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  1. Qualitative reasoning for biological network inference from systematic perturbation experiments.

    Badaloni, Silvana; Di Camillo, Barbara; Sambo, Francesco

    2012-01-01

    The systematic perturbation of the components of a biological system has been proven among the most informative experimental setups for the identification of causal relations between the components. In this paper, we present Systematic Perturbation-Qualitative Reasoning (SPQR), a novel Qualitative Reasoning approach to automate the interpretation of the results of systematic perturbation experiments. Our method is based on a qualitative abstraction of the experimental data: for each perturbation experiment, measured values of the observed variables are modeled as lower, equal or higher than the measurements in the wild type condition, when no perturbation is applied. The algorithm exploits a set of IF-THEN rules to infer causal relations between the variables, analyzing the patterns of propagation of the perturbation signals through the biological network, and is specifically designed to minimize the rate of false positives among the inferred relations. Tested on both simulated and real perturbation data, SPQR indeed exhibits a significantly higher precision than the state of the art.

  2. Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network

    Kim Hyun

    2011-12-01

    Full Text Available Abstract Background Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. Results We herein introduce a framework for network modularization and Bayesian network analysis (FMB to investigate organism’s metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. Conclusions After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis.

  3. Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network.

    Kim, Hyun Uk; Kim, Tae Yong; Lee, Sang Yup

    2011-01-01

    Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism's metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis.

  4. Strings as perturbations of evolving spin networks

    Smolin, Lee

    2000-01-01

    One step in the construction of a background independent formulation of string theory is detailed, in which it is shown how perturbative strings may arise as small fluctuations around histories in a formulation of non-perturbative dynamics of spin networks due to Markopoulou. In this formulation the dynamics of spin network states and their generalizations is described in terms of histories which have discrete analogues of the causal structure and many fingered time of Lorentzian spacetimes. Perturbations of these histories turn out to be described in terms of spin systems defined on 2-dimensional timelike surfaces embedded in the discrete spacetime. When the history has a classical limit which is Minkowski spacetime, the action of the perturbation theory is given to leading order by the spacetime area of the surface, as in bosonic string theory. This map between a non-perturbative formulation of quantum gravity and a 1+1 dimensional theory generalizes to a large class of theories in which the group SU(2) i s extended to any quantum group or supergroup. It is argued that a necessary condition for the non-perturbative theory to have a good classical limit is that the resulting 1+1 dimensional theory defines a consistent and stable perturbative string theory

  5. NEXCADE: perturbation analysis for complex networks.

    Gitanjali Yadav

    Full Text Available Recent advances in network theory have led to considerable progress in our understanding of complex real world systems and their behavior in response to external threats or fluctuations. Much of this research has been invigorated by demonstration of the 'robust, yet fragile' nature of cellular and large-scale systems transcending biology, sociology, and ecology, through application of the network theory to diverse interactions observed in nature such as plant-pollinator, seed-dispersal agent and host-parasite relationships. In this work, we report the development of NEXCADE, an automated and interactive program for inducing disturbances into complex systems defined by networks, focusing on the changes in global network topology and connectivity as a function of the perturbation. NEXCADE uses a graph theoretical approach to simulate perturbations in a user-defined manner, singly, in clusters, or sequentially. To demonstrate the promise it holds for broader adoption by the research community, we provide pre-simulated examples from diverse real-world networks including eukaryotic protein-protein interaction networks, fungal biochemical networks, a variety of ecological food webs in nature as well as social networks. NEXCADE not only enables network visualization at every step of the targeted attacks, but also allows risk assessment, i.e. identification of nodes critical for the robustness of the system of interest, in order to devise and implement context-based strategies for restructuring a network, or to achieve resilience against link or node failures. Source code and license for the software, designed to work on a Linux-based operating system (OS can be downloaded at http://www.nipgr.res.in/nexcade_download.html. In addition, we have developed NEXCADE as an OS-independent online web server freely available to the scientific community without any login requirement at http://www.nipgr.res.in/nexcade.html.

  6. Delineating social network data anonymization via random edge perturbation

    Xue, Mingqiang; Karras, Panagiotis; Raï ssi, Chedy; Kalnis, Panos; Pung, Hungkeng

    2012-01-01

    study of the probability of success of any}structural attack as a function of the perturbation probability. Our analysis provides a powerful tool for delineating the identification risk of perturbed social network data; our extensive experiments

  7. Network perturbation by recurrent regulatory variants in cancer.

    Kiwon Jang

    2017-03-01

    Full Text Available Cancer driving genes have been identified as recurrently affected by variants that alter protein-coding sequences. However, a majority of cancer variants arise in noncoding regions, and some of them are thought to play a critical role through transcriptional perturbation. Here we identified putative transcriptional driver genes based on combinatorial variant recurrence in cis-regulatory regions. The identified genes showed high connectivity in the cancer type-specific transcription regulatory network, with high outdegree and many downstream genes, highlighting their causative role during tumorigenesis. In the protein interactome, the identified transcriptional drivers were not as highly connected as coding driver genes but appeared to form a network module centered on the coding drivers. The coding and regulatory variants associated via these interactions between the coding and transcriptional drivers showed exclusive and complementary occurrence patterns across tumor samples. Transcriptional cancer drivers may act through an extensive perturbation of the regulatory network and by altering protein network modules through interactions with coding driver genes.

  8. PerturbationAnalyzer: a tool for investigating the effects of concentration perturbation on protein interaction networks.

    Li, Fei; Li, Peng; Xu, Wenjian; Peng, Yuxing; Bo, Xiaochen; Wang, Shengqi

    2010-01-15

    The propagation of perturbations in protein concentration through a protein interaction network (PIN) can shed light on network dynamics and function. In order to facilitate this type of study, PerturbationAnalyzer, which is an open source plugin for Cytoscape, has been developed. PerturbationAnalyzer can be used in manual mode for simulating user-defined perturbations, as well as in batch mode for evaluating network robustness and identifying significant proteins that cause large propagation effects in the PINs when their concentrations are perturbed. Results from PerturbationAnalyzer can be represented in an intuitive and customizable way and can also be exported for further exploration. PerturbationAnalyzer has great potential in mining the design principles of protein networks, and may be a useful tool for identifying drug targets. PerturbationAnalyzer can be accessed from the Cytoscape web site http://www.cytoscape.org/plugins/index.php or http://biotech.bmi.ac.cn/PerturbationAnalyzer. Supplementary data are available at Bioinformatics online.

  9. Delineating social network data anonymization via random edge perturbation

    Xue, Mingqiang

    2012-01-01

    Social network data analysis raises concerns about the privacy of related entities or individuals. To address this issue, organizations can publish data after simply replacing the identities of individuals with pseudonyms, leaving the overall structure of the social network unchanged. However, it has been shown that attacks based on structural identification (e.g., a walk-based attack) enable an adversary to re-identify selected individuals in an anonymized network. In this paper we explore the capacity of techniques based on random edge perturbation to thwart such attacks. We theoretically establish that any kind of structural identification attack can effectively be prevented using random edge perturbation and show that, surprisingly, important properties of the whole network, as well as of subgraphs thereof, can be accurately calculated and hence data analysis tasks performed on the perturbed data, given that the legitimate data recipient knows the perturbation probability as well. Yet we also examine ways to enhance the walk-based attack, proposing a variant we call probabilistic attack. Nevertheless, we demonstrate that such probabilistic attacks can also be prevented under sufficient perturbation. Eventually, we conduct a thorough theoretical study of the probability of success of any}structural attack as a function of the perturbation probability. Our analysis provides a powerful tool for delineating the identification risk of perturbed social network data; our extensive experiments with synthetic and real datasets confirm our expectations. © 2012 ACM.

  10. Perturbation results for the Estrada index in weighted networks

    Shang Yilun, E-mail: shylmath@hotmail.com [Institute for Cyber Security, University of Texas at San Antonio, San Antonio, TX 78249 (United States)

    2011-02-18

    The logarithm of the Estrada index has been proposed recently as a spectral measure to character efficiently the robustness of complex networks. In this paper, we explore the Estrada index in weighted networks and develop various perturbation results based on spectral graph theory. It is shown that the robustness of a network may be enhanced even when some edge weights are reduced. This is of particular theoretical and practical significance to network design and optimization.

  11. Perturbation results for the Estrada index in weighted networks

    Shang Yilun

    2011-01-01

    The logarithm of the Estrada index has been proposed recently as a spectral measure to character efficiently the robustness of complex networks. In this paper, we explore the Estrada index in weighted networks and develop various perturbation results based on spectral graph theory. It is shown that the robustness of a network may be enhanced even when some edge weights are reduced. This is of particular theoretical and practical significance to network design and optimization.

  12. Diagrammatic perturbation methods in networks and sports ranking combinatorics

    Park, Juyong

    2010-01-01

    Analytic and computational tools developed in statistical physics are being increasingly applied to the study of complex networks. Here we present recent developments in the diagrammatic perturbation methods for the exponential random graph models, and apply them to the combinatoric problem of determining the ranking of nodes in directed networks that represent pairwise competitions

  13. Creation and perturbation of planar networks of chemical oscillators

    Tompkins, Nathan; Cambria, Matthew Carl; Wang, Adam L.; Heymann, Michael; Fraden, Seth

    2015-01-01

    Methods for creating custom planar networks of diffusively coupled chemical oscillators and perturbing individual oscillators within the network are presented. The oscillators consist of the Belousov-Zhabotinsky (BZ) reaction contained in an emulsion. Networks of drops of the BZ reaction are created with either Dirichlet (constant-concentration) or Neumann (no-flux) boundary conditions in a custom planar configuration using programmable illumination for the perturbations. The differences between the observed network dynamics for each boundary condition are described. Using light, we demonstrate the ability to control the initial conditions of the network and to cause individual oscillators within the network to undergo sustained period elongation or a one-time phase delay. PMID:26117136

  14. Perturbation Biology: Inferring Signaling Networks in Cellular Systems

    Miller, Martin L.; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

    2013-01-01

    We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. PMID:24367245

  15. Customer-oriented finite perturbation analysis for queueing networks

    Heidergott, B.F.

    2000-01-01

    We consider queueing networks for which the performance measureJ ( ) depends on a parameter , which can be a service time parameter or a buffer size, and we are interested in sensitivity analysis of J ( ) with respect to . We introduce a new method, called customer-oriented finite perturbation

  16. Mathematical inference and control of molecular networks from perturbation experiments

    Mohammed-Rasheed, Mohammed

    One of the main challenges facing biologists and mathematicians in the post genomic era is to understand the behavior of molecular networks and harness this understanding into an educated intervention of the cell. The cell maintains its function via an elaborate network of interconnecting positive and negative feedback loops of genes, RNA and proteins that send different signals to a large number of pathways and molecules. These structures are referred to as genetic regulatory networks (GRNs) or molecular networks. GRNs can be viewed as dynamical systems with inherent properties and mechanisms, such as steady-state equilibriums and stability, that determine the behavior of the cell. The biological relevance of the mathematical concepts are important as they may predict the differentiation of a stem cell, the maintenance of a normal cell, the development of cancer and its aberrant behavior, and the design of drugs and response to therapy. Uncovering the underlying GRN structure from gene/protein expression data, e.g., microarrays or perturbation experiments, is called inference or reverse engineering of the molecular network. Because of the high cost and time consuming nature of biological experiments, the number of available measurements or experiments is very small compared to the number of molecules (genes, RNA and proteins). In addition, the observations are noisy, where the noise is due to the measurements imperfections as well as the inherent stochasticity of genetic expression levels. Intra-cellular activities and extra-cellular environmental attributes are also another source of variability. Thus, the inference of GRNs is, in general, an under-determined problem with a highly noisy set of observations. The ultimate goal of GRN inference and analysis is to be able to intervene within the network, in order to force it away from undesirable cellular states and into desirable ones. However, it remains a major challenge to design optimal intervention strategies

  17. Perturbation analysis of complete synchronization in networks of phase oscillators.

    Tönjes, Ralf; Blasius, Bernd

    2009-08-01

    The behavior of weakly coupled self-sustained oscillators can often be well described by phase equations. Here we use the paradigm of Kuramoto phase oscillators which are coupled in a network to calculate first- and second-order corrections to the frequency of the fully synchronized state for nonidentical oscillators. The topology of the underlying coupling network is reflected in the eigenvalues and eigenvectors of the network Laplacian which influence the synchronization frequency in a particular way. They characterize the importance of nodes in a network and the relations between them. Expected values for the synchronization frequency are obtained for oscillators with quenched random frequencies on a class of scale-free random networks and for a Erdös-Rényi random network. We briefly discuss an application of the perturbation theory in the second order to network structural analysis.

  18. Recovery time after localized perturbations in complex dynamical networks

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

    2017-10-01

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

  19. Recovery time after localized perturbations in complex dynamical networks

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

    2017-01-01

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

  20. Resilience of traffic networks: From perturbation to recovery via a dynamic restricted equilibrium model

    Nogal, Maria; O'Connor, Alan; Caulfield, Brian; Martinez-Pastor, Beatriz

    2016-01-01

    When a disruptive event takes place in a traffic network some important questions arise, such as how stressed the traffic network is, whether the system is able to respond to this stressful situation, or how long the system needs to recover a new equilibrium position after suffering this perturbation. Quantifying these aspects allows the comparison of different systems, to scale the degree of damage, to identify traffic network weaknesses, and to analyse the effect of user knowledge about the traffic network state. The indicator that accounts for performance and recovery pattern under disruptive events is known as resilience. This paper presents a methodology to assess the resilience of a traffic network when a given perturbation occurs, from the beginning of the perturbation to the total system recovery. To consider the dynamic nature of the problem, a new dynamic equilibrium-restricted assignment model is presented to simulate the network performance evolution, which takes into consideration important aspects, such as the cost increment due to the perturbation, the system impedance to alter its previous state and the user stress level. Finally, this methodology is used to evaluate the resilience indices of a real network. - Highlights: • Method to assess the resilience of a traffic network suffering progressive impacts. • It simulates the dynamic response during the perturbation and system recovery. • The resilience index is based on the travel costs and the stress level of users. • It considers the capacity of adaptation of the system to the new situations. • The model evaluates redundancy, adaptability, ability to recover, etc.

  1. Approximating spectral impact of structural perturbations in large networks

    Milanese, A; Nishikawa, Takashi; Sun, Jie

    2010-01-01

    Determining the effect of structural perturbations on the eigenvalue spectra of networks is an important problem because the spectra characterize not only their topological structures, but also their dynamical behavior, such as synchronization and cascading processes on networks. Here we develop a theory for estimating the change of the largest eigenvalue of the adjacency matrix or the extreme eigenvalues of the graph Laplacian when small but arbitrary set of links are added or removed from the network. We demonstrate the effectiveness of our approximation schemes using both real and artificial networks, showing in particular that we can accurately obtain the spectral ranking of small subgraphs. We also propose a local iterative scheme which computes the relative ranking of a subgraph using only the connectivity information of its neighbors within a few links. Our results may not only contribute to our theoretical understanding of dynamical processes on networks, but also lead to practical applications in ran...

  2. Numerical approaches to model perturbation fire in turing pattern formations

    Campagna, R.; Brancaccio, M.; Cuomo, S.; Mazzoleni, S.; Russo, L.; Siettos, K.; Giannino, F.

    2017-11-01

    Turing patterns were observed in chemical, physical and biological systems described by coupled reaction-diffusion equations. Several models have been formulated proposing the water as the causal mechanism of vegetation pattern formation, but this isn't an exhaustive hypothesis in some natural environments. An alternative explanation has been related to the plant-soil negative feedback. In Marasco et al. [1] the authors explored the hypothesis that both mechanisms contribute in the formation of regular and irregular vegetation patterns. The mathematical model consists in three partial differential equations (PDEs) that take into account for a dynamic balance between biomass, water and toxic compounds. A numerical approach is mandatory also to investigate on the predictions of this kind of models. In this paper we start from the mathematical model described in [1], set the model parameters such that the biomass reaches a stable spatial pattern (spots) and present preliminary studies about the occurrence of perturbing events, such as wildfire, that can affect the regularity of the biomass configuration.

  3. Dynamical Response of Networks Under External Perturbations: Exact Results

    Chinellato, David D.; Epstein, Irving R.; Braha, Dan; Bar-Yam, Yaneer; de Aguiar, Marcus A. M.

    2015-04-01

    We give exact statistical distributions for the dynamic response of influence networks subjected to external perturbations. We consider networks whose nodes have two internal states labeled 0 and 1. We let nodes be frozen in state 0, in state 1, and the remaining nodes change by adopting the state of a connected node with a fixed probability per time step. The frozen nodes can be interpreted as external perturbations to the subnetwork of free nodes. Analytically extending and to be smaller than 1 enables modeling the case of weak coupling. We solve the dynamical equations exactly for fully connected networks, obtaining the equilibrium distribution, transition probabilities between any two states and the characteristic time to equilibration. Our exact results are excellent approximations for other topologies, including random, regular lattice, scale-free and small world networks, when the numbers of fixed nodes are adjusted to take account of the effect of topology on coupling to the environment. This model can describe a variety of complex systems, from magnetic spins to social networks to population genetics, and was recently applied as a framework for early warning signals for real-world self-organized economic market crises.

  4. Developing feasible loading patterns using perturbation theory methods

    White, J.R.; Avila, K.M.

    1990-01-01

    This work illustrates an approach to core reload design that combines the power of integer programming with the efficiency of generalized perturbation theory. The main use of the method is as a tool to help the design engineer identify feasible loading patterns with minimum time and effort. The technique is highly successful for the burnable poison (BP) loading problem, but the unpredictable behavior of the branch-and-bound algorithm degrades overall performance for large problems. Unfortunately, the combined fuel shuffling plus BP optimization problem falls into this latter classification. Overall, however, the method shows great promise for significantly reducing the manpower time required for the reload design process. And it may even give the further benefit of better designs and improved performance

  5. Controlled perturbation-induced switching in pulse-coupled oscillator networks

    Schittler Neves, Fabio; Timme, Marc

    2009-01-01

    Pulse-coupled systems such as spiking neural networks exhibit nontrivial invariant sets in the form of attracting yet unstable saddle periodic orbits where units are synchronized into groups. Heteroclinic connections between such orbits may in principle support switching processes in these networks and enable novel kinds of neural computations. For small networks of coupled oscillators, we here investigate under which conditions and how system symmetry enforces or forbids certain switching transitions that may be induced by perturbations. For networks of five oscillators, we derive explicit transition rules that for two cluster symmetries deviate from those known from oscillators coupled continuously in time. A third symmetry yields heteroclinic networks that consist of sets of all unstable attractors with that symmetry and the connections between them. Our results indicate that pulse-coupled systems can reliably generate well-defined sets of complex spatiotemporal patterns that conform to specific transition rules. We briefly discuss possible implications for computation with spiking neural systems.

  6. Controlled perturbation-induced switching in pulse-coupled oscillator networks

    Schittler Neves, Fabio; Timme, Marc [Network Dynamics Group, Max Planck Institute for Dynamics and Self-Organization, Goettingen, D-37073 (Germany); Bernstein Center for Computational Neuroscience (BCCN), Goettingen (Germany)], E-mail: neves@nld.ds.mpg.de, E-mail: timme@nld.ds.mpg.de

    2009-08-28

    Pulse-coupled systems such as spiking neural networks exhibit nontrivial invariant sets in the form of attracting yet unstable saddle periodic orbits where units are synchronized into groups. Heteroclinic connections between such orbits may in principle support switching processes in these networks and enable novel kinds of neural computations. For small networks of coupled oscillators, we here investigate under which conditions and how system symmetry enforces or forbids certain switching transitions that may be induced by perturbations. For networks of five oscillators, we derive explicit transition rules that for two cluster symmetries deviate from those known from oscillators coupled continuously in time. A third symmetry yields heteroclinic networks that consist of sets of all unstable attractors with that symmetry and the connections between them. Our results indicate that pulse-coupled systems can reliably generate well-defined sets of complex spatiotemporal patterns that conform to specific transition rules. We briefly discuss possible implications for computation with spiking neural systems.

  7. Embarked electrical network robust control based on singular perturbation model.

    Abdeljalil Belhaj, Lamya; Ait-Ahmed, Mourad; Benkhoris, Mohamed Fouad

    2014-07-01

    This paper deals with an approach of modelling in view of control for embarked networks which can be described as strongly coupled multi-sources, multi-loads systems with nonlinear and badly known characteristics. This model has to be representative of the system behaviour and easy to handle for easy regulators synthesis. As a first step, each alternator is modelled and linearized around an operating point and then it is subdivided into two lower order systems according to the singular perturbation theory. RST regulators are designed for each subsystem and tested by means of a software test-bench which allows predicting network behaviour in both steady and transient states. Finally, the designed controllers are implanted on an experimental benchmark constituted by two alternators supplying loads in order to test the dynamic performances in realistic conditions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  8. Singular Perturbation Analysis and Gene Regulatory Networks with Delay

    Shlykova, Irina; Ponosov, Arcady

    2009-09-01

    There are different ways of how to model gene regulatory networks. Differential equations allow for a detailed description of the network's dynamics and provide an explicit model of the gene concentration changes over time. Production and relative degradation rate functions used in such models depend on the vector of steeply sloped threshold functions which characterize the activity of genes. The most popular example of the threshold functions comes from the Boolean network approach, where the threshold functions are given by step functions. The system of differential equations becomes then piecewise linear. The dynamics of this system can be described very easily between the thresholds, but not in the switching domains. For instance this approach fails to analyze stationary points of the system and to define continuous solutions in the switching domains. These problems were studied in [2], [3], but the proposed model did not take into account a time delay in cellular systems. However, analysis of real gene expression data shows a considerable number of time-delayed interactions suggesting that time delay is essential in gene regulation. Therefore, delays may have a great effect on the dynamics of the system presenting one of the critical factors that should be considered in reconstruction of gene regulatory networks. The goal of this work is to apply the singular perturbation analysis to certain systems with delay and to obtain an analog of Tikhonov's theorem, which provides sufficient conditions for constracting the limit system in the delay case.

  9. Capturing the Flatness of a peer-to-peer lending network through random and selected perturbations

    Karampourniotis, Panagiotis D.; Singh, Pramesh; Uparna, Jayaram; Horvat, Emoke-Agnes; Szymanski, Boleslaw K.; Korniss, Gyorgy; Bakdash, Jonathan Z.; Uzzi, Brian

    Null models are established tools that have been used in network analysis to uncover various structural patterns. They quantify the deviance of an observed network measure to that given by the null model. We construct a null model for weighted, directed networks to identify biased links (carrying significantly different weights than expected according to the null model) and thus quantify the flatness of the system. Using this model, we study the flatness of Kiva, a large international crownfinancing network of borrowers and lenders, aggregated to the country level. The dataset spans the years from 2006 to 2013. Our longitudinal analysis shows that flatness of the system is reducing over time, meaning the proportion of biased inter-country links is growing. We extend our analysis by testing the robustness of the flatness of the network in perturbations on the links' weights or the nodes themselves. Examples of such perturbations are event shocks (e.g. erecting walls) or regulatory shocks (e.g. Brexit). We find that flatness is unaffected by random shocks, but changes after shocks target links with a large weight or bias. The methods we use to capture the flatness are based on analytics, simulations, and numerical computations using Shannon's maximum entropy. Supported by ARL NS-CTA.

  10. Learning gene networks under SNP perturbations using eQTL datasets.

    Lingxue Zhang

    2014-02-01

    Full Text Available The standard approach for identifying gene networks is based on experimental perturbations of gene regulatory systems such as gene knock-out experiments, followed by a genome-wide profiling of differential gene expressions. However, this approach is significantly limited in that it is not possible to perturb more than one or two genes simultaneously to discover complex gene interactions or to distinguish between direct and indirect downstream regulations of the differentially-expressed genes. As an alternative, genetical genomics study has been proposed to treat naturally-occurring genetic variants as potential perturbants of gene regulatory system and to recover gene networks via analysis of population gene-expression and genotype data. Despite many advantages of genetical genomics data analysis, the computational challenge that the effects of multifactorial genetic perturbations should be decoded simultaneously from data has prevented a widespread application of genetical genomics analysis. In this article, we propose a statistical framework for learning gene networks that overcomes the limitations of experimental perturbation methods and addresses the challenges of genetical genomics analysis. We introduce a new statistical model, called a sparse conditional Gaussian graphical model, and describe an efficient learning algorithm that simultaneously decodes the perturbations of gene regulatory system by a large number of SNPs to identify a gene network along with expression quantitative trait loci (eQTLs that perturb this network. While our statistical model captures direct genetic perturbations of gene network, by performing inference on the probabilistic graphical model, we obtain detailed characterizations of how the direct SNP perturbation effects propagate through the gene network to perturb other genes indirectly. We demonstrate our statistical method using HapMap-simulated and yeast eQTL datasets. In particular, the yeast gene network

  11. Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks

    Martin Florian

    2012-05-01

    Full Text Available Abstract Background High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus. Results Four complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE cells treated with the pro-inflammatory signaling mediator TNFα, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFα-induced perturbation for each network model when compared against NF-κB nuclear localization and cell number. In addition, the degree and specificity to which CDK

  12. A network engineering perspective on probing and perturbing cognition with neurofeedback.

    Bassett, Danielle S; Khambhati, Ankit N

    2017-05-01

    Network science and engineering provide a flexible and generalizable tool set to describe and manipulate complex systems characterized by heterogeneous interaction patterns among component parts. While classically applied to social systems, these tools have recently proven to be particularly useful in the study of the brain. In this review, we describe the nascent use of these tools to understand human cognition, and we discuss their utility in informing the meaningful and predictable perturbation of cognition in combination with the emerging capabilities of neurofeedback. To blend these disparate strands of research, we build on emerging conceptualizations of how the brain functions (as a complex network) and how we can develop and target interventions or modulations (as a form of network control). We close with an outline of current frontiers that bridge neurofeedback, connectomics, and network control theory to better understand human cognition. © 2017 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals Inc. on behalf of The New York Academy of Sciences.

  13. Network periodic solutions: patterns of phase-shift synchrony

    Golubitsky, Martin; Wang, Yunjiao; Romano, David

    2012-01-01

    We prove the rigid phase conjecture of Stewart and Parker. It then follows from previous results (of Stewart and Parker and our own) that rigid phase-shifts in periodic solutions on a transitive network are produced by a cyclic symmetry on a quotient network. More precisely, let X(t) = (x 1 (t), ..., x n (t)) be a hyperbolic T-periodic solution of an admissible system on an n-node network. Two nodes c and d are phase-related if there exists a phase-shift θ cd in [0, 1) such that x d (t) = x c (t + θ cd T). The conjecture states that if phase relations persist under all small admissible perturbations (that is, the phase relations are rigid), then for each pair of phase-related cells, their input signals are also phase-related to the same phase-shift. For a transitive network, rigid phase relations can also be described abstractly as a Z m permutation symmetry of a quotient network. We discuss how patterns of phase-shift synchrony lead to rigid synchrony, rigid phase synchrony, and rigid multirhythms, and we show that for each phase pattern there exists an admissible system with a periodic solution with that phase pattern. Finally, we generalize the results to nontransitive networks where we show that the symmetry that generates rigid phase-shifts occurs on an extension of a quotient network

  14. Development of a VVER-1000 core loading pattern optimization program based on perturbation theory

    Hosseini, Mohammad; Vosoughi, Naser

    2012-01-01

    Highlights: ► We use perturbation theory to find an optimum fuel loading pattern in a VVER-1000. ► We provide a software for in-core fuel management optimization. ► We consider two objectives for our method (perturbation theory). ► We show that perturbation theory method is very fast and accurate for optimization. - Abstract: In-core nuclear fuel management is one of the most important concerns in the design of nuclear reactors. Two main goals in core fuel loading pattern design optimization are maximizing the core effective multiplication factor in order to extract the maximum energy, and keeping the local power peaking factor lower than a predetermined value to maintain the fuel integrity. Because of the numerous possible patterns of fuel assemblies in the reactor core, finding the best configuration is so important and challenging. Different techniques for optimization of fuel loading pattern in the reactor core have been introduced by now. In this study, a software is programmed in C language to find an order of the fuel loading pattern of a VVER-1000 reactor core using the perturbation theory. Our optimization method is based on minimizing the radial power peaking factor. The optimization process launches by considering an initial loading pattern and the specifications of the fuel assemblies which are given as the input of the software. The results on a typical VVER-1000 reactor reveal that the method could reach to a pattern with an allowed radial power peaking factor and increases the cycle length 1.1 days, as well.

  15. MEMBRAIN NEURAL NETWORK FOR VISUAL PATTERN RECOGNITION

    Artur Popko

    2013-06-01

    Full Text Available Recognition of visual patterns is one of significant applications of Artificial Neural Networks, which partially emulate human thinking in the domain of artificial intelligence. In the paper, a simplified neural approach to recognition of visual patterns is portrayed and discussed. This paper is dedicated for investigators in visual patterns recognition, Artificial Neural Networking and related disciplines. The document describes also MemBrain application environment as a powerful and easy to use neural networks’ editor and simulator supporting ANN.

  16. Network and external perturbation induce burst synchronisation in cat cerebral cortex

    Lameu, Ewandson L.; Borges, Fernando S.; Borges, Rafael R.; Batista, Antonio M.; Baptista, Murilo S.; Viana, Ricardo L.

    2016-05-01

    The brain of mammals are divided into different cortical areas that are anatomically connected forming larger networks which perform cognitive tasks. The cat cerebral cortex is composed of 65 areas organised into the visual, auditory, somatosensory-motor and frontolimbic cognitive regions. We have built a network of networks, in which networks are connected among themselves according to the connections observed in the cat cortical areas aiming to study how inputs drive the synchronous behaviour in this cat brain-like network. We show that without external perturbations it is possible to observe high level of bursting synchronisation between neurons within almost all areas, except for the auditory area. Bursting synchronisation appears between neurons in the auditory region when an external perturbation is applied in another cognitive area. This is a clear evidence that burst synchronisation and collective behaviour in the brain might be a process mediated by other brain areas under stimulation.

  17. Gradient Learning in Spiking Neural Networks by Dynamic Perturbation of Conductances

    Fiete, Ila R.; Seung, H. Sebastian

    2006-01-01

    We present a method of estimating the gradient of an objective function with respect to the synaptic weights of a spiking neural network. The method works by measuring the fluctuations in the objective function in response to dynamic perturbation of the membrane conductances of the neurons. It is compatible with recurrent networks of conductance-based model neurons with dynamic synapses. The method can be interpreted as a biologically plausible synaptic learning rule, if the dynamic perturbations are generated by a special class of 'empiric' synapses driven by random spike trains from an external source

  18. Generalized perturbation theory error control within PWR core-loading pattern optimization

    Imbriani, J.S.; Turinsky, P.J.; Kropaczek, D.J.

    1995-01-01

    The fuel management optimization code FORMOSA-P has been developed to determine the family of near-optimum loading patterns for PWR reactors. The code couples the optimization technique of simulated annealing (SA) with a generalized perturbation theory (GPT) model for evaluating core physics characteristics. To ensure the accuracy of the GPT predictions, as well as to maximize the efficient of the SA search, a GPT error control method has been developed

  19. Anti-synchronization control of BAM memristive neural networks with multiple proportional delays and stochastic perturbations

    Wang, Weiping; Yuan, Manman; Luo, Xiong; Liu, Linlin; Zhang, Yao

    2018-01-01

    Proportional delay is a class of unbounded time-varying delay. A class of bidirectional associative memory (BAM) memristive neural networks with multiple proportional delays is concerned in this paper. First, we propose the model of BAM memristive neural networks with multiple proportional delays and stochastic perturbations. Furthermore, by choosing suitable nonlinear variable transformations, the BAM memristive neural networks with multiple proportional delays can be transformed into the BAM memristive neural networks with constant delays. Based on the drive-response system concept, differential inclusions theory and Lyapunov stability theory, some anti-synchronization criteria are obtained. Finally, the effectiveness of proposed criteria are demonstrated through numerical examples.

  20. Joint queue-perturbed and weakly-coupled power control for wireless backbone networks

    Olwal, TO

    2012-09-01

    Full Text Available perturbation and weakly-coupled based power control approach for the WBNs. The ultimate objectives are to increase energy-efficiency and the overal network capacity. In order to achieve these objectives, a Markov chain model is first presented to describe...

  1. How pathogens use linear motifs to perturb host cell networks

    Via, Allegra; Uyar, Bora; Brun, Christine; Zanzoni, Andreas

    2015-01-01

    Molecular mimicry is one of the powerful stratagems that pathogens employ to colonise their hosts and take advantage of host cell functions to guarantee their replication and dissemination. In particular, several viruses have evolved the ability to interact with host cell components through protein short linear motifs (SLiMs) that mimic host SLiMs, thus facilitating their internalisation and the manipulation of a wide range of cellular networks. Here we present convincing evidence from the literature that motif mimicry also represents an effective, widespread hijacking strategy in prokaryotic and eukaryotic parasites. Further insights into host motif mimicry would be of great help in the elucidation of the molecular mechanisms behind host cell invasion and the development of anti-infective therapeutic strategies.

  2. Fingering patterns in magnetic fluids: Perturbative solutions and the stability of exact stationary shapes

    Anjos, Pedro H. A.; Lira, Sérgio A.; Miranda, José A.

    2018-04-01

    We examine the formation of interfacial patterns when a magnetic liquid droplet (ferrofluid, or a magnetorheological fluid), surrounded by a nonmagnetic fluid, is subjected to a radial magnetic field in a Hele-Shaw cell. By using a vortex-sheet formalism, we find exact stationary solutions for the fluid-fluid interface in the form of n -fold polygonal shapes. A weakly nonlinear, mode-coupling method is then utilized to find time-evolving perturbative solutions for the interfacial patterns. The stability of such nonzero surface tension exact solutions is checked and discussed, by trying to systematically approach the exact stationary shapes through perturbative solutions containing an increasingly larger number of participating Fourier modes. Our results indicate that the exact stationary solutions of the problem are stable, and that a good matching between exact and perturbative shape solutions is achieved just by using a few Fourier modes. The stability of such solutions is substantiated by a linearization process close to the stationary shape, where a system of mode-coupling equations is diagonalized, determining the eigenvalues which dictate the stability of a fixed point.

  3. Selection of doublet cellular patterns in directional solidification through spatially periodic perturbations

    Losert, W.; Stillman, D.A.; Cummins, H.Z.; Kopczynski, P.; Rappel, W.; Karma, A.

    1998-01-01

    Pattern formation at the solid-liquid interface of a growing crystal was studied in directional solidification using a perturbation technique. We analyzed both experimentally and numerically the stability range and dynamical selection of cellular arrays of 'doublets' with asymmetric tip shapes, separated by alternate deep and shallow grooves. Applying an initial periodic perturbation of arbitrary wavelength to the unstable planar interface allowed us to force the interface to evolve into doublet states that would not otherwise be dynamically accessible from a planar interface. We determined systematically the ranges of wavelength corresponding to stable singlets, stable doublets, and transient unstable patterns. Experimentally, this was accomplished by applying a brief UV light pulse of a desired spatial periodicity to the planar interface during the planar-cellular transient using the model alloy Succinonitrile-Coumarin 152. Numerical simulations of the nonlinear evolution of the interface were performed starting from a small sinusoidal perturbation of the steady-state planar interface. These simulations were carried out using a computationally efficient phase-field symmetric model of directional solidification with recently reformulated asymptotics and vanishing kinetics [A. Karma and W.-J. Rappel, Phys. Rev. E 53 R3017 (1996); Phys. Rev. Lett. 77, 4050 (1996); Phys. Rev. E 57, 4323 (1998)], which allowed us to simulate spatially extended arrays that can be meaningfully compared to experiments. Simulations and experiments show remarkable qualitative agreement in the dynamic evolution, steady-state structure, and instability mechanisms of doublet cellular arrays. copyright 1998 The American Physical Society

  4. Counterpart synchronization of duplex networks with delayed nodes and noise perturbation

    Wei, Xiang; Wu, Xiaoqun; Lu, Jun-an; Zhao, Junchan

    2015-01-01

    In the real world, many complex systems are represented not by single networks but rather by sets of interdependent ones. In these specific networks, nodes in one network mutually interact with nodes in other networks. This paper focuses on a simple representative case of two-layer networks (the so-called duplex networks) with unidirectional inter-layer couplings. That is, each node in one network depends on a counterpart in the other network. Accordingly, the former network is called the response layer and the latter network is the drive layer. Specifically, synchronization between each node in the drive layer and its counterpart in the response layer (counterpart synchronization (CS)) in these kinds of duplex networks with delayed nodes and noise perturbation is investigated. Based on the LaSalle-type invariance principle, a control technique is proposed and a sufficient condition is developed for realizing CS of duplex networks. Furthermore, two corollaries are derived as special cases. In addition, node dynamics within each layer can be varied and topologies of the two layers are not necessarily identical. Therefore, the proposed synchronization method can be applied to a wide range of multiplex networks. Numerical examples are provided to illustrate the feasibility and effectiveness of the results. (paper)

  5. Modeling urbanization patterns with generative adversarial networks

    Albert, Adrian; Strano, Emanuele; Kaur, Jasleen; Gonzalez, Marta

    2018-01-01

    In this study we propose a new method to simulate hyper-realistic urban patterns using Generative Adversarial Networks trained with a global urban land-use inventory. We generated a synthetic urban "universe" that qualitatively reproduces the complex spatial organization observed in global urban patterns, while being able to quantitatively recover certain key high-level urban spatial metrics.

  6. Fabrication of microstamps and patterned cell network

    Seong, Nak Seon; Pak, James Jung Ho; Choi, Ju Hee; Ahn, Dong June; Hwang, Seong Min; Lee, Kyung J.

    2002-01-01

    Elastomeric stamps with micrometer-sized grids are fabricated for building biological cell networks by design. Polymerized polydimethyl-siloxane (PDMS) stamps are cast in a variety of different molds prepared by micro-electro mechanical systems (MEMS) technology. Micro square-grid patterns of 3-aminopropyl triethoxy silane (APS) are successfully imprinted on glass plates, and patterned networks of cardiac cells are obtained as designed. The resulting cellular networks clearly demonstrate that cell attachment and growth are greatly favored on APS-treated thin tracks. Here, we report the technical details related to the fabrication of microstamps, to the stamping procedure, and to the culture method. The potential applications of patterned cellular networks are also discussed

  7. 2D heat flux pattern in ASDEX upgrade L-mode with magnetic perturbation

    Faitsch, Michael; Sieglin, Bernhard; Eich, Thomas; Herrmann, Albrecht; Suttrop, Wolfgang [Max-Planck-Institute for Plasma Physics, Boltzmannstr. 2, D-85748 Garching (Germany); Collaboration: the ASDEDX Upgrade Team

    2016-07-01

    A future fusion reactor is likely to operate in high confinement mode (H-mode). This mode is associated with a periodic instability at the plasma edge that expels particles and energy. This instability is called edge localized mode (ELM). External magnetic perturbation (MP) is one technique that is thought to be able to mitigate or even suppress large ELMs in next step fusion devices such as ITER, where the ELM induced heat load for unmitigated ELMs might limit the lifetime of the divertor. Applying an external magnetic perturbation breaks the axisymmetry and leads to a 2D steady state heat flux pattern at the divertor. The ASDEX Upgrade tokamak is equipped with 16 perturbation coils, 8 above (upper row) and 8 below (lower row) the outer mid plane, toroidal equally distributed. A high resolution infra red system is measuring the heat flux at the outer target at a fixed toroidal position with a resolution of around 0.6 mm. In order to measure the 2D structure a slow rotation of the MP field was applied (1 Hz) with a toroidal mode number n=2. The differential phase between the upper and lower row was changed to investigate the effect of the alignment with the field lines at the edge. The density was varied to study the density dependence of the heat transport with applied external MP and compare it to the axisymmetric scenario.

  8. Gene expression network reconstruction by convex feature selection when incorporating genetic perturbations.

    Benjamin A Logsdon

    Full Text Available Cellular gene expression measurements contain regulatory information that can be used to discover novel network relationships. Here, we present a new algorithm for network reconstruction powered by the adaptive lasso, a theoretically and empirically well-behaved method for selecting the regulatory features of a network. Any algorithms designed for network discovery that make use of directed probabilistic graphs require perturbations, produced by either experiments or naturally occurring genetic variation, to successfully infer unique regulatory relationships from gene expression data. Our approach makes use of appropriately selected cis-expression Quantitative Trait Loci (cis-eQTL, which provide a sufficient set of independent perturbations for maximum network resolution. We compare the performance of our network reconstruction algorithm to four other approaches: the PC-algorithm, QTLnet, the QDG algorithm, and the NEO algorithm, all of which have been used to reconstruct directed networks among phenotypes leveraging QTL. We show that the adaptive lasso can outperform these algorithms for networks of ten genes and ten cis-eQTL, and is competitive with the QDG algorithm for networks with thirty genes and thirty cis-eQTL, with rich topologies and hundreds of samples. Using this novel approach, we identify unique sets of directed relationships in Saccharomyces cerevisiae when analyzing genome-wide gene expression data for an intercross between a wild strain and a lab strain. We recover novel putative network relationships between a tyrosine biosynthesis gene (TYR1, and genes involved in endocytosis (RCY1, the spindle checkpoint (BUB2, sulfonate catabolism (JLP1, and cell-cell communication (PRM7. Our algorithm provides a synthesis of feature selection methods and graphical model theory that has the potential to reveal new directed regulatory relationships from the analysis of population level genetic and gene expression data.

  9. Perturbed cooperative-state feedback strategy for model predictive networked control of interconnected systems.

    Tran, Tri; Ha, Q P

    2018-01-01

    A perturbed cooperative-state feedback (PSF) strategy is presented for the control of interconnected systems in this paper. The subsystems of an interconnected system can exchange data via the communication network that has multiple connection topologies. The PSF strategy can resolve both issues, the sensor data losses and the communication network breaks, thanks to the two components of the control including a cooperative-state feedback and a perturbation variable, e.g., u i =K ij x j +w i . The PSF is implemented in a decentralized model predictive control scheme with a stability constraint and a non-monotonic storage function (ΔV(x(k))≥0), derived from the dissipative systems theory. Numerical simulation for the automatic generation control problem in power systems is studied to illustrate the effectiveness of the presented PSF strategy. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  10. Optimal Detection of a Localized Perturbation in Random Networks of Integrate-and-Fire Neurons

    Bernardi, Davide; Lindner, Benjamin

    2017-06-01

    Experimental and theoretical studies suggest that cortical networks are chaotic and coding relies on averages over large populations. However, there is evidence that rats can respond to the short stimulation of a single cortical cell, a theoretically unexplained fact. We study effects of single-cell stimulation on a large recurrent network of integrate-and-fire neurons and propose a simple way to detect the perturbation. Detection rates obtained from simulations and analytical estimates are similar to experimental response rates if the readout is slightly biased towards specific neurons. Near-optimal detection is attained for a broad range of intermediate values of the mean coupling between neurons.

  11. Dynamics of the cell-cycle network under genome-rewiring perturbations

    Katzir, Yair; Elhanati, Yuval; Braun, Erez; Averbukh, Inna

    2013-01-01

    The cell-cycle progression is regulated by a specific network enabling its ordered dynamics. Recent experiments supported by computational models have shown that a core of genes ensures this robust cycle dynamics. However, much less is known about the direct interaction of the cell-cycle regulators with genes outside of the cell-cycle network, in particular those of the metabolic system. Following our recent experimental work, we present here a model focusing on the dynamics of the cell-cycle core network under rewiring perturbations. Rewiring is achieved by placing an essential metabolic gene exclusively under the regulation of a cell-cycle's promoter, forcing the cell-cycle network to function under a multitasking challenging condition; operating in parallel the cell-cycle progression and a metabolic essential gene. Our model relies on simple rate equations that capture the dynamics of the relevant protein–DNA and protein–protein interactions, while making a clear distinction between these two different types of processes. In particular, we treat the cell-cycle transcription factors as limited ‘resources’ and focus on the redistribution of resources in the network during its dynamics. This elucidates the sensitivity of its various nodes to rewiring interactions. The basic model produces the correct cycle dynamics for a wide range of parameters. The simplicity of the model enables us to study the interface between the cell-cycle regulation and other cellular processes. Rewiring a promoter of the network to regulate a foreign gene, forces a multitasking regulatory load. The higher the load on the promoter, the longer is the cell-cycle period. Moreover, in agreement with our experimental results, the model shows that different nodes of the network exhibit variable susceptibilities to the rewiring perturbations. Our model suggests that the topology of the cell-cycle core network ensures its plasticity and flexible interface with other cellular processes

  12. Network approach to patterns in stratocumulus clouds

    Glassmeier, Franziska; Feingold, Graham

    2017-10-01

    Stratocumulus clouds (Sc) have a significant impact on the amount of sunlight reflected back to space, with important implications for Earth’s climate. Representing Sc and their radiative impact is one of the largest challenges for global climate models. Sc fields self-organize into cellular patterns and thus lend themselves to analysis and quantification in terms of natural cellular networks. Based on large-eddy simulations of Sc fields, we present a first analysis of the geometric structure and self-organization of Sc patterns from this network perspective. Our network analysis shows that the Sc pattern is scale-invariant as a consequence of entropy maximization that is known as Lewis’s Law (scaling parameter: 0.16) and is largely independent of the Sc regime (cloud-free vs. cloudy cell centers). Cells are, on average, hexagonal with a neighbor number variance of about 2, and larger cells tend to be surrounded by smaller cells, as described by an Aboav-Weaire parameter of 0.9. The network structure is neither completely random nor characteristic of natural convection. Instead, it emerges from Sc-specific versions of cell division and cell merging that are shaped by cell expansion. This is shown with a heuristic model of network dynamics that incorporates our physical understanding of cloud processes.

  13. Granular neural networks, pattern recognition and bioinformatics

    Pal, Sankar K; Ganivada, Avatharam

    2017-01-01

    This book provides a uniform framework describing how fuzzy rough granular neural network technologies can be formulated and used in building efficient pattern recognition and mining models. It also discusses the formation of granules in the notion of both fuzzy and rough sets. Judicious integration in forming fuzzy-rough information granules based on lower approximate regions enables the network to determine the exactness in class shape as well as to handle the uncertainties arising from overlapping regions, resulting in efficient and speedy learning with enhanced performance. Layered network and self-organizing analysis maps, which have a strong potential in big data, are considered as basic modules,. The book is structured according to the major phases of a pattern recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory, algorithm, and application. It covers the latest findings as well as directions for future research, particularly highlighting bioinf...

  14. Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data.

    Narimani, Zahra; Beigy, Hamid; Ahmad, Ashar; Masoudi-Nejad, Ali; Fröhlich, Holger

    2017-01-01

    Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods.

  15. Peeking Network States with Clustered Patterns

    Kim, Jinoh [Texas A & M Univ., Commerce, TX (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2015-10-20

    Network traffic monitoring has long been a core element for effec- tive network management and security. However, it is still a chal- lenging task with a high degree of complexity for comprehensive analysis when considering multiple variables and ever-increasing traffic volumes to monitor. For example, one of the widely con- sidered approaches is to scrutinize probabilistic distributions, but it poses a scalability concern and multivariate analysis is not gen- erally supported due to the exponential increase of the complexity. In this work, we propose a novel method for network traffic moni- toring based on clustering, one of the powerful deep-learning tech- niques. We show that the new approach enables us to recognize clustered results as patterns representing the network states, which can then be utilized to evaluate “similarity” of network states over time. In addition, we define a new quantitative measure for the similarity between two compared network states observed in dif- ferent time windows, as a supportive means for intuitive analysis. Finally, we demonstrate the clustering-based network monitoring with public traffic traces, and show that the proposed approach us- ing the clustering method has a great opportunity for feasible, cost- effective network monitoring.

  16. Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations.

    Xiaodong Cai

    Full Text Available Integrating genetic perturbations with gene expression data not only improves accuracy of regulatory network topology inference, but also enables learning of causal regulatory relations between genes. Although a number of methods have been developed to integrate both types of data, the desiderata of efficient and powerful algorithms still remains. In this paper, sparse structural equation models (SEMs are employed to integrate both gene expression data and cis-expression quantitative trait loci (cis-eQTL, for modeling gene regulatory networks in accordance with biological evidence about genes regulating or being regulated by a small number of genes. A systematic inference method named sparsity-aware maximum likelihood (SML is developed for SEM estimation. Using simulated directed acyclic or cyclic networks, the SML performance is compared with that of two state-of-the-art algorithms: the adaptive Lasso (AL based scheme, and the QTL-directed dependency graph (QDG method. Computer simulations demonstrate that the novel SML algorithm offers significantly better performance than the AL-based and QDG algorithms across all sample sizes from 100 to 1,000, in terms of detection power and false discovery rate, in all the cases tested that include acyclic or cyclic networks of 10, 30 and 300 genes. The SML method is further applied to infer a network of 39 human genes that are related to the immune function and are chosen to have a reliable eQTL per gene. The resulting network consists of 9 genes and 13 edges. Most of the edges represent interactions reasonably expected from experimental evidence, while the remaining may just indicate the emergence of new interactions. The sparse SEM and efficient SML algorithm provide an effective means of exploiting both gene expression and perturbation data to infer gene regulatory networks. An open-source computer program implementing the SML algorithm is freely available upon request.

  17. Discovering Preferential Patterns in Sectoral Trade Networks.

    Cingolani, Isabella; Piccardi, Carlo; Tajoli, Lucia

    2015-01-01

    We analyze the patterns of import/export bilateral relations, with the aim of assessing the relevance and shape of "preferentiality" in countries' trade decisions. Preferentiality here is defined as the tendency to concentrate trade on one or few partners. With this purpose, we adopt a systemic approach through the use of the tools of complex network analysis. In particular, we apply a pattern detection approach based on community and pseudocommunity analysis, in order to highlight the groups of countries within which most of members' trade occur. The method is applied to two intra-industry trade networks consisting of 221 countries, relative to the low-tech "Textiles and Textile Articles" and the high-tech "Electronics" sectors for the year 2006, to look at the structure of world trade before the start of the international financial crisis. It turns out that the two networks display some similarities and some differences in preferential trade patterns: they both include few significant communities that define narrow sets of countries trading with each other as preferential destinations markets or supply sources, and they are characterized by the presence of similar hierarchical structures, led by the largest economies. But there are also distinctive features due to the characteristics of the industries examined, in which the organization of production and the destination markets are different. Overall, the extent of preferentiality and partner selection at the sector level confirm the relevance of international trade costs still today, inducing countries to seek the highest efficiency in their trade patterns.

  18. DETECTION OF TOPOLOGICAL PATTERNS IN PROTEIN NETWORKS.

    MASLOV,S.SNEPPEN,K.

    2003-11-17

    interesting property of many biological networks that was recently brought to attention of the scientific community [3, 4, 5] is an extremely broad distribution of node connectivities defined as the number of immediate neighbors of a given node in the network. While the majority of nodes have just a few edges connecting them to other nodes in the network, there exist some nodes, that we will refer to as ''hubs'', with an unusually large number of neighbors. The connectivity of the most connected hub in such a network is typically several orders of magnitude larger than the average connectivity in the network. Often the distribution of connectivities of individual nodes can be approximated by a scale-free power law form [3] in which case the network is referred to as scale-free. Among biological networks distributions of node connectivities in metabolic [4], protein interaction [5], and brain functional [6] networks can be reasonably approximated by a power law extending for several orders of magnitude. The set of connectivities of individual nodes is an example of a low-level (single-node) topological property of a network. While it answers the question about how many neighbors a given node has, it gives no information about the identity of those neighbors. It is clear that most functional properties of networks are defined at a higher topological level in the exact pattern of connections of nodes to each other. However, such multi-node connectivity patterns are rather difficult to quantify and compare between networks. In this work we concentrate on multi-node topological properties of protein networks. These networks (as any other biological networks) lack the top-down design. Instead, selective forces of biological evolution shape them from raw material provided by random events such as mutations within individual genes, and gene duplications. As a result their connections are characterized by a large degree of randomness. One may wonder which

  19. SYNCHRONIZATION OF HETEROGENEOUS OSCILLATORS UNDER NETWORK MODIFICATIONS: PERTURBATION AND OPTIMIZATION OF THE SYNCHRONY ALIGNMENT FUNCTION

    Taylor, Dane; Skardal, Per Sebastian; Sun, Jie

    2016-01-01

    Synchronization is central to many complex systems in engineering physics (e.g., the power-grid, Josephson junction circuits, and electro-chemical oscillators) and biology (e.g., neuronal, circadian, and cardiac rhythms). Despite these widespread applications—for which proper functionality depends sensitively on the extent of synchronization—there remains a lack of understanding for how systems can best evolve and adapt to enhance or inhibit synchronization. We study how network modifications affect the synchronization properties of network-coupled dynamical systems that have heterogeneous node dynamics (e.g., phase oscillators with non-identical frequencies), which is often the case for real-world systems. Our approach relies on a synchrony alignment function (SAF) that quantifies the interplay between heterogeneity of the network and of the oscillators and provides an objective measure for a system’s ability to synchronize. We conduct a spectral perturbation analysis of the SAF for structural network modifications including the addition and removal of edges, which subsequently ranks the edges according to their importance to synchronization. Based on this analysis, we develop gradient-descent algorithms to efficiently solve optimization problems that aim to maximize phase synchronization via network modifications. We support these and other results with numerical experiments. PMID:27872501

  20. Reconstruction of cellular signal transduction networks using perturbation assays and linear programming.

    Knapp, Bettina; Kaderali, Lars

    2013-01-01

    Perturbation experiments for example using RNA interference (RNAi) offer an attractive way to elucidate gene function in a high throughput fashion. The placement of hit genes in their functional context and the inference of underlying networks from such data, however, are challenging tasks. One of the problems in network inference is the exponential number of possible network topologies for a given number of genes. Here, we introduce a novel mathematical approach to address this question. We formulate network inference as a linear optimization problem, which can be solved efficiently even for large-scale systems. We use simulated data to evaluate our approach, and show improved performance in particular on larger networks over state-of-the art methods. We achieve increased sensitivity and specificity, as well as a significant reduction in computing time. Furthermore, we show superior performance on noisy data. We then apply our approach to study the intracellular signaling of human primary nave CD4(+) T-cells, as well as ErbB signaling in trastuzumab resistant breast cancer cells. In both cases, our approach recovers known interactions and points to additional relevant processes. In ErbB signaling, our results predict an important role of negative and positive feedback in controlling the cell cycle progression.

  1. Fixed-time synchronization of complex networks with nonidentical nodes and stochastic noise perturbations

    Zhang, Wanli; Li, Chuandong; Huang, Tingwen; Huang, Junjian

    2018-02-01

    This paper investigates the fixed-time synchronization of complex networks (CNs) with nonidentical nodes and stochastic noise perturbations. By designing new controllers, constructing Lyapunov functions and using the properties of Weiner process, different synchronization criteria are derived according to whether the node systems in the CNs or the goal system satisfies the corresponding conditions. Moreover, the role of the designed controllers is analyzed in great detail by constructing a suitable comparison system and a new method is presented to estimate the settling time by utilizing the comparison system. Results of this paper can be applied to both directed and undirected weighted networks. Numerical simulations are offered to verify the effectiveness of our new results.

  2. Robust Adaptive Exponential Synchronization of Stochastic Perturbed Chaotic Delayed Neural Networks with Parametric Uncertainties

    Yang Fang

    2014-01-01

    Full Text Available This paper investigates the robust adaptive exponential synchronization in mean square of stochastic perturbed chaotic delayed neural networks with nonidentical parametric uncertainties. A robust adaptive feedback controller is proposed based on Gronwally’s inequality, drive-response concept, and adaptive feedback control technique with the update laws of nonidentical parametric uncertainties as well as linear matrix inequality (LMI approach. The sufficient conditions for robust adaptive exponential synchronization in mean square of uncoupled uncertain stochastic chaotic delayed neural networks are derived in terms of linear matrix inequalities (LMIs. The effect of nonidentical uncertain parameter uncertainties is suppressed by the designed robust adaptive feedback controller rapidly. A numerical example is provided to validate the effectiveness of the proposed method.

  3. Perturbative calculations of flow patterns in free convection between coaxial cylinders. Non-linear temperature dependences of the fluid properties

    Navarro, J. A.; Madariaga, J. A.; Santamaria, C. M.; Saviron, J. M.

    1980-01-01

    10 refs. Flow pattern calculations in natural convection between two vertical coaxial cylinders are reported. It is assumed trough the paper. that fluid properties, viscosity, thermal conductivity and density, depend no-linearly on temperature and that the aspects (height/radius) ratio of the cylinders is high. Velocity profiles are calculated trough a perturbative scheme and analytic results for the three first perturbation orders are presented. We outline also an iterative method to estimate the perturbations on the flow patterns which arise when a radial composition gradient is established by external forces in a two-component fluid. This procedure, based on semiempirical basis, is applied to gaseous convection. The influence of the molecules gas properties on tho flow is also discussed. (Author) 10 refs

  4. Traumatic Brain Injury Induces Genome-Wide Transcriptomic, Methylomic, and Network Perturbations in Brain and Blood Predicting Neurological Disorders

    Qingying Meng

    2017-02-01

    Full Text Available The complexity of the traumatic brain injury (TBI pathology, particularly concussive injury, is a serious obstacle for diagnosis, treatment, and long-term prognosis. Here we utilize modern systems biology in a rodent model of concussive injury to gain a thorough view of the impact of TBI on fundamental aspects of gene regulation, which have the potential to drive or alter the course of the TBI pathology. TBI perturbed epigenomic programming, transcriptional activities (expression level and alternative splicing, and the organization of genes in networks centered around genes such as Anax2, Ogn, and Fmod. Transcriptomic signatures in the hippocampus are involved in neuronal signaling, metabolism, inflammation, and blood function, and they overlap with those in leukocytes from peripheral blood. The homology between genomic signatures from blood and brain elicited by TBI provides proof of concept information for development of biomarkers of TBI based on composite genomic patterns. By intersecting with human genome-wide association studies, many TBI signature genes and network regulators identified in our rodent model were causally associated with brain disorders with relevant link to TBI. The overall results show that concussive brain injury reprograms genes which could lead to predisposition to neurological and psychiatric disorders, and that genomic information from peripheral leukocytes has the potential to predict TBI pathogenesis in the brain.

  5. Change in Allosteric Network Affects Binding Affinities of PDZ Domains: Analysis through Perturbation Response Scanning

    Gerek, Z. Nevin; Ozkan, S. Banu

    2011-01-01

    The allosteric mechanism plays a key role in cellular functions of several PDZ domain proteins (PDZs) and is directly linked to pharmaceutical applications; however, it is a challenge to elaborate the nature and extent of these allosteric interactions. One solution to this problem is to explore the dynamics of PDZs, which may provide insights about how intramolecular communication occurs within a single domain. Here, we develop an advancement of perturbation response scanning (PRS) that couples elastic network models with linear response theory (LRT) to predict key residues in allosteric transitions of the two most studied PDZs (PSD-95 PDZ3 domain and hPTP1E PDZ2 domain). With PRS, we first identify the residues that give the highest mean square fluctuation response upon perturbing the binding sites. Strikingly, we observe that the residues with the highest mean square fluctuation response agree with experimentally determined residues involved in allosteric transitions. Second, we construct the allosteric pathways by linking the residues giving the same directional response upon perturbation of the binding sites. The predicted intramolecular communication pathways reveal that PSD-95 and hPTP1E have different pathways through the dynamic coupling of different residue pairs. Moreover, our analysis provides a molecular understanding of experimentally observed hidden allostery of PSD-95. We show that removing the distal third alpha helix from the binding site alters the allosteric pathway and decreases the binding affinity. Overall, these results indicate that (i) dynamics plays a key role in allosteric regulations of PDZs, (ii) the local changes in the residue interactions can lead to significant changes in the dynamics of allosteric regulations, and (iii) this might be the mechanism that each PDZ uses to tailor their binding specificities regulation. PMID:21998559

  6. Extracting Association Patterns in Network Communications

    Portela, Javier; Villalba, Luis Javier García; Trujillo, Alejandra Guadalupe Silva; Orozco, Ana Lucila Sandoval; Kim, Tai-hoon

    2015-01-01

    In network communications, mixes provide protection against observers hiding the appearance of messages, patterns, length and links between senders and receivers. Statistical disclosure attacks aim to reveal the identity of senders and receivers in a communication network setting when it is protected by standard techniques based on mixes. This work aims to develop a global statistical disclosure attack to detect relationships between users. The only information used by the attacker is the number of messages sent and received by each user for each round, the batch of messages grouped by the anonymity system. A new modeling framework based on contingency tables is used. The assumptions are more flexible than those used in the literature, allowing to apply the method to multiple situations automatically, such as email data or social networks data. A classification scheme based on combinatoric solutions of the space of rounds retrieved is developed. Solutions about relationships between users are provided for all pairs of users simultaneously, since the dependence of the data retrieved needs to be addressed in a global sense. PMID:25679311

  7. Extracting Association Patterns in Network Communications

    Javier Portela

    2015-02-01

    Full Text Available In network communications, mixes provide protection against observers hiding the appearance of messages, patterns, length and links between senders and receivers. Statistical disclosure attacks aim to reveal the identity of senders and receivers in a communication network setting when it is protected by standard techniques based on mixes. This work aims to develop a global statistical disclosure attack to detect relationships between users. The only information used by the attacker is the number of messages sent and received by each user for each round, the batch of messages grouped by the anonymity system. A new modeling framework based on contingency tables is used. The assumptions are more flexible than those used in the literature, allowing to apply the method to multiple situations automatically, such as email data or social networks data. A classification scheme based on combinatoric solutions of the space of rounds retrieved is developed. Solutions about relationships between users are provided for all pairs of users simultaneously, since the dependence of the data retrieved needs to be addressed in a global sense.

  8. Global impulsive exponential synchronization of stochastic perturbed chaotic delayed neural networks

    Hua-Guang, Zhang; Tie-Dong, Ma; Jie, Fu; Shao-Cheng, Tong

    2009-01-01

    In this paper, the global impulsive exponential synchronization problem of a class of chaotic delayed neural networks (DNNs) with stochastic perturbation is studied. Based on the Lyapunov stability theory, stochastic analysis approach and an efficient impulsive delay differential inequality, some new exponential synchronization criteria expressed in the form of the linear matrix inequality (LMI) are derived. The designed impulsive controller not only can globally exponentially stabilize the error dynamics in mean square, but also can control the exponential synchronization rate. Furthermore, to estimate the stable region of the synchronization error dynamics, a novel optimization control algorithm is proposed, which can deal with the minimum problem with two nonlinear terms coexisting in LMIs effectively. Simulation results finally demonstrate the effectiveness of the proposed method

  9. Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations.

    Xin Wang

    Full Text Available Combinatorial gene perturbations provide rich information for a systematic exploration of genetic interactions. Despite successful applications to bacteria and yeast, the scalability of this approach remains a major challenge for higher organisms such as humans. Here, we report a novel experimental and computational framework to efficiently address this challenge by limiting the 'search space' for important genetic interactions. We propose to integrate rich phenotypes of multiple single gene perturbations to robustly predict functional modules, which can subsequently be subjected to further experimental investigations such as combinatorial gene silencing. We present posterior association networks (PANs to predict functional interactions between genes estimated using a Bayesian mixture modelling approach. The major advantage of this approach over conventional hypothesis tests is that prior knowledge can be incorporated to enhance predictive power. We demonstrate in a simulation study and on biological data, that integrating complementary information greatly improves prediction accuracy. To search for significant modules, we perform hierarchical clustering with multiscale bootstrap resampling. We demonstrate the power of the proposed methodologies in applications to Ewing's sarcoma and human adult stem cells using publicly available and custom generated data, respectively. In the former application, we identify a gene module including many confirmed and highly promising therapeutic targets. Genes in the module are also significantly overrepresented in signalling pathways that are known to be critical for proliferation of Ewing's sarcoma cells. In the latter application, we predict a functional network of chromatin factors controlling epidermal stem cell fate. Further examinations using ChIP-seq, ChIP-qPCR and RT-qPCR reveal that the basis of their genetic interactions may arise from transcriptional cross regulation. A Bioconductor package

  10. Energy prediction using spatiotemporal pattern networks

    Jiang, Zhanhong; Liu, Chao; Akintayo, Adedotun; Henze, Gregor P.; Sarkar, Soumik

    2017-11-01

    This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamical filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependencies among different sub-systems. To quantify causal dependencies, a mutual information based metric is presented and an energy prediction approach is subsequently proposed based on the STPN framework. To validate the proposed scheme, two case studies are presented, one involving wind turbine power prediction (supply side energy) using the Western Wind Integration data set generated by the National Renewable Energy Laboratory (NREL) for identifying spatiotemporal characteristics, and the other, residential electric energy disaggregation (demand side energy) using the Building America 2010 data set from NREL for exploring temporal features. In the energy disaggregation context, convex programming techniques beyond the STPN framework are developed and applied to achieve improved disaggregation performance.

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

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

    2017-02-01

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

  12. Social networking patterns/hazards among teenagers.

    Machold, C; Judge, G; Mavrinac, A; Elliott, J; Murphy, A M; Roche, E

    2012-05-01

    Social Networking Sites (SNSs) have grown substantially, posing new hazards to teenagers. This study aimed to determine general patterns of Internet usage among Irish teenagers aged 11-16 years, and to identify potential hazards, including; bullying, inappropriate contact, overuse, addiction and invasion of users' privacy. A cross-sectional study design was employed to survey students at three Irish secondary schools, with a sample of 474 completing a questionnaire. 202 (44%) (n = 460) accessed the Internet using a shared home computer. Two hours or less were spent online daily by 285(62%), of whom 450 (98%) were unsupervised. 306 (72%) (n = 425) reported frequent usage of SNSs, 403 (95%) of whom were Facebook users. 42 (10%) males and 51 (12%) females experienced bullying online, while 114 (27%) reported inappropriate contact from others. Concerning overuse and the risk of addiction, 140 (33%) felt they accessed SNSs too often. These patterns among Irish teenagers suggest that SNS usage poses significant dangers, which are going largely unaddressed.

  13. Social networking patterns/hazards among teenagers.

    Machold, C

    2012-05-01

    Social Networking Sites (SNSs) have grown substantially, posing new hazards to teenagers. This study aimed to determine general patterns of Internet usage among Irish teenagers aged 11-16 years, and to identify potential hazards, including; bullying, inappropriate contact, overuse, addiction and invasion of users\\' privacy. A cross-sectional study design was employed to survey students at three Irish secondary schools, with a sample of 474 completing a questionnaire. 202 (44%) (n = 460) accessed the Internet using a shared home computer. Two hours or less were spent online daily by 285(62%), of whom 450 (98%) were unsupervised. 306 (72%) (n = 425) reported frequent usage of SNSs, 403 (95%) of whom were Facebook users. 42 (10%) males and 51 (12%) females experienced bullying online, while 114 (27%) reported inappropriate contact from others. Concerning overuse and the risk of addiction, 140 (33%) felt they accessed SNSs too often. These patterns among Irish teenagers suggest that SNS usage poses significant dangers, which are going largely unaddressed.

  14. An linear matrix inequality approach to global synchronisation of non-parameter perturbations of multi-delay Hopfield neural network

    Shao Hai-Jian; Cai Guo-Liang; Wang Hao-Xiang

    2010-01-01

    In this study, a successful linear matrix inequality approach is used to analyse a non-parameter perturbation of multi-delay Hopfield neural network by constructing an appropriate Lyapunov-Krasovskii functional. This paper presents the comprehensive discussion of the approach and also extensive applications

  15. Analysis of the electrical disturbances in CERN power distribution network with pattern mining methods

    Abramenko, Oleksii

    2017-01-01

    The current research focuses on the perturbations within the electrical network of the LHC and its subsystems by analyzing measurements collected from oscilloscopes installed across different CERN sites, and alarms by electrical equipments. We analyze amplitude and duration of the glitches and, together with other relevant variables, correlate them with beam stopping events. The work also tries to identify assets affected by such perturbations using data mining and, in particular, frequent pattern mining methods. On the practical side we summarize results of our work by putting forward a prototype of a software tool enabling online monitoring of the alarms coming from the electrical network and facilitating glitch detection and analysis by a technical operator.

  16. Improving the accuracy of Møller-Plesset perturbation theory with neural networks

    McGibbon, Robert T.; Taube, Andrew G.; Donchev, Alexander G.; Siva, Karthik; Hernández, Felipe; Hargus, Cory; Law, Ka-Hei; Klepeis, John L.; Shaw, David E.

    2017-10-01

    Noncovalent interactions are of fundamental importance across the disciplines of chemistry, materials science, and biology. Quantum chemical calculations on noncovalently bound complexes, which allow for the quantification of properties such as binding energies and geometries, play an essential role in advancing our understanding of, and building models for, a vast array of complex processes involving molecular association or self-assembly. Because of its relatively modest computational cost, second-order Møller-Plesset perturbation (MP2) theory is one of the most widely used methods in quantum chemistry for studying noncovalent interactions. MP2 is, however, plagued by serious errors due to its incomplete treatment of electron correlation, especially when modeling van der Waals interactions and π-stacked complexes. Here we present spin-network-scaled MP2 (SNS-MP2), a new semi-empirical MP2-based method for dimer interaction-energy calculations. To correct for errors in MP2, SNS-MP2 uses quantum chemical features of the complex under study in conjunction with a neural network to reweight terms appearing in the total MP2 interaction energy. The method has been trained on a new data set consisting of over 200 000 complete basis set (CBS)-extrapolated coupled-cluster interaction energies, which are considered the gold standard for chemical accuracy. SNS-MP2 predicts gold-standard binding energies of unseen test compounds with a mean absolute error of 0.04 kcal mol-1 (root-mean-square error 0.09 kcal mol-1), a 6- to 7-fold improvement over MP2. To the best of our knowledge, its accuracy exceeds that of all extant density functional theory- and wavefunction-based methods of similar computational cost, and is very close to the intrinsic accuracy of our benchmark coupled-cluster methodology itself. Furthermore, SNS-MP2 provides reliable per-conformation confidence intervals on the predicted interaction energies, a feature not available from any alternative method.

  17. Stationary patterns in star networks of bistable units: Theory and application to chemical reactions.

    Kouvaris, Nikos E; Sebek, Michael; Iribarne, Albert; Díaz-Guilera, Albert; Kiss, István Z

    2017-04-01

    We present theoretical and experimental studies on pattern formation with bistable dynamical units coupled in a star network configuration. By applying a localized perturbation to the central or the peripheral elements, we demonstrate the subsequent spreading, pinning, or retraction of the activations; such analysis enables the characterization of the formation of stationary patterns of localized activity. The results are interpreted with a theoretical analysis of a simplified bistable reaction-diffusion model. Weak coupling results in trivial pinned states where the activation cannot propagate. At strong coupling, a uniform state is expected with active or inactive elements at small or large degree networks, respectively. A nontrivial stationary spatial pattern, corresponding to an activation pinning, is predicted to occur at an intermediate number of peripheral elements and at intermediate coupling strengths, where the central activation of the network is pinned, but the peripheral activation propagates toward the center. The results are confirmed in experiments with star networks of bistable electrochemical reactions. The experiments confirm the existence of the stationary spatial patterns and the dependence of coupling strength on the number of peripheral elements for transitions between pinned and retreating or spreading fronts in forced network configurations (where the central or periphery elements are forced to maintain their states).

  18. Influence of stochastic perturbations on the cluster explosive synchronization of second-order Kuramoto oscillators on networks.

    Cao, Liang; Tian, Changhai; Wang, Zhenhua; Zhang, Xiyun; Liu, Zonghua

    2018-02-01

    Explosive synchronization in networked second-order Kuramoto oscillators has been well studied recently and it is revealed that the synchronization process is featured by cluster explosive synchronization. However, little attention has been paid to the influence of noise or perturbation. We here study this problem and discuss the influences of noise and perturbation. For the former, we interestingly find that noise has significant influence on the cluster explosive synchronization of those nodes with smaller degrees, i.e., their synchronization will change from the first-order to second-order transition and the critical points for both the forward and backward synchronization depend on the strength of noise. Especially, when the strength of noise is in an optimal range, a synchronization of the nodes with smaller degrees will be induced in the region of coupling strength where they do not display synchronization in the absence of noise. For the latter, we find that the effect of perturbation is similar to that of noise when its duration W is small. However, the perturbation will induce a change from cluster explosive synchronization to explosive synchronization when W is large. Furthermore, a brief theory is provided to explain the influence of perturbations on the critical points.

  19. Control patterns in an healthcare network

    Kartseva, V.; Hulstijn, J.; Gordijn, J.; Tan, Y.H.

    2010-01-01

    To keep a network of enterprises sustainable, inter-organizational control measures are needed to detect or prevent opportunistic behaviour of network participants. We present a requirements engineering method for understanding control problems and designing solutions, based on an economic value

  20. Nexus network journal patterns in architecture

    2007-01-01

    This issue is dedicated to various kinds of patterns in architecture. Buthayna Eilouti and Amer Al-Jokhadar address patterns in shape grammars in the ground plans of Mamluk madrasas, religious schools. Giulio Magli goes back further in history, to the age of Greek colonies in Italy before they were conquered by the Romans, to examine patterns in urban design. In Traditional Patterns in Pyrgi of Chios: Mathematics and Community Charoula Stathopoulou examines the geometric patterns that decorate the buildings of the town of Pyrgi, on the Greek island of Chios. Curve Fitting is a study of ways to construct a function so that its graph most closely approximates the pattern given by a set of points. Dirk Huylebrouck’s paper examines how a pattern of points extracted from an arch might be associated to a precise mathematical curve. James Harris looks at the designs of Frank Lloyd Wright and Piet Mondrian to extract the rules of their pattern generation and propose possible applications.

  1. Pattern-Oriented Reengineering of a Network System

    Chung-Horng Lung

    2004-08-01

    Full Text Available Reengineering is to reorganize and modify existing systems to enhance them or to make them more maintainable. Reengineering is usually necessary as systems evolve due to changes in requirements, technologies, and/or personnel. Design patterns capture recurring structures and dynamics among software participants to facilitate reuse of successful designs. Design patterns are common and well studied in network systems. In this project, we reengineer part of a network system with some design patterns to support future evolution and performance improvement. We start with reverse engineering effort to understand the system and recover its high level architecture. Then we apply concurrent and networked design patterns to restructure the main sub-system. Those patterns include Half-Sync/Half-Async, Monitor Object, and Scoped Locking idiom. The resulting system is more maintainable and has better performance.

  2. Pattern Recognition for Reliability Assessment of Water Distribution Networks

    Trifunovi?, N.

    2012-01-01

    The study presented in this manuscript investigates the patterns that describe reliability of water distribution networks focusing to the node connectivity, energy balance, and economics of construction, operation and maintenance. A number of measures to evaluate the network resilience has been

  3. Pattern recognition of state variables by neural networks

    Faria, Eduardo Fernandes; Pereira, Claubia

    1996-01-01

    An artificial intelligence system based on artificial neural networks can be used to classify predefined events and emergency procedures. These systems are being used in different areas. In the nuclear reactors safety, the goal is the classification of events whose data can be processed and recognized by neural networks. In this works we present a preliminary simple system, using neural networks in the recognition of patterns the recognition of variables which define a situation. (author)

  4. Patterns recognition of electric brain activity using artificial neural networks

    Musatov, V. Yu.; Pchelintseva, S. V.; Runnova, A. E.; Hramov, A. E.

    2017-04-01

    An approach for the recognition of various cognitive processes in the brain activity in the perception of ambiguous images. On the basis of developed theoretical background and the experimental data, we propose a new classification of oscillating patterns in the human EEG by using an artificial neural network approach. After learning of the artificial neural network reliably identified cube recognition processes, for example, left-handed or right-oriented Necker cube with different intensity of their edges, construct an artificial neural network based on Perceptron architecture and demonstrate its effectiveness in the pattern recognition of the EEG in the experimental.

  5. Classification of data patterns using an autoassociative neural network topology

    Dietz, W. E.; Kiech, E. L.; Ali, M.

    1989-01-01

    A diagnostic expert system based on neural networks is developed and applied to the real-time diagnosis of jet and rocket engines. The expert system methodologies are based on the analysis of patterns of behavior of physical mechanisms. In this approach, fault diagnosis is conceptualized as the mapping or association of patterns of sensor data to patterns representing fault conditions. The approach addresses deficiencies inherent in many feedforward neural network models and greatly reduces the number of networks necessary to identify the existence of a fault condition and estimate the duration and severity of the identified fault. The network topology used in the present implementation of the diagnostic system is described, as well as the training regimen used and the response of the system to inputs representing both previously observed and unknown fault scenarios. Noise effects on the integrity of the diagnosis are also evaluated.

  6. Study on Dissemination Patterns in Location-Aware Gossiping Networks

    Kami, Nobuharu; Baba, Teruyuki; Yoshikawa, Takashi; Morikawa, Hiroyuki

    We study the properties of information dissemination over location-aware gossiping networks leveraging location-based real-time communication applications. Gossiping is a promising method for quickly disseminating messages in a large-scale system, but in its application to information dissemination for location-aware applications, it is important to consider the network topology and patterns of spatial dissemination over the network in order to achieve effective delivery of messages to potentially interested users. To this end, we propose a continuous-space network model extended from Kleinberg's small-world model applicable to actual location-based applications. Analytical and simulation-based study shows that the proposed network achieves high dissemination efficiency resulting from geographically neutral dissemination patterns as well as selective dissemination to proximate users. We have designed a highly scalable location management method capable of promptly updating the network topology in response to node movement and have implemented a distributed simulator to perform dynamic target pursuit experiments as one example of applications that are the most sensitive to message forwarding delay. The experimental results show that the proposed network surpasses other types of networks in pursuit efficiency and achieves the desirable dissemination patterns.

  7. Divisibility patterns of natural numbers on a complex network.

    Shekatkar, Snehal M; Bhagwat, Chandrasheel; Ambika, G

    2015-09-16

    Investigation of divisibility properties of natural numbers is one of the most important themes in the theory of numbers. Various tools have been developed over the centuries to discover and study the various patterns in the sequence of natural numbers in the context of divisibility. In the present paper, we study the divisibility of natural numbers using the framework of a growing complex network. In particular, using tools from the field of statistical inference, we show that the network is scale-free but has a non-stationary degree distribution. Along with this, we report a new kind of similarity pattern for the local clustering, which we call "stretching similarity", in this network. We also show that the various characteristics like average degree, global clustering coefficient and assortativity coefficient of the network vary smoothly with the size of the network. Using analytical arguments we estimate the asymptotic behavior of global clustering and average degree which is validated using numerical analysis.

  8. Social Network Mixing Patterns In Mergers & Acquisitions - A Simulation Experiment

    Robert Fabac

    2011-01-01

    Full Text Available In the contemporary world of global business and continuously growing competition, organizations tend to use mergers and acquisitions to enforce their position on the market. The future organization’s design is a critical success factor in such undertakings. The field of social network analysis can enhance our uderstanding of these processes as it lets us reason about the development of networks, regardless of their origin. The analysis of mixing patterns is particularly useful as it provides an insight into how nodes in a network connect with each other. We hypothesize that organizational networks with compatible mixing patterns will be integrated more successfully. After conducting a simulation experiment, we suggest an integration model based on the analysis of network assortativity. The model can be a guideline for organizational integration, such as occurs in mergers and acquisitions.

  9. Synchronization transmission of laser pattern signal within uncertain switched network

    Lü, Ling; Li, Chengren; Li, Gang; Sun, Ao; Yan, Zhe; Rong, Tingting; Gao, Yan

    2017-06-01

    We propose a new technology for synchronization transmission of laser pattern signal within uncertain network with controllable topology. In synchronization process, the connection of dynamic network can vary at all time according to different demands. Especially, we construct the Lyapunov function of network through designing a special semi-positive definite function, and the synchronization transmission of laser pattern signal within uncertain network with controllable topology can be realized perfectly, which effectively avoids the complicated calculation for solving the second largest eignvalue of the coupling matrix of the dynamic network in order to obtain the network synchronization condition. At the same time, the uncertain parameters in dynamic equations belonging to network nodes can also be identified accurately via designing the identification laws of uncertain parameters. In addition, there are not any limitations for the synchronization target of network in the new technology, in other words, the target can either be a state variable signal of an arbitrary node within the network or an exterior signal.

  10. Dynamical Networks for Smog Pattern Analysis

    Zong, Linqi; Gong, Xinyi; Zhu, Jia

    2015-01-01

    Smog, as a form of air pollution, poses as a serious problem to the environment, health, and economy of the world[1-4] . Previous studies on smog mostly focused on the components and the effects of smog [5-10]. However, as the smog happens with increased frequency and duration, the smog pattern which is critical for smog forecast and control, is rarely investigated, mainly due to the complexity of the components, the causes, and the spreading processes of smog. Here we report the first analys...

  11. Connectivity, excitability and activity patterns in neuronal networks

    Le Feber, Joost; Stoyanova, Irina I; Chiappalone, Michela

    2014-01-01

    Extremely synchronized firing patterns such as those observed in brain diseases like epilepsy may result from excessive network excitability. Although network excitability is closely related to (excitatory) connectivity, a direct measure for network excitability remains unavailable. Several methods currently exist for estimating network connectivity, most of which are related to cross-correlation. An example is the conditional firing probability (CFP) analysis which calculates the pairwise probability (CFP i,j ) that electrode j records an action potential at time t = τ, given that electrode i recorded a spike at t = 0. However, electrode i often records multiple spikes within the analysis interval, and CFP values are biased by the on-going dynamic state of the network. Here we show that in a linear approximation this bias may be removed by deconvoluting CFP i,j with the autocorrelation of i (i.e. CFP i,i ), to obtain the single pulse response (SPR i,j )—the average response at electrode j to a single spike at electrode i. Thus, in a linear system SPRs would be independent of the dynamic network state. Nonlinear components of synaptic transmission, such as facilitation and short term depression, will however still affect SPRs. Therefore SPRs provide a clean measure of network excitability. We used carbachol and ghrelin to moderately activate cultured cortical networks to affect their dynamic state. Both neuromodulators transformed the bursting firing patterns of the isolated networks into more dispersed firing. We show that the influence of the dynamic state on SPRs is much smaller than the effect on CFPs, but not zero. The remaining difference reflects the alteration in network excitability. We conclude that SPRs are less contaminated by the dynamic network state and that mild excitation may decrease network excitability, possibly through short term synaptic depression. (papers)

  12. Causal functional contributions and interactions in the attention network of the brain: an objective multi-perturbation analysis.

    Zavaglia, Melissa; Hilgetag, Claus C

    2016-06-01

    Spatial attention is a prime example for the distributed network functions of the brain. Lesion studies in animal models have been used to investigate intact attentional mechanisms as well as perspectives for rehabilitation in the injured brain. Here, we systematically analyzed behavioral data from cooling deactivation and permanent lesion experiments in the cat, where unilateral deactivation of the posterior parietal cortex (in the vicinity of the posterior middle suprasylvian cortex, pMS) or the superior colliculus (SC) cause a severe neglect in the contralateral hemifield. Counterintuitively, additional deactivation of structures in the opposite hemisphere reverses the deficit. Using such lesion data, we employed a game-theoretical approach, multi-perturbation Shapley value analysis (MSA), for inferring functional contributions and network interactions of bilateral pMS and SC from behavioral performance in visual attention studies. The approach provides an objective theoretical strategy for lesion inferences and allows a unique quantitative characterization of regional functional contributions and interactions on the basis of multi-perturbations. The quantitative analysis demonstrated that right posterior parietal cortex and superior colliculus made the strongest positive contributions to left-field orienting, while left brain regions had negative contributions, implying that their perturbation may reverse the effects of contralateral lesions or improve normal function. An analysis of functional modulations and interactions among the regions revealed redundant interactions (implying functional overlap) between regions within each hemisphere, and synergistic interactions between bilateral regions. To assess the reliability of the MSA method in the face of variable and incomplete input data, we performed a sensitivity analysis, investigating how much the contribution values of the four regions depended on the performance of specific configurations and on the

  13. Connectivity patterns in cognitive control networks predict naturalistic multitasking ability.

    Wen, Tanya; Liu, De-Cyuan; Hsieh, Shulan

    2018-06-01

    Multitasking is a fundamental aspect of everyday life activities. To achieve a complex, multi-component goal, the tasks must be subdivided into sub-tasks and component steps, a critical function of prefrontal networks. The prefrontal cortex is considered to be organized in a cascade of executive processes from the sensorimotor to anterior prefrontal cortex, which includes execution of specific goal-directed action, to encoding and maintaining task rules, and finally monitoring distal goals. In the current study, we used a virtual multitasking paradigm to tap into real-world performance and relate it to each individual's resting-state functional connectivity in fMRI. While did not find any correlation between global connectivity of any of the major networks with multitasking ability, global connectivity of the lateral prefrontal cortex (LPFC) was predictive of multitasking ability. Further analysis showed that multivariate connectivity patterns within the sensorimotor network (SMN), and between-network connectivity of the frontoparietal network (FPN) and dorsal attention network (DAN), predicted individual multitasking ability and could be generalized to novel individuals. Together, these results support previous research that prefrontal networks underlie multitasking abilities and show that connectivity patterns in the cascade of prefrontal networks may explain individual differences in performance. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

  14. Pattern formation and firing synchronization in networks of map neurons

    Wang Qingyun; Duan Zhisheng; Huang Lin; Chen Guanrong; Lu Qishao

    2007-01-01

    Patterns and collective phenomena such as firing synchronization are studied in networks of nonhomogeneous oscillatory neurons and mixtures of oscillatory and excitable neurons, with dynamics of each neuron described by a two-dimensional (2D) Rulkov map neuron. It is shown that as the coupling strength is increased, typical patterns emerge spatially, which propagate through the networks in the form of beautiful target waves or parallel ones depending on the size of networks. Furthermore, we investigate the transitions of firing synchronization characterized by the rate of firing when the coupling strength is increased. It is found that there exists an intermediate coupling strength; firing synchronization is minimal simultaneously irrespective of the size of networks. For further increasing the coupling strength, synchronization is enhanced. Since noise is inevitable in real neurons, we also investigate the effects of white noise on firing synchronization for different networks. For the networks of oscillatory neurons, it is shown that firing synchronization decreases when the noise level increases. For the missed networks, firing synchronization is robust under the noise conditions considered in this paper. Results presented in this paper should prove to be valuable for understanding the properties of collective dynamics in real neuronal networks

  15. Learning-induced pattern classification in a chaotic neural network

    Li, Yang; Zhu, Ping; Xie, Xiaoping; He, Guoguang; Aihara, Kazuyuki

    2012-01-01

    In this Letter, we propose a Hebbian learning rule with passive forgetting (HLRPF) for use in a chaotic neural network (CNN). We then define the indices based on the Euclidean distance to investigate the evolution of the weights in a simplified way. Numerical simulations demonstrate that, under suitable external stimulations, the CNN with the proposed HLRPF acts as a fuzzy-like pattern classifier that performs much better than an ordinary CNN. The results imply relationship between learning and recognition. -- Highlights: ► Proposing a Hebbian learning rule with passive forgetting (HLRPF). ► Defining indices to investigate the evolution of the weights simply. ► The chaotic neural network with HLRPF acts as a fuzzy-like pattern classifier. ► The pattern classifier ability of the network is improved much.

  16. Cellular-automata-based learning network for pattern recognition

    Tzionas, Panagiotis G.; Tsalides, Phillippos G.; Thanailakis, Adonios

    1991-11-01

    Most classification techniques either adopt an approach based directly on the statistical characteristics of the pattern classes involved, or they transform the patterns in a feature space and try to separate the point clusters in this space. An alternative approach based on memory networks has been presented, its novelty being that it can be implemented in parallel and it utilizes direct features of the patterns rather than statistical characteristics. This study presents a new approach for pattern classification using pseudo 2-D binary cellular automata (CA). This approach resembles the memory network classifier in the sense that it is based on an adaptive knowledge based formed during a training phase, and also in the fact that both methods utilize pattern features that are directly available. The main advantage of this approach is that the sensitivity of the pattern classifier can be controlled. The proposed pattern classifier has been designed using 1.5 micrometers design rules for an N-well CMOS process. Layout has been achieved using SOLO 1400. Binary pseudo 2-D hybrid additive CA (HACA) is described in the second section of this paper. The third section describes the operation of the pattern classifier and the fourth section presents some possible applications. The VLSI implementation of the pattern classifier is presented in the fifth section and, finally, the sixth section draws conclusions from the results obtained.

  17. Visualizing neuronal network connectivity with connectivity pattern tables

    Eilen Nordlie

    2010-01-01

    Full Text Available Complex ideas are best conveyed through well-designed illustrations. Up to now, computational neuroscientists have mostly relied on box-and-arrow diagrams of even complex neuronal networks, often using ad hoc notations with conflicting use of symbols from paper to paper. This significantly impedes the communication of ideas in neuronal network modeling. We present here Connectivity Pattern Tables (CPTs as a clutter-free visualization of connectivity in large neuronal networks containing two-dimensional populations of neurons. CPTs can be generated automatically from the same script code used to create the actual network in the NEST simulator. Through aggregation, CPTs can be viewed at different levels, providing either full detail or summary information. We also provide the open source ConnPlotter tool as a means to create connectivity pattern tables.

  18. Robust synchronization analysis in nonlinear stochastic cellular networks with time-varying delays, intracellular perturbations and intercellular noise.

    Chen, Po-Wei; Chen, Bor-Sen

    2011-08-01

    Naturally, a cellular network consisted of a large amount of interacting cells is complex. These cells have to be synchronized in order to emerge their phenomena for some biological purposes. However, the inherently stochastic intra and intercellular interactions are noisy and delayed from biochemical processes. In this study, a robust synchronization scheme is proposed for a nonlinear stochastic time-delay coupled cellular network (TdCCN) in spite of the time-varying process delay and intracellular parameter perturbations. Furthermore, a nonlinear stochastic noise filtering ability is also investigated for this synchronized TdCCN against stochastic intercellular and environmental disturbances. Since it is very difficult to solve a robust synchronization problem with the Hamilton-Jacobi inequality (HJI) matrix, a linear matrix inequality (LMI) is employed to solve this problem via the help of a global linearization method. Through this robust synchronization analysis, we can gain a more systemic insight into not only the robust synchronizability but also the noise filtering ability of TdCCN under time-varying process delays, intracellular perturbations and intercellular disturbances. The measures of robustness and noise filtering ability of a synchronized TdCCN have potential application to the designs of neuron transmitters, on-time mass production of biochemical molecules, and synthetic biology. Finally, a benchmark of robust synchronization design in Escherichia coli repressilators is given to confirm the effectiveness of the proposed methods. Copyright © 2011 Elsevier Inc. All rights reserved.

  19. Dissemination Patterns and Associated Network Effects of Sentiments in Social Networks

    Hillmann, Robert; Trier, Matthias

    2012-01-01

    . The dissemination patterns analyzed in this study consist of network motifs based on triples of actors and the ties among them. These motifs are associated with common social network effects to derive meaningful insights about dissemination activities. The data basis includes several thousand social networks...... with textual messages classified according to embedded positive and negative sentiments. Based on this data, sub-networks are extracted and analyzed with a dynamic network motif analysis to determine dissemination patterns and associated network effects. Results indicate that the emergence of digital social...... networks exhibits a strong tendency towards reciprocity, followed by the dominance of hierarchy as an intermediate step leading to social clustering with hubs and transitivity effects for both positive and negative sentiments to the same extend. Sentiments embedded in exchanged textual messages do only...

  20. Oscillator Neural Network Retrieving Sparsely Coded Phase Patterns

    Aoyagi, Toshio; Nomura, Masaki

    1999-08-01

    Little is known theoretically about the associative memory capabilities of neural networks in which information is encoded not only in the mean firing rate but also in the timing of firings. Particularly, in the case of sparsely coded patterns, it is biologically important to consider the timings of firings and to study how such consideration influences storage capacities and quality of recalled patterns. For this purpose, we propose a simple extended model of oscillator neural networks to allow for expression of a nonfiring state. Analyzing both equilibrium states and dynamical properties in recalling processes, we find that the system possesses good associative memory.

  1. Synchronization stability and pattern selection in a memristive neuronal network.

    Wang, Chunni; Lv, Mi; Alsaedi, Ahmed; Ma, Jun

    2017-11-01

    Spatial pattern formation and selection depend on the intrinsic self-organization and cooperation between nodes in spatiotemporal systems. Based on a memory neuron model, a regular network with electromagnetic induction is proposed to investigate the synchronization and pattern selection. In our model, the memristor is used to bridge the coupling between the magnetic flux and the membrane potential, and the induction current results from the time-varying electromagnetic field contributed by the exchange of ion currents and the distribution of charged ions. The statistical factor of synchronization predicts the transition of synchronization and pattern stability. The bifurcation analysis of the sampled time series for the membrane potential reveals the mode transition in electrical activity and pattern selection. A formation mechanism is outlined to account for the emergence of target waves. Although an external stimulus is imposed on each neuron uniformly, the diversity in the magnetic flux and the induction current leads to emergence of target waves in the studied network.

  2. Synchronization stability and pattern selection in a memristive neuronal network

    Wang, Chunni; Lv, Mi; Alsaedi, Ahmed; Ma, Jun

    2017-11-01

    Spatial pattern formation and selection depend on the intrinsic self-organization and cooperation between nodes in spatiotemporal systems. Based on a memory neuron model, a regular network with electromagnetic induction is proposed to investigate the synchronization and pattern selection. In our model, the memristor is used to bridge the coupling between the magnetic flux and the membrane potential, and the induction current results from the time-varying electromagnetic field contributed by the exchange of ion currents and the distribution of charged ions. The statistical factor of synchronization predicts the transition of synchronization and pattern stability. The bifurcation analysis of the sampled time series for the membrane potential reveals the mode transition in electrical activity and pattern selection. A formation mechanism is outlined to account for the emergence of target waves. Although an external stimulus is imposed on each neuron uniformly, the diversity in the magnetic flux and the induction current leads to emergence of target waves in the studied network.

  3. Network based approaches reveal clustering in protein point patterns

    Parker, Joshua; Barr, Valarie; Aldridge, Joshua; Samelson, Lawrence E.; Losert, Wolfgang

    2014-03-01

    Recent advances in super-resolution imaging have allowed for the sub-diffraction measurement of the spatial location of proteins on the surfaces of T-cells. The challenge is to connect these complex point patterns to the internal processes and interactions, both protein-protein and protein-membrane. We begin analyzing these patterns by forming a geometric network amongst the proteins and looking at network measures, such the degree distribution. This allows us to compare experimentally observed patterns to models. Specifically, we find that the experimental patterns differ from heterogeneous Poisson processes, highlighting an internal clustering structure. Further work will be to compare our results to simulated protein-protein interactions to determine clustering mechanisms.

  4. The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations.

    Keenan, Alexandra B; Jenkins, Sherry L; Jagodnik, Kathleen M; Koplev, Simon; He, Edward; Torre, Denis; Wang, Zichen; Dohlman, Anders B; Silverstein, Moshe C; Lachmann, Alexander; Kuleshov, Maxim V; Ma'ayan, Avi; Stathias, Vasileios; Terryn, Raymond; Cooper, Daniel; Forlin, Michele; Koleti, Amar; Vidovic, Dusica; Chung, Caty; Schürer, Stephan C; Vasiliauskas, Jouzas; Pilarczyk, Marcin; Shamsaei, Behrouz; Fazel, Mehdi; Ren, Yan; Niu, Wen; Clark, Nicholas A; White, Shana; Mahi, Naim; Zhang, Lixia; Kouril, Michal; Reichard, John F; Sivaganesan, Siva; Medvedovic, Mario; Meller, Jaroslaw; Koch, Rick J; Birtwistle, Marc R; Iyengar, Ravi; Sobie, Eric A; Azeloglu, Evren U; Kaye, Julia; Osterloh, Jeannette; Haston, Kelly; Kalra, Jaslin; Finkbiener, Steve; Li, Jonathan; Milani, Pamela; Adam, Miriam; Escalante-Chong, Renan; Sachs, Karen; Lenail, Alex; Ramamoorthy, Divya; Fraenkel, Ernest; Daigle, Gavin; Hussain, Uzma; Coye, Alyssa; Rothstein, Jeffrey; Sareen, Dhruv; Ornelas, Loren; Banuelos, Maria; Mandefro, Berhan; Ho, Ritchie; Svendsen, Clive N; Lim, Ryan G; Stocksdale, Jennifer; Casale, Malcolm S; Thompson, Terri G; Wu, Jie; Thompson, Leslie M; Dardov, Victoria; Venkatraman, Vidya; Matlock, Andrea; Van Eyk, Jennifer E; Jaffe, Jacob D; Papanastasiou, Malvina; Subramanian, Aravind; Golub, Todd R; Erickson, Sean D; Fallahi-Sichani, Mohammad; Hafner, Marc; Gray, Nathanael S; Lin, Jia-Ren; Mills, Caitlin E; Muhlich, Jeremy L; Niepel, Mario; Shamu, Caroline E; Williams, Elizabeth H; Wrobel, David; Sorger, Peter K; Heiser, Laura M; Gray, Joe W; Korkola, James E; Mills, Gordon B; LaBarge, Mark; Feiler, Heidi S; Dane, Mark A; Bucher, Elmar; Nederlof, Michel; Sudar, Damir; Gross, Sean; Kilburn, David F; Smith, Rebecca; Devlin, Kaylyn; Margolis, Ron; Derr, Leslie; Lee, Albert; Pillai, Ajay

    2018-01-24

    The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. Boolean network identification from perturbation time series data combining dynamics abstraction and logic programming.

    Ostrowski, M; Paulevé, L; Schaub, T; Siegel, A; Guziolowski, C

    2016-11-01

    Boolean networks (and more general logic models) are useful frameworks to study signal transduction across multiple pathways. Logic models can be learned from a prior knowledge network structure and multiplex phosphoproteomics data. However, most efficient and scalable training methods focus on the comparison of two time-points and assume that the system has reached an early steady state. In this paper, we generalize such a learning procedure to take into account the time series traces of phosphoproteomics data in order to discriminate Boolean networks according to their transient dynamics. To that end, we identify a necessary condition that must be satisfied by the dynamics of a Boolean network to be consistent with a discretized time series trace. Based on this condition, we use Answer Set Programming to compute an over-approximation of the set of Boolean networks which fit best with experimental data and provide the corresponding encodings. Combined with model-checking approaches, we end up with a global learning algorithm. Our approach is able to learn logic models with a true positive rate higher than 78% in two case studies of mammalian signaling networks; for a larger case study, our method provides optimal answers after 7min of computation. We quantified the gain in our method predictions precision compared to learning approaches based on static data. Finally, as an application, our method proposes erroneous time-points in the time series data with respect to the optimal learned logic models. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  6. Active patterning and asymmetric transport in a model actomyosin network

    Wang, Shenshen [Department of Chemical Engineering and Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 (United States); Wolynes, Peter G. [Department of Chemistry and Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005 (United States)

    2013-12-21

    Cytoskeletal networks, which are essentially motor-filament assemblies, play a major role in many developmental processes involving structural remodeling and shape changes. These are achieved by nonequilibrium self-organization processes that generate functional patterns and drive intracellular transport. We construct a minimal physical model that incorporates the coupling between nonlinear elastic responses of individual filaments and force-dependent motor action. By performing stochastic simulations we show that the interplay of motor processes, described as driving anti-correlated motion of the network vertices, and the network connectivity, which determines the percolation character of the structure, can indeed capture the dynamical and structural cooperativity which gives rise to diverse patterns observed experimentally. The buckling instability of individual filaments is found to play a key role in localizing collapse events due to local force imbalance. Motor-driven buckling-induced node aggregation provides a dynamic mechanism that stabilizes the two-dimensional patterns below the apparent static percolation limit. Coordinated motor action is also shown to suppress random thermal noise on large time scales, the two-dimensional configuration that the system starts with thus remaining planar during the structural development. By carrying out similar simulations on a three-dimensional anchored network, we find that the myosin-driven isotropic contraction of a well-connected actin network, when combined with mechanical anchoring that confers directionality to the collective motion, may represent a novel mechanism of intracellular transport, as revealed by chromosome translocation in the starfish oocyte.

  7. Do networks of social interactions reflect patterns of kinship?

    Joah R. MADDEN, Johanna F. NIELSEN, Tim H. CLUTTON-BROCK

    2012-04-01

    Full Text Available The underlying kin structure of groups of animals may be glimpsed from patterns of spatial position or temporal association between individuals, and is presumed to facilitate inclusive fitness benefits. Such structure may be evident at a finer, behavioural, scale with individuals preferentially interacting with kin. We tested whether kin structure within groups of meerkats Suricata suricatta matched three forms of social interaction networks: grooming, dominance or foraging competitions. Networks of dominance interactions were positively related to networks of kinship, with close relatives engaging in dominance interactions with each other. This relationship persisted even after excluding the breeding dominant pair and when we restricted the kinship network to only include links between first order kin, which are most likely to be able to discern kin through simple rules of thumb. Conversely, we found no relationship between kinship networks and either grooming networks or networks of foraging competitions. This is surprising because a positive association between kin in a grooming network, or a negative association between kin in a network of foraging competitions offers opportunities for inclusive fitness benefits. Indeed, the positive association between kin in a network of dominance interactions that we did detect does not offer clear inclusive fitness benefits to group members. We conclude that kin structure in behavioural interactions in meerkats may be driven by factors other than indirect fitness benefits, and that networks of cooperative behaviours such as grooming may be driven by direct benefits accruing to individuals perhaps through mutualism or manipulation [Current Zoology 58 (2: 319-328, 2012].

  8. Do networks of social interactions reflect patterns of kinship?

    Joah R. MADDEN; Johanna F. NIEL SEN; Tim H. CLUTTON-BROCK

    2012-01-01

    The underlying kin structure of groups of animals may be glimpsed from patterns of spatial position or temporal association between individuals,and is presumed to facilitate inclusive fitness benefits.Such structure may be evident at a finer,behavioural,scale with individuals preferentially interacting with kin.We tested whether kin structure within groups of meerkats Suricata suricatta matched three forms of social interaction networks:grooming,dominance or foraging competitions.Networks of dominance interactions were positively related to networks of kinship,with close relatives engaging in dominance interactions with each other.This relationship persisted even after excluding the breeding dominant pair and when we restricted the kinship network to only include links between first order kin,which are most likely to be able to discern kin through simple rules of thumb.Conversely,we found no relationship between kinship networks and either grooming networks or networks of foraging competitions.This is surprising because a positive association between kin in a grooming network,or a negative association between kin in a network of foraging competitions offers opportunities for inclusive fitness benefits.Indeed,the positive association between kin in a network of dominance interactions that we did detect does not offer clear inclusive fitness benefits to group members.We conclude that kin structure in behavioural interactions in meerkats may be driven by factors other than indirect fitness benefits,and that networks of cooperative behaviours such as grooming may be driven by direct benefits accruing to individuals perhaps through mutualism or manipulation [Current Zoology 58 (2):319-328,2012].

  9. Patterning of leaf vein networks by convergent auxin transport pathways.

    Sawchuk, Megan G; Edgar, Alexander; Scarpella, Enrico

    2013-01-01

    The formation of leaf vein patterns has fascinated biologists for centuries. Transport of the plant signal auxin has long been implicated in vein patterning, but molecular details have remained unclear. Varied evidence suggests a central role for the plasma-membrane (PM)-localized PIN-FORMED1 (PIN1) intercellular auxin transporter of Arabidopsis thaliana in auxin-transport-dependent vein patterning. However, in contrast to the severe vein-pattern defects induced by auxin transport inhibitors, pin1 mutant leaves have only mild vein-pattern defects. These defects have been interpreted as evidence of redundancy between PIN1 and the other four PM-localized PIN proteins in vein patterning, redundancy that underlies many developmental processes. By contrast, we show here that vein patterning in the Arabidopsis leaf is controlled by two distinct and convergent auxin-transport pathways: intercellular auxin transport mediated by PM-localized PIN1 and intracellular auxin transport mediated by the evolutionarily older, endoplasmic-reticulum-localized PIN6, PIN8, and PIN5. PIN6 and PIN8 are expressed, as PIN1 and PIN5, at sites of vein formation. pin6 synthetically enhances pin1 vein-pattern defects, and pin8 quantitatively enhances pin1pin6 vein-pattern defects. Function of PIN6 is necessary, redundantly with that of PIN8, and sufficient to control auxin response levels, PIN1 expression, and vein network formation; and the vein pattern defects induced by ectopic PIN6 expression are mimicked by ectopic PIN8 expression. Finally, vein patterning functions of PIN6 and PIN8 are antagonized by PIN5 function. Our data define a new level of control of vein patterning, one with repercussions on other patterning processes in the plant, and suggest a mechanism to select cell files specialized for vascular function that predates evolution of PM-localized PIN proteins.

  10. Patterning of leaf vein networks by convergent auxin transport pathways.

    Megan G Sawchuk

    Full Text Available The formation of leaf vein patterns has fascinated biologists for centuries. Transport of the plant signal auxin has long been implicated in vein patterning, but molecular details have remained unclear. Varied evidence suggests a central role for the plasma-membrane (PM-localized PIN-FORMED1 (PIN1 intercellular auxin transporter of Arabidopsis thaliana in auxin-transport-dependent vein patterning. However, in contrast to the severe vein-pattern defects induced by auxin transport inhibitors, pin1 mutant leaves have only mild vein-pattern defects. These defects have been interpreted as evidence of redundancy between PIN1 and the other four PM-localized PIN proteins in vein patterning, redundancy that underlies many developmental processes. By contrast, we show here that vein patterning in the Arabidopsis leaf is controlled by two distinct and convergent auxin-transport pathways: intercellular auxin transport mediated by PM-localized PIN1 and intracellular auxin transport mediated by the evolutionarily older, endoplasmic-reticulum-localized PIN6, PIN8, and PIN5. PIN6 and PIN8 are expressed, as PIN1 and PIN5, at sites of vein formation. pin6 synthetically enhances pin1 vein-pattern defects, and pin8 quantitatively enhances pin1pin6 vein-pattern defects. Function of PIN6 is necessary, redundantly with that of PIN8, and sufficient to control auxin response levels, PIN1 expression, and vein network formation; and the vein pattern defects induced by ectopic PIN6 expression are mimicked by ectopic PIN8 expression. Finally, vein patterning functions of PIN6 and PIN8 are antagonized by PIN5 function. Our data define a new level of control of vein patterning, one with repercussions on other patterning processes in the plant, and suggest a mechanism to select cell files specialized for vascular function that predates evolution of PM-localized PIN proteins.

  11. Modelling and predicting biogeographical patterns in river networks

    Sabela Lois

    2016-04-01

    Full Text Available Statistical analysis and interpretation of biogeographical phenomena in rivers is now possible using a spatially explicit modelling framework, which has seen significant developments in the past decade. I used this approach to identify a spatial extent (geostatistical range in which the abundance of the parasitic freshwater pearl mussel (Margaritifera margaritifera L. is spatially autocorrelated in river networks. I show that biomass and abundance of host fish are a likely explanation for the autocorrelation in mussel abundance within a 15-km spatial extent. The application of universal kriging with the empirical model enabled precise prediction of mussel abundance within segments of river networks, something that has the potential to inform conservation biogeography. Although I used a variety of modelling approaches in my thesis, I focus here on the details of this relatively new spatial stream network model, thus advancing the study of biogeographical patterns in river networks.

  12. Global patterns of interaction specialization in bird-flower networks

    Zanata, Thais B.; Dalsgaard, Bo; Passos, Fernando C.

    2017-01-01

    , such as plant species richness, asymmetry, latitude, insularity, topography, sampling methods and intensity. Results: Hummingbird–flower networks were more specialized than honeyeater–flower networks. Specifically, hummingbird–flower networks had a lower proportion of realized interactions (lower C), decreased...... in the interaction patterns with their floral resources. Location: Americas, Africa, Asia and Oceania/Australia. Methods: We compiled interaction networks between birds and floral resources for 79 hummingbird, nine sunbird and 33 honeyeater communities. Interaction specialization was quantified through connectance...... (C), complementary specialization (H2′), binary (QB) and weighted modularity (Q), with both observed and null-model corrected values. We compared interaction specialization among the three types of bird–flower communities, both independently and while controlling for potential confounding variables...

  13. A simplified memory network model based on pattern formations

    Xu, Kesheng; Zhang, Xiyun; Wang, Chaoqing; Liu, Zonghua

    2014-12-01

    Many experiments have evidenced the transition with different time scales from short-term memory (STM) to long-term memory (LTM) in mammalian brains, while its theoretical understanding is still under debate. To understand its underlying mechanism, it has recently been shown that it is possible to have a long-period rhythmic synchronous firing in a scale-free network, provided the existence of both the high-degree hubs and the loops formed by low-degree nodes. We here present a simplified memory network model to show that the self-sustained synchronous firing can be observed even without these two necessary conditions. This simplified network consists of two loops of coupled excitable neurons with different synaptic conductance and with one node being the sensory neuron to receive an external stimulus signal. This model can be further used to show how the diversity of firing patterns can be selectively formed by varying the signal frequency, duration of the stimulus and network topology, which corresponds to the patterns of STM and LTM with different time scales. A theoretical analysis is presented to explain the underlying mechanism of firing patterns.

  14. Optimization of patterns of control bars using neural networks

    Mejia S, D.M.; Ortiz S, J.J.

    2005-01-01

    In this work the RENOPBC system that is based on a recurrent multi state neural network, for the optimization of patterns of control bars in a cycle of balance of a boiling water reactor (BWR for their initials in English) is presented. The design of patterns of bars is based on the execution of operation thermal limits, to maintain criticizes the reactor and that the axial profile of power is adjusted to one predetermined along several steps of burnt. The patterns of control bars proposed by the system are comparable to those proposed by human experts with many hour-man of experience. These results are compared with those proposed by other techniques as genetic algorithms, colonies of ants and tabu search for the same operation cycle. As consequence it is appreciated that the proposed patterns of control bars, have bigger operation easiness that those proposed by the other techniques. (Author)

  15. Electromiography comparison of distal and proximal lower limb muscle activity patterns during external perturbation in subjects with and without functional ankle instability.

    Kazemi, Khadijeh; Arab, Amir Massoud; Abdollahi, Iraj; López-López, Daniel; Calvo-Lobo, César

    2017-10-01

    Ankle sprain is one of the most common injuries among athletes and the general population. Most ankle injuries commonly affect the lateral ligament complex. Changes in postural sway and hip abductor muscle strength may be generated after inversion ankle sprain. Therefore, the consequences of ankle injury may affect proximal structures of the lower limb. The aim is to describe and compare the activity patterns of distal and proximal lower limb muscles following external perturbation in individuals with and without functional ankle instability. The sample consisted of 16 women with functional ankle instability and 18 healthy women were recruited to participate in this research. The external perturbation via body jacket using surface electromyography, amplitude and onset of muscle activity of gluteus maximums, gluteus medius, tibialis anterior, and peroneus longus was recorded and analyzed during external perturbation. There were differences between the onset of muscles activity due to perturbation direction in the two groups (healthy and functional ankle instability). In the healthy group, there were statistically significant differences in amplitude of proximal muscle activity with distal muscle activity during front perturbation with eyes open and closed. In the functional ankle instability group; there were statistically significant differences in amplitude of proximal muscle activity with distal muscle activity during perturbation of the front and back with eyes open. There were statistically significant differences in the onset of muscle activity and amplitude of muscle activity, with-in and between groups (Pankle instability, activation patterns of the lower limb proximal muscles may be altered. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. Chimera patterns in two-dimensional networks of coupled neurons

    Schmidt, Alexander; Kasimatis, Theodoros; Hizanidis, Johanne; Provata, Astero; Hövel, Philipp

    2017-03-01

    We discuss synchronization patterns in networks of FitzHugh-Nagumo and leaky integrate-and-fire oscillators coupled in a two-dimensional toroidal geometry. A common feature between the two models is the presence of fast and slow dynamics, a typical characteristic of neurons. Earlier studies have demonstrated that both models when coupled nonlocally in one-dimensional ring networks produce chimera states for a large range of parameter values. In this study, we give evidence of a plethora of two-dimensional chimera patterns of various shapes, including spots, rings, stripes, and grids, observed in both models, as well as additional patterns found mainly in the FitzHugh-Nagumo system. Both systems exhibit multistability: For the same parameter values, different initial conditions give rise to different dynamical states. Transitions occur between various patterns when the parameters (coupling range, coupling strength, refractory period, and coupling phase) are varied. Many patterns observed in the two models follow similar rules. For example, the diameter of the rings grows linearly with the coupling radius.

  17. Patterns of the cosmic microwave background from evolving string networks

    Bouchet, F.R.; Bennett, D.P.; Stebbins, A.

    1988-01-01

    A network of cosmic strings generated in the early Universe may still exist today. As the strings move across the sky, they produce, by gravitational lensing, a characteristic pattern of anisotropies in the temperature of the cosmic microwave background. The observed absence of such anisotropies places constraints on theories in which galaxy formation is seeded by strings, but it is anticipated that the next generation of experiments will detect them. (author)

  18. Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks.

    Gardner, Brian; Sporea, Ioana; Grüning, André

    2015-12-01

    Information encoding in the nervous system is supported through the precise spike timings of neurons; however, an understanding of the underlying processes by which such representations are formed in the first place remains an open question. Here we examine how multilayered networks of spiking neurons can learn to encode for input patterns using a fully temporal coding scheme. To this end, we introduce a new supervised learning rule, MultilayerSpiker, that can train spiking networks containing hidden layer neurons to perform transformations between spatiotemporal input and output spike patterns. The performance of the proposed learning rule is demonstrated in terms of the number of pattern mappings it can learn, the complexity of network structures it can be used on, and its classification accuracy when using multispike-based encodings. In particular, the learning rule displays robustness against input noise and can generalize well on an example data set. Our approach contributes to both a systematic understanding of how computations might take place in the nervous system and a learning rule that displays strong technical capability.

  19. Flat electronic bands in fractal-kagomé network and the effect of perturbation

    Nandy, Atanu, E-mail: atanunandy1989@gmail.com; Chakrabarti, Arunava, E-mail: arunava-chakrabarti@yahoo.co.in [Department of Physics, University of Kalyani, Kalyani, West Bengal - 741235 (India)

    2016-05-06

    We demonstrate an analytical prescription of demonstrating the flat band [FB] states in a fractal incorporated kagomé type network that can give rise to a countable infinity of flat non-dispersive eigenstates with a multitude of localization area. The onset of localization can, in principle, be delayed in space by an appropriate choice of energy regime. The length scale, at which the onset of localization for each mode occurs, can be tuned at will following the formalism developed within the framework of real space renormalization group. This scheme leads to an exact determination of energy eigenvalue for which one can have dispersionless flat electronic bands. Furthermore, we have shown the effect ofuniform magnetic field for the same non-translationally invariant network model that has ultimately led to an‘apparent invisibility’ of such staggered localized states and to generate absolutely continuous sub-bands in the energy spectrum and again an interesting re-entrant behavior of those FB states.

  20. Maximum-entropy networks pattern detection, network reconstruction and graph combinatorics

    Squartini, Tiziano

    2017-01-01

    This book is an introduction to maximum-entropy models of random graphs with given topological properties and their applications. Its original contribution is the reformulation of many seemingly different problems in the study of both real networks and graph theory within the unified framework of maximum entropy. Particular emphasis is put on the detection of structural patterns in real networks, on the reconstruction of the properties of networks from partial information, and on the enumeration and sampling of graphs with given properties.  After a first introductory chapter explaining the motivation, focus, aim and message of the book, chapter 2 introduces the formal construction of maximum-entropy ensembles of graphs with local topological constraints. Chapter 3 focuses on the problem of pattern detection in real networks and provides a powerful way to disentangle nontrivial higher-order structural features from those that can be traced back to simpler local constraints. Chapter 4 focuses on the problem o...

  1. Application of perturbation methods and sensitivity analysis to water hammer problems in hydraulic networks

    Balino, Jorge L.; Larreteguy, Axel E.; Andrade Lima, Fernando R.

    1995-01-01

    The differential method was applied to the sensitivity analysis for water hammer problems in hydraulic networks. Starting from the classical water hammer equations in a single-phase liquid with friction, the state vector comprising the piezometric head and the velocity was defined. Applying the differential method the adjoint operator, the adjoint equations with the general form of their boundary conditions, and the general form of the bilinear concomitant were calculated. The discretized adjoint equations and the corresponding boundary conditions were programmed and solved by using the so called method of characteristics. As an example, a constant-level tank connected through a pipe to a valve discharging to atmosphere was considered. The bilinear concomitant was calculated for this particular case. The corresponding sensitivity coefficients due to the variation of different parameters by using both the differential method and the response surface generated by the computer code WHAT were also calculated. The results obtained with these methods show excellent agreement. (author). 11 refs, 2 figs, 2 tabs

  2. Integration of hormonal signaling networks and mobile microRNAs is required for vascular patterning in Arabidopsis roots

    Muraro, D.

    2013-12-31

    As multicellular organisms grow, positional information is continually needed to regulate the pattern in which cells are arranged. In the Arabidopsis root, most cell types are organized in a radially symmetric pattern; however, a symmetry-breaking event generates bisymmetric auxin and cytokinin signaling domains in the stele. Bidirectional cross-talk between the stele and the surrounding tissues involving a mobile transcription factor, SHORT ROOT (SHR), and mobile microRNA species also determines vascular pattern, but it is currently unclear how these signals integrate. We use a multicellular model to determine a minimal set of components necessary for maintaining a stable vascular pattern. Simulations perturbing the signaling network show that, in addition to the mutually inhibitory interaction between auxin and cytokinin, signaling through SHR, microRNA165/6, and PHABULOSA is required to maintain a stable bisymmetric pattern. We have verified this prediction by observing loss of bisymmetry in shr mutants. The model reveals the importance of several features of the network, namely the mutual degradation of microRNA165/6 and PHABULOSA and the existence of an additional negative regulator of cytokinin signaling. These components form a plausible mechanism capable of patterning vascular tissues in the absence of positional inputs provided by the transport of hormones from the shoot.

  3. Integration of hormonal signaling networks and mobile microRNAs is required for vascular patterning in Arabidopsis roots

    Muraro, D.; Mellor, N.; Pound, M. P.; Help, H.; Lucas, M.; Chopard, J.; Byrne, H. M.; Godin, C.; Hodgman, T. C.; King, J. R.; Pridmore, T. P.; Helariutta, Y.; Bennett, M. J.; Bishopp, A.

    2013-01-01

    As multicellular organisms grow, positional information is continually needed to regulate the pattern in which cells are arranged. In the Arabidopsis root, most cell types are organized in a radially symmetric pattern; however, a symmetry-breaking event generates bisymmetric auxin and cytokinin signaling domains in the stele. Bidirectional cross-talk between the stele and the surrounding tissues involving a mobile transcription factor, SHORT ROOT (SHR), and mobile microRNA species also determines vascular pattern, but it is currently unclear how these signals integrate. We use a multicellular model to determine a minimal set of components necessary for maintaining a stable vascular pattern. Simulations perturbing the signaling network show that, in addition to the mutually inhibitory interaction between auxin and cytokinin, signaling through SHR, microRNA165/6, and PHABULOSA is required to maintain a stable bisymmetric pattern. We have verified this prediction by observing loss of bisymmetry in shr mutants. The model reveals the importance of several features of the network, namely the mutual degradation of microRNA165/6 and PHABULOSA and the existence of an additional negative regulator of cytokinin signaling. These components form a plausible mechanism capable of patterning vascular tissues in the absence of positional inputs provided by the transport of hormones from the shoot.

  4. Spontaneous Plasticity of Multineuronal Activity Patterns in Activated Hippocampal Networks

    Atsushi Usami

    2008-01-01

    Full Text Available Using functional multineuron imaging with single-cell resolution, we examined how hippocampal networks by themselves change the spatiotemporal patterns of spontaneous activity during the course of emitting spontaneous activity. When extracellular ionic concentrations were changed to those that mimicked in vivo conditions, spontaneous activity was increased in active cell number and activity frequency. When ionic compositions were restored to the control conditions, the activity level returned to baseline, but the weighted spatial dispersion of active cells, as assessed by entropy-based metrics, did not. Thus, the networks can modify themselves by altering the internal structure of their correlated activity, even though they as a whole maintained the same level of activity in space and time.

  5. Delay-induced cluster patterns in coupled Cayley tree networks

    Singh, A.; Jalan, S.

    2013-07-01

    We study effects of delay in diffusively coupled logistic maps on the Cayley tree networks. We find that smaller coupling values exhibit sensitiveness to value of delay, and lead to different cluster patterns of self-organized and driven types. Whereas larger coupling strengths exhibit robustness against change in delay values, and lead to stable driven clusters comprising nodes from last generation of the Cayley tree. Furthermore, introduction of delay exhibits suppression as well as enhancement of synchronization depending upon coupling strength values. To the end we discuss the importance of results to understand conflicts and cooperations observed in family business.

  6. Pattern detection in stream networks: Quantifying spatialvariability in fish distribution

    Torgersen, Christian E.; Gresswell, Robert E.; Bateman, Douglas S.

    2004-01-01

    Biological and physical properties of rivers and streams are inherently difficult to sample and visualize at the resolution and extent necessary to detect fine-scale distributional patterns over large areas. Satellite imagery and broad-scale fish survey methods are effective for quantifying spatial variability in biological and physical variables over a range of scales in marine environments but are often too coarse in resolution to address conservation needs in inland fisheries management. We present methods for sampling and analyzing multiscale, spatially continuous patterns of stream fishes and physical habitat in small- to medium-size watersheds (500–1000 hectares). Geospatial tools, including geographic information system (GIS) software such as ArcInfo dynamic segmentation and ArcScene 3D analyst modules, were used to display complex biological and physical datasets. These tools also provided spatial referencing information (e.g. Cartesian and route-measure coordinates) necessary for conducting geostatistical analyses of spatial patterns (empirical semivariograms and wavelet analysis) in linear stream networks. Graphical depiction of fish distribution along a one-dimensional longitudinal profile and throughout the stream network (superimposed on a 10-metre digital elevation model) provided the spatial context necessary for describing and interpreting the relationship between landscape pattern and the distribution of coastal cutthroat trout (Oncorhynchus clarki clarki) in western Oregon, U.S.A. The distribution of coastal cutthroat trout was highly autocorrelated and exhibited a spherical semivariogram with a defined nugget, sill, and range. Wavelet analysis of the main-stem longitudinal profile revealed periodicity in trout distribution at three nested spatial scales corresponding ostensibly to landscape disturbances and the spacing of tributary junctions.

  7. SPINE: SParse eIgengene NEtwork linking gene expression clusters in Dehalococcoides mccartyi to perturbations in experimental conditions.

    Cresten B Mansfeldt

    Full Text Available We present a statistical model designed to identify the effect of experimental perturbations on the aggregate behavior of the transcriptome expressed by the bacterium Dehalococcoides mccartyi strain 195. Strains of Dehalococcoides are used in sub-surface bioremediation applications because they organohalorespire tetrachloroethene and trichloroethene (common chlorinated solvents that contaminate the environment to non-toxic ethene. However, the biochemical mechanism of this process remains incompletely described. Additionally, the response of Dehalococcoides to stress-inducing conditions that may be encountered at field-sites is not well understood. The constructed statistical model captured the aggregate behavior of gene expression phenotypes by modeling the distinct eigengenes of 100 transcript clusters, determining stable relationships among these clusters of gene transcripts with a sparse network-inference algorithm, and directly modeling the effect of changes in experimental conditions by constructing networks conditioned on the experimental state. Based on the model predictions, we discovered new response mechanisms for DMC, notably when the bacterium is exposed to solvent toxicity. The network identified a cluster containing thirteen gene transcripts directly connected to the solvent toxicity condition. Transcripts in this cluster include an iron-dependent regulator (DET0096-97 and a methylglyoxal synthase (DET0137. To validate these predictions, additional experiments were performed. Continuously fed cultures were exposed to saturating levels of tetrachloethene, thereby causing solvent toxicity, and transcripts that were predicted to be linked to solvent toxicity were monitored by quantitative reverse-transcription polymerase chain reaction. Twelve hours after being shocked with saturating levels of tetrachloroethene, the control transcripts (encoding for a key hydrogenase and the 16S rRNA did not significantly change. By contrast

  8. Patterns of work attitudes: A neural network approach

    Mengov, George D.; Zinovieva, Irina L.; Sotirov, George R.

    2000-05-01

    In this paper we introduce a neural networks based approach to analyzing empirical data and models from work and organizational psychology (WOP), and suggest possible implications for the practice of managers and business consultants. With this method it becomes possible to have quantitative answers to a bunch of questions like: What are the characteristics of an organization in terms of its employees' motivation? What distinct attitudes towards the work exist? Which pattern is most desirable from the standpoint of productivity and professional achievement? What will be the dynamics of behavior as quantified by our method, during an ongoing organizational change or consultancy intervention? Etc. Our investigation is founded on the theoretical achievements of Maslow (1954, 1970) in human motivation, and of Hackman & Oldham (1975, 1980) in job diagnostics, and applies the mathematical algorithm of the dARTMAP variation (Carpenter et al., 1998) of the Adaptive Resonance Theory (ART) neural networks introduced by Grossberg (1976). We exploit the ART capabilities to visualize the knowledge accumulated in the network's long-term memory in order to interpret the findings in organizational research.

  9. A Network Based Methodology to Reveal Patterns in Knowledge Transfer

    Orlando López-Cruz

    2015-12-01

    Full Text Available This paper motivates, presents and demonstrates in use a methodology based in complex network analysis to support research aimed at identification of sources in the process of knowledge transfer at the interorganizational level. The importance of this methodology is that it states a unified model to reveal knowledge sharing patterns and to compare results from multiple researches on data from different periods of time and different sectors of the economy. This methodology does not address the underlying statistical processes. To do this, national statistics departments (NSD provide documents and tools at their websites. But this proposal provides a guide to model information inferences gathered from data processing revealing links between sources and recipients of knowledge being transferred and that the recipient detects as main source to new knowledge creation. Some national statistics departments set as objective for these surveys the characterization of innovation dynamics in firms and to analyze the use of public support instruments. From this characterization scholars conduct different researches. Measures of dimensions of the network composed by manufacturing firms and other organizations conform the base to inquiry the structure that emerges from taking ideas from other organizations to incept innovations. These two sets of data are actors of a two- mode-network. The link between two actors (network nodes, one acting as the source of the idea. The second one acting as the destination comes from organizations or events organized by organizations that “provide” ideas to other group of firms. The resulting demonstrated design satisfies the objective of being a methodological model to identify sources in knowledge transfer of knowledge effectively used in innovation.

  10. Cultured Neural Networks: Optimization of Patterned Network Adhesiveness and Characterization of their Neural Activity

    W. L. C. Rutten

    2006-01-01

    Full Text Available One type of future, improved neural interface is the “cultured probe”. It is a hybrid type of neural information transducer or prosthesis, for stimulation and/or recording of neural activity. It would consist of a microelectrode array (MEA on a planar substrate, each electrode being covered and surrounded by a local circularly confined network (“island” of cultured neurons. The main purpose of the local networks is that they act as biofriendly intermediates for collateral sprouts from the in vivo system, thus allowing for an effective and selective neuron–electrode interface. As a secondary purpose, one may envisage future information processing applications of these intermediary networks. In this paper, first, progress is shown on how substrates can be chemically modified to confine developing networks, cultured from dissociated rat cortex cells, to “islands” surrounding an electrode site. Additional coating of neurophobic, polyimide-coated substrate by triblock-copolymer coating enhances neurophilic-neurophobic adhesion contrast. Secondly, results are given on neuronal activity in patterned, unconnected and connected, circular “island” networks. For connected islands, the larger the island diameter (50, 100 or 150 μm, the more spontaneous activity is seen. Also, activity may show a very high degree of synchronization between two islands. For unconnected islands, activity may start at 22 days in vitro (DIV, which is two weeks later than in unpatterned networks.

  11. Lag synchronization of unknown chaotic delayed Yang-Yang-type fuzzy neural networks with noise perturbation based on adaptive control and parameter identification.

    Xia, Yonghui; Yang, Zijiang; Han, Maoan

    2009-07-01

    This paper considers the lag synchronization (LS) issue of unknown coupled chaotic delayed Yang-Yang-type fuzzy neural networks (YYFCNN) with noise perturbation. Separate research work has been published on the stability of fuzzy neural network and LS issue of unknown coupled chaotic neural networks, as well as its application in secure communication. However, there have not been any studies that integrate the two. Motivated by the achievements from both fields, we explored the benefits of integrating fuzzy logic theories into the study of LS problems and applied the findings to secure communication. Based on adaptive feedback control techniques and suitable parameter identification, several sufficient conditions are developed to guarantee the LS of coupled chaotic delayed YYFCNN with or without noise perturbation. The problem studied in this paper is more general in many aspects. Various problems studied extensively in the literature can be treated as special cases of the findings of this paper, such as complete synchronization (CS), effect of fuzzy logic, and noise perturbation. This paper presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed adaptive scheme. This research also demonstrates the effectiveness of application of the proposed adaptive feedback scheme in secure communication by comparing chaotic masking with fuzziness with some previous studies. Chaotic signal with fuzziness is more complex, which makes unmasking more difficult due to the added fuzzy logic.

  12. Differential theory of learning for efficient neural network pattern recognition

    Hampshire, John B., II; Vijaya Kumar, Bhagavatula

    1993-09-01

    We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generate well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts.

  13. Inheritance Patterns in Citation Networks Reveal Scientific Memes

    Tobias Kuhn

    2014-11-01

    Full Text Available Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Our analysis of memes in the scientific literature reveals that they are governed by a surprisingly simple relationship between frequency of occurrence and the degree to which they propagate along the citation graph. We propose a simple formalization of this pattern and validate it with data from close to 50 million publication records from the Web of Science, PubMed Central, and the American Physical Society. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative approaches confirm that our formula is accurate and effective, without a dependence on linguistic or ontological knowledge and without the application of arbitrary thresholds or filters.

  14. Inheritance Patterns in Citation Networks Reveal Scientific Memes

    Kuhn, Tobias; Perc, Matjaž; Helbing, Dirk

    2014-10-01

    Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Our analysis of memes in the scientific literature reveals that they are governed by a surprisingly simple relationship between frequency of occurrence and the degree to which they propagate along the citation graph. We propose a simple formalization of this pattern and validate it with data from close to 50 million publication records from the Web of Science, PubMed Central, and the American Physical Society. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative approaches confirm that our formula is accurate and effective, without a dependence on linguistic or ontological knowledge and without the application of arbitrary thresholds or filters.

  15. Co-authorship patterns and networks of Korean radiation oncologists

    Choi, Jin Hyun; Kang, Jin Oh; Park, Seo Hyun; Kim, Sang Ki

    2011-01-01

    This research aimed to analyze the patterns of co-authorship network among the Korean radiation oncologists and to identify attributing factors for the formation of networks. A total of 1,447 articles including contents of 'Radiation Oncology' and 'Therapeutic Radiology' were searched from the KoreaMed database. The co-authorship was assorted by the author's full name, affiliation and specialties. UCINET 6.0 was used to figure out the author's network centrality and the cluster analysis, and KeyPlayer 1.44 program was used to get a result of key player index. Sociogram was analyzed with the Netdraw 2.090. The statistical comparison was performed by a t-test and ANOVA using SPSS 16.0 with p-value < 0.05 as the significant value. The number of articles written by a radiation oncologist as the fi rst author was 1,025 out of 1,447. The pattern of coauthorship was classified into fi ve groups. For articles of which the fi rst author was a radiation oncologist, the number of single author articles (type-A) was 81; single-institution articles (type-B) was 687; and multiple-author articles (type-C) was 257. For the articles which radiation oncologists participated in as a co-author, the number of single-institution articles (type-D) was 280 while multiple-institution articles (type-E) were 142. There were 8,895 authors from 1,366 co-authored articles, thus the average number of authors per article was 6.51. It was 5.73 for type-B, 6.44 for type-C, 7.90 for type-D, and 7.67 for type-E (p 0.000) in the average number of authors per article. The number of authors for articles from the hospitals published more than 100 articles was 7.23 while form others was 5.94 (p = 0.005). Its number was 5.94 and 7.16 for the articles published before and after 2001 (p = 0.000). The articles written by a radiation oncologist as the fi rst author had 5.92 authors while others for 7.82 (p = 0.025). Its number was 5.57 and 7.71 for the Journal of the Korean Society for Therapeutic Radiology

  16. Temporal prediction of epidemic patterns in community networks

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

    2013-01-01

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

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

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

    2016-11-01

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

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

    Bornholdt, S. [Heidelberg Univ., (Germany). Inst., fuer Theoretische Physik; Graudenz, D. [Lawrence Berkeley Lab., CA (United States)

    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.

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

    Bornholdt, S.

    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

  20. Perturbation theory

    Bartlett, R.; Kirtman, B.; Davidson, E.R.

    1978-01-01

    After noting some advantages of using perturbation theory some of the various types are related on a chart and described, including many-body nonlinear summations, quartic force-field fit for geometry, fourth-order correlation approximations, and a survey of some recent work. Alternative initial approximations in perturbation theory are also discussed. 25 references

  1. Power Terminal Communication Access Network Monitoring System Scheme Based on Design Patterns

    Yan, Shengchao; Wu, Desheng; Zhu, Jiang

    2018-01-01

    In order to realize patterns design for terminal communication monitoring system, this paper introduces manager-workers, tasks-workers design patterns, based on common design patterns such as factory method, chain of responsibility, facade. Using these patterns, the communication monitoring system which combines module-groups like networking communication, business data processing and the peripheral support has been designed successfully. Using these patterns makes this system have great flexibility and scalability and improves the degree of systematic pattern design structure.

  2. Effects of traffic generation patterns on the robustness of complex networks

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

    2018-02-01

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

  3. Complex networks from experimental horizontal oil–water flows: Community structure detection versus flow pattern discrimination

    Gao, Zhong-Ke; Fang, Peng-Cheng; Ding, Mei-Shuang; Yang, Dan; Jin, Ning-De

    2015-01-01

    We propose a complex network-based method to distinguish complex patterns arising from experimental horizontal oil–water two-phase flow. We first use the adaptive optimal kernel time–frequency representation (AOK TFR) to characterize flow pattern behaviors from the energy and frequency point of view. Then, we infer two-phase flow complex networks from experimental measurements and detect the community structures associated with flow patterns. The results suggest that the community detection in two-phase flow complex network allows objectively discriminating complex horizontal oil–water flow patterns, especially for the segregated and dispersed flow patterns, a task that existing method based on AOK TFR fails to work. - Highlights: • We combine time–frequency analysis and complex network to identify flow patterns. • We explore the transitional flow behaviors in terms of betweenness centrality. • Our analysis provides a novel way for recognizing complex flow patterns. • Broader applicability of our method is demonstrated and articulated

  4. Comparison of eye imaging pattern recognition using neural network

    Bukhari, W. M.; Syed A., M.; Nasir, M. N. M.; Sulaima, M. F.; Yahaya, M. S.

    2015-05-01

    The beauty of eye recognition system that it is used in automatic identifying and verifies a human weather from digital images or video source. There are various behaviors of the eye such as the color of the iris, size of pupil and shape of the eye. This study represents the analysis, design and implementation of a system for recognition of eye imaging. All the eye images that had been captured from the webcam in RGB format must through several techniques before it can be input for the pattern and recognition processes. The result shows that the final value of weight and bias after complete training 6 eye images for one subject is memorized by the neural network system and be the reference value of the weight and bias for the testing part. The target classifies to 5 different types for 5 subjects. The eye images can recognize the subject based on the target that had been set earlier during the training process. When the values between new eye image and the eye image in the database are almost equal, it is considered the eye image is matched.

  5. Mechanized extraction of topology anti-patterns in wireless networks

    Woehrle, M.; Bakhshi, R.; Mousavi, M.R.; Derrick, J.; Gnesi, S.; Latella, D.; Treharne, H.

    2012-01-01

    Exhaustive and mechanized formal verification of wireless networks is hampered by the huge number of possible topologies and the large size of the actual networks. However, the generic communication structure in such networks allows for reducing the root causes of faults to faulty (sub-)topologies,

  6. An approach to evaluate the topological significance of motifs and other patterns in regulatory networks

    Wingender Edgar

    2009-05-01

    Full Text Available Abstract Background The identification of network motifs as statistically over-represented topological patterns has become one of the most promising topics in the analysis of complex networks. The main focus is commonly made on how they operate by means of their internal organization. Yet, their contribution to a network's global architecture is poorly understood. However, this requires switching from the abstract view of a topological pattern to the level of its instances. Here, we show how a recently proposed metric, the pairwise disconnectivity index, can be adapted to survey if and which kind of topological patterns and their instances are most important for sustaining the connectivity within a network. Results The pairwise disconnectivity index of a pattern instance quantifies the dependency of the pairwise connections between vertices in a network on the presence of this pattern instance. Thereby, it particularly considers how the coherence between the unique constituents of a pattern instance relates to the rest of a network. We have applied the method exemplarily to the analysis of 3-vertex topological pattern instances in the transcription networks of a bacteria (E. coli, a unicellular eukaryote (S. cerevisiae and higher eukaryotes (human, mouse, rat. We found that in these networks only very few pattern instances break lots of the pairwise connections between vertices upon the removal of an instance. Among them network motifs do not prevail. Rather, those patterns that are shared by the three networks exhibit a conspicuously enhanced pairwise disconnectivity index. Additionally, these are often located in close vicinity to each other or are even overlapping, since only a small number of genes are repeatedly present in most of them. Moreover, evidence has gathered that the importance of these pattern instances is due to synergistic rather than merely additive effects between their constituents. Conclusion A new method has been proposed

  7. Network-level accident-mapping: Distance based pattern matching using artificial neural network.

    Deka, Lipika; Quddus, Mohammed

    2014-04-01

    The objective of an accident-mapping algorithm is to snap traffic accidents onto the correct road segments. Assigning accidents onto the correct segments facilitate to robustly carry out some key analyses in accident research including the identification of accident hot-spots, network-level risk mapping and segment-level accident risk modelling. Existing risk mapping algorithms have some severe limitations: (i) they are not easily 'transferable' as the algorithms are specific to given accident datasets; (ii) they do not perform well in all road-network environments such as in areas of dense road network; and (iii) the methods used do not perform well in addressing inaccuracies inherent in and type of road environment. The purpose of this paper is to develop a new accident mapping algorithm based on the common variables observed in most accident databases (e.g. road name and type, direction of vehicle movement before the accident and recorded accident location). The challenges here are to: (i) develop a method that takes into account uncertainties inherent to the recorded traffic accident data and the underlying digital road network data, (ii) accurately determine the type and proportion of inaccuracies, and (iii) develop a robust algorithm that can be adapted for any accident set and road network of varying complexity. In order to overcome these challenges, a distance based pattern-matching approach is used to identify the correct road segment. This is based on vectors containing feature values that are common in the accident data and the network data. Since each feature does not contribute equally towards the identification of the correct road segments, an ANN approach using the single-layer perceptron is used to assist in "learning" the relative importance of each feature in the distance calculation and hence the correct link identification. The performance of the developed algorithm was evaluated based on a reference accident dataset from the UK confirming that

  8. Application of a fuzzy neural network model in predicting polycyclic aromatic hydrocarbon-mediated perturbations of the Cyp1b1 transcriptional regulatory network in mouse skin

    Larkin, Andrew [Department of Environmental and Molecular Toxicology, Oregon State University (United States); Department of Statistics, Oregon State University (United States); Superfund Research Center, Oregon State University (United States); Siddens, Lisbeth K. [Department of Environmental and Molecular Toxicology, Oregon State University (United States); Superfund Research Center, Oregon State University (United States); Krueger, Sharon K. [Superfund Research Center, Oregon State University (United States); Linus Pauling Institute, Oregon State University (United States); Tilton, Susan C.; Waters, Katrina M. [Superfund Research Center, Oregon State University (United States); Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory, Richland, WA 99352 (United States); Williams, David E., E-mail: david.williams@oregonstate.edu [Department of Environmental and Molecular Toxicology, Oregon State University (United States); Superfund Research Center, Oregon State University (United States); Linus Pauling Institute, Oregon State University (United States); Environmental Health Sciences Center, Oregon State University, Corvallis, OR 97331 (United States); Baird, William M. [Department of Environmental and Molecular Toxicology, Oregon State University (United States); Superfund Research Center, Oregon State University (United States); Environmental Health Sciences Center, Oregon State University, Corvallis, OR 97331 (United States)

    2013-03-01

    Polycyclic aromatic hydrocarbons (PAHs) are present in the environment as complex mixtures with components that have diverse carcinogenic potencies and mostly unknown interactive effects. Non-additive PAH interactions have been observed in regulation of cytochrome P450 (CYP) gene expression in the CYP1 family. To better understand and predict biological effects of complex mixtures, such as environmental PAHs, an 11 gene input-1 gene output fuzzy neural network (FNN) was developed for predicting PAH-mediated perturbations of dermal Cyp1b1 transcription in mice. Input values were generalized using fuzzy logic into low, medium, and high fuzzy subsets, and sorted using k-means clustering to create Mamdani logic functions for predicting Cyp1b1 mRNA expression. Model testing was performed with data from microarray analysis of skin samples from FVB/N mice treated with toluene (vehicle control), dibenzo[def,p]chrysene (DBC), benzo[a]pyrene (BaP), or 1 of 3 combinations of diesel particulate extract (DPE), coal tar extract (CTE) and cigarette smoke condensate (CSC) using leave-one-out cross-validation. Predictions were within 1 log{sub 2} fold change unit of microarray data, with the exception of the DBC treatment group, where the unexpected down-regulation of Cyp1b1 expression was predicted but did not reach statistical significance on the microarrays. Adding CTE to DPE was predicted to increase Cyp1b1 expression, whereas adding CSC to CTE and DPE was predicted to have no effect, in agreement with microarray results. The aryl hydrocarbon receptor repressor (Ahrr) was determined to be the most significant input variable for model predictions using back-propagation and normalization of FNN weights. - Highlights: ► Tested a model to predict PAH mixture-mediated changes in Cyp1b1 expression ► Quantitative predictions in agreement with microarrays for Cyp1b1 induction ► Unexpected difference in expression between DBC and other treatments predicted ► Model predictions

  9. Visibility Network Patterns and Methods for Studying Visual Relational Phenomena in Archeology

    Tom Brughmans

    2017-08-01

    Full Text Available A review of the archeological and non-archeological use of visibility networks reveals the use of a limited range of formal techniques, in particular for representing visibility theories. This paper aims to contribute to the study of complex visual relational phenomena in landscape archeology by proposing a range of visibility network patterns and methods. We propose first- and second-order visibility graph representations of total and cumulative viewsheds, and two-mode representations of cumulative viewsheds. We present network patterns that can be used to represent aspects of visibility theories and that can be used in statistical simulation models to compare theorized networks with observed networks. We argue for the need to incorporate observed visibility network density in these simulation models, by illustrating strong differences in visibility network density in three example landscapes. The approach is illustrated through a brief case study of visibility networks of long barrows in Cranborne Chase.

  10. The characteristic patterns of neuronal avalanches in mice under anesthesia and at rest: An investigation using constrained artificial neural networks

    Knöpfel, Thomas; Leech, Robert

    2018-01-01

    Local perturbations within complex dynamical systems can trigger cascade-like events that spread across significant portions of the system. Cascades of this type have been observed across a broad range of scales in the brain. Studies of these cascades, known as neuronal avalanches, usually report the statistics of large numbers of avalanches, without probing the characteristic patterns produced by the avalanches themselves. This is partly due to limitations in the extent or spatiotemporal resolution of commonly used neuroimaging techniques. In this study, we overcome these limitations by using optical voltage (genetically encoded voltage indicators) imaging. This allows us to record cortical activity in vivo across an entire cortical hemisphere, at both high spatial (~30um) and temporal (~20ms) resolution in mice that are either in an anesthetized or awake state. We then use artificial neural networks to identify the characteristic patterns created by neuronal avalanches in our data. The avalanches in the anesthetized cortex are most accurately classified by an artificial neural network architecture that simultaneously connects spatial and temporal information. This is in contrast with the awake cortex, in which avalanches are most accurately classified by an architecture that treats spatial and temporal information separately, due to the increased levels of spatiotemporal complexity. This is in keeping with reports of higher levels of spatiotemporal complexity in the awake brain coinciding with features of a dynamical system operating close to criticality. PMID:29795654

  11. A Neural Network-Based Interval Pattern Matcher

    Jing Lu

    2015-07-01

    Full Text Available One of the most important roles in the machine learning area is to classify, and neural networks are very important classifiers. However, traditional neural networks cannot identify intervals, let alone classify them. To improve their identification ability, we propose a neural network-based interval matcher in our paper. After summarizing the theoretical construction of the model, we take a simple and a practical weather forecasting experiment, which show that the recognizer accuracy reaches 100% and that is promising.

  12. Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics

    Lamoš, Martin; Mareček, Radek; Slavíček, Tomáš; Mikl, Michal; Rektor, Ivan; Jan, Jiří

    2018-06-01

    Objective. Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during electroencephalogram (EEG) data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. Approach. The blind decomposition of EEG spectrogram by parallel factor analysis has been shown to be a useful technique for uncovering patterns of neural activity. The simultaneously acquired BOLD fMRI data were decomposed by independent component analysis. Dynamic functional connectivity was computed on the component’s time series using a sliding window correlation, and between-network connectivity states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of between-network connectivity states and the fluctuations of EEG spectral patterns. Main results. We found three patterns related to the dynamics of between-network connectivity states. The first pattern has dominant peaks in the alpha, beta, and gamma bands and is related to the dynamics between the auditory, sensorimotor, and attentional networks. The second pattern, with dominant peaks in the theta and low alpha bands, is related to the visual and default mode network. The third pattern, also with peaks in the theta and low alpha bands, is related to the auditory and frontal network. Significance. Our previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. In this study, we suggest that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral

  13. A comprehensive analysis on preservation patterns of gene co-expression networks during Alzheimer's disease progression.

    Ray, Sumanta; Hossain, Sk Md Mosaddek; Khatun, Lutfunnesa; Mukhopadhyay, Anirban

    2017-12-20

    Alzheimer's disease (AD) is a chronic neuro-degenerative disruption of the brain which involves in large scale transcriptomic variation. The disease does not impact every regions of the brain at the same time, instead it progresses slowly involving somewhat sequential interaction with different regions. Analysis of the expression patterns of the genes in different regions of the brain influenced in AD surely contribute for a enhanced comprehension of AD pathogenesis and shed light on the early characterization of the disease. Here, we have proposed a framework to identify perturbation and preservation characteristics of gene expression patterns across six distinct regions of the brain ("EC", "HIP", "PC", "MTG", "SFG", and "VCX") affected in AD. Co-expression modules were discovered considering a couple of regions at once. These are then analyzed to know the preservation and perturbation characteristics. Different module preservation statistics and a rank aggregation mechanism have been adopted to detect the changes of expression patterns across brain regions. Gene ontology (GO) and pathway based analysis were also carried out to know the biological meaning of preserved and perturbed modules. In this article, we have extensively studied the preservation patterns of co-expressed modules in six distinct brain regions affected in AD. Some modules are emerged as the most preserved while some others are detected as perturbed between a pair of brain regions. Further investigation on the topological properties of preserved and non-preserved modules reveals a substantial association amongst "betweenness centrality" and "degree" of the involved genes. Our findings may render a deeper realization of the preservation characteristics of gene expression patterns in discrete brain regions affected by AD.

  14. Optimizing the spatial pattern of networks for monitoring radioactive releases

    Melles, S.J.; Heuvelink, G.B.M.; Twenhofel, C.J.W.; Dijk, van A.; Hiemstra, P.H.; Baume, O.P.; Stohlker, U.

    2011-01-01

    This study presents a method to optimize the sampling design of environmental monitoring networks in a multi-objective setting. We optimize the permanent network of radiation monitoring stations in the Netherlands and parts of Germany as an example. The optimization method proposed combines

  15. Linking network usage patterns to traffic Gaussianity fit

    de Oliveira Schmidt, R.; Sadre, R.; Melnikov, Nikolay; Schönwälder, Jürgen; Pras, Aiko

    Gaussian traffic models are widely used in the domain of network traffic modeling. The central assumption is that traffic aggregates are Gaussian distributed. Due to its importance, the Gaussian character of network traffic has been extensively assessed by researchers in the past years. In 2001,

  16. Pattern Recognition and Classification of Fatal Traffic Accidents in Israel A Neural Network Approach

    Prato, Carlo Giacomo; Gitelman, Victoria; Bekhor, Shlomo

    2011-01-01

    on 1,793 fatal traffic accidents occurred during the period between 2003 and 2006 and applies Kohonen and feed-forward back-propagation neural networks with the objective of extracting from the data typical patterns and relevant factors. Kohonen neural networks reveal five compelling accident patterns....... Feed-forward back-propagation neural networks indicate that sociodemographic characteristics of drivers and victims, accident location, and period of the day are extremely relevant factors. Accident patterns suggest that countermeasures are necessary for identified problems concerning mainly vulnerable...

  17. Generalised power graph compression reveals dominant relationship patterns in complex networks.

    Ahnert, Sebastian E

    2014-03-25

    We introduce a framework for the discovery of dominant relationship patterns in complex networks, by compressing the networks into power graphs with overlapping power nodes. When paired with enrichment analysis of node classification terms, the most compressible sets of edges provide a highly informative sketch of the dominant relationship patterns that define the network. In addition, this procedure also gives rise to a novel, link-based definition of overlapping node communities in which nodes are defined by their relationships with sets of other nodes, rather than through connections within the community. We show that this completely general approach can be applied to undirected, directed, and bipartite networks, yielding valuable insights into the large-scale structure of real-world networks, including social networks and food webs. Our approach therefore provides a novel way in which network architecture can be studied, defined and classified.

  18. Traffic Perturbation

    C. Colloca TS/FM

    2004-01-01

    TS/FM group informs you that, for the progress of the works at the Prévessin site entrance, some perturbation of the traffic may occur during the week between the 14th and 18th of June for a short duration. Access will be assured at any time. For more information, please contact 160239. C. Colloca TS/FM

  19. Retrospective qualitative analysis of ecological networks under environmental perturbation: a copper-polluted intertidal community as a case study.

    Ramos-Jiliberto, Rodrigo; Garay-Narváez, Leslie; Medina, Matías H

    2012-01-01

    The coast of Chañaral Bay in northern Chile has been affected by copper mine wastes for decades. This sustained perturbation has disrupted the intertidal community in several ways, but the mechanisms behind the observed shifts in local biodiversity remain poorly understood. Our main goal was to identify the species (lumped into trophic groups) belonging to the Chañaral intertidal community that, being directly affected by copper pollution, contributed primarily to the generation of the observed changes in community structure. These groups of species were called initiators. We applied a qualitative modelling approach based only on the sign and direction of effects among species, and present a formula for predicting changes in equilibrium abundances considering stress on multiple variables simultaneously. We then applied this technique retrospectively to identify the most likely set of initiators. Our analyses allowed identification of a unique set of four initiators in the studied intertidal system (a group of algae, sessile invertebrates, a group of herbivores and starfish), which were hypothesized to be the primary drivers of the observed changes in community structure. In addition, a hypothesis was derived about how the perturbation affected these initiators. The hypothesis is that pollution affected negatively the population growth rate of both algae and sessile invertebrates and suppressed the interaction between herbivores and starfish. Our analytic approach, focused on identifying initiators, constitutes an advance towards understanding the mechanisms underlying human-driven ecosystem disruption and permits identifying species that may serve as a focal point for community management and restoration.

  20. Integration of genome-scale metabolic networks into whole-body PBPK models shows phenotype-specific cases of drug-induced metabolic perturbation.

    Cordes, Henrik; Thiel, Christoph; Baier, Vanessa; Blank, Lars M; Kuepfer, Lars

    2018-01-01

    Drug-induced perturbations of the endogenous metabolic network are a potential root cause of cellular toxicity. A mechanistic understanding of such unwanted side effects during drug therapy is therefore vital for patient safety. The comprehensive assessment of such drug-induced injuries requires the simultaneous consideration of both drug exposure at the whole-body and resulting biochemical responses at the cellular level. We here present a computational multi-scale workflow that combines whole-body physiologically based pharmacokinetic (PBPK) models and organ-specific genome-scale metabolic network (GSMN) models through shared reactions of the xenobiotic metabolism. The applicability of the proposed workflow is illustrated for isoniazid, a first-line antibacterial agent against Mycobacterium tuberculosis , which is known to cause idiosyncratic drug-induced liver injuries (DILI). We combined GSMN models of a human liver with N-acetyl transferase 2 (NAT2)-phenotype-specific PBPK models of isoniazid. The combined PBPK-GSMN models quantitatively describe isoniazid pharmacokinetics, as well as intracellular responses, and changes in the exometabolome in a human liver following isoniazid administration. Notably, intracellular and extracellular responses identified with the PBPK-GSMN models are in line with experimental and clinical findings. Moreover, the drug-induced metabolic perturbations are distributed and attenuated in the metabolic network in a phenotype-dependent manner. Our simulation results show that a simultaneous consideration of both drug pharmacokinetics at the whole-body and metabolism at the cellular level is mandatory to explain drug-induced injuries at the patient level. The proposed workflow extends our mechanistic understanding of the biochemistry underlying adverse events and may be used to prevent drug-induced injuries in the future.

  1. Extracellular magnesium enhances the damage to locomotor networks produced by metabolic perturbation mimicking spinal injury in the neonatal rat spinal cord in vitro.

    Margaryan, G; Mladinic, M; Mattioli, C; Nistri, A

    2009-10-06

    An acute injury to brain or spinal cord produces profound metabolic perturbation that extends and exacerbates tissue damage. Recent clinical interventions to treat this condition with i.v. Mg(2+) to stabilize its extracellular concentration provided disappointing results. The present study used an in vitro spinal cord model from the neonatal rat to investigate the role of extracellular Mg(2+) in the lesion evoked by a pathological medium mimicking the metabolic perturbation (hypoxia, aglycemia, oxidative stress, and acid pH) occurring in vivo. Damage was measured by taking as outcome locomotor network activity for up to 24 h after the primary insult. Pathological medium in 1 mM Mg(2+) solution (1 h) largely depressed spinal reflexes and suppressed fictive locomotion on the same and the following day. Conversely, pathological medium in either Mg(2+)-free or 5 mM Mg(2+) solution evoked temporary network depression and enabled fictive locomotion the day after. While global cell death was similar regardless of extracellular Mg(2+) solution, white matter was particularly affected. In ventral horn the number of surviving neurons was the highest in Mg(2+) free solution and the lowest in 1 mM Mg(2+), while motoneurons were unaffected. Although the excitotoxic damage elicited by kainate was insensitive to extracellular Mg(2+), 1 mM Mg(2+) potentiated the effect of combining pathological medium with kainate at low concentrations. These results indicate that preserving Mg(2+) homeostasis rendered experimental spinal injury more severe. Furthermore, analyzing ventral horn neuron numbers in relation to fictive locomotion expression might provide a first estimate of the minimal size of the functional locomotor network.

  2. Persistence of self-recruitment and patterns of larval connectivity in a marine protected area network

    Berumen, Michael L.; Almany, Glenn R; Planes, Serge; Jones, Geoffrey P; Saenz Agudelo, Pablo; Thorrold, Simon R

    2012-01-01

    to maintain local populations while simultaneously supplying larvae to other MPA nodes in the network that might otherwise suffer local extinction. Here, we use genetic parentage analysis to demonstrate that patterns of self-recruitment of two reef fishes

  3. Quasi-minimal active disturbance rejection control of MIMO perturbed linear systems based on differential neural networks and the attractive ellipsoid method.

    Salgado, Iván; Mera-Hernández, Manuel; Chairez, Isaac

    2017-11-01

    This study addresses the problem of designing an output-based controller to stabilize multi-input multi-output (MIMO) systems in the presence of parametric disturbances as well as uncertainties in the state model and output noise measurements. The controller design includes a linear state transformation which separates uncertainties matched to the control input and the unmatched ones. A differential neural network (DNN) observer produces a nonlinear approximation of the matched perturbation and the unknown states simultaneously in the transformed coordinates. This study proposes the use of the Attractive Ellipsoid Method (AEM) to optimize the gains of the controller and the gain observer in the DNN structure. As a consequence, the obtained control input minimizes the convergence zone for the estimation error. Moreover, the control design uses the estimated disturbance provided by the DNN to obtain a better performance in the stabilization task in comparison with a quasi-minimal output feedback controller based on a Luenberger observer and a sliding mode controller. Numerical results pointed out the advantages obtained by the nonlinear control based on the DNN observer. The first example deals with the stabilization of an academic linear MIMO perturbed system and the second example stabilizes the trajectories of a DC-motor into a predefined operation point. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  4. Stability of U(VI) and Tc(VII) Reducing Microbial Communities to Environmental Perturbation: Development and Testing of a Thermodynamic Network Model

    McKinley, James P.; Istok, Jonathan

    2005-01-01

    Previously published research from in situ field experiments at the NABIR Field Research Center have shown that cooperative metabolism of denitrifiers and Fe(III)/sulfate reducers is essential for creating subsurface conditions favorable for U(VI) and Tc(VII) bioreduction (Istok et al., 2004). The overall goal of this project is to develop and test a thermodynamic network model for predicting the effects of substrate additions and environmental perturbations on the composition and functional stability of subsurface microbial communities. The overall scientific hypothesis is that a thermodynamic analysis of the energy-yielding reactions performed by broadly defined groups of microorganisms can be used to make quantitative and testable predictions of the change in microbial community composition that will occur when a substrate is added to the subsurface or when environmental conditions change. An interactive computer program was developed to calculate the overall growth equation and free energy yield for microorganisms that grow by coupling selected combinations of electron acceptor and electron donor half-reactions. Each group performs a specific function (e.g. oxidation of acetate coupled to reduction of nitrate); collectively the groups provide a theoretical description of the entire natural microbial community. The microbial growth data are combined with an existing thermodynamic data base for associated geochemical reactions and used to simulate the coupled microbial-geochemical response of a complex natural system to substrate addition or any other environmental perturbations

  5. Network patterns in exponentially growing two-dimensional biofilms

    Zachreson, Cameron; Yap, Xinhui; Gloag, Erin S.; Shimoni, Raz; Whitchurch, Cynthia B.; Toth, Milos

    2017-10-01

    Anisotropic collective patterns occur frequently in the morphogenesis of two-dimensional biofilms. These patterns are often attributed to growth regulation mechanisms and differentiation based on gradients of diffusing nutrients and signaling molecules. Here, we employ a model of bacterial growth dynamics to show that even in the absence of growth regulation or differentiation, confinement by an enclosing medium such as agar can itself lead to stable pattern formation over time scales that are employed in experiments. The underlying mechanism relies on path formation through physical deformation of the enclosing environment.

  6. Design of a mutual authentication based on NTRUsign with a perturbation and inherent multipoint control protocol frames in an Ethernet-based passive optical network

    Yin, Aihan; Ding, Yisheng

    2014-11-01

    Identity-related security issues inherently present in passive optical networks (PON) still exist in the current (1G) and next-generation (10G) Ethernet-based passive optical network (EPON) systems. We propose a mutual authentication scheme that integrates an NTRUsign digital signature algorithm with inherent multipoint control protocol (MPCP) frames over an EPON system between the optical line terminal (OLT) and optical network unit (ONU). Here, a primitive NTRUsign algorithm is significantly modified through the use of a new perturbation so that it can be effectively used for simultaneously completing signature and authentication functions on the OLT and the ONU sides. Also, in order to transmit their individual sensitive messages, which include public key, signature, and random value and so forth, to each other, we redefine three unique frames according to MPCP format frame. These generated messages can be added into the frames and delivered to each other, allowing the OLT and the ONU to go ahead with a mutual identity authentication process to verify their legal identities. Our simulation results show that this proposed scheme performs very well in resisting security attacks and has low influence on the registration efficiency to to-be-registered ONUs. A performance comparison with traditional authentication algorithms is also presented. To the best of our knowledge, no detailed design of mutual authentication in EPON can be found in the literature up to now.

  7. Hypothesis Management Framework: a exible design pattern for belief networks in decision support systems

    Gosliga, S.P. van; Voorde, I. van de

    2008-01-01

    This article discusses a design pattern for building belief networks for application domains in which causal models are hard to construct. In this approach we pursue a modular belief network structure that is easily extended by the users themselves, while remaining reliable for decision support. The

  8. Dynamical patterns of calcium signaling in a functional model of neuron-astrocyte networks

    Postnov, D.E.; Koreshkov, R.N.; Brazhe, N.A.

    2009-01-01

    We propose a functional mathematical model for neuron-astrocyte networks. The model incorporates elements of the tripartite synapse and the spatial branching structure of coupled astrocytes. We consider glutamate-induced calcium signaling as a specific mode of excitability and transmission...... in astrocytic-neuronal networks. We reproduce local and global dynamical patterns observed experimentally....

  9. Numerical analysis of a neural network with hierarchically organized patterns

    Bacci, Silvia; Wiecko, Cristina; Parga, Nestor

    1988-01-01

    A numerical analysis of the retrieval behaviour of an associative memory model where the memorized patterns are stored hierarchically is performed. It is found that the model is able to categorize errors. For a finite number of categories, these are retrieved correctly even when the stored patterns are not. Instead, when they are allowed to increase with the number of neurons, their retrieval quality deteriorates above a critical category capacity. (Author)

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

    Okamoto, Hiroshi

    2016-08-01

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

  11. Unsupervised discrimination of patterns in spiking neural networks with excitatory and inhibitory synaptic plasticity.

    Srinivasa, Narayan; Cho, Youngkwan

    2014-01-01

    A spiking neural network model is described for learning to discriminate among spatial patterns in an unsupervised manner. The network anatomy consists of source neurons that are activated by external inputs, a reservoir that resembles a generic cortical layer with an excitatory-inhibitory (EI) network and a sink layer of neurons for readout. Synaptic plasticity in the form of STDP is imposed on all the excitatory and inhibitory synapses at all times. While long-term excitatory STDP enables sparse and efficient learning of the salient features in inputs, inhibitory STDP enables this learning to be stable by establishing a balance between excitatory and inhibitory currents at each neuron in the network. The synaptic weights between source and reservoir neurons form a basis set for the input patterns. The neural trajectories generated in the reservoir due to input stimulation and lateral connections between reservoir neurons can be readout by the sink layer neurons. This activity is used for adaptation of synapses between reservoir and sink layer neurons. A new measure called the discriminability index (DI) is introduced to compute if the network can discriminate between old patterns already presented in an initial training session. The DI is also used to compute if the network adapts to new patterns without losing its ability to discriminate among old patterns. The final outcome is that the network is able to correctly discriminate between all patterns-both old and new. This result holds as long as inhibitory synapses employ STDP to continuously enable current balance in the network. The results suggest a possible direction for future investigation into how spiking neural networks could address the stability-plasticity question despite having continuous synaptic plasticity.

  12. Analysis of Trends in Cooperative Network Patterns for KAERI Researchers

    Chun, Young Choon; Lee, Hyun Soo

    2016-01-01

    There has been a trend toward faster results of research and accelerating inter-disciplinary convergence, under constraints in available resources. Under such reality, national and international cooperation with inter-sectoral research on science-technology-industry is becoming inevitable as a strategic approach for enhancing competitive edge on global dimension. This study gives an analysis on the cooperative network in nuclear research which bears multi-disciplinary technical feature. The study aims to visualize the cooperative network of KAERI(Korea Atomic Energy Research Institute) researchers, as the hub of the network, including academics and industry, with a view to provide insight on strengthening the cooperative network in nuclear research. This study accounted for the paper entries in SCI(E) in 2013 (538 papers) and 2015 (551 papers) with a view to identify cooperative research activities centered for KAERI. On international cooperation, the analysis showed a trend toward, first of all, diversification of partner countries. There were 118 entries of co-authorship with 22 countries in 2013 (41 with USA, 12 with Japan, 9 with India), which evolved to 121 entries in 2015 (34 for USA, 11 with China, 10 each with Japan and India). Secondly, there was a trend toward more number of countries evenly spread in 2015 compared to 2013, except a few major countries like USA, Japan, and India

  13. Analysis of Trends in Cooperative Network Patterns for KAERI Researchers

    Chun, Young Choon; Lee, Hyun Soo [KAERI, Daejeon (Korea, Republic of)

    2016-05-15

    There has been a trend toward faster results of research and accelerating inter-disciplinary convergence, under constraints in available resources. Under such reality, national and international cooperation with inter-sectoral research on science-technology-industry is becoming inevitable as a strategic approach for enhancing competitive edge on global dimension. This study gives an analysis on the cooperative network in nuclear research which bears multi-disciplinary technical feature. The study aims to visualize the cooperative network of KAERI(Korea Atomic Energy Research Institute) researchers, as the hub of the network, including academics and industry, with a view to provide insight on strengthening the cooperative network in nuclear research. This study accounted for the paper entries in SCI(E) in 2013 (538 papers) and 2015 (551 papers) with a view to identify cooperative research activities centered for KAERI. On international cooperation, the analysis showed a trend toward, first of all, diversification of partner countries. There were 118 entries of co-authorship with 22 countries in 2013 (41 with USA, 12 with Japan, 9 with India), which evolved to 121 entries in 2015 (34 for USA, 11 with China, 10 each with Japan and India). Secondly, there was a trend toward more number of countries evenly spread in 2015 compared to 2013, except a few major countries like USA, Japan, and India.

  14. Patterning and predicting aquatic macroinvertebrate diversities using artificial neural network

    Park, Y.S.; Verdonschot, P.F.M.; Chon, T.S.; Lek, S.

    2003-01-01

    A counterpropagation neural network (CPN) was applied to predict species richness (SR) and Shannon diversity index (SH) of benthic macroinvertebrate communities using 34 environmental variables. The data were collected at 664 sites at 23 different water types such as springs, streams, rivers,

  15. Bio-inspired patterned networks (BIPS) for development of wearable/disposable biosensors

    McLamore, E. S.; Convertino, M.; Hondred, John; Das, Suprem; Claussen, J. C.; Vanegas, D. C.; Gomes, C.

    2016-05-01

    Here we demonstrate a novel approach for fabricating point of care (POC) wearable electrochemical biosensors based on 3D patterning of bionanocomposite networks. To create Bio-Inspired Patterned network (BIPS) electrodes, we first generate fractal network in silico models that optimize transport of network fluxes according to an energy function. Network patterns are then inkjet printed onto flexible substrate using conductive graphene ink. We then deposit fractal nanometal structures onto the graphene to create a 3D nanocomposite network. Finally, we biofunctionalize the surface with biorecognition agents using covalent bonding. In this paper, BIPS are used to develop high efficiency, low cost biosensors for measuring glucose as a proof of concept. Our results on the fundamental performance of BIPS sensors show that the biomimetic nanostructures significantly enhance biosensor sensitivity, accuracy, response time, limit of detection, and hysteresis compared to conventional POC non fractal electrodes (serpentine, interdigitated, and screen printed electrodes). BIPs, in particular Apollonian patterned BIPS, represent a new generation of POC biosensors based on nanoscale and microscale fractal networks that significantly improve electrical connectivity, leading to enhanced sensor performance.

  16. A deep convolutional neural network to analyze position averaged convergent beam electron diffraction patterns.

    Xu, W; LeBeau, J M

    2018-05-01

    We establish a series of deep convolutional neural networks to automatically analyze position averaged convergent beam electron diffraction patterns. The networks first calibrate the zero-order disk size, center position, and rotation without the need for pretreating the data. With the aligned data, additional networks then measure the sample thickness and tilt. The performance of the network is explored as a function of a variety of variables including thickness, tilt, and dose. A methodology to explore the response of the neural network to various pattern features is also presented. Processing patterns at a rate of  ∼ 0.1 s/pattern, the network is shown to be orders of magnitude faster than a brute force method while maintaining accuracy. The approach is thus suitable for automatically processing big, 4D STEM data. We also discuss the generality of the method to other materials/orientations as well as a hybrid approach that combines the features of the neural network with least squares fitting for even more robust analysis. The source code is available at https://github.com/subangstrom/DeepDiffraction. Copyright © 2018 Elsevier B.V. All rights reserved.

  17. A host-endoparasite network of Neotropical marine fish: are there organizational patterns?

    Bellay, Sybelle; Lima, Dilermando P; Takemoto, Ricardo M; Luque, José L

    2011-12-01

    Properties of ecological networks facilitate the understanding of interaction patterns in host-parasite systems as well as the importance of each species in the interaction structure of a community. The present study evaluates the network structure, functional role of all species and patterns of parasite co-occurrence in a host-parasite network to determine the organization level of a host-parasite system consisting of 170 taxa of gastrointestinal metazoans of 39 marine fish species on the coast of Brazil. The network proved to be nested and modular, with a low degree of connectance. Host-parasite interactions were influenced by host phylogeny. Randomness in parasite co-occurrence was observed in most modules and component communities, although species segregation patterns were also observed. The low degree of connectance in the network may be the cause of properties such as nestedness and modularity, which indicate the presence of a high number of peripheral species. Segregation patterns among parasite species in modules underscore the role of host specificity. Knowledge of ecological networks allows detection of keystone species for the maintenance of biodiversity and the conduction of further studies on the stability of networks in relation to frequent environmental changes.

  18. Retrieval of Spatial Join Pattern Instances from Sensor Networks

    Yiu, Man Lung; Mamoulis, Nikos; Bakiras, Spiridon

    2009-01-01

    We study the continuous evaluation of spatial join queries and extensions thereof, defined by interesting combinations of sensor readings (events) that co-occur in a spatial neighborhood. An example of such a pattern is "a high temperature reading in the vicinity of at least four high-pressure re...

  19. Influence and Dissemination Of Sentiments in Social Network Communication Patterns

    Hillmann, Robert; Trier, Matthias

    2013-01-01

    Previous research suggests the existence of sentiments in online social networks. In comparison to real life human interaction, in which sentiments have been shown to have an influence on human behaviour, it is not yet completely understood which mechanisms explain how sentiments influence users ...... that express the same sentiment polarization. We interpret these findings and suggest future research to advance our currently limited theories that assume perceived and generalized social influence to path-dependent social influence models that consider actual behaviour....

  20. Exploring the patterns and evolution of self-organized urban street networks through modeling

    Rui, Yikang; Ban, Yifang; Wang, Jiechen; Haas, Jan

    2013-03-01

    As one of the most important subsystems in cities, urban street networks have recently been well studied by using the approach of complex networks. This paper proposes a growing model for self-organized urban street networks. The model involves a competition among new centers with different values of attraction radius and a local optimal principle of both geometrical and topological factors. We find that with the model growth, the local optimization in the connection process and appropriate probability for the loop construction well reflect the evolution strategy in real-world cities. Moreover, different values of attraction radius in centers competition process lead to morphological change in patterns including urban network, polycentric and monocentric structures. The model succeeds in reproducing a large diversity of road network patterns by varying parameters. The similarity between the properties of our model and empirical results implies that a simple universal growth mechanism exists in self-organized cities.

  1. [Scale effect of Nanjing urban green infrastructure network pattern and connectivity analysis.

    Yu, Ya Ping; Yin, Hai Wei; Kong, Fan Hua; Wang, Jing Jing; Xu, Wen Bin

    2016-07-01

    Based on ArcGIS, Erdas, GuidosToolbox, Conefor and other software platforms, using morphological spatial pattern analysis (MSPA) and landscape connectivity analysis methods, this paper quantitatively analysed the scale effect, edge effect and distance effect of the Nanjing urban green infrastructure network pattern in 2013 by setting different pixel sizes (P) and edge widths in MSPA analysis, and setting different dispersal distance thresholds in landscape connectivity analysis. The results showed that the type of landscape acquired based on the MSPA had a clear scale effect and edge effect, and scale effects only slightly affected landscape types, whereas edge effects were more obvious. Different dispersal distances had a great impact on the landscape connectivity, 2 km or 2.5 km dispersal distance was a critical threshold for Nanjing. When selecting the pixel size 30 m of the input data and the edge wide 30 m used in the morphological model, we could get more detailed landscape information of Nanjing UGI network. Based on MSPA and landscape connectivity, analysis of the scale effect, edge effect, and distance effect on the landscape types of the urban green infrastructure (UGI) network was helpful for selecting the appropriate size, edge width, and dispersal distance when developing these networks, and for better understanding the spatial pattern of UGI networks and the effects of scale and distance on the ecology of a UGI network. This would facilitate a more scientifically valid set of design parameters for UGI network spatiotemporal pattern analysis. The results of this study provided an important reference for Nanjing UGI networks and a basis for the analysis of the spatial and temporal patterns of medium-scale UGI landscape networks in other regions.

  2. Perturbations i have Known and Loved

    Field, Robert W.

    2011-06-01

    A spectroscopic perturbation is a disruption of a ^1Σ-^1Σ-like regular pattern that can embody level-shifts, extra lines, and intensity anomalies. Once upon a time, when a band was labeled ``perturbed,'' it was considered worthless because it could at best yield molecular constants unsuited for archival tables. Nevertheless, a few brave spectroscopists, notably Albin Lagerqvist and Richard Barrow, collected perturbations because they knew that the pattern of multiple perturbations formed an intricate puzzle that would eventually reveal the presence and electronic symmetry of otherwise unobservable electronic states. There are many kinds of patterns of broken patterns. In my PhD thesis I showed how to determine absolute vibrational assignments for the perturber from patterns among the observed values of perturbation matrix elements. When a ^3Π state is perturbed, its six (Ω, parity) components capture a pattern of level shifts and intensity anomalies that reveals more about the nature of the perturber than a simple perturbation of the single component of a ^1Σ state. In perturbation-facilitated OODR, a perturbed singlet level acts as a spectroscopic doorway through which the entire triplet manifold may be systematically explored. For polyatomic molecule vibrations, a vibrational polyad (a group of mutually perturbing vibrational levels, among which the perturbation matrix elements are expected to follow harmonic oscillator scaling rules) can contain more components than a ^3Π state and intrapolyad patterns can be exquisitely sensitive not merely to the nature of an interloper within the polyad but also to the eigenvector character of the vibronic state from which the polyad is viewed. Variation of scaled polyad interaction parameters from one polyad to the next, a pattern of patterns, can signal proximity to an isomerization barrier. Everything in Rydberg-land seems to scale as N⋆-3, yet a trespassing valence state causes all scaling and propensity rules go

  3. Combined genome-wide expression profiling and targeted RNA interference in primary mouse macrophages reveals perturbation of transcriptional networks associated with interferon signalling

    Craigon Marie

    2009-08-01

    Full Text Available Abstract Background Interferons (IFNs are potent antiviral cytokines capable of reprogramming the macrophage phenotype through the induction of interferon-stimulated genes (ISGs. Here we have used targeted RNA interference to suppress the expression of a number of key genes associated with IFN signalling in murine macrophages prior to stimulation with interferon-gamma. Genome-wide changes in transcript abundance caused by siRNA activity were measured using exon-level microarrays in the presence or absence of IFNγ. Results Transfection of murine bone-marrow derived macrophages (BMDMs with a non-targeting (control siRNA and 11 sequence-specific siRNAs was performed using a cationic lipid transfection reagent (Lipofectamine2000 prior to stimulation with IFNγ. Total RNA was harvested from cells and gene expression measured on Affymetrix GeneChip Mouse Exon 1.0 ST Arrays. Network-based analysis of these data revealed six siRNAs to cause a marked shift in the macrophage transcriptome in the presence or absence IFNγ. These six siRNAs targeted the Ifnb1, Irf3, Irf5, Stat1, Stat2 and Nfkb2 transcripts. The perturbation of the transcriptome by the six siRNAs was highly similar in each case and affected the expression of over 600 downstream transcripts. Regulated transcripts were clustered based on co-expression into five major groups corresponding to transcriptional networks associated with the type I and II IFN response, cell cycle regulation, and NF-KB signalling. In addition we have observed a significant non-specific immune stimulation of cells transfected with siRNA using Lipofectamine2000, suggesting use of this reagent in BMDMs, even at low concentrations, is enough to induce a type I IFN response. Conclusion Our results provide evidence that the type I IFN response in murine BMDMs is dependent on Ifnb1, Irf3, Irf5, Stat1, Stat2 and Nfkb2, and that siRNAs targeted to these genes results in perturbation of key transcriptional networks associated

  4. Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm

    Zeng, Yong; Liu, Dacheng; Lei, Zhou

    2014-01-01

    The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history si...

  5. Pattern classification and recognition of invertebrate functional groups using self-organizing neural networks.

    Zhang, WenJun

    2007-07-01

    Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing competitive learning neural networks. Comparisons between neural network models, distance (similarity) measures, and number of neurons were conducted. The results showed that self-organizing map and self-organizing competitive learning neural network models were effective in pattern classification and recognition of sampling information. Overall the performance of one-dimensional self-organizing map neural network was better than self-organizing competitive learning neural network. The number of neurons could determine the number of classes in the classification. Different neural network models with various distance (similarity) measures yielded similar classifications. Some differences, dependent upon the specific network structure, would be found. The pattern of an unrecognized functional group was recognized with the self-organizing neural network. A relative consistent classification indicated that the following invertebrate functional groups, terrestrial blood sucker; terrestrial flyer; tourist (nonpredatory species with no known functional role other than as prey in ecosystem); gall former; collector (gather, deposit feeder); predator and parasitoid; leaf miner; idiobiont (acarine ectoparasitoid), were classified into the same group, and the following invertebrate functional groups, external plant feeder; terrestrial crawler, walker, jumper or hunter; neustonic (water surface) swimmer (semi-aquatic), were classified into another group. It was concluded that reliable conclusions could be drawn from comparisons of different neural network models that use different distance

  6. Multiple Spatial Coherence Resonances and Spatial Patterns in a Noise-Driven Heterogeneous Neuronal Network

    Li, Yu-Ye; Ding, Xue-Li

    2014-12-01

    Heterogeneity of the neurons and noise are inevitable in the real neuronal network. In this paper, Gaussian white noise induced spatial patterns including spiral waves and multiple spatial coherence resonances are studied in a network composed of Morris—Lecar neurons with heterogeneity characterized by parameter diversity. The relationship between the resonances and the transitions between ordered spiral waves and disordered spatial patterns are achieved. When parameter diversity is introduced, the maxima of multiple resonances increases first, and then decreases as diversity strength increases, which implies that the coherence degrees induced by noise are enhanced at an intermediate diversity strength. The synchronization degree of spatial patterns including ordered spiral waves and disordered patterns is identified to be a very low level. The results suggest that the nervous system can profit from both heterogeneity and noise, and the multiple spatial coherence resonances are achieved via the emergency of spiral waves instead of synchronization patterns.

  7. Multiple Spatial Coherence Resonances and Spatial Patterns in a Noise-Driven Heterogeneous Neuronal Network

    Li Yu-Ye; Ding Xue-Li

    2014-01-01

    Heterogeneity of the neurons and noise are inevitable in the real neuronal network. In this paper, Gaussian white noise induced spatial patterns including spiral waves and multiple spatial coherence resonances are studied in a network composed of Morris—Lecar neurons with heterogeneity characterized by parameter diversity. The relationship between the resonances and the transitions between ordered spiral waves and disordered spatial patterns are achieved. When parameter diversity is introduced, the maxima of multiple resonances increases first, and then decreases as diversity strength increases, which implies that the coherence degrees induced by noise are enhanced at an intermediate diversity strength. The synchronization degree of spatial patterns including ordered spiral waves and disordered patterns is identified to be a very low level. The results suggest that the nervous system can profit from both heterogeneity and noise, and the multiple spatial coherence resonances are achieved via the emergency of spiral waves instead of synchronization patterns. (interdisciplinary physics and related areas of science and technology)

  8. Organization of Anti-Phase Synchronization Pattern in Neural Networks: What are the Key Factors?

    Li, Dong; Zhou, Changsong

    2011-01-01

    Anti-phase oscillation has been widely observed in cortical neural network. Elucidating the mechanism underlying the organization of anti-phase pattern is of significance for better understanding more complicated pattern formations in brain networks. In dynamical systems theory, the organization of anti-phase oscillation pattern has usually been considered to relate to time delay in coupling. This is consistent to conduction delays in real neural networks in the brain due to finite propagation velocity of action potentials. However, other structural factors in cortical neural network, such as modular organization (connection density) and the coupling types (excitatory or inhibitory), could also play an important role. In this work, we investigate the anti-phase oscillation pattern organized on a two-module network of either neuronal cell model or neural mass model, and analyze the impact of the conduction delay times, the connection densities, and coupling types. Our results show that delay times and coupling types can play key roles in this organization. The connection densities may have an influence on the stability if an anti-phase pattern exists due to the other factors. Furthermore, we show that anti-phase synchronization of slow oscillations can be achieved with small delay times if there is interaction between slow and fast oscillations. These results are significant for further understanding more realistic spatiotemporal dynamics of cortico-cortical communications. PMID:22232576

  9. Integrating Entropy and Closed Frequent Pattern Mining for Social Network Modelling and Analysis

    Adnan, Muhaimenul; Alhajj, Reda; Rokne, Jon

    The recent increase in the explicitly available social networks has attracted the attention of the research community to investigate how it would be possible to benefit from such a powerful model in producing effective solutions for problems in other domains where the social network is implicit; we argue that social networks do exist around us but the key issue is how to realize and analyze them. This chapter presents a novel approach for constructing a social network model by an integrated framework that first preparing the data to be analyzed and then applies entropy and frequent closed patterns mining for network construction. For a given problem, we first prepare the data by identifying items and transactions, which arc the basic ingredients for frequent closed patterns mining. Items arc main objects in the problem and a transaction is a set of items that could exist together at one time (e.g., items purchased in one visit to the supermarket). Transactions could be analyzed to discover frequent closed patterns using any of the well-known techniques. Frequent closed patterns have the advantage that they successfully grab the inherent information content of the dataset and is applicable to a broader set of domains. Entropies of the frequent closed patterns arc used to keep the dimensionality of the feature vectors to a reasonable size; it is a kind of feature reduction process. Finally, we analyze the dynamic behavior of the constructed social network. Experiments were conducted on a synthetic dataset and on the Enron corpus email dataset. The results presented in the chapter show that social networks extracted from a feature set as frequent closed patterns successfully carry the community structure information. Moreover, for the Enron email dataset, we present an analysis to dynamically indicate the deviations from each user's individual and community profile. These indications of deviations can be very useful to identify unusual events.

  10. Horizontal two phase flow pattern identification by neural networks

    Crivelaro, Kelen Cristina Oliveira; Seleghim Junior, Paulo; Hervieu, Eric

    1999-01-01

    A multiphase fluid can flow according to several flow regimes. The problem associated with multiphase systems are basically related to the behavior of macroscopic parameters, such as pressure drop, thermal exchanges and so on, and their strong correlation to the flow regime. From the industrial applications point of view, the safety and longevity of equipment and systems can only be assured when they work according to the flow regimes for which they were designed to. This implies in the need to diagnose flow regimes in real time. The automatic diagnosis of flow regimes represents an objective of extreme importance, mainly for applications on nuclear and petrochemical industries. In this work, a neural network is used in association to a probe of direct visualization for the identification of a gas-liquid flow horizontal regimes, developed in an experimental circuit. More specifically, the signals produced by the probe are used to compose a qualitative image of the flow, which is promptly sent to the network for the recognition of the regimes. Results are presented for different transitions among the flow regimes, which demonstrate the extremely satisfactory performance of the diagnosis system. (author)

  11. Artificial neural network for bubbles pattern recognition on the images

    Poletaev, I E; Pervunin, K S; Tokarev, M P

    2016-01-01

    Two-phase bubble flows have been used in many technological and energy processes as processing oil, chemical and nuclear reactors. This explains large interest to experimental and numerical studies of such flows last several decades. Exploiting of optical diagnostics for analysis of the bubble flows allows researchers obtaining of instantaneous velocity fields and gaseous phase distribution with the high spatial resolution non-intrusively. Behavior of light rays exhibits an intricate manner when they cross interphase boundaries of gaseous bubbles hence the identification of the bubbles images is a complicated problem. This work presents a method of bubbles images identification based on a modern technology of deep learning called convolutional neural networks (CNN). Neural networks are able to determine overlapping, blurred, and non-spherical bubble images. They can increase accuracy of the bubble image recognition, reduce the number of outliers, lower data processing time, and significantly decrease the number of settings for the identification in comparison with standard recognition methods developed before. In addition, usage of GPUs speeds up the learning process of CNN owning to the modern adaptive subgradient optimization techniques. (paper)

  12. Effects of behavioral patterns and network topology structures on Parrondo’s paradox

    Ye, Ye; Cheong, Kang Hao; Cen, Yu-Wan; Xie, Neng-Gang

    2016-11-01

    A multi-agent Parrondo’s model based on complex networks is used in the current study. For Parrondo’s game A, the individual interaction can be categorized into five types of behavioral patterns: the Matthew effect, harmony, cooperation, poor-competition-rich-cooperation and a random mode. The parameter space of Parrondo’s paradox pertaining to each behavioral pattern, and the gradual change of the parameter space from a two-dimensional lattice to a random network and from a random network to a scale-free network was analyzed. The simulation results suggest that the size of the region of the parameter space that elicits Parrondo’s paradox is positively correlated with the heterogeneity of the degree distribution of the network. For two distinct sets of probability parameters, the microcosmic reasons underlying the occurrence of the paradox under the scale-free network are elaborated. Common interaction mechanisms of the asymmetric structure of game B, behavioral patterns and network topology are also revealed.

  13. Firing patterns transition and desynchronization induced by time delay in neural networks

    Huang, Shoufang; Zhang, Jiqian; Wang, Maosheng; Hu, Chin-Kun

    2018-06-01

    We used the Hindmarsh-Rose (HR) model (Hindmarsh and Rose, 1984) to study the effect of time delay on the transition of firing behaviors and desynchronization in neural networks. As time delay is increased, neural networks exhibit diversity of firing behaviors, including regular spiking or bursting and firing patterns transitions (FPTs). Meanwhile, the desynchronization of firing and unstable bursting with decreasing amplitude in neural system, are also increasingly enhanced with the increase of time delay. Furthermore, we also studied the effect of coupling strength and network randomness on these phenomena. Our results imply that time delays can induce transition and desynchronization of firing behaviors in neural networks. These findings provide new insight into the role of time delay in the firing activities of neural networks, and can help to better understand the firing phenomena in complex systems of neural networks. A possible mechanism in brain that can cause the increase of time delay is discussed.

  14. Spatial Distribution Characteristics of Healthcare Facilities in Nanjing: Network Point Pattern Analysis and Correlation Analysis

    Jianhua Ni

    2016-08-01

    Full Text Available The spatial distribution of urban service facilities is largely constrained by the road network. In this study, network point pattern analysis and correlation analysis were used to analyze the relationship between road network and healthcare facility distribution. The weighted network kernel density estimation method proposed in this study identifies significant differences between the outside and inside areas of the Ming city wall. The results of network K-function analysis show that private hospitals are more evenly distributed than public hospitals, and pharmacy stores tend to cluster around hospitals along the road network. After computing the correlation analysis between different categorized hospitals and street centrality, we find that the distribution of these hospitals correlates highly with the street centralities, and that the correlations are higher with private and small hospitals than with public and large hospitals. The comprehensive analysis results could help examine the reasonability of existing urban healthcare facility distribution and optimize the location of new healthcare facilities.

  15. Hypothetical Pattern Recognition Design Using Multi-Layer Perceptorn Neural Network For Supervised Learning

    Md. Abdullah-al-mamun

    2015-08-01

    Full Text Available Abstract Humans are capable to identifying diverse shape in the different pattern in the real world as effortless fashion due to their intelligence is grow since born with facing several learning process. Same way we can prepared an machine using human like brain called Artificial Neural Network that can be recognize different pattern from the real world object. Although the various techniques is exists to implementation the pattern recognition but recently the artificial neural network approaches have been giving the significant attention. Because the approached of artificial neural network is like a human brain that is learn from different observation and give a decision the previously learning rule. Over the 50 years research now a days pattern recognition for machine learning using artificial neural network got a significant achievement. For this reason many real world problem can be solve by modeling the pattern recognition process. The objective of this paper is to present the theoretical concept for pattern recognition design using Multi-Layer Perceptorn neural networkin the algorithm of artificial Intelligence as the best possible way of utilizing available resources to make a decision that can be a human like performance.

  16. Core regulatory network motif underlies the ocellar complex patterning in Drosophila melanogaster

    Aguilar-Hidalgo, D.; Lemos, M. C.; Córdoba, A.

    2015-03-01

    During organogenesis, developmental programs governed by Gene Regulatory Networks (GRN) define the functionality, size and shape of the different constituents of living organisms. Robustness, thus, is an essential characteristic that GRNs need to fulfill in order to maintain viability and reproducibility in a species. In the present work we analyze the robustness of the patterning for the ocellar complex formation in Drosophila melanogaster fly. We have systematically pruned the GRN that drives the development of this visual system to obtain the minimum pathway able to satisfy this pattern. We found that the mechanism underlying the patterning obeys to the dynamics of a 3-nodes network motif with a double negative feedback loop fed by a morphogenetic gradient that triggers the inhibition in a French flag problem fashion. A Boolean modeling of the GRN confirms robustness in the patterning mechanism showing the same result for different network complexity levels. Interestingly, the network provides a steady state solution in the interocellar part of the patterning and an oscillatory regime in the ocelli. This theoretical result predicts that the ocellar pattern may underlie oscillatory dynamics in its genetic regulation.

  17. COMPLEX NETWORK SIMULATION OF FOREST NETWORK SPATIAL PATTERN IN PEARL RIVER DELTA

    Y. Zeng

    2017-09-01

    Full Text Available Forest network-construction uses for the method and model with the scale-free features of complex network theory based on random graph theory and dynamic network nodes which show a power-law distribution phenomenon. The model is suitable for ecological disturbance by larger ecological landscape Pearl River Delta consistent recovery. Remote sensing and GIS spatial data are available through the latest forest patches. A standard scale-free network node distribution model calculates the area of forest network’s power-law distribution parameter value size; The recent existing forest polygons which are defined as nodes can compute the network nodes decaying index value of the network’s degree distribution. The parameters of forest network are picked up then make a spatial transition to GIS real world models. Hence the connection is automatically generated by minimizing the ecological corridor by the least cost rule between the near nodes. Based on scale-free network node distribution requirements, select the number compared with less, a huge point of aggregation as a future forest planning network’s main node, and put them with the existing node sequence comparison. By this theory, the forest ecological projects in the past avoid being fragmented, scattered disorderly phenomena. The previous regular forest networks can be reduced the required forest planting costs by this method. For ecological restoration of tropical and subtropical in south China areas, it will provide an effective method for the forest entering city project guidance and demonstration with other ecological networks (water, climate network, etc. for networking a standard and base datum.

  18. Early Neolithic settlement patterns and exchange networks in the Aegean

    Agathe Reingruber

    2011-12-01

    Full Text Available The Neolithisation process is one of the major issues under debate in Aegean archaeology, since the description of the basal layers of Thessalian tell-settlements some fifty years ago. The pottery, figurines or stamps seemed to be of Anatolian origin, and were presumably brought to the region by colonists. The direct linking of the so-called ‘Neolithic Package’ with groups of people leaving Central Anatolia after the collapse of the Pre-Pottery Neolithic B resulted in the colonisation model of the Aegean. This view is not supported by results obtained from natural sciences such as archaeobotany, radiocarbon analyses, and neutron activation on obsidian. When theories of social networks are brought into the discussion, the picture that emerges becomes much more differentiated and complex.

  19. Network Security via Biometric Recognition of Patterns of Gene Expression

    Shaw, Harry C.

    2016-01-01

    Molecular biology provides the ability to implement forms of information and network security completely outside the bounds of legacy security protocols and algorithms. This paper addresses an approach which instantiates the power of gene expression for security. Molecular biology provides a rich source of gene expression and regulation mechanisms, which can be adopted to use in the information and electronic communication domains. Conventional security protocols are becoming increasingly vulnerable due to more intensive, highly capable attacks on the underlying mathematics of cryptography. Security protocols are being undermined by social engineering and substandard implementations by IT organizations. Molecular biology can provide countermeasures to these weak points with the current security approaches. Future advances in instruments for analyzing assays will also enable this protocol to advance from one of cryptographic algorithms to an integrated system of cryptographic algorithms and real-time expression and assay of gene expression products.

  20. Network Security via Biometric Recognition of Patterns of Gene Expression

    Shaw, Harry C.

    2016-01-01

    Molecular biology provides the ability to implement forms of information and network security completely outside the bounds of legacy security protocols and algorithms. This paper addresses an approach which instantiates the power of gene expression for security. Molecular biology provides a rich source of gene expression and regulation mechanisms, which can be adopted to use in the information and electronic communication domains. Conventional security protocols are becoming increasingly vulnerable due to more intensive, highly capable attacks on the underlying mathematics of cryptography. Security protocols are being undermined by social engineering and substandard implementations by IT (Information Technology) organizations. Molecular biology can provide countermeasures to these weak points with the current security approaches. Future advances in instruments for analyzing assays will also enable this protocol to advance from one of cryptographic algorithms to an integrated system of cryptographic algorithms and real-time assays of gene expression products.

  1. Specific and Complete Local Integration of Patterns in Bayesian Networks

    Martin Biehl

    2017-05-01

    Full Text Available We present a first formal analysis of specific and complete local integration. Complete local integration was previously proposed as a criterion for detecting entities or wholes in distributed dynamical systems. Such entities in turn were conceived to form the basis of a theory of emergence of agents within dynamical systems. Here, we give a more thorough account of the underlying formal measures. The main contribution is the disintegration theorem which reveals a special role of completely locally integrated patterns (what we call ι-entities within the trajectories they occur in. Apart from proving this theorem we introduce the disintegration hierarchy and its refinement-free version as a way to structure the patterns in a trajectory. Furthermore, we construct the least upper bound and provide a candidate for the greatest lower bound of specific local integration. Finally, we calculate the ι -entities in small example systems as a first sanity check and find that ι -entities largely fulfil simple expectations.

  2. Historical feature pattern extraction based network attack situation sensing algorithm.

    Zeng, Yong; Liu, Dacheng; Lei, Zhou

    2014-01-01

    The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously.

  3. Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm

    Yong Zeng

    2014-01-01

    Full Text Available The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE. First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously.

  4. Patterns of interactions of a large fish-parasite network in a tropical floodplain.

    Lima, Dilermando P; Giacomini, Henrique C; Takemoto, Ricardo M; Agostinho, Angelo A; Bini, Luis M

    2012-07-01

    1. Describing and explaining the structure of species interaction networks is of paramount importance for community ecology. Yet much has to be learned about the mechanisms responsible for major patterns, such as nestedness and modularity in different kinds of systems, of which large and diverse networks are a still underrepresented and scarcely studied fraction. 2. We assembled information on fishes and their parasites living in a large floodplain of key ecological importance for freshwater ecosystems in the Paraná River basin in South America. The resulting fish-parasite network containing 72 and 324 species of fishes and parasites, respectively, was analysed to investigate the patterns of nestedness and modularity as related to fish and parasite features. 3. Nestedness was found in the entire network and among endoparasites, multiple-host life cycle parasites and native hosts, but not in networks of ectoparasites, single-host life cycle parasites and non-native fishes. All networks were significantly modular. Taxonomy was the major host's attribute influencing both nestedness and modularity: more closely related host species tended to be associated with more nested parasite compositions and had greater chance of belonging to the same network module. Nevertheless, host abundance had a positive relationship with nestedness when only native host species pairs of the same network module were considered for analysis. 4. These results highlight the importance of evolutionary history of hosts in linking patterns of nestedness and formation of modules in the network. They also show that functional attributes of parasites (i.e. parasitism mode and life cycle) and origin of host populations (i.e. natives versus non-natives) are crucial to define the relative contribution of these two network properties and their dependence on other ecological factors (e.g. host abundance), with potential implications for community dynamics and stability. © 2012 The Authors

  5. Flower-Visiting Social Wasps and Plants Interaction: Network Pattern and Environmental Complexity

    Mateus Aparecido Clemente

    2012-01-01

    Full Text Available Network analysis as a tool for ecological interactions studies has been widely used since last decade. However, there are few studies on the factors that shape network patterns in communities. In this sense, we compared the topological properties of the interaction network between flower-visiting social wasps and plants in two distinct phytophysiognomies in a Brazilian savanna (Riparian Forest and Rocky Grassland. Results showed that the landscapes differed in species richness and composition, and also the interaction networks between wasps and plants had different patterns. The network was more complex in the Riparian Forest, with a larger number of species and individuals and a greater amount of connections between them. The network specialization degree was more generalist in the Riparian Forest than in the Rocky Grassland. This result was corroborated by means of the nestedness index. In both networks was found asymmetry, with a large number of wasps per plant species. In general aspects, most wasps had low niche amplitude, visiting from one to three plant species. Our results suggest that differences in structural complexity of the environment directly influence the structure of the interaction network between flower-visiting social wasps and plants.

  6. Optimization of the kernel functions in a probabilistic neural network analyzing the local pattern distribution.

    Galleske, I; Castellanos, J

    2002-05-01

    This article proposes a procedure for the automatic determination of the elements of the covariance matrix of the gaussian kernel function of probabilistic neural networks. Two matrices, a rotation matrix and a matrix of variances, can be calculated by analyzing the local environment of each training pattern. The combination of them will form the covariance matrix of each training pattern. This automation has two advantages: First, it will free the neural network designer from indicating the complete covariance matrix, and second, it will result in a network with better generalization ability than the original model. A variation of the famous two-spiral problem and real-world examples from the UCI Machine Learning Repository will show a classification rate not only better than the original probabilistic neural network but also that this model can outperform other well-known classification techniques.

  7. Identification of Abnormal System Noise Temperature Patterns in Deep Space Network Antennas Using Neural Network Trained Fuzzy Logic

    Lu, Thomas; Pham, Timothy; Liao, Jason

    2011-01-01

    This paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected non-correlations which merit further study in the future.

  8. Patterns of energy drink advertising over US television networks.

    Emond, Jennifer A; Sargent, James D; Gilbert-Diamond, Diane

    2015-01-01

    To describe programming themes and the inclusion of adolescents in the base audience for television channels with high levels of energy drink advertising airtime. Secondary analysis of energy drink advertising airtime over US network and cable television channels (n = 139) from March, 2012 to February, 2013. Programming themes and the inclusion of adolescents in each channel's base audience were extracted from cable television trade reports. Energy drink advertising airtime. Channels were ranked by airtime; programming themes and the inclusion of adolescents in the base audience were summarized for the 10 channels with the most airtime. Over the study year, 36,501 minutes (608 hours) were devoted to energy drink advertisements; the top 10 channels accounted for 46.5% of such airtime. Programming themes for the top 10 channels were music (n = 3), sports (n = 3), action-adventure lifestyle (n = 2), African American lifestyle (n = 1), and comedy (n = 1). MTV2 ranked first in airtime devoted to energy drink advertisements. Six of the 10 channels with the most airtime included adolescents aged 12-17 years in their base audience. Energy drink manufacturers primarily advertise on channels that likely appeal to adolescents. Nutritionists may wish to consider energy drink media literacy when advising adolescents about energy drink consumption. Copyright © 2015 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

  9. The structural connectivity pattern of the default mode network and its association with memory and anxiety

    Yan eTao

    2015-11-01

    Full Text Available The default mode network (DMN is one of the most widely studied resting state functional networks. The structural basis for the DMN is of particular interest and has been studied by several researchers using diffusion tensor imaging (DTI. Most of these previous studies focused on a few regions or white matter tracts of the DMN so that the global structural connectivity pattern and network properties of the DMN remain unclear. Moreover, evidences indicate that the DMN is involved in both memory and emotion, but how the DMN regulates memory and anxiety from the perspective of the whole DMN structural network remains unknown. We used multimodal neuroimaging methods to investigate the structural connectivity pattern of the DMN and the association of its network properties with memory and anxiety in 205 young healthy subjects. Using a probabilistic fiber tractography technique based on DTI data and graph theory methods, we constructed the global structural connectivity pattern of the DMN and found that memory quotient (MQ score was significantly positively correlated with the global and local efficiency of the DMN whereas anxiety was found to be negatively correlated with the efficiency. The strong structural connectivity between multiple brain regions within DMN may reflect that the DMN has certain structural basis. Meanwhile, we found the network efficiency of the DMN were related to memory and anxiety measures, which indicated that the DMN may play a role in the memory and anxiety.

  10. Attractive target wave patterns in complex networks consisting of excitable nodes

    Zhang Li-Sheng; Mi Yuan-Yuan; Liao Xu-Hong; Qian Yu; Hu Gang

    2014-01-01

    This review describes the investigations of oscillatory complex networks consisting of excitable nodes, focusing on the target wave patterns or say the target wave attractors. A method of dominant phase advanced driving (DPAD) is introduced to reveal the dynamic structures in the networks supporting oscillations, such as the oscillation sources and the main excitation propagation paths from the sources to the whole networks. The target center nodes and their drivers are regarded as the key nodes which can completely determine the corresponding target wave patterns. Therefore, the center (say node A) and its driver (say node B) of a target wave can be used as a label, (A,B), of the given target pattern. The label can give a clue to conveniently retrieve, suppress, and control the target waves. Statistical investigations, both theoretically from the label analysis and numerically from direct simulations of network dynamics, show that there exist huge numbers of target wave attractors in excitable complex networks if the system size is large, and all these attractors can be labeled and easily controlled based on the information given by the labels. The possible applications of the physical ideas and the mathematical methods about multiplicity and labelability of attractors to memory problems of neural networks are briefly discussed. (topical review - statistical physics and complex systems)

  11. Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

    Melin, Patricia

    2012-01-01

    This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...

  12. Inferring Gene Regulatory Networks Using Conditional Regulation Pattern to Guide Candidate Genes.

    Fei Xiao

    Full Text Available Combining path consistency (PC algorithms with conditional mutual information (CMI are widely used in reconstruction of gene regulatory networks. CMI has many advantages over Pearson correlation coefficient in measuring non-linear dependence to infer gene regulatory networks. It can also discriminate the direct regulations from indirect ones. However, it is still a challenge to select the conditional genes in an optimal way, which affects the performance and computation complexity of the PC algorithm. In this study, we develop a novel conditional mutual information-based algorithm, namely RPNI (Regulation Pattern based Network Inference, to infer gene regulatory networks. For conditional gene selection, we define the co-regulation pattern, indirect-regulation pattern and mixture-regulation pattern as three candidate patterns to guide the selection of candidate genes. To demonstrate the potential of our algorithm, we apply it to gene expression data from DREAM challenge. Experimental results show that RPNI outperforms existing conditional mutual information-based methods in both accuracy and time complexity for different sizes of gene samples. Furthermore, the robustness of our algorithm is demonstrated by noisy interference analysis using different types of noise.

  13. Definition of new 3D invariants. Applications to pattern recognition problems with neural networks

    Proriol, J.

    1996-01-01

    We propose a definition of new 3D invariants. Usual pattern recognition methods use 2D descriptions of 3D objects, we propose a 2D approximation of the defined 3D invariants which can be used with neural networks to solve pattern recognition problems. We describe some methods to use the 2 D approximants. This work is an extension of previous 3D invariants used to solve some high energy physics problems. (author)

  14. Inferring Drosophila gap gene regulatory network: Pattern analysis of simulated gene expression profiles and stability analysis

    Fomekong-Nanfack, Y.; Postma, M.; Kaandorp, J.A.

    2009-01-01

    Abstract Background Inference of gene regulatory networks (GRNs) requires accurate data, a method to simulate the expression patterns and an efficient optimization algorithm to estimate the unknown parameters. Using this approach it is possible to obtain alternative circuits without making any a priori assumptions about the interactions, which all simulate the observed patterns. It is important to analyze the properties of the circuits. Findings We have analyzed the simulated gene expression ...

  15. Assembly and patterning of the vascular network of the vertebrate hindbrain

    Fujita, Misato; Cha, Young R.; Pham, Van N.; Sakurai, Atsuko; Roman, Beth L.; Gutkind, J. Silvio; Weinstein, Brant M.

    2011-01-01

    The cranial vasculature is essential for the survival and development of the central nervous system and is important in stroke and other brain pathologies. Cranial vessels form in a reproducible and evolutionarily conserved manner, but the process by which these vessels assemble and acquire their stereotypic patterning remains unclear. Here, we examine the stepwise assembly and patterning of the vascular network of the zebrafish hindbrain. The major artery supplying the hindbrain, the basilar...

  16. Activity patterns in networks stabilized by background oscillations.

    Hoppensteadt, Frank

    2009-07-01

    The brain operates in a highly oscillatory environment. We investigate here how such an oscillating background can create stable organized behavior in an array of neuro-oscillators that is not observable in the absence of oscillation, much like oscillating the support point of an inverted pendulum can stabilize its up position, which is unstable without the oscillation. We test this idea in an array of electronic circuits coming from neuroengineering: we show how the frequencies of the background oscillation create a partition of the state space into distinct basins of attraction. Thus, background signals can stabilize persistent activity that is otherwise not observable. This suggests that an image, represented as a stable firing pattern which is triggered by a voltage pulse and is sustained in synchrony or resonance with the background oscillation, can persist as a stable behavior long after the initial stimulus is removed. The background oscillations provide energy for organized behavior in the array, and these behaviors are categorized by the basins of attraction determined by the oscillation frequencies.

  17. Genetic Networks and Anticipation of Gene Expression Patterns

    Gebert, J.; Lätsch, M.; Pickl, S. W.; Radde, N.; Weber, G.-W.; Wünschiers, R.

    2004-08-01

    An interesting problem for computational biology is the analysis of time-series expression data. Here, the application of modern methods from dynamical systems, optimization theory, numerical algorithms and the utilization of implicit discrete information lead to a deeper understanding. In [1], we suggested to represent the behavior of time-series gene expression patterns by a system of ordinary differential equations, which we analytically and algorithmically investigated under the parametrical aspect of stability or instability. Our algorithm strongly exploited combinatorial information. In this paper, we deepen, extend and exemplify this study from the viewpoint of underlying mathematical modelling. This modelling consists in evaluating DNA-microarray measurements as the basis of anticipatory prediction, in the choice of a smooth model given by differential equations, in an approach of the right-hand side with parametric matrices, and in a discrete approximation which is a least squares optimization problem. We give a mathematical and biological discussion, and pay attention to the special case of a linear system, where the matrices do not depend on the state of expressions. Here, we present first numerical examples.

  18. Divergent Drinking Patterns of Restaurant Workers: The Influence of Social Networks and Job Position.

    Duke, Michael R; Ames, Genevieve M; Moore, Roland S; Cunradi, Carol B

    2013-01-01

    Restaurant workers have higher rates of problem drinking than most occupational groups. However, little is known about the environmental risks and work characteristics that may lead to these behaviors. An exploration of restaurant workers' drinking networks may provide important insights into their alcohol consumption patterns, thus guiding workplace prevention efforts. Drawing from social capital theory, this paper examines the unique characteristics of drinking networks within and between various job categories. Our research suggests that these multiple, complex networks have unique risk characteristics, and that self-selection is based on factors such as job position and college attendance, among other factors.

  19. The Science DMZ: A Network Design Pattern for Data-Intensive Science

    Eli Dart

    2014-01-01

    Full Text Available The ever-increasing scale of scientific data has become a significant challenge for researchers that rely on networks to interact with remote computing systems and transfer results to collaborators worldwide. Despite the availability of high-capacity connections, scientists struggle with inadequate cyberinfrastructure that cripples data transfer performance, and impedes scientific progress. The Science DMZ paradigm comprises a proven set of network design patterns that collectively address these problems for scientists. We explain the Science DMZ model, including network architecture, system configuration, cybersecurity, and performance tools, that creates an optimized network environment for science. We describe use cases from universities, supercomputing centers and research laboratories, highlighting the effectiveness of the Science DMZ model in diverse operational settings. In all, the Science DMZ model is a solid platform that supports any science workflow, and flexibly accommodates emerging network technologies. As a result, the Science DMZ vastly improves collaboration, accelerating scientific discovery.

  20. The Science DMZ: A Network Design Pattern for Data-Intensive Science

    Dart, Eli; Rotman, Lauren; Tierney, Brian; Hester, Mary; Zurawski, Jason

    2013-08-13

    The ever-increasing scale of scientific data has become a significant challenge for researchers that rely on networks to interact with remote computing systems and transfer results to collaborators worldwide. Despite the availability of high-capacity connections, scientists struggle with inadequate cyberinfrastructure that cripples data transfer performance, and impedes scientific progress. The Science DMZ paradigm comprises a proven set of network design patterns that collectively address these problems for scientists. We explain the Science DMZ model, including network architecture, system configuration, cybersecurity, and performance tools, that creates an optimized network environment for science. We describe use cases from universities, supercomputing centers and research laboratories, highlighting the effectiveness of the Science DMZ model in diverse operational settings. In all, the Science DMZ model is a solid platform that supports any science workflow, and flexibly accommodates emerging network technologies. As a result, the Science DMZ vastly improves collaboration, accelerating scientific discovery.

  1. On the origin of distribution patterns of motifs in biological networks

    Lesk Arthur M

    2008-08-01

    Full Text Available Abstract Background Inventories of small subgraphs in biological networks have identified commonly-recurring patterns, called motifs. The inference that these motifs have been selected for function rests on the idea that their occurrences are significantly more frequent than random. Results Our analysis of several large biological networks suggests, in contrast, that the frequencies of appearance of common subgraphs are similar in natural and corresponding random networks. Conclusion Indeed, certain topological features of biological networks give rise naturally to the common appearance of the motifs. We therefore question whether frequencies of occurrences are reasonable evidence that the structures of motifs have been selected for their functional contribution to the operation of networks.

  2. TreeNetViz: revealing patterns of networks over tree structures.

    Gou, Liang; Zhang, Xiaolong Luke

    2011-12-01

    Network data often contain important attributes from various dimensions such as social affiliations and areas of expertise in a social network. If such attributes exhibit a tree structure, visualizing a compound graph consisting of tree and network structures becomes complicated. How to visually reveal patterns of a network over a tree has not been fully studied. In this paper, we propose a compound graph model, TreeNet, to support visualization and analysis of a network at multiple levels of aggregation over a tree. We also present a visualization design, TreeNetViz, to offer the multiscale and cross-scale exploration and interaction of a TreeNet graph. TreeNetViz uses a Radial, Space-Filling (RSF) visualization to represent the tree structure, a circle layout with novel optimization to show aggregated networks derived from TreeNet, and an edge bundling technique to reduce visual complexity. Our circular layout algorithm reduces both total edge-crossings and edge length and also considers hierarchical structure constraints and edge weight in a TreeNet graph. These experiments illustrate that the algorithm can reduce visual cluttering in TreeNet graphs. Our case study also shows that TreeNetViz has the potential to support the analysis of a compound graph by revealing multiscale and cross-scale network patterns. © 2011 IEEE

  3. Spatial Pattern and Regional Relevance Analysis of the Maritime Silk Road Shipping Network

    Naixia Mou

    2018-03-01

    Full Text Available Under the strategy of “One Belt and One Road”, this paper explores the spatial pattern and the status quo of regional trade relevance of the Maritime Silk Road shipping network. Based on complex network theory, a topological structure map of shipping networks for containers, tankers, and bulk carriers was constructed, and the spatial characteristics of shipping networks were analyzed. Using the mode of spatial arrangement and the Herfindahl–Hirschman Index, this paper further analyzes the traffic flow pattern of regional trade of three kinds of goods. It is shown that the shipping network of containers, tankers and bulk carriers are unevenly distributed and have regional agglomeration phenomena. There is a strong correlation between the interior of the region and the adjacent areas, and the port competition is fierce. Among them, the container ships network is the most competitive in the region, while the competitiveness of the tankers network is relatively the lowest. The inter-regional correlation is weak, and a few transit hub ports have obvious competitive advantages. The ports in Northeast Asia and Southeast Asia are the most significant. The research results combined with the Maritime Silk Road policy can provide reference for port construction, route optimization, and coordinated development of regional trade, which will help to save time and cost of marine transportation, reduce energy consumption, and promote the sustainable development of marine environment and regional trade on the Maritime Silk Road.

  4. Network Analysis Reveals a Common Host–Pathogen Interaction Pattern in Arabidopsis Immune Responses

    Hong Li

    2017-05-01

    Full Text Available Many plant pathogens secrete virulence effectors into host cells to target important proteins in host cellular network. However, the dynamic interactions between effectors and host cellular network have not been fully understood. Here, an integrative network analysis was conducted by combining Arabidopsis thaliana protein–protein interaction network, known targets of Pseudomonas syringae and Hyaloperonospora arabidopsidis effectors, and gene expression profiles in the immune response. In particular, we focused on the characteristic network topology of the effector targets and differentially expressed genes (DEGs. We found that effectors tended to manipulate key network positions with higher betweenness centrality. The effector targets, especially those that are common targets of an individual effector, tended to be clustered together in the network. Moreover, the distances between the effector targets and DEGs increased over time during infection. In line with this observation, pathogen-susceptible mutants tended to have more DEGs surrounding the effector targets compared with resistant mutants. Our results suggest a common plant–pathogen interaction pattern at the cellular network level, where pathogens employ potent local impact mode to interfere with key positions in the host network, and plant organizes an in-depth defense by sequentially activating genes distal to the effector targets.

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

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

    2018-01-01

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

  6. Identification of global oil trade patterns: An empirical research based on complex network theory

    Ji, Qiang; Zhang, Hai-Ying; Fan, Ying

    2014-01-01

    Highlights: • A global oil trade core network is analyzed using complex network theory. • The global oil export core network displays a scale-free behaviour. • The current global oil trade network can be divided into three trading blocs. • The global oil trade network presents a ‘robust and yet fragile’ characteristic. - Abstract: The Global oil trade pattern becomes increasingly complex, which has become one of the most important factors affecting every country’s energy strategy and economic development. In this paper, a global oil trade core network is constructed to analyze the overall features, regional characteristics and stability of the oil trade using complex network theory. The results indicate that the global oil export core network displays a scale-free behaviour, in which the trade position of nodes presents obvious heterogeneity and the ‘hub nodes’ play a ‘bridge’ role in the formation process of the trade network. The current global oil trade network can be divided into three trading blocs, including the ‘South America-West Africa-North America’ trading bloc, the ‘Middle East–Asian–Pacific region’ trading bloc, and ‘the former Soviet Union–North Africa–Europe’ trading bloc. Geopolitics and diplomatic relations are the two main reasons for this regional oil trade structure. Moreover, the global oil trade network presents a ‘robust but yet fragile’ characteristic, and the impacts of trade interruption always tend to spread throughout the whole network even if the occurrence of export disruptions is localised

  7. Topological patterns in street networks of self-organized urban settlements

    Buhl, J.; Gautrais, J.; Reeves, N.; Solé, R. V.; Valverde, S.; Kuntz, P.; Theraulaz, G.

    2006-02-01

    Many urban settlements result from a spatially distributed, decentralized building process. Here we analyze the topological patterns of organization of a large collection of such settlements using the approach of complex networks. The global efficiency (based on the inverse of shortest-path lengths), robustness to disconnections and cost (in terms of length) of these graphs is studied and their possible origins analyzed. A wide range of patterns is found, from tree-like settlements (highly vulnerable to random failures) to meshed urban patterns. The latter are shown to be more robust and efficient.

  8. Mining Emerging Sequential Patterns for Activity Recognition in Body Sensor Networks

    Gu, Tao; Wang, Liang; Chen, Hanhua

    2010-01-01

    Body Sensor Networks oer many applications in healthcare, well-being and entertainment. One of the emerging applications is recognizing activities of daily living. In this paper, we introduce a novel knowledge pattern named Emerging Sequential Pattern (ESP)|a sequential pattern that discovers...... signicant class dierences|to recognize both simple (i.e., sequential) and complex (i.e., interleaved and concurrent) activities. Based on ESPs, we build our complex activity models directly upon the sequential model to recognize both activity types. We conduct comprehensive empirical studies to evaluate...

  9. Neural Network Based Recognition of Signal Patterns in Application to Automatic Testing of Rails

    Tomasz Ciszewski

    2006-01-01

    Full Text Available The paper describes the application of neural network for recognition of signal patterns in measuring data gathered by the railroad ultrasound testing car. Digital conversion of the measuring signal allows to store and process large quantities of data. The elaboration of smart, effective and automatic procedures recognizing the obtained patterns on the basisof measured signal amplitude has been presented. The test shows only two classes of pattern recognition. In authors’ opinion if we deliver big enough quantity of training data, presented method is applicable to a system that recognizes many classes.

  10. Whose Balance Sheet is this? Neural Networks for Banks' Pattern Recognition

    Leon Rincon, Carlos; Moreno, José Fernando; Cely, Jorge

    2017-01-01

    The balance sheet is a snapshot that portraits the financial position of a firm at a specific point of time. Under the reasonable assumption that the financial position of a firm is unique and representative, we use a basic artificial neural network pattern recognition method on Colombian banks’

  11. Inferring Drosophila gap gene regulatory network: Pattern analysis of simulated gene expression profiles and stability analysis

    Fomekong-Nanfack, Y.; Postma, M.; Kaandorp, J.A.

    2009-01-01

    Background: Inference of gene regulatory networks (GRNs) requires accurate data, a method to simulate the expression patterns and an efficient optimization algorithm to estimate the unknown parameters. Using this approach it is possible to obtain alternative circuits without making any a priori

  12. Behavioral and Physiological Neural Network Analyses: A Common Pathway toward Pattern Recognition and Prediction

    Ninness, Chris; Lauter, Judy L.; Coffee, Michael; Clary, Logan; Kelly, Elizabeth; Rumph, Marilyn; Rumph, Robin; Kyle, Betty; Ninness, Sharon K.

    2012-01-01

    Using 3 diversified datasets, we explored the pattern-recognition ability of the Self-Organizing Map (SOM) artificial neural network as applied to diversified nonlinear data distributions in the areas of behavioral and physiological research. Experiment 1 employed a dataset obtained from the UCI Machine Learning Repository. Data for this study…

  13. Scanless functional imaging of hippocampal networks using patterned two-photon illumination through GRIN lenses

    Moretti, Claudio; Antonini, Andrea; Bovetti, Serena; Liberale, Carlo; Fellin, Tommaso

    2016-01-01

    functional imaging in rodent hippocampal networks in vivo ~1.2 mm below the brain surface. Our results open the way to the application of patterned illumination approaches to deep regions of highly scattering biological tissues, such as the mammalian brain.

  14. Patient referral patterns and the spread of hospital-acquired infections through national health care networks.

    Tjibbe Donker

    2010-03-01

    Full Text Available Rates of hospital-acquired infections, such as methicillin-resistant Staphylococcus aureus (MRSA, are increasingly used as quality indicators for hospital hygiene. Alternatively, these rates may vary between hospitals, because hospitals differ in admission and referral of potentially colonized patients. We assessed if different referral patterns between hospitals in health care networks can influence rates of hospital-acquired infections like MRSA. We used the Dutch medical registration of 2004 to measure the connectedness between hospitals. This allowed us to reconstruct the network of hospitals in the Netherlands. We used mathematical models to assess the effect of different patient referral patterns on the potential spread of hospital-acquired infections between hospitals, and between categories of hospitals (University medical centers, top clinical hospitals and general hospitals. University hospitals have a higher number of shared patients than teaching or general hospitals, and are therefore more likely to be among the first to receive colonized patients. Moreover, as the network is directional towards university hospitals, they have a higher prevalence, even when infection control measures are equally effective in all hospitals. Patient referral patterns have a profound effect on the spread of health care-associated infections like hospital-acquired MRSA. The MRSA prevalence therefore differs between hospitals with the position of each hospital within the health care network. Any comparison of MRSA rates between hospitals, as a benchmark for hospital hygiene, should therefore take the position of a hospital within the network into account.

  15. Rotation and scale change invariant point pattern relaxation matching by the Hopfield neural network

    Sang, Nong; Zhang, Tianxu

    1997-12-01

    Relaxation matching is one of the most relevant methods for image matching. The original relaxation matching technique using point patterns is sensitive to rotations and scale changes. We improve the original point pattern relaxation matching technique to be invariant to rotations and scale changes. A method that makes the Hopfield neural network perform this matching process is discussed. An advantage of this is that the relaxation matching process can be performed in real time with the neural network's massively parallel capability to process information. Experimental results with large simulated images demonstrate the effectiveness and feasibility of the method to perform point patten relaxation matching invariant to rotations and scale changes and the method to perform this matching by the Hopfield neural network. In addition, we show that the method presented can be tolerant to small random error.

  16. Compact holographic optical neural network system for real-time pattern recognition

    Lu, Taiwei; Mintzer, David T.; Kostrzewski, Andrew A.; Lin, Freddie S.

    1996-08-01

    One of the important characteristics of artificial neural networks is their capability for massive interconnection and parallel processing. Recently, specialized electronic neural network processors and VLSI neural chips have been introduced in the commercial market. The number of parallel channels they can handle is limited because of the limited parallel interconnections that can be implemented with 1D electronic wires. High-resolution pattern recognition problems can require a large number of neurons for parallel processing of an image. This paper describes a holographic optical neural network (HONN) that is based on high- resolution volume holographic materials and is capable of performing massive 3D parallel interconnection of tens of thousands of neurons. A HONN with more than 16,000 neurons packaged in an attache case has been developed. Rotation- shift-scale-invariant pattern recognition operations have been demonstrated with this system. System parameters such as the signal-to-noise ratio, dynamic range, and processing speed are discussed.

  17. Encoding sensory and motor patterns as time-invariant trajectories in recurrent neural networks.

    Goudar, Vishwa; Buonomano, Dean V

    2018-03-14

    Much of the information the brain processes and stores is temporal in nature-a spoken word or a handwritten signature, for example, is defined by how it unfolds in time. However, it remains unclear how neural circuits encode complex time-varying patterns. We show that by tuning the weights of a recurrent neural network (RNN), it can recognize and then transcribe spoken digits. The model elucidates how neural dynamics in cortical networks may resolve three fundamental challenges: first, encode multiple time-varying sensory and motor patterns as stable neural trajectories; second, generalize across relevant spatial features; third, identify the same stimuli played at different speeds-we show that this temporal invariance emerges because the recurrent dynamics generate neural trajectories with appropriately modulated angular velocities. Together our results generate testable predictions as to how recurrent networks may use different mechanisms to generalize across the relevant spatial and temporal features of complex time-varying stimuli. © 2018, Goudar et al.

  18. Supersingular quantum perturbations

    Detwiler, L.C.; Klauder, J.R.

    1975-01-01

    A perturbation potential is called supersingular whenever generally every matrix element of the perturbation in the unperturbed eigenstates is infinite. It follows that supersingular perturbations do not have conventional perturbation expansions, say for energy eigenvalues. By invoking variational arguments, we determine the asymptotic behavior of the energy eigenvalues for asymptotically small values of the coupling constant of the supersingular perturbation

  19. Beyond ectomycorrhizal bipartite networks: projected networks demonstrate contrasted patterns between early- and late-successional plants in Corsica.

    Adrien eTaudiere

    2015-10-01

    Full Text Available The ectomycorrhizal (ECM symbiosis connects mutualistic plants and fungal species into bipartite networks. While links between one focal ECM plant and its fungal symbionts have been widely documented, systemic views of ECM networks are lacking, in particular, concerning the ability of fungal species to mediate indirect ecological interactions between ECM plant species (projected-ECM networks. We assembled a large dataset of plant-fungi associations at the species level and at the scale of Corsica using molecular data and unambiguously host-assigned records to: (i examine the correlation between the number of fungal symbionts of a plant species and the average specialization of these fungal species, (ii explore the structure of the plant-plant projected network and (iii compare plant association patterns in regard to their position along the ecological succession. Our analysis reveals no trade-off between specialization of plants and specialization of their partners and a saturation of the plant projected network. Moreover, there is a significantly lower-than-expected sharing of partners between early- and late-successional plant species, with fewer fungal partners for early-successional ones and similar average specialization of symbionts of early- and late-successional plants. Our work paves the way for ecological readings of Mediterranean landscapes that include the astonishing diversity of below-ground interactions.

  20. Spatio-Temporal Patterns of the International Merger and Acquisition Network.

    Dueñas, Marco; Mastrandrea, Rossana; Barigozzi, Matteo; Fagiolo, Giorgio

    2017-09-07

    This paper analyses the world web of mergers and acquisitions (M&As) using a complex network approach. We use data of M&As to build a temporal sequence of binary and weighted-directed networks for the period 1995-2010 and 224 countries (nodes) connected according to their M&As flows (links). We study different geographical and temporal aspects of the international M&A network (IMAN), building sequences of filtered sub-networks whose links belong to specific intervals of distance or time. Given that M&As and trade are complementary ways of reaching foreign markets, we perform our analysis using statistics employed for the study of the international trade network (ITN), highlighting the similarities and differences between the ITN and the IMAN. In contrast to the ITN, the IMAN is a low density network characterized by a persistent giant component with many external nodes and low reciprocity. Clustering patterns are very heterogeneous and dynamic. High-income economies are the main acquirers and are characterized by high connectivity, implying that most countries are targets of a few acquirers. Like in the ITN, geographical distance strongly impacts the structure of the IMAN: link-weights and node degrees have a non-linear relation with distance, and an assortative pattern is present at short distances.

  1. Decompositions of injection patterns for nodal flow allocation in renewable electricity networks

    Schäfer, Mirko; Tranberg, Bo; Hempel, Sabrina; Schramm, Stefan; Greiner, Martin

    2017-08-01

    The large-scale integration of fluctuating renewable power generation represents a challenge to the technical and economical design of a sustainable future electricity system. In this context, the increasing significance of long-range power transmission calls for innovative methods to understand the emerging complex flow patterns and to integrate price signals about the respective infrastructure needs into the energy market design. We introduce a decomposition method of injection patterns. Contrary to standard flow tracing approaches, it provides nodal allocations of link flows and costs in electricity networks by decomposing the network injection pattern into market-inspired elementary import/export building blocks. We apply the new approach to a simplified data-driven model of a European electricity grid with a high share of renewable wind and solar power generation.

  2. Changes of postural control and muscle activation pattern in response to external perturbations after neck flexor fatigue in young subjects with and without chronic neck pain.

    Cheng, Chih-Hsiu; Chien, Andy; Hsu, Wei-Li; Yen, Ling-Wei; Lin, Yang-Hua; Cheng, Hsin-Yi Kathy

    2015-03-01

    Previous studies have identified sensorimotor disturbances and greater fatigability of neck muscles in patients with neck pain. The purpose of this study was to investigate the effect of neck pain and neck flexor fatigue on standing balance following postural perturbations. Twenty patients with chronic neck pain (CNP) (24.7±3.6 year-old) and 20 age-matched asymptomatic subjects (22.1±2.2 year-old) were recruited. Subjects stood barefoot on a force plate and experienced backward perturbations before and after neck flexor fatigue. Center of pressure, electromyography of cervical and lumbar muscles, and head/trunk accelerations were recorded. Two-way ANOVA (pain×fatigue) was used for statistical analysis. CNP group showed larger body sway during quiet standing but not during perturbed standing compared with asymptomatic adults. In both groups, neck flexor fatigue resulted in greater body sway during the quiet standing but smaller body sway during perturbed standing, increased neck muscle activations and decreased lumbar muscle activations, as well as increased time to maximal head acceleration. Disturbed balance control was observed in CNP patients during the quiet standing. However, a rigid strategy was used to minimize the postural sway and to protect the head against backward perturbations in both CNP and asymptomatic young adults after neck flexor fatigue. The results facilitate the understanding of how the subjects with chronic neck pain and with neck muscle fatigue deal with the challenging condition. Further studies are needed to verify if such phenomenon could be changed after the intervention of specific flexor muscle retraining and balance control exercises. Copyright © 2015 Elsevier B.V. All rights reserved.

  3. Competition, transmission and pattern evolution: A network analysis of global oil trade

    Zhang, Hai-Ying; Ji, Qiang; Fan, Ying

    2014-01-01

    This paper studies the competition among oil importers using complex network theory, combined with several alternative measures of competition intensity, to analyze the evolution of the pattern and transmission of oil-trading competition. The results indicate that oil trade has formed a global competition pattern and that the role played by the Asian-Pacific region in the evolution of this competition pattern is becoming increasingly prominent. In addition, global competition intensity has continued to rise, and non-OECD countries have become the main driving force for this increase in global competition intensity. The large oil importers are the most significant parts of the global oil-trading competition pattern. They are not only the major participants in the competition for oil resources but also play important roles in the transmission of oil-trading competition. China and the United States especially display the feature of globalization, whose impacts of transmission reach across the whole oil-trading competition network. Finally, a “5C” (changeability, contestability, cooperation, commitment and circumstances) policy framework is put forward to maintain the stability of oil trade and improve the energy security of oil importers in various aspects. - Highlights: • An oil-trading competition network is constructed using complex network theory. • Oil trade has formed a global competition pattern and its intensity has kept rising. • The status of the Asian-Pacific region in the competition pattern becomes prominent. • Large oil importers play important roles in transmitting the trading competition. • A “5C” policy framework is put forward to cope with the intensive competition

  4. Persistence of self-recruitment and patterns of larval connectivity in a marine protected area network

    Berumen, Michael L.

    2012-02-01

    The use of marine protected area (MPA) networks to sustain fisheries and conserve biodiversity is predicated on two critical yet rarely tested assumptions. Individual MPAs must produce sufficient larvae that settle within that reserve\\'s boundaries to maintain local populations while simultaneously supplying larvae to other MPA nodes in the network that might otherwise suffer local extinction. Here, we use genetic parentage analysis to demonstrate that patterns of self-recruitment of two reef fishes (Amphiprion percula and Chaetodon vagabundus) in an MPA in Kimbe Bay, Papua New Guinea, were remarkably consistent over several years. However, dispersal from this reserve to two other nodes in an MPA network varied between species and through time. The stability of our estimates of self-recruitment suggests that even small MPAs may be self-sustaining. However, our results caution against applying optimization strategies to MPA network design without accounting for variable connectivity among species and over time. 2012 The Authors.

  5. Network Patterns of Inventor Collaboration and Their Effects on Innovation Outputs

    Wonchang Hur

    2016-03-01

    Full Text Available The purpose of this study is to examine how the collaboration structure among inventors in an R and D organization affects its capability to create impactful innovations. Specifically, this study is focused on examining whether a certain type of network mechanism found in collaboration among inventors contributes more to enhancing the future impacts of collaboration outputs, which is represented by the forward citations of their patents. To this end, co-invention networks for R and D organizations are constructed from an inventor-patent database, and the three structural patterns are measured by using network analytic constructs, namely, structural holes, strength of ties, and centralization. The results show that the presence of structural holes and strong ties are positively associated with the increasing forward citations, and that decentralized collaboration has also a positive impact. The findings offer support for both structural hole and network closure perspectives on social capital, which have been considered contradictive in the literature.

  6. Organization of anti-phase synchronization pattern in neural networks: what are the key factors?

    Dong eLi

    2011-12-01

    Full Text Available Anti-phase oscillation has been widely observed in cortical neuralnetwork. Elucidating the mechanism underlying the organization ofanti-phase pattern is of significance for better understanding morecomplicated pattern formations in brain networks. In dynamicalsystems theory, the organization of anti-phase oscillation patternhas usually been considered to relate to time-delay in coupling.This is consistent to conduction delays in real neural networks inthe brain due to finite propagation velocity of action potentials.However, other structural factors in cortical neural network, suchas modular organization (connection density and the coupling types(excitatory or inhibitory, could also play an important role. Inthis work, we investigate the anti-phase oscillation patternorganized on a two-module network of either neuronal cell model orneural mass model, and analyze the impact of the conduction delaytimes, the connection densities, and coupling types. Our resultsshow that delay times and coupling types can play key roles in thisorganization. The connection densities may have an influence on thestability if an anti-phase pattern exists due to the other factors.Furthermore, we show that anti-phase synchronization of slowoscillations can be achieved with small delay times if there isinteraction between slow and fast oscillations. These results aresignificant for further understanding more realistic spatiotemporaldynamics of cortico-cortical communications.

  7. Estimating Urban Traffic Patterns through Probabilistic Interconnectivity of Road Network Junctions.

    Ed Manley

    Full Text Available The emergence of large, fine-grained mobility datasets offers significant opportunities for the development and application of new methodologies for transportation analysis. In this paper, the link between routing behaviour and traffic patterns in urban areas is examined, introducing a method to derive estimates of traffic patterns from a large collection of fine-grained routing data. Using this dataset, the interconnectivity between road network junctions is extracted in the form of a Markov chain. This representation encodes the probability of the successive usage of adjacent road junctions, encoding routes as flows between decision points rather than flows along road segments. This network of functional interactions is then integrated within a modified Markov chain Monte Carlo (MCMC framework, adapted for the estimation of urban traffic patterns. As part of this approach, the data-derived links between major junctions influence the movement of directed random walks executed across the network to model origin-destination journeys. The simulation process yields estimates of traffic distribution across the road network. The paper presents an implementation of the modified MCMC approach for London, United Kingdom, building an MCMC model based on a dataset of nearly 700000 minicab routes. Validation of the approach clarifies how each element of the MCMC framework contributes to junction prediction performance, and finds promising results in relation to the estimation of junction choice and minicab traffic distribution. The paper concludes by summarising the potential for the development and extension of this approach to the wider urban modelling domain.

  8. Master stability functions reveal diffusion-driven pattern formation in networks

    Brechtel, Andreas; Gramlich, Philipp; Ritterskamp, Daniel; Drossel, Barbara; Gross, Thilo

    2018-03-01

    We study diffusion-driven pattern formation in networks of networks, a class of multilayer systems, where different layers have the same topology, but different internal dynamics. Agents are assumed to disperse within a layer by undergoing random walks, while they can be created or destroyed by reactions between or within a layer. We show that the stability of homogeneous steady states can be analyzed with a master stability function approach that reveals a deep analogy between pattern formation in networks and pattern formation in continuous space. For illustration, we consider a generalized model of ecological meta-food webs. This fairly complex model describes the dispersal of many different species across a region consisting of a network of individual habitats while subject to realistic, nonlinear predator-prey interactions. In this example, the method reveals the intricate dependence of the dynamics on the spatial structure. The ability of the proposed approach to deal with this fairly complex system highlights it as a promising tool for ecology and other applications.

  9. Frequency specific patterns of resting-state networks development from childhood to adolescence: A magnetoencephalography study.

    Meng, Lu; Xiang, Jing

    2016-11-01

    The present study investigated frequency dependent developmental patterns of the brain resting-state networks from childhood to adolescence. Magnetoencephalography (MEG) data were recorded from 20 healthy subjects at resting-state with eyes-open. The resting-state networks (RSNs) was analyzed at source-level. Brain network organization was characterized by mean clustering coefficient and average path length. The correlations between brain network measures and subjects' age during development from childhood to adolescence were statistically analyzed in delta (1-4Hz), theta (4-8Hz), alpha (8-12Hz), and beta (12-30Hz) frequency bands. A significant positive correlation between functional connectivity with age was found in alpha and beta frequency bands. A significant negative correlation between average path lengths with age was found in beta frequency band. The results suggest that there are significant developmental changes of resting-state networks from childhood to adolescence, which matures from a lattice network to a small-world network. Copyright © 2016 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

  10. Designing a Pattern Recognition Neural Network with a Reject Output and Many Sets of Weights and Biases

    Dung, Le; Mizukawa, Makoto

    2008-01-01

    Adding the reject output to the pattern recognition neural network is an approach to help the neural network can classify almost all patterns of a training data set by using many sets of weights and biases, even if the neural network is small. With a smaller number of neurons, we can implement the neural network on a hardware-based platform more easily and also reduce the response time of it. With the reject output the neural network can produce not only right or wrong results but also reject...

  11. Social networks and patterns of health risk behaviours over two decades: A multi-cohort study.

    Kauppi, Maarit; Elovainio, Marko; Stenholm, Sari; Virtanen, Marianna; Aalto, Ville; Koskenvuo, Markku; Kivimäki, Mika; Vahtera, Jussi

    2017-08-01

    To determine the associations between social network size and subsequent long-term health behaviour patterns, as indicated by alcohol use, smoking, and physical activity. Repeat data from up to six surveys over a 15- or 20-year follow-up were drawn from the Finnish Public Sector study (Raisio-Turku cohort, n=986; Hospital cohort, n=7307), and the Health and Social Support study (n=20,115). Social network size was determined at baseline, and health risk behaviours were assessed using repeated data from baseline and follow-up. We pooled cohort-specific results from repeated-measures log-binomial regression with the generalized estimating equations (GEE) method using fixed-effects meta-analysis. Participants with up to 10 members in their social network at baseline had an unhealthy risk factor profile throughout the follow-up. The pooled relative risks adjusted for age, gender, survey year, chronic conditions and education were 1.15 for heavy alcohol use (95% CI: 1.06-1.24), 1.19 for smoking (95% CI: 1.12-1.27), and 1.25 for low physical activity (95% CI: 1.21-1.29), as compared with those with >20 members in their social network. These associations appeared to be similar in subgroups stratified according to gender, age and education. Social network size predicted persistent behaviour-related health risk patterns up to at least two decades. Copyright © 2017 Elsevier Inc. All rights reserved.

  12. Efficient Mining and Detection of Sequential Intrusion Patterns for Network Intrusion Detection Systems

    Shyu, Mei-Ling; Huang, Zifang; Luo, Hongli

    In recent years, pervasive computing infrastructures have greatly improved the interaction between human and system. As we put more reliance on these computing infrastructures, we also face threats of network intrusion and/or any new forms of undesirable IT-based activities. Hence, network security has become an extremely important issue, which is closely connected with homeland security, business transactions, and people's daily life. Accurate and efficient intrusion detection technologies are required to safeguard the network systems and the critical information transmitted in the network systems. In this chapter, a novel network intrusion detection framework for mining and detecting sequential intrusion patterns is proposed. The proposed framework consists of a Collateral Representative Subspace Projection Modeling (C-RSPM) component for supervised classification, and an inter-transactional association rule mining method based on Layer Divided Modeling (LDM) for temporal pattern analysis. Experiments on the KDD99 data set and the traffic data set generated by a private LAN testbed show promising results with high detection rates, low processing time, and low false alarm rates in mining and detecting sequential intrusion detections.

  13. Network topology: patterns and mechanisms in plant-herbivore and host-parasitoid food webs.

    Cagnolo, Luciano; Salvo, Adriana; Valladares, Graciela

    2011-03-01

    1. Biological communities are organized in complex interaction networks such as food webs, which topology appears to be non-random. Gradients, compartments, nested subsets and even combinations of these structures have been shown in bipartite networks. However, in most studies only one pattern is tested against randomness and mechanistic hypotheses are generally lacking. 2. Here we examined the topology of regional, coexisting plant-herbivore and host-parasitoid food webs to discriminate between the mentioned network patterns. We also evaluated the role of species body size, local abundance, regional frequency and phylogeny as determinants of network topology. 3. We found both food webs to be compartmented, with interaction range boundaries imposed by host phylogeny. Species degree within compartments was mostly related to their regional frequency and local abundance. Only one compartment showed an internal nested structure in the distribution of interactions between species, but species position within this compartment was unrelated to species size or abundance. 4. These results suggest that compartmentalization may be more common than previously considered, and that network structure is a result of multiple, hierarchical, non-exclusive processes. © 2010 The Authors. Journal compilation © 2010 British Ecological Society.

  14. Gait pattern recognition in cerebral palsy patients using neural network modelling

    Muhammad, J.; Gibbs, S.; Abboud, R.; Anand, S.

    2015-01-01

    Interpretation of gait data obtained from modern 3D gait analysis is a challenging and time consuming task. The aim of this study was to create neural network models which can recognise the gait patterns from pre- and post-treatment and the normal ones. Neural network is a method which works on the principle of learning from experience and then uses the obtained knowledge to predict the unknown. Methods: Twenty-eight patients with cerebral palsy were recruited as subjects whose gait was analysed in pre- and post-treatment. A group of twenty-six normal subjects also participated in this study as control group. All subjects gait was analysed using Vicon Nexus to obtain the gait parameters and kinetic and kinematic parameters of hip, knee and ankle joints in three planes of both limbs. The gait data was used as input to create neural network models. A total of approximately 300 trials were split into 70% and 30% to train and test the models, respectively. Different models were built using different parameters. The gait was categorised as three patterns, i.e., normal, pre- and post-treatments. Result: The results showed that the models using all parameters or using the joint angles and moments could predict the gait patterns with approximately 95% accuracy. Some of the models e.g., the models using joint power and moments, had lower rate in recognition of gait patterns with approximately 70-90% successful ratio. Conclusion: Neural network model can be used in clinical practice to recognise the gait pattern for cerebral palsy patients. (author)

  15. In-vivo determination of chewing patterns using FBG and artificial neural networks

    Pegorini, Vinicius; Zen Karam, Leandro; Rocha Pitta, Christiano S.; Ribeiro, Richardson; Simioni Assmann, Tangriani; Cardozo da Silva, Jean Carlos; Bertotti, Fábio L.; Kalinowski, Hypolito J.; Cardoso, Rafael

    2015-09-01

    This paper reports the process of pattern classification of the chewing process of ruminants. We propose a simplified signal processing scheme for optical fiber Bragg grating (FBG) sensors based on machine learning techniques. The FBG sensors measure the biomechanical forces during jaw movements and an artificial neural network is responsible for the classification of the associated chewing pattern. In this study, three patterns associated to dietary supplement, hay and ryegrass were considered. Additionally, two other important events for ingestive behavior studies were monitored, rumination and idle period. Experimental results show that the proposed approach for pattern classification has been capable of differentiating the materials involved in the chewing process with a small classification error.

  16. Ex vivo determination of chewing patterns using FBG and artificial neural networks

    Karam, L. Z.; Pegorini, V.; Pitta, C. S. R.; Assmann, T. S.; Cardoso, R.; Kalinowski, H. J.; Silva, J. C. C.

    2014-05-01

    This paper reports the experimental procedures performed in a bovine head for the determination of chewing patterns during the mastication process. Mandible movements during the chewing have been simulated either by using two plasticine materials with different textures or without material. Fibre Bragg grating sensors were fixed in the jaw to monitor the biomechanical forces involved in the chewing process. The acquired signals from the sensors fed the input of an artificial neural network aiming at the classification of the measured chewing patterns for each material used in the experiment. The results obtained from the simulation of the chewing process presented different patterns for the different textures of plasticine, resulting on the determination of three chewing patterns with a classification error of 5%.

  17. Scanless functional imaging of hippocampal networks using patterned two-photon illumination through GRIN lenses

    Moretti, Claudio

    2016-09-12

    Patterned illumination through the phase modulation of light is increasingly recognized as a powerful tool to investigate biological tissues in combination with two-photon excitation and light-sensitive molecules. However, to date two-photon patterned illumination has only been coupled to traditional microscope objectives, thus limiting the applicability of these methods to superficial biological structures. Here, we show that phase modulation can be used to efficiently project complex two-photon light patterns, including arrays of points and large shapes, in the focal plane of graded index (GRIN) lenses. Moreover, using this approach in combination with the genetically encoded calcium indicator GCaMP6, we validate our system performing scanless functional imaging in rodent hippocampal networks in vivo ~1.2 mm below the brain surface. Our results open the way to the application of patterned illumination approaches to deep regions of highly scattering biological tissues, such as the mammalian brain.

  18. Geographic patterns of networks derived from extreme precipitation over the Indian subcontinent

    Stolbova, Veronika; Bookhagen, Bodo; Marwan, Norbert; Kurths, Juergen

    2014-05-01

    Complex networks (CN) and event synchronization (ES) methods have been applied to study a number of climate phenomena such as Indian Summer Monsoon (ISM), South-American Monsoon, and African Monsoon. These methods proved to be powerful tools to infer interdependencies in climate dynamics between geographical sites, spatial structures, and key regions of the considered climate phenomenon. Here, we use these methods to study the spatial temporal variability of the extreme rainfall over the Indian subcontinent, in order to filter the data by coarse-graining the network, and to identify geographic patterns that are signature features (spatial signatures) of the ISM. We find four main geographic patterns of networks derived from extreme precipitation over the Indian subcontinent using up-to-date satellite-derived, and high temporal and spatial resolution rain-gauge interpolated daily rainfall datasets. In order to prove that our results are also relevant for other climatic variables like pressure and temperature, we use re-analysis data provided by the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR). We find that two of the patterns revealed from the CN extreme rainfall analysis coincide with those obtained for the pressure and temperature fields, and all four above mentioned patterns can be explained by topography, winds, and monsoon circulation. CN and ES enable to select the most informative regions for the ISM, providing realistic description of the ISM dynamics with fewer data, and also help to infer geographic pattern that are spatial signatures of the ISM. These patterns deserve a special attention for the meteorologists and can be used as markers of the ISM variability.

  19. Evolution of networks for body plan patterning; interplay of modularity, robustness and evolvability.

    Kirsten H Ten Tusscher

    2011-10-01

    Full Text Available A major goal of evolutionary developmental biology (evo-devo is to understand how multicellular body plans of increasing complexity have evolved, and how the corresponding developmental programs are genetically encoded. It has been repeatedly argued that key to the evolution of increased body plan complexity is the modularity of the underlying developmental gene regulatory networks (GRNs. This modularity is considered essential for network robustness and evolvability. In our opinion, these ideas, appealing as they may sound, have not been sufficiently tested. Here we use computer simulations to study the evolution of GRNs' underlying body plan patterning. We select for body plan segmentation and differentiation, as these are considered to be major innovations in metazoan evolution. To allow modular networks to evolve, we independently select for segmentation and differentiation. We study both the occurrence and relation of robustness, evolvability and modularity of evolved networks. Interestingly, we observed two distinct evolutionary strategies to evolve a segmented, differentiated body plan. In the first strategy, first segments and then differentiation domains evolve (SF strategy. In the second scenario segments and domains evolve simultaneously (SS strategy. We demonstrate that under indirect selection for robustness the SF strategy becomes dominant. In addition, as a byproduct of this larger robustness, the SF strategy is also more evolvable. Finally, using a combined functional and architectural approach, we determine network modularity. We find that while SS networks generate segments and domains in an integrated manner, SF networks use largely independent modules to produce segments and domains. Surprisingly, we find that widely used, purely architectural methods for determining network modularity completely fail to establish this higher modularity of SF networks. Finally, we observe that, as a free side effect of evolving segmentation

  20. Three-dimensional chimera patterns in networks of spiking neuron oscillators

    Kasimatis, T.; Hizanidis, J.; Provata, A.

    2018-05-01

    We study the stable spatiotemporal patterns that arise in a three-dimensional (3D) network of neuron oscillators, whose dynamics is described by the leaky integrate-and-fire (LIF) model. More specifically, we investigate the form of the chimera states induced by a 3D coupling matrix with nonlocal topology. The observed patterns are in many cases direct generalizations of the corresponding two-dimensional (2D) patterns, e.g., spheres, layers, and cylinder grids. We also find cylindrical and "cross-layered" chimeras that do not have an equivalent in 2D systems. Quantitative measures are calculated, such as the ratio of synchronized and unsynchronized neurons as a function of the coupling range, the mean phase velocities, and the distribution of neurons in mean phase velocities. Based on these measures, the chimeras are categorized in two families. The first family of patterns is observed for weaker coupling and exhibits higher mean phase velocities for the unsynchronized areas of the network. The opposite holds for the second family, where the unsynchronized areas have lower mean phase velocities. The various measures demonstrate discontinuities, indicating criticality as the parameters cross from the first family of patterns to the second.

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

    Rodriguez, Marko A [Los Alamos National Laboratory; Pepe, Alberto [UCLA

    2009-01-01

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

  2. Multivoxel Patterns Reveal Functionally Differentiated Networks Underlying Auditory Feedback Processing of Speech

    Zheng, Zane Z.; Vicente-Grabovetsky, Alejandro; MacDonald, Ewen N.

    2013-01-01

    The everyday act of speaking involves the complex processes of speech motor control. An important component of control is monitoring, detection, and processing of errors when auditory feedback does not correspond to the intended motor gesture. Here we show, using fMRI and converging operations...... within a multivoxel pattern analysis framework, that this sensorimotor process is supported by functionally differentiated brain networks. During scanning, a real-time speech-tracking system was used to deliver two acoustically different types of distorted auditory feedback or unaltered feedback while...... human participants were vocalizing monosyllabic words, and to present the same auditory stimuli while participants were passively listening. Whole-brain analysis of neural-pattern similarity revealed three functional networks that were differentially sensitive to distorted auditory feedback during...

  3. Emergence of structural patterns out of synchronization in networks with competitive interactions

    Assenza, Salvatore; Gutiérrez, Ricardo; Gómez-Gardeñes, Jesús; Latora, Vito; Boccaletti, Stefano

    2011-09-01

    Synchronization is a collective phenomenon occurring in systems of interacting units, and is ubiquitous in nature, society and technology. Recent studies have enlightened the important role played by the interaction topology on the emergence of synchronized states. However, most of these studies neglect that real world systems change their interaction patterns in time. Here, we analyze synchronization features in networks in which structural and dynamical features co-evolve. The feedback of the node dynamics on the interaction pattern is ruled by the competition of two mechanisms: homophily (reinforcing those interactions with other correlated units in the graph) and homeostasis (preserving the value of the input strength received by each unit). The competition between these two adaptive principles leads to the emergence of key structural properties observed in real world networks, such as modular and scale-free structures, together with a striking enhancement of local synchronization in systems with no global order.

  4. An novel frequent probability pattern mining algorithm based on circuit simulation method in uncertain biological networks

    2014-01-01

    Background Motif mining has always been a hot research topic in bioinformatics. Most of current research on biological networks focuses on exact motif mining. However, due to the inevitable experimental error and noisy data, biological network data represented as the probability model could better reflect the authenticity and biological significance, therefore, it is more biological meaningful to discover probability motif in uncertain biological networks. One of the key steps in probability motif mining is frequent pattern discovery which is usually based on the possible world model having a relatively high computational complexity. Methods In this paper, we present a novel method for detecting frequent probability patterns based on circuit simulation in the uncertain biological networks. First, the partition based efficient search is applied to the non-tree like subgraph mining where the probability of occurrence in random networks is small. Then, an algorithm of probability isomorphic based on circuit simulation is proposed. The probability isomorphic combines the analysis of circuit topology structure with related physical properties of voltage in order to evaluate the probability isomorphism between probability subgraphs. The circuit simulation based probability isomorphic can avoid using traditional possible world model. Finally, based on the algorithm of probability subgraph isomorphism, two-step hierarchical clustering method is used to cluster subgraphs, and discover frequent probability patterns from the clusters. Results The experiment results on data sets of the Protein-Protein Interaction (PPI) networks and the transcriptional regulatory networks of E. coli and S. cerevisiae show that the proposed method can efficiently discover the frequent probability subgraphs. The discovered subgraphs in our study contain all probability motifs reported in the experiments published in other related papers. Conclusions The algorithm of probability graph isomorphism

  5. Using a multi-state recurrent neural network to optimize loading patterns in BWRs

    Ortiz, Juan Jose; Requena, Ignacio

    2004-01-01

    A Multi-State Recurrent Neural Network is used to optimize Loading Patterns (LP) in BWRs. We have proposed an energy function that depends on fuel assembly positions and their nuclear cross sections to carry out optimisation. Multi-State Recurrent Neural Networks creates LPs that satisfy the Radial Power Peaking Factor and maximize the effective multiplication factor at the Beginning of the Cycle, and also satisfy the Minimum Critical Power Ratio and Maximum Linear Heat Generation Rate at the End of the Cycle, thereby maximizing the effective multiplication factor. In order to evaluate the LPs, we have used a trained back-propagation neural network to predict the parameter values, instead of using a reactor core simulator, which saved considerable computation time in the search process. We applied this method to find optimal LPs for five cycles of Laguna Verde Nuclear Power Plant (LVNPP) in Mexico

  6. Analyzing collaboration networks and developmental patterns of nano-enabled drug delivery (NEDD for brain cancer

    Ying Huang

    2015-07-01

    Full Text Available The rapid development of new and emerging science & technologies (NESTs brings unprecedented challenges, but also opportunities. In this paper, we use bibliometric and social network analyses, at country, institution, and individual levels, to explore the patterns of scientific networking for a key nano area – nano-enabled drug delivery (NEDD. NEDD has successfully been used clinically to modulate drug release and to target particular diseased tissues. The data for this research come from a global compilation of research publication information on NEDD directed at brain cancer. We derive a family of indicators that address multiple facets of research collaboration and knowledge transfer patterns. Results show that: (1 international cooperation is increasing, but networking characteristics change over time; (2 highly productive institutions also lead in influence, as measured by citation to their work, with American institutes leading; (3 research collaboration is dominated by local relationships, with interesting information available from authorship patterns that go well beyond journal impact factors. Results offer useful technical intelligence to help researchers identify potential collaborators and to help inform R&D management and science & innovation policy for such nanotechnologies.

  7. Recurrent Neural Network For Forecasting Time Series With Long Memory Pattern

    Walid; Alamsyah

    2017-04-01

    Recurrent Neural Network as one of the hybrid models are often used to predict and estimate the issues related to electricity, can be used to describe the cause of the swelling of electrical load which experienced by PLN. In this research will be developed RNN forecasting procedures at the time series with long memory patterns. Considering the application is the national electrical load which of course has a different trend with the condition of the electrical load in any country. This research produces the algorithm of time series forecasting which has long memory pattern using E-RNN after this referred to the algorithm of integrated fractional recurrent neural networks (FIRNN).The prediction results of long memory time series using models Fractional Integrated Recurrent Neural Network (FIRNN) showed that the model with the selection of data difference in the range of [-1,1] and the model of Fractional Integrated Recurrent Neural Network (FIRNN) (24,6,1) provides the smallest MSE value, which is 0.00149684.

  8. Patterns of precipitation and soil moisture extremes in Texas, US: A complex network analysis

    Sun, Alexander Y.; Xia, Youlong; Caldwell, Todd G.; Hao, Zengchao

    2018-02-01

    Understanding of the spatial and temporal dynamics of extreme precipitation not only improves prediction skills, but also helps to prioritize hazard mitigation efforts. This study seeks to enhance the understanding of spatiotemporal covariation patterns embedded in precipitation (P) and soil moisture (SM) by using an event-based, complex-network-theoretic approach. Events concurrences are quantified using a nonparametric event synchronization measure, and spatial patterns of hydroclimate variables are analyzed by using several network measures and a community detection algorithm. SM-P coupling is examined using a directional event coincidence analysis measure that takes the order of event occurrences into account. The complex network approach is demonstrated for Texas, US, a region possessing a rich set of hydroclimate features and is frequented by catastrophic flooding. Gridded daily observed P data and simulated SM data are used to create complex networks of P and SM extremes. The uncovered high degree centrality regions and community structures are qualitatively in agreement with the overall existing knowledge of hydroclimate extremes in the study region. Our analyses provide new visual insights on the propagation, connectivity, and synchronicity of P extremes, as well as the SM-P coupling, in this flood-prone region, and can be readily used as a basis for event-driven predictive analytics for other regions.

  9. Efficient spiking neural network model of pattern motion selectivity in visual cortex.

    Beyeler, Michael; Richert, Micah; Dutt, Nikil D; Krichmar, Jeffrey L

    2014-07-01

    Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance approach to simulating large-scale neural network models in real-time is to leverage the parallel processing capability of graphics processing units (GPUs). Based on this approach, we present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction-selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and Heeger. Pattern-direction-selective (PDS) cells in MT are constructed by pooling over MT CDS cells with a wide range of preferred directions. Responses of our model neurons are comparable to electrophysiological results for grating and plaid stimuli as well as speed tuning. The behavioral response of the network in a motion discrimination task is in agreement with psychophysical data. Moreover, our implementation outperforms a previous implementation of the motion energy model by orders of magnitude in terms of computational speed and memory usage. The full network, which comprises 153,216 neurons and approximately 40 million synapses, processes 20 frames per second of a 40 × 40 input video in real-time using a single off-the-shelf GPU. To promote the use of this algorithm among neuroscientists and computer vision researchers, the source code for the simulator, the network, and analysis scripts are publicly available.

  10. Physician referral patterns and racial disparities in total hip replacement: A network analysis approach.

    Hassan M K Ghomrawi

    Full Text Available Efforts to reduce racial disparities in total hip replacement (THR have focused mainly on patient behaviors. While these efforts are no doubt important, they ignore the potentially important role of provider- and system-level factors, which may be easier to modify. We aimed to determine whether the patterns of interaction among physicians around THR episodes differ in communities with low versus high concentrations of black residents.We analyzed national Medicare claims from 2008 to 2011, identifying all fee-for-service beneficiaries who underwent THR. Based on physician encounter data, we then mapped the physician referral networks at the hospitals where beneficiaries' procedures were performed. Next, we measured two structural properties of these networks that could affect care coordination and information sharing: clustering, and the number of external ties. Finally, we estimated multivariate regression models to determine the relationship between the concentration of black residents in the community [as measured by the hospital service area (HSA] served by a given network and each of these 2 network properties.Our sample included 336,506 beneficiaries (mean age 76.3 ± SD, 63.1% of whom were women. HSAs with higher concentrations of black residents tended to be more impoverished than those with lower concentrations. While HSAs with higher concentrations of black residents had, on average, more acute care beds and medical specialists, they had fewer surgeons per capita than those with lower concentrations. After adjusting for these differences, we found that HSAs with higher concentrations of black residents were served by physician referral networks that had significantly higher within-network clustering but fewer external ties.We observed differences in the patterns of interaction among physicians around THR episodes in communities with low versus high concentrations of black residents. Studies investigating the impact of these differences

  11. Pattern-recalling processes in quantum Hopfield networks far from saturation

    Inoue, Jun-ichi

    2011-01-01

    As a mathematical model of associative memories, the Hopfield model was now well-established and a lot of studies to reveal the pattern-recalling process have been done from various different approaches. As well-known, a single neuron is itself an uncertain, noisy unit with a finite unnegligible error in the input-output relation. To model the situation artificially, a kind of 'heat bath' that surrounds neurons is introduced. The heat bath, which is a source of noise, is specified by the 'temperature'. Several studies concerning the pattern-recalling processes of the Hopfield model governed by the Glauber-dynamics at finite temperature were already reported. However, we might extend the 'thermal noise' to the quantum-mechanical variant. In this paper, in terms of the stochastic process of quantum-mechanical Markov chain Monte Carlo method (the quantum MCMC), we analytically derive macroscopically deterministic equations of order parameters such as 'overlap' in a quantum-mechanical variant of the Hopfield neural networks (let us call quantum Hopfield model or quantum Hopfield networks). For the case in which non-extensive number p of patterns are embedded via asymmetric Hebbian connections, namely, p/N → 0 for the number of neuron N → ∞ ('far from saturation'), we evaluate the recalling processes for one of the built-in patterns under the influence of quantum-mechanical noise.

  12. Mesoporous silica nanoparticles trigger mitophagy in endothelial cells and perturb neuronal network activity in a size- and time-dependent manner

    Orlando A

    2017-05-01

    polyacrylamide-gel electrophoresis analysis of the plasma proteins covering the MSNP surface. Moreover, doses of MSNPs up to 0.25 mg/mL perturbed network activity by increasing excitability, as detected by multielectrode-array technology, without affecting neuronal cell viability.Conclusion: These results suggest that MSNPs may be low-risk if prepared with a diameter <30 nm and if they reach human tissues at doses <0.25 mg/mL. These important advances could help the rational design of NPs intended for biomedical uses, demonstrating that careful toxicity evaluation is necessary before using MSNPs in patients. Keywords: mesoporous silica nanoparticles, nanotoxicity, endothelial cells, neurons, brain 

  13. A Social Network Analysis of Tourist Movement Patterns in Blogs: Korean Backpackers in Europe

    Hee Chung Chung

    2017-12-01

    Full Text Available Given recent developments in information and communication technology, the number of individual tourists enjoying free travel without the advice of travel agencies is increasing. Therefore, such tourists can visit more tourist destinations and create more complex movement patterns than mass tourists. These tourist movement patterns are a key factor in understanding tourist behavior and they contain various information that is important for tourism marketers. In this vein, this study aims to investigate tourist movement patterns in Europe. We acquired 122 data points from posts on the NAVER blog, which is the most famous social media platform in Korea. These data were transformed into matrix data for social network analysis and analyzed for centrality. The results suggest that Korean backpackers in Europe tend to enter Europe through London and Paris. Venezia and Firenze are also key cities.

  14. Analytical maximum-likelihood method to detect patterns in real networks

    Squartini, Tiziano; Garlaschelli, Diego

    2011-01-01

    In order to detect patterns in real networks, randomized graph ensembles that preserve only part of the topology of an observed network are systematically used as fundamental null models. However, the generation of them is still problematic. Existing approaches are either computationally demanding and beyond analytic control or analytically accessible but highly approximate. Here, we propose a solution to this long-standing problem by introducing a fast method that allows one to obtain expectation values and standard deviations of any topological property analytically, for any binary, weighted, directed or undirected network. Remarkably, the time required to obtain the expectation value of any property analytically across the entire graph ensemble is as short as that required to compute the same property using the adjacency matrix of the single original network. Our method reveals that the null behavior of various correlation properties is different from what was believed previously, and is highly sensitive to the particular network considered. Moreover, our approach shows that important structural properties (such as the modularity used in community detection problems) are currently based on incorrect expressions, and provides the exact quantities that should replace them.

  15. Reduction of the dimension of neural network models in problems of pattern recognition and forecasting

    Nasertdinova, A. D.; Bochkarev, V. V.

    2017-11-01

    Deep neural networks with a large number of parameters are a powerful tool for solving problems of pattern recognition, prediction and classification. Nevertheless, overfitting remains a serious problem in the use of such networks. A method of solving the problem of overfitting is proposed in this article. This method is based on reducing the number of independent parameters of a neural network model using the principal component analysis, and can be implemented using existing libraries of neural computing. The algorithm was tested on the problem of recognition of handwritten symbols from the MNIST database, as well as on the task of predicting time series (rows of the average monthly number of sunspots and series of the Lorentz system were used). It is shown that the application of the principal component analysis enables reducing the number of parameters of the neural network model when the results are good. The average error rate for the recognition of handwritten figures from the MNIST database was 1.12% (which is comparable to the results obtained using the "Deep training" methods), while the number of parameters of the neural network can be reduced to 130 times.

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

    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. Interaction patterns of nurturant support exchanged in online health social networking.

    Chuang, Katherine Y; Yang, Christopher C

    2012-05-03

    Expressing emotion in online support communities is an important aspect of enabling e-patients to connect with each other and expand their social resources. Indirectly it increases the amount of support for coping with health issues. Exploring the supportive interaction patterns in online health social networking would help us better understand how technology features impacts user behavior in this context. To build on previous research that identified different types of social support in online support communities by delving into patterns of supportive behavior across multiple computer-mediated communication formats. Each format combines different architectural elements, affecting the resulting social spaces. Our research question compared communication across different formats of text-based computer-mediated communication provided on the MedHelp.org health social networking environment. We identified messages with nurturant support (emotional, esteem, and network) across three different computer-mediated communication formats (forums, journals, and notes) of an online support community for alcoholism using content analysis. Our sample consisted of 493 forum messages, 423 journal messages, and 1180 notes. Nurturant support types occurred frequently among messages offering support (forum comments: 276/412 messages, 67.0%; journal posts: 65/88 messages, 74%; journal comments: 275/335 messages, 82.1%; and notes: 1002/1180 messages, 84.92%), but less often among messages requesting support. Of all the nurturing supports, emotional (ie, encouragement) appeared most frequently, with network and esteem support appearing in patterns of varying combinations. Members of the Alcoholism Community appeared to adapt some traditional face-to-face forms of support to their needs in becoming sober, such as provision of encouragement, understanding, and empathy to one another. The computer-mediated communication format may have the greatest influence on the supportive interactions

  18. Storage of phase-coded patterns via STDP in fully-connected and sparse network: a study of the network capacity

    Silvia Scarpetta

    2010-08-01

    Full Text Available We study the storage and retrieval of phase-coded patterns as stable dynamical attractors in recurrent neural networks, for both an analog and a integrate-and-fire spiking model. The synaptic strength is determined by a learning rule based on spike-time-dependent plasticity, with an asymmetric time window depending on the relative timing between pre- and post-synaptic activity. We store multiple patterns and study the network capacity. For the analog model, we find that the network capacity scales linearly with the network size, and that both capacity and the oscillation frequency of the retrieval state depend on the asymmetry of the learning time window. In addition to fully-connected networks, we study sparse networks, where each neuron is connected only to a small number $zll N$ of other neurons. Connections can be short range, between neighboring neurons placed on a regular lattice, or long range, between randomly chosen pairs of neurons. We find that a small fraction of long range connections is able to amplify the capacity of the network. This imply that a small-world-network topology is optimal, as a compromise between the cost of long range connections and the capacity increase. Also in the spiking integrate and fire model the crucial result of storing and retrieval of multiple phase-coded patterns is observed. The capacity of the fully-connected spiking network is investigated, together with the relation between oscillation frequency of retrieval state and window asymmetry.

  19. Probing the reaching-grasping network in humans through multivoxel pattern decoding.

    Di Bono, Maria Grazia; Begliomini, Chiara; Castiello, Umberto; Zorzi, Marco

    2015-11-01

    The quest for a putative human homolog of the reaching-grasping network identified in monkeys has been the focus of many neuropsychological and neuroimaging studies in recent years. These studies have shown that the network underlying reaching-only and reach-to-grasp movements includes the superior parieto-occipital cortex (SPOC), the anterior part of the human intraparietal sulcus (hAIP), the ventral and the dorsal portion of the premotor cortex, and the primary motor cortex (M1). Recent evidence for a wider frontoparietal network coding for different aspects of reaching-only and reach-to-grasp actions calls for a more fine-grained assessment of the reaching-grasping network in humans by exploiting pattern decoding methods (multivoxel pattern analysis--MVPA). Here, we used MPVA on functional magnetic resonance imaging (fMRI) data to assess whether regions of the frontoparietal network discriminate between reaching-only and reach-to-grasp actions, natural and constrained grasping, different grasp types, and object sizes. Participants were required to perform either reaching-only movements or two reach-to-grasp types (precision or whole hand grasp) upon spherical objects of different sizes. Multivoxel pattern analysis highlighted that, independently from the object size, all the selected regions of both hemispheres contribute in coding for grasp type, with the exception of SPOC and the right hAIP. Consistent with recent neurophysiological findings on monkeys, there was no evidence for a clear-cut distinction between a dorsomedial and a dorsolateral pathway that would be specialized for reaching-only and reach-to-grasp actions, respectively. Nevertheless, the comparison of decoding accuracy across brain areas highlighted their different contributions to reaching-only and grasping actions. Altogether, our findings enrich the current knowledge regarding the functional role of key brain areas involved in the cortical control of reaching-only and reach-to-grasp actions

  20. Multi-channels coupling-induced pattern transition in a tri-layer neuronal network

    Wu, Fuqiang; Wang, Ya; Ma, Jun; Jin, Wuyin; Hobiny, Aatef

    2018-03-01

    Neurons in nerve system show complex electrical behaviors due to complex connection types and diversity in excitability. A tri-layer network is constructed to investigate the signal propagation and pattern formation by selecting different coupling channels between layers. Each layer is set as different states, and the local kinetics is described by Hindmarsh-Rose neuron model. By changing the number of coupling channels between layers and the state of the first layer, the collective behaviors of each layer and synchronization pattern of network are investigated. A statistical factor of synchronization on each layer is calculated. It is found that quiescent state in the second layer can be excited and disordered state in the third layer is suppressed when the first layer is controlled by a pacemaker, and the developed state is dependent on the number of coupling channels. Furthermore, the collapse in the first layer can cause breakdown of other layers in the network, and the mechanism is that disordered state in the third layer is enhanced when sampled signals from the collapsed layer can impose continuous disturbance on the next layer.

  1. Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

    Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

    2014-07-01

    The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

  2. Premotor spinal network with balanced excitation and inhibition during motor patterns has high resilience to structural division

    Petersen, Peter C; Vestergaard, Mikkel; Reveles Jensen, Kristian

    2014-01-01

    Direct measurements of synaptic inhibition (I) and excitation (E) to spinal motoneurons can provide an important insight into the organization of premotor networks. Such measurements of flexor motoneurons participating in motor patterns in turtles have recently demonstrated strong concurrent E...

  3. Patterning human neuronal networks on photolithographically engineered silicon dioxide substrates functionalized with glial analogues.

    Hughes, Mark A; Brennan, Paul M; Bunting, Andrew S; Cameron, Katherine; Murray, Alan F; Shipston, Mike J

    2014-05-01

    Interfacing neurons with silicon semiconductors is a challenge being tackled through various bioengineering approaches. Such constructs inform our understanding of neuronal coding and learning and ultimately guide us toward creating intelligent neuroprostheses. A fundamental prerequisite is to dictate the spatial organization of neuronal cells. We sought to pattern neurons using photolithographically defined arrays of polymer parylene-C, activated with fetal calf serum. We used a purified human neuronal cell line [Lund human mesencephalic (LUHMES)] to establish whether neurons remain viable when isolated on-chip or whether they require a supporting cell substrate. When cultured in isolation, LUHMES neurons failed to pattern and did not show any morphological signs of differentiation. We therefore sought a cell type with which to prepattern parylene regions, hypothesizing that this cellular template would enable secondary neuronal adhesion and network formation. From a range of cell lines tested, human embryonal kidney (HEK) 293 cells patterned with highest accuracy. LUHMES neurons adhered to pre-established HEK 293 cell clusters and this coculture environment promoted morphological differentiation of neurons. Neurites extended between islands of adherent cell somata, creating an orthogonally arranged neuronal network. HEK 293 cells appear to fulfill a role analogous to glia, dictating cell adhesion, and generating an environment conducive to neuronal survival. We next replaced HEK 293 cells with slower growing glioma-derived precursors. These primary human cells patterned accurately on parylene and provided a similarly effective scaffold for neuronal adhesion. These findings advance the use of this microfabrication-compatible platform for neuronal patterning. Copyright © 2013 Wiley Periodicals, Inc.

  4. Discovery of spatio-temporal patterns from location-based social networks

    Béjar, J.; Álvarez, S.; García, D.; Gómez, I.; Oliva, L.; Tejeda, A.; Vázquez-Salceda, J.

    2016-03-01

    Location-based social networks (LBSNs) such as Twitter or Instagram are a good source for user spatio-temporal behaviour. These networks collect data from users in such a way that they can be seen as a set of collective and distributed sensors of a geographical area. A low rate sampling of user's location information can be obtained during large intervals of time that can be used to discover complex patterns, including mobility profiles, points of interest or unusual events. These patterns can be used as the elements of a knowledge base for different applications in different domains such as mobility route planning, touristic recommendation systems or city planning. The aim of this paper is twofold, first to analyse the frequent spatio-temporal patterns that users share when living and visiting a city. This behaviour is studied by means of frequent itemsets algorithms in order to establish some associations among visits that can be interpreted as interesting routes or spatio-temporal connections. Second, to analyse how the spatio-temporal behaviour of a large number of users can be segmented in different profiles. These behavioural profiles are obtained by means of clustering algorithms that show the different patterns of behaviour of visitors and citizens. The data analysed were obtained from the public data feeds of Twitter and Instagram within an area surrounding the cities of Barcelona and Milan for a period of several months. The analysis of these data shows that these kinds of algorithms can be successfully applied to data from any city (or general area) to discover useful patterns that can be interpreted on terms of singular places and areas and their temporal relationships.

  5. Deep convolutional neural networks for annotating gene expression patterns in the mouse brain.

    Zeng, Tao; Li, Rongjian; Mukkamala, Ravi; Ye, Jieping; Ji, Shuiwang

    2015-05-07

    Profiling gene expression in brain structures at various spatial and temporal scales is essential to understanding how genes regulate the development of brain structures. The Allen Developing Mouse Brain Atlas provides high-resolution 3-D in situ hybridization (ISH) gene expression patterns in multiple developing stages of the mouse brain. Currently, the ISH images are annotated with anatomical terms manually. In this paper, we propose a computational approach to annotate gene expression pattern images in the mouse brain at various structural levels over the course of development. We applied deep convolutional neural network that was trained on a large set of natural images to extract features from the ISH images of developing mouse brain. As a baseline representation, we applied invariant image feature descriptors to capture local statistics from ISH images and used the bag-of-words approach to build image-level representations. Both types of features from multiple ISH image sections of the entire brain were then combined to build 3-D, brain-wide gene expression representations. We employed regularized learning methods for discriminating gene expression patterns in different brain structures. Results show that our approach of using convolutional model as feature extractors achieved superior performance in annotating gene expression patterns at multiple levels of brain structures throughout four developing ages. Overall, we achieved average AUC of 0.894 ± 0.014, as compared with 0.820 ± 0.046 yielded by the bag-of-words approach. Deep convolutional neural network model trained on natural image sets and applied to gene expression pattern annotation tasks yielded superior performance, demonstrating its transfer learning property is applicable to such biological image sets.

  6. Discordant patterns of genetic and phenotypic differentiation in five grasshopper species codistributed across a microreserve network.

    Ortego, Joaquín; García-Navas, Vicente; Noguerales, Víctor; Cordero, Pedro J

    2015-12-01

    Conservation plans can be greatly improved when information on the evolutionary and demographic consequences of habitat fragmentation is available for several codistributed species. Here, we study spatial patterns of phenotypic and genetic variation among five grasshopper species that are codistributed across a network of microreserves but show remarkable differences in dispersal-related morphology (body size and wing length), degree of habitat specialization and extent of fragmentation of their respective habitats in the study region. In particular, we tested the hypothesis that species with preferences for highly fragmented microhabitats show stronger genetic and phenotypic structure than codistributed generalist taxa inhabiting a continuous matrix of suitable habitat. We also hypothesized a higher resemblance of spatial patterns of genetic and phenotypic variability among species that have experienced a higher degree of habitat fragmentation due to their more similar responses to the parallel large-scale destruction of their natural habitats. In partial agreement with our first hypothesis, we found that genetic structure, but not phenotypic differentiation, was higher in species linked to highly fragmented habitats. We did not find support for congruent patterns of phenotypic and genetic variability among any studied species, indicating that they show idiosyncratic evolutionary trajectories and distinctive demographic responses to habitat fragmentation across a common landscape. This suggests that conservation practices in networks of protected areas require detailed ecological and evolutionary information on target species to focus management efforts on those taxa that are more sensitive to the effects of habitat fragmentation. © 2015 John Wiley & Sons Ltd.

  7. Pore network modeling of drainage process in patterned porous media: a quasi-static study

    Zhang, Tao

    2015-04-17

    This work represents a preliminary investigation on the role of wettability conditions on the flow of a two-phase system in porous media. Since such effects have been lumped implicitly in relative permeability-saturation and capillary pressure-saturation relationships, it is quite challenging to isolate its effects explicitly in real porous media applications. However, within the framework of pore network models, it is easy to highlight the effects of wettability conditions on the transport of two-phase systems. We employ quasi-static investigation in which the system undergo slow movement based on slight increment of the imposed pressure. Several numerical experiments of the drainage process are conducted to displace a wetting fluid with a non-wetting one. In all these experiments the network is assigned different scenarios of various wettability patterns. The aim is to show that the drainage process is very much affected by the imposed pattern of wettability. The wettability conditions are imposed by assigning the value of contact angle to each pore throat according to predefined patterns.

  8. Application of approximate pattern matching in two dimensional spaces to grid layout for biochemical network maps.

    Inoue, Kentaro; Shimozono, Shinichi; Yoshida, Hideaki; Kurata, Hiroyuki

    2012-01-01

    For visualizing large-scale biochemical network maps, it is important to calculate the coordinates of molecular nodes quickly and to enhance the understanding or traceability of them. The grid layout is effective in drawing compact, orderly, balanced network maps with node label spaces, but existing grid layout algorithms often require a high computational cost because they have to consider complicated positional constraints through the entire optimization process. We propose a hybrid grid layout algorithm that consists of a non-grid, fast layout (preprocessor) algorithm and an approximate pattern matching algorithm that distributes the resultant preprocessed nodes on square grid points. To demonstrate the feasibility of the hybrid layout algorithm, it is characterized in terms of the calculation time, numbers of edge-edge and node-edge crossings, relative edge lengths, and F-measures. The proposed algorithm achieves outstanding performances compared with other existing grid layouts. Use of an approximate pattern matching algorithm quickly redistributes the laid-out nodes by fast, non-grid algorithms on the square grid points, while preserving the topological relationships among the nodes. The proposed algorithm is a novel use of the pattern matching, thereby providing a breakthrough for grid layout. This application program can be freely downloaded from http://www.cadlive.jp/hybridlayout/hybridlayout.html.

  9. Application of approximate pattern matching in two dimensional spaces to grid layout for biochemical network maps.

    Kentaro Inoue

    Full Text Available BACKGROUND: For visualizing large-scale biochemical network maps, it is important to calculate the coordinates of molecular nodes quickly and to enhance the understanding or traceability of them. The grid layout is effective in drawing compact, orderly, balanced network maps with node label spaces, but existing grid layout algorithms often require a high computational cost because they have to consider complicated positional constraints through the entire optimization process. RESULTS: We propose a hybrid grid layout algorithm that consists of a non-grid, fast layout (preprocessor algorithm and an approximate pattern matching algorithm that distributes the resultant preprocessed nodes on square grid points. To demonstrate the feasibility of the hybrid layout algorithm, it is characterized in terms of the calculation time, numbers of edge-edge and node-edge crossings, relative edge lengths, and F-measures. The proposed algorithm achieves outstanding performances compared with other existing grid layouts. CONCLUSIONS: Use of an approximate pattern matching algorithm quickly redistributes the laid-out nodes by fast, non-grid algorithms on the square grid points, while preserving the topological relationships among the nodes. The proposed algorithm is a novel use of the pattern matching, thereby providing a breakthrough for grid layout. This application program can be freely downloaded from http://www.cadlive.jp/hybridlayout/hybridlayout.html.

  10. Patterns of population differentiation and natural selection on the celiac disease background risk network.

    Sams, Aaron; Hawks, John

    2013-01-01

    Celiac disease is a common small intestinal inflammatory condition induced by wheat gluten and related proteins from rye and barley. Left untreated, the clinical presentation of CD can include failure to thrive, malnutrition, and distension in juveniles. The disease can additionally lead to vitamin deficiencies, anemia, and osteoporosis. Therefore, CD potentially negatively affected fitness in past populations utilizing wheat, barley, and rye. Previous analyses of CD risk variants have uncovered evidence for positive selection on some of these loci. These studies also suggest the possibility that risk for common autoimmune conditions such as CD may be the result of positive selection on immune related loci in the genome to fight infection. Under this evolutionary scenario, disease phenotypes may be a trade-off from positive selection on immunity. If this hypothesis is generally true, we can expect to find a signal of natural selection when we survey across the network of loci known to influence CD risk. This study examines the non-HLA autosomal network of gene loci associated with CD risk in Europe. We reject the null hypothesis of neutrality on this network of CD risk loci. Additionally, we can localize evidence of selection in time and space by adding information from the genome of the Tyrolean Iceman. While we can show significant differentiation between continental regions across the CD network, the pattern of evidence is not consistent with primarily recent (Holocene) selection across this network in Europe. Further localization of ancient selection on this network may illuminate the ecological pressures acting on the immune system during this critically interesting phase of our evolution.

  11. Patterns of population differentiation and natural selection on the celiac disease background risk network.

    Aaron Sams

    Full Text Available Celiac disease is a common small intestinal inflammatory condition induced by wheat gluten and related proteins from rye and barley. Left untreated, the clinical presentation of CD can include failure to thrive, malnutrition, and distension in juveniles. The disease can additionally lead to vitamin deficiencies, anemia, and osteoporosis. Therefore, CD potentially negatively affected fitness in past populations utilizing wheat, barley, and rye. Previous analyses of CD risk variants have uncovered evidence for positive selection on some of these loci. These studies also suggest the possibility that risk for common autoimmune conditions such as CD may be the result of positive selection on immune related loci in the genome to fight infection. Under this evolutionary scenario, disease phenotypes may be a trade-off from positive selection on immunity. If this hypothesis is generally true, we can expect to find a signal of natural selection when we survey across the network of loci known to influence CD risk. This study examines the non-HLA autosomal network of gene loci associated with CD risk in Europe. We reject the null hypothesis of neutrality on this network of CD risk loci. Additionally, we can localize evidence of selection in time and space by adding information from the genome of the Tyrolean Iceman. While we can show significant differentiation between continental regions across the CD network, the pattern of evidence is not consistent with primarily recent (Holocene selection across this network in Europe. Further localization of ancient selection on this network may illuminate the ecological pressures acting on the immune system during this critically interesting phase of our evolution.

  12. New patterns in human biogeography revealed by networks of contacts between linguistic groups.

    Capitán, José A; Bock Axelsen, Jacob; Manrubia, Susanna

    2015-03-07

    Human languages differ broadly in abundance and are distributed highly unevenly on the Earth. In many qualitative and quantitative aspects, they strongly resemble biodiversity distributions. An intriguing and previously unexplored issue is the architecture of the neighbouring relationships between human linguistic groups. Here we construct and characterize these networks of contacts and show that they represent a new kind of spatial network with uncommon structural properties. Remarkably, language networks share a meaningful property with food webs: both are quasi-interval graphs. In food webs, intervality is linked to the existence of a niche space of low dimensionality; in language networks, we show that the unique relevant variable is the area occupied by the speakers of a language. By means of a range model analogous to niche models in ecology, we show that a geometric restriction of perimeter covering by neighbouring linguistic domains explains the structural patterns observed. Our findings may be of interest in the development of models for language dynamics or regarding the propagation of cultural innovations. In relation to species distribution, they pose the question of whether the spatial features of species ranges share architecture, and eventually generating mechanism, with the distribution of human linguistic groups. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

  13. Fuzzy logic and neural networks in artificial intelligence and pattern recognition

    Sanchez, Elie

    1991-10-01

    With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.

  14. Critical regimes driven by recurrent mobility patterns of reaction-diffusion processes in networks

    Gómez-Gardeñes, J.; Soriano-Paños, D.; Arenas, A.

    2018-04-01

    Reaction-diffusion processes1 have been widely used to study dynamical processes in epidemics2-4 and ecology5 in networked metapopulations. In the context of epidemics6, reaction processes are understood as contagions within each subpopulation (patch), while diffusion represents the mobility of individuals between patches. Recently, the characteristics of human mobility7, such as its recurrent nature, have been proven crucial to understand the phase transition to endemic epidemic states8,9. Here, by developing a framework able to cope with the elementary epidemic processes, the spatial distribution of populations and the commuting mobility patterns, we discover three different critical regimes of the epidemic incidence as a function of these parameters. Interestingly, we reveal a regime of the reaction-diffussion process in which, counter-intuitively, mobility is detrimental to the spread of disease. We analytically determine the precise conditions for the emergence of any of the three possible critical regimes in real and synthetic networks.

  15. Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network.

    Zafar, Raheel; Kamel, Nidal; Naufal, Mohamad; Malik, Aamir Saeed; Dass, Sarat C; Ahmad, Rana Fayyaz; Abdullah, Jafri M; Reza, Faruque

    2017-01-01

    Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

  16. Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator.

    Hoellinger, Thomas; Petieau, Mathieu; Duvinage, Matthieu; Castermans, Thierry; Seetharaman, Karthik; Cebolla, Ana-Maria; Bengoetxea, Ana; Ivanenko, Yuri; Dan, Bernard; Cheron, Guy

    2013-01-01

    The existence of dedicated neuronal modules such as those organized in the cerebral cortex, thalamus, basal ganglia, cerebellum, or spinal cord raises the question of how these functional modules are coordinated for appropriate motor behavior. Study of human locomotion offers an interesting field for addressing this central question. The coordination of the elevation of the 3 leg segments under a planar covariation rule (Borghese et al., 1996) was recently modeled (Barliya et al., 2009) by phase-adjusted simple oscillators shedding new light on the understanding of the central pattern generator (CPG) processing relevant oscillation signals. We describe the use of a dynamic recurrent neural network (DRNN) mimicking the natural oscillatory behavior of human locomotion for reproducing the planar covariation rule in both legs at different walking speeds. Neural network learning was based on sinusoid signals integrating frequency and amplitude features of the first three harmonics of the sagittal elevation angles of the thigh, shank, and foot of each lower limb. We verified the biological plausibility of the neural networks. Best results were obtained with oscillations extracted from the first three harmonics in comparison to oscillations outside the harmonic frequency peaks. Physiological replication steadily increased with the number of neuronal units from 1 to 80, where similarity index reached 0.99. Analysis of synaptic weighting showed that the proportion of inhibitory connections consistently increased with the number of neuronal units in the DRNN. This emerging property in the artificial neural networks resonates with recent advances in neurophysiology of inhibitory neurons that are involved in central nervous system oscillatory activities. The main message of this study is that this type of DRNN may offer a useful model of physiological central pattern generator for gaining insights in basic research and developing clinical applications.

  17. Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

    Anwer, Rao Muhammad; Khan, Fahad Shahbaz; van de Weijer, Joost; Molinier, Matthieu; Laaksonen, Jorma

    2018-04-01

    Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.

  18. Semantic Network Adaptation Based on QoS Pattern Recognition for Multimedia Streams

    Exposito, Ernesto; Gineste, Mathieu; Lamolle, Myriam; Gomez, Jorge

    This article proposes an ontology based pattern recognition methodology to compute and represent common QoS properties of the Application Data Units (ADU) of multimedia streams. The use of this ontology by mechanisms located at different layers of the communication architecture will allow implementing fine per-packet self-optimization of communication services regarding the actual application requirements. A case study showing how this methodology is used by error control mechanisms in the context of wireless networks is presented in order to demonstrate the feasibility and advantages of this approach.

  19. Early Obstacle Detection and Avoidance for All to All Traffic Pattern in Wireless Sensor Networks

    Huc, Florian; Jarry, Aubin; Leone, Pierre; Moraru, Luminita; Nikoletseas, Sotiris; Rolim, Jose

    This paper deals with early obstacles recognition in wireless sensor networks under various traffic patterns. In the presence of obstacles, the efficiency of routing algorithms is increased by voluntarily avoiding some regions in the vicinity of obstacles, areas which we call dead-ends. In this paper, we first propose a fast convergent routing algorithm with proactive dead-end detection together with a formal definition and description of dead-ends. Secondly, we present a generalization of this algorithm which improves performances in all to many and all to all traffic patterns. In a third part we prove that this algorithm produces paths that are optimal up to a constant factor of 2π + 1. In a fourth part we consider the reactive version of the algorithm which is an extension of a previously known early obstacle detection algorithm. Finally we give experimental results to illustrate the efficiency of our algorithms in different scenarios.

  20. Forbidden versus permitted interactions: Disentangling processes from patterns in ecological network analysis.

    Strona, Giovanni; Veech, Joseph A

    2017-07-01

    Several studies have identified the tendency for species to share interacting partners as a key property to the functioning and stability of ecological networks. However, assessing this pattern has proved challenging in several regards, such as finding proper metrics to assess node overlap (sharing), and using robust null modeling to disentangle significance from randomness. Here, we bring attention to an additional, largely neglected challenge in assessing species' tendency to share interacting partners. In particular, we discuss and illustrate with two different case studies how identifying the set of "permitted" interactions for a given species (i.e. interactions that are not impeded, e.g. by lack of functional trait compatibility) is paramount to understand the ecological and co-evolutionary processes at the basis of node overlap and segregation patterns.

  1. Developments in perturbation theory

    Greenspan, E.

    1976-01-01

    Included are sections dealing with perturbation expressions for reactivity, methods for the calculation of perturbed fluxes, integral transport theory formulations for reactivity, generalized perturbation theory, sensitivity and optimization studies, multigroup calculations of bilinear functionals, and solution of inhomogeneous Boltzmann equations with singular operators

  2. A Perturbed MicroRNA Expression Pattern Characterizes Embryonic Neural Stem Cells Derived from a Severe Mouse Model of Spinal Muscular Atrophy (SMA

    Andrea Luchetti

    2015-08-01

    Full Text Available Spinal muscular atrophy (SMA is an inherited neuromuscular disorder and the leading genetic cause of death in infants. Despite the disease-causing gene, survival motor neuron (SMN1, encodes a ubiquitous protein, SMN1 deficiency preferentially affects spinal motor neurons (MNs, leaving the basis of this selective cell damage still unexplained. As neural stem cells (NSCs are multipotent self-renewing cells that can differentiate into neurons, they represent an in vitro model for elucidating the pathogenetic mechanism of neurodegenerative diseases such as SMA. Here we characterize for the first time neural stem cells (NSCs derived from embryonic spinal cords of a severe SMNΔ7 SMA mouse model. SMNΔ7 NSCs behave as their wild type (WT counterparts, when we consider neurosphere formation ability and the expression levels of specific regional and self-renewal markers. However, they show a perturbed cell cycle phase distribution and an increased proliferation rate compared to wild type cells. Moreover, SMNΔ7 NSCs are characterized by the differential expression of a limited number of miRNAs, among which miR-335-5p and miR-100-5p, reduced in SMNΔ7 NSCs compared to WT cells. We suggest that such miRNAs may be related to the proliferation differences characterizing SMNΔ7 NSCs, and may be potentially involved in the molecular mechanisms of SMA.

  3. A Perturbed MicroRNA Expression Pattern Characterizes Embryonic Neural Stem Cells Derived from a Severe Mouse Model of Spinal Muscular Atrophy (SMA).

    Luchetti, Andrea; Ciafrè, Silvia Anna; Murdocca, Michela; Malgieri, Arianna; Masotti, Andrea; Sanchez, Massimo; Farace, Maria Giulia; Novelli, Giuseppe; Sangiuolo, Federica

    2015-08-06

    Spinal muscular atrophy (SMA) is an inherited neuromuscular disorder and the leading genetic cause of death in infants. Despite the disease-causing gene, survival motor neuron (SMN1), encodes a ubiquitous protein, SMN1 deficiency preferentially affects spinal motor neurons (MNs), leaving the basis of this selective cell damage still unexplained. As neural stem cells (NSCs) are multipotent self-renewing cells that can differentiate into neurons, they represent an in vitro model for elucidating the pathogenetic mechanism of neurodegenerative diseases such as SMA. Here we characterize for the first time neural stem cells (NSCs) derived from embryonic spinal cords of a severe SMNΔ7 SMA mouse model. SMNΔ7 NSCs behave as their wild type (WT) counterparts, when we consider neurosphere formation ability and the expression levels of specific regional and self-renewal markers. However, they show a perturbed cell cycle phase distribution and an increased proliferation rate compared to wild type cells. Moreover, SMNΔ7 NSCs are characterized by the differential expression of a limited number of miRNAs, among which miR-335-5p and miR-100-5p, reduced in SMNΔ7 NSCs compared to WT cells. We suggest that such miRNAs may be related to the proliferation differences characterizing SMNΔ7 NSCs, and may be potentially involved in the molecular mechanisms of SMA.

  4. Improving Pattern Recognition and Neural Network Algorithms with Applications to Solar Panel Energy Optimization

    Zamora Ramos, Ernesto

    Artificial Intelligence is a big part of automation and with today's technological advances, artificial intelligence has taken great strides towards positioning itself as the technology of the future to control, enhance and perfect automation. Computer vision includes pattern recognition and classification and machine learning. Computer vision is at the core of decision making and it is a vast and fruitful branch of artificial intelligence. In this work, we expose novel algorithms and techniques built upon existing technologies to improve pattern recognition and neural network training, initially motivated by a multidisciplinary effort to build a robot that helps maintain and optimize solar panel energy production. Our contributions detail an improved non-linear pre-processing technique to enhance poorly illuminated images based on modifications to the standard histogram equalization for an image. While the original motivation was to improve nocturnal navigation, the results have applications in surveillance, search and rescue, medical imaging enhancing, and many others. We created a vision system for precise camera distance positioning motivated to correctly locate the robot for capture of solar panel images for classification. The classification algorithm marks solar panels as clean or dirty for later processing. Our algorithm extends past image classification and, based on historical and experimental data, it identifies the optimal moment in which to perform maintenance on marked solar panels as to minimize the energy and profit loss. In order to improve upon the classification algorithm, we delved into feedforward neural networks because of their recent advancements, proven universal approximation and classification capabilities, and excellent recognition rates. We explore state-of-the-art neural network training techniques offering pointers and insights, culminating on the implementation of a complete library with support for modern deep learning architectures

  5. A Pattern Construction Scheme for Neural Network-Based Cognitive Communication

    Ozgur Orcay

    2011-01-01

    Full Text Available Inefficient utilization of the frequency spectrum due to conventional regulatory limitations and physical performance limiting factors, mainly the Signal to Noise Ratio (SNR, are prominent restrictions in digital wireless communication. Pattern Based Communication System (PBCS is an adaptive and perceptual communication method based on a Cognitive Radio (CR approach. It intends an SNR oriented cognition mechanism in the physical layer for improvement of Link Spectral Efficiency (LSE. The key to this system is construction of optimal communication signals, which consist of encoded data in different pattern forms (waveforms depending on spectral availabilities. The signals distorted in the communication medium are recovered according to the pre-trained pattern glossary by the perceptual receiver. In this study, we have shown that it is possible to improve the bandwidth efficiency when largely uncorrelated signal patterns are chosen in order to form a glossary that represents symbols for different length data groups and the information can be recovered by the Artificial Neural Network (ANN in the receiver site.

  6. Flow-pattern identification and nonlinear dynamics of gas-liquid two-phase flow in complex networks.

    Gao, Zhongke; Jin, Ningde

    2009-06-01

    The identification of flow pattern is a basic and important issue in multiphase systems. Because of the complexity of phase interaction in gas-liquid two-phase flow, it is difficult to discern its flow pattern objectively. In this paper, we make a systematic study on the vertical upward gas-liquid two-phase flow using complex network. Three unique network construction methods are proposed to build three types of networks, i.e., flow pattern complex network (FPCN), fluid dynamic complex network (FDCN), and fluid structure complex network (FSCN). Through detecting the community structure of FPCN by the community-detection algorithm based on K -mean clustering, useful and interesting results are found which can be used for identifying five vertical upward gas-liquid two-phase flow patterns. To investigate the dynamic characteristics of gas-liquid two-phase flow, we construct 50 FDCNs under different flow conditions, and find that the power-law exponent and the network information entropy, which are sensitive to the flow pattern transition, can both characterize the nonlinear dynamics of gas-liquid two-phase flow. Furthermore, we construct FSCN and demonstrate how network statistic can be used to reveal the fluid structure of gas-liquid two-phase flow. In this paper, from a different perspective, we not only introduce complex network theory to the study of gas-liquid two-phase flow but also indicate that complex network may be a powerful tool for exploring nonlinear time series in practice.

  7. Bridge damage detection using spatiotemporal patterns extracted from dense sensor network

    Liu, Chao; Sarkar, Soumik; Gong, Yongqiang; Laflamme, Simon; Phares, Brent

    2017-01-01

    The alarmingly degrading state of transportation infrastructures combined with their key societal and economic importance calls for automatic condition assessment methods to facilitate smart management of maintenance and repairs. With the advent of ubiquitous sensing and communication capabilities, scalable data-driven approaches is of great interest, as it can utilize large volume of streaming data without requiring detailed physical models that can be inaccurate and computationally expensive to run. Properly designed, a data-driven methodology could enable fast and automatic evaluation of infrastructures, discovery of causal dependencies among various sub-system dynamic responses, and decision making with uncertainties and lack of labeled data. In this work, a spatiotemporal pattern network (STPN) strategy built on symbolic dynamic filtering (SDF) is proposed to explore spatiotemporal behaviors in a bridge network. Data from strain gauges installed on two bridges are generated using finite element simulation for three types of sensor networks from a density perspective (dense, nominal, sparse). Causal relationships among spatially distributed strain data streams are extracted and analyzed for vehicle identification and detection, and for localization of structural degradation in bridges. Multiple case studies show significant capabilities of the proposed approach in: (i) capturing spatiotemporal features to discover causality between bridges (geographically close), (ii) robustness to noise in data for feature extraction, (iii) detecting and localizing damage via comparison of bridge responses to similar vehicle loads, and (iv) implementing real-time health monitoring and decision making work flow for bridge networks. Also, the results demonstrate increased sensitivity in detecting damages and higher reliability in quantifying the damage level with increase in sensor network density. (paper)

  8. Aberrant topological patterns of brain structural network in temporal lobe epilepsy.

    Yasuda, Clarissa Lin; Chen, Zhang; Beltramini, Guilherme Coco; Coan, Ana Carolina; Morita, Marcia Elisabete; Kubota, Bruno; Bergo, Felipe; Beaulieu, Christian; Cendes, Fernando; Gross, Donald William

    2015-12-01

    Although altered large-scale brain network organization in patients with temporal lobe epilepsy (TLE) has been shown using morphologic measurements such as cortical thickness, these studies, have not included critical subcortical structures (such as hippocampus and amygdala) and have had relatively small sample sizes. Here, we investigated differences in topological organization of the brain volumetric networks between patients with right TLE (RTLE) and left TLE (LTLE) with unilateral hippocampal atrophy. We performed a cross-sectional analysis of 86 LTLE patients, 70 RTLE patients, and 116 controls. RTLE and LTLE groups were balanced for gender (p = 0.64), seizure frequency (Mann-Whitney U test, p = 0.94), age (p = 0.39), age of seizure onset (p = 0.21), and duration of disease (p = 0.69). Brain networks were constructed by thresholding correlation matrices of volumes from 80 cortical/subcortical regions (parcellated with Freesurfer v5.3 https://surfer.nmr.mgh.harvard.edu/) that were then analyzed using graph theoretical approaches. We identified reduced cortical/subcortical connectivity including bilateral hippocampus in both TLE groups, with the most significant interregional correlation increases occurring within the limbic system in LTLE and contralateral hemisphere in RTLE. Both TLE groups demonstrated less optimal topological organization, with decreased global efficiency and increased local efficiency and clustering coefficient. LTLE also displayed a more pronounced network disruption. Contrary to controls, hub nodes in both TLE groups were not distributed across whole brain, but rather found primarily in the paralimbic/limbic and temporal association cortices. Regions with increased centrality were concentrated in occipital lobes for LTLE and contralateral limbic/temporal areas for RTLE. These findings provide first evidence of altered topological organization of the whole brain volumetric network in TLE, with disruption of the coordinated patterns of

  9. A probabilistic approach to quantifying spatial patterns of flow regimes and network-scale connectivity

    Garbin, Silvia; Alessi Celegon, Elisa; Fanton, Pietro; Botter, Gianluca

    2017-04-01

    The temporal variability of river flow regime is a key feature structuring and controlling fluvial ecological communities and ecosystem processes. In particular, streamflow variability induced by climate/landscape heterogeneities or other anthropogenic factors significantly affects the connectivity between streams with notable implication for river fragmentation. Hydrologic connectivity is a fundamental property that guarantees species persistence and ecosystem integrity in riverine systems. In riverine landscapes, most ecological transitions are flow-dependent and the structure of flow regimes may affect ecological functions of endemic biota (i.e., fish spawning or grazing of invertebrate species). Therefore, minimum flow thresholds must be guaranteed to support specific ecosystem services, like fish migration, aquatic biodiversity and habitat suitability. In this contribution, we present a probabilistic approach aiming at a spatially-explicit, quantitative assessment of hydrologic connectivity at the network-scale as derived from river flow variability. Dynamics of daily streamflows are estimated based on catchment-scale climatic and morphological features, integrating a stochastic, physically based approach that accounts for the stochasticity of rainfall with a water balance model and a geomorphic recession flow model. The non-exceedance probability of ecologically meaningful flow thresholds is used to evaluate the fragmentation of individual stream reaches, and the ensuing network-scale connectivity metrics. A multi-dimensional Poisson Process for the stochastic generation of rainfall is used to evaluate the impact of climate signature on reach-scale and catchment-scale connectivity. The analysis shows that streamflow patterns and network-scale connectivity are influenced by the topology of the river network and the spatial variability of climatic properties (rainfall, evapotranspiration). The framework offers a robust basis for the prediction of the impact of

  10. Research on the Spatial-Temporal Distribution Pattern of the Network Attention of Fog and Haze in China

    Weng, Lingyan; Han, Xugao

    2018-01-01

    Understanding the spatial-temporal distribution pattern of fog and haze is the base to deal with them by adjusting measures to local conditions. Taking 31 provinces in China mainland as the research areas, this paper collected data from Baidu index on the network attention of fog and haze in relevant areas from 2011 to 2016, and conducted an analysis of their spatial-temporal distribution pattern by using autocorrelation analysis. The results show that the network attention of fog and haze has an overall spatial distribution pattern of “higher in the eastern and central, lower in the western China”. There are regional differences in different provinces in terms of network attention. Network attention of fog and haze indicates an obvious geographical agglomeration phenomenon, which is a gradual enlargement of the agglomeration area of higher value with a slight shrinking of those lower value agglomeration areas.

  11. Microcomb-Based True-Time-Delay Network for Microwave Beamforming With Arbitrary Beam Pattern Control

    Xue, Xiaoxiao; Xuan, Yi; Bao, Chengying; Li, Shangyuan; Zheng, Xiaoping; Zhou, Bingkun; Qi, Minghao; Weiner, Andrew M.

    2018-06-01

    Microwave phased array antennas (PAAs) are very attractive to defense applications and high-speed wireless communications for their abilities of fast beam scanning and complex beam pattern control. However, traditional PAAs based on phase shifters suffer from the beam-squint problem and have limited bandwidths. True-time-delay (TTD) beamforming based on low-loss photonic delay lines can solve this problem. But it is still quite challenging to build large-scale photonic TTD beamformers due to their high hardware complexity. In this paper, we demonstrate a photonic TTD beamforming network based on a miniature microresonator frequency comb (microcomb) source and dispersive time delay. A method incorporating optical phase modulation and programmable spectral shaping is proposed for positive and negative apodization weighting to achieve arbitrary microwave beam pattern control. The experimentally demonstrated TTD beamforming network can support a PAA with 21 elements. The microwave frequency range is $\\mathbf{8\\sim20\\ {GHz}}$, and the beam scanning range is $\\mathbf{\\pm 60.2^\\circ}$. Detailed measurements of the microwave amplitudes and phases are performed. The beamforming performances of Gaussian, rectangular beams and beam notch steering are evaluated through simulations by assuming a uniform radiating antenna array. The scheme can potentially support larger PAAs with hundreds of elements by increasing the number of comb lines with broadband microcomb generation.

  12. Plant–insect interactions from Middle Triassic (late Ladinian of Monte Agnello (Dolomites, N-Italy—initial pattern and response to abiotic environmental perturbations

    Torsten Wappler

    2015-04-01

    Full Text Available The Paleozoic–Mesozoic transition is characterized by the most massive extinction of the Phanerozoic. Nevertheless, an impressive adaptive radiation of herbivorous insects occurred on gymnosperm-dominated floras not earlier than during the Middle to Late Triassic, penecontemporaneous with similar events worldwide, all which exhibit parallel expansions of generalized and mostly specialized insect herbivory on plants, expressed as insect damage on a various plant organs and tissues. The flora from Monte Agnello is distinctive, due to its preservation in subaerially deposited pyroclastic layers with exceptionally preserved details. Thus, the para-autochthonous assemblage provides insights into environmental disturbances, caused by volcanic activity, and how they profoundly affected the structure and composition of herbivory patterns. These diverse Middle Triassic biota supply extensive evidence for insect herbivore colonization, resulting in specific and complex herbivory patterns involving the frequency and diversity of 20 distinctive damage types (DTs. These DT patterns show that external foliage feeders, piercer-and-suckers, leaf miners, gallers, and oviposition culprits were intricately using almost all tissue types from the dominant host plants of voltzialean conifers (e.g., Voltzia, horsetails, ferns (e.g., Neuropteridium, Phlebopteris, Cladophlebis and Thaumatopteris, seed ferns (e.g., Scytophyllum, and cycadophytes (e.g., Bjuvia and Nilssonia.

  13. Posting Behaviour Patterns in an Online Smoking Cessation Social Network: Implications for Intervention Design and Development

    Healey, Benjamin; Hoek, Janet; Edwards, Richard

    2014-01-01

    Objectives Online Cessation Support Networks (OCSNs) are associated with increased quit success rates, but few studies have examined their use over time. We identified usage patterns in New Zealand's largest OCSN over two years and explored implications for OCSN intervention design and evaluation. Methods We analysed metadata relating to 133,096 OCSN interactions during 2011 and 2012. Metrics covered aggregate network activity, user posting activity and longevity, and between-user commenting. Binary logistic regression models were estimated to investigate the feasibility of predicting low user engagement using early interaction data. Results Repeating periodic peaks and troughs in aggregate activity related not only to seasonality (e.g., New Year), but also to day of the week. Out of 2,062 unique users, 69 Highly Engaged Users (180+ interactions each) contributed 69% of all OCSN interactions in 2012 compared to 1.3% contributed by 864 Minimally Engaged Users (metrics including posts and comments, this change did not coincide with large gains in first-time user persistence. Researchers assessing intervention effects should therefore examine multiple measures when evaluating changes in network dynamics over time. PMID:25192174

  14. Memory and pattern storage in neural networks with activity dependent synapses

    Mejias, J. F.; Torres, J. J.

    2009-01-01

    We present recently obtained results on the influence of the interplay between several activity dependent synaptic mechanisms, such as short-term depression and facilitation, on the maximum memory storage capacity in an attractor neural network [1]. In contrast with the case of synaptic depression, which drastically reduces the capacity of the network to store and retrieve activity patterns [2], synaptic facilitation is able to enhance the memory capacity in different situations. In particular, we find that a convenient balance between depression and facilitation can enhance the memory capacity, reaching maximal values similar to those obtained with static synapses, that is, without activity-dependent processes. We also argue, employing simple arguments, that this level of balance is compatible with experimental data recorded from some cortical areas, where depression and facilitation may play an important role for both memory-oriented tasks and information processing. We conclude that depressing synapses with a certain level of facilitation allow to recover the good retrieval properties of networks with static synapses while maintaining the nonlinear properties of dynamic synapses, convenient for information processing and coding.

  15. Automated target recognition and tracking using an optical pattern recognition neural network

    Chao, Tien-Hsin

    1991-01-01

    The on-going development of an automatic target recognition and tracking system at the Jet Propulsion Laboratory is presented. This system is an optical pattern recognition neural network (OPRNN) that is an integration of an innovative optical parallel processor and a feature extraction based neural net training algorithm. The parallel optical processor provides high speed and vast parallelism as well as full shift invariance. The neural network algorithm enables simultaneous discrimination of multiple noisy targets in spite of their scales, rotations, perspectives, and various deformations. This fully developed OPRNN system can be effectively utilized for the automated spacecraft recognition and tracking that will lead to success in the Automated Rendezvous and Capture (AR&C) of the unmanned Cargo Transfer Vehicle (CTV). One of the most powerful optical parallel processors for automatic target recognition is the multichannel correlator. With the inherent advantages of parallel processing capability and shift invariance, multiple objects can be simultaneously recognized and tracked using this multichannel correlator. This target tracking capability can be greatly enhanced by utilizing a powerful feature extraction based neural network training algorithm such as the neocognitron. The OPRNN, currently under investigation at JPL, is constructed with an optical multichannel correlator where holographic filters have been prepared using the neocognitron training algorithm. The computation speed of the neocognitron-type OPRNN is up to 10(exp 14) analog connections/sec that enabling the OPRNN to outperform its state-of-the-art electronics counterpart by at least two orders of magnitude.

  16. Fibrillar organization in tendons: A pattern revealed by percolation characteristics of the respective geometric network

    Daniel Andres Dos Santos

    2014-06-01

    Full Text Available Since the tendon is composed by collagen fibrils of various sizes connected between them through molecular cross-links, it sounds logical to model it via a heterogeneous network of fibrils. Using cross sectional images, that network is operatively inferred from the respective Gabriel graph of the fibril mass centers. We focus on network percolation characteristics under an ordered activation of fibrils (progressive recruitment going from the smallest to the largest fibril. Analyses of percolation were carried out on a repository of images of digital flexor tendons obtained from samples of lizards and frogs. Observed percolation thresholds were compared against values derived from hypothetical scenarios of random activation of nodes. Strikingly, we found a significant delay for the occurrence of percolation in actual data. We interpret this finding as the consequence of some non-random packing of fibrillar units into a size-constrained geometric pattern. We erect an ideal geometric model of balanced interspersion of polymorphic units that accounts for the delayed percolating instance. We also address the circumstance of being percolation curves mirrored by the empirical curves of stress-strain obtained from the same studied tendons. By virtue of this isomorphism, we hypothesize that the inflection points of both curves are different quantitative manifestations of a common transitional process during mechanical load transference.

  17. Why and how selection patterns in classroom networks differ between students.The potential influence of networks size preferences, level of information, and group membership.

    Baerveldt, Chris

    2010-12-01

    Full Text Available High school students can select class mates for new friendships using a repertoire of patterns. They can actively pursue new friendships, make use of the existing network structure, and/ or use the scarce and often erroneous information about candidates. In this theoretical paper, we argue that such selection patterns should not be studied as the result of general rules, as is usually done in social network studies. Specifically, we state that network size preferences, the level of information about individual attributes of fellow classmates, and group membership are likely to differ among high school students, and that as a result, also their selection patterns are likely to be different. In this paper we sketch the theoretical articulations between these.

  18. Method of Parallel-Hierarchical Network Self-Training and its Application for Pattern Classification and Recognition

    TIMCHENKO, L.

    2012-11-01

    Full Text Available Propositions necessary for development of parallel-hierarchical (PH network training methods are discussed in this article. Unlike already known structures of the artificial neural network, where non-normalized (absolute similarity criteria are used for comparison, the suggested structure uses a normalized criterion. Based on the analysis of training rules, a conclusion is made that application of two training methods with a teacher is optimal for PH network training: error correction-based training and memory-based training. Mathematical models of training and a combined method of PH network training for recognition of static and dynamic patterns are developed.

  19. A spatiotemporal analysis of hydrological patterns based on a wireless sensor network system

    Plaza, F.; Slater, T. A.; Zhong, X.; Li, Y.; Liang, Y.; Liang, X.

    2017-12-01

    Understanding complicated spatiotemporal patterns of eco-hydrological variables at a small scale plays a profound role in improving predictability of high resolution distributed hydrological models. However, accurate and continuous monitoring of these complex patterns has become one of the main challenges in the environmental sciences. Wireless sensor networks (WSNs) have emerged as one of the most widespread potential solutions to achieve this. This study presents a spatiotemporal analysis of hydrological patterns (e.g., soil moisture, soil water potential, soil temperature and transpiration) based on observational data collected from a dense multi-hop wireless sensor network (WSN) in a steep-forested testbed located in Southwestern Pennsylvania, USA. At this WSN testbed with an approximate area of 3000 m2, environmental variables are collected from over 240 sensors that are connected to more than 100 heterogeneous motes. The sensors include the soil moisture of EC-5, soil temperature and soil water potential of MPS-1 and MPS-2, and sap flow sensors constructed in house. The motes consist of MICAz, IRIS and TelosB. In addition, several data loggers have been installed along the site to provide a comparative reference to the WSN measurements for the purpose of checking the WSN data quality. The edaphic properties monitored by the WSN sensors show strong agreement with the data logger measurements. Moreover, sap flow measurements, scaled to tree stand transpiration, are found to be reasonable. This study also investigates the feasibility and roles that these sensor measurements play in improving the performance of high-resolution distributed hydrological models. In particular, we explore this using a modified version of the Distributed Hydrological Soil Vegetation Model (DHSVM).

  20. Acupuncture induce the different modulation patterns of the default mode network: an fMRI study

    Liu, Peng; Qin, Wei; Tian, Jie; Zhang, Yi

    2009-02-01

    According to Traditional Chinese Medicine (TCM) theory and certain clinical treatment reports, the sustained effects of acupuncture indeed exist, which may last several minutes or hours. Furthermore, increased attention has fallen on the sustained effects of acupuncture. Recently, it is reported that the sustained acupuncture effects may alter the default mode network (DMN). It raises interesting questions: whether the modulations of acupuncture effects to the DMN are still detected at other acupoints and whether the modulation patterns are different induced by different acupoints. In the present study, we wanted to investigate the questions. An experiment fMRI design was carried out on 36 subjects with the electroacupuncture stimulation (EAS) at the three acupoints: Guangming (GB37), Kunlun (BL60) and Jiaoxin (KI8) on the left leg. The data sets were analyzed by a data driven method named independent component analysis (ICA). The results indicated that the three acupoints stimulations may modulate the DMN. Moreover, the modulation patterns were distinct. We suggest the different modulation patterns on the DMN may attribute to the distinct functional effects of acupoints.

  1. Bifurcation analysis of oscillating network model of pattern recognition in the rabbit olfactory bulb

    Baird, Bill

    1986-08-01

    A neural network model describing pattern recognition in the rabbit olfactory bulb is analysed to explain the changes in neural activity observed experimentally during classical Pavlovian conditioning. EEG activity recorded from an 8×8 arry of 64 electrodes directly on the surface on the bulb shows distinct spatial patterns of oscillation that correspond to the animal's recognition of different conditioned odors and change with conditioning to new odors. The model may be considered a variant of Hopfield's model of continuous analog neural dynamics. Excitatory and inhibitory cell types in the bulb and the anatomical architecture of their connection requires a nonsymmetric coupling matrix. As the mean input level rises during each breath of the animal, the system bifurcates from homogenous equilibrium to a spatially patterned oscillation. The theory of multiple Hopf bifurcations is employed to find coupled equations for the amplitudes of these unstable oscillatory modes independent of frequency. This allows a view of stored periodic attractors as fixed points of a gradient vector field and thereby recovers the more familiar dynamical systems picture of associative memory.

  2. From Spinal Central Pattern Generators to Cortical Network: Integrated BCI for Walking Rehabilitation

    G. Cheron

    2012-01-01

    Full Text Available Success in locomotor rehabilitation programs can be improved with the use of brain-computer interfaces (BCIs. Although a wealth of research has demonstrated that locomotion is largely controlled by spinal mechanisms, the brain is of utmost importance in monitoring locomotor patterns and therefore contains information regarding central pattern generation functioning. In addition, there is also a tight coordination between the upper and lower limbs, which can also be useful in controlling locomotion. The current paper critically investigates different approaches that are applicable to this field: the use of electroencephalogram (EEG, upper limb electromyogram (EMG, or a hybrid of the two neurophysiological signals to control assistive exoskeletons used in locomotion based on programmable central pattern generators (PCPGs or dynamic recurrent neural networks (DRNNs. Plantar surface tactile stimulation devices combined with virtual reality may provide the sensation of walking while in a supine position for use of training brain signals generated during locomotion. These methods may exploit mechanisms of brain plasticity and assist in the neurorehabilitation of gait in a variety of clinical conditions, including stroke, spinal trauma, multiple sclerosis, and cerebral palsy.

  3. Fluid pipeline system leak detection based on neural network and pattern recognition

    Tang Xiujia

    1998-01-01

    The mechanism of the stress wave propagation along the pipeline system of NPP, caused by turbulent ejection from pipeline leakage, is researched. A series of characteristic index are described in time domain or frequency domain, and compress numerical algorithm is developed for original data compression. A back propagation neural networks (BPNN) with the input matrix composed by stress wave characteristics in time domain or frequency domain is first proposed to classify various situations of the pipeline, in order to detect the leakage in the fluid flow pipelines. The capability of the new method had been demonstrated by experiments and finally used to design a handy instrument for the pipeline leakage detection. Usually a pipeline system has many inner branches and often in adjusting dynamic condition, it is difficult for traditional pipeline diagnosis facilities to identify the difference between inner pipeline operation and pipeline fault. The author first proposed pipeline wave propagation identification by pattern recognition to diagnose pipeline leak. A series of pattern primitives such as peaks, valleys, horizon lines, capstan peaks, dominant relations, slave relations, etc., are used to extract features of the negative pressure wave form. The context-free grammar of symbolic representation of the negative wave form is used, and a negative wave form parsing system with application to structural pattern recognition based on the representation is first proposed to detect and localize leaks of the fluid pipelines

  4. Genetic algorithms and artificial neural networks for loading pattern optimisation of advanced gas-cooled reactors

    Ziver, A.K. E-mail: a.k.ziver@imperial.ac.uk; Pain, C.C; Carter, J.N.; Oliveira, C.R.E. de; Goddard, A.J.H.; Overton, R.S

    2004-03-01

    A non-generational genetic algorithm (GA) has been developed for fuel management optimisation of Advanced Gas-Cooled Reactors, which are operated by British Energy and produce around 20% of the UK's electricity requirements. An evolutionary search is coded using the genetic operators; namely selection by tournament, two-point crossover, mutation and random assessment of population for multi-cycle loading pattern (LP) optimisation. A detailed description of the chromosomes in the genetic algorithm coded is presented. Artificial Neural Networks (ANNs) have been constructed and trained to accelerate the GA-based search during the optimisation process. The whole package, called GAOPT, is linked to the reactor analysis code PANTHER, which performs fresh fuel loading, burn-up and power shaping calculations for each reactor cycle by imposing station-specific safety and operational constraints. GAOPT has been verified by performing a number of tests, which are applied to the Hinkley Point B and Hartlepool reactors. The test results giving loading pattern (LP) scenarios obtained from single and multi-cycle optimisation calculations applied to realistic reactor states of the Hartlepool and Hinkley Point B reactors are discussed. The results have shown that the GA/ANN algorithms developed can help the fuel engineer to optimise loading patterns in an efficient and more profitable way than currently available for multi-cycle refuelling of AGRs. Research leading to parallel GAs applied to LP optimisation are outlined, which can be adapted to present day LWR fuel management problems.

  5. From time-series to complex networks: Application to the cerebrovascular flow patterns in atrial fibrillation.

    Scarsoglio, Stefania; Cazzato, Fabio; Ridolfi, Luca

    2017-09-01

    A network-based approach is presented to investigate the cerebrovascular flow patterns during atrial fibrillation (AF) with respect to normal sinus rhythm (NSR). AF, the most common cardiac arrhythmia with faster and irregular beating, has been recently and independently associated with the increased risk of dementia. However, the underlying hemodynamic mechanisms relating the two pathologies remain mainly undetermined so far; thus, the contribution of modeling and refined statistical tools is valuable. Pressure and flow rate temporal series in NSR and AF are here evaluated along representative cerebral sites (from carotid arteries to capillary brain circulation), exploiting reliable artificially built signals recently obtained from an in silico approach. The complex network analysis evidences, in a synthetic and original way, a dramatic signal variation towards the distal/capillary cerebral regions during AF, which has no counterpart in NSR conditions. At the large artery level, networks obtained from both AF and NSR hemodynamic signals exhibit elongated and chained features, which are typical of pseudo-periodic series. These aspects are almost completely lost towards the microcirculation during AF, where the networks are topologically more circular and present random-like characteristics. As a consequence, all the physiological phenomena at the microcerebral level ruled by periodicity-such as regular perfusion, mean pressure per beat, and average nutrient supply at the cellular level-can be strongly compromised, since the AF hemodynamic signals assume irregular behaviour and random-like features. Through a powerful approach which is complementary to the classical statistical tools, the present findings further strengthen the potential link between AF hemodynamic and cognitive decline.

  6. Neural networks engaged in tactile object manipulation: patterns of expression among healthy individuals

    Seitz Rüdiger J

    2010-11-01

    Full Text Available Abstract Background Somatosensory object discrimination has been shown to involve widespread cortical and subcortical structures in both cerebral hemispheres. In this study we aimed to identify the networks involved in tactile object manipulation by principal component analysis (PCA of individual subjects. We expected to find more than one network. Methods Seven healthy right-handed male volunteers (aged 22 to 44 yrs manipulated with their right hand aluminium spheres during 5 s with a repetition frequency of 0.5-0.7 Hz. The correlation coefficients between the principal component temporal expression coefficients and the hemodynamic response modelled by SPM (ecc determined the task-related components. To establish reproducibility within subjects and similarity of functional connectivity patterns among subjects, regional correlation coefficients (rcc were computed between task-related component image volumes. By hierarchically categorizing, selecting and averaging the task-related component image volumes across subjects according to the rccs, mean component images (MCIs were derived describing neural networks associated with tactile object manipulation. Results Two independent mean component images emerged. Each included the primary sensorimotor cortex contralateral to the manipulating hand. The region extended to the premotor cortex in MCI 1, whereas it was restricted to the hand area of the primary sensorimotor cortex in MCI 2. MCI 1 showed bilateral involvement of the paralimbic anterior cingulate cortex (ACC, whereas MCI 2 implicated the midline thalamic nuclei and two areas of the rostral dorsal pons. Conclusions Two distinct networks participate in tactile object manipulation as revealed by the intra- and interindividual comparison of individual scans. Both were employed by most subjects, suggesting that both are involved in normal somatosensory object discrimination.

  7. Combining evolutionary game theory and network theory to analyze human cooperation patterns

    Scatà, Marialisa; Di Stefano, Alessandro; La Corte, Aurelio; Liò, Pietro; Catania, Emanuele; Guardo, Ermanno; Pagano, Salvatore

    2016-01-01

    Highlights: • We investigate the evolutionary dynamics of human cooperation in a social network. • We introduce the concepts of “Critical Mass”, centrality measure and homophily. • The emergence of cooperation is affected by the spatial choice of the “Critical Mass”. • Our findings show that homophily speeds up the convergence towards cooperation. • Centrality and “Critical Mass” spatial choice partially offset the impact of homophily. - Abstract: As natural systems continuously evolve, the human cooperation dilemma represents an increasingly more challenging question. Humans cooperate in natural and social systems, but how it happens and what are the mechanisms which rule the emergence of cooperation, represent an open and fascinating issue. In this work, we investigate the evolution of cooperation through the analysis of the evolutionary dynamics of behaviours within the social network, where nodes can choose to cooperate or defect following the classical social dilemmas represented by Prisoner’s Dilemma and Snowdrift games. To this aim, we introduce a sociological concept and statistical estimator, “Critical Mass”, to detect the minimum initial seed of cooperators able to trigger the diffusion process, and the centrality measure to select within the social network. Selecting different spatial configurations of the Critical Mass nodes, we highlight how the emergence of cooperation can be influenced by this spatial choice of the initial core in the network. Moreover, we target to shed light how the concept of homophily, a social shaping factor for which “birds of a feather flock together”, can affect the evolutionary process. Our findings show that homophily allows speeding up the diffusion process and make quicker the convergence towards human cooperation, while centrality measure and thus the Critical Mass selection, play a key role in the evolution showing how the spatial configurations can create some hidden patterns, partially

  8. From time-series to complex networks: Application to the cerebrovascular flow patterns in atrial fibrillation

    Scarsoglio, Stefania; Cazzato, Fabio; Ridolfi, Luca

    2017-09-01

    A network-based approach is presented to investigate the cerebrovascular flow patterns during atrial fibrillation (AF) with respect to normal sinus rhythm (NSR). AF, the most common cardiac arrhythmia with faster and irregular beating, has been recently and independently associated with the increased risk of dementia. However, the underlying hemodynamic mechanisms relating the two pathologies remain mainly undetermined so far; thus, the contribution of modeling and refined statistical tools is valuable. Pressure and flow rate temporal series in NSR and AF are here evaluated along representative cerebral sites (from carotid arteries to capillary brain circulation), exploiting reliable artificially built signals recently obtained from an in silico approach. The complex network analysis evidences, in a synthetic and original way, a dramatic signal variation towards the distal/capillary cerebral regions during AF, which has no counterpart in NSR conditions. At the large artery level, networks obtained from both AF and NSR hemodynamic signals exhibit elongated and chained features, which are typical of pseudo-periodic series. These aspects are almost completely lost towards the microcirculation during AF, where the networks are topologically more circular and present random-like characteristics. As a consequence, all the physiological phenomena at the microcerebral level ruled by periodicity—such as regular perfusion, mean pressure per beat, and average nutrient supply at the cellular level—can be strongly compromised, since the AF hemodynamic signals assume irregular behaviour and random-like features. Through a powerful approach which is complementary to the classical statistical tools, the present findings further strengthen the potential link between AF hemodynamic and cognitive decline.

  9. Self-organized pattern formation of biomolecules at silicon surfaces: Intended application of a dislocation network

    Kittler, M.; Yu, X.; Vyvenko, O.F.; Birkholz, M.; Seifert, W.; Reiche, M.; Wilhelm, T.; Arguirov, T.; Wolff, A.; Fritzsche, W.; Seibt, M.

    2006-01-01

    Defined placement of biomolecules at Si surfaces is a precondition for a successful combination of Si electronics with biological applications. We aim to realize this by Coulomb interaction of biomolecules with dislocations in Si. The dislocations form charged lines and they will be surrounded with a space charge region being connected with an electric field. The electric stray field in a solution of biomolecules, caused by dislocations located close to the Si surface, was estimated to yield values up to few kVcm -1 . A regular dislocation network can be formed by wafer direct bonding at the interface between the bonded wafers in case of misorientation. The adjustment of misorientation allows the variation of the distance between dislocations in a range from 10 nm to a few μm. This is appropriate for nanobiotechnology dealing with protein or DNA molecules with sizes in the nm and lower μm range. Actually, we achieved a distance between the dislocations of 10-20 nm. Also the existence of a distinct electric field formed by the dislocation network was demonstrated by the technique of the electron-beam-induced current (EBIC). Because of the relatively short range of the field, the dislocations have to be placed close to the surface. We positioned the dislocation network in an interface being 200 nm parallel to the Si surface by layer transfer techniques using hydrogen implantation and bonding. Based on EBIC and luminescence data we postulate a barrier of the dislocations at the as bonded interface < 100 meV. We plan to dope the dislocations with metal atoms to increase the electric field. We demonstrated that regular periodic dislocation networks close to the Si surface formed by bonding are realistic candidates for self-organized placing of biomolecules. Experiments are underway to test whether biomolecules decorate the pattern of the dislocation lines

  10. Modelling fast spreading patterns of airborne infectious diseases using complex networks

    Brenner, Frank; Marwan, Norbert; Hoffmann, Peter

    2017-04-01

    The pandemics of SARS (2002/2003) and H1N1 (2009) have impressively shown the potential of epidemic outbreaks of infectious diseases in a world that is strongly connected. Global air travelling established an easy and fast opportunity for pathogens to migrate globally in only a few days. This made epidemiological prediction harder. By understanding this complex development and its link to climate change we can suggest actions to control a part of global human health affairs. In this study we combine the following data components to simulate the outbreak of an airborne infectious disease that is directly transmitted from human to human: em{Global Air Traffic Network (from openflights.org) with information on airports, airport location, direct flight connection, airplane type} em{Global population dataset (from SEDAC, NASA)} em{Susceptible-Infected-Recovered (SIR) compartmental model to simulate disease spreading in the vicinity of airports. A modified Susceptible-Exposed-Infected-Recovered (SEIR) model to analyze the impact of the incubation period.} em{WATCH-Forcing-Data-ERA-Interim (WFDEI) climate data: temperature, specific humidity, surface air pressure, and water vapor pressure} These elements are implemented into a complex network. Nodes inside the network represent airports. Each single node is equipped with its own SIR/SEIR compartmental model with node specific attributes. Edges between those nodes represent direct flight connections that allow infected individuals to move between linked nodes. Therefore the interaction of the set of unique SIR models creates the model dynamics we will analyze. To better figure out the influence on climate change on disease spreading patterns, we focus on Influenza-like-Illnesses (ILI). The transmission rate of ILI has a dependency on climate parameters like humidity and temperature. Even small changes of environmental variables can trigger significant differences in the global outbreak behavior. Apart from the direct

  11. Imbalanced pattern completion vs. separation in cognitive disease: network simulations of synaptic pathologies predict a personalized therapeutics strategy

    Hanson Jesse E

    2010-08-01

    Full Text Available Abstract Background Diverse Mouse genetic models of neurodevelopmental, neuropsychiatric, and neurodegenerative causes of impaired cognition exhibit at least four convergent points of synaptic malfunction: 1 Strength of long-term potentiation (LTP, 2 Strength of long-term depression (LTD, 3 Relative inhibition levels (Inhibition, and 4 Excitatory connectivity levels (Connectivity. Results To test the hypothesis that pathological increases or decreases in these synaptic properties could underlie imbalances at the level of basic neural network function, we explored each type of malfunction in a simulation of autoassociative memory. These network simulations revealed that one impact of impairments or excesses in each of these synaptic properties is to shift the trade-off between pattern separation and pattern completion performance during memory storage and recall. Each type of synaptic pathology either pushed the network balance towards intolerable error in pattern separation or intolerable error in pattern completion. Imbalances caused by pathological impairments or excesses in LTP, LTD, inhibition, or connectivity, could all be exacerbated, or rescued, by the simultaneous modulation of any of the other three synaptic properties. Conclusions Because appropriate modulation of any of the synaptic properties could help re-balance network function, regardless of the origins of the imbalance, we propose a new strategy of personalized cognitive therapeutics guided by assay of pattern completion vs. pattern separation function. Simulated examples and testable predictions of this theorized approach to cognitive therapeutics are presented.

  12. Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments

    Tahat Amani; Marti Jordi; Khwaldeh Ali; Tahat Kaher

    2014-01-01

    In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories: transfer ‘occurred’ and transfer ‘not occurred’. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation (multilayer perceptron algorithms) could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties (e.g., temperature, density) show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies. (condensed matter: structural, mechanical, and thermal properties)

  13. Dentists’ practice patterns regarding caries prevention: results from a dental practice-based research network

    Yokoyama, Yoko; Kakudate, Naoki; Sumida, Futoshi; Matsumoto, Yuki; Gilbert, Gregg H; Gordan, Valeria V

    2013-01-01

    Objective The purposes of this study were to (1) quantify dentists' practice patterns regarding caries prevention and (2) test the hypothesis that certain dentists' characteristics are associated with these practice patterns. Design The study used a cross-sectional study design consisting of a questionnaire survey. Participants The study queried dentists who worked in outpatient dental practices who were affiliated with the Dental Practice-Based Research Network Japan, which seeks to engage dentists in investigating research questions and sharing experiences and expertise (n=282). Measurement Dentists were asked about their practice patterns regarding caries preventive dentistry. Background data on patients, practice and dentist were also collected. Results 38% of dentists (n=72) provided individualised caries prevention to more than 50% of their patients. Overall, 10% of the time in daily practice was spent on caries preventive dentistry. Dentists who provided individualised caries prevention to more than 50% of their patients spent significantly more time on preventive care and less time on removable prosthetics treatment, compared to dentists who did not provide individualised caries prevention. Additionally, they provided oral hygiene instruction, patient education, fluoride recommendations, intraoral photographs taken and diet counselling to their patients significantly more often than dentists who did not provide individualised caries prevention. Multiple logistic regression analysis suggested that the percentage of patients interested in caries prevention and the percentage of patients who received hygiene instruction, were both associated with the percentage of patients who receive individualised caries prevention. Conclusions We identified substantial variation in dentists' practice patterns regarding preventive dentistry. Individualised caries prevention was significantly related to provision of other preventive services and to having a higher percentage

  14. Effect of the size of an artificial neural network used as pattern identifier

    Reynoso V, M.R.; Vega C, J.J.

    2003-01-01

    A novel way to extract relevant parameters associated with the outgoing ions from nuclear reactions, obtained by digitizing the signals provided by a Bragg curve spectrometer (BCS) is presented. This allowed the implementation of a more thorough pulse-shape analysis. Due to the complexity of this task, it was required to take advantage of new and more powerful computational paradigms. This was fulfilled using a back-propagation artificial neural network (ANN) as a pattern identifier. Over training of ANNs is a common problem during the training stage. In the performance of the ANN there is a compromise between its size and the size of the training set. Here, this effect will be illustrated in relation to the problem of Bragg Curve (BC) identification. (Author)

  15. Electrocardiogram Pattern Recognition and Analysis Based on Artificial Neural Networks and Support Vector Machines: A Review

    Mario Sansone

    2013-01-01

    Full Text Available Computer systems for Electrocardiogram (ECG analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units or in prompt detection of dangerous events (e.g., ventricular fibrillation. Together with clinical applications (arrhythmia detection and heart rate variability analysis, ECG is currently being investigated in biometrics (human identification, an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned.

  16. Effect of the size of an artificial neural network used as pattern identifier

    Reynoso V, M.R.; Vega C, J.J. [ININ, 52045 Ocoyoacac, Estado de Mexico (Mexico)

    2003-07-01

    A novel way to extract relevant parameters associated with the outgoing ions from nuclear reactions, obtained by digitizing the signals provided by a Bragg curve spectrometer (BCS) is presented. This allowed the implementation of a more thorough pulse-shape analysis. Due to the complexity of this task, it was required to take advantage of new and more powerful computational paradigms. This was fulfilled using a back-propagation artificial neural network (ANN) as a pattern identifier. Over training of ANNs is a common problem during the training stage. In the performance of the ANN there is a compromise between its size and the size of the training set. Here, this effect will be illustrated in relation to the problem of Bragg Curve (BC) identification. (Author)

  17. Network analysis of pig movements: Loyalty patterns and contact chains of different holding types in Denmark.

    Schulz, Jana; Boklund, Anette; Halasa, Tariq H B; Toft, Nils; Lentz, Hartmut H K

    2017-01-01

    Understanding animal movements is an important factor for the development of meaningful surveillance and control programs, but also for the development of disease spread models. We analysed the Danish pig movement network using static and temporal network analysis tools to provide deeper insight in the connection between holdings dealing with pigs, such as breeding and multiplier herds, production herds, slaughterhouses or traders. Pig movements, which occurred between 1st January 2006 and 31st December 2015 in Denmark, were summarized to investigate temporal trends such as the number of active holdings, the number of registered movements and the number of pigs moved. To identify holdings and holding types with potentially higher risk for introduction or spread of diseases via pig movements, we determined loyalty patterns, annual network components and contact chains for the 24 registered holding types. The total number of active holdings as well as the number of pig movements decreased during the study period while the holding sizes increased. Around 60-90% of connections between two pig holdings were present in two consecutive years and around one third of the connections persisted within the considered time period. Weaner herds showed the highest level of in-loyalty, whereas we observed an intermediate level of in-loyalty for all breeding sites and for production herds. Boar stations, production herds and trade herds showed a high level of out-loyalty. Production herds constituted the highest proportion of holdings in the largest strongly connected component. All production sites showed low levels of in-going contact chains and we observed a high level of out-going contact chain for breeding and multiplier herds. Except for livestock auctions, all transit sites also showed low levels of out-going contact chains. Our results reflect the pyramidal structure of the underlying network. Based on the considered disease, the time frame for the calculation of network

  18. Network analysis of pig movements: Loyalty patterns and contact chains of different holding types in Denmark.

    Jana Schulz

    Full Text Available Understanding animal movements is an important factor for the development of meaningful surveillance and control programs, but also for the development of disease spread models. We analysed the Danish pig movement network using static and temporal network analysis tools to provide deeper insight in the connection between holdings dealing with pigs, such as breeding and multiplier herds, production herds, slaughterhouses or traders. Pig movements, which occurred between 1st January 2006 and 31st December 2015 in Denmark, were summarized to investigate temporal trends such as the number of active holdings, the number of registered movements and the number of pigs moved. To identify holdings and holding types with potentially higher risk for introduction or spread of diseases via pig movements, we determined loyalty patterns, annual network components and contact chains for the 24 registered holding types. The total number of active holdings as well as the number of pig movements decreased during the study period while the holding sizes increased. Around 60-90% of connections between two pig holdings were present in two consecutive years and around one third of the connections persisted within the considered time period. Weaner herds showed the highest level of in-loyalty, whereas we observed an intermediate level of in-loyalty for all breeding sites and for production herds. Boar stations, production herds and trade herds showed a high level of out-loyalty. Production herds constituted the highest proportion of holdings in the largest strongly connected component. All production sites showed low levels of in-going contact chains and we observed a high level of out-going contact chain for breeding and multiplier herds. Except for livestock auctions, all transit sites also showed low levels of out-going contact chains. Our results reflect the pyramidal structure of the underlying network. Based on the considered disease, the time frame for the

  19. The patterns of organisation and structure of interactions in a fish-parasite network of a neotropical river.

    Bellay, Sybelle; Oliveira, Edson F de; Almeida-Neto, Mário; Abdallah, Vanessa D; Azevedo, Rodney K de; Takemoto, Ricardo M; Luque, José L

    2015-07-01

    The use of the complex network approach to study host-parasite interactions has helped to improve the understanding of the structure and dynamics of ecological communities. In this study, this network approach is applied to evaluate the patterns of organisation and structure of interactions in a fish-parasite network of a neotropical Atlantic Forest river. The network includes 20 fish species and 73 metazoan parasite species collected from the Guandu River, Rio de Janeiro State, Brazil. According to the usual measures in studies of networks, the organisation of the network was evaluated using measures of host susceptibility, parasite dependence, interaction asymmetry, species strength and complementary specialisation of each species as well as the network. The network structure was evaluated using connectance, nestedness and modularity measures. Host susceptibility typically presented low values, whereas parasite dependence was high. The asymmetry and species strength were correlated with host taxonomy but not with parasite taxonomy. Differences among parasite taxonomic groups in the complementary specialisation of each species on hosts were also observed. However, the complementary specialisation and species strength values were not correlated. The network had a high complementary specialisation, low connectance and nestedness, and high modularity, thus indicating variability in the roles of species in the network organisation and the expected presence of many specialist species. Copyright © 2015 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.

  20. Connectivity strategies for higher-order neural networks applied to pattern recognition

    Spirkovska, Lilly; Reid, Max B.

    1990-01-01

    Different strategies for non-fully connected HONNs (higher-order neural networks) are discussed, showing that by using such strategies an input field of 128 x 128 pixels can be attained while still achieving in-plane rotation and translation-invariant recognition. These techniques allow HONNs to be used with the larger input scenes required for practical pattern-recognition applications. The number of interconnections that must be stored has been reduced by a factor of approximately 200,000 in a T/C case and about 2000 in a Space Shuttle/F-18 case by using regional connectivity. Third-order networks have been simulated using several connection strategies. The method found to work best is regional connectivity. The main advantages of this strategy are the following: (1) it considers features of various scales within the image and thus gets a better sample of what the image looks like; (2) it is invariant to shape-preserving geometric transformations, such as translation and rotation; (3) the connections are predetermined so that no extra computations are necessary during run time; and (4) it does not require any extra storage for recording which connections were formed.

  1. An Effective and Novel Neural Network Ensemble for Shift Pattern Detection in Control Charts

    Mahmoud Barghash

    2015-01-01

    Full Text Available Pattern recognition in control charts is critical to make a balance between discovering faults as early as possible and reducing the number of false alarms. This work is devoted to designing a multistage neural network ensemble that achieves this balance which reduces rework and scrape without reducing productivity. The ensemble under focus is composed of a series of neural network stages and a series of decision points. Initially, this work compared using multidecision points and single-decision point on the performance of the ANN which showed that multidecision points are highly preferable to single-decision points. This work also tested the effect of population percentages on the ANN and used this to optimize the ANN’s performance. Also this work used optimized and nonoptimized ANNs in an ensemble and proved that using nonoptimized ANN may reduce the performance of the ensemble. The ensemble that used only optimized ANNs has improved performance over individual ANNs and three-sigma level rule. In that respect using the designed ensemble can help in reducing the number of false stops and increasing productivity. It also can be used to discover even small shifts in the mean as early as possible.

  2. A Scalable Permutation Approach Reveals Replication and Preservation Patterns of Network Modules in Large Datasets.

    Ritchie, Scott C; Watts, Stephen; Fearnley, Liam G; Holt, Kathryn E; Abraham, Gad; Inouye, Michael

    2016-07-01

    Network modules-topologically distinct groups of edges and nodes-that are preserved across datasets can reveal common features of organisms, tissues, cell types, and molecules. Many statistics to identify such modules have been developed, but testing their significance requires heuristics. Here, we demonstrate that current methods for assessing module preservation are systematically biased and produce skewed p values. We introduce NetRep, a rapid and computationally efficient method that uses a permutation approach to score module preservation without assuming data are normally distributed. NetRep produces unbiased p values and can distinguish between true and false positives during multiple hypothesis testing. We use NetRep to quantify preservation of gene coexpression modules across murine brain, liver, adipose, and muscle tissues. Complex patterns of multi-tissue preservation were revealed, including a liver-derived housekeeping module that displayed adipose- and muscle-specific association with body weight. Finally, we demonstrate the broader applicability of NetRep by quantifying preservation of bacterial networks in gut microbiota between men and women. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

  3. Abnormal network flow detection based on application execution patterns from Web of Things (WoT) platforms.

    Yoon, Young; Jung, Hyunwoo; Lee, Hana

    2018-01-01

    In this paper, we present a research work on a novel methodology of identifying abnormal behaviors at the underlying network monitor layer during runtime based on the execution patterns of Web of Things (WoT) applications. An execution pattern of a WoT application is a sequence of profiled time delays between the invocations of involved Web services, and it can be obtained from WoT platforms. We convert the execution pattern to a time sequence of network flows that are generated when the WoT applications are executed. We consider such time sequences as a whitelist. This whitelist reflects the valid application execution patterns. At the network monitor layer, our applied RETE algorithm examines whether any given runtime sequence of network flow instances does not conform to the whitelist. Through this approach, it is possible to interpret a sequence of network flows with regard to application logic. Given such contextual information, we believe that the administrators can detect and reason about any abnormal behaviors more effectively. Our empirical evaluation shows that our RETE-based algorithm outperforms the baseline algorithm in terms of memory usage.

  4. An intelligent signal processing and pattern recognition technique for defect identification using an active sensor network

    Su, Zhongqing; Ye, Lin

    2004-08-01

    The practical utilization of elastic waves, e.g. Rayleigh-Lamb waves, in high-performance structural health monitoring techniques is somewhat impeded due to the complicated wave dispersion phenomena, the existence of multiple wave modes, the high susceptibility to diverse interferences, the bulky sampled data and the difficulty in signal interpretation. An intelligent signal processing and pattern recognition (ISPPR) approach using the wavelet transform and artificial neural network algorithms was developed; this was actualized in a signal processing package (SPP). The ISPPR technique comprehensively functions as signal filtration, data compression, characteristic extraction, information mapping and pattern recognition, capable of extracting essential yet concise features from acquired raw wave signals and further assisting in structural health evaluation. For validation, the SPP was applied to the prediction of crack growth in an alloy structural beam and construction of a damage parameter database for defect identification in CF/EP composite structures. It was clearly apparent that the elastic wave propagation-based damage assessment could be dramatically streamlined by introduction of the ISPPR technique.

  5. Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

    Huo, Guanying

    2017-01-01

    As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

  6. The effect of road network patterns on pedestrian safety: A zone-based Bayesian spatial modeling approach.

    Guo, Qiang; Xu, Pengpeng; Pei, Xin; Wong, S C; Yao, Danya

    2017-02-01

    Pedestrian safety is increasingly recognized as a major public health concern. Extensive safety studies have been conducted to examine the influence of multiple variables on the occurrence of pedestrian-vehicle crashes. However, the explicit relationship between pedestrian safety and road network characteristics remains unknown. This study particularly focused on the role of different road network patterns on the occurrence of crashes involving pedestrians. A global integration index via space syntax was introduced to quantify the topological structures of road networks. The Bayesian Poisson-lognormal (PLN) models with conditional autoregressive (CAR) prior were then developed via three different proximity structures: contiguity, geometry-centroid distance, and road network connectivity. The models were also compared with the PLN counterpart without spatial correlation effects. The analysis was based on a comprehensive crash dataset from 131 selected traffic analysis zones in Hong Kong. The results indicated that higher global integration was associated with more pedestrian-vehicle crashes; the irregular pattern network was proved to be safest in terms of pedestrian crash occurrences, whereas the grid pattern was the least safe; the CAR model with a neighborhood structure based on road network connectivity was found to outperform in model goodness-of-fit, implying the importance of accurately accounting for spatial correlation when modeling spatially aggregated crash data. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

    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

  8. Dentists' dietary perception and practice patterns in a dental practice-based research network.

    Yoko Yokoyama

    Full Text Available Dental caries are largely preventable, and epidemiological evidence for a relationship between diet and oral health is abundant. To date, however, dentists' perceptions about the role of diet and dentists' practice patterns regarding diet counseling have not been clarified.THE PURPOSES OF THIS STUDY WERE TO: (1 examine discordance between dentists' perception of the importance of diet in caries treatment planning and their actual provision of diet counseling to patients, and (2 identify dentists' characteristics associated with their provision of diet counseling.The study used a cross-sectional study design consisting of a questionnaire survey in Japan.The study queried dentists working in outpatient dental practices who were affiliated with the Dental Practice-Based Research Network Japan (JDPBRN, which aims to allow dentists to investigate research questions and share experiences and expertise (n = 282.Dentists were asked about their perceptions on the importance of diet and their practice patterns regarding diet counseling, as well as patient, practice, and dentist background data.The majority of participants (n = 116, 63% recognized that diet is "more important" to oral health. However, among participants who think diet is "more important" (n = 116, only 48% (n = 56 provide diet counseling to more than 20% of their patients. Multiple logistic regression analysis suggested that several variables were associated with providing diet counseling; dentist gender, practice busyness, percentage of patients interested in caries prevention, caries risk assessment, and percentage of patients who receive blood pressure screening.Some discordance exists between dentists' perception of the importance of diet in caries treatment planning and their actual practice pattern regarding diet counseling to patients. Reducing this discordance may require additional dentist education, including nutritional and systemic disease concepts; patient

  9. Status of perturbative QCD

    Collins, J.C.

    1985-01-01

    Progress in quantum chromodynamics in the past year is reviewed in these specific areas: proof of factorization for hadron-hadron collisions, fast calculation of higher order graphs, perturbative Monte Carlo calculations for hadron-hadron scattering, applicability of perturbative methods to heavy quark production, and understanding of the small-x problem. 22 refs

  10. Analysis of spatiotemporal soil moisture patterns at the catchment scale using a wireless sensor network

    Bogena, Heye R.; Huisman, Johan A.; Rosenbaum, Ulrike; Weuthen, Ansgar; Vereecken, Harry

    2010-05-01

    Soil water content plays a key role in partitioning water and energy fluxes and controlling the pattern of groundwater recharge. Despite the importance of soil water content, it is not yet measured in an operational way at larger scales. The aim of this paper is to present the potential of real-time monitoring for the analysis of soil moisture patterns at the catchment scale using the recently developed wireless sensor network SoilNet [1], [2]. SoilNet is designed to measure soil moisture, salinity and temperature in several depths (e.g. 5, 20 and 50 cm). Recently, a small forest catchment Wüstebach (~27 ha) has been instrumented with 150 sensor nodes and more than 1200 soil sensors in the framework of the Transregio32 and the Helmholtz initiative TERENO (Terrestrial Environmental Observatories). From August to November 2009, more than 6 million soil moisture measurements have been performed. We will present first results from a statistical and geostatistical analysis of the data. The observed spatial variability of soil moisture corresponds well with the 800-m scale variability described in [3]. The very low scattering of the standard deviation versus mean soil moisture plots indicates that sensor network data shows less artificial soil moisture variations than soil moisture data originated from measurement campaigns. The variograms showed more or less the same nugget effect, which indicates that the sum of the sub-scale variability and the measurement error is rather time-invariant. Wet situations showed smaller spatial variability, which is attributed to saturated soil water content, which poses an upper limit and is typically not strongly variable in headwater catchments with relatively homogeneous soil. The spatiotemporal variability in soil moisture at 50 cm depth was significantly lower than at 5 and 20 cm. This finding indicates that the considerable variability of the top soil is buffered deeper in the soil due to lateral and vertical water fluxes

  11. Perturbative and constructive renormalization

    Veiga, P.A. Faria da

    2000-01-01

    These notes are a survey of the material treated in a series of lectures delivered at the X Summer School Jorge Andre Swieca. They are concerned with renormalization in Quantum Field Theories. At the level of perturbation series, we review classical results as Feynman graphs, ultraviolet and infrared divergences of Feynman integrals. Weinberg's theorem and Hepp's theorem, the renormalization group and the Callan-Symanzik equation, the large order behavior and the divergence of most perturbation series. Out of the perturbative regime, as an example of a constructive method, we review Borel summability and point out how it is possible to circumvent the perturbation diseases. These lectures are a preparation for the joint course given by professor V. Rivasseau at the same school, where more sophisticated non-perturbative analytical methods based on rigorous renormalization group techniques are presented, aiming at furthering our understanding about the subject and bringing field theoretical models to a satisfactory mathematical level. (author)

  12. Artificial Neural Network approach to develop unique Classification and Raga identification tools for Pattern Recognition in Carnatic Music

    Srimani, P. K.; Parimala, Y. G.

    2011-12-01

    A unique approach has been developed to study patterns in ragas of Carnatic Classical music based on artificial neural networks. Ragas in Carnatic music which have found their roots in the Vedic period, have grown on a Scientific foundation over thousands of years. However owing to its vastness and complexities it has always been a challenge for scientists and musicologists to give an all encompassing perspective both qualitatively and quantitatively. Cognition, comprehension and perception of ragas in Indian classical music have always been the subject of intensive research, highly intriguing and many facets of these are hitherto not unravelled. This paper is an attempt to view the melakartha ragas with a cognitive perspective using artificial neural network based approach which has given raise to very interesting results. The 72 ragas of the melakartha system were defined through the combination of frequencies occurring in each of them. The data sets were trained using several neural networks. 100% accurate pattern recognition and classification was obtained using linear regression, TLRN, MLP and RBF networks. Performance of the different network topologies, by varying various network parameters, were compared. Linear regression was found to be the best performing network.

  13. Perturbative calculations of flow patterns in free convection between coaxial cylinders. Non-linear temperature dependences of the fluid properties; Un metodo de perturbaciones para la obtencion de perfiles de velocidad en conveccion natural entre cilindros coaxiales, dependencias de la temperatura no-lineales de las propiedades del fluido

    Navarro, J A; Madariaga, J A; Santamaria, C M; Saviron, J M

    1980-07-01

    10 refs. Flow pattern calculations in natural convection between two vertical coaxial cylinders are reported. It is assumed trough the paper. that fluid properties, viscosity, thermal conductivity and density, depend no-linearly on temperature and that the aspects (height/radius) ratio of the cylinders is high. Velocity profiles are calculated trough a perturbative scheme and analytic results for the three first perturbation orders are presented. We outline also an iterative method to estimate the perturbations on the flow patterns which arise when a radial composition gradient is established by external forces in a two-component fluid. This procedure, based on semiempirical basis, is applied to gaseous convection. The influence of the molecules gas properties on tho flow is also discussed. (Author) 10 refs.

  14. Influences of sampling effort on detected patterns and structuring processes of a Neotropical plant-hummingbird network.

    Vizentin-Bugoni, Jeferson; Maruyama, Pietro K; Debastiani, Vanderlei J; Duarte, L da S; Dalsgaard, Bo; Sazima, Marlies

    2016-01-01

    Virtually all empirical ecological interaction networks to some extent suffer from undersampling. However, how limitations imposed by sampling incompleteness affect our understanding of ecological networks is still poorly explored, which may hinder further advances in the field. Here, we use a plant-hummingbird network with unprecedented sampling effort (2716 h of focal observations) from the Atlantic Rainforest in Brazil, to investigate how sampling effort affects the description of network structure (i.e. widely used network metrics) and the relative importance of distinct processes (i.e. species abundances vs. traits) in determining the frequency of pairwise interactions. By dividing the network into time slices representing a gradient of sampling effort, we show that quantitative metrics, such as interaction evenness, specialization (H2 '), weighted nestedness (wNODF) and modularity (Q; QuanBiMo algorithm) were less biased by sampling incompleteness than binary metrics. Furthermore, the significance of some network metrics changed along the sampling effort gradient. Nevertheless, the higher importance of traits in structuring the network was apparent even with small sampling effort. Our results (i) warn against using very poorly sampled networks as this may bias our understanding of networks, both their patterns and structuring processes, (ii) encourage the use of quantitative metrics little influenced by sampling when performing spatio-temporal comparisons and (iii) indicate that in networks strongly constrained by species traits, such as plant-hummingbird networks, even small sampling is sufficient to detect their relative importance for the frequencies of interactions. Finally, we argue that similar effects of sampling are expected for other highly specialized subnetworks. © 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society.

  15. Spike Pattern Structure Influences Synaptic Efficacy Variability Under STDP and Synaptic Homeostasis. II: Spike Shuffling Methods on LIF Networks

    Zedong Bi

    2016-08-01

    Full Text Available Synapses may undergo variable changes during plasticity because of the variability of spike patterns such as temporal stochasticity and spatial randomness. Here, we call the variability of synaptic weight changes during plasticity to be efficacy variability. In this paper, we investigate how four aspects of spike pattern statistics (i.e., synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations influence the efficacy variability under pair-wise additive spike-timing dependent plasticity (STDP and synaptic homeostasis (the mean strength of plastic synapses into a neuron is bounded, by implementing spike shuffling methods onto spike patterns self-organized by a network of excitatory and inhibitory leaky integrate-and-fire (LIF neurons. With the increase of the decay time scale of the inhibitory synaptic currents, the LIF network undergoes a transition from asynchronous state to weak synchronous state and then to synchronous bursting state. We first shuffle these spike patterns using a variety of methods, each designed to evidently change a specific pattern statistics; and then investigate the change of efficacy variability of the synapses under STDP and synaptic homeostasis, when the neurons in the network fire according to the spike patterns before and after being treated by a shuffling method. In this way, we can understand how the change of pattern statistics may cause the change of efficacy variability. Our results are consistent with those of our previous study which implements spike-generating models on converging motifs. We also find that burstiness/regularity is important to determine the efficacy variability under asynchronous states, while heterogeneity of cross-correlations is the main factor to cause efficacy variability when the network moves into synchronous bursting states (the states observed in epilepsy.

  16. Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN

    Lee, Hyeungill; Han, Sungyeob; Lee, Jungwoo

    2017-01-01

    We propose a novel technique to make neural network robust to adversarial examples using a generative adversarial network. We alternately train both classifier and generator networks. The generator network generates an adversarial perturbation that can easily fool the classifier network by using a gradient of each image. Simultaneously, the classifier network is trained to classify correctly both original and adversarial images generated by the generator. These procedures help the classifier ...

  17. Plant distribution patterns related to species characteristics and spatial and temporal habitat heterogeneity in a network of ditch banks

    Geertsema, W.; Sprangers, J.T.C.M.

    2002-01-01

    In this study we investigated the relationship between the distribution patterns of a number of herbaceous plant species and the isolation and age of habitat patches. The study was conducted for a network of ditch banks in an agricultural landscape in The Netherlands. Thirteen plant species were

  18. Functional Polycentrism and Urban Network Development in the Greater South East UK: Evidence from Commuting Patterns, 1981-2001

    B. de Goei (Bastiaan); M.J. Burger (Martijn); F.G. van Oort (Frank); M. Kitson (Michael)

    2009-01-01

    textabstractIn contemporary literature on changing urban systems, it is often argued that the traditional central place conceptualisation is outdated and should be replaced by a network view that emphasises the increasing criss-crossing pattern of interdependencies between spatial units. This paper

  19. Methods for discriminating gas-liquid two phase flow patterns based on gray neural networks and SVM

    Li Jingjing; Zhou Tao; Duan Jun; Zhang Lei

    2013-01-01

    Background: The flow patterns of two phase flow will directly influence the heat transfer and mass transfer of the flow. Purpose: By wavelet analysis of the pressure drop experimental data, the wavelet coefficients of different frequency can be obtained. Methods: Get the wavelet energy and then train them in the model of BP neural network to distinguish the flow patterns. Introduced the implant gray neural networks model and use it for the two phase flow for the first time. At the same time, set up the method of training the pressure data and wavelet energy data in the support vector machine. Results: Through treatment of the gray layer, the result of the neural network is more accuracy. It can obviously reduce the effect of data marginalization. The accuracy of the pressure drop Lib-SVM method is 95.2%. Conclusions: The results show that these three methods can make a distinction among the different flow patterns and the Lib-SVM method gets the best result, then the gray neural networks, and at last the BP neural networks. (authors)

  20. Algorithms for Regular Tree Grammar Network Search and Their Application to Mining Human-viral Infection Patterns.

    Smoly, Ilan; Carmel, Amir; Shemer-Avni, Yonat; Yeger-Lotem, Esti; Ziv-Ukelson, Michal

    2016-03-01

    Network querying is a powerful approach to mine molecular interaction networks. Most state-of-the-art network querying tools either confine the search to a prespecified topology in the form of some template subnetwork, or do not specify any topological constraints at all. Another approach is grammar-based queries, which are more flexible and expressive as they allow for expressing the topology of the sought pattern according to some grammar-based logic. Previous grammar-based network querying tools were confined to the identification of paths. In this article, we extend the patterns identified by grammar-based query approaches from paths to trees. For this, we adopt a higher order query descriptor in the form of a regular tree grammar (RTG). We introduce a novel problem and propose an algorithm to search a given graph for the k highest scoring subgraphs matching a tree accepted by an RTG. Our algorithm is based on the combination of dynamic programming with color coding, and includes an extension of previous k-best parsing optimization approaches to avoid isomorphic trees in the output. We implement the new algorithm and exemplify its application to mining viral infection patterns within molecular interaction networks. Our code is available online.

  1. Flexibility in the patterning and control of axial locomotor networks in lamprey.

    Buchanan, James T

    2011-12-01

    In lower vertebrates, locomotor burst generators for axial muscles generally produce unitary bursts that alternate between the two sides of the body. In lamprey, a lower vertebrate, locomotor activity in the axial ventral roots of the isolated spinal cord can exhibit flexibility in the timings of bursts to dorsally-located myotomal muscle fibers versus ventrally-located myotomal muscle fibers. These episodes of decreased synchrony can occur spontaneously, especially in the rostral spinal cord where the propagating body waves of swimming originate. Application of serotonin, an endogenous spinal neurotransmitter known to presynaptically inhibit excitatory synapses in lamprey, can promote decreased synchrony of dorsal-ventral bursting. These observations suggest the possible existence of dorsal and ventral locomotor networks with modifiable coupling strength between them. Intracellular recordings of motoneurons during locomotor activity provide some support for this model. Pairs of motoneurons innervating myotomal muscle fibers of similar ipsilateral dorsoventral location tend to have higher correlations of fast synaptic activity during fictive locomotion than do pairs of motoneurons innervating myotomes of different ipsilateral dorsoventral locations, suggesting their control by different populations of premotor interneurons. Further, these different motoneuron pools receive different patterns of excitatory and inhibitory inputs from individual reticulospinal neurons, conveyed in part by different sets of premotor interneurons. Perhaps, then, the locomotor network of the lamprey is not simply a unitary burst generator on each side of the spinal cord that activates all ipsilateral body muscles simultaneously. Instead, the burst generator on each side may comprise at least two coupled burst generators, one controlling motoneurons innervating dorsal body muscles and one controlling motoneurons innervating ventral body muscles. The coupling strength between these two

  2. "Newbies" and "Celebrities": Detecting Social Roles in an Online Network of Teachers via Participation Patterns

    Smith Risser, H.; Bottoms, SueAnn

    2014-01-01

    The advent of social networking tools allows teachers to create online networks and share information. While some virtual networks have a formal structure and defined boundaries, many do not. These unstructured virtual networks are difficult to study because they lack defined boundaries and a formal structure governing leadership roles and the…

  3. Patterns of severe injury in pediatric car crash victims: Crash Injury Research Engineering Network database.

    Brown, J Kristine; Jing, Yuezhou; Wang, Stewart; Ehrlich, Peter F

    2006-02-01

    Motor vehicle crashes (MVCs) account for 50% of pediatric trauma. Safety improvements are typically tested with child crash dummies using an in vitro model. The Crash Injury Research Engineering Network (CIREN) provides an in vivo validation process. Previous research suggest that children in lateral crashes or front-seat locations have higher Injury Severity Scale scores and lower Glasgow Coma Scale scores than those in frontal-impact crashes. However, specific injury patterns and crash characteristics have not been characterized. Data were collected from the CIREN multidisciplinary crash reconstruction network (10 pediatric trauma centers). Injuries were examined with regard to crash direction (frontal/lateral), restraint use, seat location, and change in velocity at impact (DeltaV). Injuries were limited to Abbreviated Injury Scale (AIS) scores of 3 or higher and included head, thoracic, abdominal, pelvic, spine, and long bone (orthopedic) injuries. Standard age groupings (0-4, 5-9, 10-14, and 15-18 years) were used. Statistical analyses used Fisher's Exact test and multiple logistic regressions. Four hundred seventeen MVCs with 2500 injuries were analyzed (males = 219, females = 198). Controlling for DeltaV and age, children in lateral-impact crashes (n = 232) were significantly more likely to suffer severe injuries to the head and thorax as compared with children in frontal crashes (n = 185), who were more likely to suffer severe spine and orthopedic injuries. Children in a front-seat (n = 236) vs those in a back-seat (n = 169) position had more injuries to the thoracic (27% vs 17%), abdominal (21% vs 13%), pelvic (11% vs 1%), and orthopedic (28% vs 10%) regions (P < .05 for all). Seat belts were protective for pelvic (5% vs 12% unbelted) and orthopedic (15% vs 40%) injuries (odds ratio = 3, P < .01 for both). A reproducible pattern of injury is noted for children involved in lateral-impact crashes characterized by head and chest injuries. The Injury Severity

  4. A latency analysis for M2M and OG-like traffic patterns in different HSPA core network configurations

    M. V. Popović

    2014-11-01

    Full Text Available In this paper we present an analysis intended to reveal possible impacts of core network features on latency for modelled M2M and Online Gaming traffic. Simulations were performed in a live 3G/HSPA network. Test traffic simulating multiplayer real-time games and M2M applications was generated on 10 mobile phones in parallel, sending data to a remote server. APNs with different combinations of hardware and features (proxy server, different GGSNs and firewalls, usage of Service Awareness feature were chosen. The traffic was recorded on the Gn interface in the mobile core. The goal of experiments was to evaluate any eventually significant variation of average recorded RTTs in the core part of mobile network that would clearly indicate either the impact of used APN on delay for a specific traffic pattern, or selectivity of the APN towards different traffic patterns.

  5. Oscillation-Driven Spike-Timing Dependent Plasticity Allows Multiple Overlapping Pattern Recognition in Inhibitory Interneuron Networks

    Garrido, Jesús A.; Luque, Niceto R.; Tolu, Silvia

    2016-01-01

    The majority of operations carried out by the brain require learning complex signal patterns for future recognition, retrieval and reuse. Although learning is thought to depend on multiple forms of long-term synaptic plasticity, the way this latter contributes to pattern recognition is still poorly...... and at the inhibitory interneuron-interneuron synapses, the interneurons rapidly learned complex input patterns. Interestingly, induction of plasticity required that the network be entrained into theta-frequency band oscillations, setting the internal phase-reference required to drive STDP. Inhibitory plasticity...... effectively distributed multiple patterns among available interneurons, thus allowing the simultaneous detection of multiple overlapping patterns. The addition of plasticity in intrinsic excitability made the system more robust allowing self-adjustment and rescaling in response to a broad range of input...

  6. Evolution‐development congruence in pattern formation dynamics: Bifurcations in gene expression and regulation of networks structures

    Kohsokabe, Takahiro

    2016-01-01

    ABSTRACT Search for possible relationships between phylogeny and ontogeny is important in evolutionary‐developmental biology. Here we uncover such relationships by numerical evolution and unveil their origin in terms of dynamical systems theory. By representing developmental dynamics of spatially located cells with gene expression dynamics with cell‐to‐cell interaction under external morphogen gradient, gene regulation networks are evolved under mutation and selection with the fitness to approach a prescribed spatial pattern of expressed genes. For most numerical evolution experiments, evolution of pattern over generations and development of pattern by an evolved network exhibit remarkable congruence. Both in the evolution and development pattern changes consist of several epochs where stripes are formed in a short time, while for other temporal regimes, pattern hardly changes. In evolution, these quasi‐stationary regimes are generations needed to hit relevant mutations, while in development, they are due to some gene expression that varies slowly and controls the pattern change. The morphogenesis is regulated by combinations of feedback or feedforward regulations, where the upstream feedforward network reads the external morphogen gradient, and generates a pattern used as a boundary condition for the later patterns. The ordering from up to downstream is common in evolution and development, while the successive epochal changes in development and evolution are represented as common bifurcations in dynamical‐systems theory, which lead to the evolution‐development congruence. Mechanism of exceptional violation of the congruence is also unveiled. Our results provide a new look on developmental stages, punctuated equilibrium, developmental bottlenecks, and evolutionary acquisition of novelty in morphogenesis. J. Exp. Zool. (Mol. Dev. Evol.) 326B:61–84, 2016. © 2015 The Authors. Journal of Experimental Zoology Part B: Molecular and Developmental Evolution

  7. Evolution-development congruence in pattern formation dynamics: Bifurcations in gene expression and regulation of networks structures.

    Kohsokabe, Takahiro; Kaneko, Kunihiko

    2016-01-01

    Search for possible relationships between phylogeny and ontogeny is important in evolutionary-developmental biology. Here we uncover such relationships by numerical evolution and unveil their origin in terms of dynamical systems theory. By representing developmental dynamics of spatially located cells with gene expression dynamics with cell-to-cell interaction under external morphogen gradient, gene regulation networks are evolved under mutation and selection with the fitness to approach a prescribed spatial pattern of expressed genes. For most numerical evolution experiments, evolution of pattern over generations and development of pattern by an evolved network exhibit remarkable congruence. Both in the evolution and development pattern changes consist of several epochs where stripes are formed in a short time, while for other temporal regimes, pattern hardly changes. In evolution, these quasi-stationary regimes are generations needed to hit relevant mutations, while in development, they are due to some gene expression that varies slowly and controls the pattern change. The morphogenesis is regulated by combinations of feedback or feedforward regulations, where the upstream feedforward network reads the external morphogen gradient, and generates a pattern used as a boundary condition for the later patterns. The ordering from up to downstream is common in evolution and development, while the successive epochal changes in development and evolution are represented as common bifurcations in dynamical-systems theory, which lead to the evolution-development congruence. Mechanism of exceptional violation of the congruence is also unveiled. Our results provide a new look on developmental stages, punctuated equilibrium, developmental bottlenecks, and evolutionary acquisition of novelty in morphogenesis. © 2015 The Authors. Journal of Experimental Zoology Part B: Molecular and Developmental Evolution Published by Wiley Periodicals, Inc.

  8. A Combination of Central Pattern Generator-based and Reflex-based Neural Networks for Dynamic, Adaptive, Robust Bipedal Locomotion

    Di Canio, Giuliano; Larsen, Jørgen Christian; Wörgötter, Florentin

    2016-01-01

    Robotic systems inspired from humans have always been lightening up the curiosity of engineers and scientists. Of many challenges, human locomotion is a very difficult one where a number of different systems needs to interact in order to generate a correct and balanced pattern. To simulate...... the interaction of these systems, implementations with reflexbased or central pattern generator (CPG)-based controllers have been tested on bipedal robot systems. In this paper we will combine the two controller types, into a controller that works with both reflex and CPG signals. We use a reflex-based neural...... network to generate basic walking patterns of a dynamic bipedal walking robot (DACBOT) and then a CPG-based neural network to ensure robust walking behavior...

  9. Detecting the Community Structure and Activity Patterns of Temporal Networks: A Non-Negative Tensor Factorization Approach

    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. PMID:24497935

  10. Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer's disease

    Daianu, Madelaine; Jahanshad, Neda; Nir, Talia M.; Leonardo, Cassandra D.; Jack, Clifford R.; Weiner, Michael W.; Bernstein, Matthew A.; Thompson, Paul M.

    2015-01-01

    Measures of network topology and connectivity aid the understanding of network breakdown as the brain degenerates in Alzheimer's disease (AD). We analyzed 3-Tesla diffusion-weighted images from 202 patients scanned by the Alzheimer's Disease Neuroimaging Initiative – 50 healthy controls, 72 with early- and 38 with late-stage mild cognitive impairment (eMCI/lMCI) and 42 with AD. Using whole-brain tractography, we reconstructed structural connectivity networks representing connections between pairs of cortical regions. We examined, for the first time in this context, the network's Laplacian matrix and its Fiedler value, describing the network's algebraic connectivity, and the Fiedler vector, used to partition a graph. We assessed algebraic connectivity and four additional supporting metrics, revealing a decrease in network robustness and increasing disarray among nodes as dementia progressed. Network components became more disconnected and segregated, and their modularity increased. These measures are sensitive to diagnostic group differences, and may help understand the complex changes in AD. PMID:26640830

  11. Perturbative QCD and jets

    Mueller, A.H.

    1986-03-01

    A brief review of some of the recent progress in perturbative QCD is given (heavy quark production, small-x physics, minijets and related topics, classical simulations in high energy reactions, coherence and the string effect)

  12. Social network analysis of mating patterns in American black bears (Ursus americanus).

    Moore, Jennifer A; Xu, Ran; Frank, Kenneth; Draheim, Hope; Scribner, Kim T

    2015-08-01

    Nonrandom mating can structure populations and has important implications for population-level processes. Investigating how and why mating deviates from random is important for understanding evolutionary processes as well as informing conservation and management. Prior to the implementation of parentage analyses, understanding mating patterns in solitary, elusive species like bears was virtually impossible. Here, we capitalize on a long-term genetic data set collected from black bears (Ursus americanus) (N = 2422) in the Northern Lower Peninsula (NLP) of Michigan, USA. We identified mated pairs using parentage analysis and applied logistic regression (selection) models that controlled for features of the social network, to quantify the effects of individual characteristics, and spatial and population demographic factors on mating dynamics. Logistic regression models revealed that black bear mating was associated with spatial proximity of mates, male age, the time a pair had coexisted, local population density and relatedness. Mated pairs were more likely to contain older males. On average, bears tended to mate with nearby individuals to whom they were related, which does not support the existence of kin recognition in black bears. Pairwise relatedness was especially high for mated pairs containing young males. Restricted dispersal and high male turnover from intensive harvest mortality of NLP black bears are probably the underlying factors associated with younger male bears mating more often with female relatives. Our findings illustrate how harvest has the potential to disrupt the social structure of game species, which warrants further attention for conservation and management. © 2015 John Wiley & Sons Ltd.

  13. Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network.

    Zhai, Xiaolong; Jelfs, Beth; Chan, Rosa H M; Tin, Chung

    2017-01-01

    Hand movement classification based on surface electromyography (sEMG) pattern recognition is a promising approach for upper limb neuroprosthetic control. However, maintaining day-to-day performance is challenged by the non-stationary nature of sEMG in real-life operation. In this study, we propose a self-recalibrating classifier that can be automatically updated to maintain a stable performance over time without the need for user retraining. Our classifier is based on convolutional neural network (CNN) using short latency dimension-reduced sEMG spectrograms as inputs. The pretrained classifier is recalibrated routinely using a corrected version of the prediction results from recent testing sessions. Our proposed system was evaluated with the NinaPro database comprising of hand movement data of 40 intact and 11 amputee subjects. Our system was able to achieve ~10.18% (intact, 50 movement types) and ~2.99% (amputee, 10 movement types) increase in classification accuracy averaged over five testing sessions with respect to the unrecalibrated classifier. When compared with a support vector machine (SVM) classifier, our CNN-based system consistently showed higher absolute performance and larger improvement as well as more efficient training. These results suggest that the proposed system can be a useful tool to facilitate long-term adoption of prosthetics for amputees in real-life applications.

  14. Textural Classification of Mammographic Parenchymal Patterns with the SONNET Selforganizing Neural Network

    Daniel Howard

    2008-01-01

    Full Text Available In nationwide mammography screening, thousands of mammography examinations must be processed. Each consists of two standard views of each breast, and each mammogram must be visually examined by an experienced radiologist to assess it for any anomalies. The ability to detect an anomaly in mammographic texture is important to successful outcomes in mammography screening and, in this study, a large number of mammograms were digitized with a highly accurate scanner; and textural features were derived from the mammograms as input data to a SONNET selforganizing neural network. The paper discusses how SONNET was used to produce a taxonomic organization of the mammography archive in an unsupervised manner. This process is subject to certain choices of SONNET parameters, in these numerical experiments using the craniocaudal view, and typically produced O(10, for example, 39 mammogram classes, by analysis of features from O(103 mammogram images. The mammogram taxonomy captured typical subtleties to discriminate mammograms, and it is submitted that this may be exploited to aid the detection of mammographic anomalies, for example, by acting as a preprocessing stage to simplify the task for a computational detection scheme, or by ordering mammography examinations by mammogram taxonomic class prior to screening in order to encourage more successful visual examination during screening. The resulting taxonomy may help train screening radiologists and conceivably help to settle legal cases concerning a mammography screening examination because the taxonomy can reveal the frequency of mammographic patterns in a population.

  15. Comparison of Pattern Recognition, Artificial Neural Network and Pedotransfer Functions for Estimation of Soil Water Parameters

    Amir LAKZIAN

    2010-09-01

    Full Text Available This paper presents the comparison of three different approaches to estimate soil water content at defined values of soil water potential based on selected parameters of soil solid phase. Forty different sampling locations in northeast of Iran were selected and undisturbed samples were taken to measure the water content at field capacity (FC, -33 kPa, and permanent wilting point (PWP, -1500 kPa. At each location solid particle of each sample including the percentage of sand, silt and clay were measured. Organic carbon percentage and soil texture were also determined for each soil sample at each location. Three different techniques including pattern recognition approach (k nearest neighbour, k-NN, Artificial Neural Network (ANN and pedotransfer functions (PTF were used to predict the soil water at each sampling location. Mean square deviation (MSD and its components, index of agreement (d, root mean square difference (RMSD and normalized RMSD (RMSDr were used to evaluate the performance of all the three approaches. Our results showed that k-NN and PTF performed better than ANN in prediction of water content at both FC and PWP matric potential. Various statistics criteria for simulation performance also indicated that between kNN and PTF, the former, predicted water content at PWP more accurate than PTF, however both approach showed a similar accuracy to predict water content at FC.

  16. Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

    Liu, Chao; Akintayo, Adedotun; Jiang, Zhanhong; Henze, Gregor P.; Sarkar, Soumik

    2018-02-01

    Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shown to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.

  17. Generalized chiral perturbation theory

    Knecht, M.; Stern, J.

    1994-01-01

    The Generalized Chiral Perturbation Theory enlarges the framework of the standard χPT (Chiral Perturbation Theory), relaxing certain assumptions which do not necessarily follow from QCD or from experiment, and which are crucial for the usual formulation of the low energy expansion. In this way, experimental tests of the foundations of the standard χPT become possible. Emphasis is put on physical aspects rather than on formal developments of GχPT. (author). 31 refs

  18. Spatiotemporal Patterns, Monitoring Network Design, and Environmental Justice of Air Pollution in the Phoenix Metropolitan Region: A Landscape Approach

    Pope, Ronald L.

    Air pollution is a serious problem in most urban areas around the world, which has a number of negative ecological and human health impacts. As a result, it's vitally important to detect and characterize air pollutants to protect the health of the urban environment and our citizens. An important early step in this process is ensuring that the air pollution monitoring network is properly designed to capture the patterns of pollution and that all social demographics in the urban population are represented. An important aspect in characterizing air pollution patterns is scale in space and time which, along with pattern and process relationships, is a key subject in the field of landscape ecology. Thus, using multiple landscape ecological methods, this dissertation research begins by characterizing and quantifying the multi-scalar patterns of ozone (O3) and particulate matter (PM10) in the Phoenix, Arizona, metropolitan region. Results showed that pollution patterns are scale-dependent, O3 is a regionally-scaled pollutant at longer temporal scales, and PM10 is a locally-scaled pollutant with patterns sensitive to season. Next, this dissertation examines the monitoring network within Maricopa County. Using a novel multiscale indicator-based approach, the adequacy of the network was quantified by integrating inputs from various academic and government stakeholders. Furthermore, deficiencies were spatially defined and recommendations were made on how to strengthen the design of the network. A sustainability ranking system also provided new insight into the strengths and weaknesses of the network. Lastly, the study addresses the question of whether distinct social groups were experiencing inequitable exposure to pollutants - a key issue of distributive environmental injustice. A novel interdisciplinary method using multi-scalar ambient pollution data and hierarchical multiple regression models revealed environmental inequities between air pollutants and race, ethnicity

  19. Controllability analysis of transcriptional regulatory networks reveals circular control patterns among transcription factors

    Österlund, Tobias; Bordel, Sergio; Nielsen, Jens

    2015-01-01

    % for the human network. The high controllability (low number of drivers needed to control the system) in yeast, mouse and human is due to the presence of internal loops in their regulatory networks where the TFs regulate each other in a circular fashion. We refer to these internal loops as circular control...... motifs (CCM). The E. coli transcriptional regulatory network, which does not have any CCMs, shows a hierarchical structure of the transcriptional regulatory network in contrast to the eukaryal networks. The presence of CCMs also has influence on the stability of these networks, as the presence of cycles...

  20. Motif distributions in phase-space networks for characterizing experimental two-phase flow patterns with chaotic features.

    Gao, Zhong-Ke; Jin, Ning-De; Wang, Wen-Xu; Lai, Ying-Cheng

    2010-07-01

    The dynamics of two-phase flows have been a challenging problem in nonlinear dynamics and fluid mechanics. We propose a method to characterize and distinguish patterns from inclined water-oil flow experiments based on the concept of network motifs that have found great usage in network science and systems biology. In particular, we construct from measured time series phase-space complex networks and then calculate the distribution of a set of distinct network motifs. To gain insight, we first test the approach using time series from classical chaotic systems and find a universal feature: motif distributions from different chaotic systems are generally highly heterogeneous. Our main finding is that the distributions from experimental two-phase flows tend to be heterogeneous as well, suggesting the underlying chaotic nature of the flow patterns. Calculation of the maximal Lyapunov exponent provides further support for this. Motif distributions can thus be a feasible tool to understand the dynamics of realistic two-phase flow patterns.

  1. Structural and dynamical patterns on online social networks: the Spanish May 15th movement as a case study.

    Javier Borge-Holthoefer

    Full Text Available The number of people using online social networks in their everyday life is continuously growing at a pace never saw before. This new kind of communication has an enormous impact on opinions, cultural trends, information spreading and even in the commercial success of new products. More importantly, social online networks have revealed as a fundamental organizing mechanism in recent country-wide social movements. In this paper, we provide a quantitative analysis of the structural and dynamical patterns emerging from the activity of an online social network around the ongoing May 15th (15M movement in Spain. Our network is made up by users that exchanged tweets in a time period of one month, which includes the birth and stabilization of the 15M movement. We characterize in depth the growth of such dynamical network and find that it is scale-free with communities at the mesoscale. We also find that its dynamics exhibits typical features of critical systems such as robustness and power-law distributions for several quantities. Remarkably, we report that the patterns characterizing the spreading dynamics are asymmetric, giving rise to a clear distinction between information sources and sinks. Our study represents a first step towards the use of data from online social media to comprehend modern societal dynamics.

  2. Adaptive Forming of the Beam Pattern of Microstrip Antenna with the Use of an Artificial Neural Network

    Janusz Dudczyk

    2012-01-01

    Full Text Available Microstrip antenna has been recently one of the most innovative fields of antenna techniques. The main advantage of such an antenna is the simplicity of its production, little weight, a narrow profile, and easiness of integration of the radiating elements with the net of generators power systems. As a result of using arrays consisting of microstrip antennas; it is possible to decrease the size and weight and also to reduce the costs of components production as well as whole application systems. This paper presents possibilities of using artificial neural networks (ANNs in the process of forming a beam from radiating complex microstrip antenna. Algorithms which base on artificial neural networks use high parallelism of actions which results in considerable acceleration of the process of forming the antenna pattern. The appropriate selection of learning constants makes it possible to get theoretically a solution which will be close to the real time. This paper presents the training neural network algorithm with the selection of optimal network structure. The analysis above was made in case of following the emission source, setting to zero the pattern of direction of expecting interference, and following emission source compared with two constant interferences. Computer simulation was made in MATLAB environment on the basis of Flex Tool, a programme which creates artificial neural networks.

  3. Structural and dynamical patterns on online social networks: the Spanish May 15th movement as a case study.

    Borge-Holthoefer, Javier; Rivero, Alejandro; García, Iñigo; Cauhé, Elisa; Ferrer, Alfredo; Ferrer, Darío; Francos, David; Iñiguez, David; Pérez, María Pilar; Ruiz, Gonzalo; Sanz, Francisco; Serrano, Fermín; Viñas, Cristina; Tarancón, Alfonso; Moreno, Yamir

    2011-01-01

    The number of people using online social networks in their everyday life is continuously growing at a pace never saw before. This new kind of communication has an enormous impact on opinions, cultural trends, information spreading and even in the commercial success of new products. More importantly, social online networks have revealed as a fundamental organizing mechanism in recent country-wide social movements. In this paper, we provide a quantitative analysis of the structural and dynamical patterns emerging from the activity of an online social network around the ongoing May 15th (15M) movement in Spain. Our network is made up by users that exchanged tweets in a time period of one month, which includes the birth and stabilization of the 15M movement. We characterize in depth the growth of such dynamical network and find that it is scale-free with communities at the mesoscale. We also find that its dynamics exhibits typical features of critical systems such as robustness and power-law distributions for several quantities. Remarkably, we report that the patterns characterizing the spreading dynamics are asymmetric, giving rise to a clear distinction between information sources and sinks. Our study represents a first step towards the use of data from online social media to comprehend modern societal dynamics.

  4. Elucidation of time-dependent systems biology cell response patterns with time course network enrichment

    Wiwie, Christian; Rauch, Alexander; Haakonsson, Anders

    2018-01-01

    , no methods exist to integrate time series data with networks, thus preventing the identification of time-dependent systems biology responses. We close this gap with Time Course Network Enrichment (TiCoNE). It combines a new kind of human-augmented clustering with a novel approach to network enrichment...

  5. Optimization of patterns of control bars using neural networks; Optimizacion de patrones de barras de control usando redes neuronales

    Mejia S, D.M. [IPN, ESFM, Depto. de Ingenieria Nuclear, 07738 Mexico D.F. (Mexico); Ortiz S, J.J. [ININ, 52045 Ocoyoacac, Estado de Mexico (Mexico)]. e-mail: dulcema6715@hotmail.com

    2005-07-01

    In this work the RENOPBC system that is based on a recurrent multi state neural network, for the optimization of patterns of control bars in a cycle of balance of a boiling water reactor (BWR for their initials in English) is presented. The design of patterns of bars is based on the execution of operation thermal limits, to maintain criticizes the reactor and that the axial profile of power is adjusted to one predetermined along several steps of burnt. The patterns of control bars proposed by the system are comparable to those proposed by human experts with many hour-man of experience. These results are compared with those proposed by other techniques as genetic algorithms, colonies of ants and tabu search for the same operation cycle. As consequence it is appreciated that the proposed patterns of control bars, have bigger operation easiness that those proposed by the other techniques. (Author)

  6. Drug-like and non drug-like pattern classification based on simple topology descriptor using hybrid neural network.

    Wan-Mamat, Wan Mohd Fahmi; Isa, Nor Ashidi Mat; Wahab, Habibah A; Wan-Mamat, Wan Mohd Fairuz

    2009-01-01

    An intelligent prediction system has been developed to discriminate drug-like and non drug-like molecules pattern. The system is constructed by using the application of advanced version of standard multilayer perceptron (MLP) neural network called Hybrid Multilayer Perceptron (HMLP) neural network and trained using Modified Recursive Prediction Error (MRPE) training algorithm. In this work, a well understood and easy excess Rule of Five + Veber filter properties are selected as the topological descriptor. The main idea behind the selection of this simple descriptor is to assure that the system could be used widely, beneficial and more advantageous regardless at all user level within a drug discovery organization.

  7. Monitoring soil moisture patterns in alpine meadows using ground sensor networks and remote sensing techniques

    Bertoldi, Giacomo; Brenner, Johannes; Notarnicola, Claudia; Greifeneder, Felix; Nicolini, Irene; Della Chiesa, Stefano; Niedrist, Georg; Tappeiner, Ulrike

    2015-04-01

    Soil moisture content (SMC) is a key factor for numerous processes, including runoff generation, groundwater recharge, evapotranspiration, soil respiration, and biological productivity. Understanding the controls on the spatial and temporal variability of SMC in mountain catchments is an essential step towards improving quantitative predictions of catchment hydrological processes and related ecosystem services. The interacting influences of precipitation, soil properties, vegetation, and topography on SMC and the influence of SMC patterns on runoff generation processes have been extensively investigated (Vereecken et al., 2014). However, in mountain areas, obtaining reliable SMC estimations is still challenging, because of the high variability in topography, soil and vegetation properties. In the last few years, there has been an increasing interest in the estimation of surface SMC at local scales. On the one hand, low cost wireless sensor networks provide high-resolution SMC time series. On the other hand, active remote sensing microwave techniques, such as Synthetic Aperture Radars (SARs), show promising results (Bertoldi et al. 2014). As these data provide continuous coverage of large spatial extents with high spatial resolution (10-20 m), they are particularly in demand for mountain areas. However, there are still limitations related to the fact that the SAR signal can penetrate only a few centimeters in the soil. Moreover, the signal is strongly influenced by vegetation, surface roughness and topography. In this contribution, we analyse the spatial and temporal dynamics of surface and root-zone SMC (2.5 - 5 - 25 cm depth) of alpine meadows and pastures in the Long Term Ecological Research (LTER) Area Mazia Valley (South Tyrol - Italy) with different techniques: (I) a network of 18 stations; (II) field campaigns with mobile ground sensors; (III) 20-m resolution RADARSAT2 SAR images; (IV) numerical simulations using the GEOtop hydrological model (Rigon et al

  8. Modeling urbanization patterns at a global scale with generative adversarial networks

    Albert, A. T.; Strano, E.; Gonzalez, M.

    2017-12-01

    Current demographic projections show that, in the next 30 years, global population growth will mostly take place in developing countries. Coupled with a decrease in density, such population growth could potentially double the land occupied by settlements by 2050. The lack of reliable and globally consistent socio-demographic data, coupled with the limited predictive performance underlying traditional urban spatial explicit models, call for developing better predictive methods, calibrated using a globally-consistent dataset. Thus, richer models of the spatial interplay between the urban built-up land, population distribution and energy use are central to the discussion around the expansion and development of cities, and their impact on the environment in the context of a changing climate. In this talk we discuss methods for, and present an analysis of, urban form, defined as the spatial distribution of macroeconomic quantities that characterize a city, using modern machine learning methods and best-available remote-sensing data for the world's largest 25,000 cities. We first show that these cities may be described by a small set of patterns in radial building density, nighttime luminosity, and population density, which highlight, to first order, differences in development and land use across the world. We observe significant, spatially-dependent variance around these typical patterns, which would be difficult to model using traditional statistical methods. We take a first step in addressing this challenge by developing CityGAN, a conditional generative adversarial network model for simulating realistic urban forms. To guide learning and measure the quality of the simulated synthetic cities, we develop a specialized loss function for GAN optimization that incorporates standard spatial statistics used by urban analysis experts. Our framework is a stark departure from both the standard physics-based approaches in the literature (that view urban forms as fractals with a

  9. Analysis of Road Network Pattern Considering Population Distribution and Central Business District.

    Fangxia Zhao

    Full Text Available This paper proposes a road network growing model with the consideration of population distribution and central business district (CBD attraction. In the model, the relative neighborhood graph (RNG is introduced as the connection mechanism to capture the characteristics of road network topology. The simulation experiment is set up to illustrate the effects of population distribution and CBD attraction on the characteristics of road network. Moreover, several topological attributes of road network is evaluated by using coverage, circuitness, treeness and total length in the experiment. Finally, the suggested model is verified in the simulation of China and Beijing Highway networks.

  10. Perturbative anyon gas

    Dasnieres de Veigy, A.; Ouvry, S.; Paris-6 Univ., 75

    1992-06-01

    The problem of the statistical mechanics of an anyon gas is addressed. A perturbative analysis in the anyonic coupling constant α is reviewed, and the thermodynamical potential is computed at first and second order. An adequate second quantized formalism (field theory at finite temperature) is proposed. At first order in perturbation theory, the results are strikingly simple: only the second virial coefficient close to bosonic statistics is corrected. At second order, however, the complexity of the anyon model appears. One can compute exactly the perturbative correction to each cluster coefficient. However, and contrary to first order, a closed expression for the equation of state seems out of reach. As an illustration, the perturbative expressions of a 3 , a 4 , a 5 and a 6 are given at second order. Finally, using the same formalism, the equation of state of an anyon gas in a constant magnetic field is analyzed at first order in perturbation theory. (K.A.) 16 refs.; 3 figs.; 7 tabs

  11. Study on pattern recognition of Raman spectrum based on fuzzy neural network

    Zheng, Xiangxiang; Lv, Xiaoyi; Mo, Jiaqing

    2017-10-01

    Hydatid disease is a serious parasitic disease in many regions worldwide, especially in Xinjiang, China. Raman spectrum of the serum of patients with echinococcosis was selected as the research object in this paper. The Raman spectrum of blood samples from healthy people and patients with echinococcosis are measured, of which the spectrum characteristics are analyzed. The fuzzy neural network not only has the ability of fuzzy logic to deal with uncertain information, but also has the ability to store knowledge of neural network, so it is combined with the Raman spectrum on the disease diagnosis problem based on Raman spectrum. Firstly, principal component analysis (PCA) is used to extract the principal components of the Raman spectrum, reducing the network input and accelerating the prediction speed and accuracy of Network based on remaining the original data. Then, the information of the extracted principal component is used as the input of the neural network, the hidden layer of the network is the generation of rules and the inference process, and the output layer of the network is fuzzy classification output. Finally, a part of samples are randomly selected for the use of training network, then the trained network is used for predicting the rest of the samples, and the predicted results are compared with general BP neural network to illustrate the feasibility and advantages of fuzzy neural network. Success in this endeavor would be helpful for the research work of spectroscopic diagnosis of disease and it can be applied in practice in many other spectral analysis technique fields.

  12. Spatial patterns of atrophy, hypometabolism, and amyloid deposition in Alzheimer's disease correspond to dissociable functional brain networks.

    Grothe, Michel J; Teipel, Stefan J

    2016-01-01

    Recent neuroimaging studies of Alzheimer's disease (AD) have emphasized topographical similarities between AD-related brain changes and a prominent cortical association network called the default-mode network (DMN). However, the specificity of distinct imaging abnormalities for the DMN compared to other intrinsic connectivity networks (ICNs) of the limbic and heteromodal association cortex has not yet been examined systematically. We assessed regional amyloid load using AV45-PET, neuronal metabolism using FDG-PET, and gray matter volume using structural MRI in 473 participants from the Alzheimer's Disease Neuroimaging Initiative, including preclinical, predementia, and clinically manifest AD stages. Complementary region-of-interest and voxel-based analyses were used to assess disease stage- and modality-specific changes within seven principle ICNs of the human brain as defined by a standardized functional connectivity atlas. Amyloid deposition in AD dementia showed a preference for the DMN, but high effect sizes were also observed for other neocortical ICNs, most notably the frontoparietal-control network. Atrophic changes were most specific for an anterior limbic network, followed by the DMN, whereas other neocortical networks were relatively spared. Hypometabolism appeared to be a mixture of both amyloid- and atrophy-related profiles. Similar patterns of modality-dependent network specificity were also observed in the predementia and, for amyloid deposition, in the preclinical stage. These quantitative data confirm a high vulnerability of the DMN for multimodal imaging abnormalities in AD. However, rather than being selective for the DMN, imaging abnormalities more generally affect higher order cognitive networks and, importantly, the vulnerability profiles of these networks markedly differ for distinct aspects of AD pathology. © 2015 Wiley Periodicals, Inc.

  13. Analysis of structural patterns in the brain with the complex network approach

    Maksimenko, Vladimir A.; Makarov, Vladimir V.; Kharchenko, Alexander A.; Pavlov, Alexey N.; Khramova, Marina V.; Koronovskii, Alexey A.; Hramov, Alexander E.

    2015-03-01

    In this paper we study mechanisms of the phase synchronization in a model network of Van der Pol oscillators and in the neural network of the brain by consideration of macroscopic parameters of these networks. As the macroscopic characteristics of the model network we consider a summary signal produced by oscillators. Similar to the model simulations, we study EEG signals reflecting the macroscopic dynamics of neural network. We show that the appearance of the phase synchronization leads to an increased peak in the wavelet spectrum related to the dynamics of synchronized oscillators. The observed correlation between the phase relations of individual elements and the macroscopic characteristics of the whole network provides a way to detect phase synchronization in the neural networks in the cases of normal and pathological activity.

  14. Spatial patterns of some trace elements in four Swedish stream networks

    J. Temnerud

    2013-03-01

    Full Text Available Four river basins in southern Sweden, with catchment sizes from 0.3 to 127 km2 (median 1.9, were sampled in October~2007. The 243 samples were analysed for 26 trace elements (Ag, As, Au, Ba, Be, Bi, Cd, Co, Cr, Cu, Ga, Ge, In, La, Li, Mo, Ni, Pb, Sb, Se, Sn, Tl, Ti, U, V and Zn to identify spatial patterns within drainage networks. The range and median of each element were defined for different stream orders, and relationships to catchment characteristics, including deposition history, were explored. The sampling design made it possible to compare the differences along 40 stream reaches, above and below 53 stream junctions with 107 tributaries and between the 77 inlets and outlets of 36 lakes. The largest concentration differences (at reaches, junctions and lakes were observed for lakes, with outlets usually having lower concentration compared to the inlets for As, Ba, Be, Bi, Cd, Co, Cr, Ga, Ge, Ni, Pb, Sn, Ti, Tl, U, V and Zn. Significantly lower concentrations were observed for Cd and Co when comparing headwaters with downstream sites in each catchment. Common factor analysis (FA revealed that As, Bi, Cr, Ga, Ge, Tl and V co-vary positively with Al, Fe and total organic carbon (TOC and negatively with La, Li and pH. The strong removal of a large number of trace elements when passing through lakes is evident though in the FA, where lake surface coverage plots opposite to many of those elements. Forest volume does not respond in a similar systematic fashion and, surprisingly, the amount of wetland does not relate strongly to either Fe or TOC at any of the rivers. A better understanding of the quantitative removal of organic carbon and iron will aid in understanding trace element fluxes from landscapes rich in organic matter and iron.

  15. Caffeine impact on working memory-related network activation patterns in early stages of cognitive decline

    Haller, Sven [Affidea Centre de Diagnostic Radiologique de Carouge CDRC, Geneva (Switzerland); Uppsala University, Department of Surgical Sciences, Radiology, Uppsala (Sweden); Montandon, Marie-Louise; Rodriguez, Cristelle; Moser, Dominik; Toma, Simona; Hofmeister, Jeremy; Giannakopoulos, Panteleimon [University Hospitals of Geneva, Department of Psychiatry, Faculty of Medicine, Medical Direction, Geneva (Switzerland)

    2017-04-15

    Recent evidence indicates that caffeine may have a beneficial effect on cognitive decline and dementia. The current investigation assessed the effect of acute caffeine administration on working memory during the earliest stage of cognitive decline in elderly participants. The study includes consecutive 45 elderly controls and 18 individuals with mild cognitive impairment (MCI, 71.6 ± 4.7 years, 7 females). During neuropsychological follow-up at 18 months, 24 controls remained stable (sCON, 70.0 ± 4.3 years, 11 women), while the remaining 21 showed subtle cognitive deterioration (dCON, 73.4 ± 5.9 years, 14 women). All participants underwent an established 2-back working task in a crossover design of 200 mg caffeine versus placebo. Data analysis included task-related general linear model and functional connectivity tensorial independent component analysis. Working memory behavioral performances did not differ between sCON and dCON, while MCI was slower and less accurate than both control groups (p < 0.05). The dCON group had a less pronounced effect of acute caffeine administration essentially restricted to the right hemisphere (p < 0.05 corrected) and reduced default mode network (DMN) deactivation compared to sCON (p < 0.01 corrected). dCON cases are characterized by decreased sensitivity to caffeine effects on brain activation and DMN deactivation. These complex fMRI patterns possibly reflect the instable status of these cases with intact behavioral performances despite already existing functional alterations in neocortical circuits. (orig.)

  16. Caffeine impact on working memory-related network activation patterns in early stages of cognitive decline

    Haller, Sven; Montandon, Marie-Louise; Rodriguez, Cristelle; Moser, Dominik; Toma, Simona; Hofmeister, Jeremy; Giannakopoulos, Panteleimon

    2017-01-01

    Recent evidence indicates that caffeine may have a beneficial effect on cognitive decline and dementia. The current investigation assessed the effect of acute caffeine administration on working memory during the earliest stage of cognitive decline in elderly participants. The study includes consecutive 45 elderly controls and 18 individuals with mild cognitive impairment (MCI, 71.6 ± 4.7 years, 7 females). During neuropsychological follow-up at 18 months, 24 controls remained stable (sCON, 70.0 ± 4.3 years, 11 women), while the remaining 21 showed subtle cognitive deterioration (dCON, 73.4 ± 5.9 years, 14 women). All participants underwent an established 2-back working task in a crossover design of 200 mg caffeine versus placebo. Data analysis included task-related general linear model and functional connectivity tensorial independent component analysis. Working memory behavioral performances did not differ between sCON and dCON, while MCI was slower and less accurate than both control groups (p < 0.05). The dCON group had a less pronounced effect of acute caffeine administration essentially restricted to the right hemisphere (p < 0.05 corrected) and reduced default mode network (DMN) deactivation compared to sCON (p < 0.01 corrected). dCON cases are characterized by decreased sensitivity to caffeine effects on brain activation and DMN deactivation. These complex fMRI patterns possibly reflect the instable status of these cases with intact behavioral performances despite already existing functional alterations in neocortical circuits. (orig.)

  17. Generalizing genetical genomics : getting added value from environmental perturbation

    Li, Yang; Breitling, Rainer; Jansen, Ritsert C.

    2008-01-01

    Genetical genomics is a useful approach for studying the effect of genetic perturbations on biological systems at the molecular level. However, molecular networks depend on the environmental conditions and, thus, a comprehensive understanding of biological systems requires studying them across

  18. Graph theoretical analysis reveals the reorganization of the brain network pattern in primary open angle glaucoma patients

    Wang, Jieqiong; Li, Ting; Xian, Junfang; Wang, Ningli; He, Huiguang

    2016-01-01

    Most previous glaucoma studies with resting-state fMRI have focused on the neuronal activity in the individual structure of the brain, yet ignored the functional communication of anatomically separated structures. The purpose of this study is to investigate the efficiency of the functional communication change or not in glaucoma patients. We applied the resting-state fMRI data to construct the connectivity network of 25 normal controls and 25 age-gender-matched primary open angle glaucoma patients. Graph theoretical analysis was performed to assess brain network pattern differences between the two groups. No significant differences of the global network measures were found between the two groups. However, the local measures were radically reorganized in glaucoma patients. Comparing with the hub regions in normal controls' network, we found that six hub regions disappeared and nine hub regions appeared in the network of patients. In addition, the betweenness centralities of two altered hub regions, right fusiform gyrus and right lingual gyrus, were significantly correlated with the visual field mean deviation. Although the efficiency of functional communication is preserved in the brain network of the glaucoma at the global level, the efficiency of functional communication is altered in some specialized regions of the glaucoma. (orig.)

  19. Graph theoretical analysis reveals the reorganization of the brain network pattern in primary open angle glaucoma patients

    Wang, Jieqiong [Chinese Academy of Sciences, State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Beijing (China); Li, Ting; Xian, Junfang [Capital Medical University, Department of Radiology, Beijing Tongren Hospital, Beijing (China); Wang, Ningli [Capital Medical University, Department of Ophthalmology, Beijing Tongren Hospital, Beijing (China); He, Huiguang [Chinese Academy of Sciences, State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Beijing (China); Chinese Academy of Sciences, Research Center for Brain-Inspired Intelligence, Institute of Automation, Beijing (China)

    2016-11-15

    Most previous glaucoma studies with resting-state fMRI have focused on the neuronal activity in the individual structure of the brain, yet ignored the functional communication of anatomically separated structures. The purpose of this study is to investigate the efficiency of the functional communication change or not in glaucoma patients. We applied the resting-state fMRI data to construct the connectivity network of 25 normal controls and 25 age-gender-matched primary open angle glaucoma patients. Graph theoretical analysis was performed to assess brain network pattern differences between the two groups. No significant differences of the global network measures were found between the two groups. However, the local measures were radically reorganized in glaucoma patients. Comparing with the hub regions in normal controls' network, we found that six hub regions disappeared and nine hub regions appeared in the network of patients. In addition, the betweenness centralities of two altered hub regions, right fusiform gyrus and right lingual gyrus, were significantly correlated with the visual field mean deviation. Although the efficiency of functional communication is preserved in the brain network of the glaucoma at the global level, the efficiency of functional communication is altered in some specialized regions of the glaucoma. (orig.)

  20. Methods for Inferring Health-Related Social Networks among Coworkers from Online Communication Patterns

    Matthews, Luke J.; DeWan, Peter; Rula, Elizabeth Y.

    2013-01-01

    Studies of social networks, mapped using self-reported contacts, have demonstrated the strong influence of social connections on the propensity for individuals to adopt or maintain healthy behaviors and on their likelihood to adopt health risks such as obesity. Social network analysis may prove useful for businesses and organizations that wish to improve the health of their populations by identifying key network positions. Health traits have been shown to correlate across friendship ties, but evaluating network effects in large coworker populations presents the challenge of obtaining sufficiently comprehensive network data. The purpose of this study was to evaluate methods for using online communication data to generate comprehensive network maps that reproduce the health-associated properties of an offline social network. In this study, we examined three techniques for inferring social relationships from email traffic data in an employee population using thresholds based on: (1) the absolute number of emails exchanged, (2) logistic regression probability of an offline relationship, and (3) the highest ranked email exchange partners. As a model of the offline social network in the same population, a network map was created using social ties reported in a survey instrument. The email networks were evaluated based on the proportion of survey ties captured, comparisons of common network metrics, and autocorrelation of body mass index (BMI) across social ties. Results demonstrated that logistic regression predicted the greatest proportion of offline social ties, thresholding on number of emails exchanged produced the best match to offline network metrics, and ranked email partners demonstrated the strongest autocorrelation of BMI. Since each method had unique strengths, researchers should choose a method based on the aspects of offline behavior of interest. Ranked email partners may be particularly useful for purposes related to health traits in a social network. PMID

  1. Methods for inferring health-related social networks among coworkers from online communication patterns.

    Matthews, Luke J; DeWan, Peter; Rula, Elizabeth Y

    2013-01-01

    Studies of social networks, mapped using self-reported contacts, have demonstrated the strong influence of social connections on the propensity for individuals to adopt or maintain healthy behaviors and on their likelihood to adopt health risks such as obesity. Social network analysis may prove useful for businesses and organizations that wish to improve the health of their populations by identifying key network positions. Health traits have been shown to correlate across friendship ties, but evaluating network effects in large coworker populations presents the challenge of obtaining sufficiently comprehensive network data. The purpose of this study was to evaluate methods for using online communication data to generate comprehensive network maps that reproduce the health-associated properties of an offline social network. In this study, we examined three techniques for inferring social relationships from email traffic data in an employee population using thresholds based on: (1) the absolute number of emails exchanged, (2) logistic regression probability of an offline relationship, and (3) the highest ranked email exchange partners. As a model of the offline social network in the same population, a network map was created using social ties reported in a survey instrument. The email networks were evaluated based on the proportion of survey ties captured, comparisons of common network metrics, and autocorrelation of body mass index (BMI) across social ties. Results demonstrated that logistic regression predicted the greatest proportion of offline social ties, thresholding on number of emails exchanged produced the best match to offline network metrics, and ranked email partners demonstrated the strongest autocorrelation of BMI. Since each method had unique strengths, researchers should choose a method based on the aspects of offline behavior of interest. Ranked email partners may be particularly useful for purposes related to health traits in a social network.

  2. Methods for inferring health-related social networks among coworkers from online communication patterns.

    Luke J Matthews

    Full Text Available Studies of social networks, mapped using self-reported contacts, have demonstrated the strong influence of social connections on the propensity for individuals to adopt or maintain healthy behaviors and on their likelihood to adopt health risks such as obesity. Social network analysis may prove useful for businesses and organizations that wish to improve the health of their populations by identifying key network positions. Health traits have been shown to correlate across friendship ties, but evaluating network effects in large coworker populations presents the challenge of obtaining sufficiently comprehensive network data. The purpose of this study was to evaluate methods for using online communication data to generate comprehensive network maps that reproduce the health-associated properties of an offline social network. In this study, we examined three techniques for inferring social relationships from email traffic data in an employee population using thresholds based on: (1 the absolute number of emails exchanged, (2 logistic regression probability of an offline relationship, and (3 the highest ranked email exchange partners. As a model of the offline social network in the same population, a network map was created using social ties reported in a survey instrument. The email networks were evaluated based on the proportion of survey ties captured, comparisons of common network metrics, and autocorrelation of body mass index (BMI across social ties. Results demonstrated that logistic regression predicted the greatest proportion of offline social ties, thresholding on number of emails exchanged produced the best match to offline network metrics, and ranked email partners demonstrated the strongest autocorrelation of BMI. Since each method had unique strengths, researchers should choose a method based on the aspects of offline behavior of interest. Ranked email partners may be particularly useful for purposes related to health traits in a

  3. Chiral perturbation theory

    Ecker, G.

    1996-06-01

    After a general introduction to the structure of effective field theories, the main ingredients of chiral perturbation theory are reviewed. Applications include the light quark mass ratios and pion-pion scattering to two-loop accuracy. In the pion-nucleon system, the linear σ model is contrasted with chiral perturbation theory. The heavy-nucleon expansion is used to construct the effective pion-nucleon Lagrangian to third order in the low-energy expansion, with applications to nucleon Compton scattering. (author)

  4. Patterning and predicting aquatic insect richness in four West-African coastal rivers using artificial neural networks

    Edia E.O.; Gevrey M.; Ouattara A.; Brosse S.; Gourène G.; Lek S.

    2010-01-01

    Despite their importance in stream management, the aquatic insect assemblages are still little known in West Africa. This is particularly true in South-Eastern Ivory Coast, where aquatic insect assemblages were hardly studied. We therefore aimed at characterising aquatic insect assemblages on four coastal rivers in South-Eastern Ivory Coast. Patterning aquatic insect assemblages was achieved using a Self-Organizing Map (SOM), an unsupervised Artificial Neural Networks (ANN) method. This metho...

  5. Robustness of cluster synchronous patterns in small-world networks with inter-cluster co-competition balance

    Zhang, Jianbao; Ma, Zhongjun; Chen, Guanrong

    2014-01-01

    All edges in the classical Watts and Strogatz's small-world network model are unweighted and cooperative (positive). By introducing competitive (negative) inter-cluster edges and assigning edge weights to mimic more realistic networks, this paper develops a modified model which possesses co-competitive weighted couplings and cluster structures while maintaining the common small-world network properties of small average shortest path lengths and large clustering coefficients. Based on theoretical analysis, it is proved that the new model with inter-cluster co-competition balance has an important dynamical property of robust cluster synchronous pattern formation. More precisely, clusters will neither merge nor split regardless of adding or deleting nodes and edges, under the condition of inter-cluster co-competition balance. Numerical simulations demonstrate the robustness of the model against the increase of the coupling strength and several topological variations

  6. Robustness of cluster synchronous patterns in small-world networks with inter-cluster co-competition balance

    Zhang, Jianbao; Ma, Zhongjun; Chen, Guanrong

    2014-06-01

    All edges in the classical Watts and Strogatz's small-world network model are unweighted and cooperative (positive). By introducing competitive (negative) inter-cluster edges and assigning edge weights to mimic more realistic networks, this paper develops a modified model which possesses co-competitive weighted couplings and cluster structures while maintaining the common small-world network properties of small average shortest path lengths and large clustering coefficients. Based on theoretical analysis, it is proved that the new model with inter-cluster co-competition balance has an important dynamical property of robust cluster synchronous pattern formation. More precisely, clusters will neither merge nor split regardless of adding or deleting nodes and edges, under the condition of inter-cluster co-competition balance. Numerical simulations demonstrate the robustness of the model against the increase of the coupling strength and several topological variations.

  7. Robustness of cluster synchronous patterns in small-world networks with inter-cluster co-competition balance

    Zhang, Jianbao [School of Science, Hangzhou Dianzi University, Hangzhou 310018 (China); Ma, Zhongjun, E-mail: mzj1234402@163.com [School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004 (China); Chen, Guanrong [Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong (China)

    2014-06-15

    All edges in the classical Watts and Strogatz's small-world network model are unweighted and cooperative (positive). By introducing competitive (negative) inter-cluster edges and assigning edge weights to mimic more realistic networks, this paper develops a modified model which possesses co-competitive weighted couplings and cluster structures while maintaining the common small-world network properties of small average shortest path lengths and large clustering coefficients. Based on theoretical analysis, it is proved that the new model with inter-cluster co-competition balance has an important dynamical property of robust cluster synchronous pattern formation. More precisely, clusters will neither merge nor split regardless of adding or deleting nodes and edges, under the condition of inter-cluster co-competition balance. Numerical simulations demonstrate the robustness of the model against the increase of the coupling strength and several topological variations.

  8. On cooperative and efficient overlay network evolution based on a group selection pattern.

    Nakao, Akihiro; Wang, Yufeng

    2010-04-01

    In overlay networks, the interplay between network structure and dynamics remains largely unexplored. In this paper, we study dynamic coevolution between individual rational strategies (cooperative or defect) and the overlay network structure, that is, the interaction between peer's local rational behaviors and the emergence of the whole network structure. We propose an evolutionary game theory (EGT)-based overlay topology evolution scheme to drive a given overlay into the small-world structure (high global network efficiency and average clustering coefficient). Our contributions are the following threefold: From the viewpoint of peers' local interactions, we explicitly consider the peer's rational behavior and introduce a link-formation game to characterize the social dilemma of forming links in an overlay network. Furthermore, in the evolutionary link-formation phase, we adopt a simple economic process: Each peer keeps one link to a cooperative neighbor in its neighborhood, which can slightly speed up the convergence of cooperation and increase network efficiency; from the viewpoint of the whole network structure, our simulation results show that the EGT-based scheme can drive an arbitrary overlay network into a fully cooperative and efficient small-world structure. Moreover, we compare our scheme with a search-based economic model of network formation and illustrate that our scheme can achieve the experimental and analytical results in the latter model. In addition, we also graphically illustrate the final overlay network structure; finally, based on the group selection model and evolutionary set theory, we theoretically obtain the approximate threshold of cost and draw the conclusion that the small value of the average degree and the large number of the total peers in an overlay network facilitate the evolution of cooperation.

  9. Deriving frequency-dependent spatial patterns in MEG-derived resting state sensorimotor network: A novel multiband ICA technique.

    Nugent, Allison C; Luber, Bruce; Carver, Frederick W; Robinson, Stephen E; Coppola, Richard; Zarate, Carlos A

    2017-02-01

    Recently, independent components analysis (ICA) of resting state magnetoencephalography (MEG) recordings has revealed resting state networks (RSNs) that exhibit fluctuations of band-limited power envelopes. Most of the work in this area has concentrated on networks derived from the power envelope of beta bandpass-filtered data. Although research has demonstrated that most networks show maximal correlation in the beta band, little is known about how spatial patterns of correlations may differ across frequencies. This study analyzed MEG data from 18 healthy subjects to determine if the spatial patterns of RSNs differed between delta, theta, alpha, beta, gamma, and high gamma frequency bands. To validate our method, we focused on the sensorimotor network, which is well-characterized and robust in both MEG and functional magnetic resonance imaging (fMRI) resting state data. Synthetic aperture magnetometry (SAM) was used to project signals into anatomical source space separately in each band before a group temporal ICA was performed over all subjects and bands. This method preserved the inherent correlation structure of the data and reflected connectivity derived from single-band ICA, but also allowed identification of spatial spectral modes that are consistent across subjects. The implications of these results on our understanding of sensorimotor function are discussed, as are the potential applications of this technique. Hum Brain Mapp 38:779-791, 2017. © 2016 Wiley Periodicals, Inc. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

  10. Exploring patterns of alteration in Alzheimer’s disease brain networks: a combined structural and functional connectomics analysis

    Fulvia Palesi

    2016-09-01

    Full Text Available Alzheimer’s disease (AD is a neurodegenerative disorder characterized by a severe derangement of cognitive functions, primarily memory, in elderly subjects. As far as the functional impairment is concerned, growing evidence supports the disconnection syndrome hypothesis. Recent investigations using fMRI have revealed a generalized alteration of resting state networks in patients affected by AD and mild cognitive impairment (MCI. However, it was unclear whether the changes in functional connectivity were accompanied by corresponding structural network changes. In this work, we have developed a novel structural/functional connectomic approach: resting state fMRI was used to identify the functional cortical network nodes and diffusion MRI to reconstruct the fiber tracts to give a weight to internodal subcortical connections. Then, local and global efficiency were determined for different networks, exploring specific alterations of integration and segregation patterns in AD and MCI patients compared to healthy controls (HC. In the default mode network (DMN, that was the most affected, axonal loss and reduced axonal integrity appeared to compromise both local and global efficiency along posterior-anterior connections. In the basal ganglia network (BGN, disruption of white matter integrity implied that main alterations occurred in local microstructure. In the anterior insular network (AIN, neuronal loss probably subtended a compromised communication with the insular cortex. Cognitive performance, evaluated by neuropsychological examinations, revealed a dependency on integration and segregation of brain networks. These findings are indicative of the fact that cognitive deficits in AD could be associated not only with cortical alterations (revealed by fMRI but also with subcortical alterations (revealed by diffusion MRI that extend beyond the areas primarily damaged by neurodegeneration, towards the support of an emerging concept of AD as a

  11. Preheating curvaton perturbations

    Bastero-Gil, M.; Di Clemente, V.; King, S.F.

    2005-01-01

    We discuss the potentially important role played by preheating in certain variants of the curvaton mechanism in which isocurvature perturbations of a D-flat (and F-flat) direction become converted to curvature perturbations during reheating. We discover that parametric resonance of the isocurvature components amplifies the superhorizon fluctuations by a significant amount. As an example of these effects we develop a particle physics motivated model which involves hybrid inflation with the waterfall field N being responsible for generating the μ term, the right-handed neutrino mass scale, and the Peccei-Quinn symmetry breaking scale. The role of the curvaton field can be played either by usual Higgs field, or the lightest right-handed sneutrino. Our new results show that it is possible to achieve the correct curvature perturbations for initial values of the curvaton fields of order the weak scale. In this model we show that the prediction for the spectral index of the final curvature perturbation only depends on the mass of the curvaton during inflation, where consistency with current observational data requires the ratio of this mass to the Hubble constant to be 0.3

  12. String perturbation theory diverges

    Gross, D.J.; Periwal, V.

    1988-01-01

    We prove that perturbation theory for the bosonic string diverges for arbitrary values of the coupling constant and is not Borel summable. This divergence is independent of the existence of the infinities that occur in the theory due to the presence of tachyons and dilaton tadpoles. We discuss the physical implications of such a divergence

  13. Divergent Perturbation Series

    Suslov, I.M.

    2005-01-01

    Various perturbation series are factorially divergent. The behavior of their high-order terms can be determined by Lipatov's method, which involves the use of instanton configurations of appropriate functional integrals. When the Lipatov asymptotic form is known and several lowest order terms of the perturbation series are found by direct calculation of diagrams, one can gain insight into the behavior of the remaining terms of the series, which can be resummed to solve various strong-coupling problems in a certain approximation. This approach is demonstrated by determining the Gell-Mann-Low functions in φ 4 theory, QED, and QCD with arbitrary coupling constants. An overview of the mathematical theory of divergent series is presented, and interpretation of perturbation series is discussed. Explicit derivations of the Lipatov asymptotic form are presented for some basic problems in theoretical physics. A solution is proposed to the problem of renormalon contributions, which hampered progress in this field in the late 1970s. Practical perturbation-series summation schemes are described both for a coupling constant of order unity and in the strong-coupling limit. An interpretation of the Borel integral is given for 'non-Borel-summable' series. Higher order corrections to the Lipatov asymptotic form are discussed

  14. Instantaneous stochastic perturbation theory

    Lüscher, Martin

    2015-01-01

    A form of stochastic perturbation theory is described, where the representative stochastic fields are generated instantaneously rather than through a Markov process. The correctness of the procedure is established to all orders of the expansion and for a wide class of field theories that includes all common formulations of lattice QCD.

  15. Cosmological perturbations in antigravity

    Oltean, Marius; Brandenberger, Robert

    2014-10-01

    We compute the evolution of cosmological perturbations in a recently proposed Weyl-symmetric theory of two scalar fields with oppositely signed conformal couplings to Einstein gravity. It is motivated from the minimal conformal extension of the standard model, such that one of these scalar fields is the Higgs while the other is a new particle, the dilaton, introduced to make the Higgs mass conformally symmetric. At the background level, the theory admits novel geodesically complete cyclic cosmological solutions characterized by a brief period of repulsive gravity, or "antigravity," during each successive transition from a big crunch to a big bang. For simplicity, we consider scalar perturbations in the absence of anisotropies, with potential set to zero and without any radiation. We show that despite the necessarily wrong-signed kinetic term of the dilaton in the full action, these perturbations are neither ghostlike nor tachyonic in the limit of strongly repulsive gravity. On this basis, we argue—pending a future analysis of vector and tensor perturbations—that, with respect to perturbative stability, the cosmological solutions of this theory are viable.

  16. Perturbed Markov chains

    Solan, Eilon; Vieille, Nicolas

    2015-01-01

    We study irreducible time-homogenous Markov chains with finite state space in discrete time. We obtain results on the sensitivity of the stationary distribution and other statistical quantities with respect to perturbations of the transition matrix. We define a new closeness relation between transition matrices, and use graph-theoretic techniques, in contrast with the matrix analysis techniques previously used.

  17. Scalar cosmological perturbations

    Uggla, Claes; Wainwright, John

    2012-01-01

    Scalar perturbations of Friedmann-Lemaitre cosmologies can be analyzed in a variety of ways using Einstein's field equations, the Ricci and Bianchi identities, or the conservation equations for the stress-energy tensor, and possibly introducing a timelike reference congruence. The common ground is the use of gauge invariants derived from the metric tensor, the stress-energy tensor, or from vectors associated with a reference congruence, as basic variables. Although there is a complication in that there is no unique choice of gauge invariants, we will show that this can be used to advantage. With this in mind our first goal is to present an efficient way of constructing dimensionless gauge invariants associated with the tensors that are involved, and of determining their inter-relationships. Our second goal is to give a unified treatment of the various ways of writing the governing equations in dimensionless form using gauge-invariant variables, showing how simplicity can be achieved by a suitable choice of variables and normalization factors. Our third goal is to elucidate the connection between the metric-based approach and the so-called 1 + 3 gauge-invariant approach to cosmological perturbations. We restrict our considerations to linear perturbations, but our intent is to set the stage for the extension to second-order perturbations. (paper)

  18. Generalized perturbation series

    Baird, L.C.; Stinchcomb, G.

    1973-01-01

    An approximate solution of the Green's function equation may be used to generate an exact solution of the Schroedinger equation. This is accomplished through an iterative procedure. The procedure is equivalent to a perturbation expansion if the approximate Green's function is exact with respect to some reference potential

  19. Perturbed S3 neutrinos

    jora, Renata; Schechter, Joseph; Naeem Shahid, M.

    2009-01-01

    We study the effects of the perturbation which violates the permutation symmetry of three Majorana neutrinos but preserves the well known (23) interchange symmetry. This is done in the presenceof an arbitrary Majorana phase which serves to insure the degeneracy of the three neutrinos at the unper...... at the unperturbed level....

  20. Characterizing heterogeneous cellular responses to perturbations.

    Slack, Michael D; Martinez, Elisabeth D; Wu, Lani F; Altschuler, Steven J

    2008-12-09

    Cellular populations have been widely observed to respond heterogeneously to perturbation. However, interpreting the observed heterogeneity is an extremely challenging problem because of the complexity of possible cellular phenotypes, the large dimension of potential perturbations, and the lack of methods for separating meaningful biological information from noise. Here, we develop an image-based approach to characterize cellular phenotypes based on patterns of signaling marker colocalization. Heterogeneous cellular populations are characterized as mixtures of phenotypically distinct subpopulations, and responses to perturbations are summarized succinctly as probabilistic redistributions of these mixtures. We apply our method to characterize the heterogeneous responses of cancer cells to a panel of drugs. We find that cells treated with drugs of (dis-)similar mechanism exhibit (dis-)similar patterns of heterogeneity. Despite the observed phenotypic diversity of cells observed within our data, low-complexity models of heterogeneity were sufficient to distinguish most classes of drug mechanism. Our approach offers a computational framework for assessing the complexity of cellular heterogeneity, investigating the degree to which perturbations induce redistributions of a limited, but nontrivial, repertoire of underlying states and revealing functional significance contained within distinct patterns of heterogeneous responses.

  1. Migration patterns and influence of support networks: A case study of West Africans in the Netherlands

    Chelpi-den Hamer, M.

    2008-01-01

    This article explores the influence of support networks in the migration process of West African migrants to the Netherlands. Taking a case-oriented biographic approach, the article analyzes the migration stories of several West African migrants with a focus on the networks that facilitated their

  2. Knitted Patterns or Contagious Hotspots?: Linking Views on Knowledge and Organizational Networked Learning

    Cornelissen, Frank; de Jong, Tjip; Kessels, Joseph

    2012-01-01

    Purpose: This paper aims to propose a framework which connects perspectives on knowledge and learning to various approaches of social networks studies. The purpose is twofold: providing input for the discourse in organizational studies about the way different views on knowledge and networks drive design choices and activities of researchers,…

  3. Mapping the geography of science: distribution patterns and networks of relations among cities and institutes

    Leydesdorff, L.; Persson, O.

    2010-01-01

    Using Google Earth, Google Maps, and/or network visualization programs such as Pajek, one can overlay the network of relations among addresses in scientific publications onto the geographic map. The authors discuss the pros and cons of various options, and provide software (freeware) for bridging

  4. Evolving patterns in a collaboration network of global R&D on monoclonal antibodies.

    Kong, Xiangjun; Wan, Jian-Bo; Hu, Hao; Su, Shibing; Hu, Yuanjia

    2017-10-01

    We investigated the evolution process of collaborative inter-organizational network of the research and development (R&D) on monoclonal antibody (mAb) over the past 30 y. The annual detection of the collaboration network provides dynamics on network structures and relationship changes among different organizations. Our research showed continuous growth of the network's scale and complexity due to the constant entry of new organizations and the forging of new partnering relationships. The evolving topological features reveal a core-periphery structure that became clearer over time and an increasing heterogeneity within the collaborative mAb R&D network. As measured by the number of network participants, dedicated biotechnology firms (DBFs) were the dominant organization form in the field of mAb development, but their average centrality was reduced during the period of 2004-2009, when pharmaceutical companies took over the positions of DBFs. Along with the network evolution, 2 waves of substitution on the leading positions were driven by technological innovations and mergers and acquisitions (M&A). In addition, this study analyzed organizational-level behaviors to help understand the evolution of network structures over the field of mAb development across the different technologically innovative or economic contexts.

  5. Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

    Firdausiah Mansur, Andi Besse; Yusof, Norazah

    2013-01-01

    Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

  6. Diluted connectivity in pattern association networks facilitates the recall of information from the hippocampus to the neocortex.

    Rolls, Edmund T

    2015-01-01

    The recall of information stored in the hippocampus involves a series of corticocortical backprojections via the entorhinal cortex, parahippocampal gyrus, and one or more neocortical stages. Each stage is considered to be a pattern association network, with the retrieval cue at each stage the firing of neurons in the previous stage. The leading factor that determines the capacity of this multistage pattern association backprojection pathway is the number of connections onto any one neuron, which provides a quantitative basis for why there are as many backprojections between adjacent stages in the hierarchy as forward projections. The issue arises of why this multistage backprojection system uses diluted connectivity. One reason is that a multistage backprojection system with expansion of neuron numbers at each stage enables the hippocampus to address during recall the very large numbers of neocortical neurons, which would otherwise require hippocampal neurons to make very large numbers of synapses if they were directly onto neocortical neurons. The second reason is that as shown here, diluted connectivity in the backprojection pathways reduces the probability of more than one connection onto a receiving neuron in the backprojecting pathways, which otherwise reduces the capacity of the system, that is the number of memories that can be recalled from the hippocampus to the neocortex. For similar reasons, diluted connectivity is advantageous in pattern association networks in other brain systems such as the orbitofrontal cortex and amygdala; for related reasons, in autoassociation networks in, for example, the hippocampal CA3 and the neocortex; and for the different reason that diluted connectivity facilitates the operation of competitive networks in forward-connected cortical systems. © 2015 Elsevier B.V. All rights reserved.

  7. A Parallel Supercomputer Implementation of a Biological Inspired Neural Network and its use for Pattern Recognition

    De Ladurantaye, Vincent; Lavoie, Jean; Bergeron, Jocelyn; Parenteau, Maxime; Lu Huizhong; Pichevar, Ramin; Rouat, Jean

    2012-01-01

    A parallel implementation of a large spiking neural network is proposed and evaluated. The neural network implements the binding by synchrony process using the Oscillatory Dynamic Link Matcher (ODLM). Scalability, speed and performance are compared for 2 implementations: Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA) running on clusters of multicore supercomputers and NVIDIA graphical processing units respectively. A global spiking list that represents at each instant the state of the neural network is described. This list indexes each neuron that fires during the current simulation time so that the influence of their spikes are simultaneously processed on all computing units. Our implementation shows a good scalability for very large networks. A complex and large spiking neural network has been implemented in parallel with success, thus paving the road towards real-life applications based on networks of spiking neurons. MPI offers a better scalability than CUDA, while the CUDA implementation on a GeForce GTX 285 gives the best cost to performance ratio. When running the neural network on the GTX 285, the processing speed is comparable to the MPI implementation on RQCHP's Mammouth parallel with 64 notes (128 cores).

  8. Evaluation of Spatial Pattern of Altered Flow Regimes on a River Network Using a Distributed Hydrological Model.

    Ryo, Masahiro; Iwasaki, Yuichi; Yoshimura, Chihiro; Saavedra V, Oliver C

    2015-01-01

    Alteration of the spatial variability of natural flow regimes has been less studied than that of the temporal variability, despite its ecological importance for river ecosystems. Here, we aimed to quantify the spatial patterns of flow regime alterations along a river network in the Sagami River, Japan, by estimating river discharge under natural and altered flow conditions. We used a distributed hydrological model, which simulates hydrological processes spatiotemporally, to estimate 20-year daily river discharge along the river network. Then, 33 hydrologic indices (i.e., Indicators of Hydrologic Alteration) were calculated from the simulated discharge to estimate the spatial patterns of their alterations. Some hydrologic indices were relatively well estimated such as the magnitude and timing of maximum flows, monthly median flows, and the frequency of low and high flow pulses. The accuracy was evaluated with correlation analysis (r > 0.4) and the Kolmogorov-Smirnov test (α = 0.05) by comparing these indices calculated from both observed and simulated discharge. The spatial patterns of the flow regime alterations varied depending on the hydrologic indices. For example, both the median flow in August and the frequency of high flow pulses were reduced by the maximum of approximately 70%, but these strongest alterations were detected at different locations (i.e., on the mainstream and the tributary, respectively). These results are likely caused by different operational purposes of multiple water control facilities. The results imply that the evaluation only at discharge gauges is insufficient to capture the alteration of the flow regime. Our findings clearly emphasize the importance of evaluating the spatial pattern of flow regime alteration on a river network where its discharge is affected by multiple water control facilities.

  9. Graph Theoretical Analysis of Developmental Patterns of the White Matter Network

    Zhang eChen

    2013-11-01

    Full Text Available Understanding the development of human brain organization is critical for gaining insight into how the enhancement of cognitive processes is related to the fine-tuning of the brain network. However, the developmental trajectory of the large-scale white matter (WM network is not fully understood. Here, using graph theory, we examine developmental changes in the organization of WM networks in 180 typically-developing participants. WM networks were constructed using whole brain tractography and 78 cortical regions of interest were extracted from each participant. The subjects were first divided into 5 equal sample size (n=36 groups (early childhood: 6.0-9.7 years; late childhood: 9.8-12.7 years; adolescence: 12.9-17.5 years; young adult: 17.6-21.8 years; adult: 21.9-29.6 years. Most prominent changes in the topological properties of developing brain networks occur at late childhood and adolescence. During late childhood period, the structural brain network showed significant increase in the global efficiency but decrease in modularity, suggesting a shift of topological organization toward a more randomized configuration. However, while preserving most topological features, there was a significant increase in the local efficiency at adolescence, suggesting the dynamic process of rewiring and rebalancing brain connections at different growth stages. In addition, several pivotal hubs were identified that are vital for the global coordination of information flow over the whole brain network across all age groups. Significant increases of nodal efficiency were present in several regions such as precuneus at late childhood. Finally, a stable and functionally/anatomically related modular organization was identified throughout the development of the WM network. This study used network analysis to elucidate the topological changes in brain maturation, paving the way for developing novel methods for analyzing disrupted brain connectivity in

  10. Fast convergence of spike sequences to periodic patterns in recurrent networks

    Jin, Dezhe Z.

    2002-01-01

    The dynamical attractors are thought to underlie many biological functions of recurrent neural networks. Here we show that stable periodic spike sequences with precise timings are the attractors of the spiking dynamics of recurrent neural networks with global inhibition. Almost all spike sequences converge within a finite number of transient spikes to these attractors. The convergence is fast, especially when the global inhibition is strong. These results support the possibility that precise spatiotemporal sequences of spikes are useful for information encoding and processing in biological neural networks

  11. On-Line Detection of Distributed Attacks from Space-Time Network Flow Patterns

    Baras, J. S; Cardenas, A. A; Ramezani, V

    2003-01-01

    .... The directionality of the change in a network flow is assumed to have an objective or target. The particular problem of detecting distributed denial of service attacks from distributed observations is presented as a working framework...

  12. Patterns of coho salmon size and survival within a stream network

    Effective habitat restoration planning requires correctly anticipating demographic responses to altered habitats. Network-scale investigations of habitat-specific growth and survival of juvenile salmonids have provided critical insights that can now better inform and help priori...

  13. Stepping stability: effects of sensory perturbation

    Krebs David E

    2005-05-01

    Full Text Available Abstract Background Few tools exist for quantifying locomotor stability in balance impaired populations. The objective of this study was to develop and evaluate a technique for quantifying stability of stepping in healthy people and people with peripheral (vestibular hypofunction, VH and central (cerebellar pathology, CB balance dysfunction by means a sensory (auditory perturbation test. Methods Balance impaired and healthy subjects performed a repeated bench stepping task. The perturbation was applied by suddenly changing the cadence of the metronome (100 beat/min to 80 beat/min at a predetermined time (but unpredictable by the subject during the trial. Perturbation response was quantified by computing the Euclidian distance, expressed as a fractional error, between the anterior-posterior center of gravity attractor trajectory before and after the perturbation was applied. The error immediately after the perturbation (Emax, error after recovery (Emin and the recovery response (Edif were documented for each participant, and groups were compared with ANOVA. Results Both balance impaired groups exhibited significantly higher Emax (p = .019 and Emin (p = .028 fractional errors compared to the healthy (HE subjects, but there were no significant differences between CB and VH groups. Although response recovery was slower for CB and VH groups compared to the HE group, the difference was not significant (p = .051. Conclusion The findings suggest that individuals with balance impairment have reduced ability to stabilize locomotor patterns following perturbation, revealing the fragility of their impairment adaptations and compensations. These data suggest that auditory perturbations applied during a challenging stepping task may be useful for measuring rehabilitation outcomes.

  14. The use of global image characteristics for neural network pattern recognitions

    Kulyas, Maksim O.; Kulyas, Oleg L.; Loshkarev, Aleksey S.

    2017-04-01

    The recognition system is observed, where the information is transferred by images of symbols generated by a television camera. For descriptors of objects the coefficients of two-dimensional Fourier transformation generated in a special way. For solution of the task of classification the one-layer neural network trained on reference images is used. Fast learning of a neural network with a single neuron calculation of coefficients is applied.

  15. Uplink Interference Analysis for Two-tier Cellular Networks with Diverse Users under Random Spatial Patterns

    Bao, Wei; Liang, Ben

    2013-01-01

    Multi-tier architecture improves the spatial reuse of radio spectrum in cellular networks, but it introduces complicated heterogeneity in the spatial distribution of transmitters, which brings new challenges in interference analysis. In this work, we present a stochastic geometric model to evaluate the uplink interference in a two-tier network considering multi-type users and base stations. Each type of tier-1 users and tier-2 base stations are modeled as independent homogeneous Poisson point...

  16. Studying the perturbative Reggeon

    Griffiths, S.; Ross, D.A.

    2000-01-01

    We consider the flavour non-singlet Reggeon within the context of perturbative QCD. This consists of ladders built out of ''reggeized'' quarks. We propose a method for the numerical solution of the integro-differential equation for the amplitude describing the exchange of such a Reggeon. The solution is known to have a sharp rise at low values of Bjorken-x when applied to non-singlet quantities in deep-inelastic scattering. We show that when the running of the coupling is taken into account this sharp rise is further enhanced, although the Q 2 dependence is suppressed by the introduction of the running coupling. We also investigate the effects of simulating non-perturbative physics by introducing a constituent mass for the soft quarks and an effective mass for the soft gluons exchanged in the t-channel. (orig.)

  17. Renormalized Lie perturbation theory

    Rosengaus, E.; Dewar, R.L.

    1981-07-01

    A Lie operator method for constructing action-angle transformations continuously connected to the identity is developed for area preserving mappings. By a simple change of variable from action to angular frequency a perturbation expansion is obtained in which the small denominators have been renormalized. The method is shown to lead to the same series as the Lagrangian perturbation method of Greene and Percival, which converges on KAM surfaces. The method is not superconvergent, but yields simple recursion relations which allow automatic algebraic manipulation techniques to be used to develop the series to high order. It is argued that the operator method can be justified by analytically continuing from the complex angular frequency plane onto the real line. The resulting picture is one where preserved primary KAM surfaces are continuously connected to one another

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

    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

  19. Gene expression patterns combined with network analysis identify hub genes associated with bladder cancer.

    Bi, Dongbin; Ning, Hao; Liu, Shuai; Que, Xinxiang; Ding, Kejia

    2015-06-01

    To explore molecular mechanisms of bladder cancer (BC), network strategy was used to find biomarkers for early detection and diagnosis. The differentially expressed genes (DEGs) between bladder carcinoma patients and normal subjects were screened using empirical Bayes method of the linear models for microarray data package. Co-expression networks were constructed by differentially co-expressed genes and links. Regulatory impact factors (RIF) metric was used to identify critical transcription factors (TFs). The protein-protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and clusters were obtained through molecular complex detection (MCODE) algorithm. Centralities analyses for complex networks were performed based on degree, stress and betweenness. Enrichment analyses were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Co-expression networks and TFs (based on expression data of global DEGs and DEGs in different stages and grades) were identified. Hub genes of complex networks, such as UBE2C, ACTA2, FABP4, CKS2, FN1 and TOP2A, were also obtained according to analysis of degree. In gene enrichment analyses of global DEGs, cell adhesion, proteinaceous extracellular matrix and extracellular matrix structural constituent were top three GO terms. ECM-receptor interaction, focal adhesion, and cell cycle were significant pathways. Our results provide some potential underlying biomarkers of BC. However, further validation is required and deep studies are needed to elucidate the pathogenesis of BC. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. Nonperturbative perturbation theory

    Bender, C.M.

    1989-01-01

    In this talk we describe a recently proposed graphical perturbative calculational scheme for quantum field theory. The basic idea is to expand in the power of the interaction term. For example, to solve a λφ 4 theory in d-dimensional space-time, we introduce a small parameter δ and consider a λ(φ 2 ) 1+δ field theory. We show how to expand such a theory as a series in powers of δ. The resulting perturbation series appears to have a finite radius of convergence and numerical results for low-dimensional models are good. We have computed the two-point and four-point Green's functions to second order in powers of δ and the 2n-point Green's functions (n>2) to order δ. We explain how to renormalize the theory and show that, to first order in powers of δ, when δ>0 and d≥4 the theory is free. This conclusion remains valid to second order in powers of δ, and we believe that it remains valid to all orders in powers of δ. The new perturbative scheme is consistent with global supersymmetry invariance. We examine a two-dimensional supersymmetric quantum field theory in which we do not know of any other means for doing analytical calculations. We illustrate the power of this new technique by computing the ground-state energy density E to second order in this new perturbation theory. We show that there is a beautiful and delicate cancellation between infinite classes of graphs which leads to the result that E=0. (orig.)

  1. Perturbed asymptotically linear problems

    Bartolo, R.; Candela, A. M.; Salvatore, A.

    2012-01-01

    The aim of this paper is investigating the existence of solutions of some semilinear elliptic problems on open bounded domains when the nonlinearity is subcritical and asymptotically linear at infinity and there is a perturbation term which is just continuous. Also in the case when the problem has not a variational structure, suitable procedures and estimates allow us to prove that the number of distinct crtitical levels of the functional associated to the unperturbed problem is "stable" unde...

  2. Characterizing genes with distinct methylation patterns in the context of protein-protein interaction network: application to human brain tissues.

    Li, Yongsheng; Xu, Juan; Chen, Hong; Zhao, Zheng; Li, Shengli; Bai, Jing; Wu, Aiwei; Jiang, Chunjie; Wang, Yuan; Su, Bin; Li, Xia

    2013-01-01

    DNA methylation is an essential epigenetic mechanism involved in transcriptional control. However, how genes with different methylation patterns are assembled in the protein-protein interaction network (PPIN) remains a mystery. In the present study, we systematically dissected the characterization of genes with different methylation patterns in the PPIN. A negative association was detected between the methylation levels in the brain tissues and topological centralities. By focusing on two classes of genes with considerably different methylation levels in the brain tissues, namely the low methylated genes (LMGs) and high methylated genes (HMGs), we found that their organizing principles in the PPIN are distinct. The LMGs tend to be the center of the PPIN, and attacking them causes a more deleterious effect on the network integrity. Furthermore, the LMGs express their functions in a modular pattern and substantial differences in functions are observed between the two types of genes. The LMGs are enriched in the basic biological functions, such as binding activity and regulation of transcription. More importantly, cancer genes, especially recessive cancer genes, essential genes, and aging-related genes were all found more often in the LMGs. Additionally, our analysis presented that the intra-classes communications are enhanced, but inter-classes communications are repressed. Finally, a functional complementation was revealed between methylation and miRNA regulation in the human genome. We have elucidated the assembling principles of genes with different methylation levels in the context of the PPIN, providing key insights into the complex epigenetic regulation mechanisms.

  3. Twisting perturbed parafermions

    A.V. Belitsky

    2017-07-01

    Full Text Available The near-collinear expansion of scattering amplitudes in maximally supersymmetric Yang–Mills theory at strong coupling is governed by the dynamics of stings propagating on the five sphere. The pentagon transitions in the operator product expansion which systematize the series get reformulated in terms of matrix elements of branch-point twist operators in the two-dimensional O(6 nonlinear sigma model. The facts that the latter is an asymptotically free field theory and that there exists no local realization of twist fields prevents one from explicit calculation of their scaling dimensions and operator product expansion coefficients. This complication is bypassed making use of the equivalence of the sigma model to the infinite-level limit of WZNW models perturbed by current–current interactions, such that one can use conformal symmetry and conformal perturbation theory for systematic calculations. Presently, to set up the formalism, we consider the O(3 sigma model which is reformulated as perturbed parafermions.

  4. Global spatio-temporal patterns in human migration: a complex network perspective.

    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.

  5. Global spatio-temporal patterns in human migration: a complex network perspective.

    Kyle F Davis

    Full Text Available 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.

  6. Retrieving infinite numbers of patterns in a spin-glass model of immune networks

    Agliari, E.; Annibale, A.; Barra, A.; Coolen, A. C. C.; Tantari, D.

    2017-01-01

    The similarity between neural and (adaptive) immune networks has been known for decades, but so far we did not understand the mechanism that allows the immune system, unlike associative neural networks, to recall and execute a large number of memorized defense strategies in parallel. The explanation turns out to lie in the network topology. Neurons interact typically with a large number of other neurons, whereas interactions among lymphocytes in immune networks are very specific, and described by graphs with finite connectivity. In this paper we use replica techniques to solve a statistical mechanical immune network model with “coordinator branches” (T-cells) and “effector branches” (B-cells), and show how the finite connectivity enables the coordinators to manage an extensive number of effectors simultaneously, even above the percolation threshold (where clonal cross-talk is not negligible). A consequence of its underlying topological sparsity is that the adaptive immune system exhibits only weak ergodicity breaking, so that also spontaneous switch-like effects as bi-stabilities are present: the latter may play a significant role in the maintenance of immune homeostasis.

  7. Uncovering robust patterns of microRNA co-expression across cancers using Bayesian Relevance Networks.

    Parameswaran Ramachandran

    Full Text Available Co-expression networks have long been used as a tool for investigating the molecular circuitry governing biological systems. However, most algorithms for constructing co-expression networks were developed in the microarray era, before high-throughput sequencing-with its unique statistical properties-became the norm for expression measurement. Here we develop Bayesian Relevance Networks, an algorithm that uses Bayesian reasoning about expression levels to account for the differing levels of uncertainty in expression measurements between highly- and lowly-expressed entities, and between samples with different sequencing depths. It combines data from groups of samples (e.g., replicates to estimate group expression levels and confidence ranges. It then computes uncertainty-moderated estimates of cross-group correlations between entities, and uses permutation testing to assess their statistical significance. Using large scale miRNA data from The Cancer Genome Atlas, we show that our Bayesian update of the classical Relevance Networks algorithm provides improved reproducibility in co-expression estimates and lower false discovery rates in the resulting co-expression networks. Software is available at www.perkinslab.ca.

  8. Asymptotic theory of time varying networks with burstiness and heterogeneous activation patterns

    Burioni, Raffaella; Ubaldi, Enrico; Vezzani, Alessandro

    2017-05-01

    The recent availability of large-scale, time-resolved and high quality digital datasets has allowed for a deeper understanding of the structure and properties of many real-world networks. The empirical evidence of a temporal dimension prompted the switch of paradigm from a static representation of networks to a time varying one. In this work we briefly review the framework of time-varying-networks in real world social systems, especially focusing on the activity-driven paradigm. We develop a framework that allows for the encoding of three generative mechanisms that seem to play a central role in the social networks’ evolution: the individual’s propensity to engage in social interactions, its strategy in allocate these interactions among its alters and the burstiness of interactions amongst social actors. The functional forms and probability distributions encoding these mechanisms are typically data driven. A natural question arises if different classes of strategies and burstiness distributions, with different local scale behavior and analogous asymptotics can lead to the same long time and large scale structure of the evolving networks. We consider the problem in its full generality, by investigating and solving the system dynamics in the asymptotic limit, for general classes of ties allocation mechanisms and waiting time probability distributions. We show that the asymptotic network evolution is driven by a few characteristics of these functional forms, that can be extracted from direct measurements on large datasets.

  9. Predicting clinical symptoms of attention deficit hyperactivity disorder based on temporal patterns between and within intrinsic connectivity networks.

    Wang, Xun-Heng; Jiao, Yun; Li, Lihua

    2017-10-24

    Attention deficit hyperactivity disorder (ADHD) is a common brain disorder with high prevalence in school-age children. Previously developed machine learning-based methods have discriminated patients with ADHD from normal controls by providing label information of the disease for individuals. Inattention and impulsivity are the two most significant clinical symptoms of ADHD. However, predicting clinical symptoms (i.e., inattention and impulsivity) is a challenging task based on neuroimaging data. The goal of this study is twofold: to build predictive models for clinical symptoms of ADHD based on resting-state fMRI and to mine brain networks for predictive patterns of inattention and impulsivity. To achieve this goal, a cohort of 74 boys with ADHD and a cohort of 69 age-matched normal controls were recruited from the ADHD-200 Consortium. Both structural and resting-state fMRI images were obtained for each participant. Temporal patterns between and within intrinsic connectivity networks (ICNs) were applied as raw features in the predictive models. Specifically, sample entropy was taken asan intra-ICN feature, and phase synchronization (PS) was used asan inter-ICN feature. The predictive models were based on the least absolute shrinkage and selectionator operator (LASSO) algorithm. The performance of the predictive model for inattention is r=0.79 (p<10 -8 ), and the performance of the predictive model for impulsivity is r=0.48 (p<10 -8 ). The ICN-related predictive patterns may provide valuable information for investigating the brain network mechanisms of ADHD. In summary, the predictive models for clinical symptoms could be beneficial for personalizing ADHD medications. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

  10. Probabilistic Neural Networks for Chemical Sensor Array Pattern Recognition: Comparison Studies, Improvements and Automated Outlier Rejection

    Shaffer, Ronald E

    1998-01-01

    For application to chemical sensor arrays, the ideal pattern recognition is accurate, fast, simple to train, robust to outliers, has low memory requirements, and has the ability to produce a measure...

  11. The use of antenna radiation pattern in node localisation algorithms for wireless sensor networks

    Mwila, MK

    2014-08-01

    Full Text Available due to the limited accuracy inherent to the current ranging model. These models, however, make the assumption that the antenna radiation pattern is omnidirectional targeted to simplifying the complexity of the algorithms. An increasing number of sensor...

  12. Training spiking neural networks to associate spatio-temporal input-output spike patterns

    Mohemmed, A; Schliebs, S; Matsuda, S; Kasabov, N

    2013-01-01

    In a previous work (Mohemmed et al., Method for training a spiking neuron to associate input–output spike trains) [1] we have proposed a supervised learning algorithm based on temporal coding to train a spiking neuron to associate input spatiotemporal spike patterns to desired output spike patterns. The algorithm is based on the conversion of spike trains into analogue signals and the application of the Widrow–Hoff learning rule. In this paper we present a mathematical formulation of the prop...

  13. Dynamic Causal Modeling of the Cortical Responses to Wrist Perturbations

    Yuan Yang

    2017-09-01

    Full Text Available Mechanical perturbations applied to the wrist joint typically evoke a stereotypical sequence of cortical and muscle responses. The early cortical responses (<100 ms are thought be involved in the “rapid” transcortical reaction to the perturbation while the late cortical responses (>100 ms are related to the “slow” transcortical reaction. Although previous studies indicated that both responses involve the primary motor cortex, it remains unclear if both responses are engaged by the same effective connectivity in the cortical network. To answer this question, we investigated the effective connectivity cortical network after a “ramp-and-hold” mechanical perturbation, in both the early (<100 ms and late (>100 ms periods, using dynamic causal modeling. Ramp-and-hold perturbations were applied to the wrist joint while the subject maintained an isometric wrist flexion. Cortical activity was recorded using a 128-channel electroencephalogram (EEG. We investigated how the perturbation modulated the effective connectivity for the early and late periods. Bayesian model comparisons suggested that different effective connectivity networks are engaged in these two periods. For the early period, we found that only a few cortico-cortical connections were modulated, while more complicated connectivity was identified in the cortical network during the late period with multiple modulated cortico-cortical connections. The limited early cortical network likely allows for a rapid muscle response without involving high-level cognitive processes, while the complexity of the late network may facilitate coordinated responses.

  14. Recognize PM2.5 sources and emission patterns via high-density sensor network: An application case in Beijing

    Ba, Yu tao; xian Liu, Bao; Sun, Feng; Wang, Li hua; Zhang, Da wei; Yin, Wen jun

    2017-04-01

    Beijing suffered severe air pollution during wintertime, 2016, with the unprecedented high level pollutants monitored. As the most dominant pollutant, fine particulate matter (PM2.5) was measured via high-density sensor network (>1000 fixed monitors across 16000 km2 area). This campaign provided precise observations (spatial resolution ≈ 3 km, temporal resolution = 10 min, error of measure Chemistry) were analyzed to elucidate the effects of atmospheric transportations across regions, both horizontal and vertical, on emission patterns during this haze period. The results quantified the main cause of regional transport and local emission, and highlighted the importance of cross-region cooperation in anti-pollution campaigns.

  15. Toward a multipoint optical fibre sensor system for use in process water systems based on artificial neural network pattern recognition

    King, D; Lyons, W B; Flanagan, C; Lewis, E

    2005-01-01

    An optical fibre sensor capable of detecting various concentrations of ethanol in water supplies is reported. The sensor is based on a U-bend sensor configuration and is incorporated into a 170-metre length of silica cladding silica core optical fibre. The sensor is interrogated using Optical Time Domain Reflectometry (OTDR) and it is proposed to apply artificial neural network (ANN) pattern recognition techniques to the resulting OTDR signals to accurately classify the sensor test conditions. It is also proposed that additional U-bend configuration sensors will be added to the fibre measurement length, in order to implement a multipoint optical fibre sensor system

  16. Networks and spatial patterns of extremist organizations in North and West Africa

    Walther, Olivier; Leuprecht, Christian; Skillicorn, David

    2018-01-01

    (Cunningham 2006; Findley and Rudloff 2012). Building on network science and spatial analysis, the overall objective of this chapter is to bridge these strands of literature and lay the foundations for a more formal approach to social and spatial networks of belligerents in the region. Examining...... of ensuring border integrity through border patrols and law enforcement. The chapter proceeds as follows. The second section reviews the literature on the social and spatial organization of state and non-state organizations, paying particular attention to the role of networks and national borders. The third...... and stability” (MaliActu 2016). The much-debated letter, which arrived one month before Ansar Dine attacked a UN convoy in the north of the country (RFI 2016), is the latest development in a tortuous military career for ag Ghaly, who, since the 1990s, has been a foreign fighter for the late Colonel Gaddafi...

  17. Order Patterns Networks (orpan – a method toestimate time-evolving functional connectivity frommultivariate time series

    Stefan eSchinkel

    2012-11-01

    Full Text Available Complex networks provide an excellent framework for studying the functionof the human brain activity. Yet estimating functional networks from mea-sured signals is not trivial, especially if the data is non-stationary and noisyas it is often the case with physiological recordings. In this article we proposea method that uses the local rank structure of the data to define functionallinks in terms of identical rank structures. The method yields temporal se-quences of networks which permits to trace the evolution of the functionalconnectivity during the time course of the observation. We demonstrate thepotentials of this approach with model data as well as with experimentaldata from an electrophysiological study on language processing.

  18. Parameter estimation of breast tumour using dynamic neural network from thermal pattern

    Elham Saniei

    2016-11-01

    Full Text Available This article presents a new approach for estimating the depth, size, and metabolic heat generation rate of a tumour. For this purpose, the surface temperature distribution of a breast thermal image and the dynamic neural network was used. The research consisted of two steps: forward and inverse. For the forward section, a finite element model was created. The Pennes bio-heat equation was solved to find surface and depth temperature distributions. Data from the analysis, then, were used to train the dynamic neural network model (DNN. Results from the DNN training/testing confirmed those of the finite element model. For the inverse section, the trained neural network was applied to estimate the depth temperature distribution (tumour position from the surface temperature profile, extracted from the thermal image. Finally, tumour parameters were obtained from the depth temperature distribution. Experimental findings (20 patients were promising in terms of the model’s potential for retrieving tumour parameters.

  19. Fabric Weave Pattern and Yarn Color Recognition and Classification Using a Deep ELM Network

    Babar Khan

    2017-10-01

    Full Text Available Usually, a fabric weave pattern is recognized using methods which identify the warp floats and weft floats. Although these methods perform well for uniform or repetitive weave patterns, in the case of complex weave patterns, these methods become computationally complex and the classification error rates are comparatively higher. Furthermore, the fault-tolerance (invariance and stability (selectivity of the existing methods are still to be enhanced. We present a novel biologically-inspired method to invariantly recognize the fabric weave pattern (fabric texture and yarn color from the color image input. We proposed a model in which the fabric weave pattern descriptor is based on the HMAX model for computer vision inspired by the hierarchy in the visual cortex, the color descriptor is based on the opponent color channel inspired by the classical opponent color theory of human vision, and the classification stage is composed of a multi-layer (deep extreme learning machine. Since the weave pattern descriptor, yarn color descriptor, and the classification stage are all biologically inspired, we propose a method which is completely biologically plausible. The classification performance of the proposed algorithm indicates that the biologically-inspired computer-aided-vision models might provide accurate, fast, reliable and cost-effective solution to industrial automation.

  20. The medical science DMZ: a network design pattern for data-intensive medical science

    Peisert, Sean [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Davis, CA (United States). Dept. of computer Science; Corporation for Education Network Initiatives in California (CENIC), Berkeley, CA (United States); Dart, Eli [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). ESnet; Barnett, William [Indiana Univ., Indianapolis, IN (United States). Indiana Clinical and Translational Sciences Inst., Regenstrief Inst.; Balas, Edward [Indiana Univ., Bloomington, IN (United States). Global Research Network Operations Center; Cuff, James [Harvard Univ., Cambridge, MA (United States). Research Computing; Grossman, Robert L. [Univ. of Chicago, IL (United States). Center for Data Intensive Science; Berman, Ari [BioTeam, Middleton, MA (United States); Shankar, Anurag [Indiana Univ., Bloomington, IN (United States). Pervasive Technology Inst.; Tierney, Brian [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). ESnet

    2017-10-06

    We describe a detailed solution for maintaining high-capacity, data-intensive network flows (eg, 10, 40, 100 Gbps+) in a scientific, medical context while still adhering to security and privacy laws and regulations.High-end networking, packet-filter firewalls, network intrusion-detection systems.We describe a "Medical Science DMZ" concept as an option for secure, high-volume transport of large, sensitive datasets between research institutions over national research networks, and give 3 detailed descriptions of implemented Medical Science DMZs.The exponentially increasing amounts of "omics" data, high-quality imaging, and other rapidly growing clinical datasets have resulted in the rise of biomedical research "Big Data." The storage, analysis, and network resources required to process these data and integrate them into patient diagnoses and treatments have grown to scales that strain the capabilities of academic health centers. Some data are not generated locally and cannot be sustained locally, and shared data repositories such as those provided by the National Library of Medicine, the National Cancer Institute, and international partners such as the European Bioinformatics Institute are rapidly growing. The ability to store and compute using these data must therefore be addressed by a combination of local, national, and industry resources that exchange large datasets. Maintaining data-intensive flows that comply with the Health Insurance Portability and Accountability Act (HIPAA) and other regulations presents a new challenge for biomedical research. We describe a strategy that marries performance and security by borrowing from and redefining the concept of a Science DMZ, a framework that is used in physical sciences and engineering research to manage high-capacity data flows.By implementing a Medical Science DMZ architecture, biomedical researchers can leverage the scale provided by high-performance computer and cloud storage facilities and national high

  1. The medical science DMZ: a network design pattern for data-intensive medical science.

    Peisert, Sean; Dart, Eli; Barnett, William; Balas, Edward; Cuff, James; Grossman, Robert L; Berman, Ari; Shankar, Anurag; Tierney, Brian

    2017-10-06

    We describe a detailed solution for maintaining high-capacity, data-intensive network flows (eg, 10, 40, 100 Gbps+) in a scientific, medical context while still adhering to security and privacy laws and regulations. High-end networking, packet-filter firewalls, network intrusion-detection systems. We describe a "Medical Science DMZ" concept as an option for secure, high-volume transport of large, sensitive datasets between research institutions over national research networks, and give 3 detailed descriptions of implemented Medical Science DMZs. The exponentially increasing amounts of "omics" data, high-quality imaging, and other rapidly growing clinical datasets have resulted in the rise of biomedical research "Big Data." The storage, analysis, and network resources required to process these data and integrate them into patient diagnoses and treatments have grown to scales that strain the capabilities of academic health centers. Some data are not generated locally and cannot be sustained locally, and shared data repositories such as those provided by the National Library of Medicine, the National Cancer Institute, and international partners such as the European Bioinformatics Institute are rapidly growing. The ability to store and compute using these data must therefore be addressed by a combination of local, national, and industry resources that exchange large datasets. Maintaining data-intensive flows that comply with the Health Insurance Portability and Accountability Act (HIPAA) and other regulations presents a new challenge for biomedical research. We describe a strategy that marries performance and security by borrowing from and redefining the concept of a Science DMZ, a framework that is used in physical sciences and engineering research to manage high-capacity data flows. By implementing a Medical Science DMZ architecture, biomedical researchers can leverage the scale provided by high-performance computer and cloud storage facilities and national high

  2. Universal principles governing multiple random searchers on complex networks: The logarithmic growth pattern and the harmonic law

    Weng, Tongfeng; Zhang, Jie; Small, Michael; Harandizadeh, Bahareh; Hui, Pan

    2018-03-01

    We propose a unified framework to evaluate and quantify the search time of multiple random searchers traversing independently and concurrently on complex networks. We find that the intriguing behaviors of multiple random searchers are governed by two basic principles—the logarithmic growth pattern and the harmonic law. Specifically, the logarithmic growth pattern characterizes how the search time increases with the number of targets, while the harmonic law explores how the search time of multiple random searchers varies relative to that needed by individual searchers. Numerical and theoretical results demonstrate these two universal principles established across a broad range of random search processes, including generic random walks, maximal entropy random walks, intermittent strategies, and persistent random walks. Our results reveal two fundamental principles governing the search time of multiple random searchers, which are expected to facilitate investigation of diverse dynamical processes like synchronization and spreading.

  3. Modularity and the spread of perturbations in complex dynamical systems.

    Kolchinsky, Artemy; Gates, Alexander J; Rocha, Luis M

    2015-12-01

    We propose a method to decompose dynamical systems based on the idea that modules constrain the spread of perturbations. We find partitions of system variables that maximize "perturbation modularity," defined as the autocovariance of coarse-grained perturbed trajectories. The measure effectively separates the fast intramodular from the slow intermodular dynamics of perturbation spreading (in this respect, it is a generalization of the "Markov stability" method of network community detection). Our approach captures variation of modular organization across different system states, time scales, and in response to different kinds of perturbations: aspects of modularity which are all relevant to real-world dynamical systems. It offers a principled alternative to detecting communities in networks of statistical dependencies between system variables (e.g., "relevance networks" or "functional networks"). Using coupled logistic maps, we demonstrate that the method uncovers hierarchical modular organization planted in a system's coupling matrix. Additionally, in homogeneously coupled map lattices, it identifies the presence of self-organized modularity that depends on the initial state, dynamical parameters, and type of perturbations. Our approach offers a powerful tool for exploring the modular organization of complex dynamical systems.

  4. Spatial coherence resonance and spatial pattern transition induced by the decrease of inhibitory effect in a neuronal network

    Tao, Ye; Gu, Huaguang; Ding, Xueli

    2017-10-01

    Spiral waves were observed in the biological experiment on rat brain cortex with the application of carbachol and bicuculline which can block inhibitory coupling from interneurons to pyramidal neurons. To simulate the experimental spiral waves, a two-dimensional neuronal network composed of pyramidal neurons and inhibitory interneurons was built. By decreasing the percentage of active inhibitory interneurons, the random-like spatial patterns change to spiral waves and to random-like spatial patterns or nearly synchronous behaviors. The spiral waves appear at a low percentage of inhibitory interneurons, which matches the experimental condition that inhibitory couplings of the interneurons were blocked. The spiral waves exhibit a higher order or signal-to-noise ratio (SNR) characterized by spatial structure function than both random-like spatial patterns and nearly synchronous behaviors, which shows that changes of the percentage of active inhibitory interneurons can induce spatial coherence resonance-like behaviors. In addition, the relationship between the coherence degree and the spatial structures of the spiral waves is identified. The results not only present a possible and reasonable interpretation to the spiral waves observed in the biological experiment on the brain cortex with disinhibition, but also reveal that the spiral waves exhibit more ordered degree in spatial patterns.

  5. Investigating univariate temporal patterns for intrinsic connectivity networks based on complexity and low-frequency oscillation: a test-retest reliability study.

    Wang, X; Jiao, Y; Tang, T; Wang, H; Lu, Z

    2013-12-19

    Intrinsic connectivity networks (ICNs) are composed of spatial components and time courses. The spatial components of ICNs were discovered with moderate-to-high reliability. So far as we know, few studies focused on the reliability of the temporal patterns for ICNs based their individual time courses. The goals of this study were twofold: to investigate the test-retest reliability of temporal patterns for ICNs, and to analyze these informative univariate metrics. Additionally, a correlation analysis was performed to enhance interpretability. Our study included three datasets: (a) short- and long-term scans, (b) multi-band echo-planar imaging (mEPI), and (c) eyes open or closed. Using dual regression, we obtained the time courses of ICNs for each subject. To produce temporal patterns for ICNs, we applied two categories of univariate metrics: network-wise complexity and network-wise low-frequency oscillation. Furthermore, we validated the test-retest reliability for each metric. The network-wise temporal patterns for most ICNs (especially for default mode network, DMN) exhibited moderate-to-high reliability and reproducibility under different scan conditions. Network-wise complexity for DMN exhibited fair reliability (ICC<0.5) based on eyes-closed sessions. Specially, our results supported that mEPI could be a useful method with high reliability and reproducibility. In addition, these temporal patterns were with physiological meanings, and certain temporal patterns were correlated to the node strength of the corresponding ICN. Overall, network-wise temporal patterns of ICNs were reliable and informative and could be complementary to spatial patterns of ICNs for further study. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

  6. Pore network modeling of drainage process in patterned porous media: a quasi-static study

    Zhang, Tao; Salama, Amgad; Sun, Shuyu; El-Amin, Mohamed

    2015-01-01

    -saturation relationships, it is quite challenging to isolate its effects explicitly in real porous media applications. However, within the framework of pore network models, it is easy to highlight the effects of wettability conditions on the transport of two-phase systems

  7. Transacting in the absence of trust : Uncertainty and network patterns in post-communism

    B. Lehmbruch (Barbara)

    2012-01-01

    textabstractThe paper critically discusses the widespread literature focusing on informal post-communist “clans,” “networks,” or “fiefdoms.” Often self-described as determinedly empirical, the “clan” paradigm is crucially shaped by its origins in the analysis of East Asia, as well as by its use as

  8. Interaction Patterns in Web-based Knowledge Communities: Two-Mode Network Approach

    Vollenbroek, Wouter Bernardus; de Vries, Sjoerd A.; Fred, Ana; Dietz, Jan; Aveiro, David; Liu, Kecheng; Bernardino, Jorge; Filipe, Joaquim

    2016-01-01

    The importance of web-based knowledge communities (WKCs) in the 'network society' is growing. This trend is seen in many disciplines, like education, government, finance and other profit- and non-profit organisations. There is a need for understanding the development of these online communities in

  9. Social networking through email : studying email usage patterns of persons with aphasia

    Al Mahmud, A.; Martens, J.B.O.S.

    2016-01-01

    Background: A social networking program has been developed by the Aphasia Union Netherlands (AVN) to enhance communication between older persons with aphasia (PWA) mutually and with their therapists. The medium of communication is email. Aims: The aim of the paper is to understand the email usage

  10. Differential Patterns of Dysconnectivity in Mirror Neuron and Mentalizing Networks in Schizophrenia

    Schilbach, Leonhard; Derntl, Birgit; Aleman, Andre; Caspers, Svenja; Clos, Mareike; Diederen, Kelly M J; Gruber, Oliver; Kogler, Lydia; Liemburg, Edith J; Sommer, Iris E; Müller, Veronika I; Cieslik, Edna C; Eickhoff, Simon B

    Impairments of social cognition are well documented in patients with schizophrenia (SCZ), but the neural basis remains poorly understood. In light of evidence that suggests that the "mirror neuron system" (MNS) and the "mentalizing network" (MENT) are key substrates of intersubjectivity and joint

  11. Non-Perturbative Renormalization

    Mastropietro, Vieri

    2008-01-01

    The notion of renormalization is at the core of several spectacular achievements of contemporary physics, and in the last years powerful techniques have been developed allowing to put renormalization on a firm mathematical basis. This book provides a self-consistent and accessible introduction to the sophisticated tools used in the modern theory of non-perturbative renormalization, allowing an unified and rigorous treatment of Quantum Field Theory, Statistical Physics and Condensed Matter models. In particular the first part of this book is devoted to Constructive Quantum Field Theory, providi

  12. Perturbative quantum chromodynamics

    1989-01-01

    This book will be of great interest to advanced students and researchers in the area of high energy theoretical physics. Being the most complete and updated review volume on Perturbative QCD, it serves as an extremely useful textbook or reference book. Some of the reviews in this volume are the best that have been written on the subject anywhere. Contents: Factorization of Hard Processes in QCD (J C Collins, D E Soper & G Sterman); Exclusive Processes in Quantum Chromodynamics (S J Brodsky & G P Lepage); Coherence and Physics of QCD Jets (Yu L Dokshitzer, V A Khoze & S I Troyan); Pomeron in Qu

  13. Perturbative quantum chromodynamics

    Radyushkin, A.V.

    1987-01-01

    The latest achievements in perturbative quantum chromodynamics (QCD) relating to the progress in factorization of small and large distances are presented. The following problems are concerned: Development of the theory of Sudakov effects on the basis of mean contour formalism. Development of nonlocal condensate formalism. Calculation of hadron wave functions and hadron distribution functions using QCD method of sum rules. Development of the theory of Regge behaviour in QCD, behaviour of structure functions at small x. Study of polarization effects in hadron processes with high momentum transfer

  14. Patterns of Twitter Behavior Among Networks of Cannabis Dispensaries in California

    Chew, Robert F; Hsieh, Yuli P; Bieler, Gayle S; Bobashev, Georgiy V; Siege, Christopher; Zarkin, Gary A

    2017-01-01

    Background Twitter represents a social media platform through which medical cannabis dispensaries can rapidly promote and advertise a multitude of retail products. Yet, to date, no studies have systematically evaluated Twitter behavior among dispensaries and how these behaviors influence the formation of social networks. Objectives This study sought to characterize common cyberbehaviors and shared follower networks among dispensaries operating in two large cannabis markets in California. Methods From a targeted sample of 119 dispensaries in the San Francisco Bay Area and Greater Los Angeles, we collected metadata from the dispensary accounts using the Twitter API. For each city, we characterized the network structure of dispensaries based upon shared followers, then empirically derived communities with the Louvain modularity algorithm. Principal components factor analysis was employed to reduce 12 Twitter measures into a more parsimonious set of cyberbehavioral dimensions. Finally, quadratic discriminant analysis was implemented to verify the ability of the extracted dimensions to classify dispensaries into their derived communities. Results The modularity algorithm yielded three communities in each city with distinct network structures. The principal components factor analysis reduced the 12 cyberbehaviors into five dimensions that encompassed account age, posting frequency, referencing, hyperlinks, and user engagement among the dispensary accounts. In the quadratic discriminant analysis, the dimensions correctly classified 75% (46/61) of the communities in the San Francisco Bay Area and 71% (41/58) in Greater Los Angeles. Conclusions The most centralized and strongly connected dispensaries in both cities had newer accounts, higher daily activity, more frequent user engagement, and increased usage of embedded media, keywords, and hyperlinks. Measures derived from both network structure and cyberbehavioral dimensions can serve as key contextual indicators for the

  15. Genetic networking of the Bemisia tabaci cryptic species complex reveals pattern of biological invasions.

    Paul De Barro

    Full Text Available BACKGROUND: A challenge within the context of cryptic species is the delimitation of individual species within the complex. Statistical parsimony network analytics offers the opportunity to explore limits in situations where there are insufficient species-specific morphological characters to separate taxa. The results also enable us to explore the spread in taxa that have invaded globally. METHODOLOGY/PRINCIPAL FINDINGS: Using a 657 bp portion of mitochondrial cytochrome oxidase 1 from 352 unique haplotypes belonging to the Bemisia tabaci cryptic species complex, the analysis revealed 28 networks plus 7 unconnected individual haplotypes. Of the networks, 24 corresponded to the putative species identified using the rule set devised by Dinsdale et al. (2010. Only two species proposed in Dinsdale et al. (2010 departed substantially from the structure suggested by the analysis. The analysis of the two invasive members of the complex, Mediterranean (MED and Middle East - Asia Minor 1 (MEAM1, showed that in both cases only a small number of haplotypes represent the majority that have spread beyond the home range; one MEAM1 and three MED haplotypes account for >80% of the GenBank records. Israel is a possible source of the globally invasive MEAM1 whereas MED has two possible sources. The first is the eastern Mediterranean which has invaded only the USA, primarily Florida and to a lesser extent California. The second are western Mediterranean haplotypes that have spread to the USA, Asia and South America. The structure for MED supports two home range distributions, a Sub-Saharan range and a Mediterranean range. The MEAM1 network supports the Middle East - Asia Minor region. CONCLUSION/SIGNIFICANCE: The network analyses show a high level of congruence with the species identified in a previous phylogenetic analysis. The analysis of the two globally invasive members of the complex support the view that global invasion often involve very small portions of

  16. Patterns of Twitter Behavior Among Networks of Cannabis Dispensaries in California.

    Peiper, Nicholas C; Baumgartner, Peter M; Chew, Robert F; Hsieh, Yuli P; Bieler, Gayle S; Bobashev, Georgiy V; Siege, Christopher; Zarkin, Gary A

    2017-07-04

    Twitter represents a social media platform through which medical cannabis dispensaries can rapidly promote and advertise a multitude of retail products. Yet, to date, no studies have systematically evaluated Twitter behavior among dispensaries and how these behaviors influence the formation of social networks. This study sought to characterize common cyberbehaviors and shared follower networks among dispensaries operating in two large cannabis markets in California. From a targeted sample of 119 dispensaries in the San Francisco Bay Area and Greater Los Angeles, we collected metadata from the dispensary accounts using the Twitter API. For each city, we characterized the network structure of dispensaries based upon shared followers, then empirically derived communities with the Louvain modularity algorithm. Principal components factor analysis was employed to reduce 12 Twitter measures into a more parsimonious set of cyberbehavioral dimensions. Finally, quadratic discriminant analysis was implemented to verify the ability of the extracted dimensions to classify dispensaries into their derived communities. The modularity algorithm yielded three communities in each city with distinct network structures. The principal components factor analysis reduced the 12 cyberbehaviors into five dimensions that encompassed account age, posting frequency, referencing, hyperlinks, and user engagement among the dispensary accounts. In the quadratic discriminant analysis, the dimensions correctly classified 75% (46/61) of the communities in the San Francisco Bay Area and 71% (41/58) in Greater Los Angeles. The most centralized and strongly connected dispensaries in both cities had newer accounts, higher daily activity, more frequent user engagement, and increased usage of embedded media, keywords, and hyperlinks. Measures derived from both network structure and cyberbehavioral dimensions can serve as key contextual indicators for the online surveillance of cannabis dispensaries and

  17. Perturbed effects at radiation physics

    Külahcı, Fatih; Şen, Zekâi

    2013-01-01

    Perturbation methodology is applied in order to assess the linear attenuation coefficient, mass attenuation coefficient and cross-section behavior with random components in the basic variables such as the radiation amounts frequently used in the radiation physics and chemistry. Additionally, layer attenuation coefficient (LAC) and perturbed LAC (PLAC) are proposed for different contact materials. Perturbation methodology provides opportunity to obtain results with random deviations from the average behavior of each variable that enters the whole mathematical expression. The basic photon intensity variation expression as the inverse exponential power law (as Beer–Lambert's law) is adopted for perturbation method exposition. Perturbed results are presented not only in terms of the mean but additionally the standard deviation and the correlation coefficients. Such perturbation expressions provide one to assess small random variability in basic variables. - Highlights: • Perturbation methodology is applied to Radiation Physics. • Layer attenuation coefficient (LAC) and perturbed LAC are proposed for contact materials. • Perturbed linear attenuation coefficient is proposed. • Perturbed mass attenuation coefficient (PMAC) is proposed. • Perturbed cross-section is proposed

  18. Pulse patterning effect in optical pulse division multiplexing for flexible single wavelength multiple access optical network

    Jung, Sun-Young; Kim, Chang-Hun; Han, Sang-Kook

    2018-05-01

    A demand for high spectral efficiency requires multiple access within a single wavelength, but the uplink signals are significantly degraded because of optical beat interference (OBI) in intensity modulation/direct detection system. An optical pulse division multiplexing (OPDM) technique was proposed that could effectively reduce the OBI via a simple method as long as near-orthogonality is satisfied, but the condition was strict, and thus, the number of multiplexing units was very limited. We propose pulse pattern enhanced OPDM (e-OPDM) to reduce the OBI and improve the flexibility in multiple access within a single wavelength. The performance of the e-OPDM and patterning effect are experimentally verified after 23-km single mode fiber transmission. By employing pulse patterning in OPDM, the tight requirement was relaxed by extending the optical delay dynamic range. This could support more number of access with reduced OBI, which could eventually enhance a multiple access function.

  19. Systematic Assessment of the Impact of User Roles on Network Flow Patterns

    2017-09-01

    a system at IP address c_IPj can be expressed as D[ui][c_ip j]. Specifying use of a specific protocol ( prk ), server port (spl) and distant end IP...address (e_ipm) can be expressed as D[ui][c_ip j][ prk ][spl][e_ipm]. 3.2 Patterns Within Flow Sets We define patterns within a set of Netflow records as...address, protocol, server port, and endpoint IP address (D[ui][c_ip j][ prk ][spl][e_ipm]) we identified sequential flow pairs, matching each flow with

  20. Fibrin network pattern changes of platelet-rich fibrin in young versus old age group of individuals: A cell block cytology study

    Shravanthi Raghav Yajamanya

    2016-01-01

    Full Text Available Background: To evaluate variations in fibrin network patterns of the platelet-rich fibrin (PRF in different age groups. Materials and Methods: Ninety-five patients were divided into three age groups: Group 1: (20–39 years; Group 2: (40–59 years; and Group 3: (60 years and above. PRF was prepared from blood samples of all patients and were subjected to cell block cytology method of histological analysis and slides were prepared to histologically assess the age-related changes in (i fibrin network patterns in terms of density and (ii entrapment of platelets and white blood cells (WBCs within fibrin meshwork. Results: Two types of fibrin network pattern arrangements noticed: Dense and loose types in three age groups. However, there was a noticeable decrease in the dense type of fibrin network with progressing age and increase in the loose type of fibrin arrangement. Furthermore, variation in a number of platelets and WBCs entrapped within fibrin network in relation to age was noticed. Conclusion: From the current study it can be concluded that age can be considered as one of the influencing factors on quality of PRF in terms of fibrin network patterns and hence, platelet and WBCs entrapment within these fibrin networks.

  1. Patterning and predicting aquatic insect richness in four West-African coastal rivers using artificial neural networks

    Edia E.O.

    2010-10-01

    Full Text Available Despite their importance in stream management, the aquatic insect assemblages are still little known in West Africa. This is particularly true in South-Eastern Ivory Coast, where aquatic insect assemblages were hardly studied. We therefore aimed at characterising aquatic insect assemblages on four coastal rivers in South-Eastern Ivory Coast. Patterning aquatic insect assemblages was achieved using a Self-Organizing Map (SOM, an unsupervised Artificial Neural Networks (ANN method. This method was applied to pattern the samples based on the richness of five major orders of aquatic insects (Diptera, Ephemeroptera, Coleoptera, Trichoptera and Odonata. This permitted to identify three clusters that were mainly related to the local environmental status of sampling sites. Then, we used the environmental characteristics of the sites to predict, using a multilayer perceptron neural network (MLP, trained by BackPropagation algorithm (BP, a supervised ANN, the richness of the five insect orders. The BP showed high predictability (0.90 for both Diptera and Trichoptera, 0.84 for both Coleoptera and Odonata, 0.69 for Ephemeroptera. The most contributing variables in predicting the five insect order richness were pH, conductivity, total dissolved solids, water temperature, percentage of rock and the canopy. This underlines the crucial influence of both instream characteristics and riparian context.

  2. Patterns of subnet usage reveal distinct scales of regulation in the transcriptional regulatory network of Escherichia coli.

    Carsten Marr

    Full Text Available The set of regulatory interactions between genes, mediated by transcription factors, forms a species' transcriptional regulatory network (TRN. By comparing this network with measured gene expression data, one can identify functional properties of the TRN and gain general insight into transcriptional control. We define the subnet of a node as the subgraph consisting of all nodes topologically downstream of the node, including itself. Using a large set of microarray expression data of the bacterium Escherichia coli, we find that the gene expression in different subnets exhibits a structured pattern in response to environmental changes and genotypic mutation. Subnets with fewer changes in their expression pattern have a higher fraction of feed-forward loop motifs and a lower fraction of small RNA targets within them. Our study implies that the TRN consists of several scales of regulatory organization: (1 subnets with more varying gene expression controlled by both transcription factors and post-transcriptional RNA regulation and (2 subnets with less varying gene expression having more feed-forward loops and less post-transcriptional RNA regulation.

  3. Network analysis of pig movements: Loyalty patterns and contact chains of different holding types in Denmark

    Schulz, Jana; Boklund, Anette; Hisham Beshara Halasa, Tariq

    2017-01-01

    proportion of holdings in the largest strongly connected component. All production sites showed low levels of in-going contact chains and we observed a high level of out-going contact chain for breeding and multiplier herds. Except for livestock auctions, all transit sites also showed low levels of out......-going contact chains. Our results reflect the pyramidal structure of the underlying network. Based on the considered disease, the time frame for the calculation of network measurements needs to be adapted. Using these adapted values for loyalty and contact chains might help to identify holdings with high...... in the connection between holdings dealing with pigs, such as breeding and multiplier herds, production herds, slaughterhouses or traders. Pig movements, which occurred between 1st January 2006 and 31st December 2015 in Denmark, were summarized to investigate temporal trends such as the number of active holdings...

  4. Relationship of trade patterns of the Danish swine industry animal movements network to potential disease spread

    Bigras-Poulin, Michel; Barfod, Kristen; Mortensen, Sten

    2007-01-01

    homogeneity in farm-to-farm relationship should only be used for large-scale interpretation and for epidemic preparedness. The network approach, based on graph theory, can be used efficiently to express more precisely, on a local scale (premise), the heterogeneity of animal movements. This approach...... number of pigs was 24. The largest percentage of movements was from farm to abattoir (82.5%); the median number of pigs per movement was 24 and the maximum number was 2018. For the whole period the median and maximum Euclidean distances observed in farm-to-farm movements were 22 km and 289 km...... respectively, while in the farm-to-abattoir movements, they were 36.2 km and 285 km. The network related to one specific premise showed that the median number of premises was mainly away from slaughter pig farms (3) or breeder farms (26) and mainly to an abattoir (1535). The assumption that animal movements...

  5. Patterns of cooperation: fairness and coordination in networks of interacting agents

    Do, Anne-Ly; Rudolf, Lars; Gross, Thilo

    2010-06-01

    We study the self-assembly of a complex network of collaborations among self-interested agents. The agents can maintain different levels of cooperation with different partners. Further, they continuously, selectively and independently adapt the amount of resources allocated to each of their collaborations in order to maximize the obtained payoff. We show analytically that the system approaches a state in which the agents make identical investments, and links produce identical benefits. Despite this high degree of social coordination, some agents manage to secure privileged topological positions in the network, enabling them to extract high payoffs. Our analytical investigations provide a rationale for the emergence of unidirectional non-reciprocal collaborations and different responses to the withdrawal of a partner from an interaction that have been reported in the psychological literature.

  6. Toward Global Communication Networks: How Television is Forging New Thinking Patterns.

    Adams, Dennis M.; Fuchs, Mary

    1986-01-01

    Recent alliances between communication providers and computer manufacturers will lead to new technological combinations that will deliver visually-based ideas and information to a worldwide audience. Urges that those in charge of future video programs to consider their effects on children's language skills, thinking patterns, and intellectual…

  7. Conservation and diversification of an ancestral chordate gene regulatory network for dorsoventral patterning

    Kozmiková, Iryna; Smolíková, Jana; Vlček, Čestmír; Kozmik, Zbyněk

    2011-01-01

    Roč. 6, č. 2 (2011), e14650 E-ISSN 1932-6203 R&D Projects: GA AV ČR IAA500520604; GA AV ČR IAA500520908 Institutional research plan: CEZ:AV0Z50520514 Keywords : Wnt Bmp * amphioxus * axial patterning Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 4.092, year: 2011

  8. VA-Index: Quantifying Assortativity Patterns in Networks with Multidimensional Nodal Attributes (Open Access)

    2016-01-27

    through a vector each element of which captures the different types of locations he visits. Simi- larly, reviewers/buyers on electronic markets such...IEEE Internet Computing 2010; 14(2):15–23. doi: 10.1109/ MIC .2010.25 21. Zhao K, Ngamassi L, Yen J, Maitland C, Tapia A. Assortativity patterns in multi

  9. Artificial immune pattern recognition for damage detection in structural health monitoring sensor networks

    Chen, Bo; Zang, Chuanzhi

    2009-03-01

    This paper presents an artificial immune pattern recognition (AIPR) approach for the damage detection and classification in structures. An AIPR-based Structure Damage Classifier (AIPR-SDC) has been developed by mimicking immune recognition and learning mechanisms. The structure damage patterns are represented by feature vectors that are extracted from the structure's dynamic response measurements. The training process is designed based on the clonal selection principle in the immune system. The selective and adaptive features of the clonal selection algorithm allow the classifier to generate recognition feature vectors that are able to match the training data. In addition, the immune learning algorithm can learn and remember various data patterns by generating a set of memory cells that contains representative feature vectors for each class (pattern). The performance of the presented structure damage classifier has been validated using a benchmark structure proposed by the IASC-ASCE (International Association for Structural Control - American Society of Civil Engineers) Structural Health Monitoring Task Group. The validation results show a better classification success rate comparing to some of other classification algorithms.

  10. Detecting tactical patterns in basketball: comparison of merge self-organising maps and dynamic controlled neural networks.

    Kempe, Matthias; Grunz, Andreas; Memmert, Daniel

    2015-01-01

    The soaring amount of data, especially spatial-temporal data, recorded in recent years demands for advanced analysis methods. Neural networks derived from self-organizing maps established themselves as a useful tool to analyse static and temporal data. In this study, we applied the merge self-organising map (MSOM) to spatio-temporal data. To do so, we investigated the ability of MSOM's to analyse spatio-temporal data and compared its performance to the common dynamical controlled network (DyCoN) approach to analyse team sport position data. The position data of 10 players were recorded via the Ubisense tracking system during a basketball game. Furthermore, three different pre-selected plays were recorded for classification. Following data preparation, the different nets were trained with the data of the first half. The training success of both networks was evaluated by achieved entropy. The second half of the basketball game was presented to both nets for automatic classification. Both approaches were able to present the trained data extremely well and to detect the pre-selected plays correctly. In conclusion, MSOMs are a useful tool to analyse spatial-temporal data, especially in team sports. By their direct inclusion of different time length of tactical patterns, they open up new opportunities within team sports.

  11. Abnormal hubs of white matter networks in the frontal-parieto circuit contribute to depression discrimination via pattern classification.

    Qin, Jiaolong; Wei, Maobin; Liu, Haiyan; Chen, Jianhuai; Yan, Rui; Hua, Lingling; Zhao, Ke; Yao, Zhijian; Lu, Qing

    2014-12-01

    Previous studies had explored the diagnostic and prognostic value of the structural neuroimaging data of MDD and treated the whole brain voxels, the fractional anisotropy and the structural connectivity as classification features. To our best knowledge, no study examined the potential diagnostic value of the hubs of anatomical brain networks in MDD. The purpose of the current study was to provide an exploratory examination of the potential diagnostic and prognostic values of hubs of white matter brain networks in MDD discrimination and the corresponding impaired hub pattern via a multi-pattern analysis. We constructed white matter brain networks from 29 depressions and 30 healthy controls based on diffusion tensor imaging data, calculated nodal measures and identified hubs. Using these measures as features, two types of feature architectures were established, one only included hubs (HUB) and the other contained both hubs and non hubs. The support vector machine classifiers with Gaussian radial basis kernel were used after the feature selection. Moreover, the relative contribution of the features was estimated by means of the consensus features. Our results presented that the hubs (including the bilateral dorsolateral part of superior frontal gyrus, the left middle frontal gyrus, the bilateral middle temporal gyrus, and the bilateral inferior temporal gyrus) played an important role in distinguishing the depressions from healthy controls with the best accuracy of 83.05%. Moreover, most of the HUB consensus features located in the frontal-parieto circuit. These findings provided evidence that the hubs could be served as valuable potential diagnostic measure for MDD, and the hub-concentrated lesion distribution of MDD was primarily anchored within the frontal-parieto circuit. Copyright © 2014 Elsevier Inc. All rights reserved.

  12. Political Parties and Interest Groups Members' Patterns of Social Network Site Usage in Kyrgyzstan

    Elira Turdubaeva

    2014-11-01

    Full Text Available Kyrgyzstan, with a high level of political participation and an avant-garde position regarding internet access in Central Asia, broadband and social media penetration in the population, is a critical case for studying social network sites (SNSs in relation to political participation. This study analyzes the practices and attitudes of SNS users in Kyrgyzstan. Two types of users – members of political parties and members of interest organizations – are interviewed in focus groups about their practices and attitudes towards political content in the social network site Facebook. The findings indicate that, to some extent, the political engagement is indeed occurring within the Facebook environment, suggesting that the popular social networking sites (SNSs are an avenue for young people to express and share their political views. Facebook allowed users to share their political beliefs, support specific candidates, and interact with others on political issues. Participants’ perceptions regarding the appropriateness of political activity on Facebook, as well as the specific types of political activities they engaged in and witnessed within the site, were also explored.

  13. Neural network pattern recognition of lingual-palatal pressure for automated detection of swallow.

    Hadley, Aaron J; Krival, Kate R; Ridgel, Angela L; Hahn, Elizabeth C; Tyler, Dustin J

    2015-04-01

    We describe a novel device and method for real-time measurement of lingual-palatal pressure and automatic identification of the oral transfer phase of deglutition. Clinical measurement of the oral transport phase of swallowing is a complicated process requiring either placement of obstructive sensors or sitting within a fluoroscope or articulograph for recording. Existing detection algorithms distinguish oral events with EMG, sound, and pressure signals from the head and neck, but are imprecise and frequently result in false detection. We placed seven pressure sensors on a molded mouthpiece fitting over the upper teeth and hard palate and recorded pressure during a variety of swallow and non-swallow activities. Pressure measures and swallow times from 12 healthy and 7 Parkinson's subjects provided training data for a time-delay artificial neural network to categorize the recordings as swallow or non-swallow events. User-specific neural networks properly categorized 96 % of swallow and non-swallow events, while a generalized population-trained network was able to properly categorize 93 % of swallow and non-swallow events across all recordings. Lingual-palatal pressure signals are sufficient to selectively and specifically recognize the initiation of swallowing in healthy and dysphagic patients.

  14. Acceleration of spiking neural network based pattern recognition on NVIDIA graphics processors.

    Han, Bing; Taha, Tarek M

    2010-04-01

    There is currently a strong push in the research community to develop biological scale implementations of neuron based vision models. Systems at this scale are computationally demanding and generally utilize more accurate neuron models, such as the Izhikevich and the Hodgkin-Huxley models, in favor of the more popular integrate and fire model. We examine the feasibility of using graphics processing units (GPUs) to accelerate a spiking neural network based character recognition network to enable such large scale systems. Two versions of the network utilizing the Izhikevich and Hodgkin-Huxley models are implemented. Three NVIDIA general-purpose (GP) GPU platforms are examined, including the GeForce 9800 GX2, the Tesla C1060, and the Tesla S1070. Our results show that the GPGPUs can provide significant speedup over conventional processors. In particular, the fastest GPGPU utilized, the Tesla S1070, provided a speedup of 5.6 and 84.4 over highly optimized implementations on the fastest central processing unit (CPU) tested, a quadcore 2.67 GHz Xeon processor, for the Izhikevich and the Hodgkin-Huxley models, respectively. The CPU implementation utilized all four cores and the vector data parallelism offered by the processor. The results indicate that GPUs are well suited for this application domain.

  15. Internal Acoustic Transceivers Reveal the Annual Social Network Patterns in a Coastal Top Predator

    Haulsee, D.; Fox, D. A.; Breece, M.; Wetherbee, B.; Brown, L.; Kneebone, J.; Skomal, G.; Oliver, M. J.

    2016-02-01

    Sand Tigers (Carcharias taurus) are large apex predators resident in the coastal ocean along the Eastern US Coast. Although Delaware Bay and surrounding coastal waters are known summer "hot spots" for Sand Tigers, our understanding of their seasonal movements is less well known. Since 2007, we have implanted more than 300 VEMCO acoustic transmitters in Sand Tigers, which have been detected from Cape Canaveral, Florida to Long Island, New York by collaborators in the Atlantic Cooperative Telemetry (ACT) Network. During the summer of 2012, 20 Sand Tigers were implanted with VEMCO Mobile Transceivers (VMTs), which are capable of both transmitting and receiving coded acoustic pings. To date, two of the 20 sharks have been recaptured, and their VMTs recovered. VMTs recorded detections of 350 individuals, from 8 different species. We analyzed their intra- and interspecific social network, which allowed us to reconstruct the approximate locations of Sand Tigers throughout the year. Changes in the interspecific population dynamics throughout the year revealed evidence of fission-fusion social behavior, which is common in mammals, but rarely documented in non-mammalian species. This project is a unique look at the social network of an apex predator and is a useful model for studies quantifying the social structures of marine animals. In addition, understanding how the aggregations of this species changes (in terms of sex and size class segregation) on spatiotemporal scales is critical for effective protection of the species and will be useful as managers develop conservation plans along the East Coast.

  16. Relation between the Macroscopic Pattern of Elephant Ivory and Its Three-Dimensional Micro-Tubular Network

    Albéric, Marie; Dean, Mason N.; Gourrier, Aurélien; Wagermaier, Wolfgang; Dunlop, John W. C.; Staude, Andreas; Fratzl, Peter; Reiche, Ina

    2017-01-01

    Macroscopic, periodic, dark and bright patterns are observed on sections of elephant tusk, in the dentin part (ivory). The motifs—also called Schreger pattern—vary depending on the orientation in the tusk: on sections perpendicular to the tusk axis, a checkerboard pattern is present whereas on sections longitudinal to it, alternating stripes are observed. This pattern has been used to identify elephant and mammoth ivory in archeological artifacts and informs on the continuous tissue growth mechanisms of tusk. However, its origin, assumed to be related to the 3D structure of empty microtubules surrounded by the ivory matrix has yet to be characterized unequivocally. Based on 2D observations of the ivory microtubules by means of a variety of imaging techniques of three different planes (transverse, longitudinal and tangential to the tusk axis), we show that the dark areas of the macroscopic pattern are due to tubules oblique to the surface whereas bright areas are related to tubules parallel to it. The different microstructures observed in the three planes as well as the 3D data obtained by SR-μCT analysis allow us to propose a 3D model of the microtubule network with helical tubules phase-shifted in the tangential direction. The phase shift is a combination of a continuous phase shift of π every 1 mm with a stepwise phase shift of π/2 every 500 μm. By using 3D modeling, we show how the 3D helical model better represents the experimental microstructure observed in 2D planes compared to previous models in the literature. This brings new information on the origin of the unique Schreger pattern of elephant ivory, crucial for better understanding how archaeological objects were processed and for opening new routes to rethink how biological materials are built. PMID:28125603

  17. Persistence and storage of activity patterns in spiking recurrent cortical networks: modulation of sigmoid signals by after-hyperpolarization currents and acetylcholine.

    Palma, Jesse; Grossberg, Stephen; Versace, Massimiliano

    2012-01-01

    Many cortical networks contain recurrent architectures that transform input patterns before storing them in short-term memory (STM). Theorems in the 1970's showed how feedback signal functions in rate-based recurrent on-center off-surround networks control this process. A sigmoid signal function induces a quenching threshold below which inputs are suppressed as noise and above which they are contrast-enhanced before pattern storage. This article describes how changes in feedback signaling, neuromodulation, and recurrent connectivity may alter pattern processing in recurrent on-center off-surround networks of spiking neurons. In spiking neurons, fast, medium, and slow after-hyperpolarization (AHP) currents control sigmoid signal threshold and slope. Modulation of AHP currents by acetylcholine (ACh) can change sigmoid shape and, with it, network dynamics. For example, decreasing signal function threshold and increasing slope can lengthen the persistence of a partially contrast-enhanced pattern, increase the number of active cells stored in STM, or, if connectivity is distance-dependent, cause cell activities to cluster. These results clarify how cholinergic modulation by the basal forebrain may alter the vigilance of category learning circuits, and thus their sensitivity to predictive mismatches, thereby controlling whether learned categories code concrete or abstract features, as predicted by Adaptive Resonance Theory. The analysis includes global, distance-dependent, and interneuron-mediated circuits. With an appropriate degree of recurrent excitation and inhibition, spiking networks maintain a partially contrast-enhanced pattern for 800 ms or longer after stimuli offset, then resolve to no stored pattern, or to winner-take-all (WTA) stored patterns with one or multiple winners. Strengthening inhibition prolongs a partially contrast-enhanced pattern by slowing the transition to stability, while strengthening excitation causes more winners when the network

  18. Persistence and storage of activity patterns in spiking recurrent cortical networks:Modulation of sigmoid signals by after-hyperpolarization currents and acetylcholine

    Jesse ePalma

    2012-06-01

    Full Text Available Many cortical networks contain recurrent architectures that transform input patterns before storing them in short-term memory (STM. Theorems in the 1970’s showed how feedback signal functions in rate-based recurrent on-center off-surround networks control this process. A sigmoid signal function induces a quenching threshold below which inputs are suppressed as noise and above which they are contrast-enhanced before pattern storage. This article describes how changes in feedback signaling, neuromodulation, and recurrent connectivity may alter pattern processing in recurrent on-center off-surround networks of spiking neurons. In spiking neurons, fast, medium, and slow after-hyperpolarization (AHP currents control sigmoid signal threshold and slope. Modulation of AHP currents by acetylcholine (ACh can change sigmoid shape and, with it, network dynamics. For example, decreasing signal function threshold and increasing slope can lengthen the persistence of a partially contrast-enhanced pattern, increase the number of active cells stored in STM, or, if connectivity is distance-dependent, cause cell activities to cluster. These results clarify how cholinergic modulation by the basal forebrain may alter the vigilance of category learning circuits, and thus their sensitivity to predictive mismatches, thereby controlling whether learned categories code concrete or abstract features, as predicted by Adaptive Resonance Theory. The analysis includes global, distance-dependent, and interneuron-mediated circuits. With an appropriate degree of recurrent excitation and inhibition, spiking networks maintain a partially contrast-enhanced pattern for 800 milliseconds or longer after stimuli offset, then resolve to no stored pattern, or to winner-take-all stored patterns with one or multiple winners. Strengthening inhibition prolongs a partially contrast-enhanced pattern by slowing the transition to stability, while strengthening excitation causes more winners

  19. A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system

    Kaplan, Bernhard A.; Lansner, Anders

    2014-01-01

    Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin–Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian–Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian–Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures. PMID

  20. Non-perturbative versus perturbative renormalization of lattice operators

    Goeckeler, M.; Technische Hochschule Aachen; Horsley, R.; Ilgenfritz, E.M.; Oelrich, H.; Forschungszentrum Juelich GmbH; Schierholz, G.; Forschungszentrum Juelich GmbH; Perlt, H.; Schiller, A.; Rakow, P.

    1995-09-01

    Our objective is to compute the moments of the deep-inelastic structure functions of the nucleon on the lattice. A major source of uncertainty is the renormalization of the lattice operators that enter the calculation. In this talk we compare the renormalization constants of the most relevant twist-two bilinear quark operators which we have computed non-perturbatively and perturbatively to one loop order. Furthermore, we discuss the use of tadpole improved perturbation theory. (orig.)