single action potential sensitivity. Our strategy for fast, efficient two-photon microscopy over spatially extended regions provides a particularly attractive solution for monitoring neuronal population activity in thick tissue, without sacrificing the signal to noise ratio or high spatial resolution associated with standard two-photon microscopy. Finally, we provide the code to make our technique generally available. PMID:18539336

  18. 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. PMID:26348834

  19. 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.

  20. 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.

  1. 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.

  2. 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. PMID:23407600

  3. 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.

  4. Temporally resolved planar measurements of transient phenomena in a partially pre-mixed swirl flame in a gas turbine model combustor

    SciTech Connect

    Boxx, I.; Stoehr, M.; Meier, W.; Carter, C.

    2010-08-15

    This paper presents observations and analysis of the time-dependent behavior of a 10 kW partially pre-mixed, swirl-stabilized methane-air flame exhibiting self-excited thermo-acoustic oscillations. This analysis is based on a series of measurements wherein particle image velocimetry (PIV) and planar laser-induced fluorescence (PLIF) of the OH radical were performed simultaneously at 5 kHz repetition rate over durations of 0.8 s. Chemiluminescence imaging of the OH{sup *} radical was performed separately, also at 5 kHz over 0.8 s acquisition runs. These measurements were of sufficient sampling frequency and duration to extract usable spatial and temporal frequency information on the medium to large-scale flow-field and heat-release characteristics of the flame. This analysis is used to more fully characterize the interaction between the self-excited thermo-acoustic oscillations and the dominant flow-field structure of this flame, a precessing vortex core (PVC) present in the inner recirculation zone. Interpretation of individual measurement sequences yielded insight into various physical phenomena and the underlying mechanisms driving flame dynamics. It is observed for this flame that location of the reaction zone tracks large-scale fluctuations in axial velocity and also conforms to the passage of large-scale vortical structures through the flow-field. Local extinction of the reaction zone in regions of persistently high principal compressive strain is observed. Such extinctions, however, are seen to be self healing and thus do not induce blowout. Indications of auto-ignition in regions of unburned gas near the exit are also observed. Probable auto-ignition events are frequently observed coincident with the centers of large-scale vortical structures, suggesting the phenomenon is linked to the enhanced mixing and longer residence times associated with fluid at the core of the PVC as it moves through the flame. (author)

  5. The mix matters: complex personal networks relate to higher cognitive functioning in old age.

    PubMed

    Ellwardt, Lea; Van Tilburg, Theo G; Aartsen, Marja J

    2015-01-01

    Stronger engagement of older adults in social activities and greater embeddedness in networks is often argued to buffer cognitive decline and lower risks of dementia. One of the explanations is that interaction with other people trains the brain, thereby enhancing cognitive functioning. However, research on the relationship between personal networks and cognitive functioning is not yet conclusive. While previous studies have focused on the size of personal networks as a proxy of cognitive stimulation, little attention has been paid to the complexity of the personal network. Adults embedded in a broad range of network relationships (i.e., various relationship types) are likely to be exposed to a wider range of stimuli than adults embedded in a homogeneous network including similar relationship types. We expect that higher numbers of personal relationship types rather than a higher number of similar contacts relate to higher levels of cognitive functioning and slower cognitive decline. Data are from the Longitudinal Aging Study Amsterdam (LASA) and include 2959 Dutch participants aged 54 to 85 at baseline in 1992 and six follow-ups covering a time span of twenty years. Cognitive functioning is assessed with the Mini-Mental State Examination (MMSE), and for network complexity we use the Social Network Index. We test our expectations using fixed-effects regression models. The results reveal that a reduction in network complexity is associated with a reduction in cognitive functioning, which is neither explained by size of the network nor by presence of specific relationship types. However, enhanced complexity has only a marginal buffering effect on decline in cognitive functioning. We conclude that network characteristics and cognitive functioning are intertwined and that their association is mostly cross-sectional in nature.

  6. 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.

  7. 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

  8. Feed-Forward Propagation of Temporal and Rate Information between Cortical Populations during Coherent Activation in Engineered In Vitro Networks.

    PubMed

    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 synchronized

  9. 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

  10. 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.

  11. Stimulating the brain's language network: syntactic ambiguity resolution after TMS to the inferior frontal gyrus and middle temporal gyrus.

    PubMed

    Acheson, Daniel J; Hagoort, Peter

    2013-10-01

    The posterior middle temporal gyrus (MTG) and inferior frontal gyrus (IFG) are two critical nodes of the brain's language network. Previous neuroimaging evidence has supported a dissociation in language comprehension in which parts of the MTG are involved in the retrieval of lexical syntactic information and the IFG in unification operations that maintain, select, and integrate multiple sources of information over time. In the present investigation, we tested for causal evidence of this dissociation by modulating activity in IFG and MTG using an offline TMS procedure: continuous theta-burst stimulation. Lexical-syntactic retrieval was manipulated by using sentences with and without a temporarily word-class (noun/verb) ambiguity (e.g., run). In one group of participants, TMS was applied to the IFG and MTG, and in a control group, no TMS was applied. Eye movements were recorded and quantified at two critical sentence regions: a temporarily ambiguous region and a disambiguating region. Results show that stimulation of the IFG led to a modulation of the ambiguity effect (ambiguous-unambiguous) at the disambiguating sentence region in three measures: first fixation durations, total reading times, and regressive eye movements into the region. Both IFG and MTG stimulation modulated the ambiguity effect for total reading times in the temporarily ambiguous sentence region relative to the control group. The current results demonstrate that an offline repetitive TMS protocol can have influences at a different point in time during online processing and provide causal evidence for IFG involvement in unification operations during sentence comprehension.

  12. 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

  13. 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

  14. Spatial and Temporal Control of Thiol-Michael Addition via Photocaged Superbase in Photopatterning and Two-Stage Polymer Networks Formation

    PubMed Central

    2015-01-01

    Photochemical processes enable spatial and temporal control of reactions, which can be implemented as an accurate external control approach in both polymer synthesis and materials applications. “Click” reactions have also been employed as efficient tools in the same field. Herein, we combined photochemical processes and thiol-Michael “click” reactions to achieve a “photo-click” reaction that can be used in surface patterning and controlled polymer network formation, owing to the ease of spatial and temporal control through use of photolabile amines as appropriate catalysts. PMID:25264379

  15. 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.

  16. Enabling synthesis of spatially and temporally diverse data collected from environmental sensors within the Long Term Ecological Research Network

    NASA Astrophysics Data System (ADS)

    Vande Castle, J. R.; Servilla, M. S.; San Gil, I.; Michener, B. K.; Waide, R. B.; Scuderi, L. A.

    2006-12-01

    The Network Office (LNO) of the 26 site Long Term Ecological Research Network (LTER) is involved in the design and implementation of cyberinfrastructure (CI) that will support and enable synthesis of spatially and temporally diverse sets of environmental sensor data. Funded by the NSF, the LTER and LNO are driven to provide a methodology and a CI that enables researchers, and ultimately society, to share and exploit integrated current and historical observations from sensor data representing twenty-five years of environmental observatory experience for purposes of projecting trends and patterns that will provide knowledge about the our changing ecosystems. An example of this includes the "Biophony Grid Portal", which provides researchers direct access to environmental acoustic data. This project involves aggregation of streaming acoustic data from field microphones sent by Internet to a staging server at Michigan State University. Metadata for the acoustic files are stored at the LNO for discovery and access purposes using the LTER Metacat, a metadata catalog and Internet accessible data repository. An application portal developed with the National Center for Supercomputer Applications (NCSA) integrate these data for analysis and synthesis using high-performance computers located at NCSA. Collaborations with the USGS National Biological Information Infrastructure (NBII) disseminates such information by sharing metadata standards and content within the NBII clearinghouse system a channel to the broader research community. Working with the NBII, the LTER Network has adopted the Ecological Metadata Language (EML) for data documentation. Conforming to FGDC standards, EML supports discovery of these data through both the LTER Metacat and the NBII data search capabilities developed by the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) for Biogeochemical Dynamics. The (LNO) maintains a rich archive of historical remote sensing data, documented

  17. 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

  18. 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. PMID:26920088

  19. 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.

  20. 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

  1. 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

  2. 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. PMID:25303102

  3. The Neural Organization of Semantic Control: TMS Evidence for a Distributed Network in Left Inferior Frontal and Posterior Middle Temporal Gyrus

    PubMed Central

    Kirk, Marie; O'Sullivan, Jamie; Lambon Ralph, Matthew A.

    2011-01-01

    Assigning meaning to words, sounds, and objects requires stored conceptual knowledge plus executive mechanisms that shape semantic retrieval according to the task or context. Despite the essential role of control in semantic cognition, its neural basis remains unclear. Neuroimaging and patient research has emphasized the importance of left inferior frontal gyrus (IFG)—however, impaired semantic control can also follow left temporoparietal lesions, suggesting that this function may be underpinned by a large-scale cortical network. We used repetitive transcranial magnetic stimulation in healthy volunteers to disrupt processing within 2 potential sites in this network—IFG and posterior middle temporal cortex. Stimulation of both sites selectively disrupted executively demanding semantic judgments: semantic decisions based on strong automatic associations were unaffected. Performance was also unchanged in nonsemantic tasks—irrespective of their executive demands—and following stimulation of a control site. These results reveal that an extended network of prefrontal and posterior temporal regions underpins semantic control. PMID:20851853

  4. The annual cycle of vertical mixing and restratification in the Northern Gulf of Eilat/Aqaba (Red Sea) based on high temporal and vertical resolution observations

    NASA Astrophysics Data System (ADS)

    Carlson, Daniel F.; Fredj, Erick; Gildor, Hezi

    2014-02-01

    The stratification in the Northern Gulf of Eilat/Aqaba follows a well-known annual cycle of well-mixed conditions in winter, surface warming in spring and summer, maximum vertical temperature gradient in late summer, and erosion of stratification in fall. The strength and structure of the stratification influences the diverse coral reef ecosystem and also affects the strength of the semi-diurnal tidal currents. Long-term (13 months) moored thermistor data, combined with high temporal and vertical resolution density profiles in deep water, show that transitions from summer to fall and winter to spring/summer occur in unpredictable, pulses and are not slow and gradual, as previously deduced from monthly hydrographic measurements and numerical simulations forced by monthly climatologies. The cooling and deepening of the surface layer in fall is marked by a transition to large amplitude, semi-diurnal isotherm displacements in the stratified intermediate layer. Stratification is rebuilt in spring and summer by intermittent pulses of warm, buoyant water that can increase the upper 100-150 m by 2 °C that force surface waters down 100-150 m over a matter of days. The stratification also varies in response to short-lived eddies and diurnal motions during winter. Thus, the variability in the stratification exhibits strong depth and seasonal dependence and occurs over range of timescales: from tidal to seasonal. We show that monthly or weekly single-cast hydrographic data under-samples the variability of the stratification in the Gulf and we estimate the error associated with single-cast assessments of the stratification.

  5. Spatio-temporal variability of ionospheric Total Electron Content (TEC) over the Indian subcontinent derived from geodetic GPS network

    NASA Astrophysics Data System (ADS)

    Vijayan, M.; Kannoth, S.; Varghese, G.; Earnest, A.; Jade, S.; Bhatt, B. C.; Gupta, S. S.

    2013-12-01

    We present, for the first time, Ionospheric Total Electron Content (TEC) computed from dual frequency GPS data observed by Indian geodetic GPS network and neighboring IGS stations for more than a decade (2001-2012) (figure 1). Indian geodetic GPS network has more than 30 stations well spread across the Indian subcontinent, primarily, to study the tectonics of the Indian plate. Each station has geodetic grade dual frequency GPS receiver which are operated in continuous mode by making observations at every 30s since 2001. The ionospheric TEC presented here is computed from the code and phase GPS measurements using the software IONODETECT developed at CSIR 4PI. This decadal scale ionospheric data set covers from maxima of 23rd to maxima of 24th solar cycle with a broad spatial coverage from 35S to 56N and 38E to 134E (figure1). The GPS TEC computed at every 30 seconds over each sub-ionospheric point correlates well with International Reference Ionosphere(IRI) 2012 model in longer time scale, however, a strong spatio-temporal dependence in correlation is clearly observed. In addition a site specific, nearly systematic night time bias between GPS TEC and IRI-12 is noted. The advantage of using the systematic bias for correcting Differential Code Bias (DCB) in computing GPS TEC is discussed. We also discuss in detail the equatorial ionospheric processes and regional characteristics of Equatorial Ionization Anomaly (EIA) through latitudinal, diurnal, seasonal, and inter-annual variability of decadal scale GPS TEC computed over Indian subcontinent. EIA anomaly crust maxima during local noon on 30th November 2004 is clearly visible in the figure 1. The TEC variations associated with solar flares and solar maxima and minima during the solar cycles are also discussed to understand the impact of space weather on equatorial and mid latitude ionosphere as well as on navigation. Vertical TEC (VTEC) at each sub ionospheric pierce points (SIP) on 30th November 2004 from 0UTC to

  6. 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

  7. 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. PMID:25741262

  8. 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

  9. Global dissipativity analysis on uncertain neural networks with mixed time-varying delays.

    PubMed

    Song, Qiankun; Cao, Jinde

    2008-12-01

    In this paper, the problems of global dissipativity and global exponential dissipativity are investigated for uncertain neural networks with discrete time-varying delay and distributed time-varying delay as well as general activation functions. By constructing appropriate Lyapunov-Krasovskii functionals and employing Newton-Leibniz formulation and linear matrix inequality (LMI) technique, several new criteria for checking the global dissipativity and global exponential dissipativity of the addressed neural networks are established in terms of LMI, which can be checked numerically using the effective LMI toolbox in MATLAB. Illustrated examples are given to show the effectiveness and decreased conservatism of the proposed criteria in comparison with some existing results. It is noteworthy that the traditional assumptions on the differentiability of the time-varying delays and the boundedness of its derivative are removed.

  10. 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

  11. Genetic Algorithm for Solving Fuzzy Shortest Path Problem in a Network with mixed fuzzy arc lengths

    NASA Astrophysics Data System (ADS)

    Mahdavi, Iraj; Tajdin, Ali; Hassanzadeh, Reza; Mahdavi-Amiri, Nezam; Shafieian, Hosna

    2011-06-01

    We are concerned with the design of a model and an algorithm for computing a shortest path in a network having various types of fuzzy arc lengths. First, we develop a new technique for the addition of various fuzzy numbers in a path using α -cuts by proposing a linear least squares model to obtain membership functions for the considered additions. Then, using a recently proposed distance function for comparison of fuzzy numbers. we propose a new approach to solve the fuzzy APSPP using of genetic algorithm. Examples are worked out to illustrate the applicability of the proposed model.

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. Children with well controlled epilepsy possess different spatio-temporal patterns of causal network connectivity during a visual working memory task.

    PubMed

    Protopapa, Foteini; Siettos, Constantinos I; Myatchin, Ivan; Lagae, Lieven

    2016-04-01

    Using spectral Granger causality (GC) we identified distinct spatio-temporal causal connectivity (CC) patterns in groups of control and epileptic children during the execution of a one-back matching visual discrimination working memory task. Differences between control and epileptic groups were determined for both GO and NOGO conditions. The analysis was performed on a set of 19-channel EEG cortical activity signals. We show that for the GO task, the highest brain activity in terms of the density of the CC networks is observed in α band for the control group while for the epileptic group the CC network seems disrupted as reflected by the small number of connections. For the NOGO task, the denser CC network was observed in θ band for the control group while widespread differences between the control and the epileptic group were located bilaterally at the left temporal-midline and parietal areas. In order to test the discriminative power of our analysis, we performed a pattern analysis approach based on fuzzy classification techniques. The performance of the classification scheme was evaluated using permutation tests. The analysis demonstrated that, on average, 87.6 % of the subjects were correctly classified in control and epileptic. Thus, our findings may provide a helpful insight on the mechanisms pertaining to the cognitive response of children with well controlled epilepsy and could potentially serve as "functional" biomarkers for early diagnosis.

  18. Self-assembly of 1D mixed-metal tubular network with coordination bonds through the interconnection of organometallic metallamacrocycles by Ag(I) centers.

    PubMed

    Wang, Guo-Liang; Lin, Yue-Jian; Jin, Guo-Xin

    2011-05-21

    The combination of a ditopic ligand containing a functional "third site" as a bridge and organometallic half-sandwich iridium unit Cp*Ir as the corner leads to the formation of the tetranuclear metallamacrocycle 1, which is reacted with silver compound, resulting in the formation of mixed-metal infinitely tubular coordination network 2.

  19. 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

  20. Repeating Spatial-Temporal Motifs of CA3 Activity Dependent on Engineered Inputs from Dentate Gyrus Neurons in Live Hippocampal Networks.

    PubMed

    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

  1. Single and mixed dyslipidaemia in Canadian primary care settings: findings from the Canadian primary care sentinel surveillance network database

    PubMed Central

    Asghari, Shabnam; Aref-Eshghi, Erfan; Godwin, Marshall; Duke, Pauline; Williamson, Tyler; Mahdavian, Masoud

    2015-01-01

    Objectives Dyslipidaemia is a major risk factor to cardiovascular disease (CVD)—the leading cause of death worldwide. Limited data are available about the prevalence of various dyslipidaemia in Canada. The objective of this study is to describe the prevalence of various single and mixed dyslipidaemia within the Canadian population in a primary care setting. Setting A cross-sectional study, using the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), was undertaken. Participants Non-pregnant adults older than 20 years were included. Outcome measures Canadian guidelines were used to define dyslipidaemia. Descriptive statistics and multivariate regression analyses were conducted to compare the prevalence of single/mixed dyslipidaemia. Results 134 074 individuals with a mean age of 59.2 (55.8% women) were identified. 34.8% of this population had no lipid abnormality, whereas 35.8%, 17.3% and 3.2% had abnormalities in one, two and three lipid components, respectively. Approximately 86% of these patients did not receive any lipid-lowering medication. Among the medication users (14%), approximately 12% were on statin monotherapy. Statin users (n=16 036) had a lower rate of low-density lipoprotein dyslipidaemia compared to non-medication users (3% vs 17%), whereas the prevalence of high-density lipoprotein (HDL) (20% vs 12%) and triglycerides (TG) (12% vs 7%) dyslipidaemia were higher in statin users. Statin users had a greater prevalence of HDL, TG and combined HDL-TG dyslipidaemia compared to non-medication users (OR 1.44, 95% CI 1.36 to 153), (OR 1.18, 95% CI 1.10 to 1.27) and (OR 1.30, 95% CI 1.22 to 1.38), respectively, (all p values<0.0001). Conclusions One of every five patients in primary care settings in Canada is suffering from mixed dyslipidaemia. The overall prevalence of dyslipidaemia remains the same between treated and untreated groups, although the type of abnormal lipid component is considerably different. Among the CVD risk factors

  2. 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.

  3. 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)

  4. 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

  5. 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. PMID:26101832

  6. 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.

  7. 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).

  8. 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.

  9. 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

  10. Effects of pH and mixing agents on the temporal setting of tooth-colored and gray mineral trioxide aggregate.

    PubMed

    Watts, J Dustin; Holt, Dennis M; Beeson, Thomas J; Kirkpatrick, Timothy C; Rutledge, Richard E

    2007-08-01

    The purpose of this study was to test the compressive strength of white mineral trioxide aggregate (WMTA) and gray mineral trioxide aggregate (GMTA) when mixed with sterile water or local anesthetic and exposed to an acidic environment. A total of 248 samples of WMTA and GMTA were mixed and placed in phosphate-buffered saline (PBS), at pH 5.0 or 7.4, for a period of 7 or 28 days. When WMTA and GMTA were mixed with local anesthetic, the following were observed: 1) pH 5.0 caused a significant decrease in compressive strength (p<0.0001); 2) WMTA was significantly stronger than GMTA (p<0.0001); and 3) more time in PBS (total 28 days) caused a significant decrease in compressive strength (p<0.001). There were no consistent differences in compressive strength for WMTA or GMTA when mixed with sterile water. Variability of results suggests both types of MTA be mixed with sterile water in acidic and neutral environments.

  11. 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

  12. Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks.

    PubMed

    Erfanian Saeedi, Nafise; Blamey, Peter J; Burkitt, Anthony N; Grayden, David B

    2016-04-01

    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

  13. 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. PMID:26752413

  14. 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

  15. 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.

  16. 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).

  17. Temporal and spatial variability of radium in the coastal ocean and its impact on computation of nearshore cross-shelf mixing rates

    NASA Astrophysics Data System (ADS)

    Colbert, Steven L.; Hammond, Douglas E.

    2007-06-01

    Constraining the exchange of water from the shoreline to the mid-shelf is necessary for the development of accurate and predictive models of nearshore circulation. Ra isotopes, which emanate from sediments and have a variety of half-lives, may be useful in measuring cross-shelf mixing rates. The distributions of Ra isotopes were measured in transects extending perpendicular from the shoreline at Sunset Beach and Huntington Beach, CA. The average inventory at Sunset Beach was four times greater than at Huntington Beach. Building on previous research on Ra inputs and circulation in San Pedro Bay, a two-dimensional model for surface water Ra was developed to identify the importance of onshore flow and cross-shelf mixing near Huntington Beach. For the mean summertime conditions, the eddy diffusivity ( Kh) was 1.4±0.4 m 2 s -1, with 8% of the water from Sunset Beach moving down the coast. The remaining water must be low-Ra water that has moved onshore. At time scales greater than a week, the short-lived Ra inventory at Huntington Beach varied by 50%, which reflects changes in the fractions of water moving down-coast and/or in the longshore advection rate. The shoreline Ra concentration varied on time scales of hours, which may be generated by tidal changes in the Ra input at the shoreline and short-period fluctuations in the mixing rate. The low Kh observed in this study in comparison to higher values measured further offshore is evidence that Kh increases with distance offshore. When scale-dependent mixing beyond 455 m offshore is incorporated into the model, the results are consistent with the observed data for 223Ra, 224Ra, and 228Ra. Using the model, the 228Ra input flux to the summertime mixed layer was between 3.4×10 6 and 4.0×10 6 atoms s -1 (m shoreline) -1.

  18. Spatio-temporal prediction of leaf area index of rubber plantation using HJ-1A/1B CCD images and recurrent neural network

    NASA Astrophysics Data System (ADS)

    Chen, Bangqian; Wu, Zhixiang; Wang, Jikun; Dong, Jinwei; Guan, Liming; Chen, Junming; Yang, Kai; Xie, Guishui

    2015-04-01

    Rubber (Hevea brasiliensis) plantations are one of the most important economic forest in tropical area. Retrieving leaf area index (LAI) and its dynamics by remote sensing is of great significance in ecological study and production management, such as yield prediction and post-hurricane damage evaluation. Thirteen HJ-1A/1B CCD images, which possess the spatial advantage of Landsat TM/ETM+ and 2-days temporal resolution of MODIS, were introduced to predict the spatial-temporal LAI of rubber plantation on Hainan Island by Nonlinear AutoRegressive networks with eXogenous inputs (NARX) model. Monthly measured LAIs at 30 stands by LAI-2000 between 2012 and 2013 were used to explore the LAI dynamics and their relationship with spectral bands and seven vegetation indices, and to develop and validate model. The NARX model, which was built base on input variables of day of year (DOY), four spectral bands and weight difference vegetation index (WDVI), possessed good accuracies during the model building for the data set of training (N = 202, R2 = 0.98, RMSE = 0.13), validation (N = 43, R2 = 0.93, RMSE = 0.24) and testing (N = 43, R2 = 0.87, RMSE = 0.31), respectively. The model performed well during field validation (N = 24, R2 = 0.88, RMSE = 0.24) and most of its mapping results showed better agreement (R2 = 0.54-0.58, RMSE = 0.47-0.71) with the field data than the results of corresponding stepwise regression models (R2 = 0.43-0.51, RMSE = 0.52-0.82). Besides, the LAI statistical values from the spatio-temporal LAI maps and their dynamics, which increased dramatically from late March (2.36 ± 0.59) to early May (3.22 ± 0.64) and then gradually slow down until reached the maximum value in early October (4.21 ± 0.87), were quite consistent with the statistical results of the field data. The study demonstrates the feasibility and reliability of retrieving spatio-temporal LAI of rubber plantations by an artificial neural network (ANN) approach, and provides some insight on the

  19. 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.

  20. Potential of a low-cost sensor network to understand the spatial and temporal dynamics of a mountain snow cover

    NASA Astrophysics Data System (ADS)

    Pohl, Stefan; Garvelmann, Jakob; Wawerla, Jens; Weiler, Markus

    2014-03-01

    The spatial and temporal dynamics of seasonal snow covers play a critical role for many hydrological, ecological, and climatic processes. This paper presents a new, innovative approach to continuously monitor these dynamics using numerous low-cost, standalone snow monitoring stations (SnoMoS). These stations provide snow and related meteorological data with a high temporal and spatial resolution. Data collected by SnoMoS include: snow depth, surface temperature, air temperature and humidity, total precipitation, global radiation, wind speed, and barometric pressure. A total of 99 sensors were placed over the winters 2010/2011 and 2011/2012 at multiple locations within three 40-180 km2 basins in the Black Forest region of Southern Germany. The locations were chosen to cover a wide range of slopes, elevations, and expositions in a stratified sampling design. Furthermore, "paired stations" located in close proximity to each other, one in the open and one underneath various forest canopies, were set up to investigate the influence of vegetation on snow dynamics. The results showed that considerable differences in snow depth and, therefore, snow water equivalent (SWE) are present within the study area despite its moderate temperatures and medium elevation range (400-1500 m). The relative impact of topographical factors like elevation, aspect, and of different types of forest vegetation were quantified continuously and were found to change considerably over the winter period. The recorded differences in SWE and snow cover duration were large enough that they should be considered in hydrologic and climate models.

  1. Statistical modelling of particle number concentration in Zurich at high spatio-temporal resolution utilizing data from a mobile sensor network

    NASA Astrophysics Data System (ADS)

    Mueller, M. D.; Hasenfratz, David; Saukh, Olga; Fierz, Martin; Hueglin, Christoph

    2016-02-01

    Highly resolved pollution maps are a valuable resource for many issues related to air quality including exposure modelling and urban planning. We present an approach for their generation based on data from a mobile sensor network and statistical modelling. An extensive record of particle number concentrations (PNCs) spanning more than 1.5 years was compiled by the tram-based OpenSense mobile sensor network in the City of Zurich. The sensor network consists of 10 sensor nodes installed on the roof of trams operating on different services according to their regular operation schedules. We developed a statistical modelling approach based on Generalized Additive models (GAMs) utilizing the PNC data obtained along the tram tracks as well as georeferenced information as predictor variables. Our approach includes a variable selection algorithm to ensure that individual models rely on the optimal set of predictor variables. Our models have high temporal and spatial resolutions of 30 min and 10 m by 10 m, respectively, and allow the spatial prediction of PNC in the municipal area of Zurich. We applied our approach to PNC data from two dedicated time periods: July-Sept. 2013 and Dec. 2013-Feb. 2014. The models strongly rely on traffic related predictor variables (vehicle counts) and, due to the hilly topography of Zurich, on elevation. We assessed the model performance by leave-one-out cross-validation and by comparing PNC predictions to measurements at fixed reference sites and to PNC measurements obtained by pedestrians. Model predictions reproduce well the main features of the PNC field in environment types similar to those passed by individual trams. Model performance is worse at elevated background locations probably due to the weak coverage of similar spots by the tram network. We end the paper by outlining a route finding algorithm which utilizes the highly resolved PNC maps providing the exposure minimal route for cyclists.

  2. 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 wer