Sample records for order correlation network

  1. Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks.

    PubMed

    Jovanović, Stojan; Rotter, Stefan

    2016-06-01

    The study of processes evolving on networks has recently become a very popular research field, not only because of the rich mathematical theory that underpins it, but also because of its many possible applications, a number of them in the field of biology. Indeed, molecular signaling pathways, gene regulation, predator-prey interactions and the communication between neurons in the brain can be seen as examples of networks with complex dynamics. The properties of such dynamics depend largely on the topology of the underlying network graph. In this work, we want to answer the following question: Knowing network connectivity, what can be said about the level of third-order correlations that will characterize the network dynamics? We consider a linear point process as a model for pulse-coded, or spiking activity in a neuronal network. Using recent results from theory of such processes, we study third-order correlations between spike trains in such a system and explain which features of the network graph (i.e. which topological motifs) are responsible for their emergence. Comparing two different models of network topology-random networks of Erdős-Rényi type and networks with highly interconnected hubs-we find that, in random networks, the average measure of third-order correlations does not depend on the local connectivity properties, but rather on global parameters, such as the connection probability. This, however, ceases to be the case in networks with a geometric out-degree distribution, where topological specificities have a strong impact on average correlations.

  2. Identification of Correlated GRACE Monthly Harmonic Coefficients Using Pattern Recognition and Neural Networks

    NASA Astrophysics Data System (ADS)

    Piretzidis, D.; Sra, G.; Sideris, M. G.

    2016-12-01

    This study explores new methods for identifying correlation errors in harmonic coefficients derived from monthly solutions of the Gravity Recovery and Climate Experiment (GRACE) satellite mission using pattern recognition and neural network algorithms. These correlation errors are evidenced in the differences between monthly solutions and can be suppressed using a de-correlation filter. In all studies so far, the implementation of the de-correlation filter starts from a specific minimum order (i.e., 11 for RL04 and 38 for RL05) until the maximum order of the monthly solution examined. This implementation method has two disadvantages, namely, the omission of filtering correlated coefficients of order less than the minimum order and the filtering of uncorrelated coefficients of order higher than the minimum order. In the first case, the filtered solution is not completely free of correlated errors, whereas the second case results in a monthly solution that suffers from loss of geophysical signal. In the present study, a new method of implementing the de-correlation filter is suggested, by identifying and filtering only the coefficients that show indications of high correlation. Several numerical and geometric properties of the harmonic coefficient series of all orders are examined. Extreme cases of both correlated and uncorrelated coefficients are selected, and their corresponding properties are used to train a two-layer feed-forward neural network. The objective of the neural network is to identify and quantify the correlation by providing the probability of an order of coefficients to be correlated. Results show good performance of the neural network, both in the validation stage of the training procedure and in the subsequent use of the trained network to classify independent coefficients. The neural network is also capable of identifying correlated coefficients even when a small number of training samples and neurons are used (e.g.,100 and 10, respectively).

  3. Tree Tensor Network State with Variable Tensor Order: An Efficient Multireference Method for Strongly Correlated Systems

    PubMed Central

    2015-01-01

    We study the tree-tensor-network-state (TTNS) method with variable tensor orders for quantum chemistry. TTNS is a variational method to efficiently approximate complete active space (CAS) configuration interaction (CI) wave functions in a tensor product form. TTNS can be considered as a higher order generalization of the matrix product state (MPS) method. The MPS wave function is formulated as products of matrices in a multiparticle basis spanning a truncated Hilbert space of the original CAS-CI problem. These matrices belong to active orbitals organized in a one-dimensional array, while tensors in TTNS are defined upon a tree-like arrangement of the same orbitals. The tree-structure is advantageous since the distance between two arbitrary orbitals in the tree scales only logarithmically with the number of orbitals N, whereas the scaling is linear in the MPS array. It is found to be beneficial from the computational costs point of view to keep strongly correlated orbitals in close vicinity in both arrangements; therefore, the TTNS ansatz is better suited for multireference problems with numerous highly correlated orbitals. To exploit the advantages of TTNS a novel algorithm is designed to optimize the tree tensor network topology based on quantum information theory and entanglement. The superior performance of the TTNS method is illustrated on the ionic-neutral avoided crossing of LiF. It is also shown that the avoided crossing of LiF can be localized using only ground state properties, namely one-orbital entanglement. PMID:25844072

  4. Higher-order clustering in networks

    NASA Astrophysics Data System (ADS)

    Yin, Hao; Benson, Austin R.; Leskovec, Jure

    2018-05-01

    A fundamental property of complex networks is the tendency for edges to cluster. The extent of the clustering is typically quantified by the clustering coefficient, which is the probability that a length-2 path is closed, i.e., induces a triangle in the network. However, higher-order cliques beyond triangles are crucial to understanding complex networks, and the clustering behavior with respect to such higher-order network structures is not well understood. Here we introduce higher-order clustering coefficients that measure the closure probability of higher-order network cliques and provide a more comprehensive view of how the edges of complex networks cluster. Our higher-order clustering coefficients are a natural generalization of the traditional clustering coefficient. We derive several properties about higher-order clustering coefficients and analyze them under common random graph models. Finally, we use higher-order clustering coefficients to gain new insights into the structure of real-world networks from several domains.

  5. Effects of high-order correlations on personalized recommendations for bipartite networks

    NASA Astrophysics Data System (ADS)

    Liu, Jian-Guo; Zhou, Tao; Che, Hong-An; Wang, Bing-Hong; Zhang, Yi-Cheng

    2010-02-01

    In this paper, we introduce a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the MCF, instead of the cosine similarity index, the user-user correlations are obtained by a diffusion process. Furthermore, by considering the second-order correlations, we design an effective algorithm that depresses the influence of mainstream preferences. Simulation results show that the algorithmic accuracy, measured by the average ranking score, is further improved by 20.45% and 33.25% in the optimal cases of MovieLens and Netflix data. More importantly, the optimal value λ depends approximately monotonously on the sparsity of the training set. Given a real system, we could estimate the optimal parameter according to the data sparsity, which makes this algorithm easy to be applied. In addition, two significant criteria of algorithmic performance, diversity and popularity, are also taken into account. Numerical results show that as the sparsity increases, the algorithm considering the second-order correlation can outperform the MCF simultaneously in all three criteria.

  6. Clustering Coefficients for Correlation Networks.

    PubMed

    Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

    2018-01-01

    Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly

  7. Clustering Coefficients for Correlation Networks

    PubMed Central

    Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

    2018-01-01

    Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly

  8. Self-adaptive tensor network states with multi-site correlators

    NASA Astrophysics Data System (ADS)

    Kovyrshin, Arseny; Reiher, Markus

    2017-12-01

    We introduce the concept of self-adaptive tensor network states (SATNSs) based on multi-site correlators. The SATNS ansatz gradually extends its variational space incorporating the most important next-order correlators into the ansatz for the wave function. The selection of these correlators is guided by entanglement-entropy measures from quantum information theory. By sequentially introducing variational parameters and adjusting them to the system under study, the SATNS ansatz achieves keeping their number significantly smaller than the total number of full-configuration interaction parameters. The SATNS ansatz is studied for manganocene in its lowest-energy sextet and doublet states; the latter of which is known to be difficult to describe. It is shown that the SATNS parametrization solves the convergence issues found for previous correlator-based tensor network states.

  9. Measuring and modeling correlations in multiplex networks.

    PubMed

    Nicosia, Vincenzo; Latora, Vito

    2015-09-01

    The interactions among the elementary components of many complex systems can be qualitatively different. Such systems are therefore naturally described in terms of multiplex or multilayer networks, i.e., networks where each layer stands for a different type of interaction between the same set of nodes. There is today a growing interest in understanding when and why a description in terms of a multiplex network is necessary and more informative than a single-layer projection. Here we contribute to this debate by presenting a comprehensive study of correlations in multiplex networks. Correlations in node properties, especially degree-degree correlations, have been thoroughly studied in single-layer networks. Here we extend this idea to investigate and characterize correlations between the different layers of a multiplex network. Such correlations are intrinsically multiplex, and we first study them empirically by constructing and analyzing several multiplex networks from the real world. In particular, we introduce various measures to characterize correlations in the activity of the nodes and in their degree at the different layers and between activities and degrees. We show that real-world networks exhibit indeed nontrivial multiplex correlations. For instance, we find cases where two layers of the same multiplex network are positively correlated in terms of node degrees, while other two layers are negatively correlated. We then focus on constructing synthetic multiplex networks, proposing a series of models to reproduce the correlations observed empirically and/or to assess their relevance.

  10. Robustness of Oscillatory Behavior in Correlated Networks

    PubMed Central

    Sasai, Takeyuki; Morino, Kai; Tanaka, Gouhei; Almendral, Juan A.; Aihara, Kazuyuki

    2015-01-01

    Understanding network robustness against failures of network units is useful for preventing large-scale breakdowns and damages in real-world networked systems. The tolerance of networked systems whose functions are maintained by collective dynamical behavior of the network units has recently been analyzed in the framework called dynamical robustness of complex networks. The effect of network structure on the dynamical robustness has been examined with various types of network topology, but the role of network assortativity, or degree–degree correlations, is still unclear. Here we study the dynamical robustness of correlated (assortative and disassortative) networks consisting of diffusively coupled oscillators. Numerical analyses for the correlated networks with Poisson and power-law degree distributions show that network assortativity enhances the dynamical robustness of the oscillator networks but the impact of network disassortativity depends on the detailed network connectivity. Furthermore, we theoretically analyze the dynamical robustness of correlated bimodal networks with two-peak degree distributions and show the positive impact of the network assortativity. PMID:25894574

  11. Earthquake correlations and networks: A comparative study

    NASA Astrophysics Data System (ADS)

    Krishna Mohan, T. R.; Revathi, P. G.

    2011-04-01

    We quantify the correlation between earthquakes and use the same to extract causally connected earthquake pairs. Our correlation metric is a variation on the one introduced by Baiesi and Paczuski [M. Baiesi and M. Paczuski, Phys. Rev. E EULEEJ1539-375510.1103/PhysRevE.69.06610669, 066106 (2004)]. A network of earthquakes is then constructed from the time-ordered catalog and with links between the more correlated ones. A list of recurrences to each of the earthquakes is identified employing correlation thresholds to demarcate the most meaningful ones in each cluster. Data pertaining to three different seismic regions (viz., California, Japan, and the Himalayas) are comparatively analyzed using such a network model. The distribution of recurrence lengths and recurrence times are two of the key features analyzed to draw conclusions about the universal aspects of such a network model. We find that the unimodal feature of recurrence length distribution, which helps to associate typical rupture lengths with different magnitude earthquakes, is robust across the different seismic regions. The out-degree of the networks shows a hub structure rooted on the large magnitude earthquakes. In-degree distribution is seen to be dependent on the density of events in the neighborhood. Power laws, with two regimes having different exponents, are obtained with recurrence time distribution. The first regime confirms the Omori law for aftershocks while the second regime, with a faster falloff for the larger recurrence times, establishes that pure spatial recurrences also follow a power-law distribution. The crossover to the second power-law regime can be taken to be signaling the end of the aftershock regime in an objective fashion.

  12. Correlated network of networks enhances robustness against catastrophic failures.

    PubMed

    Min, Byungjoon; Zheng, Muhua

    2018-01-01

    Networks in nature rarely function in isolation but instead interact with one another with a form of a network of networks (NoN). A network of networks with interdependency between distinct networks contains instability of abrupt collapse related to the global rule of activation. As a remedy of the collapse instability, here we investigate a model of correlated NoN. We find that the collapse instability can be removed when hubs provide the majority of interconnections and interconnections are convergent between hubs. Thus, our study identifies a stable structure of correlated NoN against catastrophic failures. Our result further suggests a plausible way to enhance network robustness by manipulating connection patterns, along with other methods such as controlling the state of node based on a local rule.

  13. Correlated network of networks enhances robustness against catastrophic failures

    PubMed Central

    Zheng, Muhua

    2018-01-01

    Networks in nature rarely function in isolation but instead interact with one another with a form of a network of networks (NoN). A network of networks with interdependency between distinct networks contains instability of abrupt collapse related to the global rule of activation. As a remedy of the collapse instability, here we investigate a model of correlated NoN. We find that the collapse instability can be removed when hubs provide the majority of interconnections and interconnections are convergent between hubs. Thus, our study identifies a stable structure of correlated NoN against catastrophic failures. Our result further suggests a plausible way to enhance network robustness by manipulating connection patterns, along with other methods such as controlling the state of node based on a local rule. PMID:29668730

  14. Using Neural Networks to Describe Tracer Correlations

    NASA Technical Reports Server (NTRS)

    Lary, D. J.; Mueller, M. D.; Mussa, H. Y.

    2003-01-01

    Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). In this study a neural network using Quickprop learning and one hidden layer with eight nodes was able to reproduce the CH4-N2O correlation with a correlation co- efficient of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. Such as the dataset from the Halogen Occultation Experiment (HALOE) which has continuously observed CH4, (but not N2O) from 1991 till the present. The neural network Fortran code used is available for download.

  15. Temporal correlation coefficient for directed networks.

    PubMed

    Büttner, Kathrin; Salau, Jennifer; Krieter, Joachim

    2016-01-01

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

  16. Correlations in star networks: from Bell inequalities to network inequalities

    NASA Astrophysics Data System (ADS)

    Tavakoli, Armin; Olivier Renou, Marc; Gisin, Nicolas; Brunner, Nicolas

    2017-07-01

    The problem of characterizing classical and quantum correlations in networks is considered. Contrary to the usual Bell scenario, where distant observers share a physical system emitted by one common source, a network features several independent sources, each distributing a physical system to a subset of observers. In the quantum setting, the observers can perform joint measurements on initially independent systems, which may lead to strong correlations across the whole network. In this work, we introduce a technique to systematically map a Bell inequality to a family of Bell-type inequalities bounding classical correlations on networks in a star-configuration. Also, we show that whenever a given Bell inequality can be violated by some entangled state ρ, then all the corresponding network inequalities can be violated by considering many copies of ρ distributed in the star network. The relevance of these ideas is illustrated by applying our method to a specific multi-setting Bell inequality. We derive the corresponding network inequalities, and study their quantum violations.

  17. Fourth-Order Spatial Correlation of Thermal Light

    NASA Astrophysics Data System (ADS)

    Wen, Feng; Zhang, Xun; Xue, Xin-Xin; Sun, Jia; Song, Jian-Ping; Zhang, Yan-Peng

    2014-11-01

    We investigate the fourth-order spatial correlation properties of pseudo-thermal light in the photon counting regime, and apply the Klyshko advanced-wave picture to describe the process of four-photon coincidence counting measurement. We deduce the theory of a proof-of-principle four-photon coincidence counting configuration, and find that if the four randomly radiated photons come from the same radiation area and are indistinguishable in principle, the fourth-order correlation of them is 24 times larger than that when four photons come from different radiation areas. In addition, we also show that the higher-order spatial correlation function can be decomposed into multiple lower-order correlation functions, and the contrast and visibility of low-order correlation peaks are less than those of higher orders, while the resolutions all are identical. This study may be useful for better understanding the four-photon interference and multi-channel correlation imaging.

  18. Neural Classifiers for Learning Higher-Order Correlations

    NASA Astrophysics Data System (ADS)

    Güler, Marifi

    1999-01-01

    Studies by various authors suggest that higher-order networks can be more powerful and are biologically more plausible with respect to the more traditional multilayer networks. These architectures make explicit use of nonlinear interactions between input variables in the form of higher-order units or product units. If it is known a priori that the problem to be implemented possesses a given set of invariances like in the translation, rotation, and scale invariant pattern recognition problems, those invariances can be encoded, thus eliminating all higher-order terms which are incompatible with the invariances. In general, however, it is a serious set-back that the complexity of learning increases exponentially with the size of inputs. This paper reviews higher-order networks and introduces an implicit representation in which learning complexity is mainly decided by the number of higher-order terms to be learned and increases only linearly with the input size.

  19. The Dichotomy in Degree Correlation of Biological Networks

    PubMed Central

    Hao, Dapeng; Li, Chuanxing

    2011-01-01

    Most complex networks from different areas such as biology, sociology or technology, show a correlation on node degree where the possibility of a link between two nodes depends on their connectivity. It is widely believed that complex networks are either disassortative (links between hubs are systematically suppressed) or assortative (links between hubs are enhanced). In this paper, we analyze a variety of biological networks and find that they generally show a dichotomous degree correlation. We find that many properties of biological networks can be explained by this dichotomy in degree correlation, including the neighborhood connectivity, the sickle-shaped clustering coefficient distribution and the modularity structure. This dichotomy distinguishes biological networks from real disassortative networks or assortative networks such as the Internet and social networks. We suggest that the modular structure of networks accounts for the dichotomy in degree correlation and vice versa, shedding light on the source of modularity in biological networks. We further show that a robust and well connected network necessitates the dichotomy of degree correlation, suggestive of an evolutionary motivation for its existence. Finally, we suggest that a dichotomous degree correlation favors a centrally connected modular network, by which the integrity of network and specificity of modules might be reconciled. PMID:22164269

  20. Quantum correlations with no causal order

    PubMed Central

    Oreshkov, Ognyan; Costa, Fabio; Brukner, Časlav

    2012-01-01

    The idea that events obey a definite causal order is deeply rooted in our understanding of the world and at the basis of the very notion of time. But where does causal order come from, and is it a necessary property of nature? Here, we address these questions from the standpoint of quantum mechanics in a new framework for multipartite correlations that does not assume a pre-defined global causal structure but only the validity of quantum mechanics locally. All known situations that respect causal order, including space-like and time-like separated experiments, are captured by this framework in a unified way. Surprisingly, we find correlations that cannot be understood in terms of definite causal order. These correlations violate a 'causal inequality' that is satisfied by all space-like and time-like correlations. We further show that in a classical limit causal order always arises, which suggests that space-time may emerge from a more fundamental structure in a quantum-to-classical transition. PMID:23033068

  1. Frontal parietal control network regulates the anti-correlated default and dorsal attention networks.

    PubMed

    Gao, Wei; Lin, Weili

    2012-01-01

    Recent reports demonstrate the anti-correlated behaviors between the default (DF) and the dorsal attention (DA) networks. We aimed to investigate the roles of the frontal parietal control (FPC) network in regulating the two anti-correlated networks through three experimental conditions, including resting, continuous self-paced/attended sequential finger tapping (FT), and natural movie watching (MW), respectively. The two goal-directed tasks were chosen to engage either one of the two competing networks-FT for DA whereas MW for default. We hypothesized that FPC will selectively augment/suppress either network depending on how the task targets the specific network; FPC will positively correlate with the target network, but negatively correlate with the network anti-correlated with the target network. We further hypothesized that significant causal links from FPC to both DA and DF are present during all three experimental conditions, supporting the initiative regulating role of FPC over the two opposing systems. Consistent with our hypotheses, FPC exhibited a significantly higher positive correlation with DA (P = 0.0095) whereas significantly more negative correlation with default (P = 0.0025) during FT when compared to resting. Completely opposite to that observed during FT, the FPC was significantly anti-correlated with DA (P = 2.1e-6) whereas positively correlated with default (P = 0.0035) during MW. Furthermore, extensive causal links from FPC to both DA and DF were observed across all three experimental states. Together, our results strongly support the notion that the FPC regulates the anti-correlated default and DA networks. Copyright © 2011 Wiley Periodicals, Inc.

  2. Default Mode and Executive Networks Areas: Association with the Serial Order in Divergent Thinking

    PubMed Central

    Heinonen, Jarmo; Numminen, Jussi; Hlushchuk, Yevhen; Antell, Henrik; Taatila, Vesa; Suomala, Jyrki

    2016-01-01

    Scientific findings have suggested a two-fold structure of the cognitive process. By using the heuristic thinking mode, people automatically process information that tends to be invariant across days, whereas by using the explicit thinking mode people explicitly process information that tends to be variant compared to typical previously learned information patterns. Previous studies on creativity found an association between creativity and the brain regions in the prefrontal cortex, the anterior cingulate cortex, the default mode network and the executive network. However, which neural networks contribute to the explicit mode of thinking during idea generation remains an open question. We employed an fMRI paradigm to examine which brain regions were activated when participants (n = 16) mentally generated alternative uses for everyday objects. Most previous creativity studies required participants to verbalize responses during idea generation, whereas in this study participants produced mental alternatives without verbalizing. This study found activation in the left anterior insula when contrasting idea generation and object identification. This finding suggests that the insula (part of the brain’s salience network) plays a role in facilitating both the central executive and default mode networks to activate idea generation. We also investigated closely the effect of the serial order of idea being generated on brain responses: The amplitude of fMRI responses correlated positively with the serial order of idea being generated in the anterior cingulate cortex, which is part of the central executive network. Positive correlation with the serial order was also observed in the regions typically assigned to the default mode network: the precuneus/cuneus, inferior parietal lobule and posterior cingulate cortex. These networks support the explicit mode of thinking and help the individual to convert conventional mental models to new ones. The serial order correlated

  3. Higher-Order Neural Networks Recognize Patterns

    NASA Technical Reports Server (NTRS)

    Reid, Max B.; Spirkovska, Lilly; Ochoa, Ellen

    1996-01-01

    Networks of higher order have enhanced capabilities to distinguish between different two-dimensional patterns and to recognize those patterns. Also enhanced capabilities to "learn" patterns to be recognized: "trained" with far fewer examples and, therefore, in less time than necessary to train comparable first-order neural networks.

  4. Multifractal cross-correlation effects in two-variable time series of complex network vertex observables

    NASA Astrophysics Data System (ADS)

    OświÈ©cimka, Paweł; Livi, Lorenzo; DroŻdŻ, Stanisław

    2016-10-01

    We investigate the scaling of the cross-correlations calculated for two-variable time series containing vertex properties in the context of complex networks. Time series of such observables are obtained by means of stationary, unbiased random walks. We consider three vertex properties that provide, respectively, short-, medium-, and long-range information regarding the topological role of vertices in a given network. In order to reveal the relation between these quantities, we applied the multifractal cross-correlation analysis technique, which provides information about the nonlinear effects in coupling of time series. We show that the considered network models are characterized by unique multifractal properties of the cross-correlation. In particular, it is possible to distinguish between Erdös-Rényi, Barabási-Albert, and Watts-Strogatz networks on the basis of fractal cross-correlation. Moreover, the analysis of protein contact networks reveals characteristics shared with both scale-free and small-world models.

  5. Frontal Parietal Control Network Regulates the Anti-Correlated Default and Dorsal Attention Networks

    PubMed Central

    Gao, Wei; Lin, Weili

    2011-01-01

    Recent reports demonstrate the anti-correlated behaviors between the default and the dorsal attention (DA) networks. We aimed to investigate the roles of the frontal parietal control (FPC) network in regulating the two anti-correlated networks through three experimental conditions, including resting, continuous self-paced/attended sequential finger tapping (FT), and natural movie watching (MW), respectively. The two goal-directed tasks were chosen to engage either one of the two competing networks—FT for DA whereas MW for default. We hypothesized that FPC will selectively augment/suppress either network depending on how the task targets the specific network; FPC will positively correlate with the target network, but negatively correlate with the network anti-correlated with the target network. We further hypothesized that significant causal links from FPC to both DA and DF are present during all three experimental conditions, supporting the initiative regulating role of FPC over the two opposing systems. Consistent with our hypotheses, FPC exhibited a significantly higher positive correlation with DA (P = 0.0095) whereas significantly more negative correlation with default (P = 0.0025) during FT when compared to resting. Completely opposite to that observed during FT, the FPC was significantly anti-correlated with DA (P = 2.1e-6) whereas positively correlated with default (P = 0.0035) during MW. Furthermore, extensive causal links from FPC to both DA and DF were observed across all three experimental states. Together, our results strongly support the notion that the FPC regulates the anti-correlated default and DA networks. PMID:21391263

  6. Reconstructing networks from dynamics with correlated noise

    NASA Astrophysics Data System (ADS)

    Tam, H. C.; Ching, Emily S. C.; Lai, Pik-Yin

    2018-07-01

    Reconstructing the structure of complex networks from measurements of the nodes is a challenge in many branches of science. External influences are always present and act as a noise to the networks of interest. In this paper, we present a method for reconstructing networks from measured dynamics of the nodes subjected to correlated noise that cannot be approximated by a white noise. This method can reconstruct the links of both bidirectional and directed networks, the correlation time and strength of the noise, and also the relative coupling strength of the links when the coupling functions have certain properties. Our method is built upon theoretical relations between network structure and measurable quantities from the dynamics that we have derived for systems that have fixed point dynamics in the noise-free limit. Using these theoretical results, we can further explain the shortcomings of two common practices of inferring links for bidirectional networks using the Pearson correlation coefficient and the partial correlation coefficient.

  7. Change Point Detection in Correlation Networks

    NASA Astrophysics Data System (ADS)

    Barnett, Ian; Onnela, Jukka-Pekka

    2016-01-01

    Many systems of interacting elements can be conceptualized as networks, where network nodes represent the elements and network ties represent interactions between the elements. In systems where the underlying network evolves, it is useful to determine the points in time where the network structure changes significantly as these may correspond to functional change points. We propose a method for detecting change points in correlation networks that, unlike previous change point detection methods designed for time series data, requires minimal distributional assumptions. We investigate the difficulty of change point detection near the boundaries of the time series in correlation networks and study the power of our method and competing methods through simulation. We also show the generalizable nature of the method by applying it to stock price data as well as fMRI data.

  8. Attack tolerance of correlated time-varying social networks with well-defined communities

    NASA Astrophysics Data System (ADS)

    Sur, Souvik; Ganguly, Niloy; Mukherjee, Animesh

    2015-02-01

    In this paper, we investigate the efficiency and the robustness of information transmission for real-world social networks, modeled as time-varying instances, under targeted attack in shorter time spans. We observe that these quantities are markedly higher than that of the randomized versions of the considered networks. An important factor that drives this efficiency or robustness is the presence of short-time correlations across the network instances which we quantify by a novel metric the-edge emergence factor, denoted as ξ. We find that standard targeted attacks are not effective in collapsing this network structure. Remarkably, if the hourly community structures of the temporal network instances are attacked with the largest size community attacked first, the second largest next and so on, the network soon collapses. This behavior, we show is an outcome of the fact that the edge emergence factor bears a strong positive correlation with the size ordered community structures.

  9. Unidimensional factor models imply weaker partial correlations than zero-order correlations.

    PubMed

    van Bork, Riet; Grasman, Raoul P P P; Waldorp, Lourens J

    2018-06-01

    In this paper we present a new implication of the unidimensional factor model. We prove that the partial correlation between two observed variables that load on one factor given any subset of other observed variables that load on this factor lies between zero and the zero-order correlation between these two observed variables. We implement this result in an empirical bootstrap test that rejects the unidimensional factor model when partial correlations are identified that are either stronger than the zero-order correlation or have a different sign than the zero-order correlation. We demonstrate the use of the test in an empirical data example with data consisting of fourteen items that measure extraversion.

  10. Optical-Correlator Neural Network Based On Neocognitron

    NASA Technical Reports Server (NTRS)

    Chao, Tien-Hsin; Stoner, William W.

    1994-01-01

    Multichannel optical correlator implements shift-invariant, high-discrimination pattern-recognizing neural network based on paradigm of neocognitron. Selected as basic building block of this neural network because invariance under shifts is inherent advantage of Fourier optics included in optical correlators in general. Neocognitron is conceptual electronic neural-network model for recognition of visual patterns. Multilayer processing achieved by iteratively feeding back output of feature correlator to input spatial light modulator and updating Fourier filters. Neural network trained by use of characteristic features extracted from target images. Multichannel implementation enables parallel processing of large number of selected features.

  11. Node Survival in Networks under Correlated Attacks

    PubMed Central

    Hao, Yan; Armbruster, Dieter; Hütt, Marc-Thorsten

    2015-01-01

    We study the interplay between correlations, dynamics, and networks for repeated attacks on a socio-economic network. As a model system we consider an insurance scheme against disasters that randomly hit nodes, where a node in need receives support from its network neighbors. The model is motivated by gift giving among the Maasai called Osotua. Survival of nodes under different disaster scenarios (uncorrelated, spatially, temporally and spatio-temporally correlated) and for different network architectures are studied with agent-based numerical simulations. We find that the survival rate of a node depends dramatically on the type of correlation of the disasters: Spatially and spatio-temporally correlated disasters increase the survival rate; purely temporally correlated disasters decrease it. The type of correlation also leads to strong inequality among the surviving nodes. We introduce the concept of disaster masking to explain some of the results of our simulations. We also analyze the subsets of the networks that were activated to provide support after fifty years of random disasters. They show qualitative differences for the different disaster scenarios measured by path length, degree, clustering coefficient, and number of cycles. PMID:25932635

  12. High-order nonuniformly correlated beams

    NASA Astrophysics Data System (ADS)

    Wu, Dan; Wang, Fei; Cai, Yangjian

    2018-02-01

    We have introduced a class of partially coherent beams with spatially varying correlations named high-order nonuniformly correlated (HNUC) beams, as an extension of conventional nonuniformly correlated (NUC) beams. Such beams bring a new parameter (mode order) which is used to tailor the spatial coherence properties. The behavior of the spectral density of the HNUC beams on propagation has been investigated through numerical examples with the help of discrete model decomposition and fast Fourier transform (FFT) algorithm. Our results reveal that by selecting the mode order appropriately, the more sharpened intensity maxima can be achieved at a certain propagation distance compared to that of the NUC beams, and the lateral shift of the intensity maxima on propagation is closed related to the mode order. Furthermore, analytical expressions for the r.m.s width and the propagation factor of the HNUC beams on free-space propagation are derived by means of Wigner distribution function. The influence of initial beam parameters on the evolution of the r.m.s width and the propagation factor, and the relation between the r.m.s width and the occurring of the sharpened intensity maxima on propagation have been studied and discussed in detail.

  13. Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations

    PubMed Central

    van Albada, Sacha Jennifer; Helias, Moritz; Diesmann, Markus

    2015-01-01

    Network models are routinely downscaled compared to nature in terms of numbers of nodes or edges because of a lack of computational resources, often without explicit mention of the limitations this entails. While reliable methods have long existed to adjust parameters such that the first-order statistics of network dynamics are conserved, here we show that limitations already arise if also second-order statistics are to be maintained. The temporal structure of pairwise averaged correlations in the activity of recurrent networks is determined by the effective population-level connectivity. We first show that in general the converse is also true and explicitly mention degenerate cases when this one-to-one relationship does not hold. The one-to-one correspondence between effective connectivity and the temporal structure of pairwise averaged correlations implies that network scalings should preserve the effective connectivity if pairwise averaged correlations are to be held constant. Changes in effective connectivity can even push a network from a linearly stable to an unstable, oscillatory regime and vice versa. On this basis, we derive conditions for the preservation of both mean population-averaged activities and pairwise averaged correlations under a change in numbers of neurons or synapses in the asynchronous regime typical of cortical networks. We find that mean activities and correlation structure can be maintained by an appropriate scaling of the synaptic weights, but only over a range of numbers of synapses that is limited by the variance of external inputs to the network. Our results therefore show that the reducibility of asynchronous networks is fundamentally limited. PMID:26325661

  14. The correlation of metrics in complex networks with applications in functional brain networks

    NASA Astrophysics Data System (ADS)

    Li, C.; Wang, H.; de Haan, W.; Stam, C. J.; Van Mieghem, P.

    2011-11-01

    An increasing number of network metrics have been applied in network analysis. If metric relations were known better, we could more effectively characterize networks by a small set of metrics to discover the association between network properties/metrics and network functioning. In this paper, we investigate the linear correlation coefficients between widely studied network metrics in three network models (Bárabasi-Albert graphs, Erdös-Rényi random graphs and Watts-Strogatz small-world graphs) as well as in functional brain networks of healthy subjects. The metric correlations, which we have observed and theoretically explained, motivate us to propose a small representative set of metrics by including only one metric from each subset of mutually strongly dependent metrics. The following contributions are considered important. (a) A network with a given degree distribution can indeed be characterized by a small representative set of metrics. (b) Unweighted networks, which are obtained from weighted functional brain networks with a fixed threshold, and Erdös-Rényi random graphs follow a similar degree distribution. Moreover, their metric correlations and the resultant representative metrics are similar as well. This verifies the influence of degree distribution on metric correlations. (c) Most metric correlations can be explained analytically. (d) Interestingly, the most studied metrics so far, the average shortest path length and the clustering coefficient, are strongly correlated and, thus, redundant. Whereas spectral metrics, though only studied recently in the context of complex networks, seem to be essential in network characterizations. This representative set of metrics tends to both sufficiently and effectively characterize networks with a given degree distribution. In the study of a specific network, however, we have to at least consider the representative set so that important network properties will not be neglected.

  15. Negative Correlations in Visual Cortical Networks

    PubMed Central

    Chelaru, Mircea I.; Dragoi, Valentin

    2016-01-01

    The amount of information encoded by cortical circuits depends critically on the capacity of nearby neurons to exhibit trial-to-trial (noise) correlations in their responses. Depending on their sign and relationship to signal correlations, noise correlations can either increase or decrease the population code accuracy relative to uncorrelated neuronal firing. Whereas positive noise correlations have been extensively studied using experimental and theoretical tools, the functional role of negative correlations in cortical circuits has remained elusive. We addressed this issue by performing multiple-electrode recording in the superficial layers of the primary visual cortex (V1) of alert monkey. Despite the fact that positive noise correlations decayed exponentially with the difference in the orientation preference between cells, negative correlations were uniformly distributed across the population. Using a statistical model for Fisher Information estimation, we found that a mild increase in negative correlations causes a sharp increase in network accuracy even when mean correlations were held constant. To examine the variables controlling the strength of negative correlations, we implemented a recurrent spiking network model of V1. We found that increasing local inhibition and reducing excitation causes a decrease in the firing rates of neurons while increasing the negative noise correlations, which in turn increase the population signal-to-noise ratio and network accuracy. Altogether, these results contribute to our understanding of the neuronal mechanism involved in the generation of negative correlations and their beneficial impact on cortical circuit function. PMID:25217468

  16. Generalization of Clustering Coefficients to Signed Correlation Networks

    PubMed Central

    Costantini, Giulio; Perugini, Marco

    2014-01-01

    The recent interest in network analysis applications in personality psychology and psychopathology has put forward new methodological challenges. Personality and psychopathology networks are typically based on correlation matrices and therefore include both positive and negative edge signs. However, some applications of network analysis disregard negative edges, such as computing clustering coefficients. In this contribution, we illustrate the importance of the distinction between positive and negative edges in networks based on correlation matrices. The clustering coefficient is generalized to signed correlation networks: three new indices are introduced that take edge signs into account, each derived from an existing and widely used formula. The performances of the new indices are illustrated and compared with the performances of the unsigned indices, both on a signed simulated network and on a signed network based on actual personality psychology data. The results show that the new indices are more resistant to sample variations in correlation networks and therefore have higher convergence compared with the unsigned indices both in simulated networks and with real data. PMID:24586367

  17. Tensor Spectral Clustering for Partitioning Higher-order Network Structures.

    PubMed

    Benson, Austin R; Gleich, David F; Leskovec, Jure

    2015-01-01

    Spectral graph theory-based methods represent an important class of tools for studying the structure of networks. Spectral methods are based on a first-order Markov chain derived from a random walk on the graph and thus they cannot take advantage of important higher-order network substructures such as triangles, cycles, and feed-forward loops. Here we propose a Tensor Spectral Clustering (TSC) algorithm that allows for modeling higher-order network structures in a graph partitioning framework. Our TSC algorithm allows the user to specify which higher-order network structures (cycles, feed-forward loops, etc.) should be preserved by the network clustering. Higher-order network structures of interest are represented using a tensor, which we then partition by developing a multilinear spectral method. Our framework can be applied to discovering layered flows in networks as well as graph anomaly detection, which we illustrate on synthetic networks. In directed networks, a higher-order structure of particular interest is the directed 3-cycle, which captures feedback loops in networks. We demonstrate that our TSC algorithm produces large partitions that cut fewer directed 3-cycles than standard spectral clustering algorithms.

  18. Tensor Spectral Clustering for Partitioning Higher-order Network Structures

    PubMed Central

    Benson, Austin R.; Gleich, David F.; Leskovec, Jure

    2016-01-01

    Spectral graph theory-based methods represent an important class of tools for studying the structure of networks. Spectral methods are based on a first-order Markov chain derived from a random walk on the graph and thus they cannot take advantage of important higher-order network substructures such as triangles, cycles, and feed-forward loops. Here we propose a Tensor Spectral Clustering (TSC) algorithm that allows for modeling higher-order network structures in a graph partitioning framework. Our TSC algorithm allows the user to specify which higher-order network structures (cycles, feed-forward loops, etc.) should be preserved by the network clustering. Higher-order network structures of interest are represented using a tensor, which we then partition by developing a multilinear spectral method. Our framework can be applied to discovering layered flows in networks as well as graph anomaly detection, which we illustrate on synthetic networks. In directed networks, a higher-order structure of particular interest is the directed 3-cycle, which captures feedback loops in networks. We demonstrate that our TSC algorithm produces large partitions that cut fewer directed 3-cycles than standard spectral clustering algorithms. PMID:27812399

  19. Percolation of secret correlations in a network

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Leverrier, Anthony; Garcia-Patron, Raul; Research Laboratory of Electronics, MIT, Cambridge, MA 02139

    In this work, we explore the analogy between entanglement and secret classical correlations in the context of large networks--more precisely, the question of percolation of secret correlations in a network. It is known that entanglement percolation in quantum networks can display a highly nontrivial behavior depending on the topology of the network and on the presence of entanglement between the nodes. Here we show that this behavior, thought to be of a genuine quantum nature, also occurs in a classical context.

  20. Characterization of complex networks by higher order neighborhood properties

    NASA Astrophysics Data System (ADS)

    Andrade, R. F. S.; Miranda, J. G. V.; Pinho, S. T. R.; Lobão, T. P.

    2008-01-01

    A concept of higher order neighborhood in complex networks, introduced previously [Phys. Rev. E 73, 046101 (2006)], is systematically explored to investigate larger scale structures in complex networks. The basic idea is to consider each higher order neighborhood as a network in itself, represented by a corresponding adjacency matrix, and to settle a plenty of new parameters in order to obtain a best characterization of the whole network. Usual network indices are then used to evaluate the properties of each neighborhood. The identification of high order neighborhoods is also regarded as intermediary step towards the evaluation of global network properties, like the diameter, average shortest path between node, and network fractal dimension. Results for a large number of typical networks are presented and discussed.

  1. A generative spike train model with time-structured higher order correlations.

    PubMed

    Trousdale, James; Hu, Yu; Shea-Brown, Eric; Josić, Krešimir

    2013-01-01

    Emerging technologies are revealing the spiking activity in ever larger neural ensembles. Frequently, this spiking is far from independent, with correlations in the spike times of different cells. Understanding how such correlations impact the dynamics and function of neural ensembles remains an important open problem. Here we describe a new, generative model for correlated spike trains that can exhibit many of the features observed in data. Extending prior work in mathematical finance, this generalized thinning and shift (GTaS) model creates marginally Poisson spike trains with diverse temporal correlation structures. We give several examples which highlight the model's flexibility and utility. For instance, we use it to examine how a neural network responds to highly structured patterns of inputs. We then show that the GTaS model is analytically tractable, and derive cumulant densities of all orders in terms of model parameters. The GTaS framework can therefore be an important tool in the experimental and theoretical exploration of neural dynamics.

  2. Learning Bayesian Networks from Correlated Data

    NASA Astrophysics Data System (ADS)

    Bae, Harold; Monti, Stefano; Montano, Monty; Steinberg, Martin H.; Perls, Thomas T.; Sebastiani, Paola

    2016-05-01

    Bayesian networks are probabilistic models that represent complex distributions in a modular way and have become very popular in many fields. There are many methods to build Bayesian networks from a random sample of independent and identically distributed observations. However, many observational studies are designed using some form of clustered sampling that introduces correlations between observations within the same cluster and ignoring this correlation typically inflates the rate of false positive associations. We describe a novel parameterization of Bayesian networks that uses random effects to model the correlation within sample units and can be used for structure and parameter learning from correlated data without inflating the Type I error rate. We compare different learning metrics using simulations and illustrate the method in two real examples: an analysis of genetic and non-genetic factors associated with human longevity from a family-based study, and an example of risk factors for complications of sickle cell anemia from a longitudinal study with repeated measures.

  3. Influence of Choice of Null Network on Small-World Parameters of Structural Correlation Networks

    PubMed Central

    Hosseini, S. M. Hadi; Kesler, Shelli R.

    2013-01-01

    In recent years, coordinated variations in brain morphology (e.g., volume, thickness) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1) networks constructed by topology randomization (TOP), 2) networks matched to the distributional properties of the observed covariance matrix (HQS), and 3) networks generated from correlation of randomized input data (COR). The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures. PMID:23840672

  4. WGCNA: an R package for weighted correlation network analysis

    PubMed Central

    Langfelder, Peter; Horvath, Steve

    2008-01-01

    Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at . PMID:19114008

  5. WGCNA: an R package for weighted correlation network analysis.

    PubMed

    Langfelder, Peter; Horvath, Steve

    2008-12-29

    Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA.

  6. Effect of correlations on controllability transition in network control

    PubMed Central

    Nie, Sen; Wang, Xu-Wen; Wang, Bing-Hong; Jiang, Luo-Luo

    2016-01-01

    The network control problem has recently attracted an increasing amount of attention, owing to concerns including the avoidance of cascading failures of power-grids and the management of ecological networks. It has been proven that numerical control can be achieved if the number of control inputs exceeds a certain transition point. In the present study, we investigate the effect of degree correlation on the numerical controllability in networks whose topological structures are reconstructed from both real and modeling systems, and we find that the transition point of the number of control inputs depends strongly on the degree correlation in both undirected and directed networks with moderately sparse links. More interestingly, the effect of the degree correlation on the transition point cannot be observed in dense networks for numerical controllability, which contrasts with the corresponding result for structural controllability. In particular, for directed random networks and scale-free networks, the influence of the degree correlation is determined by the types of correlations. Our approach provides an understanding of control problems in complex sparse networks. PMID:27063294

  7. Social contagions on correlated multiplex networks

    NASA Astrophysics Data System (ADS)

    Wang, Wei; Cai, Meng; Zheng, Muhua

    2018-06-01

    The existence of interlayer degree correlations has been disclosed by abundant multiplex network analysis. However, how they impose on the dynamics of social contagions are remain largely unknown. In this paper, we propose a non-Markovian social contagion model in multiplex networks with inter-layer degree correlations to delineate the behavior spreading, and develop an edge-based compartmental (EBC) theory to describe the model. We find that multiplex networks promote the final behavior adoption size. Remarkably, it can be observed that the growth pattern of the final behavior adoption size, versus the behavioral information transmission probability, changes from discontinuous to continuous once decreasing the behavior adoption threshold in one layer. We finally unravel that the inter-layer degree correlations play a role on the final behavior adoption size but have no effects on the growth pattern, which is coincidence with our prediction by using the suggested theory.

  8. The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced?

    PubMed Central

    Murphy, Kevin; Birn, Rasmus M.; Handwerker, Daniel A.; Jones, Tyler B.; Bandettini, Peter A.

    2009-01-01

    Low-frequency fluctuations in fMRI signal have been used to map several consistent resting state networks in the brain. Using the posterior cingulate cortex as a seed region, functional connectivity analyses have found not only positive correlations in the default mode network but negative correlations in another resting state network related to attentional processes. The interpretation is that the human brain is intrinsically organized into dynamic, anti-correlated functional networks. Global variations of the BOLD signal are often considered nuisance effects and are commonly removed using a general linear model (GLM) technique. This global signal regression method has been shown to introduce negative activation measures in standard fMRI analyses. The topic of this paper is whether such a correction technique could be the cause of anti-correlated resting state networks in functional connectivity analyses. Here we show that, after global signal regression, correlation values to a seed voxel must sum to a negative value. Simulations also show that small phase differences between regions can lead to spurious negative correlation values. A combination breath holding and visual task demonstrates that the relative phase of global and local signals can affect connectivity measures and that, experimentally, global signal regression leads to bell-shaped correlation value distributions, centred on zero. Finally, analyses of negatively correlated networks in resting state data show that global signal regression is most likely the cause of anti-correlations. These results call into question the interpretation of negatively correlated regions in the brain when using global signal regression as an initial processing step. PMID:18976716

  9. The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?

    PubMed

    Murphy, Kevin; Birn, Rasmus M; Handwerker, Daniel A; Jones, Tyler B; Bandettini, Peter A

    2009-02-01

    Low-frequency fluctuations in fMRI signal have been used to map several consistent resting state networks in the brain. Using the posterior cingulate cortex as a seed region, functional connectivity analyses have found not only positive correlations in the default mode network but negative correlations in another resting state network related to attentional processes. The interpretation is that the human brain is intrinsically organized into dynamic, anti-correlated functional networks. Global variations of the BOLD signal are often considered nuisance effects and are commonly removed using a general linear model (GLM) technique. This global signal regression method has been shown to introduce negative activation measures in standard fMRI analyses. The topic of this paper is whether such a correction technique could be the cause of anti-correlated resting state networks in functional connectivity analyses. Here we show that, after global signal regression, correlation values to a seed voxel must sum to a negative value. Simulations also show that small phase differences between regions can lead to spurious negative correlation values. A combination breath holding and visual task demonstrates that the relative phase of global and local signals can affect connectivity measures and that, experimentally, global signal regression leads to bell-shaped correlation value distributions, centred on zero. Finally, analyses of negatively correlated networks in resting state data show that global signal regression is most likely the cause of anti-correlations. These results call into question the interpretation of negatively correlated regions in the brain when using global signal regression as an initial processing step.

  10. Effects of temporal correlations in social multiplex networks.

    PubMed

    Starnini, Michele; Baronchelli, Andrea; Pastor-Satorras, Romualdo

    2017-08-17

    Multi-layered networks represent a major advance in the description of natural complex systems, and their study has shed light on new physical phenomena. Despite its importance, however, the role of the temporal dimension in their structure and function has not been investigated in much detail so far. Here we study the temporal correlations between layers exhibited by real social multiplex networks. At a basic level, the presence of such correlations implies a certain degree of predictability in the contact pattern, as we quantify by an extension of the entropy and mutual information analyses proposed for the single-layer case. At a different level, we demonstrate that temporal correlations are a signature of a 'multitasking' behavior of network agents, characterized by a higher level of switching between different social activities than expected in a uncorrelated pattern. Moreover, temporal correlations significantly affect the dynamics of coupled epidemic processes unfolding on the network. Our work opens the way for the systematic study of temporal multiplex networks and we anticipate it will be of interest to researchers in a broad array of fields.

  11. Lagged correlation networks

    NASA Astrophysics Data System (ADS)

    Curme, Chester

    Technological advances have provided scientists with large high-dimensional datasets that describe the behaviors of complex systems: from the statistics of energy levels in complex quantum systems, to the time-dependent transcription of genes, to price fluctuations among assets in a financial market. In this environment, where it may be difficult to infer the joint distribution of the data, network science has flourished as a way to gain insight into the structure and organization of such systems by focusing on pairwise interactions. This work focuses on a particular setting, in which a system is described by multivariate time series data. We consider time-lagged correlations among elements in this system, in such a way that the measured interactions among elements are asymmetric. Finally, we allow these interactions to be characteristically weak, so that statistical uncertainties may be important to consider when inferring the structure of the system. We introduce a methodology for constructing statistically validated networks to describe such a system, extend the methodology to accommodate interactions with a periodic component, and show how consideration of bipartite community structures in these networks can aid in the construction of robust statistical models. An example of such a system is a financial market, in which high frequency returns data may be used to describe contagion, or the spreading of shocks in price among assets. These data provide the experimental testing ground for our methodology. We study NYSE data from both the present day and one decade ago, examine the time scales over which the validated lagged correlation networks exist, and relate differences in the topological properties of the networks to an increasing economic efficiency. We uncover daily periodicities in the validated interactions, and relate our findings to explanations of the Epps Effect, an empirical phenomenon of financial time series. We also study bipartite community

  12. Correlational Neural Networks.

    PubMed

    Chandar, Sarath; Khapra, Mitesh M; Larochelle, Hugo; Ravindran, Balaraman

    2016-02-01

    Common representation learning (CRL), wherein different descriptions (or views) of the data are embedded in a common subspace, has been receiving a lot of attention recently. Two popular paradigms here are canonical correlation analysis (CCA)-based approaches and autoencoder (AE)-based approaches. CCA-based approaches learn a joint representation by maximizing correlation of the views when projected to the common subspace. AE-based methods learn a common representation by minimizing the error of reconstructing the two views. Each of these approaches has its own advantages and disadvantages. For example, while CCA-based approaches outperform AE-based approaches for the task of transfer learning, they are not as scalable as the latter. In this work, we propose an AE-based approach, correlational neural network (CorrNet), that explicitly maximizes correlation among the views when projected to the common subspace. Through a series of experiments, we demonstrate that the proposed CorrNet is better than AE and CCA with respect to its ability to learn correlated common representations. We employ CorrNet for several cross-language tasks and show that the representations learned using it perform better than the ones learned using other state-of-the-art approaches.

  13. Sampling networks with prescribed degree correlations

    NASA Astrophysics Data System (ADS)

    Del Genio, Charo; Bassler, Kevin; Erdos, Péter; Miklos, István; Toroczkai, Zoltán

    2014-03-01

    A feature of a network known to affect its structural and dynamical properties is the presence of correlations amongst the node degrees. Degree correlations are a measure of how much the connectivity of a node influences the connectivity of its neighbours, and they are fundamental in the study of processes such as the spreading of information or epidemics, the cascading failures of damaged systems and the evolution of social relations. We introduce a method, based on novel mathematical results, that allows the exact sampling of networks where the number of connections between nodes of any given connectivity is specified. Our algorithm provides a weight associated to each sample, thereby allowing network observables to be measured according to any desired distribution, and it is guaranteed to always terminate successfully in polynomial time. Thus, our new approach provides a preferred tool for scientists to model complex systems of current relevance, and enables researchers to precisely study correlated networks with broad societal importance. CIDG acknowledges support by the European Commission's FP7 through grant No. 288021. KEB acknowledges support from the NSF through grant DMR?1206839. KEB, PE, IM and ZT acknowledge support from AFSOR and DARPA through grant FA?9550-12-1-0405.

  14. Robustness of networks formed from interdependent correlated networks under intentional attacks

    NASA Astrophysics Data System (ADS)

    Liu, Long; Meng, Ke; Dong, Zhaoyang

    2018-02-01

    We study the problem of intentional attacks targeting to interdependent networks generated with known degree distribution (in-degree oriented model) or distribution of interlinks (out-degree oriented model). In both models, each node's degree is correlated with the number of its links that connect to the other network. For both models, varying the correlation coefficient has a significant effect on the robustness of a system undergoing random attacks or attacks targeting nodes with low degree. For a system with an assortative relationship between in-degree and out-degree, reducing the broadness of networks' degree distributions can increase the resistance of systems against intentional attacks.

  15. Synchronization from Second Order Network Connectivity Statistics

    PubMed Central

    Zhao, Liqiong; Beverlin, Bryce; Netoff, Theoden; Nykamp, Duane Q.

    2011-01-01

    We investigate how network structure can influence the tendency for a neuronal network to synchronize, or its synchronizability, independent of the dynamical model for each neuron. The synchrony analysis takes advantage of the framework of second order networks, which defines four second order connectivity statistics based on the relative frequency of two-connection network motifs. The analysis identifies two of these statistics, convergent connections, and chain connections, as highly influencing the synchrony. Simulations verify that synchrony decreases with the frequency of convergent connections and increases with the frequency of chain connections. These trends persist with simulations of multiple models for the neuron dynamics and for different types of networks. Surprisingly, divergent connections, which determine the fraction of shared inputs, do not strongly influence the synchrony. The critical role of chains, rather than divergent connections, in influencing synchrony can be explained by their increasing the effective coupling strength. The decrease of synchrony with convergent connections is primarily due to the resulting heterogeneity in firing rates. PMID:21779239

  16. Functional cortical network in alpha band correlates with social bargaining.

    PubMed

    Billeke, Pablo; Zamorano, Francisco; Chavez, Mario; Cosmelli, Diego; Aboitiz, Francisco

    2014-01-01

    Solving demanding tasks requires fast and flexible coordination among different brain areas. Everyday examples of this are the social dilemmas in which goals tend to clash, requiring one to weigh alternative courses of action in limited time. In spite of this fact, there are few studies that directly address the dynamics of flexible brain network integration during social interaction. To study the preceding, we carried out EEG recordings while subjects played a repeated version of the Ultimatum Game in both human (social) and computer (non-social) conditions. We found phase synchrony (inter-site-phase-clustering) modulation in alpha band that was specific to the human condition and independent of power modulation. The strength and patterns of the inter-site-phase-clustering of the cortical networks were also modulated, and these modulations were mainly in frontal and parietal regions. Moreover, changes in the individuals' alpha network structure correlated with the risk of the offers made only in social conditions. This correlation was independent of changes in power and inter-site-phase-clustering strength. Our results indicate that, when subjects believe they are participating in a social interaction, a specific modulation of functional cortical networks in alpha band takes place, suggesting that phase synchrony of alpha oscillations could serve as a mechanism by which different brain areas flexibly interact in order to adapt ongoing behavior in socially demanding contexts.

  17. Functional Cortical Network in Alpha Band Correlates with Social Bargaining

    PubMed Central

    Billeke, Pablo; Zamorano, Francisco; Chavez, Mario; Cosmelli, Diego; Aboitiz, Francisco

    2014-01-01

    Solving demanding tasks requires fast and flexible coordination among different brain areas. Everyday examples of this are the social dilemmas in which goals tend to clash, requiring one to weigh alternative courses of action in limited time. In spite of this fact, there are few studies that directly address the dynamics of flexible brain network integration during social interaction. To study the preceding, we carried out EEG recordings while subjects played a repeated version of the Ultimatum Game in both human (social) and computer (non-social) conditions. We found phase synchrony (inter-site-phase-clustering) modulation in alpha band that was specific to the human condition and independent of power modulation. The strength and patterns of the inter-site-phase-clustering of the cortical networks were also modulated, and these modulations were mainly in frontal and parietal regions. Moreover, changes in the individuals’ alpha network structure correlated with the risk of the offers made only in social conditions. This correlation was independent of changes in power and inter-site-phase-clustering strength. Our results indicate that, when subjects believe they are participating in a social interaction, a specific modulation of functional cortical networks in alpha band takes place, suggesting that phase synchrony of alpha oscillations could serve as a mechanism by which different brain areas flexibly interact in order to adapt ongoing behavior in socially demanding contexts. PMID:25286240

  18. General formulation of long-range degree correlations in complex networks

    NASA Astrophysics Data System (ADS)

    Fujiki, Yuka; Takaguchi, Taro; Yakubo, Kousuke

    2018-06-01

    We provide a general framework for analyzing degree correlations between nodes separated by more than one step (i.e., beyond nearest neighbors) in complex networks. One joint and four conditional probability distributions are introduced to fully describe long-range degree correlations with respect to degrees k and k' of two nodes and shortest path length l between them. We present general relations among these probability distributions and clarify the relevance to nearest-neighbor degree correlations. Unlike nearest-neighbor correlations, some of these probability distributions are meaningful only in finite-size networks. Furthermore, as a baseline to determine the existence of intrinsic long-range degree correlations in a network other than inevitable correlations caused by the finite-size effect, the functional forms of these probability distributions for random networks are analytically evaluated within a mean-field approximation. The utility of our argument is demonstrated by applying it to real-world networks.

  19. Revisiting node-based SIR models in complex networks with degree correlations

    NASA Astrophysics Data System (ADS)

    Wang, Yi; Cao, Jinde; Alofi, Abdulaziz; AL-Mazrooei, Abdullah; Elaiw, Ahmed

    2015-11-01

    In this paper, we consider two growing networks which will lead to the degree-degree correlations between two nearest neighbors in the network. When the network grows to some certain size, we introduce an SIR-like disease such as pandemic influenza H1N1/09 to the population. Due to its rapid spread, the population size changes slowly, and thus the disease spreads on correlated networks with approximately fixed size. To predict the disease evolution on correlated networks, we first review two node-based SIR models incorporating degree correlations and an edge-based SIR model without considering degree correlation, and then compare the predictions of these models with stochastic SIR simulations, respectively. We find that the edge-based model, even without considering degree correlations, agrees much better than the node-based models incorporating degree correlations with stochastic SIR simulations in many respects. Moreover, simulation results show that for networks with positive correlation, the edge-based model provides a better upper bound of the cumulative incidence than the node-based SIR models, whereas for networks with negative correlation, it provides a lower bound of the cumulative incidence.

  20. The Correlation Fractal Dimension of Complex Networks

    NASA Astrophysics Data System (ADS)

    Wang, Xingyuan; Liu, Zhenzhen; Wang, Mogei

    2013-05-01

    The fractality of complex networks is studied by estimating the correlation dimensions of the networks. Comparing with the previous algorithms of estimating the box dimension, our algorithm achieves a significant reduction in time complexity. For four benchmark cases tested, that is, the Escherichia coli (E. Coli) metabolic network, the Homo sapiens protein interaction network (H. Sapiens PIN), the Saccharomyces cerevisiae protein interaction network (S. Cerevisiae PIN) and the World Wide Web (WWW), experiments are provided to demonstrate the validity of our algorithm.

  1. Higher order correlations of IRAS galaxies

    NASA Technical Reports Server (NTRS)

    Meiksin, Avery; Szapudi, Istvan; Szalay, Alexander

    1992-01-01

    The higher order irreducible angular correlation functions are derived up to the eight-point function, for a sample of 4654 IRAS galaxies, flux-limited at 1.2 Jy in the 60 microns band. The correlations are generally found to be somewhat weaker than those for the optically selected galaxies, consistent with the visual impression of looser clusters in the IRAS sample. It is found that the N-point correlation functions can be expressed as the symmetric sum of products of N - 1 two-point functions, although the correlations above the four-point function are consistent with zero. The coefficients are consistent with the hierarchical clustering scenario as modeled by Hamilton and by Schaeffer.

  2. Design of order statistics filters using feedforward neural networks

    NASA Astrophysics Data System (ADS)

    Maslennikova, Yu. S.; Bochkarev, V. V.

    2016-08-01

    In recent years significant progress have been made in the development of nonlinear data processing techniques. Such techniques are widely used in digital data filtering and image enhancement. Many of the most effective nonlinear filters based on order statistics. The widely used median filter is the best known order statistic filter. Generalized form of these filters could be presented based on Lloyd's statistics. Filters based on order statistics have excellent robustness properties in the presence of impulsive noise. In this paper, we present special approach for synthesis of order statistics filters using artificial neural networks. Optimal Lloyd's statistics are used for selecting of initial weights for the neural network. Adaptive properties of neural networks provide opportunities to optimize order statistics filters for data with asymmetric distribution function. Different examples demonstrate the properties and performance of presented approach.

  3. Improving the Quality of Service and Security of Military Networks with a Network Tasking Order Process

    DTIC Science & Technology

    2010-09-01

    IMPROVING THE QUALITY OF SERVICE AND SECURITY OF MILITARY NETWORKS WITH A NETWORK TASKING ORDER...United States. AFIT/DCS/ENG/10-09 IMPROVING THE QUALITY OF SERVICE AND SECURITY OF MILITARY NETWORKS WITH A NETWORK TASKING ORDER PROCESS...USAF September 2010 APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED AFIT/DCS/ENG/10-09 IMPROVING THE QUALITY OF SERVICE AND

  4. Ring correlations in random networks.

    PubMed

    Sadjadi, Mahdi; Thorpe, M F

    2016-12-01

    We examine the correlations between rings in random network glasses in two dimensions as a function of their separation. Initially, we use the topological separation (measured by the number of intervening rings), but this leads to pseudo-long-range correlations due to a lack of topological charge neutrality in the shells surrounding a central ring. This effect is associated with the noncircular nature of the shells. It is, therefore, necessary to use the geometrical distance between ring centers. Hence we find a generalization of the Aboav-Weaire law out to larger distances, with the correlations between rings decaying away when two rings are more than about three rings apart.

  5. Higher-order neural network software for distortion invariant object recognition

    NASA Technical Reports Server (NTRS)

    Reid, Max B.; Spirkovska, Lilly

    1991-01-01

    The state-of-the-art in pattern recognition for such applications as automatic target recognition and industrial robotic vision relies on digital image processing. We present a higher-order neural network model and software which performs the complete feature extraction-pattern classification paradigm required for automatic pattern recognition. Using a third-order neural network, we demonstrate complete, 100 percent accurate invariance to distortions of scale, position, and in-plate rotation. In a higher-order neural network, feature extraction is built into the network, and does not have to be learned. Only the relatively simple classification step must be learned. This is key to achieving very rapid training. The training set is much smaller than with standard neural network software because the higher-order network only has to be shown one view of each object to be learned, not every possible view. The software and graphical user interface run on any Sun workstation. Results of the use of the neural software in autonomous robotic vision systems are presented. Such a system could have extensive application in robotic manufacturing.

  6. BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks.

    PubMed

    Richiardi, Jonas; Altmann, Andre; Milazzo, Anna-Clare; Chang, Catie; Chakravarty, M Mallar; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Bromberg, Uli; Büchel, Christian; Conrod, Patricia; Fauth-Bühler, Mira; Flor, Herta; Frouin, Vincent; Gallinat, Jürgen; Garavan, Hugh; Gowland, Penny; Heinz, Andreas; Lemaître, Hervé; Mann, Karl F; Martinot, Jean-Luc; Nees, Frauke; Paus, Tomáš; Pausova, Zdenka; Rietschel, Marcella; Robbins, Trevor W; Smolka, Michael N; Spanagel, Rainer; Ströhle, Andreas; Schumann, Gunter; Hawrylycz, Mike; Poline, Jean-Baptiste; Greicius, Michael D

    2015-06-12

    During rest, brain activity is synchronized between different regions widely distributed throughout the brain, forming functional networks. However, the molecular mechanisms supporting functional connectivity remain undefined. We show that functional brain networks defined with resting-state functional magnetic resonance imaging can be recapitulated by using measures of correlated gene expression in a post mortem brain tissue data set. The set of 136 genes we identify is significantly enriched for ion channels. Polymorphisms in this set of genes significantly affect resting-state functional connectivity in a large sample of healthy adolescents. Expression levels of these genes are also significantly associated with axonal connectivity in the mouse. The results provide convergent, multimodal evidence that resting-state functional networks correlate with the orchestrated activity of dozens of genes linked to ion channel activity and synaptic function. Copyright © 2015, American Association for the Advancement of Science.

  7. Neural network post-processing of grayscale optical correlator

    NASA Technical Reports Server (NTRS)

    Lu, Thomas T; Hughlett, Casey L.; Zhoua, Hanying; Chao, Tien-Hsin; Hanan, Jay C.

    2005-01-01

    In this paper we present the use of a radial basis function neural network (RBFNN) as a post-processor to assist the optical correlator to identify the objects and to reject false alarms. Image plane features near the correlation peaks are extracted and fed to the neural network for analysis. The approach is capable of handling large number of object variations and filter sets. Preliminary experimental results are presented and the performance is analyzed.

  8. Functional network connectivity analysis based on partial correlation in Alzheimer's disease

    NASA Astrophysics Data System (ADS)

    Zhang, Nan; Guan, Xiaoting; Zhang, Yumei; Li, Jingjing; Chen, Hongyan; Chen, Kewei; Fleisher, Adam; Yao, Li; Wu, Xia

    2009-02-01

    Functional network connectivity (FNC) measures the temporal dependency among the time courses of functional networks. However, the marginal correlation between two networks used in the classic FNC analysis approach doesn't separate the FNC from the direct/indirect effects of other networks. In this study, we proposed an alternative approach based on partial correlation to evaluate the FNC, since partial correlation based FNC can reveal the direct interaction between a pair of networks, removing dependencies or influences from others. Previous studies have demonstrated less task-specific activation and less rest-state activity in Alzheimer's disease (AD). We applied present approach to contrast FNC differences of resting state network (RSN) between AD and normal controls (NC). The fMRI data under resting condition were collected from 15 AD and 16 NC. FNC was calculated for each pair of six RSNs identified using Group ICA, thus resulting in 15 (2 out of 6) pairs for each subject. Partial correlation based FNC analysis indicated 6 pairs significant differences between groups, while marginal correlation only revealed 2 pairs (involved in the partial correlation results). Additionally, patients showed lower correlation than controls among most of the FNC differences. Our results provide new evidences for the disconnection hypothesis in AD.

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

    NASA Astrophysics Data System (ADS)

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

    2013-03-01

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

  10. Critical behavior and correlations on scale-free small-world networks: Application to network design

    NASA Astrophysics Data System (ADS)

    Ostilli, M.; Ferreira, A. L.; Mendes, J. F. F.

    2011-06-01

    We analyze critical phenomena on networks generated as the union of hidden variable models (networks with any desired degree sequence) with arbitrary graphs. The resulting networks are general small worlds similar to those à la Watts and Strogatz, but with a heterogeneous degree distribution. We prove that the critical behavior (thermal or percolative) remains completely unchanged by the presence of finite loops (or finite clustering). Then, we show that, in large but finite networks, correlations of two given spins may be strong, i.e., approximately power-law-like, at any temperature. Quite interestingly, if γ is the exponent for the power-law distribution of the vertex degree, for γ⩽3 and with or without short-range couplings, such strong correlations persist even in the thermodynamic limit, contradicting the common opinion that, in mean-field models, correlations always disappear in this limit. Finally, we provide the optimal choice of rewiring under which percolation phenomena in the rewired network are best performed, a natural criterion to reach best communication features, at least in noncongested regimes.

  11. The Software Correlator of the Chinese VLBI Network

    NASA Technical Reports Server (NTRS)

    Zheng, Weimin; Quan, Ying; Shu, Fengchun; Chen, Zhong; Chen, Shanshan; Wang, Weihua; Wang, Guangli

    2010-01-01

    The software correlator of the Chinese VLBI Network (CVN) has played an irreplaceable role in the CVN routine data processing, e.g., in the Chinese lunar exploration project. This correlator will be upgraded to process geodetic and astronomical observation data. In the future, with several new stations joining the network, CVN will carry out crustal movement observations, quick UT1 measurements, astrophysical observations, and deep space exploration activities. For the geodetic or astronomical observations, we need a wide-band 10-station correlator. For spacecraft tracking, a realtime and highly reliable correlator is essential. To meet the scientific and navigation requirements of CVN, two parallel software correlators in the multiprocessor environments are under development. A high speed, 10-station prototype correlator using the mixed Pthreads and MPI (Massage Passing Interface) parallel algorithm on a computer cluster platform is being developed. Another real-time software correlator for spacecraft tracking adopts the thread-parallel technology, and it runs on the SMP (Symmetric Multiple Processor) servers. Both correlators have the characteristic of flexible structure and scalability.

  12. Business cycles' correlation and systemic risk of the Japanese supplier-customer network.

    PubMed

    Krichene, Hazem; Chakraborty, Abhijit; Inoue, Hiroyasu; Fujiwara, Yoshi

    2017-01-01

    This work aims to study and explain the business cycle correlations of the Japanese production network. We consider the supplier-customer network, which is a directed network representing the trading links between Japanese firms (links from suppliers to customers). The community structure of this network is determined by applying the Infomap algorithm. Each community is defined by its GDP and its associated business cycle. Business cycle correlations between communities are estimated based on copula theory. Then, based on firms' attributes and network topology, these correlations are explained through linear econometric models. The results show strong evidence of business cycle correlations in the Japanese production network. A significant systemic risk is found for high negative or positive shocks. These correlations are explained mainly by the sector and by geographic similarities. Moreover, our results highlight the higher vulnerability of small communities and small firms, which is explained by the disassortative mixing of the production network.

  13. Business cycles’ correlation and systemic risk of the Japanese supplier-customer network

    PubMed Central

    Chakraborty, Abhijit; Inoue, Hiroyasu; Fujiwara, Yoshi

    2017-01-01

    This work aims to study and explain the business cycle correlations of the Japanese production network. We consider the supplier-customer network, which is a directed network representing the trading links between Japanese firms (links from suppliers to customers). The community structure of this network is determined by applying the Infomap algorithm. Each community is defined by its GDP and its associated business cycle. Business cycle correlations between communities are estimated based on copula theory. Then, based on firms’ attributes and network topology, these correlations are explained through linear econometric models. The results show strong evidence of business cycle correlations in the Japanese production network. A significant systemic risk is found for high negative or positive shocks. These correlations are explained mainly by the sector and by geographic similarities. Moreover, our results highlight the higher vulnerability of small communities and small firms, which is explained by the disassortative mixing of the production network. PMID:29059233

  14. Revealing cancer subtypes with higher-order correlations applied to imaging and omics data.

    PubMed

    Graim, Kiley; Liu, Tiffany Ting; Achrol, Achal S; Paull, Evan O; Newton, Yulia; Chang, Steven D; Harsh, Griffith R; Cordero, Sergio P; Rubin, Daniel L; Stuart, Joshua M

    2017-03-31

    Patient stratification to identify subtypes with different disease manifestations, severity, and expected survival time is a critical task in cancer diagnosis and treatment. While stratification approaches using various biomarkers (including high-throughput gene expression measurements) for patient-to-patient comparisons have been successful in elucidating previously unseen subtypes, there remains an untapped potential of incorporating various genotypic and phenotypic data to discover novel or improved groupings. Here, we present HOCUS, a unified analytical framework for patient stratification that uses a community detection technique to extract subtypes out of sparse patient measurements. HOCUS constructs a patient-to-patient network from similarities in the data and iteratively groups and reconstructs the network into higher order clusters. We investigate the merits of using higher-order correlations to cluster samples of cancer patients in terms of their associations with survival outcomes. In an initial test of the method, the approach identifies cancer subtypes in mutation data of glioblastoma, ovarian, breast, prostate, and bladder cancers. In several cases, HOCUS provides an improvement over using the molecular features directly to compare samples. Application of HOCUS to glioblastoma images reveals a size and location classification of tumors that improves over human expert-based stratification. Subtypes based on higher order features can reveal comparable or distinct groupings. The distinct solutions can provide biologically- and treatment-relevant solutions that are just as significant as solutions based on the original data.

  15. ICA model order selection of task co-activation networks.

    PubMed

    Ray, Kimberly L; McKay, D Reese; Fox, Peter M; Riedel, Michael C; Uecker, Angela M; Beckmann, Christian F; Smith, Stephen M; Fox, Peter T; Laird, Angela R

    2013-01-01

    Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders.

  16. ICA model order selection of task co-activation networks

    PubMed Central

    Ray, Kimberly L.; McKay, D. Reese; Fox, Peter M.; Riedel, Michael C.; Uecker, Angela M.; Beckmann, Christian F.; Smith, Stephen M.; Fox, Peter T.; Laird, Angela R.

    2013-01-01

    Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders. PMID:24339802

  17. Estimating order statistics of network degrees

    NASA Astrophysics Data System (ADS)

    Chu, J.; Nadarajah, S.

    2018-01-01

    We model the order statistics of network degrees of big data sets by a range of generalised beta distributions. A three parameter beta distribution due to Libby and Novick (1982) is shown to give the best overall fit for at least four big data sets. The fit of this distribution is significantly better than the fit suggested by Olhede and Wolfe (2012) across the whole range of order statistics for all four data sets.

  18. Nonlinear Transfer of Signal and Noise Correlations in Cortical Networks

    PubMed Central

    Lyamzin, Dmitry R.; Barnes, Samuel J.; Donato, Roberta; Garcia-Lazaro, Jose A.; Keck, Tara

    2015-01-01

    Signal and noise correlations, a prominent feature of cortical activity, reflect the structure and function of networks during sensory processing. However, in addition to reflecting network properties, correlations are also shaped by intrinsic neuronal mechanisms. Here we show that spike threshold transforms correlations by creating nonlinear interactions between signal and noise inputs; even when input noise correlation is constant, spiking noise correlation varies with both the strength and correlation of signal inputs. We characterize these effects systematically in vitro in mice and demonstrate their impact on sensory processing in vivo in gerbils. We also find that the effects of nonlinear correlation transfer on cortical responses are stronger in the synchronized state than in the desynchronized state, and show that they can be reproduced and understood in a model with a simple threshold nonlinearity. Since these effects arise from an intrinsic neuronal property, they are likely to be present across sensory systems and, thus, our results are a critical step toward a general understanding of how correlated spiking relates to the structure and function of cortical networks. PMID:26019325

  19. Anti-correlated cortical networks of intrinsic connectivity in the rat brain.

    PubMed

    Schwarz, Adam J; Gass, Natalia; Sartorius, Alexander; Risterucci, Celine; Spedding, Michael; Schenker, Esther; Meyer-Lindenberg, Andreas; Weber-Fahr, Wolfgang

    2013-01-01

    In humans, resting-state blood oxygen level-dependent (BOLD) signals in the default mode network (DMN) are temporally anti-correlated with those from a lateral cortical network involving the frontal eye fields, secondary somatosensory and posterior insular cortices. Here, we demonstrate the existence of an analogous lateral cortical network in the rat brain, extending laterally from anterior secondary sensorimotor regions to the insular cortex and exhibiting low-frequency BOLD fluctuations that are temporally anti-correlated with a midline "DMN-like" network comprising posterior/anterior cingulate and prefrontal cortices. The primary nexus for this anti-correlation relationship was the anterior secondary motor cortex, close to regions that have been identified with frontal eye fields in the rat brain. The anti-correlation relationship was corroborated after global signal removal, underscoring this finding as a robust property of the functional connectivity signature in the rat brain. These anti-correlated networks demonstrate strong anatomical homology to networks identified in human and monkey connectivity studies, extend the known preserved functional connectivity relationships between rodent and primates, and support the use of resting-state functional magnetic resonance imaging as a translational imaging method between rat models and humans.

  20. Anti-Correlated Cortical Networks of Intrinsic Connectivity in the Rat Brain

    PubMed Central

    Gass, Natalia; Sartorius, Alexander; Risterucci, Celine; Spedding, Michael; Schenker, Esther; Meyer-Lindenberg, Andreas; Weber-Fahr, Wolfgang

    2013-01-01

    Abstract In humans, resting-state blood oxygen level-dependent (BOLD) signals in the default mode network (DMN) are temporally anti-correlated with those from a lateral cortical network involving the frontal eye fields, secondary somatosensory and posterior insular cortices. Here, we demonstrate the existence of an analogous lateral cortical network in the rat brain, extending laterally from anterior secondary sensorimotor regions to the insular cortex and exhibiting low-frequency BOLD fluctuations that are temporally anti-correlated with a midline “DMN-like” network comprising posterior/anterior cingulate and prefrontal cortices. The primary nexus for this anti-correlation relationship was the anterior secondary motor cortex, close to regions that have been identified with frontal eye fields in the rat brain. The anti-correlation relationship was corroborated after global signal removal, underscoring this finding as a robust property of the functional connectivity signature in the rat brain. These anti-correlated networks demonstrate strong anatomical homology to networks identified in human and monkey connectivity studies, extend the known preserved functional connectivity relationships between rodent and primates, and support the use of resting-state functional magnetic resonance imaging as a translational imaging method between rat models and humans. PMID:23919836

  1. Speaker normalization and adaptation using second-order connectionist networks.

    PubMed

    Watrous, R L

    1993-01-01

    A method for speaker normalization and adaption using connectionist networks is developed. A speaker-specific linear transformation of observations of the speech signal is computed using second-order network units. Classification is accomplished by a multilayer feedforward network that operates on the normalized speech data. The network is adapted for a new talker by modifying the transformation parameters while leaving the classifier fixed. This is accomplished by backpropagating classification error through the classifier to the second-order transformation units. This method was evaluated for the classification of ten vowels for 76 speakers using the first two formant values of the Peterson-Barney data. The results suggest that rapid speaker adaptation resulting in high classification accuracy can be accomplished by this method.

  2. Experimental characterization of a quantum many-body system via higher-order correlations.

    PubMed

    Schweigler, Thomas; Kasper, Valentin; Erne, Sebastian; Mazets, Igor; Rauer, Bernhard; Cataldini, Federica; Langen, Tim; Gasenzer, Thomas; Berges, Jürgen; Schmiedmayer, Jörg

    2017-05-17

    Quantum systems can be characterized by their correlations. Higher-order (larger than second order) correlations, and the ways in which they can be decomposed into correlations of lower order, provide important information about the system, its structure, its interactions and its complexity. The measurement of such correlation functions is therefore an essential tool for reading, verifying and characterizing quantum simulations. Although higher-order correlation functions are frequently used in theoretical calculations, so far mainly correlations up to second order have been studied experimentally. Here we study a pair of tunnel-coupled one-dimensional atomic superfluids and characterize the corresponding quantum many-body problem by measuring correlation functions. We extract phase correlation functions up to tenth order from interference patterns and analyse whether, and under what conditions, these functions factorize into correlations of lower order. This analysis characterizes the essential features of our system, the relevant quasiparticles, their interactions and topologically distinct vacua. From our data we conclude that in thermal equilibrium our system can be seen as a quantum simulator of the sine-Gordon model, relevant for diverse disciplines ranging from particle physics to condensed matter. The measurement and evaluation of higher-order correlation functions can easily be generalized to other systems and to study correlations of any other observable such as density, spin and magnetization. It therefore represents a general method for analysing quantum many-body systems from experimental data.

  3. Global asymptotical ω-periodicity of a fractional-order non-autonomous neural networks.

    PubMed

    Chen, Boshan; Chen, Jiejie

    2015-08-01

    We study the global asymptotic ω-periodicity for a fractional-order non-autonomous neural networks. Firstly, based on the Caputo fractional-order derivative it is shown that ω-periodic or autonomous fractional-order neural networks cannot generate exactly ω-periodic signals. Next, by using the contraction mapping principle we discuss the existence and uniqueness of S-asymptotically ω-periodic solution for a class of fractional-order non-autonomous neural networks. Then by using a fractional-order differential and integral inequality technique, we study global Mittag-Leffler stability and global asymptotical periodicity of the fractional-order non-autonomous neural networks, which shows that all paths of the networks, starting from arbitrary points and responding to persistent, nonconstant ω-periodic external inputs, asymptotically converge to the same nonconstant ω-periodic function that may be not a solution. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. Importance of small-degree nodes in assortative networks with degree-weight correlations

    NASA Astrophysics Data System (ADS)

    Ma, Sijuan; Feng, Ling; Monterola, Christopher Pineda; Lai, Choy Heng

    2017-10-01

    It has been known that assortative network structure plays an important role in spreading dynamics for unweighted networks. Yet its influence on weighted networks is not clear, in particular when weight is strongly correlated with the degrees of the nodes as we empirically observed in Twitter. Here we use the self-consistent probability method and revised nonperturbative heterogenous mean-field theory method to investigate this influence on both susceptible-infective-recovered (SIR) and susceptible-infective-susceptible (SIS) spreading dynamics. Both our simulation and theoretical results show that while the critical threshold is not significantly influenced by the assortativity, the prevalence in the supercritical regime shows a crossover under different degree-weight correlations. In particular, unlike the case of random mixing networks, in assortative networks, the negative degree-weight correlation leads to higher prevalence in their spreading beyond the critical transmissivity than that of the positively correlated. In addition, the previously observed inhibition effect on spreading velocity by assortative structure is not apparent in negatively degree-weight correlated networks, while it is enhanced for that of the positively correlated. Detailed investigation into the degree distribution of the infected nodes reveals that small-degree nodes play essential roles in the supercritical phase of both SIR and SIS spreadings. Our results have direct implications in understanding viral information spreading over online social networks and epidemic spreading over contact networks.

  5. Effect of inhibitory feedback on correlated firing of spiking neural network.

    PubMed

    Xie, Jinli; Wang, Zhijie

    2013-08-01

    Understanding the properties and mechanisms that generate different forms of correlation is critical for determining their role in cortical processing. Researches on retina, visual cortex, sensory cortex, and computational model have suggested that fast correlation with high temporal precision appears consistent with common input, and correlation on a slow time scale likely involves feedback. Based on feedback spiking neural network model, we investigate the role of inhibitory feedback in shaping correlations on a time scale of 100 ms. Notably, the relationship between the correlation coefficient and inhibitory feedback strength is non-monotonic. Further, computational simulations show how firing rate and oscillatory activity form the basis of the mechanisms underlying this relationship. When the mean firing rate holds unvaried, the correlation coefficient increases monotonically with inhibitory feedback, but the correlation coefficient keeps decreasing when the network has no oscillatory activity. Our findings reveal that two opposing effects of the inhibitory feedback on the firing activity of the network contribute to the non-monotonic relationship between the correlation coefficient and the strength of the inhibitory feedback. The inhibitory feedback affects the correlated firing activity by modulating the intensity and regularity of the spike trains. Finally, the non-monotonic relationship is replicated with varying transmission delay and different spatial network structure, demonstrating the universality of the results.

  6. Aging-related changes in the default mode network and its anti-correlated networks: a resting-state fMRI study.

    PubMed

    Wu, Jing-Tao; Wu, Hui-Zhen; Yan, Chao-Gan; Chen, Wen-Xin; Zhang, Hong-Ying; He, Yong; Yang, Hai-Shan

    2011-10-17

    Intrinsic brain activity in a resting state incorporates components of the task negative network called default mode network (DMN) and task-positive networks called attentional networks. In the present study, the reciprocal neuronal networks in the elder group were compared with the young group to investigate the differences of the intrinsic brain activity using a method of temporal correlation analysis based on seed regions of posterior cingulate cortex (PCC) and ventromedial prefrontal cortex (vmPFC). We found significant decreased positive correlations and negative correlations with the seeds of PCC and vmPFC in the old group. The decreased coactivations in the DMN network components and their negative networks in the old group may reflect age-related alterations in various brain functions such as attention, motor control and inhibition modulation in cognitive processing. These alterations in the resting state anti-correlative networks could provide neuronal substrates for the aging brain. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  7. When do correlations increase with firing rates in recurrent networks?

    PubMed Central

    2017-01-01

    A central question in neuroscience is to understand how noisy firing patterns are used to transmit information. Because neural spiking is noisy, spiking patterns are often quantified via pairwise correlations, or the probability that two cells will spike coincidentally, above and beyond their baseline firing rate. One observation frequently made in experiments, is that correlations can increase systematically with firing rate. Theoretical studies have determined that stimulus-dependent correlations that increase with firing rate can have beneficial effects on information coding; however, we still have an incomplete understanding of what circuit mechanisms do, or do not, produce this correlation-firing rate relationship. Here, we studied the relationship between pairwise correlations and firing rates in recurrently coupled excitatory-inhibitory spiking networks with conductance-based synapses. We found that with stronger excitatory coupling, a positive relationship emerged between pairwise correlations and firing rates. To explain these findings, we used linear response theory to predict the full correlation matrix and to decompose correlations in terms of graph motifs. We then used this decomposition to explain why covariation of correlations with firing rate—a relationship previously explained in feedforward networks driven by correlated input—emerges in some recurrent networks but not in others. Furthermore, when correlations covary with firing rate, this relationship is reflected in low-rank structure in the correlation matrix. PMID:28448499

  8. Stability analysis of fractional-order Hopfield neural networks with time delays.

    PubMed

    Wang, Hu; Yu, Yongguang; Wen, Guoguang

    2014-07-01

    This paper investigates the stability for fractional-order Hopfield neural networks with time delays. Firstly, the fractional-order Hopfield neural networks with hub structure and time delays are studied. Some sufficient conditions for stability of the systems are obtained. Next, two fractional-order Hopfield neural networks with different ring structures and time delays are developed. By studying the developed neural networks, the corresponding sufficient conditions for stability of the systems are also derived. It is shown that the stability conditions are independent of time delays. Finally, numerical simulations are given to illustrate the effectiveness of the theoretical results obtained in this paper. Copyright © 2014 Elsevier Ltd. All rights reserved.

  9. Distributions of positive correlations in sectoral value added growth in the global economic network*

    NASA Astrophysics Data System (ADS)

    Maluck, Julian; Donner, Reik V.

    2017-02-01

    International trade has grown considerably during the process of globalization. Complex supply chains for the production of goods have resulted in an increasingly connected International Trade Network (ITN). Traditionally, direct trade relations between industries have been regarded as mediators of supply and demand spillovers. With increasing network connectivity the question arises if higher-order relations become more important in explaining a national sector's susceptibility to supply and demand changes of its trading partner. In this study we address this question by investigating empirically to what extent the topological properties of the ITN provide information about positive correlations in the production of two industry sectors. We observe that although direct trade relations between industries serve as important indicators for correlations in the industries' value added growth, opportunities of substitution for required production inputs as well as second-order trade relations cannot be neglected. Our results contribute to a better understanding of the relation between trade and economic productivity and can serve as a basis for the improvement of crisis spreading models that evaluate contagion threats in the case of a node's failure in the ITN.

  10. Assessing the utility of gene co-expression stability in combination with correlation in the analysis of protein-protein interaction networks

    PubMed Central

    2011-01-01

    Background Gene co-expression, in the form of a correlation coefficient, has been valuable in the analysis, classification and prediction of protein-protein interactions. However, it is susceptible to bias from a few samples having a large effect on the correlation coefficient. Gene co-expression stability is a means of quantifying this bias, with high stability indicating robust, unbiased co-expression correlation coefficients. We assess the utility of gene co-expression stability as an additional measure to support the co-expression correlation in the analysis of protein-protein interaction networks. Results We studied the patterns of co-expression correlation and stability in interacting proteins with respect to their interaction promiscuity, levels of intrinsic disorder, and essentiality or disease-relatedness. Co-expression stability, along with co-expression correlation, acts as a better classifier of hub proteins in interaction networks, than co-expression correlation alone, enabling the identification of a class of hubs that are functionally distinct from the widely accepted transient (date) and obligate (party) hubs. Proteins with high levels of intrinsic disorder have low co-expression correlation and high stability with their interaction partners suggesting their involvement in transient interactions, except for a small group that have high co-expression correlation and are typically subunits of stable complexes. Similar behavior was seen for disease-related and essential genes. Interacting proteins that are both disordered have higher co-expression stability than ordered protein pairs. Using co-expression correlation and stability, we found that transient interactions are more likely to occur between an ordered and a disordered protein while obligate interactions primarily occur between proteins that are either both ordered, or disordered. Conclusions We observe that co-expression stability shows distinct patterns in structurally and functionally

  11. Pulse transmission receiver with higher-order time derivative pulse correlator

    DOEpatents

    Dress, Jr., William B.; Smith, Stephen F.

    2003-09-16

    Systems and methods for pulse-transmission low-power communication modes are disclosed. A pulse transmission receiver includes: a higher-order time derivative pulse correlator; a demodulation decoder coupled to the higher-order time derivative pulse correlator; a clock coupled to the demodulation decoder; and a pseudorandom polynomial generator coupled to both the higher-order time derivative pulse correlator and the clock. The systems and methods significantly reduce lower-frequency emissions from pulse transmission spread-spectrum communication modes, which reduces potentially harmful interference to existing radio frequency services and users and also simultaneously permit transmission of multiple data bits by utilizing specific pulse shapes.

  12. Effective and Efficient Correlation Analysis with Application to Market Basket Analysis and Network Community Detection

    ERIC Educational Resources Information Center

    Duan, Lian

    2012-01-01

    Finding the most interesting correlations among items is essential for problems in many commercial, medical, and scientific domains. For example, what kinds of items should be recommended with regard to what has been purchased by a customer? How to arrange the store shelf in order to increase sales? How to partition the whole social network into…

  13. Covariance, correlation matrix, and the multiscale community structure of networks.

    PubMed

    Shen, Hua-Wei; Cheng, Xue-Qi; Fang, Bin-Xing

    2010-07-01

    Empirical studies show that real world networks often exhibit multiple scales of topological descriptions. However, it is still an open problem how to identify the intrinsic multiple scales of networks. In this paper, we consider detecting the multiscale community structure of network from the perspective of dimension reduction. According to this perspective, a covariance matrix of network is defined to uncover the multiscale community structure through the translation and rotation transformations. It is proved that the covariance matrix is the unbiased version of the well-known modularity matrix. We then point out that the translation and rotation transformations fail to deal with the heterogeneous network, which is very common in nature and society. To address this problem, a correlation matrix is proposed through introducing the rescaling transformation into the covariance matrix. Extensive tests on real world and artificial networks demonstrate that the correlation matrix significantly outperforms the covariance matrix, identically the modularity matrix, as regards identifying the multiscale community structure of network. This work provides a novel perspective to the identification of community structure and thus various dimension reduction methods might be used for the identification of community structure. Through introducing the correlation matrix, we further conclude that the rescaling transformation is crucial to identify the multiscale community structure of network, as well as the translation and rotation transformations.

  14. Weak Higher-Order Interactions in Macroscopic Functional Networks of the Resting Brain.

    PubMed

    Huang, Xuhui; Xu, Kaibin; Chu, Congying; Jiang, Tianzi; Yu, Shan

    2017-10-25

    Interactions among different brain regions are usually examined through functional connectivity (FC) analysis, which is exclusively based on measuring pairwise correlations in activities. However, interactions beyond the pairwise level, that is, higher-order interactions (HOIs), are vital in understanding the behavior of many complex systems. So far, whether HOIs exist among brain regions and how they can affect the brain's activities remains largely elusive. To address these issues, here, we analyzed blood oxygenation level-dependent (BOLD) signals recorded from six typical macroscopic functional networks of the brain in 100 human subjects (46 males and 54 females) during the resting state. Through examining the binarized BOLD signals, we found that HOIs within and across individual networks were both very weak regardless of the network size, topology, degree of spatial proximity, spatial scales, and whether the global signal was regressed. To investigate the potential mechanisms underlying the weak HOIs, we analyzed the dynamics of a network model and also found that HOIs were generally weak within a wide range of key parameters provided that the overall dynamic feature of the model was similar to the empirical data and it was operating close to a linear fluctuation regime. Our results suggest that weak HOI may be a general property of brain's macroscopic functional networks, which implies the dominance of pairwise interactions in shaping brain activities at such a scale and warrants the validity of widely used pairwise-based FC approaches. SIGNIFICANCE STATEMENT To explain how activities of different brain areas are coordinated through interactions is essential to revealing the mechanisms underlying various brain functions. Traditionally, such an interaction structure is commonly studied using pairwise-based functional network analyses. It is unclear whether the interactions beyond the pairwise level (higher-order interactions or HOIs) play any role in this

  15. Cascade Error Projection with Low Bit Weight Quantization for High Order Correlation Data

    NASA Technical Reports Server (NTRS)

    Duong, Tuan A.; Daud, Taher

    1998-01-01

    In this paper, we reinvestigate the solution for chaotic time series prediction problem using neural network approach. The nature of this problem is such that the data sequences are never repeated, but they are rather in chaotic region. However, these data sequences are correlated between past, present, and future data in high order. We use Cascade Error Projection (CEP) learning algorithm to capture the high order correlation between past and present data to predict a future data using limited weight quantization constraints. This will help to predict a future information that will provide us better estimation in time for intelligent control system. In our earlier work, it has been shown that CEP can sufficiently learn 5-8 bit parity problem with 4- or more bits, and color segmentation problem with 7- or more bits of weight quantization. In this paper, we demonstrate that chaotic time series can be learned and generalized well with as low as 4-bit weight quantization using round-off and truncation techniques. The results show that generalization feature will suffer less as more bit weight quantization is available and error surfaces with the round-off technique are more symmetric around zero than error surfaces with the truncation technique. This study suggests that CEP is an implementable learning technique for hardware consideration.

  16. Convergence of third order correlation energy in atoms and molecules.

    PubMed

    Kahn, Kalju; Granovsky, Alex A; Noga, Jozef

    2007-01-30

    We have investigated the convergence of third order correlation energy within the hierarchies of correlation consistent basis sets for helium, neon, and water, and for three stationary points of hydrogen peroxide. This analysis confirms that singlet pair energies converge much slower than triplet pair energies. In addition, singlet pair energies with (aug)-cc-pVDZ and (aug)-cc-pVTZ basis sets do not follow a converging trend and energies with three basis sets larger than aug-cc-pVTZ are generally required for reliable extrapolations of third order correlation energies, making so the explicitly correlated R12 calculations preferable.

  17. Unifying neural-network quantum states and correlator product states via tensor networks

    NASA Astrophysics Data System (ADS)

    Clark, Stephen R.

    2018-04-01

    Correlator product states (CPS) are a powerful and very broad class of states for quantum lattice systems whose (unnormalised) amplitudes in a fixed basis can be sampled exactly and efficiently. They work by gluing together states of overlapping clusters of sites on the lattice, called correlators. Recently Carleo and Troyer (2017 Science 355 602) introduced a new type sampleable ansatz called neural-network quantum states (NQS) that are inspired by the restricted Boltzmann model used in machine learning. By employing the formalism of tensor networks we show that NQS are a special form of CPS with novel properties. Diagramatically a number of simple observations become transparent. Namely, that NQS are CPS built from extensively sized GHZ-form correlators making them uniquely unbiased geometrically. The appearance of GHZ correlators also relates NQS to canonical polyadic decompositions of tensors. Another immediate implication of the NQS equivalence to CPS is that we are able to formulate exact NQS representations for a wide range of paradigmatic states, including superpositions of weighed-graph states, the Laughlin state, toric code states, and the resonating valence bond state. These examples reveal the potential of using higher dimensional hidden units and a second hidden layer in NQS. The major outlook of this study is the elevation of NQS to correlator operators allowing them to enhance conventional well-established variational Monte Carlo approaches for strongly correlated fermions.

  18. Quasi-projective synchronization of fractional-order complex-valued recurrent neural networks.

    PubMed

    Yang, Shuai; Yu, Juan; Hu, Cheng; Jiang, Haijun

    2018-08-01

    In this paper, without separating the complex-valued neural networks into two real-valued systems, the quasi-projective synchronization of fractional-order complex-valued neural networks is investigated. First, two new fractional-order inequalities are established by using the theory of complex functions, Laplace transform and Mittag-Leffler functions, which generalize traditional inequalities with the first-order derivative in the real domain. Additionally, different from hybrid control schemes given in the previous work concerning the projective synchronization, a simple and linear control strategy is designed in this paper and several criteria are derived to ensure quasi-projective synchronization of the complex-valued neural networks with fractional-order based on the established fractional-order inequalities and the theory of complex functions. Moreover, the error bounds of quasi-projective synchronization are estimated. Especially, some conditions are also presented for the Mittag-Leffler synchronization of the addressed neural networks. Finally, some numerical examples with simulations are provided to show the effectiveness of the derived theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

  19. Functional connectivity of default mode network components: correlation, anticorrelation, and causality

    PubMed Central

    Uddin, Lucina Q.; Clare Kelly, A. M.; Biswal, Bharat B.; Castellanos, F. Xavier; Milham, Michael P.

    2013-01-01

    The default mode network (DMN), based in ventromedial prefrontal cortex (vmPFC) and posterior cingulate cortex (PCC), exhibits higher metabolic activity at rest than during performance of externally-oriented cognitive tasks. Recent studies have suggested that competitive relationships between the DMN and various task-positive networks involved in task performance are intrinsically represented in the brain in the form of strong negative correlations (anticorrelations) between spontaneous fluctuations in these networks. Most neuroimaging studies characterize the DMN as a homogenous network, thus few have examined the differential contributions of DMN components to such competitive relationships. Here we examined functional differentiation within the default mode network, with an emphasis on understanding competitive relationships between this and other networks. We used a seed correlation approach on resting-state data to assess differences in functional connectivity between these two regions and their anticorrelated networks. While the positively correlated networks for the vmPFC and PCC seeds largely overlapped, the anticorrelated networks for each showed striking differences. Activity in vmPFC negatively predicted activity in parietal visual spatial and temporal attention networks, whereas activity in PCC negatively predicted activity in prefrontal-based motor control circuits. Granger causality analyses suggest that vmPFC and PCC exert greater influence on their anticorrelated networks than the other way around, suggesting that these two default mode nodes may directly modulate activity in task-positive networks. Thus, the two major nodes comprising the default mode network are differentiated with respect to the specific brain systems with which they interact, suggesting greater heterogeneity within this network than is commonly appreciated. PMID:18219617

  20. Knowledge extraction from evolving spiking neural networks with rank order population coding.

    PubMed

    Soltic, Snjezana; Kasabov, Nikola

    2010-12-01

    This paper demonstrates how knowledge can be extracted from evolving spiking neural networks with rank order population coding. Knowledge discovery is a very important feature of intelligent systems. Yet, a disproportionally small amount of research is centered on the issue of knowledge extraction from spiking neural networks which are considered to be the third generation of artificial neural networks. The lack of knowledge representation compatibility is becoming a major detriment to end users of these networks. We show that a high-level knowledge can be obtained from evolving spiking neural networks. More specifically, we propose a method for fuzzy rule extraction from an evolving spiking network with rank order population coding. The proposed method was used for knowledge discovery on two benchmark taste recognition problems where the knowledge learnt by an evolving spiking neural network was extracted in the form of zero-order Takagi-Sugeno fuzzy IF-THEN rules.

  1. Correlation functions in first-order phase transitions

    NASA Astrophysics Data System (ADS)

    Garrido, V.; Crespo, D.

    1997-09-01

    Most of the physical properties of systems underlying first-order phase transitions can be obtained from the spatial correlation functions. In this paper, we obtain expressions that allow us to calculate all the correlation functions from the droplet size distribution. Nucleation and growth kinetics is considered, and exact solutions are obtained for the case of isotropic growth by using self-similarity properties. The calculation is performed by using the particle size distribution obtained by a recently developed model (populational Kolmogorov-Johnson-Mehl-Avrami model). Since this model is less restrictive than that used in previously existing theories, the result is that the correlation functions can be obtained for any dependence of the kinetic parameters. The validity of the method is tested by comparison with the exact correlation functions, which had been obtained in the available cases by the time-cone method. Finally, the correlation functions corresponding to the microstructure developed in partitioning transformations are obtained.

  2. Theoretical scheme of thermal-light many-ghost imaging by Nth-order intensity correlation

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Liu Yingchuan; College of Mathematics and Physics, University of South China, Hengyang 421001; Kuang Leman

    2011-05-15

    In this paper, we propose a theoretical scheme of many-ghost imaging in terms of Nth-order correlated thermal light. We obtain the Gaussian thin lens equations in the many-ghost imaging protocol. We show that it is possible to produce N-1 ghost images of an object at different places in a nonlocal fashion by means of a higher order correlated imaging process with an Nth-order correlated thermal source and correlation measurements. We investigate the visibility of the ghost images in the scheme and obtain the upper bounds of the visibility for the Nth-order correlated thermal-light ghost imaging. It is found that themore » visibility of the ghost images can be dramatically enhanced when the order of correlation becomes larger. It is pointed out that the many-ghost imaging phenomenon is an observable physical effect induced by higher order coherence or higher order correlations of optical fields.« less

  3. Assessment of higher order correlation effects with the help of Moller-Plesset perturbation theory up to sixth order

    NASA Astrophysics Data System (ADS)

    He, Yuan; Cremer, Dieter

    For 30 molecules and two atoms, MP n correlation energies up to n = 6 are computed and used to analyse higher order correlation effects and the initial convergence behaviour of the MP n series. Particularly useful is the analysis of correlation contributions E(n)XY ...( n = 4,5,6; X , Y ,... = S, D, T, Q denoting single, double, triple, and quadruple excitations) in the form of correlation energy spectra. Two classes of system are distinguished, namely class A systems possessing well separated electron pairs and class B systems which are characterized by electron clustering in certain regions of atomic and molecular space. For class A systems, electron pair correlation effects as described by D, Q, DD, DQ, QQ, DDD, etc., contributions are most important, which are stepwise included at MP n with n = 2,... ,6. Class A systems are reasonably described by MP n theory, which is reflected by the fact that convergence of the MP n series is monotonic (but relatively slow) for class A systems. The description of class B systems is difficult since three- and four-electron correlation effects and couplings between two-, three-, and four-electron correlation effects missing for lower order perturbation theory are significant. MP n methods, which do not cover these effects, simulate higher order with lower order correlation effects thus exaggerating the latter, which has to be corrected with increasing n. Consequently, the MP n series oscillates for class B systems at low orders. A possible divergence of the MP n series is mostly a consequence of an unbalanced basis set. For example, diffuse functions added to an unsaturated sp basis lead to an exaggeration of higher order correlation effects, which can cause enhanced oscillations and divergence of the MP n series.

  4. Statistics and dynamics of attractor networks with inter-correlated patterns

    NASA Astrophysics Data System (ADS)

    Kropff, E.

    2007-02-01

    In an embodied feature representation view, the semantic memory represents concepts in the brain by the associated activation of the features that describe it, each one of them processed in a differentiated region of the cortex. This system has been modeled with a Potts attractor network. Several studies of feature representation show that the correlation between patterns plays a crucial role in semantic memory. The present work focuses on two aspects of the effect of correlations in attractor networks. In first place, it assesses how a Potts network can store a set of patterns with non-trivial correlations between them. This is done through a simple and biologically plausible modification to the classical learning rule. In second place, it studies the complexity of latching transitions between attractor states, and how this complexity can be controlled.

  5. Correlation between hierarchical structure of crystal networks and macroscopic performance of mesoscopic soft materials and engineering principles.

    PubMed

    Lin, Naibo; Liu, Xiang Yang

    2015-11-07

    This review examines how the concepts and ideas of crystallization can be extended further and applied to the field of mesoscopic soft materials. It concerns the structural characteristics vs. the macroscopic performance, and the formation mechanism of crystal networks. Although this subject can be discussed in a broad sense across the area of mesoscopic soft materials, our main focus is on supramolecular materials, spider and silkworm silks, and biominerals. First, the occurrence of a hierarchical structure, i.e. crystal network and domain network structures, will facilitate the formation kinetics of mesoscopic phases and boost up the macroscopic performance of materials in some cases (i.e. spider silk fibres). Second, the structure and performance of materials can be correlated in some way by the four factors: topology, correlation length, symmetry/ordering, and strength of association of crystal networks. Moreover, four different kinetic paths of crystal network formation are identified, namely, one-step process of assembly, two-step process of assembly, mixed mode of assembly and foreign molecule mediated assembly. Based on the basic mechanisms of crystal nucleation and growth, the formation of crystal networks, such as crystallographic mismatch (or noncrystallographic) branching (tip branching and fibre side branching) and fibre/polymeric side merging, are reviewed. This facilitates the rational design and construction of crystal networks in supramolecular materials. In this context, the (re-)construction of a hierarchical crystal network structure can be implemented by thermal, precipitate, chemical, and sonication stimuli. As another important class of soft materials, the unusual mechanical performance of spider and silkworm silk fibres are reviewed in comparison with the regenerated silk protein derivatives. It follows that the considerably larger breaking stress and unusual breaking strain of spider silk fibres vs. silkworm silk fibres can be interpreted

  6. Theory of correlation in a network with synaptic depression

    NASA Astrophysics Data System (ADS)

    Igarashi, Yasuhiko; Oizumi, Masafumi; Okada, Masato

    2012-01-01

    Synaptic depression affects not only the mean responses of neurons but also the correlation of response variability in neural populations. Although previous studies have constructed a theory of correlation in a spiking neuron model by using the mean-field theory framework, synaptic depression has not been taken into consideration. We expanded the previous theoretical framework in this study to spiking neuron models with short-term synaptic depression. On the basis of this theory we analytically calculated neural correlations in a ring attractor network with Mexican-hat-type connectivity, which was used as a model of the primary visual cortex. The results revealed that synaptic depression reduces neural correlation, which could be beneficial for sensory coding. Furthermore, our study opens the way for theoretical studies on the effect of interaction change on the linear response function in large stochastic networks.

  7. Plenoptic imaging with second-order correlations of light

    NASA Astrophysics Data System (ADS)

    Pepe, Francesco V.; Scarcelli, Giuliano; Garuccio, Augusto; D'Angelo, Milena

    2016-01-01

    Plenoptic imaging is a promising optical modality that simultaneously captures the location and the propagation direction of light in order to enable tridimensional imaging in a single shot. We demonstrate that it is possible to implement plenoptic imaging through second-order correlations of chaotic light, thus enabling to overcome the typical limitations of classical plenoptic devices.

  8. Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

    NASA Technical Reports Server (NTRS)

    Yao, Weigang; Liou, Meng-Sing

    2012-01-01

    The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis

  9. The default mode network and the working memory network are not anti-correlated during all phases of a working memory task.

    PubMed

    Piccoli, Tommaso; Valente, Giancarlo; Linden, David E J; Re, Marta; Esposito, Fabrizio; Sack, Alexander T; Di Salle, Francesco

    2015-01-01

    The default mode network and the working memory network are known to be anti-correlated during sustained cognitive processing, in a load-dependent manner. We hypothesized that functional connectivity among nodes of the two networks could be dynamically modulated by task phases across time. To address the dynamic links between default mode network and the working memory network, we used a delayed visuo-spatial working memory paradigm, which allowed us to separate three different phases of working memory (encoding, maintenance, and retrieval), and analyzed the functional connectivity during each phase within and between the default mode network and the working memory network networks. We found that the two networks are anti-correlated only during the maintenance phase of working memory, i.e. when attention is focused on a memorized stimulus in the absence of external input. Conversely, during the encoding and retrieval phases, when the external stimulation is present, the default mode network is positively coupled with the working memory network, suggesting the existence of a dynamically switching of functional connectivity between "task-positive" and "task-negative" brain networks. Our results demonstrate that the well-established dichotomy of the human brain (anti-correlated networks during rest and balanced activation-deactivation during cognition) has a more nuanced organization than previously thought and engages in different patterns of correlation and anti-correlation during specific sub-phases of a cognitive task. This nuanced organization reinforces the hypothesis of a direct involvement of the default mode network in cognitive functions, as represented by a dynamic rather than static interaction with specific task-positive networks, such as the working memory network.

  10. The Default Mode Network and the Working Memory Network Are Not Anti-Correlated during All Phases of a Working Memory Task

    PubMed Central

    Piccoli, Tommaso; Valente, Giancarlo; Linden, David E. J.; Re, Marta; Esposito, Fabrizio; Sack, Alexander T.; Salle, Francesco Di

    2015-01-01

    Introduction The default mode network and the working memory network are known to be anti-correlated during sustained cognitive processing, in a load-dependent manner. We hypothesized that functional connectivity among nodes of the two networks could be dynamically modulated by task phases across time. Methods To address the dynamic links between default mode network and the working memory network, we used a delayed visuo-spatial working memory paradigm, which allowed us to separate three different phases of working memory (encoding, maintenance, and retrieval), and analyzed the functional connectivity during each phase within and between the default mode network and the working memory network networks. Results We found that the two networks are anti-correlated only during the maintenance phase of working memory, i.e. when attention is focused on a memorized stimulus in the absence of external input. Conversely, during the encoding and retrieval phases, when the external stimulation is present, the default mode network is positively coupled with the working memory network, suggesting the existence of a dynamically switching of functional connectivity between “task-positive” and “task-negative” brain networks. Conclusions Our results demonstrate that the well-established dichotomy of the human brain (anti-correlated networks during rest and balanced activation-deactivation during cognition) has a more nuanced organization than previously thought and engages in different patterns of correlation and anti-correlation during specific sub-phases of a cognitive task. This nuanced organization reinforces the hypothesis of a direct involvement of the default mode network in cognitive functions, as represented by a dynamic rather than static interaction with specific task-positive networks, such as the working memory network. PMID:25848951

  11. Unveiling correlations between financial variables and topological metrics of trading networks: Evidence from a stock and its warrant

    NASA Astrophysics Data System (ADS)

    Li, Ming-Xia; Jiang, Zhi-Qiang; Xie, Wen-Jie; Xiong, Xiong; Zhang, Wei; Zhou, Wei-Xing

    2015-02-01

    Traders develop and adopt different trading strategies attempting to maximize their profits in financial markets. These trading strategies not only result in specific topological structures in trading networks, which connect the traders with the pairwise buy-sell relationships, but also have potential impacts on market dynamics. Here, we present a detailed analysis on how the market behaviors are correlated with the structures of traders in trading networks based on audit trail data for the Baosteel stock and its warrant at the transaction level from 22 August 2005 to 23 August 2006. In our investigation, we divide each trade day into 48 rolling time windows with a length of 5 min, construct a trading network within each window, and obtain a time series of over 11,600 trading networks. We find that there are strongly simultaneous correlations between the topological metrics (including network centralization, assortative index, and average path length) of trading networks that characterize the patterns of order execution and the financial variables (including return, volatility, intertrade duration, and trading volume) for the stock and its warrant. Our analysis may shed new lights on how the microscopic interactions between elements within complex system affect the system's performance.

  12. Consensus Algorithms for Networks of Systems with Second- and Higher-Order Dynamics

    NASA Astrophysics Data System (ADS)

    Fruhnert, Michael

    This thesis considers homogeneous networks of linear systems. We consider linear feedback controllers and require that the directed graph associated with the network contains a spanning tree and systems are stabilizable. We show that, in continuous-time, consensus with a guaranteed rate of convergence can always be achieved using linear state feedback. For networks of continuous-time second-order systems, we provide a new and simple derivation of the conditions for a second-order polynomials with complex coefficients to be Hurwitz. We apply this result to obtain necessary and sufficient conditions to achieve consensus with networks whose graph Laplacian matrix may have complex eigenvalues. Based on the conditions found, methods to compute feedback gains are proposed. We show that gains can be chosen such that consensus is achieved robustly over a variety of communication structures and system dynamics. We also consider the use of static output feedback. For networks of discrete-time second-order systems, we provide a new and simple derivation of the conditions for a second-order polynomials with complex coefficients to be Schur. We apply this result to obtain necessary and sufficient conditions to achieve consensus with networks whose graph Laplacian matrix may have complex eigenvalues. We show that consensus can always be achieved for marginally stable systems and discretized systems. Simple conditions for consensus achieving controllers are obtained when the Laplacian eigenvalues are all real. For networks of continuous-time time-variant higher-order systems, we show that uniform consensus can always be achieved if systems are quadratically stabilizable. In this case, we provide a simple condition to obtain a linear feedback control. For networks of discrete-time higher-order systems, we show that constant gains can be chosen such that consensus is achieved for a variety of network topologies. First, we develop simple results for networks of time

  13. Correlated stopping, proton clusters and higher order proton cumulants

    DOE PAGES

    Bzdak, Adam; Koch, Volker; Skokov, Vladimir

    2017-05-05

    Here, we investigate possible effects of correlations between stopped nucleons on higher order proton cumulants at low energy heavy-ion collisions. We find that fluctuations of the number of wounded nucleons N part lead to rather nontrivial dependence of the correlations on the centrality; however, this effect is too small to explain the large and positive four-proton correlations found in the preliminary data collected by the STAR collaboration at √s = 7.7 GeV. We further demonstrate that, by taking into account additional proton clustering, we are able to qualitatively reproduce the preliminary experimental data. We speculate that this clustering may originatemore » either from collective/multi-collision stopping which is expected to be effective at lower energies or from a possible first-order phase transition, or from (attractive) final state interactions. To test these ideas we propose to measure a mixed multi-particle correlation between stopped protons and a produced particle (e.g. pion, antiproton).« less

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

    PubMed Central

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

    2015-01-01

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

  15. A Maximum Entropy Test for Evaluating Higher-Order Correlations in Spike Counts

    PubMed Central

    Onken, Arno; Dragoi, Valentin; Obermayer, Klaus

    2012-01-01

    Evaluating the importance of higher-order correlations of neural spike counts has been notoriously hard. A large number of samples are typically required in order to estimate higher-order correlations and resulting information theoretic quantities. In typical electrophysiology data sets with many experimental conditions, however, the number of samples in each condition is rather small. Here we describe a method that allows to quantify evidence for higher-order correlations in exactly these cases. We construct a family of reference distributions: maximum entropy distributions, which are constrained only by marginals and by linear correlations as quantified by the Pearson correlation coefficient. We devise a Monte Carlo goodness-of-fit test, which tests - for a given divergence measure of interest - whether the experimental data lead to the rejection of the null hypothesis that it was generated by one of the reference distributions. Applying our test to artificial data shows that the effects of higher-order correlations on these divergence measures can be detected even when the number of samples is small. Subsequently, we apply our method to spike count data which were recorded with multielectrode arrays from the primary visual cortex of anesthetized cat during an adaptation experiment. Using mutual information as a divergence measure we find that there are spike count bin sizes at which the maximum entropy hypothesis can be rejected for a substantial number of neuronal pairs. These results demonstrate that higher-order correlations can matter when estimating information theoretic quantities in V1. They also show that our test is able to detect their presence in typical in-vivo data sets, where the number of samples is too small to estimate higher-order correlations directly. PMID:22685392

  16. Currency co-movement and network correlation structure of foreign exchange market

    NASA Astrophysics Data System (ADS)

    Mai, Yong; Chen, Huan; Zou, Jun-Zhong; Li, Sai-Ping

    2018-02-01

    We study the correlations of exchange rate volatility in the global foreign exchange(FX) market based on complex network graphs. Correlation matrices (CM) and the theoretical information flow method (Infomap) are employed to analyze the modular structure of the global foreign exchange network. The analysis demonstrates that there exist currency modules in the network, which is consistent with the geographical nature of currencies. The European and the East Asian currency modules in the FX network are most significant. We introduce a measure of the impact of individual currency based on its partial correlations with other currencies. We further incorporate an impact elimination method to filter out the impact of core nodes and construct subnetworks after the removal of these core nodes. The result reveals that (i) the US Dollar has prominent global influence on the FX market while the Euro has great impact on European currencies; (ii) the East Asian currency module is more strongly correlated than the European currency module. The strong correlation is a result of the strong co-movement of currencies in the region. The co-movement of currencies is further used to study the formation of international monetary bloc and the result is in good agreement with the consideration based on international trade.

  17. Use of probabilistic neural networks for emitter correlation

    NASA Astrophysics Data System (ADS)

    Maloney, P. S.

    1990-08-01

    The Probabilistic Neural Network (PNN) as described by Specht''3 has been successfully applied to a number of emitter correlation problems involving operational data for training and testing of the neural net work. The PNN has been found to be a reliable classification tool for determining emitter type or even identifying specific emitter platforms given appropriate representative data sets for training con sisting only of parametric data from electronic intelligence (ELINT) reports. Four separate feasibility studies have been conducted to prove the usefulness of PNN in this application area: . Hull-to-emitter correlation (HULTEC) for identification of seagoing emitter platforms . Identification of landbased emitters from airborne sensors . Pulse sorting according to emitter of origin . Emitter typing based on a dynamically learning neural network. 1 .

  18. Weighted network analysis of high-frequency cross-correlation measures

    NASA Astrophysics Data System (ADS)

    Iori, Giulia; Precup, Ovidiu V.

    2007-03-01

    In this paper we implement a Fourier method to estimate high-frequency correlation matrices from small data sets. The Fourier estimates are shown to be considerably less noisy than the standard Pearson correlation measures and thus capable of detecting subtle changes in correlation matrices with just a month of data. The evolution of correlation at different time scales is analyzed from the full correlation matrix and its minimum spanning tree representation. The analysis is performed by implementing measures from the theory of random weighted networks.

  19. Medium range order and structural relaxation in As–Se network glasses through FSDP analysis

    DOE PAGES

    Golovchak, R.; Lucas, P.; Oelgoetz, J.; ...

    2015-01-13

    We performed synchrotron X-ray diffraction and neutron scattering studies on As-Se glasses in two states: as-prepared (rejuvenated) and aged for similar to 27 years. The first sharp diffraction peak (FSDP) obtained from the structure factor data as a function of composition and temperature indicates that the cooperative processes that are responsible for structural relaxation do not affect FSDP. The results are correlated with the composition dependence of the complex heat capacity of the glasses and concentration of different structural fragments in the glass network. Furthermore, the comparison of structural information shows that density fluctuations, which were thought previously to havemore » a significant contribution to FSDP, have much smaller effect than the cation-cation correlations, presence of ordered structural fragments or cage molecules.« less

  20. Covalent bond orders and atomic valences from correlated wavefunctions

    NASA Astrophysics Data System (ADS)

    Ángyán, János G.; Rosta, Edina; Surján, Péter R.

    1999-01-01

    A comparison is made between two alternative definitions for covalent bond orders: one derived from the exchange part of the two-particle density matrix and the other expressed as the correlation of fluctuations (covariance) of the number of electrons between the atomic centers. Although these definitions lead to identical formulae for mono-determinantal SCF wavefunctions, they predict different bond orders for correlated wavefunctions. It is shown that, in this case, the fluctuation-based definition leads to slightly lower values of the bond order than does the exchange-based definition, provided one uses an appropriate space-partitioning technique like that of Bader's topological theory of atoms in a molecule; however, use of Mulliken partitioning in this context leads to unphysical behaviour. The example of H 2 is discussed in detail.

  1. Hybrid optical CDMA-FSO communications network under spatially correlated gamma-gamma scintillation.

    PubMed

    Jurado-Navas, Antonio; Raddo, Thiago R; Garrido-Balsells, José María; Borges, Ben-Hur V; Olmos, Juan José Vegas; Monroy, Idelfonso Tafur

    2016-07-25

    In this paper, we propose a new hybrid network solution based on asynchronous optical code-division multiple-access (OCDMA) and free-space optical (FSO) technologies for last-mile access networks, where fiber deployment is impractical. The architecture of the proposed hybrid OCDMA-FSO network is thoroughly described. The users access the network in a fully asynchronous manner by means of assigned fast frequency hopping (FFH)-based codes. In the FSO receiver, an equal gain-combining technique is employed along with intensity modulation and direct detection. New analytical formalisms for evaluating the average bit error rate (ABER) performance are also proposed. These formalisms, based on the spatially correlated gamma-gamma statistical model, are derived considering three distinct scenarios, namely, uncorrelated, totally correlated, and partially correlated channels. Numerical results show that users can successfully achieve error-free ABER levels for the three scenarios considered as long as forward error correction (FEC) algorithms are employed. Therefore, OCDMA-FSO networks can be a prospective alternative to deliver high-speed communication services to access networks with deficient fiber infrastructure.

  2. Impact of leakage delay on bifurcation in high-order fractional BAM neural networks.

    PubMed

    Huang, Chengdai; Cao, Jinde

    2018-02-01

    The effects of leakage delay on the dynamics of neural networks with integer-order have lately been received considerable attention. It has been confirmed that fractional neural networks more appropriately uncover the dynamical properties of neural networks, but the results of fractional neural networks with leakage delay are relatively few. This paper primarily concentrates on the issue of bifurcation for high-order fractional bidirectional associative memory(BAM) neural networks involving leakage delay. The first attempt is made to tackle the stability and bifurcation of high-order fractional BAM neural networks with time delay in leakage terms in this paper. The conditions for the appearance of bifurcation for the proposed systems with leakage delay are firstly established by adopting time delay as a bifurcation parameter. Then, the bifurcation criteria of such system without leakage delay are successfully acquired. Comparative analysis wondrously detects that the stability performance of the proposed high-order fractional neural networks is critically weakened by leakage delay, they cannot be overlooked. Numerical examples are ultimately exhibited to attest the efficiency of the theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. 78 FR 14359 - Verizon Business Networks Services, Inc., Senior Analysts-Order Management, Voice Over Internet...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-03-05

    ... Business Networks Services, Inc., Senior Analysts-Order Management, Voice Over Internet Protocol, Small And Medium Business, Tampa, Florida; Verizon Business Networks Services, Inc., Senior Coordinator-Order... Business Networks Services, Inc., Senior Analysts-Order Management, Voice Over Internet Protocol, Small and...

  4. Development of stock correlation networks using mutual information and financial big data.

    PubMed

    Guo, Xue; Zhang, Hu; Tian, Tianhai

    2018-01-01

    Stock correlation networks use stock price data to explore the relationship between different stocks listed in the stock market. Currently this relationship is dominantly measured by the Pearson correlation coefficient. However, financial data suggest that nonlinear relationships may exist in the stock prices of different shares. To address this issue, this work uses mutual information to characterize the nonlinear relationship between stocks. Using 280 stocks traded at the Shanghai Stocks Exchange in China during the period of 2014-2016, we first compare the effectiveness of the correlation coefficient and mutual information for measuring stock relationships. Based on these two measures, we then develop two stock networks using the Minimum Spanning Tree method and study the topological properties of these networks, including degree, path length and the power-law distribution. The relationship network based on mutual information has a better distribution of the degree and larger value of the power-law distribution than those using the correlation coefficient. Numerical results show that mutual information is a more effective approach than the correlation coefficient to measure the stock relationship in a stock market that may undergo large fluctuations of stock prices.

  5. Development of stock correlation networks using mutual information and financial big data

    PubMed Central

    Guo, Xue; Zhang, Hu

    2018-01-01

    Stock correlation networks use stock price data to explore the relationship between different stocks listed in the stock market. Currently this relationship is dominantly measured by the Pearson correlation coefficient. However, financial data suggest that nonlinear relationships may exist in the stock prices of different shares. To address this issue, this work uses mutual information to characterize the nonlinear relationship between stocks. Using 280 stocks traded at the Shanghai Stocks Exchange in China during the period of 2014-2016, we first compare the effectiveness of the correlation coefficient and mutual information for measuring stock relationships. Based on these two measures, we then develop two stock networks using the Minimum Spanning Tree method and study the topological properties of these networks, including degree, path length and the power-law distribution. The relationship network based on mutual information has a better distribution of the degree and larger value of the power-law distribution than those using the correlation coefficient. Numerical results show that mutual information is a more effective approach than the correlation coefficient to measure the stock relationship in a stock market that may undergo large fluctuations of stock prices. PMID:29668715

  6. Will electrical cyber-physical interdependent networks undergo first-order transition under random attacks?

    NASA Astrophysics Data System (ADS)

    Ji, Xingpei; Wang, Bo; Liu, Dichen; Dong, Zhaoyang; Chen, Guo; Zhu, Zhenshan; Zhu, Xuedong; Wang, Xunting

    2016-10-01

    Whether the realistic electrical cyber-physical interdependent networks will undergo first-order transition under random failures still remains a question. To reflect the reality of Chinese electrical cyber-physical system, the "partial one-to-one correspondence" interdependent networks model is proposed and the connectivity vulnerabilities of three realistic electrical cyber-physical interdependent networks are analyzed. The simulation results show that due to the service demands of power system the topologies of power grid and its cyber network are highly inter-similar which can effectively avoid the first-order transition. By comparing the vulnerability curves between electrical cyber-physical interdependent networks and its single-layer network, we find that complex network theory is still useful in the vulnerability analysis of electrical cyber-physical interdependent networks.

  7. Order priors for Bayesian network discovery with an application to malware phylogeny

    DOE PAGES

    Oyen, Diane; Anderson, Blake; Sentz, Kari; ...

    2017-09-15

    Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges)more » in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.« less

  8. Order priors for Bayesian network discovery with an application to malware phylogeny

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Oyen, Diane; Anderson, Blake; Sentz, Kari

    Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges)more » in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.« less

  9. Supercooperation in evolutionary games on correlated weighted networks.

    PubMed

    Buesser, Pierre; Tomassini, Marco

    2012-01-01

    In this work we study the behavior of classical two-person, two-strategies evolutionary games on a class of weighted networks derived from Barabási-Albert and random scale-free unweighted graphs. Using customary imitative dynamics, our numerical simulation results show that the presence of link weights that are correlated in a particular manner with the degree of the link end points leads to unprecedented levels of cooperation in the whole games' phase space, well above those found for the corresponding unweighted complex networks. We provide intuitive explanations for this favorable behavior by transforming the weighted networks into unweighted ones with particular topological properties. The resulting structures help us to understand why cooperation can thrive and also give ideas as to how such supercooperative networks might be built.

  10. Autocorrel I: A Neural Network Based Network Event Correlation Approach

    DTIC Science & Technology

    2005-05-01

    which concern any component of the network. 2.1.1 Existing Intrusion Detection Systems EMERALD [8] is a distributed, scalable, hierarchal, customizable...writing this paper, the updaters of this system had not released their correlation unit to the public. EMERALD ex- plicitly divides statistical analysis... EMERALD , NetSTAT is scalable and composi- ble. QuidSCOR [12] is an open-source IDS, though it requires a subscription from its publisher, Qualys Inc

  11. A Novel Higher Order Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Xu, Shuxiang

    2010-05-01

    In this paper a new Higher Order Neural Network (HONN) model is introduced and applied in several data mining tasks. Data Mining extracts hidden patterns and valuable information from large databases. A hyperbolic tangent function is used as the neuron activation function for the new HONN model. Experiments are conducted to demonstrate the advantages and disadvantages of the new HONN model, when compared with several conventional Artificial Neural Network (ANN) models: Feedforward ANN with the sigmoid activation function; Feedforward ANN with the hyperbolic tangent activation function; and Radial Basis Function (RBF) ANN with the Gaussian activation function. The experimental results seem to suggest that the new HONN holds higher generalization capability as well as abilities in handling missing data.

  12. A neural network for noise correlation classification

    NASA Astrophysics Data System (ADS)

    Paitz, Patrick; Gokhberg, Alexey; Fichtner, Andreas

    2018-02-01

    We present an artificial neural network (ANN) for the classification of ambient seismic noise correlations into two categories, suitable and unsuitable for noise tomography. By using only a small manually classified data subset for network training, the ANN allows us to classify large data volumes with low human effort and to encode the valuable subjective experience of data analysts that cannot be captured by a deterministic algorithm. Based on a new feature extraction procedure that exploits the wavelet-like nature of seismic time-series, we efficiently reduce the dimensionality of noise correlation data, still keeping relevant features needed for automated classification. Using global- and regional-scale data sets, we show that classification errors of 20 per cent or less can be achieved when the network training is performed with as little as 3.5 per cent and 16 per cent of the data sets, respectively. Furthermore, the ANN trained on the regional data can be applied to the global data, and vice versa, without a significant increase of the classification error. An experiment where four students manually classified the data, revealed that the classification error they would assign to each other is substantially larger than the classification error of the ANN (>35 per cent). This indicates that reproducibility would be hampered more by human subjectivity than by imperfections of the ANN.

  13. Functional Network Development During the First Year: Relative Sequence and Socioeconomic Correlations

    PubMed Central

    Gao, Wei; Alcauter, Sarael; Elton, Amanda; Hernandez-Castillo, Carlos R.; Smith, J. Keith; Ramirez, Juanita; Lin, Weili

    2015-01-01

    The first postnatal year is characterized by the most dramatic functional network development of the human lifespan. Yet, the relative sequence of the maturation of different networks and the impact of socioeconomic status (SES) on their development during this critical period remains poorly characterized. Leveraging a large, normally developing infant sample with multiple longitudinal resting-state functional magnetic resonance imaging scans during the first year (N = 65, scanned every 3 months), we aimed to delineate the relative maturation sequence of 9 key brain functional networks and examine their SES correlations. Our results revealed a maturation sequence from primary sensorimotor/auditory to visual to attention/default-mode, and finally to executive control networks. Network-specific critical growth periods were also identified. Finally, marginally significant positive SES–brain correlations were observed at 6 months of age for both the sensorimotor and default-mode networks, indicating interesting SES effects on functional brain maturation. To the best of our knowledge, this is the first study delineating detailed longitudinal growth trajectories of all major functional networks during the first year of life and their SES correlations. Insights from this study not only improve our understanding of early brain development, but may also inform the critical periods for SES expression during infancy. PMID:24812084

  14. Co-occurrence correlations of heavy metals in sediments revealed using network analysis.

    PubMed

    Liu, Lili; Wang, Zhiping; Ju, Feng; Zhang, Tong

    2015-01-01

    In this study, the correlation-based study was used to identify the co-occurrence correlations among metals in marine sediment of Hong Kong, based on the long-term (from 1991 to 2011) temporal and spatial monitoring data. 14 stations out of the total 45 marine sediment monitoring stations were selected from three representative areas, including Deep Bay, Victoria Harbour and Mirs Bay. Firstly, Spearman's rank correlation-based network analysis was conducted as the first step to identify the co-occurrence correlations of metals from raw metadata, and then for further analysis using the normalized metadata. The correlations patterns obtained by network were consistent with those obtained by the other statistic normalization methods, including annual ratios, R-squared coefficient and Pearson correlation coefficient. Both Deep Bay and Victoria Harbour have been polluted by heavy metals, especially for Pb and Cu, which showed strong co-occurrence with other heavy metals (e.g. Cr, Ni, Zn and etc.) and little correlations with the reference parameters (Fe or Al). For Mirs Bay, which has better marine sediment quality compared with Deep Bay and Victoria Harbour, the co-occurrence patterns revealed by network analysis indicated that the metals in sediment dominantly followed the natural geography process. Besides the wide applications in biology, sociology and informatics, it is the first time to apply network analysis in the researches of environment pollutions. This study demonstrated its powerful application for revealing the co-occurrence correlations among heavy metals in marine sediments, which could be further applied for other pollutants in various environment systems. Copyright © 2014 Elsevier Ltd. All rights reserved.

  15. ICE-Based Custom Full-Mesh Network for the CHIME High Bandwidth Radio Astronomy Correlator

    NASA Astrophysics Data System (ADS)

    Bandura, K.; Cliche, J. F.; Dobbs, M. A.; Gilbert, A. J.; Ittah, D.; Mena Parra, J.; Smecher, G.

    2016-03-01

    New generation radio interferometers encode signals from thousands of antenna feeds across large bandwidth. Channelizing and correlating this data requires networking capabilities that can handle unprecedented data rates with reasonable cost. The Canadian Hydrogen Intensity Mapping Experiment (CHIME) correlator processes 8-bits from N=2,048 digitizer inputs across 400MHz of bandwidth. Measured in N2× bandwidth, it is the largest radio correlator that is currently commissioning. Its digital back-end must exchange and reorganize the 6.6terabit/s produced by its 128 digitizing and channelizing nodes, and feed it to the 256 graphics processing unit (GPU) node spatial correlator in a way that each node obtains data from all digitizer inputs but across a small fraction of the bandwidth (i.e. ‘corner-turn’). In order to maximize performance and reliability of the corner-turn system while minimizing cost, a custom networking solution has been implemented. The system makes use of Field Programmable Gate Array (FPGA) transceivers to implement direct, passive copper, full-mesh, high speed serial connections between sixteen circuit boards in a crate, to exchange data between crates, and to offload the data to a cluster of 256 GPU nodes using standard 10Gbit/s Ethernet links. The GPU nodes complete the corner-turn by combining data from all crates and then computing visibilities. Eye diagrams and frame error counters confirm error-free operation of the corner-turn network in both the currently operating CHIME Pathfinder telescope (a prototype for the full CHIME telescope) and a representative fraction of the full CHIME hardware providing an end-to-end system validation. An analysis of an equivalent corner-turn system built with Ethernet switches instead of custom passive data links is provided.

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

    PubMed

    Büttner, Kathrin; Salau, Jennifer; Krieter, Joachim

    2016-01-01

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

  17. Network Connectivity for Permanent, Transient, Independent, and Correlated Faults

    NASA Technical Reports Server (NTRS)

    White, Allan L.; Sicher, Courtney; henry, Courtney

    2012-01-01

    This paper develops a method for the quantitative analysis of network connectivity in the presence of both permanent and transient faults. Even though transient noise is considered a common occurrence in networks, a survey of the literature reveals an emphasis on permanent faults. Transient faults introduce a time element into the analysis of network reliability. With permanent faults it is sufficient to consider the faults that have accumulated by the end of the operating period. With transient faults the arrival and recovery time must be included. The number and location of faults in the system is a dynamic variable. Transient faults also introduce system recovery into the analysis. The goal is the quantitative assessment of network connectivity in the presence of both permanent and transient faults. The approach is to construct a global model that includes all classes of faults: permanent, transient, independent, and correlated. A theorem is derived about this model that give distributions for (1) the number of fault occurrences, (2) the type of fault occurrence, (3) the time of the fault occurrences, and (4) the location of the fault occurrence. These results are applied to compare and contrast the connectivity of different network architectures in the presence of permanent, transient, independent, and correlated faults. The examples below use a Monte Carlo simulation, but the theorem mentioned above could be used to guide fault-injections in a laboratory.

  18. Infinitely robust order and local order-parameter tulips in Apollonian networks with quenched disorder

    NASA Astrophysics Data System (ADS)

    Kaplan, C. Nadir; Hinczewski, Michael; Berker, A. Nihat

    2009-06-01

    For a variety of quenched random spin systems on an Apollonian network, including ferromagnetic and antiferromagnetic bond percolation and the Ising spin glass, we find the persistence of ordered phases up to infinite temperature over the entire range of disorder. We develop a renormalization-group technique that yields highly detailed information, including the exact distributions of local magnetizations and local spin-glass order parameters, which turn out to exhibit, as function of temperature, complex and distinctive tulip patterns.

  19. Order or chaos in Boolean gene networks depends on the mean fraction of canalizing functions

    NASA Astrophysics Data System (ADS)

    Karlsson, Fredrik; Hörnquist, Michael

    2007-10-01

    We explore the connection between order/chaos in Boolean networks and the naturally occurring fraction of canalizing functions in such systems. This fraction turns out to give a very clear indication of whether the system possesses ordered or chaotic dynamics, as measured by Derrida plots, and also the degree of order when we compare different networks with the same number of vertices and edges. By studying also a wide distribution of indegrees in a network, we show that the mean probability of canalizing functions is a more reliable indicator of the type of dynamics for a finite network than the classical result on stability relating the bias to the mean indegree. Finally, we compare by direct simulations two biologically derived networks with networks of similar sizes but with power-law and Poisson distributions of indegrees, respectively. The biologically motivated networks are not more ordered than the latter, and in one case the biological network is even chaotic while the others are not.

  20. Higher-Order Statistical Correlations and Mutual Information Among Particles in a Quantum Well

    NASA Astrophysics Data System (ADS)

    Yépez, V. S.; Sagar, R. P.; Laguna, H. G.

    2017-12-01

    The influence of wave function symmetry on statistical correlation is studied for the case of three non-interacting spin-free quantum particles in a unidimensional box, in position and in momentum space. Higher-order statistical correlations occurring among the three particles in this quantum system is quantified via higher-order mutual information and compared to the correlation between pairs of variables in this model, and to the correlation in the two-particle system. The results for the higher-order mutual information show that there are states where the symmetric wave functions are more correlated than the antisymmetric ones with same quantum numbers. This holds in position as well as in momentum space. This behavior is opposite to that observed for the correlation between pairs of variables in this model, and the two-particle system, where the antisymmetric wave functions are in general more correlated. These results are also consistent with those observed in a system of three uncoupled oscillators. The use of higher-order mutual information as a correlation measure, is monitored and examined by considering a superposition of states or systems with two Slater determinants.

  1. Scaling and correlations in three bus-transport networks of China

    NASA Astrophysics Data System (ADS)

    Xu, Xinping; Hu, Junhui; Liu, Feng; Liu, Lianshou

    2007-01-01

    We report the statistical properties of three bus-transport networks (BTN) in three different cities of China. These networks are composed of a set of bus lines and stations serviced by these. Network properties, including the degree distribution, clustering and average path length are studied in different definitions of network topology. We explore scaling laws and correlations that may govern intrinsic features of such networks. Besides, we create a weighted network representation for BTN with lines mapped to nodes and number of common stations to weights between lines. In such a representation, the distributions of degree, strength and weight are investigated. A linear behavior between strength and degree s(k)∼k is also observed.

  2. Conditions for Viral Influence Spreading through Multiplex Correlated Social Networks

    NASA Astrophysics Data System (ADS)

    Hu, Yanqing; Havlin, Shlomo; Makse, Hernán A.

    2014-04-01

    A fundamental problem in network science is to predict how certain individuals are able to initiate new networks to spring up "new ideas." Frequently, these changes in trends are triggered by a few innovators who rapidly impose their ideas through "viral" influence spreading, producing cascades of followers and fragmenting an old network to create a new one. Typical examples include the rise of scientific ideas or abrupt changes in social media, like the rise of Facebook to the detriment of Myspace. How this process arises in practice has not been conclusively demonstrated. Here, we show that a condition for sustaining a viral spreading process is the existence of a multiplex-correlated graph with hidden "influence links." Analytical solutions predict percolation-phase transitions, either abrupt or continuous, where networks are disintegrated through viral cascades of followers, as in empirical data. Our modeling predicts the strict conditions to sustain a large viral spreading via a scaling form of the local correlation function between multilayers, which we also confirm empirically. Ultimately, the theory predicts the conditions for viral cascading in a large class of multiplex networks ranging from social to financial systems and markets.

  3. Correlation and network topologies in global and local stock indices

    NASA Astrophysics Data System (ADS)

    Nobi, Ashadun; Lee, Sungmin; Kim, Doo Hwan; Lee, Jae Woo

    2014-07-01

    We examined how the correlation and network structure of the global indices and local Korean indices have changed during years 2000-2012. The average correlations of the global indices increased with time, while the local indices showed a decreasing trend except for drastic changes during the crises. A significant change in the network topologies was observed due to the financial crises in both markets. The Jaccard similarities identified the change in the market state due to a crisis in both markets. The dynamic change of the Jaccard index can be used as an indicator of systemic risk or precursors of the crisis.

  4. Correlation and network analysis of global financial indices

    NASA Astrophysics Data System (ADS)

    Kumar, Sunil; Deo, Nivedita

    2012-08-01

    Random matrix theory (RMT) and network methods are applied to investigate the correlation and network properties of 20 financial indices. The results are compared before and during the financial crisis of 2008. In the RMT method, the components of eigenvectors corresponding to the second largest eigenvalue form two clusters of indices in the positive and negative directions. The components of these two clusters switch in opposite directions during the crisis. The network analysis uses the Fruchterman-Reingold layout to find clusters in the network of indices at different thresholds. At a threshold of 0.6, before the crisis, financial indices corresponding to the Americas, Europe, and Asia-Pacific form separate clusters. On the other hand, during the crisis at the same threshold, the American and European indices combine together to form a strongly linked cluster while the Asia-Pacific indices form a separate weakly linked cluster. If the value of the threshold is further increased to 0.9 then the European indices (France, Germany, and the United Kingdom) are found to be the most tightly linked indices. The structure of the minimum spanning tree of financial indices is more starlike before the crisis and it changes to become more chainlike during the crisis. The average linkage hierarchical clustering algorithm is used to find a clearer cluster structure in the network of financial indices. The cophenetic correlation coefficients are calculated and found to increase significantly, which indicates that the hierarchy increases during the financial crisis. These results show that there is substantial change in the structure of the organization of financial indices during a financial crisis.

  5. Correlation and network analysis of global financial indices.

    PubMed

    Kumar, Sunil; Deo, Nivedita

    2012-08-01

    Random matrix theory (RMT) and network methods are applied to investigate the correlation and network properties of 20 financial indices. The results are compared before and during the financial crisis of 2008. In the RMT method, the components of eigenvectors corresponding to the second largest eigenvalue form two clusters of indices in the positive and negative directions. The components of these two clusters switch in opposite directions during the crisis. The network analysis uses the Fruchterman-Reingold layout to find clusters in the network of indices at different thresholds. At a threshold of 0.6, before the crisis, financial indices corresponding to the Americas, Europe, and Asia-Pacific form separate clusters. On the other hand, during the crisis at the same threshold, the American and European indices combine together to form a strongly linked cluster while the Asia-Pacific indices form a separate weakly linked cluster. If the value of the threshold is further increased to 0.9 then the European indices (France, Germany, and the United Kingdom) are found to be the most tightly linked indices. The structure of the minimum spanning tree of financial indices is more starlike before the crisis and it changes to become more chainlike during the crisis. The average linkage hierarchical clustering algorithm is used to find a clearer cluster structure in the network of financial indices. The cophenetic correlation coefficients are calculated and found to increase significantly, which indicates that the hierarchy increases during the financial crisis. These results show that there is substantial change in the structure of the organization of financial indices during a financial crisis.

  6. Cubic law with aperture-length correlation: implications for network scale fluid flow

    NASA Astrophysics Data System (ADS)

    Klimczak, Christian; Schultz, Richard A.; Parashar, Rishi; Reeves, Donald M.

    2010-06-01

    Previous studies have computed and modeled fluid flow through fractured rock with the parallel plate approach where the volumetric flow per unit width normal to the direction of flow is proportional to the cubed aperture between the plates, referred to as the traditional cubic law. When combined with the square root relationship of displacement to length scaling of opening-mode fractures, total flow rates through natural opening-mode fractures are found to be proportional to apertures to the fifth power. This new relationship was explored by examining a suite of flow simulations through fracture networks using the discrete fracture network model (DFN). Flow was modeled through fracture networks with the same spatial distribution of fractures for both correlated and uncorrelated fracture length-to-aperture relationships. Results indicate that flow rates are significantly higher for correlated DFNs. Furthermore, the length-to-aperture relations lead to power-law distributions of network hydraulic conductivity which greatly influence equivalent permeability tensor values. These results confirm the importance of the correlated square root relationship of displacement to length scaling for total flow through natural opening-mode fractures and, hence, emphasize the role of these correlations for flow modeling.

  7. Network analysis of a financial market based on genuine correlation and threshold method

    NASA Astrophysics Data System (ADS)

    Namaki, A.; Shirazi, A. H.; Raei, R.; Jafari, G. R.

    2011-10-01

    A financial market is an example of an adaptive complex network consisting of many interacting units. This network reflects market’s behavior. In this paper, we use Random Matrix Theory (RMT) notion for specifying the largest eigenvector of correlation matrix as the market mode of stock network. For a better risk management, we clean the correlation matrix by removing the market mode from data and then construct this matrix based on the residuals. We show that this technique has an important effect on correlation coefficient distribution by applying it for Dow Jones Industrial Average (DJIA). To study the topological structure of a network we apply the removing market mode technique and the threshold method to Tehran Stock Exchange (TSE) as an example. We show that this network follows a power-law model in certain intervals. We also show the behavior of clustering coefficients and component numbers of this network for different thresholds. These outputs are useful for both theoretical and practical purposes such as asset allocation and risk management.

  8. Spatially correlated heterogeneous aspirations to enhance network reciprocity

    NASA Astrophysics Data System (ADS)

    Tanimoto, Jun; Nakata, Makoto; Hagishima, Aya; Ikegaya, Naoki

    2012-02-01

    Perc & Wang demonstrated that aspiring to be the fittest under conditions of pairwise strategy updating enhances network reciprocity in structured populations playing 2×2 Prisoner's Dilemma games (Z. Wang, M. Perc, Aspiring to the fittest and promoted of cooperation in the Prisoner's Dilemma game, Physical Review E 82 (2010) 021115; M. Perc, Z. Wang, Heterogeneous aspiration promotes cooperation in the Prisoner's Dilemma game, PLOS one 5 (12) (2010) e15117). Through numerical simulations, this paper shows that network reciprocity is even greater if heterogeneous aspirations are imposed. We also suggest why heterogeneous aspiration fosters network reciprocity. It distributes strategy updating speed among agents in a manner that fortifies the initially allocated cooperators' clusters against invasion. This finding prompted us to further enhance the usual heterogeneous aspiration cases for heterogeneous network topologies. We find that a negative correlation between degree and aspiration level does extend cooperation among heterogeneously structured agents.

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

    PubMed

    Ni, Jianhua; Qian, Tianlu; Xi, Changbai; Rui, Yikang; Wang, Jiechen

    2016-08-18

    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.

  10. Musical Imagery Involves Wernicke's Area in Bilateral and Anti-Correlated Network Interactions in Musicians.

    PubMed

    Zhang, Yizhen; Chen, Gang; Wen, Haiguang; Lu, Kun-Han; Liu, Zhongming

    2017-12-06

    Musical imagery is the human experience of imagining music without actually hearing it. The neural basis of this mental ability is unclear, especially for musicians capable of engaging in accurate and vivid musical imagery. Here, we created a visualization of an 8-minute symphony as a silent movie and used it as real-time cue for musicians to continuously imagine the music for repeated and synchronized sessions during functional magnetic resonance imaging (fMRI). The activations and networks evoked by musical imagery were compared with those elicited by the subjects directly listening to the same music. Musical imagery and musical perception resulted in overlapping activations at the anterolateral belt and Wernicke's area, where the responses were correlated with the auditory features of the music. Whereas Wernicke's area interacted within the intrinsic auditory network during musical perception, it was involved in much more complex networks during musical imagery, showing positive correlations with the dorsal attention network and the motor-control network and negative correlations with the default-mode network. Our results highlight the important role of Wernicke's area in forming vivid musical imagery through bilateral and anti-correlated network interactions, challenging the conventional view of segregated and lateralized processing of music versus language.

  11. Using an Extended Kalman Filter Learning Algorithm for Feed-Forward Neural Networks to Describe Tracer Correlations

    NASA Technical Reports Server (NTRS)

    Lary, David J.; Mussa, Yussuf

    2004-01-01

    In this study a new extended Kalman filter (EKF) learning algorithm for feed-forward neural networks (FFN) is used. With the EKF approach, the training of the FFN can be seen as state estimation for a non-linear stationary process. The EKF method gives excellent convergence performances provided that there is enough computer core memory and that the machine precision is high. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). The neural network was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9997. The neural network Fortran code used is available for download.

  12. Degree Correlations Optimize Neuronal Network Sensitivity to Sub-Threshold Stimuli

    PubMed Central

    Schmeltzer, Christian; Kihara, Alexandre Hiroaki; Sokolov, Igor Michailovitsch; Rüdiger, Sten

    2015-01-01

    Information processing in the brain crucially depends on the topology of the neuronal connections. We investigate how the topology influences the response of a population of leaky integrate-and-fire neurons to a stimulus. We devise a method to calculate firing rates from a self-consistent system of equations taking into account the degree distribution and degree correlations in the network. We show that assortative degree correlations strongly improve the sensitivity for weak stimuli and propose that such networks possess an advantage in signal processing. We moreover find that there exists an optimum in assortativity at an intermediate level leading to a maximum in input/output mutual information. PMID:26115374

  13. Stereoscopic depth increases intersubject correlations of brain networks.

    PubMed

    Gaebler, Michael; Biessmann, Felix; Lamke, Jan-Peter; Müller, Klaus-Robert; Walter, Henrik; Hetzer, Stefan

    2014-10-15

    Three-dimensional movies presented via stereoscopic displays have become more popular in recent years aiming at a more engaging viewing experience. However, neurocognitive processes associated with the perception of stereoscopic depth in complex and dynamic visual stimuli remain understudied. Here, we investigate the influence of stereoscopic depth on both neurophysiology and subjective experience. Using multivariate statistical learning methods, we compare the brain activity of subjects when freely watching the same movies in 2D and in 3D. Subjective reports indicate that 3D movies are more strongly experienced than 2D movies. On the neural level, we observe significantly higher intersubject correlations of cortical networks when subjects are watching 3D movies relative to the same movies in 2D. We demonstrate that increases in intersubject correlations of brain networks can serve as neurophysiological marker for stereoscopic depth and for the strength of the viewing experience. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  14. Anti-correlations in the degree distribution increase stimulus detection performance in noisy spiking neural networks.

    PubMed

    Martens, Marijn B; Houweling, Arthur R; E Tiesinga, Paul H

    2017-02-01

    Neuronal circuits in the rodent barrel cortex are characterized by stable low firing rates. However, recent experiments show that short spike trains elicited by electrical stimulation in single neurons can induce behavioral responses. Hence, the underlying neural networks provide stability against internal fluctuations in the firing rate, while simultaneously making the circuits sensitive to small external perturbations. Here we studied whether stability and sensitivity are affected by the connectivity structure in recurrently connected spiking networks. We found that anti-correlation between the number of afferent (in-degree) and efferent (out-degree) synaptic connections of neurons increases stability against pathological bursting, relative to networks where the degrees were either positively correlated or uncorrelated. In the stable network state, stimulation of a few cells could lead to a detectable change in the firing rate. To quantify the ability of networks to detect the stimulation, we used a receiver operating characteristic (ROC) analysis. For a given level of background noise, networks with anti-correlated degrees displayed the lowest false positive rates, and consequently had the highest stimulus detection performance. We propose that anti-correlation in the degree distribution may be a computational strategy employed by sensory cortices to increase the detectability of external stimuli. We show that networks with anti-correlated degrees can in principle be formed by applying learning rules comprised of a combination of spike-timing dependent plasticity, homeostatic plasticity and pruning to networks with uncorrelated degrees. To test our prediction we suggest a novel experimental method to estimate correlations in the degree distribution.

  15. New Insights on Temporal Lobe Epilepsy Based on Plasticity-Related Network Changes and High-Order Statistics.

    PubMed

    Kinjo, Erika Reime; Rodríguez, Pedro Xavier Royero; Dos Santos, Bianca Araújo; Higa, Guilherme Shigueto Vilar; Ferraz, Mariana Sacrini Ayres; Schmeltzer, Christian; Rüdiger, Sten; Kihara, Alexandre Hiroaki

    2018-05-01

    Epilepsy is a disorder of the brain characterized by the predisposition to generate recurrent unprovoked seizures, which involves reshaping of neuronal circuitries based on intense neuronal activity. In this review, we first detailed the regulation of plasticity-associated genes, such as ARC, GAP-43, PSD-95, synapsin, and synaptophysin. Indeed, reshaping of neuronal connectivity after the primary, acute epileptogenesis event increases the excitability of the temporal lobe. Herein, we also discussed the heterogeneity of neuronal populations regarding the number of synaptic connections, which in the theoretical field is commonly referred as degree. Employing integrate-and-fire neuronal model, we determined that in addition to increased synaptic strength, degree correlations might play essential and unsuspected roles in the control of network activity. Indeed, assortativity, which can be described as a condition where high-degree correlations are observed, increases the excitability of neural networks. In this review, we summarized recent topics in the field, and data were discussed according to newly developed or unusual tools, as provided by mathematical graph analysis and high-order statistics. With this, we were able to present new foundations for the pathological activity observed in temporal lobe epilepsy.

  16. Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset

    PubMed Central

    Guo, Hao; Liu, Lei; Chen, Junjie; Xu, Yong; Jie, Xiang

    2017-01-01

    Functional magnetic resonance imaging (fMRI) is one of the most useful methods to generate functional connectivity networks of the brain. However, conventional network generation methods ignore dynamic changes of functional connectivity between brain regions. Previous studies proposed constructing high-order functional connectivity networks that consider the time-varying characteristics of functional connectivity, and a clustering method was performed to decrease computational cost. However, random selection of the initial clustering centers and the number of clusters negatively affected classification accuracy, and the network lost neurological interpretability. Here we propose a novel method that introduces the minimum spanning tree method to high-order functional connectivity networks. As an unbiased method, the minimum spanning tree simplifies high-order network structure while preserving its core framework. The dynamic characteristics of time series are not lost with this approach, and the neurological interpretation of the network is guaranteed. Simultaneously, we propose a multi-parameter optimization framework that involves extracting discriminative features from the minimum spanning tree high-order functional connectivity networks. Compared with the conventional methods, our resting-state fMRI classification method based on minimum spanning tree high-order functional connectivity networks greatly improved the diagnostic accuracy for Alzheimer's disease. PMID:29249926

  17. 76 FR 28117 - Order of Suspension of Trading; City Network, Inc.

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-05-13

    ... SECURITIES AND EXCHANGE COMMISSION [File No. 500-1] Order of Suspension of Trading; City Network, Inc. May 11, 2011. It appears to the Securities and Exchange Commission that there is a lack of current and accurate information concerning the securities of City Network, Inc. because it has not filed...

  18. Socioeconomic correlations and stratification in social-communication networks.

    PubMed

    Leo, Yannick; Fleury, Eric; Alvarez-Hamelin, J Ignacio; Sarraute, Carlos; Karsai, Márton

    2016-12-01

    The uneven distribution of wealth and individual economic capacities are among the main forces, which shape modern societies and arguably bias the emerging social structures. However, the study of correlations between the social network and economic status of individuals is difficult due to the lack of large-scale multimodal data disclosing both the social ties and economic indicators of the same population. Here, we close this gap through the analysis of coupled datasets recording the mobile phone communications and bank transaction history of one million anonymized individuals living in a Latin American country. We show that wealth and debt are unevenly distributed among people in agreement with the Pareto principle; the observed social structure is strongly stratified, with people being better connected to others of their own socioeconomic class rather than to others of different classes; the social network appears to have assortative socioeconomic correlations and tightly connected 'rich clubs'; and that individuals from the same class live closer to each other but commute further if they are wealthier. These results are based on a representative, society-large population, and empirically demonstrate some long-lasting hypotheses on socioeconomic correlations, which potentially lay behind social segregation, and induce differences in human mobility. © 2016 The Author(s).

  19. Socioeconomic correlations and stratification in social-communication networks

    PubMed Central

    Leo, Yannick; Fleury, Eric; Sarraute, Carlos

    2016-01-01

    The uneven distribution of wealth and individual economic capacities are among the main forces, which shape modern societies and arguably bias the emerging social structures. However, the study of correlations between the social network and economic status of individuals is difficult due to the lack of large-scale multimodal data disclosing both the social ties and economic indicators of the same population. Here, we close this gap through the analysis of coupled datasets recording the mobile phone communications and bank transaction history of one million anonymized individuals living in a Latin American country. We show that wealth and debt are unevenly distributed among people in agreement with the Pareto principle; the observed social structure is strongly stratified, with people being better connected to others of their own socioeconomic class rather than to others of different classes; the social network appears to have assortative socioeconomic correlations and tightly connected ‘rich clubs’; and that individuals from the same class live closer to each other but commute further if they are wealthier. These results are based on a representative, society-large population, and empirically demonstrate some long-lasting hypotheses on socioeconomic correlations, which potentially lay behind social segregation, and induce differences in human mobility. PMID:27974571

  20. Functional connectivity within and between intrinsic brain networks correlates with trait mind wandering.

    PubMed

    Godwin, Christine A; Hunter, Michael A; Bezdek, Matthew A; Lieberman, Gregory; Elkin-Frankston, Seth; Romero, Victoria L; Witkiewitz, Katie; Clark, Vincent P; Schumacher, Eric H

    2017-08-01

    Individual differences across a variety of cognitive processes are functionally associated with individual differences in intrinsic networks such as the default mode network (DMN). The extent to which these networks correlate or anticorrelate has been associated with performance in a variety of circumstances. Despite the established role of the DMN in mind wandering processes, little research has investigated how large-scale brain networks at rest relate to mind wandering tendencies outside the laboratory. Here we examine the extent to which the DMN, along with the dorsal attention network (DAN) and frontoparietal control network (FPCN) correlate with the tendency to mind wander in daily life. Participants completed the Mind Wandering Questionnaire and a 5-min resting state fMRI scan. In addition, participants completed measures of executive function, fluid intelligence, and creativity. We observed significant positive correlations between trait mind wandering and 1) increased DMN connectivity at rest and 2) increased connectivity between the DMN and FPCN at rest. Lastly, we found significant positive correlations between trait mind wandering and fluid intelligence (Ravens) and creativity (Remote Associates Task). We interpret these findings within the context of current theories of mind wandering and executive function and discuss the possibility that certain instances of mind wandering may not be inherently harmful. Due to the controversial nature of global signal regression (GSReg) in functional connectivity analyses, we performed our analyses with and without GSReg and contrast the results from each set of analyses. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. Early warning model based on correlated networks in global crude oil markets

    NASA Astrophysics Data System (ADS)

    Yu, Jia-Wei; Xie, Wen-Jie; Jiang, Zhi-Qiang

    2018-01-01

    Applying network tools on predicting and warning the systemic risks provides a novel avenue to manage risks in financial markets. Here, we construct a series of global crude oil correlated networks based on the historical 57 oil prices covering a period from 1993 to 2012. Two systemic risk indicators are constructed based on the density and modularity of correlated networks. The local maximums of the risk indicators are found to have the ability to predict the trends of oil prices. In our sample periods, the indicator based on the network density sends five signals and the indicator based on the modularity index sends four signals. The four signals sent by both indicators are able to warn the drop of future oil prices and the signal only sent by the network density is followed by a huge rise of oil prices. Our results deepen the application of network measures on building early warning models of systemic risks and can be applied to predict the trends of future prices in financial markets.

  2. Short-range second order screened exchange correction to RPA correlation energies

    NASA Astrophysics Data System (ADS)

    Beuerle, Matthias; Ochsenfeld, Christian

    2017-11-01

    Direct random phase approximation (RPA) correlation energies have become increasingly popular as a post-Kohn-Sham correction, due to significant improvements over DFT calculations for properties such as long-range dispersion effects, which are problematic in conventional density functional theory. On the other hand, RPA still has various weaknesses, such as unsatisfactory results for non-isogyric processes. This can in parts be attributed to the self-correlation present in RPA correlation energies, leading to significant self-interaction errors. Therefore a variety of schemes have been devised to include exchange in the calculation of RPA correlation energies in order to correct this shortcoming. One of the most popular RPA plus exchange schemes is the second order screened exchange (SOSEX) correction. RPA + SOSEX delivers more accurate absolute correlation energies and also improves upon RPA for non-isogyric processes. On the other hand, RPA + SOSEX barrier heights are worse than those obtained from plain RPA calculations. To combine the benefits of RPA correlation energies and the SOSEX correction, we introduce a short-range RPA + SOSEX correction. Proof of concept calculations and benchmarks showing the advantages of our method are presented.

  3. Short-range second order screened exchange correction to RPA correlation energies.

    PubMed

    Beuerle, Matthias; Ochsenfeld, Christian

    2017-11-28

    Direct random phase approximation (RPA) correlation energies have become increasingly popular as a post-Kohn-Sham correction, due to significant improvements over DFT calculations for properties such as long-range dispersion effects, which are problematic in conventional density functional theory. On the other hand, RPA still has various weaknesses, such as unsatisfactory results for non-isogyric processes. This can in parts be attributed to the self-correlation present in RPA correlation energies, leading to significant self-interaction errors. Therefore a variety of schemes have been devised to include exchange in the calculation of RPA correlation energies in order to correct this shortcoming. One of the most popular RPA plus exchange schemes is the second order screened exchange (SOSEX) correction. RPA + SOSEX delivers more accurate absolute correlation energies and also improves upon RPA for non-isogyric processes. On the other hand, RPA + SOSEX barrier heights are worse than those obtained from plain RPA calculations. To combine the benefits of RPA correlation energies and the SOSEX correction, we introduce a short-range RPA + SOSEX correction. Proof of concept calculations and benchmarks showing the advantages of our method are presented.

  4. Boundedness, Mittag-Leffler stability and asymptotical ω-periodicity of fractional-order fuzzy neural networks.

    PubMed

    Wu, Ailong; Zeng, Zhigang

    2016-02-01

    We show that the ω-periodic fractional-order fuzzy neural networks cannot generate non-constant ω-periodic signals. In addition, several sufficient conditions are obtained to ascertain the boundedness and global Mittag-Leffler stability of fractional-order fuzzy neural networks. Furthermore, S-asymptotical ω-periodicity and global asymptotical ω-periodicity of fractional-order fuzzy neural networks is also characterized. The obtained criteria improve and extend the existing related results. To illustrate and compare the theoretical criteria, some numerical examples with simulation results are discussed in detail. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

  5. Dynamical analysis of continuous higher-order hopfield networks for combinatorial optimization.

    PubMed

    Atencia, Miguel; Joya, Gonzalo; Sandoval, Francisco

    2005-08-01

    In this letter, the ability of higher-order Hopfield networks to solve combinatorial optimization problems is assessed by means of a rigorous analysis of their properties. The stability of the continuous network is almost completely clarified: (1) hyperbolic interior equilibria, which are unfeasible, are unstable; (2) the state cannot escape from the unitary hypercube; and (3) a Lyapunov function exists. Numerical methods used to implement the continuous equation on a computer should be designed with the aim of preserving these favorable properties. The case of nonhyperbolic fixed points, which occur when the Hessian of the target function is the null matrix, requires further study. We prove that these nonhyperbolic interior fixed points are unstable in networks with three neurons and order two. The conjecture that interior equilibria are unstable in the general case is left open.

  6. Infinitely Robust Order and Local Order-Parameter Tulips in Apollonian Networks with Quenched Disorder

    NASA Astrophysics Data System (ADS)

    Nadir Kaplan, C.; Hinczewski, Michael; Berker, A. Nihat

    2009-03-01

    For a variety of quenched random spin systems on an Apollonian network, including ferromagnetic and antiferromagnetic bond percolation and the Ising spin glass, we find the persistence of ordered phases up to infinite temperature over the entire range of disorder.[1] We develop a renormalization-group technique that yields highly detailed information, including the exact distributions of local magnetizations and local spin-glass order parameters, which turn out to exhibit, as function of temperature, complex and distinctive tulip patterns. [1] C.N. Kaplan, M. Hinczewski, and A.N. Berker, arXiv:0811.3437v1 [cond-mat.dis-nn] (2008).

  7. Node-node correlations and transport properties in scale-free networks

    NASA Astrophysics Data System (ADS)

    Obregon, Bibiana; Guzman, Lev

    2011-03-01

    We study some transport properties of complex networks. We focus our attention on transport properties of scale-free and small-world networks and compare two types of transport: Electric and max-flow cases. In particular, we construct scale-free networks, with a given degree sequence, to estimate the distribution of conductances for different values of assortative/dissortative mixing. For the electric case we find that the distributions of conductances are affect ed by the assortative mixing of the network whereas for the max-flow case, the distributions almost do not show changes when node-node correlations are altered. Finally, we compare local and global transport in terms of the average conductance for the small-world (Watts-Strogatz) model

  8. A Multi-Objective Partition Method for Marine Sensor Networks Based on Degree of Event Correlation.

    PubMed

    Huang, Dongmei; Xu, Chenyixuan; Zhao, Danfeng; Song, Wei; He, Qi

    2017-09-21

    Existing marine sensor networks acquire data from sea areas that are geographically divided, and store the data independently in their affiliated sea area data centers. In the case of marine events across multiple sea areas, the current network structure needs to retrieve data from multiple data centers, and thus severely affects real-time decision making. In this study, in order to provide a fast data retrieval service for a marine sensor network, we use all the marine sensors as the vertices, establish the edge based on marine events, and abstract the marine sensor network as a graph. Then, we construct a multi-objective balanced partition method to partition the abstract graph into multiple regions and store them in the cloud computing platform. This method effectively increases the correlation of the sensors and decreases the retrieval cost. On this basis, an incremental optimization strategy is designed to dynamically optimize existing partitions when new sensors are added into the network. Experimental results show that the proposed method can achieve the optimal layout for distributed storage in the process of disaster data retrieval in the China Sea area, and effectively optimize the result of partitions when new buoys are deployed, which eventually will provide efficient data access service for marine events.

  9. Adaptive Synchronization of Fractional Order Complex-Variable Dynamical Networks via Pinning Control

    NASA Astrophysics Data System (ADS)

    Ding, Da-Wei; Yan, Jie; Wang, Nian; Liang, Dong

    2017-09-01

    In this paper, the synchronization of fractional order complex-variable dynamical networks is studied using an adaptive pinning control strategy based on close center degree. Some effective criteria for global synchronization of fractional order complex-variable dynamical networks are derived based on the Lyapunov stability theory. From the theoretical analysis, one concludes that under appropriate conditions, the complex-variable dynamical networks can realize the global synchronization by using the proper adaptive pinning control method. Meanwhile, we succeed in solving the problem about how much coupling strength should be applied to ensure the synchronization of the fractional order complex networks. Therefore, compared with the existing results, the synchronization method in this paper is more general and convenient. This result extends the synchronization condition of the real-variable dynamical networks to the complex-valued field, which makes our research more practical. Finally, two simulation examples show that the derived theoretical results are valid and the proposed adaptive pinning method is effective. Supported by National Natural Science Foundation of China under Grant No. 61201227, National Natural Science Foundation of China Guangdong Joint Fund under Grant No. U1201255, the Natural Science Foundation of Anhui Province under Grant No. 1208085MF93, 211 Innovation Team of Anhui University under Grant Nos. KJTD007A and KJTD001B, and also supported by Chinese Scholarship Council

  10. Dynamics analysis of SIR epidemic model with correlation coefficients and clustering coefficient in networks.

    PubMed

    Zhang, Juping; Yang, Chan; Jin, Zhen; Li, Jia

    2018-07-14

    In this paper, the correlation coefficients between nodes in states are used as dynamic variables, and we construct SIR epidemic dynamic models with correlation coefficients by using the pair approximation method in static networks and dynamic networks, respectively. Considering the clustering coefficient of the network, we analytically investigate the existence and the local asymptotic stability of each equilibrium of these models and derive threshold values for the prevalence of diseases. Additionally, we obtain two equivalent epidemic thresholds in dynamic networks, which are compared with the results of the mean field equations. Copyright © 2018 Elsevier Ltd. All rights reserved.

  11. Geometric evolution of complex networks with degree correlations

    NASA Astrophysics Data System (ADS)

    Murphy, Charles; Allard, Antoine; Laurence, Edward; St-Onge, Guillaume; Dubé, Louis J.

    2018-03-01

    We present a general class of geometric network growth mechanisms by homogeneous attachment in which the links created at a given time t are distributed homogeneously between a new node and the existing nodes selected uniformly. This is achieved by creating links between nodes uniformly distributed in a homogeneous metric space according to a Fermi-Dirac connection probability with inverse temperature β and general time-dependent chemical potential μ (t ) . The chemical potential limits the spatial extent of newly created links. Using a hidden variable framework, we obtain an analytical expression for the degree sequence and show that μ (t ) can be fixed to yield any given degree distributions, including a scale-free degree distribution. Additionally, we find that depending on the order in which nodes appear in the network—its history—the degree-degree correlations can be tuned to be assortative or disassortative. The effect of the geometry on the structure is investigated through the average clustering coefficient 〈c 〉 . In the thermodynamic limit, we identify a phase transition between a random regime where 〈c 〉→0 when β <βc and a geometric regime where 〈c 〉>0 when β >βc .

  12. Network reliability maximization for stochastic-flow network subject to correlated failures using genetic algorithm and tabu\\xA0search

    NASA Astrophysics Data System (ADS)

    Yeh, Cheng-Ta; Lin, Yi-Kuei; Yang, Jo-Yun

    2018-07-01

    Network reliability is an important performance index for many real-life systems, such as electric power systems, computer systems and transportation systems. These systems can be modelled as stochastic-flow networks (SFNs) composed of arcs and nodes. Most system supervisors respect the network reliability maximization by finding the optimal multi-state resource assignment, which is one resource to each arc. However, a disaster may cause correlated failures for the assigned resources, affecting the network reliability. This article focuses on determining the optimal resource assignment with maximal network reliability for SFNs. To solve the problem, this study proposes a hybrid algorithm integrating the genetic algorithm and tabu search to determine the optimal assignment, called the hybrid GA-TS algorithm (HGTA), and integrates minimal paths, recursive sum of disjoint products and the correlated binomial distribution to calculate network reliability. Several practical numerical experiments are adopted to demonstrate that HGTA has better computational quality than several popular soft computing algorithms.

  13. Quantification of the impact of a confounding variable on functional connectivity confirms anti-correlated networks in the resting-state.

    PubMed

    Carbonell, F; Bellec, P; Shmuel, A

    2014-02-01

    The effect of regressing out the global average signal (GAS) in resting state fMRI data has become a concern for interpreting functional connectivity analyses. It is not clear whether the reported anti-correlations between the Default Mode and the Dorsal Attention Networks are intrinsic to the brain, or are artificially created by regressing out the GAS. Here we introduce a concept, Impact of the Global Average on Functional Connectivity (IGAFC), for quantifying the sensitivity of seed-based correlation analyses to the regression of the GAS. This voxel-wise IGAFC index is defined as the product of two correlation coefficients: the correlation between the GAS and the fMRI time course of a voxel, times the correlation between the GAS and the seed time course. This definition enables the calculation of a threshold at which the impact of regressing-out the GAS would be large enough to introduce spurious negative correlations. It also yields a post-hoc impact correction procedure via thresholding, which eliminates spurious correlations introduced by regressing out the GAS. In addition, we introduce an Artificial Negative Correlation Index (ANCI), defined as the absolute difference between the IGAFC index and the impact threshold. The ANCI allows a graded confidence scale for ranking voxels according to their likelihood of showing artificial correlations. By applying this method, we observed regions in the Default Mode and Dorsal Attention Networks that were anti-correlated. These findings confirm that the previously reported negative correlations between the Dorsal Attention and Default Mode Networks are intrinsic to the brain and not the result of statistical manipulations. Our proposed quantification of the impact that a confound may have on functional connectivity can be generalized to global effect estimators other than the GAS. It can be readily applied to other confounds, such as systemic physiological or head movement interferences, in order to quantify their

  14. Order parameter analysis of synchronization transitions on star networks

    NASA Astrophysics Data System (ADS)

    Chen, Hong-Bin; Sun, Yu-Ting; Gao, Jian; Xu, Can; Zheng, Zhi-Gang

    2017-12-01

    The collective behaviors of populations of coupled oscillators have attracted significant attention in recent years. In this paper, an order parameter approach is proposed to study the low-dimensional dynamical mechanism of collective synchronizations, by adopting the star-topology of coupled oscillators as a prototype system. The order parameter equation of star-linked phase oscillators can be obtained in terms of the Watanabe-Strogatz transformation, Ott-Antonsen ansatz, and the ensemble order parameter approach. Different solutions of the order parameter equation correspond to the diverse collective states, and different bifurcations reveal various transitions among these collective states. The properties of various transitions in the star-network model are revealed by using tools of nonlinear dynamics such as time reversibility analysis and linear stability analysis.

  15. Infinite order quantum-gravitational correlations

    NASA Astrophysics Data System (ADS)

    Knorr, Benjamin

    2018-06-01

    A new approximation scheme for nonperturbative renormalisation group equations for quantum gravity is introduced. Correlation functions of arbitrarily high order can be studied by resolving the full dependence of the renormalisation group equations on the fluctuation field (graviton). This is reminiscent of a local potential approximation in O(N)-symmetric field theories. As a first proof of principle, we derive the flow equation for the ‘graviton potential’ induced by a conformal fluctuation and corrections induced by a gravitational wave fluctuation. Indications are found that quantum gravity might be in a non-metric phase in the deep ultraviolet. The present setup significantly improves the quality of previous fluctuation vertex studies by including infinitely many couplings, thereby testing the reliability of schemes to identify different couplings to close the equations, and represents an important step towards the resolution of the Nielsen identity. The setup further allows one, in principle, to address the question of putative gravitational condensates.

  16. SPECIAL ISSUE ON OPTICAL PROCESSING OF INFORMATION: Optical neural networks based on holographic correlators

    NASA Astrophysics Data System (ADS)

    Sokolov, V. K.; Shubnikov, E. I.

    1995-10-01

    The three most important models of neural networks — a bidirectional associative memory, Hopfield networks, and adaptive resonance networks — are used as examples to show that a holographic correlator has its place in the neural computing paradigm.

  17. HONTIOR - HIGHER-ORDER NEURAL NETWORK FOR TRANSFORMATION INVARIANT OBJECT RECOGNITION

    NASA Technical Reports Server (NTRS)

    Spirkovska, L.

    1994-01-01

    Neural networks have been applied in numerous fields, including transformation invariant object recognition, wherein an object is recognized despite changes in the object's position in the input field, size, or rotation. One of the more successful neural network methods used in invariant object recognition is the higher-order neural network (HONN) method. With a HONN, known relationships are exploited and the desired invariances are built directly into the architecture of the network, eliminating the need for the network to learn invariance to transformations. This results in a significant reduction in the training time required, since the network needs to be trained on only one view of each object, not on numerous transformed views. Moreover, one hundred percent accuracy is guaranteed for images characterized by the built-in distortions, providing noise is not introduced through pixelation. The program HONTIOR implements a third-order neural network having invariance to translation, scale, and in-plane rotation built directly into the architecture, Thus, for 2-D transformation invariance, the network needs only to be trained on just one view of each object. HONTIOR can also be used for 3-D transformation invariant object recognition by training the network only on a set of out-of-plane rotated views. Historically, the major drawback of HONNs has been that the size of the input field was limited to the memory required for the large number of interconnections in a fully connected network. HONTIOR solves this problem by coarse coding the input images (coding an image as a set of overlapping but offset coarser images). Using this scheme, large input fields (4096 x 4096 pixels) can easily be represented using very little virtual memory (30Mb). The HONTIOR distribution consists of three main programs. The first program contains the training and testing routines for a third-order neural network. The second program contains the same training and testing procedures as the

  18. A novel untargeted metabolomics correlation-based network analysis incorporating human metabolic reconstructions

    PubMed Central

    2013-01-01

    Background Metabolomics has become increasingly popular in the study of disease phenotypes and molecular pathophysiology. One branch of metabolomics that encompasses the high-throughput screening of cellular metabolism is metabolic profiling. In the present study, the metabolic profiles of different tumour cells from colorectal carcinoma and breast adenocarcinoma were exposed to hypoxic and normoxic conditions and these have been compared to reveal the potential metabolic effects of hypoxia on the biochemistry of the tumour cells; this may contribute to their survival in oxygen compromised environments. In an attempt to analyse the complex interactions between metabolites beyond routine univariate and multivariate data analysis methods, correlation analysis has been integrated with a human metabolic reconstruction to reveal connections between pathways that are associated with normoxic or hypoxic oxygen environments. Results Correlation analysis has revealed statistically significant connections between metabolites, where differences in correlations between cells exposed to different oxygen levels have been highlighted as markers of hypoxic metabolism in cancer. Network mapping onto reconstructed human metabolic models is a novel addition to correlation analysis. Correlated metabolites have been mapped onto the Edinburgh human metabolic network (EHMN) with the aim of interlinking metabolites found to be regulated in a similar fashion in response to oxygen. This revealed novel pathways within the metabolic network that may be key to tumour cell survival at low oxygen. Results show that the metabolic responses to lowering oxygen availability can be conserved or specific to a particular cell line. Network-based correlation analysis identified conserved metabolites including malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate. In this way, this method has revealed metabolites not previously linked, or less well recognised, with respect to hypoxia

  19. Correlation between centrality metrics and their application to the opinion model

    NASA Astrophysics Data System (ADS)

    Li, Cong; Li, Qian; Van Mieghem, Piet; Stanley, H. Eugene; Wang, Huijuan

    2015-03-01

    In recent decades, a number of centrality metrics describing network properties of nodes have been proposed to rank the importance of nodes. In order to understand the correlations between centrality metrics and to approximate a high-complexity centrality metric by a strongly correlated low-complexity metric, we first study the correlation between centrality metrics in terms of their Pearson correlation coefficient and their similarity in ranking of nodes. In addition to considering the widely used centrality metrics, we introduce a new centrality measure, the degree mass. The mth-order degree mass of a node is the sum of the weighted degree of the node and its neighbors no further than m hops away. We find that the betweenness, the closeness, and the components of the principal eigenvector of the adjacency matrix are strongly correlated with the degree, the 1st-order degree mass and the 2nd-order degree mass, respectively, in both network models and real-world networks. We then theoretically prove that the Pearson correlation coefficient between the principal eigenvector and the 2nd-order degree mass is larger than that between the principal eigenvector and a lower order degree mass. Finally, we investigate the effect of the inflexible contrarians selected based on different centrality metrics in helping one opinion to compete with another in the inflexible contrarian opinion (ICO) model. Interestingly, we find that selecting the inflexible contrarians based on the leverage, the betweenness, or the degree is more effective in opinion-competition than using other centrality metrics in all types of networks. This observation is supported by our previous observations, i.e., that there is a strong linear correlation between the degree and the betweenness, as well as a high centrality similarity between the leverage and the degree.

  20. A generalized LSTM-like training algorithm for second-order recurrent neural networks

    PubMed Central

    Monner, Derek; Reggia, James A.

    2011-01-01

    The Long Short Term Memory (LSTM) is a second-order recurrent neural network architecture that excels at storing sequential short-term memories and retrieving them many time-steps later. LSTM’s original training algorithm provides the important properties of spatial and temporal locality, which are missing from other training approaches, at the cost of limiting it’s applicability to a small set of network architectures. Here we introduce the Generalized Long Short-Term Memory (LSTM-g) training algorithm, which provides LSTM-like locality while being applicable without modification to a much wider range of second-order network architectures. With LSTM-g, all units have an identical set of operating instructions for both activation and learning, subject only to the configuration of their local environment in the network; this is in contrast to the original LSTM training algorithm, where each type of unit has its own activation and training instructions. When applied to LSTM architectures with peephole connections, LSTM-g takes advantage of an additional source of back-propagated error which can enable better performance than the original algorithm. Enabled by the broad architectural applicability of LSTM-g, we demonstrate that training recurrent networks engineered for specific tasks can produce better results than single-layer networks. We conclude that LSTM-g has the potential to both improve the performance and broaden the applicability of spatially and temporally local gradient-based training algorithms for recurrent neural networks. PMID:21803542

  1. Structural and psychosocial correlates of birth order anomalies in schizophrenia and homicide.

    PubMed

    Schug, Robert A; Yang, Yaling; Raine, Adrian; Han, Chenbo; Liu, Jianghong

    2010-12-01

    Birth order--a unique index of both neurodevelopmental and/or psychosocial factors in the pathogenesis of psychiatric disorder--remains largely unexplored in violent schizophrenia. We examined whether murderers with schizophrenia would demonstrate birth order anomalies, distinguishing them from both nonviolent schizophrenia patients and murderers without schizophrenia. Self-report birth order, psychosocial history data (i.e., maternal birth age, family size, parental criminality, parental SES), and structural magnetic resonance imaging data were collected from normal controls, nonviolent schizophrenia patients, murderers with schizophrenia, murderers without schizophrenia, and murderers with psychiatric conditions other than schizophrenia at a brain hospital in Nanjing, China. Results indicated that murderers with schizophrenia were characterized by significantly increased (i.e., later) birth order compared with both nonviolent schizophrenia patients and murderers without schizophrenia. Additionally, birth order was negatively correlated with gray matter volume in key frontal subregions for schizophrenic murderers, and was negatively correlated with parental SES. Findings may suggest biological, psychosocial, or interactional trajectories which may lead to a homicidally violent outcome in schizophrenia.

  2. Dynamic stability analysis of fractional order leaky integrator echo state neural networks

    NASA Astrophysics Data System (ADS)

    Pahnehkolaei, Seyed Mehdi Abedi; Alfi, Alireza; Tenreiro Machado, J. A.

    2017-06-01

    The Leaky integrator echo state neural network (Leaky-ESN) is an improved model of the recurrent neural network (RNN) and adopts an interconnected recurrent grid of processing neurons. This paper presents a new proof for the convergence of a Lyapunov candidate function to zero when time tends to infinity by means of the Caputo fractional derivative with order lying in the range (0, 1). The stability of Fractional-Order Leaky-ESN (FO Leaky-ESN) is then analyzed, and the existence, uniqueness and stability of the equilibrium point are provided. A numerical example demonstrates the feasibility of the proposed method.

  3. Attention Performance Measured by Attention Network Test Is Correlated with Global and Regional Efficiency of Structural Brain Networks

    PubMed Central

    Xiao, Min; Ge, Haitao; Khundrakpam, Budhachandra S.; Xu, Junhai; Bezgin, Gleb; Leng, Yuan; Zhao, Lu; Tang, Yuchun; Ge, Xinting; Jeon, Seun; Xu, Wenjian; Evans, Alan C.; Liu, Shuwei

    2016-01-01

    Functional neuroimaging studies have indicated the involvement of separate brain areas in three distinct attention systems: alerting, orienting, and executive control (EC). However, the structural correlates underlying attention remains unexplored. Here, we utilized graph theory to examine the neuroanatomical substrates of the three attention systems measured by attention network test (ANT) in 65 healthy subjects. White matter connectivity, assessed with diffusion tensor imaging deterministic tractography was modeled as a structural network comprising 90 nodes defined by the automated anatomical labeling (AAL) template. Linear regression analyses were conducted to explore the relationship between topological parameters and the three attentional effects. We found a significant positive correlation between EC function and global efficiency of the whole brain network. At the regional level, node-specific correlations were discovered between regional efficiency and all three ANT components, including dorsolateral superior frontal gyrus, thalamus and parahippocampal gyrus for EC, thalamus and inferior parietal gyrus for alerting, and paracentral lobule and inferior occipital gyrus for orienting. Our findings highlight the fundamental architecture of interregional structural connectivity involved in attention and could provide new insights into the anatomical basis underlying human behavior. PMID:27777556

  4. Higher-order kinetic expansion of quantum dissipative dynamics: mapping quantum networks to kinetic networks.

    PubMed

    Wu, Jianlan; Cao, Jianshu

    2013-07-28

    We apply a new formalism to derive the higher-order quantum kinetic expansion (QKE) for studying dissipative dynamics in a general quantum network coupled with an arbitrary thermal bath. The dynamics of system population is described by a time-convoluted kinetic equation, where the time-nonlocal rate kernel is systematically expanded of the order of off-diagonal elements of the system Hamiltonian. In the second order, the rate kernel recovers the expression of the noninteracting-blip approximation method. The higher-order corrections in the rate kernel account for the effects of the multi-site quantum coherence and the bath relaxation. In a quantum harmonic bath, the rate kernels of different orders are analytically derived. As demonstrated by four examples, the higher-order QKE can reliably predict quantum dissipative dynamics, comparing well with the hierarchic equation approach. More importantly, the higher-order rate kernels can distinguish and quantify distinct nontrivial quantum coherent effects, such as long-range energy transfer from quantum tunneling and quantum interference arising from the phase accumulation of interactions.

  5. Adaptative synchronization in multi-output fractional-order complex dynamical networks and secure communications

    NASA Astrophysics Data System (ADS)

    Mata-Machuca, Juan L.; Aguilar-López, Ricardo

    2018-01-01

    This work deals with the adaptative synchronization of complex dynamical networks with fractional-order nodes and its application in secure communications employing chaotic parameter modulation. The complex network is composed of multiple fractional-order systems with mismatch parameters and the coupling functions are given to realize the network synchronization. We introduce a fractional algebraic synchronizability condition (FASC) and a fractional algebraic identifiability condition (FAIC) which are used to know if the synchronization and parameters estimation problems can be solved. To overcome these problems, an adaptative synchronization methodology is designed; the strategy consists in proposing multiple receiver systems which tend to follow asymptotically the uncertain transmitters systems. The coupling functions and parameters of the receiver systems are adjusted continually according to a convenient sigmoid-like adaptative controller (SLAC), until the measurable output errors converge to zero, hence, synchronization between transmitter and receivers is achieved and message signals are recovered. Indeed, the stability analysis of the synchronization error is based on the fractional Lyapunov direct method. Finally, numerical results corroborate the satisfactory performance of the proposed scheme by means of the synchronization of a complex network consisting of several fractional-order unified chaotic systems.

  6. Template-directed assembly of metal-chalcogenide nanocrystals into ordered mesoporous networks.

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Vamvasakis, Ioannis; Subrahmanyam, Kota S.; Kanatzidis, Mercouri G.

    Although great progress in the synthesis of porous networks of metal and metal oxide nanoparticles with highly accessible pore surface and ordered mesoscale pores has been achieved, synthesis of assembled 3D mesostructures of metal-chalcogenide nanocrystals is still challenging. In this work we demonstrate that ordered mesoporous networks, which comprise well-defined interconnected metal sulfide nanocrystals, can be prepared through a polymer-templated oxidative polymerization process. The resulting self-assembled mesostructures that were obtained after solvent extraction of the polymer template impart the unique combination of light-emitting metal chalcogenide nanocrystals, three-dimensional open-pore structure, high surface area, and uniform pores. We show that the poremore » surface of these materials is active and accessible to incoming molecules, exhibiting high photocatalytic activity and stability, for instance, in oxidation of 1-phenylethanol into acetophenone. We demonstrate through appropriate selection of the synthetic components that this method is general to prepare ordered mesoporous materials from metal chalcogenide nanocrystals with various sizes and compositions.« less

  7. A KST framework for correlation network construction from time series signals

    NASA Astrophysics Data System (ADS)

    Qi, Jin-Peng; Gu, Quan; Zhu, Ying; Zhang, Ping

    2018-04-01

    A KST (Kolmogorov-Smirnov test and T statistic) method is used for construction of a correlation network based on the fluctuation of each time series within the multivariate time signals. In this method, each time series is divided equally into multiple segments, and the maximal data fluctuation in each segment is calculated by a KST change detection procedure. Connections between each time series are derived from the data fluctuation matrix, and are used for construction of the fluctuation correlation network (FCN). The method was tested with synthetic simulations and the result was compared with those from using KS or T only for detection of data fluctuation. The novelty of this study is that the correlation analyses was based on the data fluctuation in each segment of each time series rather than on the original time signals, which would be more meaningful for many real world applications and for analysis of large-scale time signals where prior knowledge is uncertain.

  8. Correlation analysis of a ground-water level monitoring network, Miami-Dade County, Florida

    USGS Publications Warehouse

    Prinos, Scott T.

    2005-01-01

    The U.S. Geological Survey cooperative ground-water monitoring program in Miami-Dade County, Florida, expanded from 4 to 98 continuously recording water-level monitoring wells during the 1939-2001 period. Network design was based on area specific assessments; however, no countywide statistical assessments of network coverage had been performed for the purpose of assessing network redundancy. To aid in the assessment of network redundancy, correlation analyses were performed using S-PLUS 2000 statistical analysis software for daily maximum water-level data from 98 monitoring wells for the November 1, 1973, to October 31, 2000 period. Because of the complexities of the hydrologic, water-supply, and water-management systems in Miami-Dade County and the changes that have occurred to these systems through time, spatial and temporal variations in the degree of correlation had to be considered. To assess temporal variation in correlation, water-level data from each well were subdivided by year and by wet and dry seasons. For each well, year, and season, correlation analyses were performed on the data from those wells that had available data. For selected wells, the resulting correlation coefficients from each year and season were plotted with respect to time. To assess spatial variation in correlation, the coefficients determined from the correlation analysis were averaged. These average wet- and dry-season correlation coefficients were plotted spatially using geographic information system software. Wells with water-level data that correlated with a coefficient of 0.95 or greater were almost always located in relatively close proximity to each other. Five areas were identified where the water-level data from wells within the area remained correlated with that of other wells in the area during the wet and dry seasons. These areas are located in or near the C-1 and C-102 basins (2 wells), in or near the C-6 and C-7 basins (2 wells), near the Florida Keys Aqueduct Authority

  9. Synchronization of fractional-order complex-valued neural networks with time delay.

    PubMed

    Bao, Haibo; Park, Ju H; Cao, Jinde

    2016-09-01

    This paper deals with the problem of synchronization of fractional-order complex-valued neural networks with time delays. By means of linear delay feedback control and a fractional-order inequality, sufficient conditions are obtained to guarantee the synchronization of the drive-response systems. Numerical simulations are provided to show the effectiveness of the obtained results. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Effects of different correlation metrics and preprocessing factors on small-world brain functional networks: a resting-state functional MRI study.

    PubMed

    Liang, Xia; Wang, Jinhui; Yan, Chaogan; Shu, Ni; Xu, Ke; Gong, Gaolang; He, Yong

    2012-01-01

    Graph theoretical analysis of brain networks based on resting-state functional MRI (R-fMRI) has attracted a great deal of attention in recent years. These analyses often involve the selection of correlation metrics and specific preprocessing steps. However, the influence of these factors on the topological properties of functional brain networks has not been systematically examined. Here, we investigated the influences of correlation metric choice (Pearson's correlation versus partial correlation), global signal presence (regressed or not) and frequency band selection [slow-5 (0.01-0.027 Hz) versus slow-4 (0.027-0.073 Hz)] on the topological properties of both binary and weighted brain networks derived from them, and we employed test-retest (TRT) analyses for further guidance on how to choose the "best" network modeling strategy from the reliability perspective. Our results show significant differences in global network metrics associated with both correlation metrics and global signals. Analysis of nodal degree revealed differing hub distributions for brain networks derived from Pearson's correlation versus partial correlation. TRT analysis revealed that the reliability of both global and local topological properties are modulated by correlation metrics and the global signal, with the highest reliability observed for Pearson's-correlation-based brain networks without global signal removal (WOGR-PEAR). The nodal reliability exhibited a spatially heterogeneous distribution wherein regions in association and limbic/paralimbic cortices showed moderate TRT reliability in Pearson's-correlation-based brain networks. Moreover, we found that there were significant frequency-related differences in topological properties of WOGR-PEAR networks, and brain networks derived in the 0.027-0.073 Hz band exhibited greater reliability than those in the 0.01-0.027 Hz band. Taken together, our results provide direct evidence regarding the influences of correlation metrics and specific

  11. Effects of Different Correlation Metrics and Preprocessing Factors on Small-World Brain Functional Networks: A Resting-State Functional MRI Study

    PubMed Central

    Liang, Xia; Wang, Jinhui; Yan, Chaogan; Shu, Ni; Xu, Ke; Gong, Gaolang; He, Yong

    2012-01-01

    Graph theoretical analysis of brain networks based on resting-state functional MRI (R-fMRI) has attracted a great deal of attention in recent years. These analyses often involve the selection of correlation metrics and specific preprocessing steps. However, the influence of these factors on the topological properties of functional brain networks has not been systematically examined. Here, we investigated the influences of correlation metric choice (Pearson's correlation versus partial correlation), global signal presence (regressed or not) and frequency band selection [slow-5 (0.01–0.027 Hz) versus slow-4 (0.027–0.073 Hz)] on the topological properties of both binary and weighted brain networks derived from them, and we employed test-retest (TRT) analyses for further guidance on how to choose the “best” network modeling strategy from the reliability perspective. Our results show significant differences in global network metrics associated with both correlation metrics and global signals. Analysis of nodal degree revealed differing hub distributions for brain networks derived from Pearson's correlation versus partial correlation. TRT analysis revealed that the reliability of both global and local topological properties are modulated by correlation metrics and the global signal, with the highest reliability observed for Pearson's-correlation-based brain networks without global signal removal (WOGR-PEAR). The nodal reliability exhibited a spatially heterogeneous distribution wherein regions in association and limbic/paralimbic cortices showed moderate TRT reliability in Pearson's-correlation-based brain networks. Moreover, we found that there were significant frequency-related differences in topological properties of WOGR-PEAR networks, and brain networks derived in the 0.027–0.073 Hz band exhibited greater reliability than those in the 0.01–0.027 Hz band. Taken together, our results provide direct evidence regarding the influences of correlation metrics

  12. The Ordered Network Structure and Prediction Summary for M≥7 Earthquakes in Xinjiang Region of China

    NASA Astrophysics Data System (ADS)

    Men, Ke-Pei; Zhao, Kai

    2014-12-01

    M ≥7 earthquakes have showed an obvious commensurability and orderliness in Xinjiang of China and its adjacent region since 1800. The main orderly values are 30 a × k (k = 1,2,3), 11 12 a, 41 43 a, 18 19 a, and 5 6 a. In the guidance of the information forecasting theory of Wen-Bo Weng, based on previous research results, combining ordered network structure analysis with complex network technology, we focus on the prediction summary of M ≥ 7 earthquakes by using the ordered network structure, and add new information to further optimize network, hence construct the 2D- and 3D-ordered network structure of M ≥ 7 earthquakes. In this paper, the network structure revealed fully the regularity of seismic activity of M ≥ 7 earthquakes in the study region during the past 210 years. Based on this, the Karakorum M7.1 earthquake in 1996, the M7.9 earthquake on the frontier of Russia, Mongol, and China in 2003, and two Yutian M7.3 earthquakes in 2008 and 2014 were predicted successfully. At the same time, a new prediction opinion is presented that the future two M ≥ 7 earthquakes will probably occur around 2019 - 2020 and 2025 - 2026 in this region. The results show that large earthquake occurred in defined region can be predicted. The method of ordered network structure analysis produces satisfactory results for the mid-and-long term prediction of M ≥ 7 earthquakes.

  13. Correlations between Community Structure and Link Formation in Complex Networks

    PubMed Central

    Liu, Zhen; He, Jia-Lin; Kapoor, Komal; Srivastava, Jaideep

    2013-01-01

    Background Links in complex networks commonly represent specific ties between pairs of nodes, such as protein-protein interactions in biological networks or friendships in social networks. However, understanding the mechanism of link formation in complex networks is a long standing challenge for network analysis and data mining. Methodology/Principal Findings Links in complex networks have a tendency to cluster locally and form so-called communities. This widely existed phenomenon reflects some underlying mechanism of link formation. To study the correlations between community structure and link formation, we present a general computational framework including a theory for network partitioning and link probability estimation. Our approach enables us to accurately identify missing links in partially observed networks in an efficient way. The links having high connection likelihoods in the communities reveal that links are formed preferentially to create cliques and accordingly promote the clustering level of the communities. The experimental results verify that such a mechanism can be well captured by our approach. Conclusions/Significance Our findings provide a new insight into understanding how links are created in the communities. The computational framework opens a wide range of possibilities to develop new approaches and applications, such as community detection and missing link prediction. PMID:24039818

  14. Stability switches of arbitrary high-order consensus in multiagent networks with time delays.

    PubMed

    Yang, Bo

    2013-01-01

    High-order consensus seeking, in which individual high-order dynamic agents share a consistent view of the objectives and the world in a distributed manner, finds its potential broad applications in the field of cooperative control. This paper presents stability switches analysis of arbitrary high-order consensus in multiagent networks with time delays. By employing a frequency domain method, we explicitly derive analytical equations that clarify a rigorous connection between the stability of general high-order consensus and the system parameters such as the network topology, communication time-delays, and feedback gains. Particularly, our results provide a general and a fairly precise notion of how increasing communication time-delay causes the stability switches of consensus. Furthermore, under communication constraints, the stability and robustness problems of consensus algorithms up to third order are discussed in details to illustrate our central results. Numerical examples and simulation results for fourth-order consensus are provided to demonstrate the effectiveness of our theoretical results.

  15. Multireference second order perturbation theory with a simplified treatment of dynamical correlation.

    PubMed

    Xu, Enhua; Zhao, Dongbo; Li, Shuhua

    2015-10-13

    A multireference second order perturbation theory based on a complete active space configuration interaction (CASCI) function or density matrix renormalized group (DMRG) function has been proposed. This method may be considered as an approximation to the CAS/A approach with the same reference, in which the dynamical correlation is simplified with blocked correlated second order perturbation theory based on the generalized valence bond (GVB) reference (GVB-BCPT2). This method, denoted as CASCI-BCPT2/GVB or DMRG-BCPT2/GVB, is size consistent and has a similar computational cost as the conventional second order perturbation theory (MP2). We have applied it to investigate a number of problems of chemical interest. These problems include bond-breaking potential energy surfaces in four molecules, the spectroscopic constants of six diatomic molecules, the reaction barrier for the automerization of cyclobutadiene, and the energy difference between the monocyclic and bicyclic forms of 2,6-pyridyne. Our test applications demonstrate that CASCI-BCPT2/GVB can provide comparable results with CASPT2 (second order perturbation theory based on the complete active space self-consistent-field wave function) for systems under study. Furthermore, the DMRG-BCPT2/GVB method is applicable to treat strongly correlated systems with large active spaces, which are beyond the capability of CASPT2.

  16. STOCK Market Differences in Correlation-Based Weighted Network

    NASA Astrophysics Data System (ADS)

    Youn, Janghyuk; Lee, Junghoon; Chang, Woojin

    We examined the sector dynamics of Korean stock market in relation to the market volatility. The daily price data of 360 stocks for 5019 trading days (from January, 1990 to August, 2008) in Korean stock market are used. We performed the weighted network analysis and employed four measures: the average, the variance, the intensity, and the coherence of network weights (absolute values of stock return correlations) to investigate the network structure of Korean stock market. We performed regression analysis using the four measures in the seven major industry sectors and the market (seven sectors combined). We found that the average, the intensity, and the coherence of sector (subnetwork) weights increase as market becomes volatile. Except for the "Financials" sector, the variance of sector weights also grows as market volatility increases. Based on the four measures, we can categorize "Financials," "Information Technology" and "Industrials" sectors into one group, and "Materials" and "Consumer Discretionary" sectors into another group. We investigated the distributions of intrasector and intersector weights for each sector and found the differences in "Financials" sector are most distinct.

  17. Frequency-specific electrophysiologic correlates of resting state fMRI networks

    PubMed Central

    Hacker, Carl D.; Snyder, Abraham Z.; Pahwa, Mrinal; Corbetta, Maurizio; Leuthardt, Eric C.

    2017-01-01

    Resting state functional MRI (R-fMRI) studies have shown that slow (< 0.1 Hz), intrinsic fluctuations of the blood oxygen level dependent (BOLD) signal are temporally correlated within hierarchically organized functional systems known as resting state networks (RSNs) (Doucet et al., 2011). Most broadly, this hierarchy exhibits a dichotomy between two opposed systems (Fox et al., 2005). One system engages with the environment and includes the visual, auditory, and sensorimotor (SMN) networks as well as the dorsal attention network (DAN), which controls spatial attention. The other system includes the default mode network (DMN) and the fronto-parietal control system (FPC), RSNs that instantiate episodic memory and executive control, respectively. Here, we test the hypothesis, based on the spectral specificity of electrophysiologic responses to perceptual vs. memory tasks (Klimesch, 1999; Pfurtscheller and Lopes da Silva, 1999), that these two large-scale neural systems also manifest frequency specificity in the resting state. We measured the spatial correspondence between electrocorticographic (ECoG) band-limited power (BLP) and R-fMRI correlation patterns in awake, resting, human subjects. Our results show that, while gamma BLP correspondence was common throughout the brain, theta (4–8 Hz) BLP correspondence was stronger in the DMN and FPC, whereas alpha (8–12 Hz) correspondence was stronger in the SMN and DAN. Thus, the human brain, at rest, exhibits frequency specific electrophysiology, respecting both the spectral structure of task responses and the hierarchical organization of RSNs. PMID:28159686

  18. 77 FR 36305 - Stream Communications Network & Media, Inc.; Order of Suspension of Trading

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-06-18

    ... SECURITIES AND EXCHANGE COMMISSION [File No. 500-1] Stream Communications Network & Media, Inc.; Order of Suspension of Trading June 14, 2012. It appears to the Securities and Exchange Commission that... Network & Media, Inc. because it has not filed any periodic reports since the period ended December 31...

  19. Superconductivity in BaPtSb with an Ordered Honeycomb Network

    NASA Astrophysics Data System (ADS)

    Kudo, Kazutaka; Saito, Yuki; Takeuchi, Takaaki; Ayukawa, Shin-ya; Kawamata, Takayuki; Nakamura, Shinichiro; Koike, Yoji; Nohara, Minoru

    2018-06-01

    Superconductivity in BaPtSb with the SrPtSb-type structure (space group P\\bar{6}m2, D3h1, No. 187) is reported. The structure consists of a PtSb ordered honeycomb network that stacks along the c-axis so that spatial inversion symmetry is broken globally. Electrical resistivity and specific-heat measurements revealed that the compound exhibited superconductivity at 1.64 K. The noncentrosymmetric structure and the strong spin-orbit coupling of Pt and Sb make BaPtSb an attractive compound for studying the exotic superconductivity predicted for a honeycomb network.

  20. Nearest neighbor imputation using spatial–temporal correlations in wireless sensor networks

    PubMed Central

    Li, YuanYuan; Parker, Lynne E.

    2016-01-01

    Missing data is common in Wireless Sensor Networks (WSNs), especially with multi-hop communications. There are many reasons for this phenomenon, such as unstable wireless communications, synchronization issues, and unreliable sensors. Unfortunately, missing data creates a number of problems for WSNs. First, since most sensor nodes in the network are battery-powered, it is too expensive to have the nodes retransmit missing data across the network. Data re-transmission may also cause time delays when detecting abnormal changes in an environment. Furthermore, localized reasoning techniques on sensor nodes (such as machine learning algorithms to classify states of the environment) are generally not robust enough to handle missing data. Since sensor data collected by a WSN is generally correlated in time and space, we illustrate how replacing missing sensor values with spatially and temporally correlated sensor values can significantly improve the network’s performance. However, our studies show that it is important to determine which nodes are spatially and temporally correlated with each other. Simple techniques based on Euclidean distance are not sufficient for complex environmental deployments. Thus, we have developed a novel Nearest Neighbor (NN) imputation method that estimates missing data in WSNs by learning spatial and temporal correlations between sensor nodes. To improve the search time, we utilize a kd-tree data structure, which is a non-parametric, data-driven binary search tree. Instead of using traditional mean and variance of each dimension for kd-tree construction, and Euclidean distance for kd-tree search, we use weighted variances and weighted Euclidean distances based on measured percentages of missing data. We have evaluated this approach through experiments on sensor data from a volcano dataset collected by a network of Crossbow motes, as well as experiments using sensor data from a highway traffic monitoring application. Our experimental results

  1. Anti-correlated Networks, Global Signal Regression, and the Effects of Caffeine in Resting-State Functional MRI

    PubMed Central

    Wong, Chi Wah; Olafsson, Valur; Tal, Omer; Liu, Thomas T.

    2012-01-01

    Resting-state functional connectivity magnetic resonance imaging is proving to be an essential tool for the characterization of functional networks in the brain. Two of the major networks that have been identified are the default mode network (DMN) and the task positive network (TPN). Although prior work indicates that these two networks are anti-correlated, the findings are controversial because the anti-correlations are often found only after the application of a pre-processing step, known as global signal regression, that can produce artifactual anti-correlations. In this paper, we show that, for subjects studied in an eyes-closed rest state, caffeine can significantly enhance the detection of anti-correlations between the DMN and TPN without the need for global signal regression. In line with these findings, we find that caffeine also leads to widespread decreases in connectivity and global signal amplitude. Using a recently introduced geometric model of global signal effects, we demonstrate that these decreases are consistent with the removal of an additive global signal confound. In contrast to the effects observed in the eyes-closed rest state, caffeine did not lead to significant changes in global functional connectivity in the eyes-open rest state. PMID:22743194

  2. Global and system-specific resting-state fMRI fluctuations are uncorrelated: principal component analysis reveals anti-correlated networks.

    PubMed

    Carbonell, Felix; Bellec, Pierre; Shmuel, Amir

    2011-01-01

    The influence of the global average signal (GAS) on functional-magnetic resonance imaging (fMRI)-based resting-state functional connectivity is a matter of ongoing debate. The global average fluctuations increase the correlation between functional systems beyond the correlation that reflects their specific functional connectivity. Hence, removal of the GAS is a common practice for facilitating the observation of network-specific functional connectivity. This strategy relies on the implicit assumption of a linear-additive model according to which global fluctuations, irrespective of their origin, and network-specific fluctuations are super-positioned. However, removal of the GAS introduces spurious negative correlations between functional systems, bringing into question the validity of previous findings of negative correlations between fluctuations in the default-mode and the task-positive networks. Here we present an alternative method for estimating global fluctuations, immune to the complications associated with the GAS. Principal components analysis was applied to resting-state fMRI time-series. A global-signal effect estimator was defined as the principal component (PC) that correlated best with the GAS. The mean correlation coefficient between our proposed PC-based global effect estimator and the GAS was 0.97±0.05, demonstrating that our estimator successfully approximated the GAS. In 66 out of 68 runs, the PC that showed the highest correlation with the GAS was the first PC. Since PCs are orthogonal, our method provides an estimator of the global fluctuations, which is uncorrelated to the remaining, network-specific fluctuations. Moreover, unlike the regression of the GAS, the regression of the PC-based global effect estimator does not introduce spurious anti-correlations beyond the decrease in seed-based correlation values allowed by the assumed additive model. After regressing this PC-based estimator out of the original time-series, we observed robust anti-correlations

  3. Global and System-Specific Resting-State fMRI Fluctuations Are Uncorrelated: Principal Component Analysis Reveals Anti-Correlated Networks

    PubMed Central

    Carbonell, Felix; Bellec, Pierre

    2011-01-01

    Abstract The influence of the global average signal (GAS) on functional-magnetic resonance imaging (fMRI)–based resting-state functional connectivity is a matter of ongoing debate. The global average fluctuations increase the correlation between functional systems beyond the correlation that reflects their specific functional connectivity. Hence, removal of the GAS is a common practice for facilitating the observation of network-specific functional connectivity. This strategy relies on the implicit assumption of a linear-additive model according to which global fluctuations, irrespective of their origin, and network-specific fluctuations are super-positioned. However, removal of the GAS introduces spurious negative correlations between functional systems, bringing into question the validity of previous findings of negative correlations between fluctuations in the default-mode and the task-positive networks. Here we present an alternative method for estimating global fluctuations, immune to the complications associated with the GAS. Principal components analysis was applied to resting-state fMRI time-series. A global-signal effect estimator was defined as the principal component (PC) that correlated best with the GAS. The mean correlation coefficient between our proposed PC-based global effect estimator and the GAS was 0.97±0.05, demonstrating that our estimator successfully approximated the GAS. In 66 out of 68 runs, the PC that showed the highest correlation with the GAS was the first PC. Since PCs are orthogonal, our method provides an estimator of the global fluctuations, which is uncorrelated to the remaining, network-specific fluctuations. Moreover, unlike the regression of the GAS, the regression of the PC-based global effect estimator does not introduce spurious anti-correlations beyond the decrease in seed-based correlation values allowed by the assumed additive model. After regressing this PC-based estimator out of the original time-series, we observed

  4. Wavelet-based higher-order neural networks for mine detection in thermal IR imagery

    NASA Astrophysics Data System (ADS)

    Baertlein, Brian A.; Liao, Wen-Jiao

    2000-08-01

    An image processing technique is described for the detection of miens in RI imagery. The proposed technique is based on a third-order neural network, which processes the output of a wavelet packet transform. The technique is inherently invariant to changes in signature position, rotation and scaling. The well-known memory limitations that arise with higher-order neural networks are addressed by (1) the data compression capabilities of wavelet packets, (2) protections of the image data into a space of similar triangles, and (3) quantization of that 'triangle space'. Using these techniques, image chips of size 28 by 28, which would require 0(109) neural net weights, are processed by a network having 0(102) weights. ROC curves are presented for mine detection in real and simulated imagery.

  5. A canonical correlation neural network for multicollinearity and functional data.

    PubMed

    Gou, Zhenkun; Fyfe, Colin

    2004-03-01

    We review a recent neural implementation of Canonical Correlation Analysis and show, using ideas suggested by Ridge Regression, how to make the algorithm robust. The network is shown to operate on data sets which exhibit multicollinearity. We develop a second model which not only performs as well on multicollinear data but also on general data sets. This model allows us to vary a single parameter so that the network is capable of performing Partial Least Squares regression (at one extreme) to Canonical Correlation Analysis (at the other)and every intermediate operation between the two. On multicollinear data, the parameter setting is shown to be important but on more general data no particular parameter setting is required. Finally, we develop a second penalty term which acts on such data as a smoother in that the resulting weight vectors are much smoother and more interpretable than the weights without the robustification term. We illustrate our algorithms on both artificial and real data.

  6. Alternative formulation of explicitly correlated third-order Møller-Plesset perturbation theory

    NASA Astrophysics Data System (ADS)

    Ohnishi, Yu-ya; Ten-no, Seiichiro

    2013-09-01

    The second-order wave operator in the explicitly correlated wave function theory has been newly defined as an extension of the conventional s- and p-wave (SP) ansatz (also referred to as the FIXED amplitude ansatz) based on the linked-diagram theorem. The newly defined second-order wave operator has been applied to the calculation of the F12 correction to the third-order many-body perturbation (MP3) energy. In addition to this new wave operator, the F12 correction with the conventional first-order wave operator has been derived and calculated. Among three components of the MP3 correlation energy, the particle ladder contribution, which has shown the slowest convergence with respect to the basis set size, is fairly ameliorated by employing these F12 corrections. Both the newly defined and conventional formalisms of the F12 corrections exhibit a similar recovery of over 90% of the complete basis set limit of the particle ladder contribution of the MP3 correlation energy with a triple-zeta quality basis set for the neon atom, while the amount is about 75% without the F12 correction. The corrections to the ring term are small but the corrected energy has shown similar recovery as the particle ladder term. The hole ladder term has shown a rapid convergence even without the F12 corrections. Owing to these balanced recoveries, the deviation of the total MP3 correlation energy from the complete basis set limit has been calculated to be about 1 kcal/mol with the triple-zeta quality basis set, which is more than five times smaller than the error without the F12 correction.

  7. Order recall in verbal short-term memory: The role of semantic networks.

    PubMed

    Poirier, Marie; Saint-Aubin, Jean; Mair, Ali; Tehan, Gerry; Tolan, Anne

    2015-04-01

    In their recent article, Acheson, MacDonald, and Postle (Journal of Experimental Psychology: Learning, Memory, and Cognition 37:44-59, 2011) made an important but controversial suggestion: They hypothesized that (a) semantic information has an effect on order information in short-term memory (STM) and (b) order recall in STM is based on the level of activation of items within the relevant lexico-semantic long-term memory (LTM) network. However, verbal STM research has typically led to the conclusion that factors such as semantic category have a large effect on the number of correctly recalled items, but little or no impact on order recall (Poirier & Saint-Aubin, Quarterly Journal of Experimental Psychology 48A:384-404, 1995; Saint-Aubin, Ouellette, & Poirier, Psychonomic Bulletin & Review 12:171-177, 2005; Tse, Memory 17:874-891, 2009). Moreover, most formal models of short-term order memory currently suggest a separate mechanism for order coding-that is, one that is separate from item representation and not associated with LTM lexico-semantic networks. Both of the experiments reported here tested the predictions that we derived from Acheson et al. The findings show that, as predicted, manipulations aiming to affect the activation of item representations significantly impacted order memory.

  8. Frequency-specific electrophysiologic correlates of resting state fMRI networks.

    PubMed

    Hacker, Carl D; Snyder, Abraham Z; Pahwa, Mrinal; Corbetta, Maurizio; Leuthardt, Eric C

    2017-04-01

    Resting state functional MRI (R-fMRI) studies have shown that slow (<0.1Hz), intrinsic fluctuations of the blood oxygen level dependent (BOLD) signal are temporally correlated within hierarchically organized functional systems known as resting state networks (RSNs) (Doucet et al., 2011). Most broadly, this hierarchy exhibits a dichotomy between two opposed systems (Fox et al., 2005). One system engages with the environment and includes the visual, auditory, and sensorimotor (SMN) networks as well as the dorsal attention network (DAN), which controls spatial attention. The other system includes the default mode network (DMN) and the fronto-parietal control system (FPC), RSNs that instantiate episodic memory and executive control, respectively. Here, we test the hypothesis, based on the spectral specificity of electrophysiologic responses to perceptual vs. memory tasks (Klimesch, 1999; Pfurtscheller and Lopes da Silva, 1999), that these two large-scale neural systems also manifest frequency specificity in the resting state. We measured the spatial correspondence between electrocorticographic (ECoG) band-limited power (BLP) and R-fMRI correlation patterns in awake, resting, human subjects. Our results show that, while gamma BLP correspondence was common throughout the brain, theta (4-8Hz) BLP correspondence was stronger in the DMN and FPC, whereas alpha (8-12Hz) correspondence was stronger in the SMN and DAN. Thus, the human brain, at rest, exhibits frequency specific electrophysiology, respecting both the spectral structure of task responses and the hierarchical organization of RSNs. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  9. A time correlation function theory describing static field enhanced third order optical effects at interfaces.

    PubMed

    Neipert, Christine; Space, Brian

    2006-12-14

    Sum vibrational frequency spectroscopy, a second order optical process, is interface specific in the dipole approximation. At charged interfaces, there exists a static field, and as a direct consequence, the experimentally detected signal is a combination of enhanced second and static field induced third order contributions. There is significant evidence in the literature of the importance/relative magnitude of this third order contribution, but no previous molecularly detailed approach existed to separately calculate the second and third order contributions. Thus, for the first time, a molecularly detailed time correlation function theory is derived here that allows for the second and third order contributions to sum frequency vibrational spectra to be individually determined. Further, a practical, molecular dynamics based, implementation procedure for the derived correlation functions that describe the third order phenomenon is also presented. This approach includes a novel generalization of point atomic polarizability models to calculate the hyperpolarizability of a molecular system. The full system hyperpolarizability appears in the time correlation functions responsible for third order contributions in the presence of a static field.

  10. Social networking profile correlates of schizotypy

    PubMed Central

    Martin, Elizabeth A.; Bailey, Drew H.; Cicero, David C.; Kerns, John G.

    2015-01-01

    Social networking sites, such as Facebook, are extremely popular and have become a primary method for socialization and communication. Despite a report of increased use among those on the schizophrenia-spectrum, few details are known about their actual practices. In the current research, undergraduate participants completed measures of schizotypy and personality, and provided access to their Facebook profiles. Information from the profiles were then systematically coded and compared to the questionnaire data. As predicted, social anhedonia (SocAnh) was associated with a decrease in social participation variables, including a decrease in number of friends and number of photos, and an increase in length of time since communication with a friend, but SocAnh was also associated with an increase in profile length. Also, SocAnh was highly correlated with extraversion. Relatedly, extraversion uniquely predicted the number of friends and photos and length of time since communication with a friend. In addition, perceptual aberration/magical ideation (PerMag) was associated with an increased number of “black outs” on Facebook profile print-outs, a measure of paranoia. Overall, results from this naturalistic-like study show that SocAnh and extraversion are associated with decreased social participation and PerMag with increased paranoia related to information on social networking sites. PMID:22796101

  11. Analytical Computation of Energy-Energy Correlation at Next-to-Leading Order in QCD [The Energy-Energy Correlation at Next-to-Leading Order in QCD, Analytically

    DOE PAGES

    Dixon, Lance J.; Luo, Ming-xing; Shtabovenko, Vladyslav; ...

    2018-03-09

    Here, the energy-energy correlation (EEC) between two detectors in e +e – annihilation was computed analytically at leading order in QCD almost 40 years ago, and numerically at next-to-leading order (NLO) starting in the 1980s. We present the first analytical result for the EEC at NLO, which is remarkably simple, and facilitates analytical study of the perturbative structure of the EEC. We provide the expansion of the EEC in the collinear and back-to-back regions through next-to-leading power, information which should aid resummation in these regions.

  12. Analytical Computation of Energy-Energy Correlation at Next-to-Leading Order in QCD [The Energy-Energy Correlation at Next-to-Leading Order in QCD, Analytically

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Dixon, Lance J.; Luo, Ming-xing; Shtabovenko, Vladyslav

    Here, the energy-energy correlation (EEC) between two detectors in e +e – annihilation was computed analytically at leading order in QCD almost 40 years ago, and numerically at next-to-leading order (NLO) starting in the 1980s. We present the first analytical result for the EEC at NLO, which is remarkably simple, and facilitates analytical study of the perturbative structure of the EEC. We provide the expansion of the EEC in the collinear and back-to-back regions through next-to-leading power, information which should aid resummation in these regions.

  13. A novel nonlinear adaptive filter using a pipelined second-order Volterra recurrent neural network.

    PubMed

    Zhao, Haiquan; Zhang, Jiashu

    2009-12-01

    To enhance the performance and overcome the heavy computational complexity of recurrent neural networks (RNN), a novel nonlinear adaptive filter based on a pipelined second-order Volterra recurrent neural network (PSOVRNN) is proposed in this paper. A modified real-time recurrent learning (RTRL) algorithm of the proposed filter is derived in much more detail. The PSOVRNN comprises of a number of simple small-scale second-order Volterra recurrent neural network (SOVRNN) modules. In contrast to the standard RNN, these modules of a PSOVRNN can be performed simultaneously in a pipelined parallelism fashion, which can lead to a significant improvement in its total computational efficiency. Moreover, since each module of the PSOVRNN is a SOVRNN in which nonlinearity is introduced by the recursive second-order Volterra (RSOV) expansion, its performance can be further improved. Computer simulations have demonstrated that the PSOVRNN performs better than the pipelined recurrent neural network (PRNN) and RNN for nonlinear colored signals prediction and nonlinear channel equalization. However, the superiority of the PSOVRNN over the PRNN is at the cost of increasing computational complexity due to the introduced nonlinear expansion of each module.

  14. Percolation of spatially constrained Erdős-Rényi networks with degree correlations.

    PubMed

    Schmeltzer, C; Soriano, J; Sokolov, I M; Rüdiger, S

    2014-01-01

    Motivated by experiments on activity in neuronal cultures [ J. Soriano, M. Rodríguez Martínez, T. Tlusty and E. Moses Proc. Natl. Acad. Sci. 105 13758 (2008)], we investigate the percolation transition and critical exponents of spatially embedded Erdős-Rényi networks with degree correlations. In our model networks, nodes are randomly distributed in a two-dimensional spatial domain, and the connection probability depends on Euclidian link length by a power law as well as on the degrees of linked nodes. Generally, spatial constraints lead to higher percolation thresholds in the sense that more links are needed to achieve global connectivity. However, degree correlations favor or do not favor percolation depending on the connectivity rules. We employ two construction methods to introduce degree correlations. In the first one, nodes stay homogeneously distributed and are connected via a distance- and degree-dependent probability. We observe that assortativity in the resulting network leads to a decrease of the percolation threshold. In the second construction methods, nodes are first spatially segregated depending on their degree and afterwards connected with a distance-dependent probability. In this segregated model, we find a threshold increase that accompanies the rising assortativity. Additionally, when the network is constructed in a disassortative way, we observe that this property has little effect on the percolation transition.

  15. Anti-correlated networks, global signal regression, and the effects of caffeine in resting-state functional MRI.

    PubMed

    Wong, Chi Wah; Olafsson, Valur; Tal, Omer; Liu, Thomas T

    2012-10-15

    Resting-state functional connectivity magnetic resonance imaging is proving to be an essential tool for the characterization of functional networks in the brain. Two of the major networks that have been identified are the default mode network (DMN) and the task positive network (TPN). Although prior work indicates that these two networks are anti-correlated, the findings are controversial because the anti-correlations are often found only after the application of a pre-processing step, known as global signal regression, that can produce artifactual anti-correlations. In this paper, we show that, for subjects studied in an eyes-closed rest state, caffeine can significantly enhance the detection of anti-correlations between the DMN and TPN without the need for global signal regression. In line with these findings, we find that caffeine also leads to widespread decreases in connectivity and global signal amplitude. Using a recently introduced geometric model of global signal effects, we demonstrate that these decreases are consistent with the removal of an additive global signal confound. In contrast to the effects observed in the eyes-closed rest state, caffeine did not lead to significant changes in global functional connectivity in the eyes-open rest state. Copyright © 2012 Elsevier Inc. All rights reserved.

  16. Assessing Higher-Order Thinking Using a Networked Portfolio System with Peer Assessment

    ERIC Educational Resources Information Center

    Liu, Eric Zhi-Feng; Zhuo, Yi-Chin; Yuan, Shyan-Ming

    2004-01-01

    In the past, the quantitative evidences of portfolio assessment have been explored under online instruction. Liu, Lin, and Yuan provide a long-term measure of peer-self, peer-instructor and self-instructor correlation coefficients under networked innovative assessment procedures. Analytical results indicated that undergraduate students could…

  17. Analytic tools for investigating the structure of network reliability measures with regard to observation correlations

    NASA Astrophysics Data System (ADS)

    Prószyński, W.; Kwaśniak, M.

    2018-03-01

    A global measure of observation correlations in a network is proposed, together with the auxiliary indices related to non-diagonal elements of the correlation matrix. Based on the above global measure, a specific representation of the correlation matrix is presented, being the result of rigorously proven theorem formulated within the present research. According to the theorem, each positive definite correlation matrix can be expressed by a scale factor and a so-called internal weight matrix. Such a representation made it possible to investigate the structure of the basic reliability measures with regard to observation correlations. Numerical examples carried out for two test networks illustrate the structure of those measures that proved to be dependent on global correlation index. Also, the levels of global correlation are proposed. It is shown that one can readily find an approximate value of the global correlation index, and hence the correlation level, for the expected values of auxiliary indices being the only knowledge about a correlation matrix of interest. The paper is an extended continuation of the previous study of authors that was confined to the elementary case termed uniform correlation. The extension covers arbitrary correlation matrices and a structure of correlation effect.

  18. Finite-time synchronization of fractional-order memristor-based neural networks with time delays.

    PubMed

    Velmurugan, G; Rakkiyappan, R; Cao, Jinde

    2016-01-01

    In this paper, we consider the problem of finite-time synchronization of a class of fractional-order memristor-based neural networks (FMNNs) with time delays and investigated it potentially. By using Laplace transform, the generalized Gronwall's inequality, Mittag-Leffler functions and linear feedback control technique, some new sufficient conditions are derived to ensure the finite-time synchronization of addressing FMNNs with fractional order α:1<α<2 and 0<α<1. The results from the theory of fractional-order differential equations with discontinuous right-hand sides are used to investigate the problem under consideration. The derived results are extended to some previous related works on memristor-based neural networks. Finally, three numerical examples are presented to show the effectiveness of our proposed theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

  19. Complex-network description of thermal quantum states in the Ising spin chain

    NASA Astrophysics Data System (ADS)

    Sundar, Bhuvanesh; Valdez, Marc Andrew; Carr, Lincoln D.; Hazzard, Kaden R. A.

    2018-05-01

    We use network analysis to describe and characterize an archetypal quantum system—an Ising spin chain in a transverse magnetic field. We analyze weighted networks for this quantum system, with link weights given by various measures of spin-spin correlations such as the von Neumann and Rényi mutual information, concurrence, and negativity. We analytically calculate the spin-spin correlations in the system at an arbitrary temperature by mapping the Ising spin chain to fermions, as well as numerically calculate the correlations in the ground state using matrix product state methods, and then analyze the resulting networks using a variety of network measures. We demonstrate that the network measures show some traits of complex networks already in this spin chain, arguably the simplest quantum many-body system. The network measures give insight into the phase diagram not easily captured by more typical quantities, such as the order parameter or correlation length. For example, the network structure varies with transverse field and temperature, and the structure in the quantum critical fan is different from the ordered and disordered phases.

  20. Structure and evolution of a European Parliament via a network and correlation analysis

    NASA Astrophysics Data System (ADS)

    Puccio, Elena; Pajala, Antti; Piilo, Jyrki; Tumminello, Michele

    2016-11-01

    We present a study of the network of relationships among elected members of the Finnish parliament, based on a quantitative analysis of initiative co-signatures, and its evolution over 16 years. To understand the structure of the parliament, we constructed a statistically validated network of members, based on the similarity between the patterns of initiatives they signed. We looked for communities within the network and characterized them in terms of members' attributes, such as electoral district and party. To gain insight on the nested structure of communities, we constructed a hierarchical tree of members from the correlation matrix. Afterwards, we studied parliament dynamics yearly, with a focus on correlations within and between parties, by also distinguishing between government and opposition. Finally, we investigated the role played by specific individuals, at a local level. In particular, whether they act as proponents who gather consensus, or as signers. Our results provide a quantitative background to current theories in political science. From a methodological point of view, our network approach has proven able to highlight both local and global features of a complex social system.

  1. An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation

    PubMed Central

    Wang, Yikai; Kang, Jian; Kemmer, Phebe B.; Guo, Ying

    2016-01-01

    Currently, network-oriented analysis of fMRI data has become an important tool for understanding brain organization and brain networks. Among the range of network modeling methods, partial correlation has shown great promises in accurately detecting true brain network connections. However, the application of partial correlation in investigating brain connectivity, especially in large-scale brain networks, has been limited so far due to the technical challenges in its estimation. In this paper, we propose an efficient and reliable statistical method for estimating partial correlation in large-scale brain network modeling. Our method derives partial correlation based on the precision matrix estimated via Constrained L1-minimization Approach (CLIME), which is a recently developed statistical method that is more efficient and demonstrates better performance than the existing methods. To help select an appropriate tuning parameter for sparsity control in the network estimation, we propose a new Dens-based selection method that provides a more informative and flexible tool to allow the users to select the tuning parameter based on the desired sparsity level. Another appealing feature of the Dens-based method is that it is much faster than the existing methods, which provides an important advantage in neuroimaging applications. Simulation studies show that the Dens-based method demonstrates comparable or better performance with respect to the existing methods in network estimation. We applied the proposed partial correlation method to investigate resting state functional connectivity using rs-fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) study. Our results show that partial correlation analysis removed considerable between-module marginal connections identified by full correlation analysis, suggesting these connections were likely caused by global effects or common connection to other nodes. Based on partial correlation, we find that the most significant

  2. An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation.

    PubMed

    Wang, Yikai; Kang, Jian; Kemmer, Phebe B; Guo, Ying

    2016-01-01

    Currently, network-oriented analysis of fMRI data has become an important tool for understanding brain organization and brain networks. Among the range of network modeling methods, partial correlation has shown great promises in accurately detecting true brain network connections. However, the application of partial correlation in investigating brain connectivity, especially in large-scale brain networks, has been limited so far due to the technical challenges in its estimation. In this paper, we propose an efficient and reliable statistical method for estimating partial correlation in large-scale brain network modeling. Our method derives partial correlation based on the precision matrix estimated via Constrained L1-minimization Approach (CLIME), which is a recently developed statistical method that is more efficient and demonstrates better performance than the existing methods. To help select an appropriate tuning parameter for sparsity control in the network estimation, we propose a new Dens-based selection method that provides a more informative and flexible tool to allow the users to select the tuning parameter based on the desired sparsity level. Another appealing feature of the Dens-based method is that it is much faster than the existing methods, which provides an important advantage in neuroimaging applications. Simulation studies show that the Dens-based method demonstrates comparable or better performance with respect to the existing methods in network estimation. We applied the proposed partial correlation method to investigate resting state functional connectivity using rs-fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) study. Our results show that partial correlation analysis removed considerable between-module marginal connections identified by full correlation analysis, suggesting these connections were likely caused by global effects or common connection to other nodes. Based on partial correlation, we find that the most significant

  3. Simulating superradiance from higher-order-intensity-correlation measurements: Single atoms

    NASA Astrophysics Data System (ADS)

    Wiegner, R.; Oppel, S.; Bhatti, D.; von Zanthier, J.; Agarwal, G. S.

    2015-09-01

    Superradiance typically requires preparation of atoms in highly entangled multiparticle states, the so-called Dicke states. In this paper we discuss an alternative route where we prepare such states from initially uncorrelated atoms by a measurement process. By measuring higher-order intensity-intensity correlations we demonstrate that we can simulate the emission characteristics of Dicke superradiance by starting with atoms in the fully excited state. We describe the essence of the scheme by first investigating two excited atoms. Here we demonstrate how via Hanbury Brown and Twiss type of measurements we can produce Dicke superradiance and subradiance displayed commonly with two atoms in the single excited symmetric and antisymmetric Dicke states, respectively. We thereafter generalize the scheme to arbitrary numbers of atoms and detectors, and explain in detail the mechanism which leads to this result. The approach shows that the Hanbury Brown and Twiss type of intensity interference and the phenomenon of Dicke superradiance can be regarded as two sides of the same coin. We also present a compact result for the characteristic functional which generates all order intensity-intensity correlations.

  4. Spatio-Chromatic Adaptation via Higher-Order Canonical Correlation Analysis of Natural Images

    PubMed Central

    Gutmann, Michael U.; Laparra, Valero; Hyvärinen, Aapo; Malo, Jesús

    2014-01-01

    Independent component and canonical correlation analysis are two general-purpose statistical methods with wide applicability. In neuroscience, independent component analysis of chromatic natural images explains the spatio-chromatic structure of primary cortical receptive fields in terms of properties of the visual environment. Canonical correlation analysis explains similarly chromatic adaptation to different illuminations. But, as we show in this paper, neither of the two methods generalizes well to explain both spatio-chromatic processing and adaptation at the same time. We propose a statistical method which combines the desirable properties of independent component and canonical correlation analysis: It finds independent components in each data set which, across the two data sets, are related to each other via linear or higher-order correlations. The new method is as widely applicable as canonical correlation analysis, and also to more than two data sets. We call it higher-order canonical correlation analysis. When applied to chromatic natural images, we found that it provides a single (unified) statistical framework which accounts for both spatio-chromatic processing and adaptation. Filters with spatio-chromatic tuning properties as in the primary visual cortex emerged and corresponding-colors psychophysics was reproduced reasonably well. We used the new method to make a theory-driven testable prediction on how the neural response to colored patterns should change when the illumination changes. We predict shifts in the responses which are comparable to the shifts reported for chromatic contrast habituation. PMID:24533049

  5. Spatio-chromatic adaptation via higher-order canonical correlation analysis of natural images.

    PubMed

    Gutmann, Michael U; Laparra, Valero; Hyvärinen, Aapo; Malo, Jesús

    2014-01-01

    Independent component and canonical correlation analysis are two general-purpose statistical methods with wide applicability. In neuroscience, independent component analysis of chromatic natural images explains the spatio-chromatic structure of primary cortical receptive fields in terms of properties of the visual environment. Canonical correlation analysis explains similarly chromatic adaptation to different illuminations. But, as we show in this paper, neither of the two methods generalizes well to explain both spatio-chromatic processing and adaptation at the same time. We propose a statistical method which combines the desirable properties of independent component and canonical correlation analysis: It finds independent components in each data set which, across the two data sets, are related to each other via linear or higher-order correlations. The new method is as widely applicable as canonical correlation analysis, and also to more than two data sets. We call it higher-order canonical correlation analysis. When applied to chromatic natural images, we found that it provides a single (unified) statistical framework which accounts for both spatio-chromatic processing and adaptation. Filters with spatio-chromatic tuning properties as in the primary visual cortex emerged and corresponding-colors psychophysics was reproduced reasonably well. We used the new method to make a theory-driven testable prediction on how the neural response to colored patterns should change when the illumination changes. We predict shifts in the responses which are comparable to the shifts reported for chromatic contrast habituation.

  6. Higher-order neural networks, Polyà polynomials, and Fermi cluster diagrams

    NASA Astrophysics Data System (ADS)

    Kürten, K. E.; Clark, J. W.

    2003-09-01

    The problem of controlling higher-order interactions in neural networks is addressed with techniques commonly applied in the cluster analysis of quantum many-particle systems. For multineuron synaptic weights chosen according to a straightforward extension of the standard Hebbian learning rule, we show that higher-order contributions to the stimulus felt by a given neuron can be readily evaluated via Polyà’s combinatoric group-theoretical approach or equivalently by exploiting a precise formal analogy with fermion diagrammatics.

  7. Role of Weak Measurements on States Ordering and Monogamy of Quantum Correlation

    NASA Astrophysics Data System (ADS)

    Hu, Ming-Liang; Fan, Heng; Tian, Dong-Ping

    2015-01-01

    The information-theoretic definition of quantum correlation, e.g., quantum discord, is measurement dependent. By considering the more general quantum measurements, weak measurements, which include the projective measurement as a limiting case, we show that while weak measurements can enable one to capture more quantumness of correlation in a state, it can also induce other counterintuitive quantum effects. Specifically, we show that the general measurements with different strengths can impose different orderings for quantum correlations of some states. It can also modify the monogamous character for certain classes of states as well which may diminish the usefulness of quantum correlation as a resource in some protocols. In this sense, we say that the weak measurements play a dual role in defining quantum correlation.

  8. Theories and applications of second-order correlation of longitudinal velocity increments at three points in isotropic turbulence

    NASA Astrophysics Data System (ADS)

    Wu, J. Z.; Fang, L.; Shao, L.; Lu, L. P.

    2018-06-01

    In order to introduce new physics to traditional two-point correlations, we define the second-order correlation of longitudinal velocity increments at three points and obtain the analytical expressions in isotropic turbulence. By introducing the Kolmogorov 4/5 law, this three-point correlation explicitly contains velocity second- and third-order moments, which correspond to energy and energy transfer respectively. The combination of them then shows additional information of non-equilibrium turbulence by comparing to two-point correlations. Moreover, this three-point correlation shows the underlying inconsistency between numerical interpolation and three-point scaling law in numerical calculations, and inspires a preliminary model to correct this problem in isotropic turbulence.

  9. Correlation between standardized assessment of concussion scores and small-world brain network in mild traumatic brain injury.

    PubMed

    Yan, Yan; Song, Jian; Xu, Guozheng; Yao, Shun; Cao, Chenglong; Li, Chang; Peng, Guibao; Du, Hao

    2017-10-01

    This study investigated the characteristics of the small-world brain network architecture of patients with mild traumatic brain injury (MTBI), and a correlation between brain functional connectivity network properties in the resting-state fMRI and Standardized Assessment of Concussion (SAC) parameters. The neurological conditions of 22 MTBI patients and 17 normal control individuals were evaluated according to the SAC. Resting-state fMRI was performed in all subjects 3 and 7days after injury respectively. After preprocessing the fMRI data, cortex functional regions were marked using AAL90 and Dosenbach160 templates. The small-world network parameters and areas under the integral curves were computed in the range of sparsity from 0.01 to 0.5. Independent-sample t-tests were used to compare these parameters between the MTBI and control group. Significantly different parameters were investigated for correlations with SAC scores; those that correlated were chosen for further curve fitting. The clustering coefficient, the communication efficiency across in local networks, and the strength of connectivity were all higher in MTBI patients relative to control individuals. Parameters in 160 brain regions of the MTBI group significantly correlated with total SAC score and score for attention; the network parameters may be a quadratic function of attention scores of SAC and a cubic function of SAC scores. MTBI patients were characterized by elevated communication efficiency across global brain regions, and in local networks, and strength of mean connectivity. These features may be associated with brain function compensation. The network parameters significantly correlated with SAC total and attention scores. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. CA1 cell activity sequences emerge after reorganization of network correlation structure during associative learning

    PubMed Central

    Modi, Mehrab N; Dhawale, Ashesh K; Bhalla, Upinder S

    2014-01-01

    Animals can learn causal relationships between pairs of stimuli separated in time and this ability depends on the hippocampus. Such learning is believed to emerge from alterations in network connectivity, but large-scale connectivity is difficult to measure directly, especially during learning. Here, we show that area CA1 cells converge to time-locked firing sequences that bridge the two stimuli paired during training, and this phenomenon is coupled to a reorganization of network correlations. Using two-photon calcium imaging of mouse hippocampal neurons we find that co-time-tuned neurons exhibit enhanced spontaneous activity correlations that increase just prior to learning. While time-tuned cells are not spatially organized, spontaneously correlated cells do fall into distinct spatial clusters that change as a result of learning. We propose that the spatial re-organization of correlation clusters reflects global network connectivity changes that are responsible for the emergence of the sequentially-timed activity of cell-groups underlying the learned behavior. DOI: http://dx.doi.org/10.7554/eLife.01982.001 PMID:24668171

  11. The correlated network of acupuncture effect: a functional connectivity study.

    PubMed

    Qin, Wei; Tian, Jie; Pan, Xiaohong; Yang, Lin; Zhen, Zonglei

    2006-01-01

    A functional connectivity, which are temporally correlated in functionally related brain regions, before and after acupuncture manipulation was measured by MRI. Amygdala, as the control system of endogenetic analgesia, was selected for "seed" point. We found that compelling similarity existed in the network of resting state before and after acupuncture manipulation. A paired student t-test was implemented to investigate under the different conditions. The main difference was found in the limbic system, brainstem and cerebellum. We conclude that the default endogenous analgesia functional network exists in human brain at a low level, and it could be increased to a higher level by acupuncture modulation.

  12. Time-Ordered Networks Reveal Limitations to Information Flow in Ant Colonies

    PubMed Central

    Blonder, Benjamin; Dornhaus, Anna

    2011-01-01

    Background An important function of many complex networks is to inhibit or promote the transmission of disease, resources, or information between individuals. However, little is known about how the temporal dynamics of individual-level interactions affect these networks and constrain their function. Ant colonies are a model comparative system for understanding general principles linking individual-level interactions to network-level functions because interactions among individuals enable integration of multiple sources of information to collectively make decisions, and allocate tasks and resources. Methodology/Findings Here we show how the temporal and spatial dynamics of such individual interactions provide upper bounds to rates of colony-level information flow in the ant Temnothorax rugatulus. We develop a general framework for analyzing dynamic networks and a mathematical model that predicts how information flow scales with individual mobility and group size. Conclusions/Significance Using thousands of time-stamped interactions between uniquely marked ants in four colonies of a range of sizes, we demonstrate that observed maximum rates of information flow are always slower than predicted, and are constrained by regulation of individual mobility and contact rate. By accounting for the ordering and timing of interactions, we can resolve important difficulties with network sampling frequency and duration, enabling a broader understanding of interaction network functioning across systems and scales. PMID:21625450

  13. Synergistic Modification Induced Specific Recognition between Histone and TRIM24 via Fluctuation Correlation Network Analysis

    NASA Astrophysics Data System (ADS)

    Zhang, Jinmai; Luo, Huajie; Liu, Hao; Ye, Wei; Luo, Ray; Chen, Hai-Feng

    2016-04-01

    Histone modification plays a key role in gene regulation and gene expression. TRIM24 as a histone reader can recognize histone modification. However the specific recognition mechanism between TRIM24 and histone modification is unsolved. Here, systems biology method of dynamics correlation network based on molecular dynamics simulation was used to answer the question. Our network analysis shows that the dynamics correlation network of H3K23ac is distinctly different from that of wild type and other modifications. A hypothesis of “synergistic modification induced recognition” is then proposed to link histone modification and TRIM24 binding. These observations were further confirmed from community analysis of networks with mutation and network perturbation. Finally, a possible recognition pathway is also identified based on the shortest path search for H3K23ac. Significant difference of recognition pathway was found among different systems due to methylation and acetylation modifications. The analysis presented here and other studies show that the dynamic network-based analysis might be a useful general strategy to study the biology of protein post-translational modification and associated recognition.

  14. [Correlations between features of social network and outcomes in those suffering from schizophrenia seven years from the first hospitalisation].

    PubMed

    Cechnicki, Andrzej; Wojciechowska, Anna

    2007-01-01

    A research had been conducted upon the correlations between selected parameters of social networks of 64 patients ill with schizophrenia who were diagnosed according to DSM-III, and the aims of treatment such as: motivation to receive treatment, insight, compliance in taking medication, satisfaction with treatment, and treatment outcomes in the area of clinical and social functioning as well as family functioning seven years after the first admission. The indices of social networks were studied with Bizon's questionnaire. It serves storing of data on persons who have supportive functions as well as allows to work out characteristic properties of the support system such as: range of the network, size of the extra-familial network, level and localisation of the support, network and support system age. A compound system of social support and large social network, with a high level of support, correlate in a beneficial way with higher subjective satisfaction with treatment. Whereas a large extra-familial network with high level of support, correlates with better insight into illness. The larger the social network was (its range to be precise), including extra-familial network and the high level of incoming support, the fewer positive and negative symptoms the patients had and much more remissions appeared then. The larger network's range correlates with smaller number of relapses and global time of being hospitalised. People with a larger network, with high level of support located in family and outside the family, have been rarely hospitalised. The connection between network's parameters and number of daily hospitalisations had been rated. People with a larger network, including extra-familial network, with high level of social support function better in the society didn't become regressive in their professional lives and they have smaller burden in their family life. The high level of social support correlates with better family function. In families of people ill

  15. Social networking profile correlates of schizotypy.

    PubMed

    Martin, Elizabeth A; Bailey, Drew H; Cicero, David C; Kerns, John G

    2012-12-30

    Social networking sites, such as Facebook, are extremely popular and have become a primary method for socialization and communication. Despite a report of increased use among those on the schizophrenia-spectrum, few details are known about their actual practices. In the current research, undergraduate participants completed measures of schizotypy and personality, and provided access to their Facebook profiles. Information from the profiles were then systematically coded and compared to the questionnaire data. As predicted, social anhedonia (SocAnh) was associated with a decrease in social participation variables, including a decrease in number of friends and number of photos, and an increase in length of time since communication with a friend, but SocAnh was also associated with an increase in profile length. Also, SocAnh was highly correlated with extraversion. Relatedly, extraversion uniquely predicted the number of friends and photos and length of time since communication with a friend. In addition, perceptual aberration/magical ideation (PerMag) was associated with an increased number of "black outs" on Facebook profile print-outs, a measure of paranoia. Overall, results from this naturalistic-like study show that SocAnh and extraversion are associated with decreased social participation and PerMag with increased paranoia related to information on social networking sites. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  16. Standardized network order sets in rural Ontario: a follow-up report on successes and sustainability.

    PubMed

    Rawn, Andrea; Wilson, Katrina

    2011-01-01

    Unifying, implementing and sustaining a large order set project requires strategic placement of key organizational professionals to provide ongoing user education, communication and support. This article will outline the successful strategies implemented by the Grey Bruce Health Network, Evidence-Based Care Program to reduce length of stay, increase patient satisfaction and increase the use of best practices resulting in quality outcomes, safer practice and better allocation of resources by using standardized Order Sets within a network of 11 hospital sites. Audits conducted in 2007 and again in 2008 revealed a reduced length of stay of 0.96 in-patient days when order sets were used on admission and readmission for the same or a related diagnosis within one month decreased from 5.5% without order sets to 3.5% with order sets.

  17. First-order design of geodetic networks using the simulated annealing method

    NASA Astrophysics Data System (ADS)

    Berné, J. L.; Baselga, S.

    2004-09-01

    The general problem of the optimal design for a geodetic network subject to any extrinsic factors, namely the first-order design problem, can be dealt with as a numeric optimization problem. The classic theory of this problem and the optimization methods are revised. Then the innovative use of the simulated annealing method, which has been successfully applied in other fields, is presented for this classical geodetic problem. This method, belonging to iterative heuristic techniques in operational research, uses a thermodynamical analogy to crystalline networks to offer a solution that converges probabilistically to the global optimum. Basic formulation and some examples are studied.

  18. Cognitive reserve moderates the association between functional network anti-correlations and memory in MCI.

    PubMed

    Franzmeier, Nicolai; Buerger, Katharina; Teipel, Stefan; Stern, Yaakov; Dichgans, Martin; Ewers, Michael

    2017-02-01

    Cognitive reserve (CR) shows protective effects on cognitive function in older adults. Here, we focused on the effects of CR at the functional network level. We assessed in patients with amnestic mild cognitive impairment (aMCI) whether higher CR moderates the association between low internetwork cross-talk on memory performance. In 2 independent aMCI samples (n = 76 and 93) and healthy controls (HC, n = 36), CR was assessed via years of education and intelligence (IQ). We focused on the anti-correlation between the dorsal attention network (DAN) and an anterior and posterior default mode network (DMN), assessed via sliding time window analysis of resting-state functional magnetic resonance imaging (fMRI). The DMN-DAN anti-correlation was numerically but not significantly lower in aMCI compared to HC. However, in aMCI, lower anterior DMN-DAN anti-correlation was associated with lower memory performance. This association was moderated by CR proxies, where the association between the internetwork anti-correlation and memory performance was alleviated at higher levels of education or IQ. In conclusion, lower DAN-DMN cross-talk is associated with lower memory in aMCI, where such effects are buffered by higher CR. Copyright © 2016 Elsevier Inc. All rights reserved.

  19. Network analysis reveals seasonal variation of co-occurrence correlations between Cyanobacteria and other bacterioplankton.

    PubMed

    Zhao, Dayong; Shen, Feng; Zeng, Jin; Huang, Rui; Yu, Zhongbo; Wu, Qinglong L

    2016-12-15

    Association network approaches have recently been proposed as a means for exploring the associations between bacterial communities. In the present study, high-throughput sequencing was employed to investigate the seasonal variations in the composition of bacterioplankton communities in six eutrophic urban lakes of Nanjing City, China. Over 150,000 16S rRNA sequences were derived from 52 water samples, and correlation-based network analyses were conducted. Our results demonstrated that the architecture of the co-occurrence networks varied in different seasons. Cyanobacteria played various roles in the ecological networks during different seasons. Co-occurrence patterns revealed that members of Cyanobacteria shared a very similar niche and they had weak positive correlations with other phyla in summer. To explore the effect of environmental factors on species-species co-occurrence networks and to determine the most influential environmental factors, the original positive network was simplified by module partitioning and by calculating module eigengenes. Module eigengene analysis indicated that temperature only affected some Cyanobacteria; the rest were mainly affected by nitrogen associated factors throughout the year. Cyanobacteria were dominant in summer which may result from strong co-occurrence patterns and suitable living conditions. Overall, this study has improved our understanding of the roles of Cyanobacteria and other bacterioplankton in ecological networks. Copyright © 2016 Elsevier B.V. All rights reserved.

  20. "Time-dependent flow-networks"

    NASA Astrophysics Data System (ADS)

    Tupikina, Liubov; Molkentin, Nora; Lopez, Cristobal; Hernandez-Garcia, Emilio; Marwan, Norbert; Kurths, Jürgen

    2015-04-01

    Complex networks have been successfully applied to various systems such as society, technology, and recently climate. Links in a climate network are defined between two geographical locations if the correlation between the time series of some climate variable is higher than a threshold. Therefore, network links are considered to imply information or heat exchange. However, the relationship between the oceanic and atmospheric flows and the climate network's structure is still unclear. Recently, a theoretical approach verifying the correlation between ocean currents and surface air temperature networks has been introduced, where the Pearson correlation networks were constructed from advection-diffusion dynamics on an underlying flow. Since the continuous approach has its limitations, i.e. high computational complexity and fixed variety of the flows in the underlying system, we introduce a new, method of flow-networks for changing in time velocity fields including external forcing in the system, noise and temperature-decay. Method of the flow-network construction can be divided into several steps: first we obtain the linear recursive equation for the temperature time-series. Then we compute the correlation matrix for time-series averaging the tensor product over all realizations of the noise, which we interpret as a weighted adjacency matrix of the flow-network and analyze using network measures. We apply the method to different types of moving flows with geographical relevance such as meandering flow. Analyzing the flow-networks using network measures we find that our approach can highlight zones of high velocity by degree and transition zones by betweenness, while the combination of these network measures can uncover how the flow propagates within time. Flow-networks can be powerful tool to understand the connection between system's dynamics and network's topology analyzed using network measures in order to shed light on different climatic phenomena.

  1. Electron spin polarization by isospin ordering in correlated two-layer quantum Hall systems.

    PubMed

    Tiemann, L; Wegscheider, W; Hauser, M

    2015-05-01

    Enhancement of the electron spin polarization in a correlated two-layer, two-dimensional electron system at a total Landau level filling factor of 1 is reported. Using resistively detected nuclear magnetic resonance, we demonstrate that the electron spin polarization of two closely spaced two-dimensional electron systems becomes maximized when interlayer Coulomb correlations establish spontaneous isospin ferromagnetic order. This correlation-driven polarization dominates over the spin polarizations of competing single-layer fractional quantum Hall states under electron density imbalances.

  2. Decorrelation of Neural-Network Activity by Inhibitory Feedback

    PubMed Central

    Einevoll, Gaute T.; Diesmann, Markus

    2012-01-01

    Correlations in spike-train ensembles can seriously impair the encoding of information by their spatio-temporal structure. An inevitable source of correlation in finite neural networks is common presynaptic input to pairs of neurons. Recent studies demonstrate that spike correlations in recurrent neural networks are considerably smaller than expected based on the amount of shared presynaptic input. Here, we explain this observation by means of a linear network model and simulations of networks of leaky integrate-and-fire neurons. We show that inhibitory feedback efficiently suppresses pairwise correlations and, hence, population-rate fluctuations, thereby assigning inhibitory neurons the new role of active decorrelation. We quantify this decorrelation by comparing the responses of the intact recurrent network (feedback system) and systems where the statistics of the feedback channel is perturbed (feedforward system). Manipulations of the feedback statistics can lead to a significant increase in the power and coherence of the population response. In particular, neglecting correlations within the ensemble of feedback channels or between the external stimulus and the feedback amplifies population-rate fluctuations by orders of magnitude. The fluctuation suppression in homogeneous inhibitory networks is explained by a negative feedback loop in the one-dimensional dynamics of the compound activity. Similarly, a change of coordinates exposes an effective negative feedback loop in the compound dynamics of stable excitatory-inhibitory networks. The suppression of input correlations in finite networks is explained by the population averaged correlations in the linear network model: In purely inhibitory networks, shared-input correlations are canceled by negative spike-train correlations. In excitatory-inhibitory networks, spike-train correlations are typically positive. Here, the suppression of input correlations is not a result of the mere existence of correlations between

  3. Disrupted Structural and Functional Networks and Their Correlation with Alertness in Right Temporal Lobe Epilepsy: A Graph Theory Study.

    PubMed

    Jiang, Wenyu; Li, Jianping; Chen, Xuemei; Ye, Wei; Zheng, Jinou

    2017-01-01

    Previous studies have shown that temporal lobe epilepsy (TLE) involves abnormal structural or functional connectivity in specific brain areas. However, limited comprehensive studies have been conducted on TLE associated changes in the topological organization of structural and functional networks. Additionally, epilepsy is associated with impairment in alertness, a fundamental component of attention. In this study, structural networks were constructed using diffusion tensor imaging tractography, and functional networks were obtained from resting-state functional MRI temporal series correlations in 20 right temporal lobe epilepsy (rTLE) patients and 19 healthy controls. Global network properties were computed by graph theoretical analysis, and correlations were assessed between global network properties and alertness. The results from these analyses showed that rTLE patients exhibit abnormal small-world attributes in structural and functional networks. Structural networks shifted toward more regular attributes, but functional networks trended toward more random attributes. After controlling for the influence of the disease duration, negative correlations were found between alertness, small-worldness, and the cluster coefficient. However, alertness did not correlate with either the characteristic path length or global efficiency in rTLE patients. Our findings show that disruptions of the topological construction of brain structural and functional networks as well as small-world property bias are associated with deficits in alertness in rTLE patients. These data suggest that reorganization of brain networks develops as a mechanism to compensate for altered structural and functional brain function during disease progression.

  4. Processing speed in recurrent visual networks correlates with general intelligence.

    PubMed

    Jolij, Jacob; Huisman, Danielle; Scholte, Steven; Hamel, Ronald; Kemner, Chantal; Lamme, Victor A F

    2007-01-08

    Studies on the neural basis of general fluid intelligence strongly suggest that a smarter brain processes information faster. Different brain areas, however, are interconnected by both feedforward and feedback projections. Whether both types of connections or only one of the two types are faster in smarter brains remains unclear. Here we show, by measuring visual evoked potentials during a texture discrimination task, that general fluid intelligence shows a strong correlation with processing speed in recurrent visual networks, while there is no correlation with speed of feedforward connections. The hypothesis that a smarter brain runs faster may need to be refined: a smarter brain's feedback connections run faster.

  5. Preliminary design work on a DSN VLBI correlator. [Deep Space Network

    NASA Technical Reports Server (NTRS)

    Lushbaugh, W. A.; Layland, J. W.

    1978-01-01

    The Deep Space Network is in the process of fielding high-density digital instrumentation recorders for support of the Pioneer Venus 1978 entry experiment and other related tasks. It has long been obvious that these recorders would also serve well as the recording medium for very long base interferometry (VLBI) experiments with relatively weak radio sources, provided that a suitable correlation processor for these tape recordings could be established. The overall design and current status of a VLBI correlator designed to mate with these tape recorders are described.

  6. Simultaneous stability and sensitivity in model cortical networks is achieved through anti-correlations between the in- and out-degree of connectivity

    PubMed Central

    Vasquez, Juan C.; Houweling, Arthur R.; Tiesinga, Paul

    2013-01-01

    Neuronal networks in rodent barrel cortex are characterized by stable low baseline firing rates. However, they are sensitive to the action potentials of single neurons as suggested by recent single-cell stimulation experiments that reported quantifiable behavioral responses in response to short spike trains elicited in single neurons. Hence, these networks are stable against internally generated fluctuations in firing rate but at the same time remain sensitive to similarly-sized externally induced perturbations. We investigated stability and sensitivity in a simple recurrent network of stochastic binary neurons and determined numerically the effects of correlation between the number of afferent (“in-degree”) and efferent (“out-degree”) connections in neurons. The key advance reported in this work is that anti-correlation between in-/out-degree distributions increased the stability of the network in comparison to networks with no correlation or positive correlations, while being able to achieve the same level of sensitivity. The experimental characterization of degree distributions is difficult because all pre-synaptic and post-synaptic neurons have to be identified and counted. We explored whether the statistics of network motifs, which requires the characterization of connections between small subsets of neurons, could be used to detect evidence for degree anti-correlations. We find that the sample frequency of the 3-neuron “ring” motif (1→2→3→1), can be used to detect degree anti-correlation for sub-networks of size 30 using about 50 samples, which is of significance because the necessary measurements are achievable experimentally in the near future. Taken together, we hypothesize that barrel cortex networks exhibit degree anti-correlations and specific network motif statistics. PMID:24223550

  7. A new cross-correlation algorithm for the analysis of "in vitro" neuronal network activity aimed at pharmacological studies.

    PubMed

    Biffi, E; Menegon, A; Regalia, G; Maida, S; Ferrigno, G; Pedrocchi, A

    2011-08-15

    Modern drug discovery for Central Nervous System pathologies has recently focused its attention to in vitro neuronal networks as models for the study of neuronal activities. Micro Electrode Arrays (MEAs), a widely recognized tool for pharmacological investigations, enable the simultaneous study of the spiking activity of discrete regions of a neuronal culture, providing an insight into the dynamics of networks. Taking advantage of MEAs features and making the most of the cross-correlation analysis to assess internal parameters of a neuronal system, we provide an efficient method for the evaluation of comprehensive neuronal network activity. We developed an intra network burst correlation algorithm, we evaluated its sensitivity and we explored its potential use in pharmacological studies. Our results demonstrate the high sensitivity of this algorithm and the efficacy of this methodology in pharmacological dose-response studies, with the advantage of analyzing the effect of drugs on the comprehensive correlative properties of integrated neuronal networks. Copyright © 2011 Elsevier B.V. All rights reserved.

  8. Quasiprobability behind the out-of-time-ordered correlator

    NASA Astrophysics Data System (ADS)

    Yunger Halpern, Nicole; Swingle, Brian; Dressel, Justin

    2018-04-01

    Two topics, evolving rapidly in separate fields, were combined recently: the out-of-time-ordered correlator (OTOC) signals quantum-information scrambling in many-body systems. The Kirkwood-Dirac (KD) quasiprobability represents operators in quantum optics. The OTOC was shown to equal a moment of a summed quasiprobability [Yunger Halpern, Phys. Rev. A 95, 012120 (2017), 10.1103/PhysRevA.95.012120]. That quasiprobability, we argue, is an extension of the KD distribution. We explore the quasiprobability's structure from experimental, numerical, and theoretical perspectives. First, we simplify and analyze Yunger Halpern's weak-measurement and interference protocols for measuring the OTOC and its quasiprobability. We decrease, exponentially in system size, the number of trials required to infer the OTOC from weak measurements. We also construct a circuit for implementing the weak-measurement scheme. Next, we calculate the quasiprobability (after coarse graining) numerically and analytically: we simulate a transverse-field Ising model first. Then, we calculate the quasiprobability averaged over random circuits, which model chaotic dynamics. The quasiprobability, we find, distinguishes chaotic from integrable regimes. We observe nonclassical behaviors: the quasiprobability typically has negative components. It becomes nonreal in some regimes. The onset of scrambling breaks a symmetry that bifurcates the quasiprobability, as in classical-chaos pitchforks. Finally, we present mathematical properties. We define an extended KD quasiprobability that generalizes the KD distribution. The quasiprobability obeys a Bayes-type theorem, for example, that exponentially decreases the memory required to calculate weak values, in certain cases. A time-ordered correlator analogous to the OTOC, insensitive to quantum-information scrambling, depends on a quasiprobability closer to a classical probability. This work not only illuminates the OTOC's underpinnings, but also generalizes

  9. Optical nonclassicality test based on third-order intensity correlations

    NASA Astrophysics Data System (ADS)

    Rigovacca, L.; Kolthammer, W. S.; Di Franco, C.; Kim, M. S.

    2018-03-01

    We develop a nonclassicality criterion for the interference of three delayed, but otherwise identical, light fields in a three-mode Bell interferometer. We do so by comparing the prediction of quantum mechanics with those of a classical framework in which independent sources emit electric fields with random phases. In particular, we evaluate third-order correlations among output intensities as a function of the delays, and show how the presence of a correlation revival for small delays cannot be explained by the classical model of light. The observation of a revival is thus a nonclassicality signature, which can be achieved only by sources with a photon-number statistics that is highly sub-Poissonian. Our analysis provides strong evidence for the nonclassicality of the experiment discussed by Menssen et al. [Phys. Rev. Lett. 118, 153603 (2017), 10.1103/PhysRevLett.118.153603], and shows how a collective "triad" phase affects the interference of any three or more light fields, irrespective of their quantum or classical character.

  10. Transition to synchrony in degree-frequency correlated Sakaguchi-Kuramoto model

    NASA Astrophysics Data System (ADS)

    Kundu, Prosenjit; Khanra, Pitambar; Hens, Chittaranjan; Pal, Pinaki

    2017-11-01

    We investigate transition to synchrony in degree-frequency correlated Sakaguchi-Kuramoto (SK) model on complex networks both analytically and numerically. We analytically derive self-consistent equations for group angular velocity and order parameter for the model in the thermodynamic limit. Using the self-consistent equations we investigate transition to synchronization in SK model on uncorrelated scale-free (SF) and Erdős-Rényi (ER) networks in detail. Depending on the degree distribution exponent (γ ) of SF networks and phase-frustration parameter, the population undergoes from first-order transition [explosive synchronization (ES)] to second-order transition and vice versa. In ER networks transition is always second order irrespective of the values of the phase-lag parameter. We observe that the critical coupling strength for the onset of synchronization is decreased by phase-frustration parameter in case of SF network where as in ER network, the phase-frustration delays the onset of synchronization. Extensive numerical simulations using SF and ER networks are performed to validate the analytical results. An analytical expression of critical coupling strength for the onset of synchronization is also derived from the self-consistent equations considering the vanishing order parameter limit.

  11. SurvNet: a web server for identifying network-based biomarkers that most correlate with patient survival data.

    PubMed

    Li, Jun; Roebuck, Paul; Grünewald, Stefan; Liang, Han

    2012-07-01

    An important task in biomedical research is identifying biomarkers that correlate with patient clinical data, and these biomarkers then provide a critical foundation for the diagnosis and treatment of disease. Conventionally, such an analysis is based on individual genes, but the results are often noisy and difficult to interpret. Using a biological network as the searching platform, network-based biomarkers are expected to be more robust and provide deep insights into the molecular mechanisms of disease. We have developed a novel bioinformatics web server for identifying network-based biomarkers that most correlate with patient survival data, SurvNet. The web server takes three input files: one biological network file, representing a gene regulatory or protein interaction network; one molecular profiling file, containing any type of gene- or protein-centred high-throughput biological data (e.g. microarray expression data or DNA methylation data); and one patient survival data file (e.g. patients' progression-free survival data). Given user-defined parameters, SurvNet will automatically search for subnetworks that most correlate with the observed patient survival data. As the output, SurvNet will generate a list of network biomarkers and display them through a user-friendly interface. SurvNet can be accessed at http://bioinformatics.mdanderson.org/main/SurvNet.

  12. Dissipativity and stability analysis of fractional-order complex-valued neural networks with time delay.

    PubMed

    Velmurugan, G; Rakkiyappan, R; Vembarasan, V; Cao, Jinde; Alsaedi, Ahmed

    2017-02-01

    As we know, the notion of dissipativity is an important dynamical property of neural networks. Thus, the analysis of dissipativity of neural networks with time delay is becoming more and more important in the research field. In this paper, the authors establish a class of fractional-order complex-valued neural networks (FCVNNs) with time delay, and intensively study the problem of dissipativity, as well as global asymptotic stability of the considered FCVNNs with time delay. Based on the fractional Halanay inequality and suitable Lyapunov functions, some new sufficient conditions are obtained that guarantee the dissipativity of FCVNNs with time delay. Moreover, some sufficient conditions are derived in order to ensure the global asymptotic stability of the addressed FCVNNs with time delay. Finally, two numerical simulations are posed to ensure that the attention of our main results are valuable. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Environmentally induced changes in correlated responses to selection reveal variable pleiotropy across a complex genetic network.

    PubMed

    Sikkink, Kristin L; Reynolds, Rose M; Cresko, William A; Phillips, Patrick C

    2015-05-01

    Selection in novel environments can lead to a coordinated evolutionary response across a suite of characters. Environmental conditions can also potentially induce changes in the genetic architecture of complex traits, which in turn could alter the pattern of the multivariate response to selection. We describe a factorial selection experiment using the nematode Caenorhabditis remanei in which two different stress-related phenotypes (heat and oxidative stress resistance) were selected under three different environmental conditions. The pattern of covariation in the evolutionary response between phenotypes or across environments differed depending on the environment in which selection occurred, including asymmetrical responses to selection in some cases. These results indicate that variation in pleiotropy across the stress response network is highly sensitive to the external environment. Our findings highlight the complexity of the interaction between genes and environment that influences the ability of organisms to acclimate to novel environments. They also make clear the need to identify the underlying genetic basis of genetic correlations in order understand how patterns of pleiotropy are distributed across complex genetic networks. © 2015 The Author(s).

  14. ENVIRONMENTALLY INDUCED CHANGES IN CORRELATED RESPONSES TO SELECTION REVEAL VARIABLE PLEIOTROPY ACROSS A COMPLEX GENETIC NETWORK

    PubMed Central

    Sikkink, Kristin L.; Reynolds, Rose M.; Cresko, William A.; Phillips, Patrick C.

    2017-01-01

    Selection in novel environments can lead to a coordinated evolutionary response across a suite of characters. Environmental conditions can also potentially induce changes in the genetic architecture of complex traits, which in turn could alter the pattern of the multivariate response to selection. We describe a factorial selection experiment using the nematode Caenorhabditis remanei in which two different stress-related phenotypes (heat and oxidative stress resistance) were selected under three different environmental conditions. The pattern of covariation in the evolutionary response between phenotypes or across environments differed depending on the environment in which selection occurred, including asymmetrical responses to selection in some cases. These results indicate that variation in pleiotropy across the stress response network is highly sensitive to the external environment. Our findings highlight the complexity of the interaction between genes and environment that influences the ability of organisms to acclimate to novel environments. They also make clear the need to identify the underlying genetic basis of genetic correlations in order understand how patterns of pleiotropy are distributed across complex genetic networks. PMID:25809411

  15. Some new results on stability and synchronization for delayed inertial neural networks based on non-reduced order method.

    PubMed

    Li, Xuanying; Li, Xiaotong; Hu, Cheng

    2017-12-01

    In this paper, without transforming the second order inertial neural networks into the first order differential systems by some variable substitutions, asymptotic stability and synchronization for a class of delayed inertial neural networks are investigated. Firstly, a new Lyapunov functional is constructed to directly propose the asymptotic stability of the inertial neural networks, and some new stability criteria are derived by means of Barbalat Lemma. Additionally, by designing a new feedback control strategy, the asymptotic synchronization of the addressed inertial networks is studied and some effective conditions are obtained. To reduce the control cost, an adaptive control scheme is designed to realize the asymptotic synchronization. It is noted that the dynamical behaviors of inertial neural networks are directly analyzed in this paper by constructing some new Lyapunov functionals, this is totally different from the traditional reduced-order variable substitution method. Finally, some numerical simulations are given to demonstrate the effectiveness of the derived theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. 78 FR 50480 - In the Matter of Redfin Network, Inc.; Order of Suspension of Trading

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-08-19

    ... SECURITIES AND EXCHANGE COMMISSION [File No. 500-1] In the Matter of Redfin Network, Inc.; Order of Suspension of Trading August 15, 2013. It appears to the Securities and Exchange Commission that there is a lack of current and accurate information concerning the securities of Redfin Network, Inc...

  17. Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network.

    PubMed

    Guo, Hao; Qin, Mengna; Chen, Junjie; Xu, Yong; Xiang, Jie

    2017-01-01

    High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional methods for processing high-order functional connectivity networks generally include the clustering method, which reduces data dimensionality. As a result, such networks cannot be effectively interpreted in the context of neurology. Additionally, due to the large scale of high-order functional connectivity networks, it can be computationally very expensive to use complex network or graph theory to calculate certain topological properties. Here, we propose a novel method of generating a high-order minimum spanning tree functional connectivity network. This method increases the neurological significance of the high-order functional connectivity network, reduces network computing consumption, and produces a network scale that is conducive to subsequent network analysis. To ensure the quality of the topological information in the network structure, we used frequent subgraph mining technology to capture the discriminative subnetworks as features and combined this with quantifiable local network features. Then we applied a multikernel learning technique to the corresponding selected features to obtain the final classification results. We evaluated our proposed method using a data set containing 38 patients with major depressive disorder and 28 healthy controls. The experimental results showed a classification accuracy of up to 97.54%.

  18. Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network

    PubMed Central

    Qin, Mengna; Chen, Junjie; Xu, Yong; Xiang, Jie

    2017-01-01

    High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional methods for processing high-order functional connectivity networks generally include the clustering method, which reduces data dimensionality. As a result, such networks cannot be effectively interpreted in the context of neurology. Additionally, due to the large scale of high-order functional connectivity networks, it can be computationally very expensive to use complex network or graph theory to calculate certain topological properties. Here, we propose a novel method of generating a high-order minimum spanning tree functional connectivity network. This method increases the neurological significance of the high-order functional connectivity network, reduces network computing consumption, and produces a network scale that is conducive to subsequent network analysis. To ensure the quality of the topological information in the network structure, we used frequent subgraph mining technology to capture the discriminative subnetworks as features and combined this with quantifiable local network features. Then we applied a multikernel learning technique to the corresponding selected features to obtain the final classification results. We evaluated our proposed method using a data set containing 38 patients with major depressive disorder and 28 healthy controls. The experimental results showed a classification accuracy of up to 97.54%. PMID:29387141

  19. Inferring gene regression networks with model trees

    PubMed Central

    2010-01-01

    Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database) is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear regressions to separate

  20. Discriminative analysis of early Alzheimer's disease based on two intrinsically anti-correlated networks with resting-state fMRI.

    PubMed

    Wang, Kun; Jiang, Tianzi; Liang, Meng; Wang, Liang; Tian, Lixia; Zhang, Xinqing; Li, Kuncheng; Liu, Zhening

    2006-01-01

    In this work, we proposed a discriminative model of Alzheimer's disease (AD) on the basis of multivariate pattern classification and functional magnetic resonance imaging (fMRI). This model used the correlation/anti-correlation coefficients of two intrinsically anti-correlated networks in resting brains, which have been suggested by two recent studies, as the feature of classification. Pseudo-Fisher Linear Discriminative Analysis (pFLDA) was then performed on the feature space and a linear classifier was generated. Using leave-one-out (LOO) cross validation, our results showed a correct classification rate of 83%. We also compared the proposed model with another one based on the whole brain functional connectivity. Our proposed model outperformed the other one significantly, and this implied that the two intrinsically anti-correlated networks may be a more susceptible part of the whole brain network in the early stage of AD.

  1. Extension of local-type inequality for the higher order correlation functions

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Suyama, Teruaki; Yokoyama, Shuichiro, E-mail: suyama@resceu.s.u-tokyo.ac.jp, E-mail: shu@a.phys.nagoya-u.ac.jp

    2011-07-01

    For the local-type primordial perturbation, it is known that there is an inequality between the bispectrum and the trispectrum. By using the diagrammatic method, we develop a general formalism to systematically construct the similar inequalities up to any order correlation function. As an application, we explicitly derive all the inequalities up to six and eight-point functions.

  2. Estimating individual contribution from group-based structural correlation networks.

    PubMed

    Saggar, Manish; Hosseini, S M Hadi; Bruno, Jennifer L; Quintin, Eve-Marie; Raman, Mira M; Kesler, Shelli R; Reiss, Allan L

    2015-10-15

    Coordinated variations in brain morphology (e.g., cortical thickness) across individuals have been widely used to infer large-scale population brain networks. These structural correlation networks (SCNs) have been shown to reflect synchronized maturational changes in connected brain regions. Further, evidence suggests that SCNs, to some extent, reflect both anatomical and functional connectivity and hence provide a complementary measure of brain connectivity in addition to diffusion weighted networks and resting-state functional networks. Although widely used to study between-group differences in network properties, SCNs are inferred only at the group-level using brain morphology data from a set of participants, thereby not providing any knowledge regarding how the observed differences in SCNs are associated with individual behavioral, cognitive and disorder states. In the present study, we introduce two novel distance-based approaches to extract information regarding individual differences from the group-level SCNs. We applied the proposed approaches to a moderately large dataset (n=100) consisting of individuals with fragile X syndrome (FXS; n=50) and age-matched typically developing individuals (TD; n=50). We tested the stability of proposed approaches using permutation analysis. Lastly, to test the efficacy of our method, individual contributions extracted from the group-level SCNs were examined for associations with intelligence scores and genetic data. The extracted individual contributions were stable and were significantly related to both genetic and intelligence estimates, in both typically developing individuals and participants with FXS. We anticipate that the approaches developed in this work could be used as a putative biomarker for altered connectivity in individuals with neurodevelopmental disorders. Copyright © 2015 Elsevier Inc. All rights reserved.

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

    NASA Astrophysics Data System (ADS)

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

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

  4. Periodic fluctuations in correlation-based connectivity density time series: Application to wind speed-monitoring network in Switzerland

    NASA Astrophysics Data System (ADS)

    Laib, Mohamed; Telesca, Luciano; Kanevski, Mikhail

    2018-02-01

    In this paper, we study the periodic fluctuations of connectivity density time series of a wind speed-monitoring network in Switzerland. By using the correlogram-based robust periodogram annual periodic oscillations were found in the correlation-based network. The intensity of such annual periodic oscillations is larger for lower correlation thresholds and smaller for higher. The annual periodicity in the connectivity density seems reasonably consistent with the seasonal meteo-climatic cycle.

  5. Substrates of metacognition on perception and metacognition on higher-order cognition relate to different subsystems of the mentalizing network.

    PubMed

    Valk, Sofie L; Bernhardt, Boris C; Böckler, Anne; Kanske, Philipp; Singer, Tania

    2016-10-01

    Humans have the ability to reflect upon their perception, thoughts, and actions, known as metacognition (MC). The brain basis of MC is incompletely understood, and it is debated whether MC on different processes is subserved by common or divergent networks. We combined behavioral phenotyping with multi-modal neuroimaging to investigate whether structural substrates of individual differences in MC on higher-order cognition (MC-C) are dissociable from those underlying MC on perceptual accuracy (MC-P). Motivated by conceptual work suggesting a link between MC and cognitive perspective taking, we furthermore tested for overlaps between MC substrates and mentalizing networks. In a large sample of healthy adults, individual differences in MC-C and MC-P did not correlate. MRI-based cortical thickness mapping revealed a structural basis of this independence, by showing that individual differences in MC-P related to right prefrontal cortical thickness, while MC-C scores correlated with measures in lateral prefrontal, temporo-parietal, and posterior midline regions. Surface-based superficial white matter diffusivity analysis revealed substrates resembling those seen for cortical thickness, confirming the divergence of both MC faculties using an independent imaging marker. Despite their specificity, substrates of MC-C and MC-P fell clearly within networks known to participate in mentalizing, confirmed by task-based fMRI in the same subjects, previous meta-analytical findings, and ad-hoc Neurosynth-based meta-analyses. Our integrative multi-method approach indicates domain-specific substrates of MC; despite their divergence, these nevertheless likely rely on component processes mediated by circuits also involved in mentalizing. Hum Brain Mapp 37:3388-3399, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  6. Evaluation model of distribution network development based on ANP and grey correlation analysis

    NASA Astrophysics Data System (ADS)

    Ma, Kaiqiang; Zhan, Zhihong; Zhou, Ming; Wu, Qiang; Yan, Jun; Chen, Genyong

    2018-06-01

    The existing distribution network evaluation system cannot scientifically and comprehensively reflect the distribution network development status. Furthermore, the evaluation model is monotonous and it is not suitable for horizontal analysis of many regional power grids. For these reason, this paper constructs a set of universal adaptability evaluation index system and model of distribution network development. Firstly, distribution network evaluation system is set up by power supply capability, power grid structure, technical equipment, intelligent level, efficiency of the power grid and development benefit of power grid. Then the comprehensive weight of indices is calculated by combining the AHP with the grey correlation analysis. Finally, the index scoring function can be obtained by fitting the index evaluation criterion to the curve, and then using the multiply plus operator to get the result of sample evaluation. The example analysis shows that the model can reflect the development of distribution network and find out the advantages and disadvantages of distribution network development. Besides, the model provides suggestions for the development and construction of distribution network.

  7. The role of communication and imitation in limit order markets

    NASA Astrophysics Data System (ADS)

    Tedeschi, G.; Iori, G.; Gallegati, M.

    2009-10-01

    In this paper we develop an order driver market model with heterogeneous traders that imitate each other on different network structures. We assess how imitations among otherway noise traders, can give rise to well known stylized facts such as fat tails and volatility clustering. We examine the impact of communication and imitation on the statistical properties of prices and order flows when changing the networks' structure, and show that the imitation of a given, fixed agent, called “guru", can generate clustering of volatility in the model. We also find a positive correlation between volatility and bid-ask spread, and between fat-tailed fluctuations in asset prices and gap sizes in the order book. in here

  8. Correlated mRNAs and miRNAs from co-expression and regulatory networks affect porcine muscle and finally meat properties.

    PubMed

    Ponsuksili, Siriluck; Du, Yang; Hadlich, Frieder; Siengdee, Puntita; Murani, Eduard; Schwerin, Manfred; Wimmers, Klaus

    2013-08-05

    Physiological processes aiding the conversion of muscle to meat involve many genes associated with muscle structure and metabolic processes. MicroRNAs regulate networks of genes to orchestrate cellular functions, in turn regulating phenotypes. We applied weighted gene co-expression network analysis to identify co-expression modules that correlated to meat quality phenotypes and were highly enriched for genes involved in glucose metabolism, response to wounding, mitochondrial ribosome, mitochondrion, and extracellular matrix. Negative correlation of miRNA with mRNA and target prediction were used to select transcripts out of the modules of trait-associated mRNAs to further identify those genes that are correlated with post mortem traits. Porcine muscle co-expression transcript networks that correlated to post mortem traits were identified. The integration of miRNA and mRNA expression analyses, as well as network analysis, enabled us to interpret the differentially-regulated genes from a systems perspective. Linking co-expression networks of transcripts and hierarchically organized pairs of miRNAs and mRNAs to meat properties yields new insight into several biological pathways underlying phenotype differences. These pathways may also be diagnostic for many myopathies, which are accompanied by deficient nutrient and oxygen supply of muscle fibers.

  9. International Network of Passive Correlation Ranging for Orbit Determination of a Geostationary Satellite

    NASA Astrophysics Data System (ADS)

    Kaliuzhnyi, Mykola; Bushuev, Felix; Shulga, Oleksandr; Sybiryakova, Yevgeniya; Shakun, Leonid; Bezrukovs, Vladislavs; Moskalenko, Sergiy; Kulishenko, Vladislav; Malynovskyi, Yevgen

    2016-12-01

    An international network of passive correlation ranging of a geostationary telecommunication satellite is considered in the article. The network is developed by the RI "MAO". The network consists of five spatially separated stations of synchronized reception of DVB-S signals of digital satellite TV. The stations are located in Ukraine and Latvia. The time difference of arrival (TDOA) on the network stations of the DVB-S signals, radiated by the satellite, is a measured parameter. The results of TDOA estimation obtained by the network in May-August 2016 are presented in the article. Orbital parameters of the tracked satellite are determined using measured values of the TDOA and two models of satellite motion: the analytical model SGP4/SDP4 and the model of numerical integration of the equations of satellite motion. Both models are realized using the free low-level space dynamics library OREKIT (ORbit Extrapolation KIT).

  10. A unified view on weakly correlated recurrent networks

    PubMed Central

    Grytskyy, Dmytro; Tetzlaff, Tom; Diesmann, Markus; Helias, Moritz

    2013-01-01

    The diversity of neuron models used in contemporary theoretical neuroscience to investigate specific properties of covariances in the spiking activity raises the question how these models relate to each other. In particular it is hard to distinguish between generic properties of covariances and peculiarities due to the abstracted model. Here we present a unified view on pairwise covariances in recurrent networks in the irregular regime. We consider the binary neuron model, the leaky integrate-and-fire (LIF) model, and the Hawkes process. We show that linear approximation maps each of these models to either of two classes of linear rate models (LRM), including the Ornstein–Uhlenbeck process (OUP) as a special case. The distinction between both classes is the location of additive noise in the rate dynamics, which is located on the output side for spiking models and on the input side for the binary model. Both classes allow closed form solutions for the covariance. For output noise it separates into an echo term and a term due to correlated input. The unified framework enables us to transfer results between models. For example, we generalize the binary model and the Hawkes process to the situation with synaptic conduction delays and simplify derivations for established results. Our approach is applicable to general network structures and suitable for the calculation of population averages. The derived averages are exact for fixed out-degree network architectures and approximate for fixed in-degree. We demonstrate how taking into account fluctuations in the linearization procedure increases the accuracy of the effective theory and we explain the class dependent differences between covariances in the time and the frequency domain. Finally we show that the oscillatory instability emerging in networks of LIF models with delayed inhibitory feedback is a model-invariant feature: the same structure of poles in the complex frequency plane determines the population power

  11. Classification of Acousto-Optic Correlation Signatures of Spread Spectrum Signals Using Artificial Neural Networks

    DTIC Science & Technology

    1989-12-01

    Ohio ’aPw iorlipuab muo i 0I2, AFIT/GE/ENG/89D-10 CLASSIFICATION OF ACOUSTO - OPTIC CORRELATION SIGNATURES OF SPREAD SPECTRUM SIGNALS USING ARTIFICIAL...ENG/89D- 10 CLASSIFICATION OF ACOUSTO - OPTIC CORRELATION SIGNATURES OF SPREAD SPECTRUM SIGNALS USING ARTIFICIAL NEURAL NETWORKS THESIS John W. DeBerry...Captain, USAF AFIT/GE/ENG/89D- 10 Approved for public release; distribution unlimited. AFIT/GE/ENG/89D-10 CLASSIFICATION OF ACOUSTO - OPTIC CORRELATION

  12. On synchronization in power-grids modelled as networks of second-order Kuramoto oscillators

    NASA Astrophysics Data System (ADS)

    Grzybowski, J. M. V.; Macau, E. E. N.; Yoneyama, T.

    2016-11-01

    This work concerns analytical results on the role of coupling strength in the phenomenon of onset of complete frequency locking in power-grids modelled as a network of second-order Kuramoto oscillators. Those results allow estimation of the coupling strength for the onset of complete frequency locking and to assess the features of network and oscillators that favor synchronization. The analytical results are evaluated using an order parameter defined as the normalized sum of absolute values of phase deviations of the oscillators over time. The investigation of the frequency synchronization within the subsets of the parameter space involved in the synchronization problem is also carried out. It is shown that the analytical results are in good agreement with those observed in the numerical simulations. In order to illustrate the methodology, a case study is presented, involving the Brazilian high-voltage transmission system under a load peak condition to study the effect of load on the syncronizability of the grid. The results show that both the load and the centralized generation might have concurred to the 2014 blackout.

  13. [Internet addiction disorder and social networks: statistical analysis of correlation and study of the association with social interaction anxiousness].

    PubMed

    Rusconi, Anna Carlotta; Valeriani, Giuseppe; Carlone, Cristiano; Raimondo, Pasquale; Quartini, Adele; Coccanari de' Fornari, Maria Antonietta; Biondi, Massimo

    2012-01-01

    Internet Addiction Disorder (IAD) is an emerging psychiatric disorder, assimilable to impulse control problems and related to maladaptive use of new networks and social and virtual technologies. Our study aims to analyze the presence of IAD among adolescents and to study the correlation with social interaction anxiousness. We investigated also the possibility that the Social Network (SN) represent a source of risk for the development of IAD. The test group was composed of 250 subjects, aged between 14 and 18 years. They were administered: Young's IAT; IAS (Interaction Anxiousness Scale), AAS (Audience Anxiousness Scale) and SISST (Social Interaction Self-Statement Test) to analyze the dimension of social interaction anxiousness. We found a rate of 2% of the IAD. The SN are the most common use of the Net in our sample, but not the most clicked sites by subjects with IAD. It should be noted, finally, a correlation between social interaction anxiety and IAD, but not a significant difference in scores of social anxiousness scales based on the SN use/non-use. The use of SN intended as single variable doesn't correlate with increased risk for IAD, or for increased social interaction anxiousness. However, if associated with prolonged use of the net for 5-6 hours or more, or concomitant use of chat rooms and/or net gambling, we find a more significant risk of psychopathology. The data presented require further investigations, in order to guide new pathogenetic models and appropriate intervention strategies.

  14. Long-range correlations improve understanding of the influence of network structure on contact dynamics.

    PubMed

    Peyrard, N; Dieckmann, U; Franc, A

    2008-05-01

    Models of infectious diseases are characterized by a phase transition between extinction and persistence. A challenge in contemporary epidemiology is to understand how the geometry of a host's interaction network influences disease dynamics close to the critical point of such a transition. Here we address this challenge with the help of moment closures. Traditional moment closures, however, do not provide satisfactory predictions close to such critical points. We therefore introduce a new method for incorporating longer-range correlations into existing closures. Our method is technically simple, remains computationally tractable and significantly improves the approximation's performance. Our extended closures thus provide an innovative tool for quantifying the influence of interaction networks on spatially or socially structured disease dynamics. In particular, we examine the effects of a network's clustering coefficient, as well as of new geometrical measures, such as a network's square clustering coefficients. We compare the relative performance of different closures from the literature, with or without our long-range extension. In this way, we demonstrate that the normalized version of the Bethe approximation-extended to incorporate long-range correlations according to our method-is an especially good candidate for studying influences of network structure. Our numerical results highlight the importance of the clustering coefficient and the square clustering coefficient for predicting disease dynamics at low and intermediate values of transmission rate, and demonstrate the significance of path redundancy for disease persistence.

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

    NASA Technical Reports Server (NTRS)

    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.

  16. Comparisons of neural networks to standard techniques for image classification and correlation

    NASA Technical Reports Server (NTRS)

    Paola, Justin D.; Schowengerdt, Robert A.

    1994-01-01

    Neural network techniques for multispectral image classification and spatial pattern detection are compared to the standard techniques of maximum-likelihood classification and spatial correlation. The neural network produced a more accurate classification than maximum-likelihood of a Landsat scene of Tucson, Arizona. Some of the errors in the maximum-likelihood classification are illustrated using decision region and class probability density plots. As expected, the main drawback to the neural network method is the long time required for the training stage. The network was trained using several different hidden layer sizes to optimize both the classification accuracy and training speed, and it was found that one node per class was optimal. The performance improved when 3x3 local windows of image data were entered into the net. This modification introduces texture into the classification without explicit calculation of a texture measure. Larger windows were successfully used for the detection of spatial features in Landsat and Magellan synthetic aperture radar imagery.

  17. Acupuncture mobilizes the brain's default mode and its anti-correlated network in healthy subjects.

    PubMed

    Hui, Kathleen K S; Marina, Ovidiu; Claunch, Joshua D; Nixon, Erika E; Fang, Jiliang; Liu, Jing; Li, Ming; Napadow, Vitaly; Vangel, Mark; Makris, Nikos; Chan, Suk-Tak; Kwong, Kenneth K; Rosen, Bruce R

    2009-09-01

    Previous work has shown that acupuncture stimulation evokes deactivation of a limbic-paralimbic-neocortical network (LPNN) as well as activation of somatosensory brain regions. This study explores the activity and functional connectivity of these regions during acupuncture vs. tactile stimulation and vs. acupuncture associated with inadvertent sharp pain. Acupuncture during 201 scans and tactile stimulation during 74 scans for comparison at acupoints LI4, ST36 and LV3 was monitored with fMRI and psychophysical response in 48 healthy subjects. Clusters of deactivated regions in the medial prefrontal, medial parietal and medial temporal lobes as well as activated regions in the sensorimotor and a few paralimbic structures can be identified during acupuncture by general linear model analysis and seed-based cross correlation analysis. Importantly, these clusters showed virtual identity with the default mode network and the anti-correlated task-positive network in response to stimulation. In addition, the amygdala and hypothalamus, structures not routinely reported in the default mode literature, were frequently involved in acupuncture. When acupuncture induced sharp pain, the deactivation was attenuated or became activated instead. Tactile stimulation induced greater activation of the somatosensory regions but less extensive deactivation of the LPNN. These results indicate that the deactivation of the LPNN during acupuncture cannot be completely explained by the demand of attention that is commonly proposed in the default mode literature. Our results suggest that acupuncture mobilizes the anti-correlated functional networks of the brain to mediate its actions, and that the effect is dependent on the psychophysical response.

  18. Characteristics of Venture Capital Network and Its Correlation with Regional Economy: Evidence from China.

    PubMed

    Jin, Yonghong; Zhang, Qi; Shan, Lifei; Li, Sai-Ping

    2015-01-01

    Financial networks have been extensively studied as examples of real world complex networks. In this paper, we establish and study the network of venture capital (VC) firms in China. We compute and analyze the statistical properties of the network, including parameters such as degrees, mean lengths of the shortest paths, clustering coefficient and robustness. We further study the topology of the network and find that it has small-world behavior. A multiple linear regression model is introduced to study the relation between network parameters and major regional economic indices in China. From the result of regression, we find that, economic aggregate (including the total GDP, investment, consumption and net export), upgrade of industrial structure, employment and remuneration of a region are all positively correlated with the degree and the clustering coefficient of the VC sub-network of the region, which suggests that the development of the VC industry has substantial effects on regional economy in China.

  19. Characteristics of Venture Capital Network and Its Correlation with Regional Economy: Evidence from China

    PubMed Central

    Jin, Yonghong; Zhang, Qi; Shan, Lifei; Li, Sai-Ping

    2015-01-01

    Financial networks have been extensively studied as examples of real world complex networks. In this paper, we establish and study the network of venture capital (VC) firms in China. We compute and analyze the statistical properties of the network, including parameters such as degrees, mean lengths of the shortest paths, clustering coefficient and robustness. We further study the topology of the network and find that it has small-world behavior. A multiple linear regression model is introduced to study the relation between network parameters and major regional economic indices in China. From the result of regression, we find that, economic aggregate (including the total GDP, investment, consumption and net export), upgrade of industrial structure, employment and remuneration of a region are all positively correlated with the degree and the clustering coefficient of the VC sub-network of the region, which suggests that the development of the VC industry has substantial effects on regional economy in China. PMID:26340555

  20. High-order Path Integral Monte Carlo methods for solving strongly correlated fermion problems

    NASA Astrophysics Data System (ADS)

    Chin, Siu A.

    2015-03-01

    In solving for the ground state of a strongly correlated many-fermion system, the conventional second-order Path Integral Monte Carlo method is plagued with the sign problem. This is due to the large number of anti-symmetric free fermion propagators that are needed to extract the square of the ground state wave function at large imaginary time. In this work, I show that optimized fourth-order Path Integral Monte Carlo methods, which uses no more than 5 free-fermion propagators, in conjunction with the use of the Hamiltonian energy estimator, can yield accurate ground state energies for quantum dots with up to 20 polarized electrons. The correlations are directly built-in and no explicit wave functions are needed. This work is supported by the Qatar National Research Fund NPRP GRANT #5-674-1-114.

  1. Network analysis reveals that bacteria and fungi form modules that correlate independently with soil parameters.

    PubMed

    de Menezes, Alexandre B; Prendergast-Miller, Miranda T; Richardson, Alan E; Toscas, Peter; Farrell, Mark; Macdonald, Lynne M; Baker, Geoff; Wark, Tim; Thrall, Peter H

    2015-08-01

    Network and multivariate statistical analyses were performed to determine interactions between bacterial and fungal community terminal restriction length polymorphisms as well as soil properties in paired woodland and pasture sites. Canonical correspondence analysis (CCA) revealed that shifts in woodland community composition correlated with soil dissolved organic carbon, while changes in pasture community composition correlated with moisture, nitrogen and phosphorus. Weighted correlation network analysis detected two distinct microbial modules per land use. Bacterial and fungal ribotypes did not group separately, rather all modules comprised of both bacterial and fungal ribotypes. Woodland modules had a similar fungal : bacterial ribotype ratio, while in the pasture, one module was fungal dominated. There was no correspondence between pasture and woodland modules in their ribotype composition. The modules had different relationships to soil variables, and these contrasts were not detected without the use of network analysis. This study demonstrated that fungi and bacteria, components of the soil microbial communities usually treated as separate functional groups as in a CCA approach, were co-correlated and formed distinct associations in these adjacent habitats. Understanding these distinct modular associations may shed more light on their niche space in the soil environment, and allow a more realistic description of soil microbial ecology and function. © 2014 Society for Applied Microbiology and John Wiley & Sons Ltd.

  2. A Quantum Field Approach for Advancing Optical Coherence Tomography Part I: First Order Correlations, Single Photon Interference, and Quantum Noise

    PubMed Central

    Brezinski, ME

    2018-01-01

    Optical coherence tomography has become an important imaging technology in cardiology and ophthalmology, with other applications under investigations. Major advances in optical coherence tomography (OCT) imaging are likely to occur through a quantum field approach to the technology. In this paper, which is the first part in a series on the topic, the quantum basis of OCT first order correlations is expressed in terms of full field quantization. Specifically first order correlations are treated as the linear sum of single photon interferences along indistinguishable paths. Photons and the electromagnetic (EM) field are described in terms of quantum harmonic oscillators. While the author feels the study of quantum second order correlations will lead to greater paradigm shifts in the field, addressed in part II, advances from the study of quantum first order correlations are given. In particular, ranging errors are discussed (with remedies) from vacuum fluctuations through the detector port, photon counting errors, and position probability amplitude uncertainty. In addition, the principles of quantum field theory and first order correlations are needed for studying second order correlations in part II. PMID:29863177

  3. A Quantum Field Approach for Advancing Optical Coherence Tomography Part I: First Order Correlations, Single Photon Interference, and Quantum Noise.

    PubMed

    Brezinski, M E

    2018-01-01

    Optical coherence tomography has become an important imaging technology in cardiology and ophthalmology, with other applications under investigations. Major advances in optical coherence tomography (OCT) imaging are likely to occur through a quantum field approach to the technology. In this paper, which is the first part in a series on the topic, the quantum basis of OCT first order correlations is expressed in terms of full field quantization. Specifically first order correlations are treated as the linear sum of single photon interferences along indistinguishable paths. Photons and the electromagnetic (EM) field are described in terms of quantum harmonic oscillators. While the author feels the study of quantum second order correlations will lead to greater paradigm shifts in the field, addressed in part II, advances from the study of quantum first order correlations are given. In particular, ranging errors are discussed (with remedies) from vacuum fluctuations through the detector port, photon counting errors, and position probability amplitude uncertainty. In addition, the principles of quantum field theory and first order correlations are needed for studying second order correlations in part II.

  4. The network property of the thalamus in the default mode network is correlated with trait mindfulness.

    PubMed

    Wang, X; Xu, M; Song, Y; Li, X; Zhen, Z; Yang, Z; Liu, J

    2014-10-10

    Mindfulness is typically defined as nonjudgmental awareness of experiences in the present moment, which is beneficial for mental and physical well-being. Previous studies have identified multiple regions in the default mode network (DMN) that are involved in mindfulness, but little is known about how these regions work collaboratively as a network. Here, we used resting-state functional magnetic resonance imaging to investigate the role of the DMN in trait mindfulness by correlating spontaneous functional connectivity among DMN nodes with self-reported trait mindfulness in a large population of young human adults. Among all pairs of the DMN nodes, we found that individuals with weaker functional connectivity between the thalamus and posterior cingulate cortex (PCC) were more mindful of the present. Post-hoc analyses of these two nodes further revealed that graph-based nodal properties of the thalamus, not the PCC, were negatively correlated with trait mindfulness, suggesting that a low involvement of the thalamus in the DMN is relevant for high trait mindfulness. Our findings not only suggest the thalamus as a switch between mind-wandering and mindfulness, but also invite future studies on mechanisms of how mindfulness produces beneficial effects by modulating the thalamus. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

  5. Joint statistics of strongly correlated neurons via dimensionality reduction

    NASA Astrophysics Data System (ADS)

    Deniz, Taşkın; Rotter, Stefan

    2017-06-01

    The relative timing of action potentials in neurons recorded from local cortical networks often shows a non-trivial dependence, which is then quantified by cross-correlation functions. Theoretical models emphasize that such spike train correlations are an inevitable consequence of two neurons being part of the same network and sharing some synaptic input. For non-linear neuron models, however, explicit correlation functions are difficult to compute analytically, and perturbative methods work only for weak shared input. In order to treat strong correlations, we suggest here an alternative non-perturbative method. Specifically, we study the case of two leaky integrate-and-fire neurons with strong shared input. Correlation functions derived from simulated spike trains fit our theoretical predictions very accurately. Using our method, we computed the non-linear correlation transfer as well as correlation functions that are asymmetric due to inhomogeneous intrinsic parameters or unequal input.

  6. Correlated Fluctuations in Strongly Coupled Binary Networks Beyond Equilibrium

    NASA Astrophysics Data System (ADS)

    Dahmen, David; Bos, Hannah; Helias, Moritz

    2016-07-01

    Randomly coupled Ising spins constitute the classical model of collective phenomena in disordered systems, with applications covering glassy magnetism and frustration, combinatorial optimization, protein folding, stock market dynamics, and social dynamics. The phase diagram of these systems is obtained in the thermodynamic limit by averaging over the quenched randomness of the couplings. However, many applications require the statistics of activity for a single realization of the possibly asymmetric couplings in finite-sized networks. Examples include reconstruction of couplings from the observed dynamics, representation of probability distributions for sampling-based inference, and learning in the central nervous system based on the dynamic and correlation-dependent modification of synaptic connections. The systematic cumulant expansion for kinetic binary (Ising) threshold units with strong, random, and asymmetric couplings presented here goes beyond mean-field theory and is applicable outside thermodynamic equilibrium; a system of approximate nonlinear equations predicts average activities and pairwise covariances in quantitative agreement with full simulations down to hundreds of units. The linearized theory yields an expansion of the correlation and response functions in collective eigenmodes, leads to an efficient algorithm solving the inverse problem, and shows that correlations are invariant under scaling of the interaction strengths.

  7. A Higher-Order Neural Network Design for Improving Segmentation Performance in Medical Image Series

    NASA Astrophysics Data System (ADS)

    Selvi, Eşref; Selver, M. Alper; Güzeliş, Cüneyt; Dicle, Oǧuz

    2014-03-01

    Segmentation of anatomical structures from medical image series is an ongoing field of research. Although, organs of interest are three-dimensional in nature, slice-by-slice approaches are widely used in clinical applications because of their ease of integration with the current manual segmentation scheme. To be able to use slice-by-slice techniques effectively, adjacent slice information, which represents likelihood of a region to be the structure of interest, plays critical role. Recent studies focus on using distance transform directly as a feature or to increase the feature values at the vicinity of the search area. This study presents a novel approach by constructing a higher order neural network, the input layer of which receives features together with their multiplications with the distance transform. This allows higher-order interactions between features through the non-linearity introduced by the multiplication. The application of the proposed method to 9 CT datasets for segmentation of the liver shows higher performance than well-known higher order classification neural networks.

  8. Intergenerational Correlations in Educational Attainment: Birth Order and Family Size Effects Using Canadian Data

    ERIC Educational Resources Information Center

    Sen, Anindya; Clemente, Anthony

    2010-01-01

    We exploit the 1986, 1994, and 2001 waves of the Canadian general social surveys in order to estimate intergenerational correlations in education. The use of these specific data is important because of available information on the final educational attainment of survey respondents and both parents, as well as family size and birth order. OLS…

  9. Google Correlations: New approaches to collecting data for statistical network analysis

    NASA Astrophysics Data System (ADS)

    Mahdavi, Paasha

    This thesis introduces a new method for data collection on political elite networks using non-obtrusive web-based techniques. One possible indicator of elite connectivity is the frequency with which individuals appear at the same political events. Using a Google search scraping algorithm (Lee 2010) to capture how often pairs of individuals appear in the same news articles reporting on these events, I construct network matrices for a given list of individuals that I identify as elites using a variety of criteria. To assess cross-validity and conceptual accuracy, I compare data from this method to previously collected data on the network connectedness of three separate populations. I then supply an application of the Google method to collect network data on the Nigerian oil elite in 2012. Conducting a network analysis, I show that appointments to the Nigerian National Petroleum Corporation board of directors are made on the basis of political connectivity and not necessarily on technical experience or merit. These findings lend support to hypotheses that leaders use patronage appointments to lucrative bureaucratic positions in order to satisfy political elites. Given that many political theories on elite behavior aim to understand individual- and group-level interactions, the potential applicability of network data using the proposed technique is very large, especially in situations where collecting network data intrusively is costly or prohibitive.

  10. Quantitative analysis of volatile organic compounds using ion mobility spectra and cascade correlation neural networks

    NASA Technical Reports Server (NTRS)

    Harrington, Peter DEB.; Zheng, Peng

    1995-01-01

    Ion Mobility Spectrometry (IMS) is a powerful technique for trace organic analysis in the gas phase. Quantitative measurements are difficult, because IMS has a limited linear range. Factors that may affect the instrument response are pressure, temperature, and humidity. Nonlinear calibration methods, such as neural networks, may be ideally suited for IMS. Neural networks have the capability of modeling complex systems. Many neural networks suffer from long training times and overfitting. Cascade correlation neural networks train at very fast rates. They also build their own topology, that is a number of layers and number of units in each layer. By controlling the decay parameter in training neural networks, reproducible and general models may be obtained.

  11. Application of the differentiation process into the correlation-based leak detection in urban pipeline networks

    NASA Astrophysics Data System (ADS)

    Gao, Yan; Liu, Yuyou; Ma, Yifan; Cheng, Xiaobin; Yang, Jun

    2018-11-01

    One major challenge currently facing pipeline networks across the world is the improvement of leak detection technologies in urban environments. There is an imperative to locate accurately leaks in buried water pipes to avoid serious environmental, social and economic consequences. Much attention has been paid to time delay estimation (TDE) in determining the position of a leak by utilising cross-correlation, which has been proven to be effective with varying degrees of success over the past half century. Previous research in published literature has demonstrated the effectiveness of the pre-whitening process for accentuating the peak in the cross-correlation associated with the time delay. This paper is concerned with the implementation of the differentiation process for TDE, with particular focus on the problem of determining a leak in pipelines by means of pipe pressure measurements. Rather than the pre-whitening operation, the proposed cross-correlation via the differentiation process, termed here DIF, changes the characteristics of the pipe system so that the pipe effectively acts as a band-pass filter. This method has the potential to eliminate some ambiguity caused by the interference at low frequencies and to allow more high frequency information to pass. Given an appropriate differentiation order, a more pronounced and reliable peak is obtained in the cross-correlation result. The use of differentiation process may provide a viable cross-correlation method suited to water leak detection. Its performance in relation to leak detection is further compared to the basic cross-correlation and pre-whitening methods for TDE in detecting a leak from actual PVC water pipes. Experimental results are presented to show an additional property of the DIF compensating for the resonance effects that may exist in cross-spectral density measurements, and hence better performance for TDE.

  12. Whole-brain analytic measures of network communication reveal increased structure-function correlation in right temporal lobe epilepsy.

    PubMed

    Wirsich, Jonathan; Perry, Alistair; Ridley, Ben; Proix, Timothée; Golos, Mathieu; Bénar, Christian; Ranjeva, Jean-Philippe; Bartolomei, Fabrice; Breakspear, Michael; Jirsa, Viktor; Guye, Maxime

    2016-01-01

    The in vivo structure-function relationship is key to understanding brain network reorganization due to pathologies. This relationship is likely to be particularly complex in brain network diseases such as temporal lobe epilepsy, in which disturbed large-scale systems are involved in both transient electrical events and long-lasting functional and structural impairments. Herein, we estimated this relationship by analyzing the correlation between structural connectivity and functional connectivity in terms of analytical network communication parameters. As such, we targeted the gradual topological structure-function reorganization caused by the pathology not only at the whole brain scale but also both in core and peripheral regions of the brain. We acquired diffusion (dMRI) and resting-state fMRI (rsfMRI) data in seven right-lateralized TLE (rTLE) patients and fourteen healthy controls and analyzed the structure-function relationship by using analytical network communication metrics derived from the structural connectome. In rTLE patients, we found a widespread hypercorrelated functional network. Network communication analysis revealed greater unspecific branching of the shortest path (search information) in the structural connectome and a higher global correlation between the structural and functional connectivity for the patient group. We also found evidence for a preserved structural rich-club in the patient group. In sum, global augmentation of structure-function correlation might be linked to a smaller functional repertoire in rTLE patients, while sparing the central core of the brain which may represent a pathway that facilitates the spread of seizures.

  13. [Analysis of the Characteristics of Infantile Small World Neural Network Node Properties Correlated with the Influencing Factors].

    PubMed

    Qu, Haibo; Lu, Su; Zhang, Wenjing; Xiao, Yuan; Ning, Gang; Sun, Huaiqiang

    2016-10-01

    We applied resting-state functional magnetic resonance imaging(rfMRI)combined with graph theory to analyze 90 regions of the infantile small world neural network of the whole brain.We tried to get the following two points clear:1 whether the parameters of the node property of the infantile small world neural network are correlated with the level of infantile intelligence development;2 whether the parameters of the infantile small world neural network are correlated with the children’s baseline parameters,i.e.,the demographic parameters such as gender,age,parents’ education level,etc.Twelve cases of healthy infants were included in the investigation(9males and 3females with the average age of 33.42±8.42 months.)We then evaluated the level of infantile intelligence of all the cases and graded by Gesell Development Scale Test.We used a Siemens 3.0T Trio imaging system to perform resting-state(rs)EPI scans,and collected the BOLD functional Magnetic Resonance Imaging(fMRI)data.We performed the data processing with Statistical Parametric Mapping 5(SPM5)based on Matlab environment.Furthermore,we got the attributes of the whole brain small world and node attributes of 90 encephalic regions of templates of Anatomatic Automatic Labeling(ALL).At last,we carried out correlation study between the above-mentioned attitudes,intelligence scale parameters and demographic data.The results showed that many node attributes of small world neural network were closely correlated with intelligence scale parameters.Betweeness was mainly centered in thalamus,superior frontal gyrus,and occipital lobe(negative correlation).The r value of superior occipital gyrus associated with the individual and social intelligent scale was-0.729(P=0.007);degree was mainly centered in amygdaloid nucleus,superior frontal gyrus,and inferior parietal gyrus(positive correlation).The r value of inferior parietal gyrus associated with the gross motor intelligent scale was 0.725(P=0.008);efficiency was mainly

  14. Correlation analysis on real-time tab-delimited network monitoring data

    DOE PAGES

    Pan, Aditya; Majumdar, Jahin; Bansal, Abhay; ...

    2016-01-01

    End-End performance monitoring in the Internet, also called PingER is a part of SLAC National Accelerator Laboratory’s research project. It was created to answer the growing need to monitor network both to analyze current performance and to designate resources to optimize execution between research centers, and the universities and institutes co-operating on present and future operations. The monitoring support reflects the broad geographical area of the collaborations and requires a comprehensive number of research and financial channels. The data architecture retrieval and methodology of the interpretation have emerged over numerous years. Analyzing this data is the main challenge due tomore » its high volume. Finally, by using correlation analysis, we can make crucial conclusions about how the network data affects the performance of the hosts and how it depends from countries to countries.« less

  15. The q-dependent detrended cross-correlation analysis of stock market

    NASA Astrophysics Data System (ADS)

    Zhao, Longfeng; Li, Wei; Fenu, Andrea; Podobnik, Boris; Wang, Yougui; Stanley, H. Eugene

    2018-02-01

    Properties of the q-dependent cross-correlation matrices of the stock market have been analyzed by using random matrix theory and complex networks. The correlation structures of the fluctuations at different magnitudes have unique properties. The cross-correlations among small fluctuations are much stronger than those among large fluctuations. The large and small fluctuations are dominated by different groups of stocks. We use complex network representation to study these q-dependent matrices and discover some new identities. By utilizing those q-dependent correlation-based networks, we are able to construct some portfolios of those more independent stocks which consistently perform better. The optimal multifractal order for portfolio optimization is around q  =  2 under the mean-variance portfolio framework, and q\\in[2, 6] under the expected shortfall criterion. These results have deepened our understanding regarding the collective behavior of the complex financial system.

  16. Cluster and propensity based approximation of a network

    PubMed Central

    2013-01-01

    Background The models in this article generalize current models for both correlation networks and multigraph networks. Correlation networks are widely applied in genomics research. In contrast to general networks, it is straightforward to test the statistical significance of an edge in a correlation network. It is also easy to decompose the underlying correlation matrix and generate informative network statistics such as the module eigenvector. However, correlation networks only capture the connections between numeric variables. An open question is whether one can find suitable decompositions of the similarity measures employed in constructing general networks. Multigraph networks are attractive because they support likelihood based inference. Unfortunately, it is unclear how to adjust current statistical methods to detect the clusters inherent in many data sets. Results Here we present an intuitive and parsimonious parametrization of a general similarity measure such as a network adjacency matrix. The cluster and propensity based approximation (CPBA) of a network not only generalizes correlation network methods but also multigraph methods. In particular, it gives rise to a novel and more realistic multigraph model that accounts for clustering and provides likelihood based tests for assessing the significance of an edge after controlling for clustering. We present a novel Majorization-Minimization (MM) algorithm for estimating the parameters of the CPBA. To illustrate the practical utility of the CPBA of a network, we apply it to gene expression data and to a bi-partite network model for diseases and disease genes from the Online Mendelian Inheritance in Man (OMIM). Conclusions The CPBA of a network is theoretically appealing since a) it generalizes correlation and multigraph network methods, b) it improves likelihood based significance tests for edge counts, c) it directly models higher-order relationships between clusters, and d) it suggests novel clustering

  17. LMI-based approach to stability analysis for fractional-order neural networks with discrete and distributed delays

    NASA Astrophysics Data System (ADS)

    Zhang, Hai; Ye, Renyu; Liu, Song; Cao, Jinde; Alsaedi, Ahmad; Li, Xiaodi

    2018-02-01

    This paper is concerned with the asymptotic stability of the Riemann-Liouville fractional-order neural networks with discrete and distributed delays. By constructing a suitable Lyapunov functional, two sufficient conditions are derived to ensure that the addressed neural network is asymptotically stable. The presented stability criteria are described in terms of the linear matrix inequalities. The advantage of the proposed method is that one may avoid calculating the fractional-order derivative of the Lyapunov functional. Finally, a numerical example is given to show the validity and feasibility of the theoretical results.

  18. Wrinkles of graphene on Ir(111): Macroscopic network ordering and internal multi-lobed structure

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Petrovic, Marin; Sadowski, Jerzy T.; Siber, Antonio

    The large-scale production of graphene monolayer greatly relies on epitaxial samples which often display stress-relaxation features in the form of wrinkles. Wrinkles of graphene on Ir(111) are found to exhibit a fairly well ordered interconnecting network which is characterized by low-energy electron microscopy (LEEM). The high degree of quasi-hexagonal network arrangement for the graphene aligned to the underlying substrate can be well described as a (non-Poissonian) Voronoi partition of a plane. The results obtained strongly suggest that the wrinkle network is frustrated at low temperatures, retaining the order inherited from elevated temperatures when the wrinkles interconnect in junctions which mostmore » often join three wrinkles. Such frustration favors the formation of multi-lobed wrinkles which are found in scanning tunneling microscopy (STM) measurements. The existence of multiple lobes is explained within a model accounting for the interplay of the van der Waals attraction between graphene and iridium and bending energy of the wrinkle. The presented study provides new insights into wrinkling of epitaxial graphene and can be exploited to further expedite its application.« less

  19. Wrinkles of graphene on Ir(111): Macroscopic network ordering and internal multi-lobed structure

    DOE PAGES

    Petrovic, Marin; Sadowski, Jerzy T.; Siber, Antonio; ...

    2015-07-17

    The large-scale production of graphene monolayer greatly relies on epitaxial samples which often display stress-relaxation features in the form of wrinkles. Wrinkles of graphene on Ir(111) are found to exhibit a fairly well ordered interconnecting network which is characterized by low-energy electron microscopy (LEEM). The high degree of quasi-hexagonal network arrangement for the graphene aligned to the underlying substrate can be well described as a (non-Poissonian) Voronoi partition of a plane. The results obtained strongly suggest that the wrinkle network is frustrated at low temperatures, retaining the order inherited from elevated temperatures when the wrinkles interconnect in junctions which mostmore » often join three wrinkles. Such frustration favors the formation of multi-lobed wrinkles which are found in scanning tunneling microscopy (STM) measurements. The existence of multiple lobes is explained within a model accounting for the interplay of the van der Waals attraction between graphene and iridium and bending energy of the wrinkle. The presented study provides new insights into wrinkling of epitaxial graphene and can be exploited to further expedite its application.« less

  20. Dynamic brain glucose metabolism identifies anti-correlated cortical-cerebellar networks at rest.

    PubMed

    Tomasi, Dardo G; Shokri-Kojori, Ehsan; Wiers, Corinde E; Kim, Sunny W; Demiral, Şukru B; Cabrera, Elizabeth A; Lindgren, Elsa; Miller, Gregg; Wang, Gene-Jack; Volkow, Nora D

    2017-12-01

    It remains unclear whether resting state functional magnetic resonance imaging (rfMRI) networks are associated with underlying synchrony in energy demand, as measured by dynamic 2-deoxy-2-[ 18 F]fluoroglucose (FDG) positron emission tomography (PET). We measured absolute glucose metabolism, temporal metabolic connectivity (t-MC) and rfMRI patterns in 53 healthy participants at rest. Twenty-two rfMRI networks emerged from group independent component analysis (gICA). In contrast, only two anti-correlated t-MC emerged from FDG-PET time series using gICA or seed-voxel correlations; one included frontal, parietal and temporal cortices, the other included the cerebellum and medial temporal regions. Whereas cerebellum, thalamus, globus pallidus and calcarine cortex arose as the strongest t-MC hubs, the precuneus and visual cortex arose as the strongest rfMRI hubs. The strength of the t-MC linearly increased with the metabolic rate of glucose suggesting that t-MC measures are strongly associated with the energy demand of the brain tissue, and could reflect regional differences in glucose metabolism, counterbalanced metabolic network demand, and/or differential time-varying delivery of FDG. The mismatch between metabolic and functional connectivity patterns computed as a function of time could reflect differences in the temporal characteristics of glucose metabolism as measured with PET-FDG and brain activation as measured with rfMRI.

  1. The correlation function for density perturbations in an expanding universe. III The three-point and predictions of the four-point and higher order correlation functions

    NASA Technical Reports Server (NTRS)

    Mcclelland, J.; Silk, J.

    1978-01-01

    Higher-order correlation functions for the large-scale distribution of galaxies in space are investigated. It is demonstrated that the three-point correlation function observed by Peebles and Groth (1975) is not consistent with a distribution of perturbations that at present are randomly distributed in space. The two-point correlation function is shown to be independent of how the perturbations are distributed spatially, and a model of clustered perturbations is developed which incorporates a nonuniform perturbation distribution and which explains the three-point correlation function. A model with hierarchical perturbations incorporating the same nonuniform distribution is also constructed; it is found that this model also explains the three-point correlation function, but predicts different results for the four-point and higher-order correlation functions than does the model with clustered perturbations. It is suggested that the model of hierarchical perturbations might be explained by the single assumption of having density fluctuations or discrete objects all of the same mass randomly placed at some initial epoch.

  2. Evolution of worldwide stock markets, correlation structure, and correlation-based graphs

    NASA Astrophysics Data System (ADS)

    Song, Dong-Ming; Tumminello, Michele; Zhou, Wei-Xing; Mantegna, Rosario N.

    2011-08-01

    We investigate the daily correlation present among market indices of stock exchanges located all over the world in the time period January 1996 to July 2009. We discover that the correlation among market indices presents both a fast and a slow dynamics. The slow dynamics reflects the development and consolidation of globalization. The fast dynamics is associated with critical events that originate in a specific country or region of the world and rapidly affect the global system. We provide evidence that the short term time scale of correlation among market indices is less than 3 trading months (about 60 trading days). The average values of the nondiagonal elements of the correlation matrix, correlation-based graphs, and the spectral properties of the largest eigenvalues and eigenvectors of the correlation matrix are carrying information about the fast and slow dynamics of the correlation of market indices. We introduce a measure of mutual information based on link co-occurrence in networks in order to detect the fast dynamics of successive changes of correlation-based graphs in a quantitative way.

  3. Topological enslavement in evolutionary games on correlated multiplex networks

    NASA Astrophysics Data System (ADS)

    Kleineberg, Kaj-Kolja; Helbing, Dirk

    2018-05-01

    Governments and enterprises strongly rely on incentives to generate favorable outcomes from social and strategic interactions between individuals. The incentives are usually modeled by payoffs in evolutionary games, such as the prisoners dilemma or the harmony game, with imitation dynamics. Adjusting the incentives by changing the payoff parameters can favor cooperation, as found in the harmony game, over defection, which prevails in the prisoner’s dilemma. Here, we show that this is not always the case if individuals engage in strategic interactions in multiple domains. In particular, we investigate evolutionary games on multiplex networks where individuals obtain an aggregate payoff. We explicitly control the strength of degree correlations between nodes in the different layers of the multiplex. We find that if the multiplex is composed of many layers and degree correlations are strong, the topology of the system enslaves the dynamics and the final outcome, cooperation or defection, becomes independent of the payoff parameters. The fate of the system is then determined by the initial conditions.

  4. Network Routing Using the Network Tasking Order, a Chron Approach

    DTIC Science & Technology

    2015-03-26

    57  Figure 4.6: Maximum bandwidth possible. ..................................................................... 58  Figure 4.7: MikroTik Router...iv xii RouterOS MikroTik Router Operating System...traffic through the network. Within the ESXi platform, four MikroTik Router Operating Systems (RouterOS) are installed. Within ESXi, the network

  5. Out-of-time-order correlators in finite open systems

    NASA Astrophysics Data System (ADS)

    Syzranov, S. V.; Gorshkov, A. V.; Galitski, V.

    2018-04-01

    We study out-of-time-order correlators (OTOCs) of the form for a quantum system weakly coupled to a dissipative environment. Such an open system may serve as a model of, e.g., a small region in a disordered interacting medium coupled to the rest of this medium considered as an environment. We demonstrate that for a system with discrete energy levels the OTOC saturates exponentially ∝∑aie-t /τi+const to a constant value at t →∞ , in contrast with quantum-chaotic systems which exhibit exponential growth of OTOCs. Focusing on the case of a two-level system, we calculate microscopically the decay times τi and the value of the saturation constant. Because some OTOCs are immune to dephasing processes and some are not, such correlators may decay on two sets of parametrically different time scales related to inelastic transitions between the system levels and to pure dephasing processes, respectively. In the case of a classical environment, the evolution of the OTOC can be mapped onto the evolution of the density matrix of two systems coupled to the same dissipative environment.

  6. Decomposition of conditional probability for high-order symbolic Markov chains.

    PubMed

    Melnik, S S; Usatenko, O V

    2017-07-01

    The main goal of this paper is to develop an estimate for the conditional probability function of random stationary ergodic symbolic sequences with elements belonging to a finite alphabet. We elaborate on a decomposition procedure for the conditional probability function of sequences considered to be high-order Markov chains. We represent the conditional probability function as the sum of multilinear memory function monomials of different orders (from zero up to the chain order). This allows us to introduce a family of Markov chain models and to construct artificial sequences via a method of successive iterations, taking into account at each step increasingly high correlations among random elements. At weak correlations, the memory functions are uniquely expressed in terms of the high-order symbolic correlation functions. The proposed method fills the gap between two approaches, namely the likelihood estimation and the additive Markov chains. The obtained results may have applications for sequential approximation of artificial neural network training.

  7. Decomposition of conditional probability for high-order symbolic Markov chains

    NASA Astrophysics Data System (ADS)

    Melnik, S. S.; Usatenko, O. V.

    2017-07-01

    The main goal of this paper is to develop an estimate for the conditional probability function of random stationary ergodic symbolic sequences with elements belonging to a finite alphabet. We elaborate on a decomposition procedure for the conditional probability function of sequences considered to be high-order Markov chains. We represent the conditional probability function as the sum of multilinear memory function monomials of different orders (from zero up to the chain order). This allows us to introduce a family of Markov chain models and to construct artificial sequences via a method of successive iterations, taking into account at each step increasingly high correlations among random elements. At weak correlations, the memory functions are uniquely expressed in terms of the high-order symbolic correlation functions. The proposed method fills the gap between two approaches, namely the likelihood estimation and the additive Markov chains. The obtained results may have applications for sequential approximation of artificial neural network training.

  8. Phylogenetic structure of European Salmonella Enteritidis outbreak correlates with national and international egg distribution network

    PubMed Central

    Inns, Thomas; Jombart, Thibaut; Ashton, Philip; Loman, Nicolas; Chatt, Carol; Messelhaeusser, Ute; Rabsch, Wolfgang; Simon, Sandra; Nikisins, Sergejs; Bernard, Helen; le Hello, Simon; Jourdan da-Silva, Nathalie; Kornschober, Christian; Mossong, Joel; Hawkey, Peter; de Pinna, Elizabeth; Grant, Kathie; Cleary, Paul

    2016-01-01

    Outbreaks of Salmonella Enteritidis have long been associated with contaminated poultry and eggs. In the summer of 2014 a large multi-national outbreak of Salmonella Enteritidis phage type 14b occurred with over 350 cases reported in the United Kingdom, Germany, Austria, France and Luxembourg. Egg supply network investigation and microbiological sampling identified the source to be a Bavarian egg producer. As part of the international investigation into the outbreak, over 400 isolates were sequenced including isolates from cases, implicated UK premises and eggs from the suspected source producer. We were able to show a clear statistical correlation between the topology of the UK egg distribution network and the phylogenetic network of outbreak isolates. This correlation can most plausibly be explained by different parts of the egg distribution network being supplied by eggs solely from independent premises of the Bavarian egg producer (Company X). Microbiological sampling from the source premises, traceback information and information on the interventions carried out at the egg production premises all supported this conclusion. The level of insight into the outbreak epidemiology provided by whole-genome sequencing (WGS) would not have been possible using traditional microbial typing methods. PMID:28348865

  9. Phylogenetic structure of European Salmonella Enteritidis outbreak correlates with national and international egg distribution network.

    PubMed

    Dallman, Tim; Inns, Thomas; Jombart, Thibaut; Ashton, Philip; Loman, Nicolas; Chatt, Carol; Messelhaeusser, Ute; Rabsch, Wolfgang; Simon, Sandra; Nikisins, Sergejs; Bernard, Helen; le Hello, Simon; Jourdan da-Silva, Nathalie; Kornschober, Christian; Mossong, Joel; Hawkey, Peter; de Pinna, Elizabeth; Grant, Kathie; Cleary, Paul

    2016-08-01

    Outbreaks of Salmonella Enteritidis have long been associated with contaminated poultry and eggs. In the summer of 2014 a large multi-national outbreak of Salmonella Enteritidis phage type 14b occurred with over 350 cases reported in the United Kingdom, Germany, Austria, France and Luxembourg. Egg supply network investigation and microbiological sampling identified the source to be a Bavarian egg producer. As part of the international investigation into the outbreak, over 400 isolates were sequenced including isolates from cases, implicated UK premises and eggs from the suspected source producer. We were able to show a clear statistical correlation between the topology of the UK egg distribution network and the phylogenetic network of outbreak isolates. This correlation can most plausibly be explained by different parts of the egg distribution network being supplied by eggs solely from independent premises of the Bavarian egg producer (Company X). Microbiological sampling from the source premises, traceback information and information on the interventions carried out at the egg production premises all supported this conclusion. The level of insight into the outbreak epidemiology provided by whole-genome sequencing (WGS) would not have been possible using traditional microbial typing methods.

  10. Time Allocation in Social Networks: Correlation Between Social Structure and Human Communication Dynamics

    NASA Astrophysics Data System (ADS)

    Miritello, Giovanna; Lara, Rubén; Moro, Esteban

    Recent research has shown the deep impact of the dynamics of human interactions (or temporal social networks) on the spreading of information, opinion formation, etc. In general, the bursty nature of human interactions lowers the interaction between people to the extent that both the speed and reach of information diffusion are diminished. Using a large database of 20 million users of mobile phone calls we show evidence this effect is not homogeneous in the social network but in fact, there is a large correlation between this effect and the social topological structure around a given individual. In particular, we show that social relations of hubs in a network are relatively weaker from the dynamical point than those that are poorer connected in the information diffusion process. Our results show the importance of the temporal patterns of communication when analyzing and modeling dynamical process on social networks.

  11. Analytical estimation of the correlation dimension of integer lattices

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Lacasa, Lucas, E-mail: l.lacasa@qmul.ac.uk; Gómez-Gardeñes, Jesús, E-mail: gardenes@gmail.com; Departamento de Fisica de la Materia Condensada, Universidad de Zaragoza, Zaragoza

    2014-12-01

    Recently [L. Lacasa and J. Gómez-Gardeñes, Phys. Rev. Lett. 110, 168703 (2013)], a fractal dimension has been proposed to characterize the geometric structure of networks. This measure is an extension to graphs of the so called correlation dimension, originally proposed by Grassberger and Procaccia to describe the geometry of strange attractors in dissipative chaotic systems. The calculation of the correlation dimension of a graph is based on the local information retrieved from a random walker navigating the network. In this contribution, we study such quantity for some limiting synthetic spatial networks and obtain analytical results on agreement with the previouslymore » reported numerics. In particular, we show that up to first order, the correlation dimension β of integer lattices ℤ{sup d} coincides with the Haussdorf dimension of their coarsely equivalent Euclidean spaces, β = d.« less

  12. Identifying module biomarkers from gastric cancer by differential correlation network

    PubMed Central

    Liu, Xiaoping; Chang, Xiao

    2016-01-01

    Gastric cancer (stomach cancer) is a severe disease caused by dysregulation of many functionally correlated genes or pathways instead of the mutation of individual genes. Systematic identification of gastric cancer biomarkers can provide insights into the mechanisms underlying this deadly disease and help in the development of new drugs. In this paper, we present a novel network-based approach to predict module biomarkers of gastric cancer that can effectively distinguish the disease from normal samples. Specifically, by assuming that gastric cancer has mainly resulted from dysfunction of biomolecular networks rather than individual genes in an organism, the genes in the module biomarkers are potentially related to gastric cancer. Finally, we identified a module biomarker with 27 genes, and by comparing the module biomarker with known gastric cancer biomarkers, we found that our module biomarker exhibited a greater ability to diagnose the samples with gastric cancer. PMID:27703371

  13. Nodal infection in Markovian susceptible-infected-susceptible and susceptible-infected-removed epidemics on networks are non-negatively correlated

    NASA Astrophysics Data System (ADS)

    Cator, E.; Van Mieghem, P.

    2014-05-01

    By invoking the famous Fortuin, Kasteleyn, and Ginibre (FKG) inequality, we prove the conjecture that the correlation of infection at the same time between any pair of nodes in a network cannot be negative for (exact) Markovian susceptible-infected-susceptible (SIS) and susceptible-infected-removed (SIR) epidemics on networks. The truth of the conjecture establishes that the N-intertwined mean-field approximation (NIMFA) upper bounds the infection probability in any graph so that network design based on NIMFA always leads to safe protections against malware spread. However, when the infection or/and curing are not Poisson processes, the infection correlation between two nodes can be negative.

  14. Nodal infection in Markovian susceptible-infected-susceptible and susceptible-infected-removed epidemics on networks are non-negatively correlated.

    PubMed

    Cator, E; Van Mieghem, P

    2014-05-01

    By invoking the famous Fortuin, Kasteleyn, and Ginibre (FKG) inequality, we prove the conjecture that the correlation of infection at the same time between any pair of nodes in a network cannot be negative for (exact) Markovian susceptible-infected-susceptible (SIS) and susceptible-infected-removed (SIR) epidemics on networks. The truth of the conjecture establishes that the N-intertwined mean-field approximation (NIMFA) upper bounds the infection probability in any graph so that network design based on NIMFA always leads to safe protections against malware spread. However, when the infection or/and curing are not Poisson processes, the infection correlation between two nodes can be negative.

  15. Global Mittag-Leffler stability analysis of fractional-order impulsive neural networks with one-side Lipschitz condition.

    PubMed

    Zhang, Xinxin; Niu, Peifeng; Ma, Yunpeng; Wei, Yanqiao; Li, Guoqiang

    2017-10-01

    This paper is concerned with the stability analysis issue of fractional-order impulsive neural networks. Under the one-side Lipschitz condition or the linear growth condition of activation function, the existence of solution is analyzed respectively. In addition, the existence, uniqueness and global Mittag-Leffler stability of equilibrium point of the fractional-order impulsive neural networks with one-side Lipschitz condition are investigated by the means of contraction mapping principle and Lyapunov direct method. Finally, an example with numerical simulation is given to illustrate the validity and feasibility of the proposed results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. RNA binding protein RNPC1 inhibits breast cancer cells metastasis via activating STARD13-correlated ceRNA network.

    PubMed

    Zhang, Zhiting; Guo, Qianqian; Zhang, Shufang; Xiang, Chenxi; Guo, Xinwei; Zhang, Feng; Gao, Lanlan; Ni, Haiwei; Xi, Tao; Zheng, Lufeng

    2018-05-07

    RNA binding proteins (RBPs) are pivotal post-transcriptional regulators. RNPC1, an RBP, acts as a tumor suppressor through binding and regulating the expression of target genes in cancer cells. This study disclosed that RNPC1 expression was positively correlated with breast cancer patients' relapse free and overall survival, and RNPC1suppressed breast cancer cells metastasis. Mechanistically, RNPC1 promoting a competing endogenous network (ceRNA) crosstalk between STARD13, CDH5, HOXD10, and HOXD1 (STARD13-correlated ceRNA network) that we previously confirmed in breast cancer cells through stabilizing the transcripts and thus facilitating the expression of these four genes in breast cancer cells. Furthermore, RNPC1 overexpression restrained the promotion of STARD13, CDH5, HOXD10, and HOXD1 knockdown on cell metastasis. Notably, RNPC1 expression was positively correlated with CDH5, HOXD1 and HOXD10 expression in breast cancer tissues, and attenuated adriamycin resistance. Taken together, these results identified that RNPC1 could inhibit breast cancer cells metastasis via promoting STARD13-correlated ceRNA network.

  17. Diffusion in Colocation Contact Networks: The Impact of Nodal Spatiotemporal Dynamics.

    PubMed

    Thomas, Bryce; Jurdak, Raja; Zhao, Kun; Atkinson, Ian

    2016-01-01

    Temporal contact networks are studied to understand dynamic spreading phenomena such as communicable diseases or information dissemination. To establish how spatiotemporal dynamics of nodes impact spreading potential in colocation contact networks, we propose "inducement-shuffling" null models which break one or more correlations between times, locations and nodes. By reconfiguring the time and/or location of each node's presence in the network, these models induce alternative sets of colocation events giving rise to contact networks with varying spreading potential. This enables second-order causal reasoning about how correlations in nodes' spatiotemporal preferences not only lead to a given contact network but ultimately influence the network's spreading potential. We find the correlation between nodes and times to be the greatest impediment to spreading, while the correlation between times and locations slightly catalyzes spreading. Under each of the presented null models we measure both the number of contacts and infection prevalence as a function of time, with the surprising finding that the two have no direct causality.

  18. Design of cryptographically secure AES like S-Box using second-order reversible cellular automata for wireless body area network applications.

    PubMed

    Gangadari, Bhoopal Rao; Rafi Ahamed, Shaik

    2016-09-01

    In biomedical, data security is the most expensive resource for wireless body area network applications. Cryptographic algorithms are used in order to protect the information against unauthorised access. Advanced encryption standard (AES) cryptographic algorithm plays a vital role in telemedicine applications. The authors propose a novel approach for design of substitution bytes (S-Box) using second-order reversible one-dimensional cellular automata (RCA 2 ) as a replacement to the classical look-up-table (LUT) based S-Box used in AES algorithm. The performance of proposed RCA 2 based S-Box and conventional LUT based S-Box is evaluated in terms of security using the cryptographic properties such as the nonlinearity, correlation immunity bias, strict avalanche criteria and entropy. Moreover, it is also shown that RCA 2 based S-Boxes are dynamic in nature, invertible and provide high level of security. Further, it is also found that the RCA 2 based S-Box have comparatively better performance than that of conventional LUT based S-Box.

  19. Design of cryptographically secure AES like S-Box using second-order reversible cellular automata for wireless body area network applications

    PubMed Central

    Rafi Ahamed, Shaik

    2016-01-01

    In biomedical, data security is the most expensive resource for wireless body area network applications. Cryptographic algorithms are used in order to protect the information against unauthorised access. Advanced encryption standard (AES) cryptographic algorithm plays a vital role in telemedicine applications. The authors propose a novel approach for design of substitution bytes (S-Box) using second-order reversible one-dimensional cellular automata (RCA2) as a replacement to the classical look-up-table (LUT) based S-Box used in AES algorithm. The performance of proposed RCA2 based S-Box and conventional LUT based S-Box is evaluated in terms of security using the cryptographic properties such as the nonlinearity, correlation immunity bias, strict avalanche criteria and entropy. Moreover, it is also shown that RCA2 based S-Boxes are dynamic in nature, invertible and provide high level of security. Further, it is also found that the RCA2 based S-Box have comparatively better performance than that of conventional LUT based S-Box. PMID:27733924

  20. Doping-dependent charge order correlations in electron-doped cuprates

    PubMed Central

    da Silva Neto, Eduardo H.; Yu, Biqiong; Minola, Matteo; Sutarto, Ronny; Schierle, Enrico; Boschini, Fabio; Zonno, Marta; Bluschke, Martin; Higgins, Joshua; Li, Yangmu; Yu, Guichuan; Weschke, Eugen; He, Feizhou; Le Tacon, Mathieu; Greene, Richard L.; Greven, Martin; Sawatzky, George A.; Keimer, Bernhard; Damascelli, Andrea

    2016-01-01

    Understanding the interplay between charge order (CO) and other phenomena (for example, pseudogap, antiferromagnetism, and superconductivity) is one of the central questions in the cuprate high-temperature superconductors. The discovery that similar forms of CO exist in both hole- and electron-doped cuprates opened a path to determine what subset of the CO phenomenology is universal to all the cuprates. We use resonant x-ray scattering to measure the CO correlations in electron-doped cuprates (La2−xCexCuO4 and Nd2−xCexCuO4) and their relationship to antiferromagnetism, pseudogap, and superconductivity. Detailed measurements of Nd2−xCexCuO4 show that CO is present in the x = 0.059 to 0.166 range and that its doping-dependent wave vector is consistent with the separation between straight segments of the Fermi surface. The CO onset temperature is highest between x = 0.106 and 0.166 but decreases at lower doping levels, indicating that it is not tied to the appearance of antiferromagnetic correlations or the pseudogap. Near optimal doping, where the CO wave vector is also consistent with a previously observed phonon anomaly, measurements of the CO below and above the superconducting transition temperature, or in a magnetic field, show that the CO is insensitive to superconductivity. Overall, these findings indicate that, although verified in the electron-doped cuprates, material-dependent details determine whether the CO correlations acquire sufficient strength to compete for the ground state of the cuprates. PMID:27536726

  1. Doping-dependent charge order correlations in electron-doped cuprates.

    PubMed

    da Silva Neto, Eduardo H; Yu, Biqiong; Minola, Matteo; Sutarto, Ronny; Schierle, Enrico; Boschini, Fabio; Zonno, Marta; Bluschke, Martin; Higgins, Joshua; Li, Yangmu; Yu, Guichuan; Weschke, Eugen; He, Feizhou; Le Tacon, Mathieu; Greene, Richard L; Greven, Martin; Sawatzky, George A; Keimer, Bernhard; Damascelli, Andrea

    2016-08-01

    Understanding the interplay between charge order (CO) and other phenomena (for example, pseudogap, antiferromagnetism, and superconductivity) is one of the central questions in the cuprate high-temperature superconductors. The discovery that similar forms of CO exist in both hole- and electron-doped cuprates opened a path to determine what subset of the CO phenomenology is universal to all the cuprates. We use resonant x-ray scattering to measure the CO correlations in electron-doped cuprates (La2-x Ce x CuO4 and Nd2-x Ce x CuO4) and their relationship to antiferromagnetism, pseudogap, and superconductivity. Detailed measurements of Nd2-x Ce x CuO4 show that CO is present in the x = 0.059 to 0.166 range and that its doping-dependent wave vector is consistent with the separation between straight segments of the Fermi surface. The CO onset temperature is highest between x = 0.106 and 0.166 but decreases at lower doping levels, indicating that it is not tied to the appearance of antiferromagnetic correlations or the pseudogap. Near optimal doping, where the CO wave vector is also consistent with a previously observed phonon anomaly, measurements of the CO below and above the superconducting transition temperature, or in a magnetic field, show that the CO is insensitive to superconductivity. Overall, these findings indicate that, although verified in the electron-doped cuprates, material-dependent details determine whether the CO correlations acquire sufficient strength to compete for the ground state of the cuprates.

  2. Network modelling methods for FMRI.

    PubMed

    Smith, Stephen M; Miller, Karla L; Salimi-Khorshidi, Gholamreza; Webster, Matthew; Beckmann, Christian F; Nichols, Thomas E; Ramsey, Joseph D; Woolrich, Mark W

    2011-01-15

    There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution. Copyright © 2010 Elsevier Inc. All rights reserved.

  3. Effect of interacting second- and third-order stimulus-dependent correlations on population-coding asymmetries.

    PubMed

    Montangie, Lisandro; Montani, Fernando

    2016-10-01

    Spike correlations among neurons are widely encountered in the brain. Although models accounting for pairwise interactions have proved able to capture some of the most important features of population activity at the level of the retina, the evidence shows that pairwise neuronal correlation analysis does not resolve cooperative population dynamics by itself. By means of a series expansion for short time scales of the mutual information conveyed by a population of neurons, the information transmission can be broken down into firing rate and correlational components. In a proposed extension of this framework, we investigate the information components considering both second- and higher-order correlations. We show that the existence of a mixed stimulus-dependent correlation term defines a new scenario for the interplay between pairwise and higher-than-pairwise interactions in noise and signal correlations that would lead either to redundancy or synergy in the information-theoretic sense.

  4. High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia

    PubMed Central

    Plis, Sergey M; Sui, Jing; Lane, Terran; Roy, Sushmita; Clark, Vincent P; Potluru, Vamsi K; Huster, Rene J; Michael, Andrew; Sponheim, Scott R; Weisend, Michael P; Calhoun, Vince D

    2013-01-01

    Identifying the complex activity relationships present in rich, modern neuroimaging data sets remains a key challenge for neuroscience. The problem is hard because (a) the underlying spatial and temporal networks may be nonlinear and multivariate and (b) the observed data may be driven by numerous latent factors. Further, modern experiments often produce data sets containing multiple stimulus contexts or tasks processed by the same subjects. Fusing such multi-session data sets may reveal additional structure, but raises further statistical challenges. We present a novel analysis method for extracting complex activity networks from such multifaceted imaging data sets. Compared to previous methods, we choose a new point in the trade-off space, sacrificing detailed generative probability models and explicit latent variable inference in order to achieve robust estimation of multivariate, nonlinear group factors (“network clusters”). We apply our method to identify relationships of task-specific intrinsic networks in schizophrenia patients and control subjects from a large fMRI study. After identifying network-clusters characterized by within- and between-task interactions, we find significant differences between patient and control groups in interaction strength among networks. Our results are consistent with known findings of brain regions exhibiting deviations in schizophrenic patients. However, we also find high-order, nonlinear interactions that discriminate groups but that are not detected by linear, pair-wise methods. We additionally identify high-order relationships that provide new insights into schizophrenia but that have not been found by traditional univariate or second-order methods. Overall, our approach can identify key relationships that are missed by existing analysis methods, without losing the ability to find relationships that are known to be important. PMID:23876245

  5. Finite-Time Stability for Fractional-Order Bidirectional Associative Memory Neural Networks with Time Delays

    NASA Astrophysics Data System (ADS)

    Xu, Chang-Jin; Li, Pei-Luan; Pang, Yi-Cheng

    2017-02-01

    This paper is concerned with fractional-order bidirectional associative memory (BAM) neural networks with time delays. Applying Laplace transform, the generalized Gronwall inequality and estimates of Mittag-Leffler functions, some sufficient conditions which ensure the finite-time stability of fractional-order bidirectional associative memory neural networks with time delays are obtained. Two examples with their simulations are given to illustrate the theoretical findings. Our results are new and complement previously known results. Supported by National Natural Science Foundation of China under Grant Nos.~61673008, 11261010, 11101126, Project of High-Level Innovative Talents of Guizhou Province ([2016]5651), Natural Science and Technology Foundation of Guizhou Province (J[2015]2025 and J[2015]2026), 125 Special Major Science and Technology of Department of Education of Guizhou Province ([2012]011) and Natural Science Foundation of the Education Department of Guizhou Province (KY[2015]482)

  6. Threshold network of a financial market using the P-value of correlation coefficients

    NASA Astrophysics Data System (ADS)

    Ha, Gyeong-Gyun; Lee, Jae Woo; Nobi, Ashadun

    2015-06-01

    Threshold methods in financial networks are important tools for obtaining important information about the financial state of a market. Previously, absolute thresholds of correlation coefficients have been used; however, they have no relation to the length of time. We assign a threshold value depending on the size of the time window by using the P-value concept of statistics. We construct a threshold network (TN) at the same threshold value for two different time window sizes in the Korean Composite Stock Price Index (KOSPI). We measure network properties, such as the edge density, clustering coefficient, assortativity coefficient, and modularity. We determine that a significant difference exists between the network properties of the two time windows at the same threshold, especially during crises. This implies that the market information depends on the length of the time window when constructing the TN. We apply the same technique to Standard and Poor's 500 (S&P500) and observe similar results.

  7. Correlation of higher order aberrations in the anterior corneal surface and degree of keratoconus measured with a Scheimpflug camera.

    PubMed

    Delgado, S; Velazco, J; Delgado Pelayo, R M; Ruiz-Quintero, N

    2016-07-01

    To determine the correlation of higher order aberrations in anterior corneal surface and degree of keratoconus measured with a Scheimpflug camera. A descriptive, cross-sectional study was conducted on 152 eyes (both eyes of each patient) of patients with keratoconus, from January 2009 to April 2014. An examination was performed on the corneal aberrometry in the anterior corneal surface, and topographic mapping (by Amsler and Muckenhirn classification) was used to determine the degree of keratoconus. The correlation between high-order aberrations in anterior corneal surface and the degree of keratoconus was determined. Coma aberration significantly correlated with keratoconus severity (r=.60, P<.01), as well as with the high order aberration (r=.61, P<.01). Trefoil and keratoconus were weakly correlated (r=.34, P<.01). Higher order aberrations in anterior corneal surface were positively correlated with the degree of keratoconus in a similar way to the entire optical system. Copyright © 2016 Sociedad Española de Oftalmología. Published by Elsevier España, S.L.U. All rights reserved.

  8. Highly-ordered supportless three-dimensional nanowire networks with tunable complexity and interwire connectivity for device integration.

    PubMed

    Rauber, Markus; Alber, Ina; Müller, Sven; Neumann, Reinhard; Picht, Oliver; Roth, Christina; Schökel, Alexander; Toimil-Molares, Maria Eugenia; Ensinger, Wolfgang

    2011-06-08

    The fabrication of three-dimensional assemblies consisting of large quantities of nanowires is of great technological importance for various applications including (electro-)catalysis, sensitive sensing, and improvement of electronic devices. Because the spatial distribution of the nanostructured material can strongly influence the properties, architectural design is required in order to use assembled nanowires to their full potential. In addition, special effort has to be dedicated to the development of efficient methods that allow precise control over structural parameters of the nanoscale building blocks as a means of tuning their characteristics. This paper reports the direct synthesis of highly ordered large-area nanowire networks by a method based on hard templates using electrodeposition within nanochannels of ion track-etched polymer membranes. Control over the complexity of the networks and the dimensions of the integrated nanostructures are achieved by a modified template fabrication. The networks possess high surface area and excellent transport properties, turning them into a promising electrocatalyst material as demonstrated by cyclic voltammetry studies on platinum nanowire networks catalyzing methanol oxidation. Our method opens up a new general route for interconnecting nanowires to stable macroscopic network structures of very high integration level that allow easy handling of nanowires while maintaining their connectivity.

  9. Inferring cultural regions from correlation networks of given baby names

    NASA Astrophysics Data System (ADS)

    Pomorski, Mateusz; Krawczyk, Małgorzata J.; Kułakowski, Krzysztof; Kwapień, Jarosław; Ausloos, Marcel

    2016-03-01

    We report investigations on the statistical characteristics of the baby names given between 1910 and 2010 in the United States of America. For each year, the 100 most frequent names in the USA are sorted out. For these names, the correlations between the names profiles are calculated for all pairs of states (minus Hawaii and Alaska). The correlations are used to form a weighted network which is found to vary mildly in time. In fact, the structure of communities in the network remains quite stable till about 1980. The goal is that the calculated structure approximately reproduces the usually accepted geopolitical regions: the Northeast, the South, and the "Midwest + West" as the third one. Furthermore, the dataset reveals that the name distribution satisfies the Zipf law, separately for each state and each year, i.e. the name frequency f ∝r-α, where r is the name rank. Between 1920 and 1980, the exponent α is the largest one for the set of states classified as 'the South', but the smallest one for the set of states classified as "Midwest + West". Our interpretation is that the pool of selected names was quite narrow in the Southern states. The data is compared with some related statistics of names in Belgium, a country also with different regions, but having quite a different scale than the USA. There, the Zipf exponent is low for young people and for the Brussels citizens.

  10. Characterizing structure connectivity correlation with the default mode network in Alzheimer's patients and normal controls

    NASA Astrophysics Data System (ADS)

    Guo, Jia; Xu, Peng; Song, Chao; Yao, Li; Zhao, Xiaojie

    2012-03-01

    Magnetic resonance diffusion tensor imaging (DTI) is a kind of effective measure to do non-invasive investigation on brain fiber structure at present. Studies of fiber tracking based on DTI showed that there was structural connection of white matter fiber among the nodes of resting-state functional network, denoting that the connection of white matter was the basis of gray matter regions in functional network. Nevertheless, relationship between these structure connectivity regions and functional network has not been clearly indicated. Moreover, research of fMRI found that activation of default mode network (DMN) in Alzheimer's disease (AD) was significantly descended, especially in hippocampus and posterior cingulated cortex (PCC). The relationship between this change of DMN activity and structural connection among functional networks needs further research. In this study, fast marching tractography (FMT) algorithm was adopted to quantitative calculate fiber connectivity value between regions, and hippocampus and PCC which were two important regions in DMN related with AD were selected to compute white matter connection region between them in elderly normal control (NC) and AD patient. The fiber connectivity value was extracted to do the correlation analysis with activity intensity of DMN. Results showed that, between PCC and hippocampus of NC, there exited region with significant high connectivity value of white matter fiber whose performance has relatively strong correlation with the activity of DMN, while there was no significant white matter connection region between them for AD patient which might be related with reduced network activation in these two regions of AD.

  11. Nano-phase separation and structural ordering in silica-rich mixed network former glasses.

    PubMed

    Liu, Hao; Youngman, Randall E; Kapoor, Saurabh; Jensen, Lars R; Smedskjaer, Morten M; Yue, Yuanzheng

    2018-06-13

    We investigate the structure, phase separation, glass transition, and crystallization in a mixed network former glass series, i.e., B2O3-Al2O3-SiO2-P2O5 glasses with varying SiO2/B2O3 molar ratio. All the studied glasses exhibit two separate glassy phases: droplet phase (G1) with the size of 50-100 nm and matrix phase (G2), corresponding to a lower calorimetric glass transition temperature (Tg1) and a higher one (Tg2), respectively. Both Tg values decrease linearly with the substitution of B2O3 for SiO2, but the magnitude of the decrease is larger for Tg1. Based on nuclear magnetic resonance and Raman spectroscopy results, we infer that the G1 phase is rich in boroxol rings, while the G2 phase mainly involves the B-O-Si network. Both phases contain BPO4- and AlPO4-like units. Ordered domains occur in G2 upon isothermal and dynamic heating, driven by the structural heterogeneity in the as-prepared glasses. The structural ordering lowers the activation energy of crystal growth, thus promoting partial crystallization of G2. These findings are useful for understanding glass formation and phase separation in mixed network former oxide systems, and for tailoring their properties.

  12. Triangular Alignment (TAME). A Tensor-based Approach for Higher-order Network Alignment

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Mohammadi, Shahin; Gleich, David F.; Kolda, Tamara G.

    2015-11-01

    Network alignment is an important tool with extensive applications in comparative interactomics. Traditional approaches aim to simultaneously maximize the number of conserved edges and the underlying similarity of aligned entities. We propose a novel formulation of the network alignment problem that extends topological similarity to higher-order structures and provide a new objective function that maximizes the number of aligned substructures. This objective function corresponds to an integer programming problem, which is NP-hard. Consequently, we approximate this objective function as a surrogate function whose maximization results in a tensor eigenvalue problem. Based on this formulation, we present an algorithm called Triangularmore » AlignMEnt (TAME), which attempts to maximize the number of aligned triangles across networks. We focus on alignment of triangles because of their enrichment in complex networks; however, our formulation and resulting algorithms can be applied to general motifs. Using a case study on the NAPABench dataset, we show that TAME is capable of producing alignments with up to 99% accuracy in terms of aligned nodes. We further evaluate our method by aligning yeast and human interactomes. Our results indicate that TAME outperforms the state-of-art alignment methods both in terms of biological and topological quality of the alignments.« less

  13. A weighted higher-order network analysis of fine particulate matter (PM2.5) transport in Yangtze River Delta

    NASA Astrophysics Data System (ADS)

    Wang, Yufang; Wang, Haiyan; Zhang, Shuhua

    2018-04-01

    Specification of PM2.5 transmission characteristics is important for pollution control, policymaking and prediction. In this paper, we propose weights for motif instances, thereby to implement a weighted higher-order clustering algorithm for a weighted, directed PM2.5 network in the Yangtze River Delta (YRD) of China. The weighted, directed network we create in this paper includes information on meteorological conditions of wind speed and wind direction, plus data on geographic distance and PM2.5 concentrations. We aim to reveal PM2.5 mobility between cities in the YRD. Major potential PM2.5 contributors and closely interacted clusters are identified in the network of 178 air quality stations in the YRD. To our knowledge, it is the first work to incorporate weight information into the higher-order network analysis to study PM2.5 transport.

  14. Hierarchical structures of correlations networks among Turkey’s exports and imports by currencies

    NASA Astrophysics Data System (ADS)

    Kocakaplan, Yusuf; Deviren, Bayram; Keskin, Mustafa

    2012-12-01

    We have examined the hierarchical structures of correlations networks among Turkey’s exports and imports by currencies for the 1996-2010 periods, using the concept of a minimal spanning tree (MST) and hierarchical tree (HT) which depend on the concept of ultrametricity. These trees are useful tools for understanding and detecting the global structure, taxonomy and hierarchy in financial markets. We derived a hierarchical organization and build the MSTs and HTs during the 1996-2001 and 2002-2010 periods. The reason for studying two different sub-periods, namely 1996-2001 and 2002-2010, is that the Euro (EUR) came into use in 2001, and some countries have made their exports and imports with Turkey via the EUR since 2002, and in order to test various time-windows and observe temporal evolution. We have carried out bootstrap analysis to associate a value of the statistical reliability to the links of the MSTs and HTs. We have also used the average linkage cluster analysis (ALCA) to observe the cluster structure more clearly. Moreover, we have obtained the bidimensional minimal spanning tree (BMST) due to economic trade being a bidimensional problem. From the structural topologies of these trees, we have identified different clusters of currencies according to their proximity and economic ties. Our results show that some currencies are more important within the network, due to a tighter connection with other currencies. We have also found that the obtained currencies play a key role for Turkey’s exports and imports and have important implications for the design of portfolio and investment strategies.

  15. Quantitative description and modeling of real networks

    NASA Astrophysics Data System (ADS)

    Capocci, Andrea; Caldarelli, Guido; de Los Rios, Paolo

    2003-10-01

    We present data analysis and modeling of two particular cases of study in the field of growing networks. We analyze World Wide Web data set and authorship collaboration networks in order to check the presence of correlation in the data. The results are reproduced with good agreement through a suitable modification of the standard Albert-Barabási model of network growth. In particular, intrinsic relevance of sites plays a role in determining the future degree of the vertex.

  16. Visualizing spatial correlation: structural and electronic orders in iron-based superconductors on atomic scale

    NASA Astrophysics Data System (ADS)

    Maksov, Artem; Ziatdinov, Maxim; Li, Li; Sefat, Athena; Maksymovych, Petro; Kalinin, Sergei

    Crystalline matter on the nanoscale level often exhibits strongly inhomogeneous structural and electronic orders, which have a profound effect on macroscopic properties. This may be caused by subtle interplay between chemical disorder, strain, magnetic, and structural order parameters. We present a novel approach based on combination of high resolution scanning tunneling microscopy/spectroscopy (STM/S) and deep data style analysis for automatic separation, extraction, and correlation of structural and electronic behavior which might lead us to uncovering the underlying sources of inhomogeneity in in iron-based family of superconductors (FeSe, BaFe2As2) . We identify STS spectral features using physically robust Bayesian linear unmixing, and show their direct relevance to the fundamental physical properties of the system, including electronic states associated with individual defects and impurities. We collect structural data from individual unit cells on the crystalline lattice, and calculate both global and local indicators of spatial correlation with electronic features, demonstrating, for the first time, a direct quantifiable connection between observed structural order parameters extracted from the STM data and electronic order parameters identified within the STS data. This research was sponsored by the Division of Materials Sciences and Engineering, Office of Science, Basic Energy Sciences, US DOE.

  17. Analytical Computation of Energy-Energy Correlation at Next-to-Leading Order in QCD

    NASA Astrophysics Data System (ADS)

    Dixon, Lance J.; Luo, Ming-xing; Shtabovenko, Vladyslav; Yang, Tong-Zhi; Zhu, Hua Xing

    2018-03-01

    The energy-energy correlation (EEC) between two detectors in e+e- annihilation was computed analytically at leading order in QCD almost 40 years ago, and numerically at next-to-leading order (NLO) starting in the 1980s. We present the first analytical result for the EEC at NLO, which is remarkably simple, and facilitates analytical study of the perturbative structure of the EEC. We provide the expansion of the EEC in the collinear and back-to-back regions through next-to-leading power, information which should aid resummation in these regions.

  18. Periodic oscillation of higher-order bidirectional associative memory neural networks with periodic coefficients and delays

    NASA Astrophysics Data System (ADS)

    Ren, Fengli; Cao, Jinde

    2007-03-01

    In this paper, several sufficient conditions are obtained ensuring existence, global attractivity and global asymptotic stability of the periodic solution for the higher-order bidirectional associative memory neural networks with periodic coefficients and delays by using the continuation theorem of Mawhin's coincidence degree theory, the Lyapunov functional and the non-singular M-matrix. Two examples are exploited to illustrate the effectiveness of the proposed criteria. These results are more effective than the ones in the literature for some neural networks, and can be applied to the design of globally attractive or globally asymptotically stable networks and thus have important significance in both theory and applications.

  19. Accounting for correlation in network meta-analysis with multi-arm trials.

    PubMed

    Franchini, A J; Dias, S; Ades, A E; Jansen, J P; Welton, N J

    2012-06-01

    Multi-arm trials (trials with more than two arms) are particularly valuable forms of evidence for network meta-analysis (NMA). Trial results are available either as arm-level summaries, where effect measures are reported for each arm, or as contrast-level summaries, where the differences in effect between arms compare with the control arm chosen for the trial. We show that likelihood-based inference in both contrast-level and arm-level formats is identical if there are only two-arm trials, but that if there are multi-arm trials, results from the contrast-level format will be incorrect unless correlations are accounted for in the likelihood. We review Bayesian and frequentist software for NMA with multi-arm trials that can account for this correlation and give an illustrative example of the difference in estimates that can be introduced if the correlations are not incorporated. We discuss methods of imputing correlations when they cannot be derived from the reported results and urge trialists to report the standard error for the control arm even if only contrast-level summaries are reported. Copyright © 2012 John Wiley & Sons, Ltd. Copyright © 2012 John Wiley & Sons, Ltd.

  20. The Bass diffusion model on networks with correlations and inhomogeneous advertising

    NASA Astrophysics Data System (ADS)

    Bertotti, M. L.; Brunner, J.; Modanese, G.

    2016-09-01

    The Bass model, which is an effective forecasting tool for innovation diffusion based on large collections of empirical data, assumes an homogeneous diffusion process. We introduce a network structure into this model and we investigate numerically the dynamics in the case of networks with link density $P(k)=c/k^\\gamma$, where $k=1, \\ldots , N$. The resulting curve of the total adoptions in time is qualitatively similar to the homogeneous Bass curve corresponding to a case with the same average number of connections. The peak of the adoptions, however, tends to occur earlier, particularly when $\\gamma$ and $N$ are large (i.e., when there are few hubs with a large maximum number of connections). Most interestingly, the adoption curve of the hubs anticipates the total adoption curve in a predictable way, with peak times which can be, for instance when $N=100$, between 10% and 60% of the total adoptions peak. This may allow to monitor the hubs for forecasting purposes. We also consider the case of networks with assortative and disassortative correlations and a case of inhomogeneous advertising where the publicity terms are "targeted" on the hubs while maintaining their total cost constant.

  1. Common neural correlates of intertemporal choices and intelligence in adolescents.

    PubMed

    Ripke, Stephan; Hübner, Thomas; Mennigen, Eva; Müller, Kathrin U; Li, Shu-Chen; Smolka, Michael N

    2015-02-01

    Converging behavioral evidence indicates that temporal discounting, measured by intertemporal choice tasks, is inversely related to intelligence. At the neural level, the parieto-frontal network is pivotal for complex, higher-order cognitive processes. Relatedly, underrecruitment of the pFC during a working memory task has been found to be associated with steeper temporal discounting. Furthermore, this network has also been shown to be related to the consistency of intertemporal choices. Here we report an fMRI study that directly investigated the association of neural correlates of intertemporal choice behavior with intelligence in an adolescent sample (n = 206; age 13.7-15.5 years). After identifying brain regions where the BOLD response during intertemporal choice was correlated with individual differences in intelligence, we further tested whether BOLD responses in these areas would mediate the associations between intelligence, the discounting rate, and choice consistency. We found positive correlations between BOLD response in a value-independent decision network (i.e., dorsolateral pFC, precuneus, and occipital areas) and intelligence. Furthermore, BOLD response in a value-dependent decision network (i.e., perigenual ACC, inferior frontal gyrus, ventromedial pFC, ventral striatum) was positively correlated with intelligence. The mediation analysis revealed that BOLD responses in the value-independent network mediated the association between intelligence and choice consistency, whereas BOLD responses in the value-dependent network mediated the association between intelligence and the discounting rate. In summary, our findings provide evidence for common neural correlates of intertemporal choice and intelligence, possibly linked by valuation as well as executive functions.

  2. Combination of DTI and fMRI reveals the white matter changes correlating with the decline of default-mode network activity in Alzheimer's disease

    NASA Astrophysics Data System (ADS)

    Wu, Xianjun; Di, Qian; Li, Yao; Zhao, Xiaojie

    2009-02-01

    Recently, evidences from fMRI studies have shown that there was decreased activity among the default-mode network in Alzheimer's disease (AD), and DTI researches also demonstrated that demyelinations exist in white matter of AD patients. Therefore, combining these two MRI methods may help to reveal the relationship between white matter damages and alterations of the resting state functional connectivity network. In the present study, we tried to address this issue by means of correlation analysis between DTI and resting state fMRI images. The default-mode networks of AD and normal control groups were compared to find the areas with significantly declined activity firstly. Then, the white matter regions whose fractional anisotropy (FA) value correlated with this decline were located through multiple regressions between the FA values and the BOLD response of the default networks. Among these correlating white matter regions, those whose FA values also declined were found by a group comparison between AD patients and healthy elderly control subjects. Our results showed that the areas with decreased activity among default-mode network included left posterior cingulated cortex (PCC), left medial temporal gyrus et al. And the damaged white matter areas correlated with the default-mode network alterations were located around left sub-gyral temporal lobe. These changes may relate to the decreased connectivity between PCC and medial temporal lobe (MTL), and thus correlate with the deficiency of default-mode network activity.

  3. Dynamic Functional Connectivity States Between the Dorsal and Ventral Sensorimotor Networks Revealed by Dynamic Conditional Correlation Analysis of Resting-State Functional Magnetic Resonance Imaging.

    PubMed

    Syed, Maleeha F; Lindquist, Martin A; Pillai, Jay J; Agarwal, Shruti; Gujar, Sachin K; Choe, Ann S; Caffo, Brian; Sair, Haris I

    2017-12-01

    Functional connectivity in resting-state functional magnetic resonance imaging (rs-fMRI) has received substantial attention since the initial findings of Biswal et al. Traditional network correlation metrics assume that the functional connectivity in the brain remains stationary over time. However, recent studies have shown that robust temporal fluctuations of functional connectivity among as well as within functional networks exist, challenging this assumption. In this study, these dynamic correlation differences were investigated between the dorsal and ventral sensorimotor networks by applying the dynamic conditional correlation model to rs-fMRI data of 20 healthy subjects. k-Means clustering was used to determine an optimal number of discrete connectivity states (k = 10) of the sensorimotor system across all subjects. Our analysis confirms the existence of differences in dynamic correlation between the dorsal and ventral networks, with highest connectivity found within the ventral motor network.

  4. Theory of population coupling and applications to describe high order correlations in large populations of interacting neurons

    NASA Astrophysics Data System (ADS)

    Huang, Haiping

    2017-03-01

    To understand the collective spiking activity in neuronal populations, it is essential to reveal basic circuit variables responsible for these emergent functional states. Here, I develop a mean field theory for the population coupling recently proposed in the studies of the visual cortex of mouse and monkey, relating the individual neuron activity to the population activity, and extend the original form to the second order, relating neuron-pair’s activity to the population activity, to explain the high order correlations observed in the neural data. I test the computational framework on the salamander retinal data and the cortical spiking data of behaving rats. For the retinal data, the original form of population coupling and its advanced form can explain a significant fraction of two-cell correlations and three-cell correlations, respectively. For the cortical data, the performance becomes much better, and the second order population coupling reveals non-local effects in local cortical circuits.

  5. Applying complex networks to evaluate precipitation patterns over South America

    NASA Astrophysics Data System (ADS)

    Ciemer, Catrin; Boers, Niklas; Barbosa, Henrique; Kurths, Jürgen; Rammig, Anja

    2016-04-01

    The climate of South America exhibits pronounced differences between the wet- and the dry-season, which are accompanied by specific synoptic events like changes in the location of the South American Low Level Jet (SALLJ) and the establishment of the South American Convergence Zone (SACZ). The onset of these events can be related to the presence of typical large-scale precipitation patterns over South America, as previous studies have shown[1,2]. The application of complex network methods to precipitation data recently received increased scientific attention for the special case of extreme events, as it is possible with such methods to analyze the spatiotemporal correlation structure as well as possible teleconnections of these events[3,4]. In these approaches the correlation between precipitation datasets is calculated by means of Event Synchronization which restricts their applicability to extreme precipitation events. In this work, we propose a method which is able to consider not only extreme precipitation but complete time series. A direct application of standard similarity measures in order to correlate precipitation time series is impossible due to their intricate statistical properties as the large amount of zeros. Therefore, we introduced and evaluated a suitable modification of Pearson's correlation coefficient to construct spatial correlation networks of precipitation. By analyzing the characteristics of spatial correlation networks constructed on the basis of this new measure, we are able to determine coherent areas of similar precipitation patterns, spot teleconnections of correlated areas, and detect central regions for precipitation correlation. By analyzing the change of the network over the year[5], we are also able to determine local and global changes in precipitation correlation patterns. Additionally, global network characteristics as the network connectivity yield indications for beginning and end of wet- and dry season. In order to identify

  6. Hysteresis, neural avalanches, and critical behavior near a first-order transition of a spiking neural network

    NASA Astrophysics Data System (ADS)

    Scarpetta, Silvia; Apicella, Ilenia; Minati, Ludovico; de Candia, Antonio

    2018-06-01

    Many experimental results, both in vivo and in vitro, support the idea that the brain cortex operates near a critical point and at the same time works as a reservoir of precise spatiotemporal patterns. However, the mechanism at the basis of these observations is still not clear. In this paper we introduce a model which combines both these features, showing that scale-free avalanches are the signature of a system posed near the spinodal line of a first-order transition, with many spatiotemporal patterns stored as dynamical metastable attractors. Specifically, we studied a network of leaky integrate-and-fire neurons whose connections are the result of the learning of multiple spatiotemporal dynamical patterns, each with a randomly chosen ordering of the neurons. We found that the network shows a first-order transition between a low-spiking-rate disordered state (down), and a high-rate state characterized by the emergence of collective activity and the replay of one of the stored patterns (up). The transition is characterized by hysteresis, or alternation of up and down states, depending on the lifetime of the metastable states. In both cases, critical features and neural avalanches are observed. Notably, critical phenomena occur at the edge of a discontinuous phase transition, as recently observed in a network of glow lamps.

  7. Correlates of gender and achievement in introductory algebra based physics

    NASA Astrophysics Data System (ADS)

    Smith, Rachel Clara

    The field of physics is heavily male dominated in America. Thus, half of the population of our country is underrepresented and underserved. The identification of factors that contribute to gender disparity in physics is necessary for educators to address the individual needs of students, and, in particular, the separate and specific needs of female students. In an effort to determine if any correlations could be established or strengthened between sex, gender identity, social network, algebra skill, scientific reasoning ability, and/or student attitude, a study was performed on a group of 82 students in an introductory algebra based physics course. The subjects each filled out a survey at the beginning of the semester of their first semester of algebra based physics. They filled out another survey at the end of that same semester. These surveys included physics content pretests and posttests, as well as questions about the students' habits, attitudes, and social networks. Correlates of posttest score were identified, in order of significance, as pretest score, emphasis on conceptual learning, preference for male friends, number of siblings (negatively correlated), motivation in physics, algebra score, and parents' combined education level. Number of siblings was also found to negatively correlate with, in order of significance, gender identity, preference for male friends, emphasis on conceptual learning, and motivation in physics. Preference for male friends was found to correlate with, in order of significance, emphasis on conceptual learning, gender identity, and algebra score. Also, gender identity was found to correlate with emphasis on conceptual learning, the strongest predictor of posttest score other than pretest score.

  8. Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

    NASA Astrophysics Data System (ADS)

    Hyman, J.; Aldrich, G. A.; Viswanathan, H. S.; Makedonska, N.; Karra, S.

    2016-12-01

    We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semi-correlation, and non-correlation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected so that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same.We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. These observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.

  9. Quantifying randomness in real networks

    NASA Astrophysics Data System (ADS)

    Orsini, Chiara; Dankulov, Marija M.; Colomer-de-Simón, Pol; Jamakovic, Almerima; Mahadevan, Priya; Vahdat, Amin; Bassler, Kevin E.; Toroczkai, Zoltán; Boguñá, Marián; Caldarelli, Guido; Fortunato, Santo; Krioukov, Dmitri

    2015-10-01

    Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks--the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain--and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs.

  10. Motor deficits correlate with resting state motor network connectivity in patients with brain tumours

    PubMed Central

    Mikell, Charles B.; Youngerman, Brett E.; Liston, Conor; Sisti, Michael B.; Bruce, Jeffrey N.; Small, Scott A.; McKhann, Guy M.

    2012-01-01

    While a tumour in or abutting primary motor cortex leads to motor weakness, how tumours elsewhere in the frontal or parietal lobes affect functional connectivity in a weak patient is less clear. We hypothesized that diminished functional connectivity in a distributed network of motor centres would correlate with motor weakness in subjects with brain masses. Furthermore, we hypothesized that interhemispheric connections would be most vulnerable to subtle disruptions in functional connectivity. We used task-free functional magnetic resonance imaging connectivity to probe motor networks in control subjects and patients with brain tumours (n = 22). Using a control dataset, we developed a method for automated detection of key nodes in the motor network, including the primary motor cortex, supplementary motor area, premotor area and superior parietal lobule, based on the anatomic location of the hand-motor knob in the primary motor cortex. We then calculated functional connectivity between motor network nodes in control subjects, as well as patients with and without brain masses. We used this information to construct weighted, undirected graphs, which were then compared to variables of interest, including performance on a motor task, the grooved pegboard. Strong connectivity was observed within the identified motor networks between all nodes bilaterally, and especially between the primary motor cortex and supplementary motor area. Reduced connectivity was observed in subjects with motor weakness versus subjects with normal strength (P < 0.001). This difference was driven mostly by decreases in interhemispheric connectivity between the primary motor cortices (P < 0.05) and between the left primary motor cortex and the right premotor area (P < 0.05), as well as other premotor area connections. In the subjects without motor weakness, however, performance on the grooved pegboard did not relate to interhemispheric connectivity, but rather was inversely

  11. Multistability of second-order competitive neural networks with nondecreasing saturated activation functions.

    PubMed

    Nie, Xiaobing; Cao, Jinde

    2011-11-01

    In this paper, second-order interactions are introduced into competitive neural networks (NNs) and the multistability is discussed for second-order competitive NNs (SOCNNs) with nondecreasing saturated activation functions. Firstly, based on decomposition of state space, Cauchy convergence principle, and inequality technique, some sufficient conditions ensuring the local exponential stability of 2N equilibrium points are derived. Secondly, some conditions are obtained for ascertaining equilibrium points to be locally exponentially stable and to be located in any designated region. Thirdly, the theory is extended to more general saturated activation functions with 2r corner points and a sufficient criterion is given under which the SOCNNs can have (r+1)N locally exponentially stable equilibrium points. Even if there is no second-order interactions, the obtained results are less restrictive than those in some recent works. Finally, three examples with their simulations are presented to verify the theoretical analysis.

  12. CMB internal delensing with general optimal estimator for higher-order correlations

    DOE PAGES

    Namikawa, Toshiya

    2017-05-24

    We present here a new method for delensing B modes of the cosmic microwave background (CMB) using a lensing potential reconstructed from the same realization of the CMB polarization (CMB internal delensing). The B -mode delensing is required to improve sensitivity to primary B modes generated by, e.g., the inflationary gravitational waves, axionlike particles, modified gravity, primordial magnetic fields, and topological defects such as cosmic strings. However, the CMB internal delensing suffers from substantial biases due to correlations between observed CMB maps to be delensed and that used for reconstructing a lensing potential. Since the bias depends on realizations, wemore » construct a realization-dependent (RD) estimator for correcting these biases by deriving a general optimal estimator for higher-order correlations. The RD method is less sensitive to simulation uncertainties. Compared to the previous ℓ -splitting method, we find that the RD method corrects the biases without substantial degradation of the delensing efficiency.« less

  13. An fMRI study of joint action–varying levels of cooperation correlates with activity in control networks

    PubMed Central

    Chaminade, Thierry; Marchant, Jennifer L.; Kilner, James; Frith, Christopher D.

    2012-01-01

    As social agents, humans continually interact with the people around them. Here, motor cooperation was investigated using a paradigm in which pairs of participants, one being scanned with fMRI, jointly controlled a visually presented object with joystick movements. The object oscillated dynamically along two dimensions, color and width of gratings, corresponding to the two cardinal directions of joystick movements. While the overall control of each participant on the object was kept constant, the amount of cooperation along the two dimensions varied along four levels, from no (each participant controlled one dimension exclusively) to full (each participant controlled half of each dimension) cooperation. Increasing cooperation correlated with BOLD signal in the left parietal operculum and anterior cingulate cortex (ACC), while decreasing cooperation correlated with activity in the right inferior frontal and superior temporal gyri, the intraparietal sulci and inferior temporal gyri bilaterally, and the dorsomedial prefrontal cortex. As joint performance improved with the level of cooperation, we assessed the brain responses correlating with behavior, and found that activity in most of the areas associated with levels of cooperation also correlated with the joint performance. The only brain area found exclusively in the negative correlation with cooperation was in the dorso medial frontal cortex, involved in monitoring action outcome. Given the cluster location and condition-related signal change, we propose that this region monitored actions to extract the level of cooperation in order to optimize the joint response. Our results, therefore, indicate that, in the current experimental paradigm involving joint control of a visually presented object with joystick movements, the level of cooperation affected brain networks involved in action control, but not mentalizing. PMID:22715326

  14. Gene network analysis identifies rumen epithelial cell proliferation, differentiation and metabolic pathways perturbed by diet and correlated with methane production

    PubMed Central

    Xiang, Ruidong; McNally, Jody; Rowe, Suzanne; Jonker, Arjan; Pinares-Patino, Cesar S.; Oddy, V. Hutton; Vercoe, Phil E.; McEwan, John C.; Dalrymple, Brian P.

    2016-01-01

    Ruminants obtain nutrients from microbial fermentation of plant material, primarily in their rumen, a multilayered forestomach. How the different layers of the rumen wall respond to diet and influence microbial fermentation, and how these process are regulated, is not well understood. Gene expression correlation networks were constructed from full thickness rumen wall transcriptomes of 24 sheep fed two different amounts and qualities of a forage and measured for methane production. The network contained two major negatively correlated gene sub-networks predominantly representing the epithelial and muscle layers of the rumen wall. Within the epithelium sub-network gene clusters representing lipid/oxo-acid metabolism, general metabolism and proliferating and differentiating cells were identified. The expression of cell cycle and metabolic genes was positively correlated with dry matter intake, ruminal short chain fatty acid concentrations and methane production. A weak correlation between lipid/oxo-acid metabolism genes and methane yield was observed. Feed consumption level explained the majority of gene expression variation, particularly for the cell cycle genes. Many known stratified epithelium transcription factors had significantly enriched targets in the epithelial gene clusters. The expression patterns of the transcription factors and their targets in proliferating and differentiating skin is mirrored in the rumen, suggesting conservation of regulatory systems. PMID:27966600

  15. Practical approximation method for firing-rate models of coupled neural networks with correlated inputs

    NASA Astrophysics Data System (ADS)

    Barreiro, Andrea K.; Ly, Cheng

    2017-08-01

    Rapid experimental advances now enable simultaneous electrophysiological recording of neural activity at single-cell resolution across large regions of the nervous system. Models of this neural network activity will necessarily increase in size and complexity, thus increasing the computational cost of simulating them and the challenge of analyzing them. Here we present a method to approximate the activity and firing statistics of a general firing rate network model (of the Wilson-Cowan type) subject to noisy correlated background inputs. The method requires solving a system of transcendental equations and is fast compared to Monte Carlo simulations of coupled stochastic differential equations. We implement the method with several examples of coupled neural networks and show that the results are quantitatively accurate even with moderate coupling strengths and an appreciable amount of heterogeneity in many parameters. This work should be useful for investigating how various neural attributes qualitatively affect the spiking statistics of coupled neural networks.

  16. The LAAS network observation for studying time correlations in extensive air showers

    NASA Astrophysics Data System (ADS)

    Ochi, Nobuaki; Iyono, A.; Kimura, Hitoomi; Konishi, Takeharu; Nakamura, Toru; Nakatsuka, Takao; Ohara, Soji; Ohmori, Nobuharu; Saito, Katsuhiko; Takahashi, Nobusuke; Tsuji, Shuhei; Wada, Tomonori; Yamamoto, Isao; Yamashita, Yoshihiko; Yanagimoto, Yukio

    2003-02-01

    The Large Area Air Shower (LAAS) group has been performing a network observation of extensive air showers (EAS) since 1996 in Japan. Ten compact EAS arrays are operating simultaneously at distant stations (up to ≍1000 km) and detecting EAS with mean energy of ≍1015 eV. Each station has 4--12 scintillation counters and a Global Positioning System (GPS), which provides time stamps of EAS triggers with an accuracy of 1μs. As a consequence of the comparable time stamps, uniformly-adjusted detectors and a standardized data format among all stations, we can treat the independent observations as a gigantic EAS detector system as a whole. The primary purpose of the network observation is to study large-scale correlations in ultra-high-energy cosmic rays. On the other hand, three nearby stations within 1~km distance at Okayama area have a possibility to detect extremely-high-energy EAS (≍1019 eV) as coincident triggers of the three stations. The present status of the network and some results from computer simulations are reported here.

  17. Doped YbRh2Si2: not only ferromagnetic correlations but ferromagnetic order.

    PubMed

    Lausberg, S; Hannaske, A; Steppke, A; Steinke, L; Gruner, T; Pedrero, L; Krellner, C; Klingner, C; Brando, M; Geibel, C; Steglich, F

    2013-06-21

    YbRh2Si2 is a prototypical system for studying unconventional antiferromagnetic quantum criticality. However, ferromagnetic correlations are present which can be enhanced via isoelectronic cobalt substitution for rhodium in Yb(Rh(1-x)Co(x))2Si2. So far, the magnetic order with increasing x was believed to remain antiferromagnetic. Here, we present the discovery of ferromagnetism for x = 0.27 below T(C) = 1.30  K in single crystalline samples. Unexpectedly, ordering occurs along the c axis, the hard crystalline electric field direction, where the g factor is an order of magnitude smaller than in the basal plane. Although the spontaneous magnetization is only 0.1 μB/Yb it corresponds to the full expected saturation moment along c taking into account partial Kondo screening.

  18. Efficient Simulation of Wing Modal Response: Application of 2nd Order Shape Sensitivities and Neural Networks

    NASA Technical Reports Server (NTRS)

    Kapania, Rakesh K.; Liu, Youhua

    2000-01-01

    At the preliminary design stage of a wing structure, an efficient simulation, one needing little computation but yielding adequately accurate results for various response quantities, is essential in the search of optimal design in a vast design space. In the present paper, methods of using sensitivities up to 2nd order, and direct application of neural networks are explored. The example problem is how to decide the natural frequencies of a wing given the shape variables of the structure. It is shown that when sensitivities cannot be obtained analytically, the finite difference approach is usually more reliable than a semi-analytical approach provided an appropriate step size is used. The use of second order sensitivities is proved of being able to yield much better results than the case where only the first order sensitivities are used. When neural networks are trained to relate the wing natural frequencies to the shape variables, a negligible computation effort is needed to accurately determine the natural frequencies of a new design.

  19. Network motif frequency vectors reveal evolving metabolic network organisation.

    PubMed

    Pearcy, Nicole; Crofts, Jonathan J; Chuzhanova, Nadia

    2015-01-01

    At the systems level many organisms of interest may be described by their patterns of interaction, and as such, are perhaps best characterised via network or graph models. Metabolic networks, in particular, are fundamental to the proper functioning of many important biological processes, and thus, have been widely studied over the past decade or so. Such investigations have revealed a number of shared topological features, such as a short characteristic path-length, large clustering coefficient and hierarchical modular structure. However, the extent to which evolutionary and functional properties of metabolism manifest via this underlying network architecture remains unclear. In this paper, we employ a novel graph embedding technique, based upon low-order network motifs, to compare metabolic network structure for 383 bacterial species categorised according to a number of biological features. In particular, we introduce a new global significance score which enables us to quantify important evolutionary relationships that exist between organisms and their physical environments. Using this new approach, we demonstrate a number of significant correlations between environmental factors, such as growth conditions and habitat variability, and network motif structure, providing evidence that organism adaptability leads to increased complexities in the resultant metabolic networks.

  20. Spatiotemporal image correlation analysis of blood flow in branched vessel networks of zebrafish embryos

    NASA Astrophysics Data System (ADS)

    Ceffa, Nicolo G.; Cesana, Ilaria; Collini, Maddalena; D'Alfonso, Laura; Carra, Silvia; Cotelli, Franco; Sironi, Laura; Chirico, Giuseppe

    2017-10-01

    Ramification of blood circulation is relevant in a number of physiological and pathological conditions. The oxygen exchange occurs largely in the capillary bed, and the cancer progression is closely linked to the angiogenesis around the tumor mass. Optical microscopy has made impressive improvements in in vivo imaging and dynamic studies based on correlation analysis of time stacks of images. Here, we develop and test advanced methods that allow mapping the flow fields in branched vessel networks at the resolution of 10 to 20 μm. The methods, based on the application of spatiotemporal image correlation spectroscopy and its extension to cross-correlation analysis, are applied here to the case of early stage embryos of zebrafish.

  1. Anti-correlated cortical networks arise from spontaneous neuronal dynamics at slow timescales.

    PubMed

    Kodama, Nathan X; Feng, Tianyi; Ullett, James J; Chiel, Hillel J; Sivakumar, Siddharth S; Galán, Roberto F

    2018-01-12

    In the highly interconnected architectures of the cerebral cortex, recurrent intracortical loops disproportionately outnumber thalamo-cortical inputs. These networks are also capable of generating neuronal activity without feedforward sensory drive. It is unknown, however, what spatiotemporal patterns may be solely attributed to intrinsic connections of the local cortical network. Using high-density microelectrode arrays, here we show that in the isolated, primary somatosensory cortex of mice, neuronal firing fluctuates on timescales from milliseconds to tens of seconds. Slower firing fluctuations reveal two spatially distinct neuronal ensembles, which correspond to superficial and deeper layers. These ensembles are anti-correlated: when one fires more, the other fires less and vice versa. This interplay is clearest at timescales of several seconds and is therefore consistent with shifts between active sensing and anticipatory behavioral states in mice.

  2. Improving Multidimensional Wireless Sensor Network Lifetime Using Pearson Correlation and Fractal Clustering.

    PubMed

    Almeida, Fernando R; Brayner, Angelo; Rodrigues, Joel J P C; Maia, Jose E Bessa

    2017-06-07

    An efficient strategy for reducing message transmission in a wireless sensor network (WSN) is to group sensors by means of an abstraction denoted cluster. The key idea behind the cluster formation process is to identify a set of sensors whose sensed values present some data correlation. Nowadays, sensors are able to simultaneously sense multiple different physical phenomena, yielding in this way multidimensional data. This paper presents three methods for clustering sensors in WSNs whose sensors collect multidimensional data. The proposed approaches implement the concept of multidimensional behavioral clustering . To show the benefits introduced by the proposed methods, a prototype has been implemented and experiments have been carried out on real data. The results prove that the proposed methods decrease the amount of data flowing in the network and present low root-mean-square error (RMSE).

  3. Heterogeneous fractionation profiles of meta-analytic coactivation networks.

    PubMed

    Laird, Angela R; Riedel, Michael C; Okoe, Mershack; Jianu, Radu; Ray, Kimberly L; Eickhoff, Simon B; Smith, Stephen M; Fox, Peter T; Sutherland, Matthew T

    2017-04-01

    Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10,000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i.e., d=20-300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how "parent" functional brain systems decompose into constituent "child" sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication. Copyright © 2017 Elsevier Inc. All rights reserved.

  4. Heterogeneous fractionation profiles of meta-analytic coactivation networks

    PubMed Central

    Laird, Angela R.; Riedel, Michael C.; Okoe, Mershack; Jianu, Radu; Ray, Kimberly L.; Eickhoff, Simon B.; Smith, Stephen M.; Fox, Peter T.; Sutherland, Matthew T.

    2017-01-01

    Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10,000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i.e., d = 20 to 300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how “parent” functional brain systems decompose into constituent “child” sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication. PMID:28222386

  5. Structured teleconnections reveal the South American monsoon onset: A network approach

    NASA Astrophysics Data System (ADS)

    Ciemer, Catrin; Ekhtiari, Nikoo; Barbosa, Henrique; Boers, Niklas; Donner, Reik; Kurths, Jürgen; Rammig, Anja; Winkelmann, Ricarda

    2017-04-01

    The regional onset dates of the global monsoon systems are, to first order, determined by the seasonal shift of the intertropical convergence zone. However, precise onset dates vary substantially from year to year due to the complexity of the involved mechanisms. In this study, we investigate processes determining the onset of the South American monsoon system (SAMS). In recent years, a trend towards later onset dates of the SAMS has been observed. A later onset of the monsoon can have severe impacts on agriculture and infrastructure such as farming, water transport routes, and the stability of the Amazon rainforest in the long term. Possible reasons for this shift involve a multitude of climatic phenomena and variables relevant for the SAMS. To account for the highly interactive nature of the SAMS, we here investigate it with the help of complex networks. By studying the temporal changes of the correlation structure in spatial rainfall networks, we are able to determine coherent areas of similar precipitation patterns, spot teleconnections in terms of strongly correlated areas, detect key regions for precipitation correlations, and finally reveal the monsoon onset by an abrupt shift from an unordered to an ordered correlation structure of the network. To further evaluate the shift in the monsoon onset, we couple our rainfall network to a network of climate networks using sea surface temperature as a second variable. We are thereby able to emphasize oceanic regions that are particularly important for the SAMS and anticipate the influence of future changes of sea-surface temperature on the SAMS.

  6. Fractional-order gradient descent learning of BP neural networks with Caputo derivative.

    PubMed

    Wang, Jian; Wen, Yanqing; Gou, Yida; Ye, Zhenyun; Chen, Hua

    2017-05-01

    Fractional calculus has been found to be a promising area of research for information processing and modeling of some physical systems. In this paper, we propose a fractional gradient descent method for the backpropagation (BP) training of neural networks. In particular, the Caputo derivative is employed to evaluate the fractional-order gradient of the error defined as the traditional quadratic energy function. The monotonicity and weak (strong) convergence of the proposed approach are proved in detail. Two simulations have been implemented to illustrate the performance of presented fractional-order BP algorithm on three small datasets and one large dataset. The numerical simulations effectively verify the theoretical observations of this paper as well. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. High order neural correlates of social behavior in the honeybee brain.

    PubMed

    Duer, Aron; Paffhausen, Benjamin H; Menzel, Randolf

    2015-10-30

    Honeybees are well established models of neural correlates of sensory function, learning and memory formation. Here we report a novel approach allowing to record high-order mushroom body-extrinsic interneurons in the brain of worker bees within a functional colony. New method The use of two 100 cm long twisted copper electrodes allowed recording of up to four units of mushroom body-extrinsic neurons simultaneously for up to 24h in animals moving freely between members of the colony. Every worker, including the recorded bee, hatched in the experimental environment. The group consisted of 200 animals in average. Animals explored different regions of the comb and interacted with other colony members. The activities of the units were not selective for locations on the comb, body directions with respect to gravity and olfactory signals on the comb, or different social interactions. However, combinations of these parameters defined neural activity in a unit-specific way. In addition, units recorded from the same animal co-varied according to unknown factors. Comparison with existing method(s): All electrophysiological studies with honey bees were performed so far on constrained animals outside their natural behavioral contexts. Yet no neuronal correlates were measured in a social context. Free mobility of recoded insects over a range of a quarter square meter allows addressing questions concerning neural correlates of social communication, planning of tasks within the colony and attention-like processes. The method makes it possible to study neural correlates of social behavior in a near-natural setting within the honeybee colony. Copyright © 2015 Elsevier B.V. All rights reserved.

  8. Cooperative Adaptive Output Regulation for Second-Order Nonlinear Multiagent Systems With Jointly Connected Switching Networks.

    PubMed

    Liu, Wei; Huang, Jie

    2018-03-01

    This paper studies the cooperative global robust output regulation problem for a class of heterogeneous second-order nonlinear uncertain multiagent systems with jointly connected switching networks. The main contributions consist of the following three aspects. First, we generalize the result of the adaptive distributed observer from undirected jointly connected switching networks to directed jointly connected switching networks. Second, by performing a new coordinate and input transformation, we convert our problem into the cooperative global robust stabilization problem of a more complex augmented system via the distributed internal model principle. Third, we solve the stabilization problem by a distributed state feedback control law. Our result is illustrated by the leader-following consensus problem for a group of Van der Pol oscillators.

  9. Discriminating topology in galaxy distributions using network analysis

    NASA Astrophysics Data System (ADS)

    Hong, Sungryong; Coutinho, Bruno C.; Dey, Arjun; Barabási, Albert-L.; Vogelsberger, Mark; Hernquist, Lars; Gebhardt, Karl

    2016-07-01

    The large-scale distribution of galaxies is generally analysed using the two-point correlation function. However, this statistic does not capture the topology of the distribution, and it is necessary to resort to higher order correlations to break degeneracies. We demonstrate that an alternate approach using network analysis can discriminate between topologically different distributions that have similar two-point correlations. We investigate two galaxy point distributions, one produced by a cosmological simulation and the other by a Lévy walk. For the cosmological simulation, we adopt the redshift z = 0.58 slice from Illustris and select galaxies with stellar masses greater than 108 M⊙. The two-point correlation function of these simulated galaxies follows a single power law, ξ(r) ˜ r-1.5. Then, we generate Lévy walks matching the correlation function and abundance with the simulated galaxies. We find that, while the two simulated galaxy point distributions have the same abundance and two-point correlation function, their spatial distributions are very different; most prominently, filamentary structures, absent in Lévy fractals. To quantify these missing topologies, we adopt network analysis tools and measure diameter, giant component, and transitivity from networks built by a conventional friends-of-friends recipe with various linking lengths. Unlike the abundance and two-point correlation function, these network quantities reveal a clear separation between the two simulated distributions; therefore, the galaxy distribution simulated by Illustris is not a Lévy fractal quantitatively. We find that the described network quantities offer an efficient tool for discriminating topologies and for comparing observed and theoretical distributions.

  10. Chemiomics: network reconstruction and kinetics of port wine aging.

    PubMed

    Monforte, Ana Rita; Jacobson, Dan; Silva Ferreira, A C

    2015-03-11

    Network reconstruction (NR) has proven to be useful in the detection and visualization of relationships among the compounds present in a Port wine aging data set. This view of the data provides a considerable amount of information with which to understand the kinetic contexts of the molecules represented by peaks in each chromatogram. The aim of this study was to use NR together with the determination of kinetic parameters to extract more information about the mechanisms involved in Port wine aging. The volatile compounds present in samples of Port wines spanning 128 years in age were measured with the use of GC-MS. After chromatogram alignment, a peak matrix was created, and all peak vectors were compared to one another to determine their Pearson correlations over time. A correlation network was created and filtered on the basis of the resulting correlation values. Some nodes in the network were further studied in experiments on Port wines stored under different conditions of oxygen and temperature in order to determine their kinetic parameters. The resulting network can be divided into three main branches. The first branch is related to compounds that do not directly correlate to age, the second branch contains compounds affected by temperature, and the third branch contains compounds associated with oxygen. Compounds clustered in the same branch of the network have similar expression patterns over time as well as the same kinetic order, thus are likely to be dependent on the same technological parameters. Network construction and visualization provides more information with which to understand the probable kinetic contexts of the molecules represented by peaks in each chromatogram. The approach described here is a powerful tool for the study of mechanisms and kinetics in complex systems and should aid in the understanding and monitoring of wine quality.

  11. Chemiomics: Network Reconstruction and Kinetics of Port Wine Aging

    DOE PAGES

    Monforte, Ana Rita; Jacobson, Dan; Silva Ferreira, A. C.

    2015-02-11

    Network reconstruction (NR) has proven to be useful in the detection and visualization of relationships among the compounds present in a Port wine aging data set. This view of the data provides a considerable amount of information with which to understand the kinetic contexts of the molecules represented by peaks in each chromatogram. The aim of this paper was to use NR together with the determination of kinetic parameters to extract more information about the mechanisms involved in Port wine aging. The volatile compounds present in samples of Port wines spanning 128 years in age were measured with the usemore » of GC-MS. After chromatogram alignment, a peak matrix was created, and all peak vectors were compared to one another to determine their Pearson correlations over time. A correlation network was created and filtered on the basis of the resulting correlation values. Some nodes in the network were further studied in experiments on Port wines stored under different conditions of oxygen and temperature in order to determine their kinetic parameters. The resulting network can be divided into three main branches. The first branch is related to compounds that do not directly correlate to age, the second branch contains compounds affected by temperature, and the third branch contains compounds associated with oxygen. Compounds clustered in the same branch of the network have similar expression patterns over time as well as the same kinetic order, thus are likely to be dependent on the same technological parameters. Network construction and visualization provides more information with which to understand the probable kinetic contexts of the molecules represented by peaks in each chromatogram. Finally, the approach described here is a powerful tool for the study of mechanisms and kinetics in complex systems and should aid in the understanding and monitoring of wine quality.« less

  12. Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation.

    PubMed

    Guo, Xiaobo; Zhang, Ye; Hu, Wenhao; Tan, Haizhu; Wang, Xueqin

    2014-01-01

    Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs). It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC) has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI)-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC) curve and the precision-recall (PR) curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference.

  13. Inferring Nonlinear Gene Regulatory Networks from Gene Expression Data Based on Distance Correlation

    PubMed Central

    Guo, Xiaobo; Zhang, Ye; Hu, Wenhao; Tan, Haizhu; Wang, Xueqin

    2014-01-01

    Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs). It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC) has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI)-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC) curve and the precision-recall (PR) curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference. PMID:24551058

  14. Real-time networked control of an industrial robot manipulator via discrete-time second-order sliding modes

    NASA Astrophysics Data System (ADS)

    Massimiliano Capisani, Luca; Facchinetti, Tullio; Ferrara, Antonella

    2010-08-01

    This article presents the networked control of a robotic anthropomorphic manipulator based on a second-order sliding mode technique, where the control objective is to track a desired trajectory for the manipulator. The adopted control scheme allows an easy and effective distribution of the control algorithm over two networked machines. While the predictability of real-time tasks execution is achieved by the Soft Hard Real-Time Kernel (S.Ha.R.K.) real-time operating system, the communication is established via a standard Ethernet network. The performances of the control system are evaluated under different experimental system configurations using, to perform the experiments, a COMAU SMART3-S2 industrial robot, and the results are analysed to put into evidence the robustness of the proposed approach against possible network delays, packet losses and unmodelled effects.

  15. Altered intra- and inter-network functional coupling of resting-state networks associated with motor dysfunction in stroke.

    PubMed

    Zhao, Zhiyong; Wu, Jie; Fan, Mingxia; Yin, Dazhi; Tang, Chaozheng; Gong, Jiayu; Xu, Guojun; Gao, Xinjie; Yu, Qiurong; Yang, Hao; Sun, Limin; Jia, Jie

    2018-04-24

    Motor functions are supported through functional integration across the extended motor system network. Individuals following stroke often show deficits on motor performance requiring coordination of multiple brain networks; however, the assessment of connectivity patterns after stroke was still unclear. This study aimed to investigate the changes in intra- and inter-network functional connectivity (FC) of multiple networks following stroke and further correlate FC with motor performance. Thirty-three left subcortical chronic stroke patients and 34 healthy controls underwent resting-state functional magnetic resonance imaging. Eleven resting-state networks were identified via independent component analysis (ICA). Compared with healthy controls, the stroke group showed abnormal FC within the motor network (MN), visual network (VN), dorsal attention network (DAN), and executive control network (ECN). Additionally, the FC values of the ipsilesional inferior parietal lobule (IPL) within the ECN were negatively correlated with the Fugl-Meyer Assessment (FMA) scores (hand + wrist). With respect to inter-network interactions, the ipsilesional frontoparietal network (FPN) decreased FC with the MN and DAN; the contralesional FPN decreased FC with the ECN, but it increased FC with the default mode network (DMN); and the posterior DMN decreased FC with the VN. In sum, this study demonstrated the coexistence of intra- and inter-network alterations associated with motor-visual attention and high-order cognitive control function in chronic stroke, which might provide insights into brain network plasticity following stroke. © 2018 Wiley Periodicals, Inc.

  16. Neural-network Kohn-Sham exchange-correlation potential and its out-of-training transferability

    NASA Astrophysics Data System (ADS)

    Nagai, Ryo; Akashi, Ryosuke; Sasaki, Shu; Tsuneyuki, Shinji

    2018-06-01

    We incorporate in the Kohn-Sham self-consistent equation a trained neural-network projection from the charge density distribution to the Hartree-exchange-correlation potential n → VHxc for a possible numerical approach to the exact Kohn-Sham scheme. The potential trained through a newly developed scheme enables us to evaluate the total energy without explicitly treating the formula of the exchange-correlation energy. With a case study of a simple model, we show that the well-trained neural-network VHxc achieves accuracy for the charge density and total energy out of the model parameter range used for the training, indicating that the property of the elusive ideal functional form of VHxc can approximately be encapsulated by the machine-learning construction. We also exemplify a factor that crucially limits the transferability—the boundary in the model parameter space where the number of the one-particle bound states changes—and see that this is cured by setting the training parameter range across that boundary. The training scheme and insights from the model study apply to more general systems, opening a novel path to numerically efficient Kohn-Sham potential.

  17. Proposed correlation of structure network inherited from producing techniques and deformation behavior for Ni-Ti-Mo metallic glasses via atomistic simulations

    PubMed Central

    Yang, M. H.; Li, J. H.; Liu, B. X.

    2016-01-01

    Based on the newly constructed n-body potential of Ni-Ti-Mo system, Molecular Dynamics and Monte Carlo simulations predict an energetically favored glass formation region and an optimal composition sub-region with the highest glass-forming ability. In order to compare the producing techniques between liquid melt quenching (LMQ) and solid-state amorphization (SSA), inherent hierarchical structure and its effect on mechanical property were clarified via atomistic simulations. It is revealed that both producing techniques exhibit no pronounced differences in the local atomic structure and mechanical behavior, while the LMQ method makes a relatively more ordered structure and a higher intrinsic strength. Meanwhile, it is found that the dominant short-order clusters of Ni-Ti-Mo metallic glasses obtained by LMQ and SSA are similar. By analyzing the structural evolution upon uniaxial tensile deformation, it is concluded that the gradual collapse of the spatial structure network is intimately correlated to the mechanical response of metallic glasses and acts as a structural signature of the initiation and propagation of shear bands. PMID:27418115

  18. Network diffusion accurately models the relationship between structural and functional brain connectivity networks

    PubMed Central

    Abdelnour, Farras; Voss, Henning U.; Raj, Ashish

    2014-01-01

    The relationship between anatomic connectivity of large-scale brain networks and their functional connectivity is of immense importance and an area of active research. Previous attempts have required complex simulations which model the dynamics of each cortical region, and explore the coupling between regions as derived by anatomic connections. While much insight is gained from these non-linear simulations, they can be computationally taxing tools for predicting functional from anatomic connectivities. Little attention has been paid to linear models. Here we show that a properly designed linear model appears to be superior to previous non-linear approaches in capturing the brain’s long-range second order correlation structure that governs the relationship between anatomic and functional connectivities. We derive a linear network of brain dynamics based on graph diffusion, whereby the diffusing quantity undergoes a random walk on a graph. We test our model using subjects who underwent diffusion MRI and resting state fMRI. The network diffusion model applied to the structural networks largely predicts the correlation structures derived from their fMRI data, to a greater extent than other approaches. The utility of the proposed approach is that it can routinely be used to infer functional correlation from anatomic connectivity. And since it is linear, anatomic connectivity can also be inferred from functional data. The success of our model confirms the linearity of ensemble average signals in the brain, and implies that their long-range correlation structure may percolate within the brain via purely mechanistic processes enacted on its structural connectivity pathways. PMID:24384152

  19. A multivariate extension of mutual information for growing neural networks.

    PubMed

    Ball, Kenneth R; Grant, Christopher; Mundy, William R; Shafer, Timothy J

    2017-11-01

    Recordings of neural network activity in vitro are increasingly being used to assess the development of neural network activity and the effects of drugs, chemicals and disease states on neural network function. The high-content nature of the data derived from such recordings can be used to infer effects of compounds or disease states on a variety of important neural functions, including network synchrony. Historically, synchrony of networks in vitro has been assessed either by determination of correlation coefficients (e.g. Pearson's correlation), by statistics estimated from cross-correlation histograms between pairs of active electrodes, and/or by pairwise mutual information and related measures. The present study examines the application of Normalized Multiinformation (NMI) as a scalar measure of shared information content in a multivariate network that is robust with respect to changes in network size. Theoretical simulations are designed to investigate NMI as a measure of complexity and synchrony in a developing network relative to several alternative approaches. The NMI approach is applied to these simulations and also to data collected during exposure of in vitro neural networks to neuroactive compounds during the first 12 days in vitro, and compared to other common measures, including correlation coefficients and mean firing rates of neurons. NMI is shown to be more sensitive to developmental effects than first order synchronous and nonsynchronous measures of network complexity. Finally, NMI is a scalar measure of global (rather than pairwise) mutual information in a multivariate network, and hence relies on less assumptions for cross-network comparisons than historical approaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Computer Simulation for Calculating the Second-Order Correlation Function of Classical and Quantum Light

    ERIC Educational Resources Information Center

    Facao, M.; Lopes, A.; Silva, A. L.; Silva, P.

    2011-01-01

    We propose an undergraduate numerical project for simulating the results of the second-order correlation function as obtained by an intensity interference experiment for two kinds of light, namely bunched light with Gaussian or Lorentzian power density spectrum and antibunched light obtained from single-photon sources. While the algorithm for…

  1. Visualization and Analysis of Wireless Sensor Network Data for Smart Civil Structure Applications Based On Spatial Correlation Technique

    NASA Astrophysics Data System (ADS)

    Chowdhry, Bhawani Shankar; White, Neil M.; Jeswani, Jai Kumar; Dayo, Khalil; Rathi, Manorma

    2009-07-01

    Disasters affecting infrastructure, such as the 2001 earthquakes in India, 2005 in Pakistan, 2008 in China and the 2004 tsunami in Asia, provide a common need for intelligent buildings and smart civil structures. Now, imagine massive reductions in time to get the infrastructure working again, realtime information on damage to buildings, massive reductions in cost and time to certify that structures are undamaged and can still be operated, reductions in the number of structures to be rebuilt (if they are known not to be damaged). Achieving these ideas would lead to huge, quantifiable, long-term savings to government and industry. Wireless sensor networks (WSNs) can be deployed in buildings to make any civil structure both smart and intelligent. WSNs have recently gained much attention in both public and research communities because they are expected to bring a new paradigm to the interaction between humans, environment, and machines. This paper presents the deployment of WSN nodes in the Top Quality Centralized Instrumentation Centre (TQCIC). We created an ad hoc networking application to collect real-time data sensed from the nodes that were randomly distributed throughout the building. If the sensors are relocated, then the application automatically reconfigures itself in the light of the new routing topology. WSNs are event-based systems that rely on the collective effort of several micro-sensor nodes, which are continuously observing a physical phenomenon. WSN applications require spatially dense sensor deployment in order to achieve satisfactory coverage. The degree of spatial correlation increases with the decreasing inter-node separation. Energy consumption is reduced dramatically by having only those sensor nodes with unique readings transmit their data. We report on an algorithm based on a spatial correlation technique that assures high QoS (in terms of SNR) of the network as well as proper utilization of energy, by suppressing redundant data transmission

  2. Fermionic topological quantum states as tensor networks

    NASA Astrophysics Data System (ADS)

    Wille, C.; Buerschaper, O.; Eisert, J.

    2017-06-01

    Tensor network states, and in particular projected entangled pair states, play an important role in the description of strongly correlated quantum lattice systems. They do not only serve as variational states in numerical simulation methods, but also provide a framework for classifying phases of quantum matter and capture notions of topological order in a stringent and rigorous language. The rapid development in this field for spin models and bosonic systems has not yet been mirrored by an analogous development for fermionic models. In this work, we introduce a tensor network formalism capable of capturing notions of topological order for quantum systems with fermionic components. At the heart of the formalism are axioms of fermionic matrix-product operator injectivity, stable under concatenation. Building upon that, we formulate a Grassmann number tensor network ansatz for the ground state of fermionic twisted quantum double models. A specific focus is put on the paradigmatic example of the fermionic toric code. This work shows that the program of describing topologically ordered systems using tensor networks carries over to fermionic models.

  3. Improving Multidimensional Wireless Sensor Network Lifetime Using Pearson Correlation and Fractal Clustering

    PubMed Central

    Almeida, Fernando R.; Brayner, Angelo; Rodrigues, Joel J. P. C.; Maia, Jose E. Bessa

    2017-01-01

    An efficient strategy for reducing message transmission in a wireless sensor network (WSN) is to group sensors by means of an abstraction denoted cluster. The key idea behind the cluster formation process is to identify a set of sensors whose sensed values present some data correlation. Nowadays, sensors are able to simultaneously sense multiple different physical phenomena, yielding in this way multidimensional data. This paper presents three methods for clustering sensors in WSNs whose sensors collect multidimensional data. The proposed approaches implement the concept of multidimensional behavioral clustering. To show the benefits introduced by the proposed methods, a prototype has been implemented and experiments have been carried out on real data. The results prove that the proposed methods decrease the amount of data flowing in the network and present low root-mean-square error (RMSE). PMID:28590450

  4. Multi-tissue analysis of co-expression networks by higher-order generalized singular value decomposition identifies functionally coherent transcriptional modules.

    PubMed

    Xiao, Xiaolin; Moreno-Moral, Aida; Rotival, Maxime; Bottolo, Leonardo; Petretto, Enrico

    2014-01-01

    Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e.g., cell-types and disease states). Leveraging these data is especially important for network-based approaches to human disease, for instance to identify coherent transcriptional modules (subnetworks) that can inform functional disease mechanisms and pathological pathways. Yet, genome-scale network analysis across conditions is significantly hampered by the paucity of robust and computationally-efficient methods. Building on the Higher-Order Generalized Singular Value Decomposition, we introduce a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions. Our method can accommodate weighted (and unweighted) networks of any size and can similarly use co-expression or raw gene expression input data, without hinging upon the definition and stability of the correlation used to assess gene co-expression. In simulation studies, we demonstrated distinctive advantages of our method over existing methods, which was able to recover accurately both common and condition-specific network-modules without entailing ad-hoc input parameters as required by other approaches. We applied our method to genome-scale and multi-tissue transcriptomic datasets from rats (microarray-based) and humans (mRNA-sequencing-based) and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods. In humans we recapitulated the crosstalk between cell-cycle progression and cell-extracellular matrix interactions processes in ventricular zones during neocortex expansion and further, we uncovered pathways related to development of later cognitive functions in the cortical plate of the developing brain which were previously unappreciated. Analyses of seven rat tissues identified a multi-tissue subnetwork of co

  5. Concepts relating magnetic interactions, intertwined electronic orders, and strongly correlated superconductivity

    PubMed Central

    Davis, J. C. Séamus; Lee, Dung-Hai

    2013-01-01

    Unconventional superconductivity (SC) is said to occur when Cooper pair formation is dominated by repulsive electron–electron interactions, so that the symmetry of the pair wave function is other than an isotropic s-wave. The strong, on-site, repulsive electron–electron interactions that are the proximate cause of such SC are more typically drivers of commensurate magnetism. Indeed, it is the suppression of commensurate antiferromagnetism (AF) that usually allows this type of unconventional superconductivity to emerge. Importantly, however, intervening between these AF and SC phases, intertwined electronic ordered phases (IP) of an unexpected nature are frequently discovered. For this reason, it has been extremely difficult to distinguish the microscopic essence of the correlated superconductivity from the often spectacular phenomenology of the IPs. Here we introduce a model conceptual framework within which to understand the relationship between AF electron–electron interactions, IPs, and correlated SC. We demonstrate its effectiveness in simultaneously explaining the consequences of AF interactions for the copper-based, iron-based, and heavy-fermion superconductors, as well as for their quite distinct IPs. PMID:24114268

  6. Keldysh meets Lindblad: Correlated Gain and Loss in Higher Order Perturbation Theory

    NASA Astrophysics Data System (ADS)

    Stace, Tom; Mueller, Clemens

    Motivated by correlated decay processes driving gain, loss and lasing in driven artificial quantum systems, we develop a theoretical technique using Keldysh diagrammatic perturbation theory to derive a Lindblad master equation that goes beyond the usual second order perturbation theory. We demonstrate the method on the driven dissipative Rabi model, including terms up to fourth order in the interaction between the qubit and both the resonator and environment. This results in a large class of Lindblad dissipators and associated rates which go beyond the terms that have previously been proposed to describe similar systems. All of the additional terms contribute to the system behaviour at the same order of perturbation theory. We then apply these results to analyse the phonon-assisted steady-state gain of a microwave field driving a double quantum-dot in a resonator. We show that resonator gain and loss are substantially affected by dephasing- assisted dissipative processes in the quantum-dot system. These additional processes, which go beyond recently proposed polaronic theories, are in good quantitative agreement with experimental observations.

  7. A robust fractional-order PID controller design based on active queue management for TCP network

    NASA Astrophysics Data System (ADS)

    Hamidian, Hamideh; Beheshti, Mohammad T. H.

    2018-01-01

    In this paper, a robust fractional-order controller is designed to control the congestion in transmission control protocol (TCP) networks with time-varying parameters. Fractional controllers can increase the stability and robustness. Regardless of advantages of fractional controllers, they are still not common in congestion control in TCP networks. The network parameters are time-varying, so the robust stability is important in congestion controller design. Therefore, we focused on the robust controller design. The fractional PID controller is developed based on active queue management (AQM). D-partition technique is used. The most important property of designed controller is the robustness to the time-varying parameters of the TCP network. The vertex quasi-polynomials of the closed-loop characteristic equation are obtained, and the stability boundaries are calculated for each vertex quasi-polynomial. The intersection of all stability regions is insensitive to network parameter variations, and results in robust stability of TCP/AQM system. NS-2 simulations show that the proposed algorithm provides a stable queue length. Moreover, simulations show smaller oscillations of the queue length and less packet drop probability for FPID compared to PI and PID controllers. We can conclude from NS-2 simulations that the average packet loss probability variations are negligible when the network parameters change.

  8. Strong correlations in gravity and biophysics

    NASA Astrophysics Data System (ADS)

    Krotov, Dmitry

    The unifying theme of this dissertation is the use of correlations. In the first part (chapter 2), we investigate correlations in quantum field theories in de Sitter space. In the second part (chapters 3,4,5), we use correlations to investigate a theoretical proposal that real (observed in nature) transcriptional networks of biological organisms are operating at a critical point in their phase diagram. In chapter 2 we study the infrared dependence of correlators in various external backgrounds. Using the Schwinger-Keldysh formalism we calculate loop corrections to the correlators in the case of the Poincare patch and the complete de Sitter space. In the case of the Poincare patch, the loop correction modifies the behavior of the correlator at large distances. In the case of the complete de Sitter space, the loop correction has a strong dependence on the infrared cutoff in the past. It grows linearly with time, suggesting that at some point the correlations become strong and break the symmetry of the classical background. In chapter 3 we derive the signatures of critical behavior in a model organism, the embryo of Drosophila melanogaster. They are: strong correlations in the fluctuations of different genes, a slowing of dynamics, long range correlations in space, and departures from a Gaussian distribution of these fluctuations. We argue that these signatures are observed experimentally. In chapter 4 we construct an effective theory for the zero mode in this system. This theory is different from the standard Landau-Ginsburg description. It contains gauge fields (the result of the broken translational symmetry inside the cell), which produce observable contributions to the two-point function of the order parameter. We show that the behavior of the two-point function for the network of N genes is described by the action of a relativistic particle moving on the surface of the N - 1 dimensional sphere. We derive a theoretical bound on the decay of the correlations and

  9. Evaluation of Residential Consumers Knowledge of Wireless Network Security and Its Correlation with Identity Theft

    ERIC Educational Resources Information Center

    Kpaduwa, Fidelis Iheanyi

    2010-01-01

    This current quantitative correlational research study evaluated the residential consumers' knowledge of wireless network security and its relationship with identity theft. Data analysis was based on a sample of 254 randomly selected students. All the study participants completed a survey questionnaire designed to measure their knowledge of…

  10. SLIDER: a generic metaheuristic for the discovery of correlated motifs in protein-protein interaction networks.

    PubMed

    Boyen, Peter; Van Dyck, Dries; Neven, Frank; van Ham, Roeland C H J; van Dijk, Aalt D J

    2011-01-01

    Correlated motif mining (cmm) is the problem of finding overrepresented pairs of patterns, called motifs, in sequences of interacting proteins. Algorithmic solutions for cmm thereby provide a computational method for predicting binding sites for protein interaction. In this paper, we adopt a motif-driven approach where the support of candidate motif pairs is evaluated in the network. We experimentally establish the superiority of the Chi-square-based support measure over other support measures. Furthermore, we obtain that cmm is an np-hard problem for a large class of support measures (including Chi-square) and reformulate the search for correlated motifs as a combinatorial optimization problem. We then present the generic metaheuristic slider which uses steepest ascent with a neighborhood function based on sliding motifs and employs the Chi-square-based support measure. We show that slider outperforms existing motif-driven cmm methods and scales to large protein-protein interaction networks. The slider-implementation and the data used in the experiments are available on http://bioinformatics.uhasselt.be.

  11. Game-theoretic strategies for asymmetric networked systems

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Rao, Nageswara S.; Ma, Chris Y. T.; Hausken, Kjell

    Abstract—We consider an infrastructure consisting of a network of systems each composed of discrete components that can be reinforced at a certain cost to guard against attacks. The network provides the vital connectivity between systems, and hence plays a critical, asymmetric role in the infrastructure operations. We characterize the system-level correlations using the aggregate failure correlation function that specifies the infrastructure failure probability given the failure of an individual system or network. The survival probabilities of systems and network satisfy first-order differential conditions that capture the component-level correlations. We formulate the problem of ensuring the infrastructure survival as a gamemore » between anattacker and a provider, using the sum-form and product-form utility functions, each composed of a survival probability term and a cost term. We derive Nash Equilibrium conditions which provide expressions for individual system survival probabilities, and also the expected capacity specified by the total number of operational components. These expressions differ only in a single term for the sum-form and product-form utilities, despite their significant differences.We apply these results to simplified models of distributed cloud computing infrastructures.« less

  12. Mapping eQTL Networks with Mixed Graphical Markov Models

    PubMed Central

    Tur, Inma; Roverato, Alberto; Castelo, Robert

    2014-01-01

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

  13. Development of structural correlations and synchronization from adaptive rewiring in networks of Kuramoto oscillators

    NASA Astrophysics Data System (ADS)

    Papadopoulos, Lia; Kim, Jason Z.; Kurths, Jürgen; Bassett, Danielle S.

    2017-07-01

    Synchronization of non-identical oscillators coupled through complex networks is an important example of collective behavior, and it is interesting to ask how the structural organization of network interactions influences this process. Several studies have explored and uncovered optimal topologies for synchronization by making purposeful alterations to a network. On the other hand, the connectivity patterns of many natural systems are often not static, but are rather modulated over time according to their dynamics. However, this co-evolution and the extent to which the dynamics of the individual units can shape the organization of the network itself are less well understood. Here, we study initially randomly connected but locally adaptive networks of Kuramoto oscillators. In particular, the system employs a co-evolutionary rewiring strategy that depends only on the instantaneous, pairwise phase differences of neighboring oscillators, and that conserves the total number of edges, allowing the effects of local reorganization to be isolated. We find that a simple rule—which preserves connections between more out-of-phase oscillators while rewiring connections between more in-phase oscillators—can cause initially disordered networks to organize into more structured topologies that support enhanced synchronization dynamics. We examine how this process unfolds over time, finding a dependence on the intrinsic frequencies of the oscillators, the global coupling, and the network density, in terms of how the adaptive mechanism reorganizes the network and influences the dynamics. Importantly, for large enough coupling and after sufficient adaptation, the resulting networks exhibit interesting characteristics, including degree-frequency and frequency-neighbor frequency correlations. These properties have previously been associated with optimal synchronization or explosive transitions in which the networks were constructed using global information. On the contrary, by

  14. Trends of the World Input and Output Network of Global Trade

    PubMed Central

    del Río-Chanona, Rita María; Grujić, Jelena; Jeldtoft Jensen, Henrik

    2017-01-01

    The international trade naturally maps onto a complex networks. Theoretical analysis of this network gives valuable insights about the global economic system. Although different economic data sets have been investigated from the network perspective, little attention has been paid to its dynamical behaviour. Here we take the World Input Output Data set, which has values of the annual transactions between 40 different countries of 35 different sectors for the period of 15 years, and infer the time interdependence between countries and sectors. As a measure of interdependence we use correlations between various time series of the network characteristics. First we form 15 primary networks for each year of the data we have, where nodes are countries and links are annual exports from one country to the other. Then we calculate the strengths (weighted degree) and PageRank of each country in each of the 15 networks for 15 different years. This leads to sets of time series and by calculating the correlations between these we form a secondary network where the links are the positive correlations between different countries or sectors. Furthermore, we also form a secondary network where the links are negative correlations in order to study the competition between countries and sectors. By analysing this secondary network we obtain a clearer picture of the mutual influences between countries. As one might expect, we find that political and geographical circumstances play an important role. However, the derived correlation network reveals surprising aspects which are hidden in the primary network. Sometimes countries which belong to the same community in the original network are found to be competitors in the secondary networks. E.g. Spain and Portugal are always in the same trade flow community, nevertheless secondary network analysis reveal that they exhibit contrary time evolution. PMID:28125656

  15. Trends of the World Input and Output Network of Global Trade.

    PubMed

    Del Río-Chanona, Rita María; Grujić, Jelena; Jeldtoft Jensen, Henrik

    2017-01-01

    The international trade naturally maps onto a complex networks. Theoretical analysis of this network gives valuable insights about the global economic system. Although different economic data sets have been investigated from the network perspective, little attention has been paid to its dynamical behaviour. Here we take the World Input Output Data set, which has values of the annual transactions between 40 different countries of 35 different sectors for the period of 15 years, and infer the time interdependence between countries and sectors. As a measure of interdependence we use correlations between various time series of the network characteristics. First we form 15 primary networks for each year of the data we have, where nodes are countries and links are annual exports from one country to the other. Then we calculate the strengths (weighted degree) and PageRank of each country in each of the 15 networks for 15 different years. This leads to sets of time series and by calculating the correlations between these we form a secondary network where the links are the positive correlations between different countries or sectors. Furthermore, we also form a secondary network where the links are negative correlations in order to study the competition between countries and sectors. By analysing this secondary network we obtain a clearer picture of the mutual influences between countries. As one might expect, we find that political and geographical circumstances play an important role. However, the derived correlation network reveals surprising aspects which are hidden in the primary network. Sometimes countries which belong to the same community in the original network are found to be competitors in the secondary networks. E.g. Spain and Portugal are always in the same trade flow community, nevertheless secondary network analysis reveal that they exhibit contrary time evolution.

  16. Do all roads lead to Rome? A comparison of brain networks derived from inter-subject volumetric and metabolic covariance and moment-to-moment hemodynamic correlations in old individuals.

    PubMed

    Di, Xin; Gohel, Suril; Thielcke, Andre; Wehrl, Hans F; Biswal, Bharat B

    2017-11-01

    Relationships between spatially remote brain regions in human have typically been estimated by moment-to-moment correlations of blood-oxygen-level dependent signals in resting-state using functional MRI (fMRI). Recently, studies using subject-to-subject covariance of anatomical volumes, cortical thickness, and metabolic activity are becoming increasingly popular. However, question remains on whether these measures reflect the same inter-region connectivity and brain network organizations. In the current study, we systematically analyzed inter-subject volumetric covariance from anatomical MRI images, metabolic covariance from fluorodeoxyglucose positron emission tomography images from 193 healthy subjects, and resting-state moment-to-moment correlations from fMRI images of a subset of 44 subjects. The correlation matrices calculated from the three methods were found to be minimally correlated, with higher correlation in the range of 0.31, as well as limited proportion of overlapping connections. The volumetric network showed the highest global efficiency and lowest mean clustering coefficient, leaning toward random-like network, while the metabolic and resting-state networks conveyed properties more resembling small-world networks. Community structures of the volumetric and metabolic networks did not reflect known functional organizations, which could be observed in resting-state network. The current results suggested that inter-subject volumetric and metabolic covariance do not necessarily reflect the inter-regional relationships and network organizations as resting-state correlations, thus calling for cautions on interpreting results of inter-subject covariance networks.

  17. Adaptation and implementation of standardized order sets in a network of multi-hospital corporations in rural Ontario.

    PubMed

    Meleskie, Jessica; Eby, Don

    2009-01-01

    Standardized, preprinted or computer-generated physician orders are an attractive project for organizations that wish to improve the quality of patient care. The successful development and maintenance of order sets is a major undertaking. This article recounts the collaborative experience of the Grey Bruce Health Network in adapting and implementing an existing set of physician orders for use in its three hospital corporations. An Order Set Committee composed of primarily front-line staff was given authority over the order set development, approval and implementation processes. This arrangement bypassed the traditional approval process and facilitated the rapid implementation of a large number of order sets in a short time period.

  18. Point model equations for neutron correlation counting: Extension of Böhnel's equations to any order

    DOE PAGES

    Favalli, Andrea; Croft, Stephen; Santi, Peter

    2015-06-15

    Various methods of autocorrelation neutron analysis may be used to extract information about a measurement item containing spontaneously fissioning material. The two predominant approaches being the time correlation analysis (that make use of a coincidence gate) methods of multiplicity shift register logic and Feynman sampling. The common feature is that the correlated nature of the pulse train can be described by a vector of reduced factorial multiplet rates. We call these singlets, doublets, triplets etc. Within the point reactor model the multiplet rates may be related to the properties of the item, the parameters of the detector, and basic nuclearmore » data constants by a series of coupled algebraic equations – the so called point model equations. Solving, or inverting, the point model equations using experimental calibration model parameters is how assays of unknown items is performed. Currently only the first three multiplets are routinely used. In this work we develop the point model equations to higher order multiplets using the probability generating functions approach combined with the general derivative chain rule, the so called Faà di Bruno Formula. Explicit expression up to 5th order are provided, as well the general iterative formula to calculate any order. This study represents the first necessary step towards determining if higher order multiplets can add value to nondestructive measurement practice for nuclear materials control and accountancy.« less

  19. Higher-order derivative correlations and the alignment of small-scale structures in isotropic numerical turbulence

    NASA Technical Reports Server (NTRS)

    Kerr, R. A.

    1983-01-01

    In a three dimensional simulation higher order derivative correlations, including skewness and flatness factors, are calculated for velocity and passive scalar fields and are compared with structures in the flow. The equations are forced to maintain steady state turbulence and collect statistics. It is found that the scalar derivative flatness increases much faster with Reynolds number than the velocity derivative flatness, and the velocity and mixed derivative skewness do not increase with Reynolds number. Separate exponents are found for the various fourth order velocity derivative correlations, with the vorticity flatness exponent the largest. Three dimensional graphics show strong alignment between the vorticity, rate of strain, and scalar-gradient fields. The vorticity is concentrated in tubes with the scalar gradient and the largest principal rate of strain aligned perpendicular to the tubes. Velocity spectra, in Kolmogorov variables, collapse to a single curve and a short minus 5/3 spectral regime is observed.

  20. Unravelling the architecture and dynamics of tropane alkaloid biosynthesis pathways using metabolite correlation networks.

    PubMed

    Nguyen, Thi-Kieu-Oanh; Jamali, Arash; Lanoue, Arnaud; Gontier, Eric; Dauwe, Rebecca

    2015-08-01

    The tropane alkaloid spectrum in Solanaceae is highly variable within and between species. Little is known about the topology and the coordination of the biosynthetic pathways leading to the variety of tropine and pseudotropine derived esters in the alkaloid spectrum, or about the metabolic dynamics induced by tropane alkaloid biosynthesis stimulating conditions. A good understanding of the metabolism, including all ramifications, is however necessary for the development of strategies to increase the abundance of pharmacologically interesting compounds such as hyoscyamine and scopolamine. The present study explores the tropane alkaloid metabolic pathways in an untargeted approach involving a correlation-based network analysis. Using GC-MS metabolite profiling, the variation and co-variation among tropane alkaloids and primary metabolites was monitored in 60 Datura innoxia Mill. individuals, of which half were exposed to tropane alkaloid biosynthesis stimulating conditions by co-culture with Agrobacterium rhizogenes. Considerable variation was evident in the relative proportions of the tropane alkaloids. Remodeling of the tropane alkaloid spectrum under co-culture with A. rhizogenes involved a specific and strong increase of hyoscyamine production and revealed that the accumulation of hyoscyamine, 3-tigloyloxy-6,7-epoxytropane, and 3-methylbutyryloxytropane was controlled independently of the majority of tropane alkaloids. Based on correlations between metabolites, we propose a biosynthetic origin of hygrine, the order of esterification of certain di-oxygenated tropanes, and that the rate of acetoxylation contributes to control of hyoscyamine production. Overall, this study shows that the biosynthesis of tropane alkaloids may be far more complex and finely controlled than previously expected. Copyright © 2015 Elsevier Ltd. All rights reserved.

  1. Single-trial extraction of cognitive evoked potentials by combination of third-order correlation and wavelet denoising.

    PubMed

    Zhang, Z; Tian, X

    2005-01-01

    The application of a recently proposed denoising implementation for obtaining cognitive evoked potentials (CEPs) at the single-trial level is shown. The aim of this investigation is to develop the technique of extracting CEPs by combining both the third-order correlation and the wavelet denoising methods. First, the noisy CEPs was passed through a finite impulse response filter whose impulse response is matched with the shape of the noise-free signal. It was shown that it is possible to estimate the filter impulse response on basis of a select third-order correlation slice (TOCS) of the input noisy CEPs. Second, the output from the third-order correlation filter is decomposed with bi-orthogonal splines at 5 levels. The CEPs is reconstructed by wavelet final approximation a5. We study its performance in simulated data as well as in cognitive evoked potentials of normal rat and Alzheimer's disease (AD) model rat. For the simulated data, the method gives a significantly better reconstruction of the single-trial cognitive evoked potentials responses in comparison with the simulated data. Moreover, with this approach we obtain a significantly better estimation of the amplitudes and latencies of the simulated CEPs. For the real data, the method clearly improves the visualization of single-trial CEPs. This allows the calculation of better averages as well as the study of systematic or unsystematic variations between trials.

  2. Mode analysis of higher-order transverse-mode correlation beams in a turbulent atmosphere.

    PubMed

    Avetisyan, H; Monken, C H

    2017-01-01

    Due to the transfer of the angular spectrum of the pump beam to the two-photon state in spontaneous parametric downconversion, the generated twin photons are entangled in their spatial degrees of freedom. This spatial entanglement can be observed through correlation measurements in any set of modes in which one may choose to perform measurements. Choosing, e.g., a Hermite-Gaussian (HG) set of spatial modes as a basis, one can observe correlations present in their spatial degrees of freedom. In addition, these modes can be used as alphabets for quantum communication. For global quantum communication purposes, we derive an analytic expression for two-photon detection probability in terms of HG modes, taking into account the effects of the turbulent atmosphere. Our result is more general as it accounts for the propagation of both signal and idler photons through the atmosphere, as opposed to other works considering one photon's propagation in vacuum. We show that while the restrictions on both the parity and order of the downconverted HG fields no longer hold, due to the crosstalk between modes when propagating in the atmosphere, the crosstalk is not uniform: there are more robust modes that tend to keep the photons in them. These modes can be employed in order to increase the fidelity of quantum communication.

  3. Combined metabolomic and correlation networks analyses reveal fumarase insufficiency altered amino acid metabolism.

    PubMed

    Hou, Entai; Li, Xian; Liu, Zerong; Zhang, Fuchang; Tian, Zhongmin

    2018-04-01

    Fumarase catalyzes the interconversion of fumarate and l-malate in the tricarboxylic acid cycle. Fumarase insufficiencies were associated with increased levels of fumarate, decreased levels of malate and exacerbated salt-induced hypertension. To gain insights into the metabolism profiles induced by fumarase insufficiency and identify key regulatory metabolites, we applied a GC-MS based metabolomics platform coupled with a network approach to analyze fumarase insufficient human umbilical vein endothelial cells (HUVEC) and negative controls. A total of 24 altered metabolites involved in seven metabolic pathways were identified as significantly altered, and enriched for the biological module of amino acids metabolism. In addition, Pearson correlation network analysis revealed that fumaric acid, l-malic acid, l-aspartic acid, glycine and l-glutamic acid were hub metabolites according to Pagerank based on their three centrality indices. Alanine aminotransferase and glutamate dehydrogenase activities increased significantly in fumarase deficiency HUVEC. These results confirmed that fumarase insufficiency altered amino acid metabolism. The combination of metabolomics and network methods would provide another perspective on expounding the molecular mechanism at metabolomics level. Copyright © 2017 John Wiley & Sons, Ltd.

  4. In-network processing of joins in wireless sensor networks.

    PubMed

    Kang, Hyunchul

    2013-03-11

    The join or correlated filtering of sensor readings is one of the fundamental query operations in wireless sensor networks (WSNs). Although the join in centralized or distributed databases is a well-researched problem, join processing in WSNs has quite different characteristics and is much more difficult to perform due to the lack of statistics on sensor readings and the resource constraints of sensor nodes. Since data transmission is orders of magnitude more costly than processing at a sensor node, in-network processing of joins is essential. In this paper, the state-of-the-art techniques for join implementation in WSNs are surveyed. The requirements and challenges, join types, and components of join implementation are described. The open issues for further research are identified.

  5. In-Network Processing of Joins in Wireless Sensor Networks

    PubMed Central

    Kang, Hyunchul

    2013-01-01

    The join or correlated filtering of sensor readings is one of the fundamental query operations in wireless sensor networks (WSNs). Although the join in centralized or distributed databases is a well-researched problem, join processing in WSNs has quite different characteristics and is much more difficult to perform due to the lack of statistics on sensor readings and the resource constraints of sensor nodes. Since data transmission is orders of magnitude more costly than processing at a sensor node, in-network processing of joins is essential. In this paper, the state-of-the-art techniques for join implementation in WSNs are surveyed. The requirements and challenges, join types, and components of join implementation are described. The open issues for further research are identified. PMID:23478603

  6. Correlation-driven charge order at the interface between a Mott and a band insulator.

    PubMed

    Pentcheva, Rossitza; Pickett, Warren E

    2007-07-06

    To study digital Mott insulator LaTiO3 and band insulator SrTiO3 interfaces, we apply correlated band theory within the local density approximation including a Hubbard U to (n, m) multilayers, 1ordering, and Ti3+ dxy-orbital ordering, with antiferromagnetic exchange coupling between the spins in the interface layer. Lattice relaxations lead to conducting behavior by shifting (slightly but importantly) the lower Hubbard band, but the charge and orbital order is robust against relaxation.

  7. Computerized implementation of higher-order electron-correlation methods and their linear-scaling divide-and-conquer extensions.

    PubMed

    Nakano, Masahiko; Yoshikawa, Takeshi; Hirata, So; Seino, Junji; Nakai, Hiromi

    2017-11-05

    We have implemented a linear-scaling divide-and-conquer (DC)-based higher-order coupled-cluster (CC) and Møller-Plesset perturbation theories (MPPT) as well as their combinations automatically by means of the tensor contraction engine, which is a computerized symbolic algebra system. The DC-based energy expressions of the standard CC and MPPT methods and the CC methods augmented with a perturbation correction were proposed for up to high excitation orders [e.g., CCSDTQ, MP4, and CCSD(2) TQ ]. The numerical assessment for hydrogen halide chains, polyene chains, and first coordination sphere (C1) model of photoactive yellow protein has revealed that the DC-based correlation methods provide reliable correlation energies with significantly less computational cost than that of the conventional implementations. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  8. Predicting missing links via correlation between nodes

    NASA Astrophysics Data System (ADS)

    Liao, Hao; Zeng, An; Zhang, Yi-Cheng

    2015-10-01

    As a fundamental problem in many different fields, link prediction aims to estimate the likelihood of an existing link between two nodes based on the observed information. Since this problem is related to many applications ranging from uncovering missing data to predicting the evolution of networks, link prediction has been intensively investigated recently and many methods have been proposed so far. The essential challenge of link prediction is to estimate the similarity between nodes. Most of the existing methods are based on the common neighbor index and its variants. In this paper, we propose to calculate the similarity between nodes by the Pearson correlation coefficient. This method is found to be very effective when applied to calculate similarity based on high order paths. We finally fuse the correlation-based method with the resource allocation method, and find that the combined method can substantially outperform the existing methods, especially in sparse networks.

  9. Pattern Storage, Bifurcations, and Groupwise Correlation Structure of an Exactly Solvable Asymmetric Neural Network Model.

    PubMed

    Fasoli, Diego; Cattani, Anna; Panzeri, Stefano

    2018-05-01

    Despite their biological plausibility, neural network models with asymmetric weights are rarely solved analytically, and closed-form solutions are available only in some limiting cases or in some mean-field approximations. We found exact analytical solutions of an asymmetric spin model of neural networks with arbitrary size without resorting to any approximation, and we comprehensively studied its dynamical and statistical properties. The network had discrete time evolution equations and binary firing rates, and it could be driven by noise with any distribution. We found analytical expressions of the conditional and stationary joint probability distributions of the membrane potentials and the firing rates. By manipulating the conditional probability distribution of the firing rates, we extend to stochastic networks the associating learning rule previously introduced by Personnaz and coworkers. The new learning rule allowed the safe storage, under the presence of noise, of point and cyclic attractors, with useful implications for content-addressable memories. Furthermore, we studied the bifurcation structure of the network dynamics in the zero-noise limit. We analytically derived examples of the codimension 1 and codimension 2 bifurcation diagrams of the network, which describe how the neuronal dynamics changes with the external stimuli. This showed that the network may undergo transitions among multistable regimes, oscillatory behavior elicited by asymmetric synaptic connections, and various forms of spontaneous symmetry breaking. We also calculated analytically groupwise correlations of neural activity in the network in the stationary regime. This revealed neuronal regimes where, statistically, the membrane potentials and the firing rates are either synchronous or asynchronous. Our results are valid for networks with any number of neurons, although our equations can be realistically solved only for small networks. For completeness, we also derived the network

  10. Internal fracture heterogeneity in discrete fracture network modelling: Effect of correlation length and textures with connected and disconnected permeability field

    NASA Astrophysics Data System (ADS)

    Frampton, A.; Hyman, J.; Zou, L.

    2017-12-01

    Analysing flow and transport in sparsely fractured media is important for understanding how crystalline bedrock environments function as barriers to transport of contaminants, with important applications towards subsurface repositories for storage of spent nuclear fuel. Crystalline bedrocks are particularly favourable due to their geological stability, low advective flow and strong hydrogeochemical retention properties, which can delay transport of radionuclides, allowing decay to limit release to the biosphere. There are however many challenges involved in quantifying and modelling subsurface flow and transport in fractured media, largely due to geological complexity and heterogeneity, where the interplay between advective and dispersive flow strongly impacts both inert and reactive transport. A key to modelling transport in a Lagrangian framework involves quantifying pathway travel times and the hydrodynamic control of retention, and both these quantities strongly depend on heterogeneity of the fracture network at different scales. In this contribution, we present recent analysis of flow and transport considering fracture networks with single-fracture heterogeneity described by different multivariate normal distributions. A coherent triad of fields with identical correlation length and variance are created but which greatly differ in structure, corresponding to textures with well-connected low, medium and high permeability structures. Through numerical modelling of multiple scales in a stochastic setting we quantify the relative impact of texture type and correlation length against network topological measures, and identify key thresholds for cases where flow dispersion is controlled by single-fracture heterogeneity versus network-scale heterogeneity. This is achieved by using a recently developed novel numerical discrete fracture network model. Furthermore, we highlight enhanced flow channelling for cases where correlation structure continues across

  11. Individual, network, and neighborhood correlates of exchange sex among female non-injection drug users in Baltimore, MD (2005–2007)

    PubMed Central

    Rudolph, Abby E; Linton, Sabriya; Dyer, Typhanye Penniman; Latkin, Carl

    2012-01-01

    The “HIV risk environment” has been characterized as a dynamic interplay between structural and network factors. However, most HIV prevention research has not examined the independent and combined impact of network and structural factors. We aimed to identify individual, network, and neighborhood correlates of exchange sex (≥1 exchange sex partner, past 90 days) among female non-injection drug users (NIDUs). We used baseline data from 417 NIDUs enrolled in a randomized HIV prevention trial in Baltimore (2005–2007). Surveys ascertained demographic variables, drug/sex risk behaviors, neighborhood perceptions, and social/sexual network characteristics. Correlates of exchange sex were identified with descriptive statistics and log-binomial regression. Our findings suggest that sex and drug relationships among female NIDUs are interlinked and may be difficult to modify without altering social norms. Strengthening ties that provide social support but not drug support and reducing ties that provide both drug and social support may facilitate reductions in individual-level HIV-risk behaviors. PMID:22983502

  12. Correlation Filter Synthesis Using Neural Networks.

    DTIC Science & Technology

    1993-12-01

    trained neural networks may be understood as "smart" data interpolators, the stored filter and the filter synthesis approaches have much in common: in...the former new filters are found by searching a data bank consisting of the filters themselves; in the latter filters are formed from a distributed... data bank that contains neural network interaction strengths or weights. 1.2 Key Results and Outputs Excellent computer simulation results were

  13. 77 FR 26796 - Order of Suspension of Trading; Airtrax, Inc., Amedia Networks, Inc., American Business Financial...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-05-07

    ... SECURITIES AND EXCHANGE COMMISSION [File No. 500-1] Order of Suspension of Trading; Airtrax, Inc., Amedia Networks, Inc., American Business Financial Services, Inc., Appalachian Bancshares, Inc., and... information concerning the securities of American Business Financial Services, Inc. because it has not filed...

  14. Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

    NASA Astrophysics Data System (ADS)

    Hyman, J. D.; Aldrich, G.; Viswanathan, H.; Makedonska, N.; Karra, S.

    2016-08-01

    We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semicorrelation, and noncorrelation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected so that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same. We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. These observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.

  15. Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

    DOE PAGES

    Hyman, Jeffrey De'Haven; Aldrich, Garrett Allen; Viswanathan, Hari S.; ...

    2016-08-01

    We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semicorrelation, and noncorrelation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected somore » that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same. We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. Lastly, these observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.« less

  16. Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Hyman, Jeffrey De'Haven; Aldrich, Garrett Allen; Viswanathan, Hari S.

    We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semicorrelation, and noncorrelation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected somore » that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same. We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. Lastly, these observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.« less

  17. Friendship Network and Dental Brushing Behavior among Middle School Students: An Agent Based Modeling Approach.

    PubMed

    Sadeghipour, Maryam; Khoshnevisan, Mohammad Hossein; Jafari, Afshin; Shariatpanahi, Seyed Peyman

    2017-01-01

    By using a standard questionnaire, the level of dental brushing frequency was assessed among 201 adolescent female middle school students in Tehran. The initial assessment was repeated after 5 months, in order to observe the dynamics in dental health behavior level. Logistic Regression model was used to evaluate the correlation among individuals' dental health behavior in their social network. A significant correlation on dental brushing habits was detected among groups of friends. This correlation was further spread over the network within the 5 months period. Moreover, it was identified that the average brushing level was improved within the 5 months period. Given that there was a significant correlation between social network's nodes' in-degree value, and brushing level, it was suggested that the observed improvement was partially due to more popularity of individuals with better tooth brushing habit. Agent Based Modeling (ABM) was used to demonstrate the dynamics of dental brushing frequency within a sample of friendship network. Two models with static and dynamic assumptions for the network structure were proposed. The model with dynamic network structure successfully described the dynamics of dental health behavior. Based on this model, on average, every 43 weeks a student changes her brushing habit due to learning from her friends. Finally, three training scenarios were tested by these models in order to evaluate their effectiveness. When training more popular students, considerable improvement in total students' brushing frequency was demonstrated by simulation results.

  18. Magnetoresistance in organic semiconductors: Including pair correlations in the kinetic equations for hopping transport

    NASA Astrophysics Data System (ADS)

    Shumilin, A. V.; Kabanov, V. V.; Dediu, V. I.

    2018-03-01

    We derive kinetic equations for polaron hopping in organic materials that explicitly take into account the double occupation possibility and pair intersite correlations. The equations include simplified phenomenological spin dynamics and provide a self-consistent framework for the description of the bipolaron mechanism of the organic magnetoresistance. At low applied voltages, the equations can be reduced to those for an effective resistor network that generalizes the Miller-Abrahams network and includes the effect of spin relaxation on the system resistivity. Our theory discloses the close relationship between the organic magnetoresistance and the intersite correlations. Moreover, in the absence of correlations, as in an ordered system with zero Hubbard energy, the magnetoresistance vanishes.

  19. Neuroimaging correlates of language network impairment and reorganization in temporal lobe epilepsy

    PubMed Central

    Balter, S.; Lin, G.; Leyden, K.M.; Paul, B.M.; McDonald, C.R.

    2016-01-01

    Advanced, noninvasive imaging has revolutionized our understanding of language networks in the brain and is reshaping our approach to the presurgical evaluation of patients with epilepsy. Functional magnetic resonance imaging (fMRI) has had the greatest impact, unveiling the complexity of language organization and reorganization in patients with epilepsy both pre- and postoperatively, while volumetric MRI and diffusion tensor imaging have led to a greater appreciation of structural and microstructural correlates of language dysfunction in different epilepsy syndromes. In this article, we review recent literature describing how unimodal and multimodal imaging has advanced our knowledge of language networks and their plasticity in epilepsy, with a focus on the most frequently studied epilepsy syndrome in adults, temporal lobe epilepsy (TLE). We also describe how new analytic techniques (i.e., graph theory) are leading to a refined characterization of abnormal brain connectivity, and how subject-specific imaging profiles combined with clinical data may enhance the prediction of both seizure and language outcomes following surgical interventions. PMID:27393391

  20. Coarse-coded higher-order neural networks for PSRI object recognition. [position, scale, and rotation invariant

    NASA Technical Reports Server (NTRS)

    Spirkovska, Lilly; Reid, Max B.

    1993-01-01

    A higher-order neural network (HONN) can be designed to be invariant to changes in scale, translation, and inplane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Consequently, fewer training passes and a smaller training set are required to learn to distinguish between objects. The size of the input field is limited, however, because of the memory required for the large number of interconnections in a fully connected HONN. By coarse coding the input image, the input field size can be increased to allow the larger input scenes required for practical object recognition problems. We describe a coarse coding technique and present simulation results illustrating its usefulness and its limitations. Our simulations show that a third-order neural network can be trained to distinguish between two objects in a 4096 x 4096 pixel input field independent of transformations in translation, in-plane rotation, and scale in less than ten passes through the training set. Furthermore, we empirically determine the limits of the coarse coding technique in the object recognition domain.

  1. Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient.

    PubMed

    Mohamed Salleh, Faridah Hani; Arif, Shereena Mohd; Zainudin, Suhaila; Firdaus-Raih, Mohd

    2015-12-01

    A gene regulatory network (GRN) is a large and complex network consisting of interacting elements that, over time, affect each other's state. The dynamics of complex gene regulatory processes are difficult to understand using intuitive approaches alone. To overcome this problem, we propose an algorithm for inferring the regulatory interactions from knock-out data using a Gaussian model combines with Pearson Correlation Coefficient (PCC). There are several problems relating to GRN construction that have been outlined in this paper. We demonstrated the ability of our proposed method to (1) predict the presence of regulatory interactions between genes, (2) their directionality and (3) their states (activation or suppression). The algorithm was applied to network sizes of 10 and 50 genes from DREAM3 datasets and network sizes of 10 from DREAM4 datasets. The predicted networks were evaluated based on AUROC and AUPR. We discovered that high false positive values were generated by our GRN prediction methods because the indirect regulations have been wrongly predicted as true relationships. We achieved satisfactory results as the majority of sub-networks achieved AUROC values above 0.5. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. Direct reconstruction of the two-dimensional pair distribution function in partially ordered systems with angular correlations.

    PubMed

    Zaluzhnyy, I A; Kurta, R P; Menushenkov, A P; Ostrovskii, B I; Vartanyants, I A

    2016-09-01

    An x-ray scattering approach to determine the two-dimensional (2D) pair distribution function (PDF) in partially ordered 2D systems is proposed. We derive relations between the structure factor and PDF that enable quantitative studies of positional and bond-orientational (BO) order in real space. We apply this approach in the x-ray study of a liquid crystal (LC) film undergoing the smectic-A-hexatic-B phase transition, to analyze the interplay between the positional and BO order during the temperature evolution of the LC film. We analyze the positional correlation length in different directions in real space.

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

    PubMed

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

    2015-06-30

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

  4. Bond Order Correlations in the 2D Hubbard Model

    NASA Astrophysics Data System (ADS)

    Moore, Conrad; Abu Asal, Sameer; Yang, Shuxiang; Moreno, Juana; Jarrell, Mark

    We use the dynamical cluster approximation to study the bond correlations in the Hubbard model with next nearest neighbor (nnn) hopping to explore the region of the phase diagram where the Fermi liquid phase is separated from the pseudogap phase by the Lifshitz line at zero temperature. We implement the Hirsch-Fye cluster solver that has the advantage of providing direct access to the computation of the bond operators via the decoupling field. In the pseudogap phase, the parallel bond order susceptibility is shown to persist at zero temperature while it vanishes for the Fermi liquid phase which allows the shape of the Lifshitz line to be mapped as a function of filling and nnn hopping. Our cluster solver implements NVIDIA's CUDA language to accelerate the linear algebra of the Quantum Monte Carlo to help alleviate the sign problem by allowing for more Monte Carlo updates to be performed in a reasonable amount of computation time. Work supported by the NSF EPSCoR Cooperative Agreement No. EPS-1003897 with additional support from the Louisiana Board of Regents.

  5. Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks

    PubMed Central

    Cavallari, Stefano; Panzeri, Stefano; Mazzoni, Alberto

    2014-01-01

    Models of networks of Leaky Integrate-and-Fire (LIF) neurons are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single neuron and neural population dynamics of conductance-based networks (COBNs) and current-based networks (CUBNs) of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity). However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-modulated in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, the network activity of COBN showed stronger synchronization in the gamma band, and spectral information about the input higher and spread over a broader range of frequencies. These results suggest that the second order statistics of network dynamics depend strongly on the choice of synaptic model. PMID:24634645

  6. Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks.

    PubMed

    Cavallari, Stefano; Panzeri, Stefano; Mazzoni, Alberto

    2014-01-01

    Models of networks of Leaky Integrate-and-Fire (LIF) neurons are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single neuron and neural population dynamics of conductance-based networks (COBNs) and current-based networks (CUBNs) of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity). However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-modulated in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, the network activity of COBN showed stronger synchronization in the gamma band, and spectral information about the input higher and spread over a broader range of frequencies. These results suggest that the second order statistics of network dynamics depend strongly on the choice of synaptic model.

  7. Application of power spectrum, cepstrum, higher order spectrum and neural network analyses for induction motor fault diagnosis

    NASA Astrophysics Data System (ADS)

    Liang, B.; Iwnicki, S. D.; Zhao, Y.

    2013-08-01

    The power spectrum is defined as the square of the magnitude of the Fourier transform (FT) of a signal. The advantage of FT analysis is that it allows the decomposition of a signal into individual periodic frequency components and establishes the relative intensity of each component. It is the most commonly used signal processing technique today. If the same principle is applied for the detection of periodicity components in a Fourier spectrum, the process is called the cepstrum analysis. Cepstrum analysis is a very useful tool for detection families of harmonics with uniform spacing or the families of sidebands commonly found in gearbox, bearing and engine vibration fault spectra. Higher order spectra (HOS) (also known as polyspectra) consist of higher order moment of spectra which are able to detect non-linear interactions between frequency components. For HOS, the most commonly used is the bispectrum. The bispectrum is the third-order frequency domain measure, which contains information that standard power spectral analysis techniques cannot provide. It is well known that neural networks can represent complex non-linear relationships, and therefore they are extremely useful for fault identification and classification. This paper presents an application of power spectrum, cepstrum, bispectrum and neural network for fault pattern extraction of induction motors. The potential for using the power spectrum, cepstrum, bispectrum and neural network as a means for differentiating between healthy and faulty induction motor operation is examined. A series of experiments is done and the advantages and disadvantages between them are discussed. It has been found that a combination of power spectrum, cepstrum and bispectrum plus neural network analyses could be a very useful tool for condition monitoring and fault diagnosis of induction motors.

  8. Network Metamodeling: The Effect of Correlation Metric Choice on Phylogenomic and Transcriptomic Network Topology

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Weighill, Deborah A; Jacobson, Daniel A

    We explore the use of a network meta-modeling approach to compare the effects of similarity metrics used to construct biological networks on the topology of the resulting networks. This work reviews various similarity metrics for the construction of networks and various topology measures for the characterization of resulting network topology, demonstrating the use of these metrics in the construction and comparison of phylogenomic and transcriptomic networks.

  9. Complex networks repair strategies: Dynamic models

    NASA Astrophysics Data System (ADS)

    Fu, Chaoqi; Wang, Ying; Gao, Yangjun; Wang, Xiaoyang

    2017-09-01

    Network repair strategies are tactical methods that restore the efficiency of damaged networks; however, unreasonable repair strategies not only waste resources, they are also ineffective for network recovery. Most extant research on network repair focuses on static networks, but results and findings on static networks cannot be applied to evolutionary dynamic networks because, in dynamic models, complex network repair has completely different characteristics. For instance, repaired nodes face more severe challenges, and require strategic repair methods in order to have a significant effect. In this study, we propose the Shell Repair Strategy (SRS) to minimize the risk of secondary node failures due to the cascading effect. Our proposed method includes the identification of a set of vital nodes that have a significant impact on network repair and defense. Our identification of these vital nodes reduces the number of switching nodes that face the risk of secondary failures during the dynamic repair process. This is positively correlated with the size of the average degree 〈 k 〉 and enhances network invulnerability.

  10. Elastic collapse in disordered isostatic networks

    NASA Astrophysics Data System (ADS)

    Moukarzel, C. F.

    2012-02-01

    Isostatic networks are minimally rigid and therefore have, generically, nonzero elastic moduli. Regular isostatic networks have finite moduli in the limit of large sizes. However, numerical simulations show that all elastic moduli of geometrically disordered isostatic networks go to zero with system size. This holds true for positional as well as for topological disorder. In most cases, elastic moduli decrease as inverse power laws of system size. On directed isostatic networks, however, of which the square and cubic lattices are particular cases, the decrease of the moduli is exponential with size. For these, the observed elastic weakening can be quantitatively described in terms of the multiplicative growth of stresses with system size, giving rise to bulk and shear moduli of order e-bL. The case of sphere packings, which only accept compressive contact forces, is considered separately. It is argued that these have a finite bulk modulus because of specific correlations in contact disorder, introduced by the constraint of compressivity. We discuss why their shear modulus, nevertheless, is again zero for large sizes. A quantitative model is proposed that describes the numerically measured shear modulus, both as a function of the loading angle and system size. In all cases, if a density p>0 of overconstraints is present, as when a packing is deformed by compression or when a glass is outside its isostatic composition window, all asymptotic moduli become finite. For square networks with periodic boundary conditions, these are of order \\sqrt{p} . For directed networks, elastic moduli are of order e-c/p, indicating the existence of an "isostatic length scale" of order 1/p.

  11. Inducing Order from Disordered Copolymers: On Demand Generation of Triblock Morphologies Including Networks

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Tureau, Maëva S.; Kuan, Wei-Fan; Rong, Lixia

    Disordered block copolymers are generally impractical in nanopatterning applications due to their inability to self-assemble into well-defined nanostructures. However, inducing order in low molecular weight disordered systems permits the design of periodic structures with smaller characteristic sizes. Here, we have induced nanoscale phase separation from disordered triblock copolymer melts to form well-ordered lamellae, hexagonally packed cylinders, and a triply periodic gyroid network structure, using a copolymer/homopolymer blending approach, which incorporates constituent homopolymers into selective block domains. This versatile blending approach allows one to precisely target multiple nanostructures from a single disordered material and can be applied to a wide varietymore » of triblock copolymer systems for nanotemplating and nanoscale separation applications requiring nanoscale feature sizes and/or high areal feature densities.« less

  12. Projective synchronization of nonidentical fractional-order neural networks based on sliding mode controller.

    PubMed

    Ding, Zhixia; Shen, Yi

    2016-04-01

    This paper investigates global projective synchronization of nonidentical fractional-order neural networks (FNNs) based on sliding mode control technique. We firstly construct a fractional-order integral sliding surface. Then, according to the sliding mode control theory, we design a sliding mode controller to guarantee the occurrence of the sliding motion. Based on fractional Lyapunov direct methods, system trajectories are driven to the proposed sliding surface and remain on it evermore, and some novel criteria are obtained to realize global projective synchronization of nonidentical FNNs. As the special cases, some sufficient conditions are given to ensure projective synchronization of identical FNNs, complete synchronization of nonidentical FNNs and anti-synchronization of nonidentical FNNs. Finally, one numerical example is given to demonstrate the effectiveness of the obtained results. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Self-Organized Lattices of Nonlinear Optochemical Waves in Photopolymerizable Fluids: The Spontaneous Emergence of 3-D Order in a Weakly Correlated System.

    PubMed

    Ponte, Matthew R; Hudson, Alexander D; Saravanamuttu, Kalaichelvi

    2018-03-01

    Many of the extraordinary three-dimensional architectures that pattern our physical world emerge from complex nonlinear systems or dynamic populations whose individual constituents are only weakly correlated to each other. Shoals of fish, murmuration behaviors in birds, congestion patterns in traffic, and even networks of social conventions are examples of spontaneous pattern formation, which cannot be predicted from the properties of individual elements alone. Pattern formation at a different scale has been observed or predicted in weakly correlated systems including superconductors, atomic gases near Bose Einstein condensation, and incoherent optical fields. Understanding pattern formation in nonlinear weakly correlated systems, which are often unified through mathematical expression, could pave intelligent self-organizing pathways to functional materials, architectures, and computing technologies. However, it is experimentally difficult to directly visualize the nonlinear dynamics of pattern formation in most populations-especially in three dimensions. Here, we describe the collective behavior of large populations of nonlinear optochemical waves, which are poorly correlated in both space and time. The optochemical waves-microscopic filaments of white light entrapped within polymer channels-originate from the modulation instability of incandescent light traveling in photopolymerizable fluids. By tracing the three-dimensional distribution of optical intensity in the nascent polymerizing system, we find that populations of randomly distributed, optochemical waves synergistically and collectively shift in space to form highly ordered lattices of specific symmetries. These, to our knowledge, are the first three-dimensionally periodic structures to emerge from a system of weakly correlated waves. Their spontaneous formation in an incoherent and effectively chaotic field is counterintuitive, but the apparent contradiction of known behaviors of light including the laws

  14. Total MRI Small Vessel Disease Burden Correlates with Cognitive Performance, Cortical Atrophy, and Network Measures in a Memory Clinic Population.

    PubMed

    Banerjee, Gargi; Jang, Hyemin; Kim, Hee Jin; Kim, Sung Tae; Kim, Jae Seung; Lee, Jae Hong; Im, Kiho; Kwon, Hunki; Lee, Jong Min; Na, Duk L; Seo, Sang Won; Werring, David John

    2018-01-01

    Recent evidence suggests that combining individual imaging markers of cerebral small vessel disease (SVD) may more accurately reflect its overall burden and better correlate with clinical measures. We wished to establish the clinical relevance of the total SVD score in a memory clinic population by investigating the association with SVD score and cognitive performance, cortical atrophy, and structural network measures, after adjusting for amyloid-β burden. We included 243 patients with amnestic mild cognitive impairment (MCI), Alzheimer's disease dementia, subcortical vascular MCI, or subcortical vascular dementia. All underwent MR and [11C] PiB-PET scanning and had standardized cognitive testing. Multiple linear regression was used to evaluate the relationships between SVD score and cognition, cortical thickness, and structural network measures. Path analyses were performed to evaluate whether network disruption mediates the effects of SVD score on cortical thickness and cognition. Total SVD score was associated with the performance of frontal (β - 4.31, SE 2.09, p = 0.040) and visuospatial (β - 0.95, SE 0.44, p = 0.032) tasks, and with reduced cortical thickness in widespread brain regions. Total SVD score was negatively correlated with nodal efficiency, as well as changes in brain network organization, with evidence of reduced integration and increasing segregation. Path analyses showed that the associations between SVD score and frontal and visuospatial scores were partially mediated by decreases in their corresponding nodal efficiency and cortical thickness. Total SVD burden has clinical relevance in a memory clinic population and correlates with cognition, and cortical atrophy, as well as structural network disruption.

  15. Higher-Order Neural Networks Applied to 2D and 3D Object Recognition

    NASA Technical Reports Server (NTRS)

    Spirkovska, Lilly; Reid, Max B.

    1994-01-01

    A Higher-Order Neural Network (HONN) can be designed to be invariant to geometric transformations such as scale, translation, and in-plane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Thus, for 2D object recognition, the network needs to be trained on just one view of each object class, not numerous scaled, translated, and rotated views. Because the 2D object recognition task is a component of the 3D object recognition task, built-in 2D invariance also decreases the size of the training set required for 3D object recognition. We present results for 2D object recognition both in simulation and within a robotic vision experiment and for 3D object recognition in simulation. We also compare our method to other approaches and show that HONNs have distinct advantages for position, scale, and rotation-invariant object recognition. The major drawback of HONNs is that the size of the input field is limited due to the memory required for the large number of interconnections in a fully connected network. We present partial connectivity strategies and a coarse-coding technique for overcoming this limitation and increasing the input field to that required by practical object recognition problems.

  16. Adaptive fuzzy wavelet network control of second order multi-agent systems with unknown nonlinear dynamics.

    PubMed

    Taheri, Mehdi; Sheikholeslam, Farid; Najafi, Majddedin; Zekri, Maryam

    2017-07-01

    In this paper, consensus problem is considered for second order multi-agent systems with unknown nonlinear dynamics under undirected graphs. A novel distributed control strategy is suggested for leaderless systems based on adaptive fuzzy wavelet networks. Adaptive fuzzy wavelet networks are employed to compensate for the effect of unknown nonlinear dynamics. Moreover, the proposed method is developed for leader following systems and leader following systems with state time delays. Lyapunov functions are applied to prove uniformly ultimately bounded stability of closed loop systems and to obtain adaptive laws. Three simulation examples are presented to illustrate the effectiveness of the proposed control algorithms. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  17. Correlation between elastic and plastic deformations of partially cured epoxy networks

    NASA Astrophysics Data System (ADS)

    Müller, Michael; Böhm, Robert; Geller, Sirko; Kupfer, Robert; Jäger, Hubert; Gude, Maik

    2018-05-01

    The thermo-mechanical behavior of polymer matrix materials is strongly dependent on the curing reaction as well as temperature and time. To date, investigations of epoxy resins and their composites mainly focused on the elastic domain because plastic deformation of cross-linked polymer networks was considered as irrelevant or not feasible. This paper presents a novel approach which combines both elastic and plastic domain. Based on an analytical framework describing the storage modulus, analogous parameter combinations are defined in order to reduce complexity when variations in temperature, strain rate and degree of cure are encountered.

  18. Are social network correlates of heavy drinking similar among black homeless youth and white homeless youth?

    PubMed

    Wenzel, Suzanne L; Hsu, Hsun-Ta; Zhou, Annie; Tucker, Joan S

    2012-11-01

    Understanding factors associated with heavy drinking among homeless youth is important for prevention efforts. Social networks are associated with drinking among homeless youth, and studies have called for attention to racial differences in networks that may affect drinking behavior. This study investigates differences in network characteristics by the racial background of homeless youth, and associations of network characteristics with heavy drinking. (Heavy drinking was defined as having five or more drinks of alcohol in a row within a couple of hours on at least one day within the past 30 days.) A probability sample of 235 Black and White homeless youths ages 13-24 were interviewed in Los Angeles County. We used chi-square or one-way analysis of variance tests to examine network differences by race and logistic regressions to identify network correlates of heavy drinking among Black and White homeless youth. The networks of Black youth included significantly more relatives and students who attend school regularly, whereas the networks of White youth were more likely to include homeless persons, relatives who drink to intoxication, and peers who drink to intoxication. Having peers who drink heavily was significantly associated with heavy drinking only among White youth. For all homeless youth, having more students in the network who regularly attend school was associated with less risk of heavy drinking. This study is the first to our knowledge to investigate racial differences in network characteristics and associations of network characteristics with heavy drinking among homeless youth. White homeless youth may benefit from interventions that reduce their ties with peers who drink. Enhancing ties to school-involved peers may be a promising intervention focus for both Black and White homeless youth.

  19. Universal features of dendrites through centripetal branch ordering

    PubMed Central

    Effenberger, Felix; Muellerleile, Julia

    2017-01-01

    Dendrites form predominantly binary trees that are exquisitely embedded in the networks of the brain. While neuronal computation is known to depend on the morphology of dendrites, their underlying topological blueprint remains unknown. Here, we used a centripetal branch ordering scheme originally developed to describe river networks—the Horton-Strahler order (SO)–to examine hierarchical relationships of branching statistics in reconstructed and model dendritic trees. We report on a number of universal topological relationships with SO that are true for all binary trees and distinguish those from SO-sorted metric measures that appear to be cell type-specific. The latter are therefore potential new candidates for categorising dendritic tree structures. Interestingly, we find a faithful correlation of branch diameters with centripetal branch orders, indicating a possible functional importance of SO for dendritic morphology and growth. Also, simulated local voltage responses to synaptic inputs are strongly correlated with SO. In summary, our study identifies important SO-dependent measures in dendritic morphology that are relevant for neural function while at the same time it describes other relationships that are universal for all dendrites. PMID:28671947

  20. Control mechanism to prevent correlated message arrivals from degrading signaling no. 7 network performance

    NASA Astrophysics Data System (ADS)

    Kosal, Haluk; Skoog, Ronald A.

    1994-04-01

    Signaling System No. 7 (SS7) is designed to provide a connection-less transfer of signaling messages of reasonable length. Customers having access to user signaling bearer capabilities as specified in the ANSI T1.623 and CCITT Q.931 standards can send bursts of correlated messages (e.g., by doing a file transfer that results in the segmentation of a block of data into a number of consecutive signaling messages) through SS7 networks. These message bursts with short interarrival times could have an adverse impact on the delay performance of the SS7 networks. A control mechanism, Credit Manager, is investigated in this paper to regulate incoming traffic to the SS7 network by imposing appropriate time separation between messages when the incoming stream is too bursty. The credit manager has a credit bank where credits accrue at a fixed rate up to a prespecified credit bank capacity. When a message arrives, the number of octets in that message is compared to the number of credits in the bank. If the number of credits is greater than or equal to the number of octets, then the message is accepted for transmission and the number of credits in the bank is decremented by the number of octets. If the number of credits is less than the number of octets, then the message is delayed until enough credits are accumulated. This paper presents simulation results showing delay performance of the SS7 ISUP and TCAP message traffic with a range of correlated message traffic, and control parameters of the credit manager (i.e., credit generation rate and bank capacity) are determined that ensure the traffic entering the SS7 network is acceptable. The results show that control parameters can be set so that for any incoming traffic stream there is no detrimental impact on the SS7 ISUP and TCAP message delay, and the credit manager accepts a wide range of traffic patterns without causing significant delay.

  1. Influence of reciprocal edges on degree distribution and degree correlations

    NASA Astrophysics Data System (ADS)

    Zlatić, Vinko; Štefančić, Hrvoje

    2009-07-01

    Reciprocal edges represent the lowest-order cycle possible to find in directed graphs without self-loops. Representing also a measure of feedback between vertices, it is interesting to understand how reciprocal edges influence other properties of complex networks. In this paper, we focus on the influence of reciprocal edges on vertex degree distribution and degree correlations. We show that there is a fundamental difference between properties observed on the static network compared to the properties of networks, which are obtained by simple evolution mechanism driven by reciprocity. We also present a way to statistically infer the portion of reciprocal edges, which can be explained as a consequence of feedback process on the static network. In the rest of the paper, the influence of reciprocal edges on a model of growing network is also presented. It is shown that our model of growing network nicely interpolates between Barabási-Albert (BA) model for undirected and the BA model for directed networks.

  2. Distributed Containment Control for Multiple Unknown Second-Order Nonlinear Systems With Application to Networked Lagrangian Systems.

    PubMed

    Mei, Jie; Ren, Wei; Li, Bing; Ma, Guangfu

    2015-09-01

    In this paper, we consider the distributed containment control problem for multiagent systems with unknown nonlinear dynamics. More specifically, we focus on multiple second-order nonlinear systems and networked Lagrangian systems. We first study the distributed containment control problem for multiple second-order nonlinear systems with multiple dynamic leaders in the presence of unknown nonlinearities and external disturbances under a general directed graph that characterizes the interaction among the leaders and the followers. A distributed adaptive control algorithm with an adaptive gain design based on the approximation capability of neural networks is proposed. We present a necessary and sufficient condition on the directed graph such that the containment error can be reduced as small as desired. As a byproduct, the leaderless consensus problem is solved with asymptotical convergence. Because relative velocity measurements between neighbors are generally more difficult to obtain than relative position measurements, we then propose a distributed containment control algorithm without using neighbors' velocity information. A two-step Lyapunov-based method is used to study the convergence of the closed-loop system. Next, we apply the ideas to deal with the containment control problem for networked unknown Lagrangian systems under a general directed graph. All the proposed algorithms are distributed and can be implemented using only local measurements in the absence of communication. Finally, simulation examples are provided to show the effectiveness of the proposed control algorithms.

  3. Markov models for fMRI correlation structure: Is brain functional connectivity small world, or decomposable into networks?

    PubMed

    Varoquaux, G; Gramfort, A; Poline, J B; Thirion, B

    2012-01-01

    Correlations in the signal observed via functional Magnetic Resonance Imaging (fMRI), are expected to reveal the interactions in the underlying neural populations through hemodynamic response. In particular, they highlight distributed set of mutually correlated regions that correspond to brain networks related to different cognitive functions. Yet graph-theoretical studies of neural connections give a different picture: that of a highly integrated system with small-world properties: local clustering but with short pathways across the complete structure. We examine the conditional independence properties of the fMRI signal, i.e. its Markov structure, to find realistic assumptions on the connectivity structure that are required to explain the observed functional connectivity. In particular we seek a decomposition of the Markov structure into segregated functional networks using decomposable graphs: a set of strongly-connected and partially overlapping cliques. We introduce a new method to efficiently extract such cliques on a large, strongly-connected graph. We compare methods learning different graph structures from functional connectivity by testing the goodness of fit of the model they learn on new data. We find that summarizing the structure as strongly-connected networks can give a good description only for very large and overlapping networks. These results highlight that Markov models are good tools to identify the structure of brain connectivity from fMRI signals, but for this purpose they must reflect the small-world properties of the underlying neural systems. Copyright © 2012 Elsevier Ltd. All rights reserved.

  4. Quality assessment of remote sensing image fusion using feature-based fourth-order correlation coefficient

    NASA Astrophysics Data System (ADS)

    Ma, Dan; Liu, Jun; Chen, Kai; Li, Huali; Liu, Ping; Chen, Huijuan; Qian, Jing

    2016-04-01

    In remote sensing fusion, the spatial details of a panchromatic (PAN) image and the spectrum information of multispectral (MS) images will be transferred into fused images according to the characteristics of the human visual system. Thus, a remote sensing image fusion quality assessment called feature-based fourth-order correlation coefficient (FFOCC) is proposed. FFOCC is based on the feature-based coefficient concept. Spatial features related to spatial details of the PAN image and spectral features related to the spectrum information of MS images are first extracted from the fused image. Then, the fourth-order correlation coefficient between the spatial and spectral features is calculated and treated as the assessment result. FFOCC was then compared with existing widely used indices, such as Erreur Relative Globale Adimensionnelle de Synthese, and quality assessed with no reference. Results of the fusion and distortion experiments indicate that the FFOCC is consistent with subjective evaluation. FFOCC significantly outperforms the other indices in evaluating fusion images that are produced by different fusion methods and that are distorted in spatial and spectral features by blurring, adding noise, and changing intensity. All the findings indicate that the proposed method is an objective and effective quality assessment for remote sensing image fusion.

  5. Identification of functional modules that correlate with phenotypic difference: the influence of network topology

    PubMed Central

    2010-01-01

    One of the important challenges to post-genomic biology is relating observed phenotypic alterations to the underlying collective alterations in genes. Current inferential methods, however, invariably omit large bodies of information on the relationships between genes. We present a method that takes account of such information - expressed in terms of the topology of a correlation network - and we apply the method in the context of current procedures for gene set enrichment analysis. PMID:20187943

  6. Propagation of spiking regularity and double coherence resonance in feedforward networks.

    PubMed

    Men, Cong; Wang, Jiang; Qin, Ying-Mei; Deng, Bin; Tsang, Kai-Ming; Chan, Wai-Lok

    2012-03-01

    We investigate the propagation of spiking regularity in noisy feedforward networks (FFNs) based on FitzHugh-Nagumo neuron model systematically. It is found that noise could modulate the transmission of firing rate and spiking regularity. Noise-induced synchronization and synfire-enhanced coherence resonance are also observed when signals propagate in noisy multilayer networks. It is interesting that double coherence resonance (DCR) with the combination of synaptic input correlation and noise intensity is finally attained after the processing layer by layer in FFNs. Furthermore, inhibitory connections also play essential roles in shaping DCR phenomena. Several properties of the neuronal network such as noise intensity, correlation of synaptic inputs, and inhibitory connections can serve as control parameters in modulating both rate coding and the order of temporal coding.

  7. Defining Higher-Order Turbulent Moment Closures with an Artificial Neural Network and Random Forest

    NASA Astrophysics Data System (ADS)

    McGibbon, J.; Bretherton, C. S.

    2017-12-01

    Unresolved turbulent advection and clouds must be parameterized in atmospheric models. Modern higher-order closure schemes depend on analytic moment closure assumptions that diagnose higher-order moments in terms of lower-order ones. These are then tested against Large-Eddy Simulation (LES) higher-order moment relations. However, these relations may not be neatly analytic in nature. Rather than rely on an analytic higher-order moment closure, can we use machine learning on LES data itself to define a higher-order moment closure?We assess the ability of a deep artificial neural network (NN) and random forest (RF) to perform this task using a set of observationally-based LES runs from the MAGIC field campaign. By training on a subset of 12 simulations and testing on remaining simulations, we avoid over-fitting the training data.Performance of the NN and RF will be assessed and compared to the Analytic Double Gaussian 1 (ADG1) closure assumed by Cloudy Layers Unified By Binormals (CLUBB), a higher-order turbulence closure currently used in the Community Atmosphere Model (CAM). We will show that the RF outperforms the NN and the ADG1 closure for the MAGIC cases within this diagnostic framework. Progress and challenges in using a diagnostic machine learning closure within a prognostic cloud and turbulence parameterization will also be discussed.

  8. Anisotropic Dye Adsorption and Anhydrous Proton Conductivity in Smectic Liquid Crystal Networks: The Role of Cross-Link Density, Order, and Orientation.

    PubMed

    Liang, Ting; van Kuringen, Huub P C; Mulder, Dirk J; Tan, Shuai; Wu, Yong; Borneman, Zandrie; Nijmeijer, Kitty; Schenning, Albertus P H J

    2017-10-11

    In this work, the decisive role of rigidity, orientation, and order in the smectic liquid crystalline network on the anisotropic proton and adsorbent properties is reported. The rigidity in the hydrogen-bonded polymer network has been altered by changing the cross-link density, the order by using different mesophases (smectic, nematic, and isotropic phases), whereas the orientation of the mesogens was controlled by alignment layers. Adding more cross-linkers improved the integrity of the polymer films. For the proton conduction, an optimum was found in the amount of cross-linker and the smectic organization results in the highest anhydrous proton conduction. The polymer films show anisotropic proton conductivity with a 54 times higher conductivity in the direction perpendicular to the molecular director. After a base treatment of the smectic liquid crystalline network, a nanoporous polymer film is obtained that also shows anisotropic adsorption of dye molecules and again straight smectic pores are favored over disordered pores in nematic and isotropic networks. The highly cross-linked films show size-selective adsorption of dyes. Low cross-linked materials do not show this difference due to swelling, which decreases the order and creates openings in the two-dimensional polymer layers. The latter is, however, beneficial for fast adsorption kinetics.

  9. Network dynamics and systems biology

    NASA Astrophysics Data System (ADS)

    Norrell, Johannes A.

    transition point, called critical, exhibit many of the features of regulatory networks, and recent studies suggest that some specific regulatory networks are indeed near-critical. We ask whether certain statistical measures of the ensemble behavior of large continuous networks are reproduced by Boolean models. We find that, in spite of the lack of correspondence between attractors observed in smaller systems, the statistical characterization given by the continuous and Boolean models show close agreement, and the transition between order and disorder known in Boolean systems can occur in continuous systems as well. One effect that is not present in Boolean systems, the failure of information to propagate down chains of elements of arbitrary length, is present in a class of continuous networks. In these systems, a modified Boolean theory that takes into account the collective effect of propagation failure on chains throughout the network gives a good description of the observed behavior. We find that propagation failure pushes the system toward greater order, resulting in a partial or complete suppression of the disordered phase. Finally, we explore a dynamical process of direct biological relevance: asymmetric cell division in A. thaliana. The long term goal is to develop a model for the process that accurately accounts for both wild type and mutant behavior. To contribute to this endeavor, we use confocal microscopy to image roots in a SHORT-ROOT inducible mutant. We compute correlation functions between the locations of asymmetrically divided cells, and we construct stochastic models based on a few simple assumptions that accurately predict the non-zero correlations. Our result shows that intracellular processes alone cannot be responsible for the observed divisions, and that an intercell signaling mechanism could account for the measured correlations.

  10. Are Social Network Correlates of Heavy Drinking Similar Among Black Homeless Youth and White Homeless Youth?

    PubMed Central

    Wenzel, Suzanne L.; Hsu, Hsun-Ta; Zhou, Annie; Tucker, Joan S.

    2012-01-01

    Objective: Understanding factors associated with heavy drinking among homeless youth is important for prevention efforts. Social networks are associated with drinking among homeless youth, and studies have called for attention to racial differences in networks that may affect drinking behavior. This study investigates differences in network characteristics by the racial background of homeless youth, and associations of network characteristics with heavy drinking. (Heavy drinking was defined as having five or more drinks of alcohol in a row within a couple of hours on at least one day within the past 30 days.) Method: A probability sample of 235 Black and White homeless youths ages 13–24 were interviewed in Los Angeles County. We used chi-square or one-way analysis of variance tests to examine network differences by race and logistic regressions to identify network correlates of heavy drinking among Black and White homeless youth. Results: The networks of Black youth included significantly more relatives and students who attend school regularly, whereas the networks of White youth were more likely to include homeless persons, relatives who drink to intoxication, and peers who drink to intoxication. Having peers who drink heavily was significantly associated with heavy drinking only among White youth. For all homeless youth, having more students in the network who regularly attend school was associated with less risk of heavy drinking. Conclusions: This study is the first to our knowledge to investigate racial differences in network characteristics and associations of network characteristics with heavy drinking among homeless youth. White homeless youth may benefit from interventions that reduce their ties with peers who drink. Enhancing ties to school-involved peers may be a promising intervention focus for both Black and White homeless youth. PMID:23036205

  11. Spectra of random networks in the weak clustering regime

    NASA Astrophysics Data System (ADS)

    Peron, Thomas K. DM.; Ji, Peng; Kurths, Jürgen; Rodrigues, Francisco A.

    2018-03-01

    The asymptotic behavior of dynamical processes in networks can be expressed as a function of spectral properties of the corresponding adjacency and Laplacian matrices. Although many theoretical results are known for the spectra of traditional configuration models, networks generated through these models fail to describe many topological features of real-world networks, in particular non-null values of the clustering coefficient. Here we study effects of cycles of order three (triangles) in network spectra. By using recent advances in random matrix theory, we determine the spectral distribution of the network adjacency matrix as a function of the average number of triangles attached to each node for networks without modular structure and degree-degree correlations. Implications to network dynamics are discussed. Our findings can shed light in the study of how particular kinds of subgraphs influence network dynamics.

  12. Neural correlates of gender differences in reputation building.

    PubMed

    Garbarini, Francesca; Boero, Riccardo; D'Agata, Federico; Bravo, Giangiacomo; Mosso, Cristina; Cauda, Franco; Duca, Sergio; Geminiani, Giuliano; Sacco, Katiuscia

    2014-01-01

    Gender differences in cooperative choices and their neural correlates were investigated in a situation where reputation represented a crucial issue. Males and females were involved in an economic exchange (trust game) where economic and reputational payoffs had to be balanced in order to increase personal welfare. At the behavioral level, females showed a stronger reaction to negative reputation judgments that led to higher cooperation than males, measured by back transfers in the game. The neuroanatomical counterpart of this gender difference was found within the reward network (engaged in producing expectations of positive results) and reputation-related brain networks, such as the self-control network (engaged in strategically resisting the temptation to defect) and the mentalizing network (engaged in thinking about how one is viewed by others), in which the dorsolateral prefrontal cortex (DLPFC) and the medial (M)PFC respectively play a crucial role. Furthermore, both DLPFC and MPFC activity correlated with the amount of back transfer, as well as with the personality dimensions assessed with the Big-Five Questionnaire (BFQ-2). Males, according to their greater DLPFC recruitment and their higher level of the BFQ-2 subscale of Dominance, were more focused on implementing a profit-maximizing strategy, pursuing this target irrespectively of others' judgments. On the contrary, females, according to their greater MPFC activity and their lower level of Dominance, were more focused on the reputation per se and not on the strategic component of reputation building. These findings shed light on the sexual dimorphism related to cooperative behavior and its neural correlates.

  13. Neural Correlates of Gender Differences in Reputation Building

    PubMed Central

    Garbarini, Francesca; Boero, Riccardo; D'Agata, Federico; Bravo, Giangiacomo; Mosso, Cristina; Cauda, Franco; Duca, Sergio; Geminiani, Giuliano; Sacco, Katiuscia

    2014-01-01

    Gender differences in cooperative choices and their neural correlates were investigated in a situation where reputation represented a crucial issue. Males and females were involved in an economic exchange (trust game) where economic and reputational payoffs had to be balanced in order to increase personal welfare. At the behavioral level, females showed a stronger reaction to negative reputation judgments that led to higher cooperation than males, measured by back transfers in the game. The neuroanatomical counterpart of this gender difference was found within the reward network (engaged in producing expectations of positive results) and reputation-related brain networks, such as the self-control network (engaged in strategically resisting the temptation to defect) and the mentalizing network (engaged in thinking about how one is viewed by others), in which the dorsolateral prefrontal cortex (DLPFC) and the medial (M)PFC respectively play a crucial role. Furthermore, both DLPFC and MPFC activity correlated with the amount of back transfer, as well as with the personality dimensions assessed with the Big-Five Questionnaire (BFQ-2). Males, according to their greater DLPFC recruitment and their higher level of the BFQ-2 subscale of Dominance, were more focused on implementing a profit-maximizing strategy, pursuing this target irrespectively of others' judgments. On the contrary, females, according to their greater MPFC activity and their lower level of Dominance, were more focused on the reputation per se and not on the strategic component of reputation building. These findings shed light on the sexual dimorphism related to cooperative behavior and its neural correlates. PMID:25180581

  14. Interdependent networks - Topological percolation research and application in finance

    NASA Astrophysics Data System (ADS)

    Zhou, Di

    This dissertation covers the two major parts of my Ph.D. research: i) developing a theoretical framework of complex networks and applying simulation and numerical methods to study the robustness of the network system, and ii) applying statistical physics concepts and methods to quantitatively analyze complex systems and applying the theoretical framework to study real-world systems. In part I, we focus on developing theories of interdependent networks as well as building computer simulation models, which includes three parts: 1) We report on the effects of topology on failure propagation for a model system consisting of two interdependent networks. We find that the internal node correlations in each of the networks significantly changes the critical density of failures, which can trigger the total disruption of the two-network system. Specifically, we find that the assortativity within a single network decreases the robustness of the entire system. 2) We study the percolation behavior of two interdependent scale-free (SF) networks under random failure of 1-p fraction of nodes. We find that as the coupling strength q between the two networks reduces from 1 (fully coupled) to 0 (no coupling), there exist two critical coupling strengths q1 and q2 , which separate the behaviors of the giant component as a function of p into three different regions, and for q2 < q < q 1 , we observe a hybrid order phase transition phenomenon. 3) We study the robustness of n interdependent networks with partially support-dependent relationship both analytically and numerically. We study a starlike network of n Erdos-Renyi (ER), SF networks and a looplike network of n ER networks, and we find for starlike networks, their phase transition regions change with n, but for looplike networks the phase regions change with average degree k . In part II, we apply concepts and methods developed in statistical physics to study economic systems. We analyze stock market indices and foreign exchange

  15. Complex network approach to fractional time series

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Manshour, Pouya

    In order to extract correlation information inherited in stochastic time series, the visibility graph algorithm has been recently proposed, by which a time series can be mapped onto a complex network. We demonstrate that the visibility algorithm is not an appropriate one to study the correlation aspects of a time series. We then employ the horizontal visibility algorithm, as a much simpler one, to map fractional processes onto complex networks. The degree distributions are shown to have parabolic exponential forms with Hurst dependent fitting parameter. Further, we take into account other topological properties such as maximum eigenvalue of the adjacencymore » matrix and the degree assortativity, and show that such topological quantities can also be used to predict the Hurst exponent, with an exception for anti-persistent fractional Gaussian noises. To solve this problem, we take into account the Spearman correlation coefficient between nodes' degrees and their corresponding data values in the original time series.« less

  16. Polymer density functional theory approach based on scaling second-order direct correlation function.

    PubMed

    Zhou, Shiqi

    2006-06-01

    A second-order direct correlation function (DCF) from solving the polymer-RISM integral equation is scaled up or down by an equation of state for bulk polymer, the resultant scaling second-order DCF is in better agreement with corresponding simulation results than the un-scaling second-order DCF. When the scaling second-order DCF is imported into a recently proposed LTDFA-based polymer DFT approach, an originally associated adjustable but mathematically meaningless parameter now becomes mathematically meaningful, i.e., the numerical value lies now between 0 and 1. When the adjustable parameter-free version of the LTDFA is used instead of the LTDFA, i.e., the adjustable parameter is fixed at 0.5, the resultant parameter-free version of the scaling LTDFA-based polymer DFT is also in good agreement with the corresponding simulation data for density profiles. The parameter-free version of the scaling LTDFA-based polymer DFT is employed to investigate the density profiles of a freely jointed tangent hard sphere chain near a variable sized central hard sphere, again the predictions reproduce accurately the simulational results. Importance of the present adjustable parameter-free version lies in its combination with a recently proposed universal theoretical way, in the resultant formalism, the contact theorem is still met by the adjustable parameter associated with the theoretical way.

  17. Generalized activity equations for spiking neural network dynamics.

    PubMed

    Buice, Michael A; Chow, Carson C

    2013-01-01

    Much progress has been made in uncovering the computational capabilities of spiking neural networks. However, spiking neurons will always be more expensive to simulate compared to rate neurons because of the inherent disparity in time scales-the spike duration time is much shorter than the inter-spike time, which is much shorter than any learning time scale. In numerical analysis, this is a classic stiff problem. Spiking neurons are also much more difficult to study analytically. One possible approach to making spiking networks more tractable is to augment mean field activity models with some information about spiking correlations. For example, such a generalized activity model could carry information about spiking rates and correlations between spikes self-consistently. Here, we will show how this can be accomplished by constructing a complete formal probabilistic description of the network and then expanding around a small parameter such as the inverse of the number of neurons in the network. The mean field theory of the system gives a rate-like description. The first order terms in the perturbation expansion keep track of covariances.

  18. Correlations induced by depressing synapses in critically self-organized networks with quenched dynamics

    NASA Astrophysics Data System (ADS)

    Campos, João Guilherme Ferreira; Costa, Ariadne de Andrade; Copelli, Mauro; Kinouchi, Osame

    2017-04-01

    In a recent work, mean-field analysis and computer simulations were employed to analyze critical self-organization in networks of excitable cellular automata where randomly chosen synapses in the network were depressed after each spike (the so-called annealed dynamics). Calculations agree with simulations of the annealed version, showing that the nominal branching ratio σ converges to unity in the thermodynamic limit, as expected of a self-organized critical system. However, the question remains whether the same results apply to the biological case where only the synapses of firing neurons are depressed (the so-called quenched dynamics). We show that simulations of the quenched model yield significant deviations from σ =1 due to spatial correlations. However, the model is shown to be critical, as the largest eigenvalue of the synaptic matrix approaches unity in the thermodynamic limit, that is, λc=1 . We also study the finite size effects near the critical state as a function of the parameters of the synaptic dynamics.

  19. Next-to-leading order weighted Sivers asymmetry in semi-inclusive deep inelastic scattering: three-gluon correlator

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Dai, Lingyun; Prokudin, Alexei; Kang, Zhong-Bo

    2015-09-01

    We study the three-gluon correlation function contribution to the Sivers asymmetry in semi-inclusive deep inelastic scattering. We first establish the matching between the usual twist-3 collinear factorization approach and transverse momentum dependent factorization formalism for the moderate transverse momentum region. We then derive the so-called coefficient functions used in the usual TMD evolution formalism. Finally, we perform the next-to-leading order calculation for the transverse-momentum-weighted spin-dependent differential cross section, from which we identify the QCD collinear evolution of the twist-3 Qiu-Sterman function: the off-diagonal contribution from the three-gluon correlation functions.

  20. Defense strategies for asymmetric networked systems under composite utilities

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Rao, Nageswara S.; Ma, Chris Y. T.; Hausken, Kjell

    We consider an infrastructure of networked systems with discrete components that can be reinforced at certain costs to guard against attacks. The communications network plays a critical, asymmetric role of providing the vital connectivity between the systems. We characterize the correlations within this infrastructure at two levels using (a) aggregate failure correlation function that specifies the infrastructure failure probability giventhe failure of an individual system or network, and (b) first order differential conditions on system survival probabilities that characterize component-level correlations. We formulate an infrastructure survival game between an attacker and a provider, who attacks and reinforces individual components, respectively.more » They use the composite utility functions composed of a survival probability term and a cost term, and the previously studiedsum-form and product-form utility functions are their special cases. At Nash Equilibrium, we derive expressions for individual system survival probabilities and the expected total number of operational components. We apply and discuss these estimates for a simplified model of distributed cloud computing infrastructure« less

  1. Correlation and interaction visualization of altmetric indicators extracted from scholarly social network activities: dimensions and structure.

    PubMed

    Liu, Chun Li; Xu, Yue Quan; Wu, Hui; Chen, Si Si; Guo, Ji Jun

    2013-11-25

    Citation counts for peer-reviewed articles and the impact factor of journals have long been indicators of article importance or quality. In the Web 2.0 era, growing numbers of scholars are using scholarly social network tools to communicate scientific ideas with colleagues, thereby making traditional indicators less sufficient, immediate, and comprehensive. In these new situations, the altmetric indicators offer alternative measures that reflect the multidimensional nature of scholarly impact in an immediate, open, and individualized way. In this direction of research, some studies have demonstrated the correlation between altmetrics and traditional metrics with different samples. However, up to now, there has been relatively little research done on the dimension and interaction structure of altmetrics. Our goal was to reveal the number of dimensions that altmetric indicators should be divided into and the structure in which altmetric indicators interact with each other. Because an article-level metrics dataset is collected from scholarly social media and open access platforms, it is one of the most robust samples available to study altmetric indicators. Therefore, we downloaded a large dataset containing activity data in 20 types of metrics present in 33,128 academic articles from the application programming interface website. First, we analyzed the correlation among altmetric indicators using Spearman rank correlation. Second, we visualized the multiple correlation coefficient matrixes with graduated colors. Third, inputting the correlation matrix, we drew an MDS diagram to demonstrate the dimension for altmetric indicators. For correlation structure, we used a social network map to represent the social relationships and the strength of relations. We found that the distribution of altmetric indicators is significantly non-normal and positively skewed. The distribution of downloads and page views follows the Pareto law. Moreover, we found that the Spearman

  2. Chaos and Correlated Avalanches in Excitatory Neural Networks with Synaptic Plasticity

    NASA Astrophysics Data System (ADS)

    Pittorino, Fabrizio; Ibáñez-Berganza, Miguel; di Volo, Matteo; Vezzani, Alessandro; Burioni, Raffaella

    2017-03-01

    A collective chaotic phase with power law scaling of activity events is observed in a disordered mean field network of purely excitatory leaky integrate-and-fire neurons with short-term synaptic plasticity. The dynamical phase diagram exhibits two transitions from quasisynchronous and asynchronous regimes to the nontrivial, collective, bursty regime with avalanches. In the homogeneous case without disorder, the system synchronizes and the bursty behavior is reflected into a period doubling transition to chaos for a two dimensional discrete map. Numerical simulations show that the bursty chaotic phase with avalanches exhibits a spontaneous emergence of persistent time correlations and enhanced Kolmogorov complexity. Our analysis reveals a mechanism for the generation of irregular avalanches that emerges from the combination of disorder and deterministic underlying chaotic dynamics.

  3. Origin of hyperbolicity in brain-to-brain coordination networks

    NASA Astrophysics Data System (ADS)

    Tadić, Bosiljka; Andjelković, Miroslav; Šuvakov, Milovan

    2018-02-01

    Hyperbolicity or negative curvature of complex networks is the intrinsic geometric proximity of nodes in the graph metric space, which implies an improved network function. Here, we investigate hidden combinatorial geometries in brain-to-brain coordination networks arising through social communications. The networks originate from correlations among EEG signals previously recorded during spoken communications comprising of 14 individuals with 24 speaker-listener pairs. We find that the corresponding networks are delta-hyperbolic with delta_max=1 and the graph diameter D=3 in each brain. While the emergent hyperbolicity in the two-brain networks satisfies delta_max/D/2 < 1 and can be attributed to the topology of the subgraph formed around the cross-brains linking channels. We identify these subgraphs in each studied two-brain network and decompose their structure into simple geometric descriptors (triangles, tetrahedra and cliques of higher orders) that contribute to hyperbolicity. Considering topologies that exceed two separate brain networks as a measure of coordination synergy between the brains, we identify different neuronal correlation patterns ranging from weak coordination to super-brain structure. These topology features are in qualitative agreement with the listener’s self-reported ratings of own experience and quality of the speaker, suggesting that studies of the cross-brain connector networks can reveal new insight into the neural mechanisms underlying human social behavior.

  4. Simultaneous use of in silico design and a correlated mutation network as a tool to efficiently guide enzyme engineering.

    PubMed

    Nobili, Alberto; Tao, Yifeng; Pavlidis, Ioannis V; van den Bergh, Tom; Joosten, Henk-Jan; Tan, Tianwei; Bornscheuer, Uwe T

    2015-03-23

    In order to improve the efficiency of directed evolution experiments, in silico multiple-substrate clustering was combined with an analysis of the variability of natural enzymes within a protein superfamily. This was applied to a Pseudomonas fluorescens esterase (PFE I) targeting the enantioselective hydrolysis of 3-phenylbutyric acid esters. Data reported in the literature for nine substrates were used for the clustering meta-analysis of the docking conformations in wild-type PFE I, and this highlighted a tryptophan residue (W28) as an interesting target. Exploration of the most frequently, naturally occurring amino acids at this position suggested that the reduced flexibility observed in the case of the W28F variant leads to enhancement of the enantioselectivity. This mutant was subsequently combined with mutations identified in a library based on analysis of a correlated mutation network. By interrogation of <80 variants a mutant with 15-fold improved enantioselectivity was found. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  5. Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks

    PubMed Central

    Valdivieso Caraguay, Ángel Leonardo; García Villalba, Luis Javier

    2017-01-01

    This paper presents the Monitoring and Discovery Framework of the Self-Organized Network Management in Virtualized and Software Defined Networks SELFNET project. This design takes into account the scalability and flexibility requirements needed by 5G infrastructures. In this context, the present framework focuses on gathering and storing the information (low-level metrics) related to physical and virtual devices, cloud environments, flow metrics, SDN traffic and sensors. Similarly, it provides the monitoring data as a generic information source in order to allow the correlation and aggregation tasks. Our design enables the collection and storing of information provided by all the underlying SELFNET sublayers, including the dynamically onboarded and instantiated SDN/NFV Apps, also known as SELFNET sensors. PMID:28362346

  6. Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks.

    PubMed

    Caraguay, Ángel Leonardo Valdivieso; Villalba, Luis Javier García

    2017-03-31

    This paper presents the Monitoring and Discovery Framework of the Self-Organized Network Management in Virtualized and Software Defined Networks SELFNET project. This design takes into account the scalability and flexibility requirements needed by 5G infrastructures. In this context, the present framework focuses on gathering and storing the information (low-level metrics) related to physical and virtual devices, cloud environments, flow metrics, SDN traffic and sensors. Similarly, it provides the monitoring data as a generic information source in order to allow the correlation and aggregation tasks. Our design enables the collection and storing of information provided by all the underlying SELFNET sublayers, including the dynamically onboarded and instantiated SDN/NFV Apps, also known as SELFNET sensors.

  7. Social Network Influence and Personal Financial Status

    NASA Astrophysics Data System (ADS)

    Luo, Shaojun; Morone, Flaviano; Sarraute, Carlos; Makse, Hernan

    Networks of social ties emerging from individual economic needs display a highly structured architecture. In response to socio-economic demands, people reshape their circle of contacts for maximizing their social status, and ipso facto, the pattern of their interconnections is strongly correlates with their personal financial situation. In this work we transform this qualitative and verbal statement into an operative definition, which allows us to quantify the economic wellness of individuals trough a measure of their collective influence. We consider the network of mobile phone calls made by the Mexican population during three months, in order to study the correlation of person's economic situation with her network location. Notably, we find that rich people tend to be also the most influential nodes, i.e., they self-organize to optimally position themselves in the network. This finding may be also raised at the level of a principle, a fact that would explain the emergence of the phenomenon of collective influence itself as the result of the local optimization of socio-economic interactions. Our method represents a powerful and efficient indicator of socio-economic robustness, which may be applied to maximize the effect of large scale economic intervention and stimulus policies

  8. Global exponential stability of neutral high-order stochastic Hopfield neural networks with Markovian jump parameters and mixed time delays.

    PubMed

    Huang, Haiying; Du, Qiaosheng; Kang, Xibing

    2013-11-01

    In this paper, a class of neutral high-order stochastic Hopfield neural networks with Markovian jump parameters and mixed time delays is investigated. The jumping parameters are modeled as a continuous-time finite-state Markov chain. At first, the existence of equilibrium point for the addressed neural networks is studied. By utilizing the Lyapunov stability theory, stochastic analysis theory and linear matrix inequality (LMI) technique, new delay-dependent stability criteria are presented in terms of linear matrix inequalities to guarantee the neural networks to be globally exponentially stable in the mean square. Numerical simulations are carried out to illustrate the main results. © 2013 ISA. Published by ISA. All rights reserved.

  9. Analyses of Third Order Bose-Einstein Correlation by Means of Coulomb Wave Function

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Biyajima, Minoru; Mizoguchi, Takuya; Suzuki, Naomichi

    2006-04-11

    In order to include a correction by the Coulomb interaction in Bose-Einstein correlation (BEC), the wave function for the Coulomb scattering were introduced in the quantum optical approach to BEC in the previous work. If we formulate the amplitude written by Coulomb wave functions according to the diagram for BEC in the plane wave formulation, the formula for 3{pi} -BEC becomes simpler than that of our previous work. We re-analyze the raw data of 3{pi} -BEC by NA44 and STAR Collaborations by this formula. Results are compared with the previous ones.

  10. A network model of correlated growth of tissue stiffening in pulmonary fibrosis

    NASA Astrophysics Data System (ADS)

    Oliveira, Cláudio L. N.; Bates, Jason H. T.; Suki, Béla

    2014-06-01

    During the progression of pulmonary fibrosis, initially isolated regions of high stiffness form and grow in the lung tissue due to collagen deposition by fibroblast cells. We have previously shown that ongoing collagen deposition may not lead to significant increases in the bulk modulus of the lung until these local remodeled regions have become sufficiently numerous and extensive to percolate in a continuous path across the entire tissue (Bates et al 2007 Am. J. Respir. Crit. Care Med. 176 617). This model, however, did not include the possibility of spatially correlated deposition of collagen. In the present study, we investigate whether spatial correlations influence the bulk modulus in a two-dimensional elastic network model of lung tissue. Random collagen deposition at a single site is modeled by increasing the elastic constant of the spring at that site by a factor of 100. By contrast, correlated collagen deposition is represented by stiffening the springs encountered along a random walk starting from some initial spring, the rationale being that excess collagen deposition is more likely in the vicinity of an already stiff region. A combination of random and correlated deposition is modeled by performing random walks of length N from randomly selected initial sites, the balance between the two processes being determined by N. We found that the dependence of bulk modulus, B(N,c), on both N and the fraction of stiff springs, c, can be described by a strikingly simple set of empirical equations. For c<0.3, B(N,c) exhibits exponential growth from its initial value according to B(N,c)\\approx {{B}_{0}}exp (2c)\\left[ 1+{{c}^{\\beta }}ln \\left( {{N}^{{{a}_{I}}}} \\right) \\right], where \\beta =0.994+/- 0.024 and {{a}_{I}}=0.54+/- 0.026. For intermediate concentrations of stiffening, 0.3\\leqslant c\\leqslant 0.8, another exponential rule describes the bulk modulus as B(N,c)=4{{B}_{0}}exp \\left[ {{a}_{II}}\\left( c-{{c}_{c}} \\right) \\right], where {{a

  11. Defense Strategies for Asymmetric Networked Systems with Discrete Components.

    PubMed

    Rao, Nageswara S V; Ma, Chris Y T; Hausken, Kjell; He, Fei; Yau, David K Y; Zhuang, Jun

    2018-05-03

    We consider infrastructures consisting of a network of systems, each composed of discrete components. The network provides the vital connectivity between the systems and hence plays a critical, asymmetric role in the infrastructure operations. The individual components of the systems can be attacked by cyber and physical means and can be appropriately reinforced to withstand these attacks. We formulate the problem of ensuring the infrastructure performance as a game between an attacker and a provider, who choose the numbers of the components of the systems and network to attack and reinforce, respectively. The costs and benefits of attacks and reinforcements are characterized using the sum-form, product-form and composite utility functions, each composed of a survival probability term and a component cost term. We present a two-level characterization of the correlations within the infrastructure: (i) the aggregate failure correlation function specifies the infrastructure failure probability given the failure of an individual system or network, and (ii) the survival probabilities of the systems and network satisfy first-order differential conditions that capture the component-level correlations using multiplier functions. We derive Nash equilibrium conditions that provide expressions for individual system survival probabilities and also the expected infrastructure capacity specified by the total number of operational components. We apply these results to derive and analyze defense strategies for distributed cloud computing infrastructures using cyber-physical models.

  12. Defense Strategies for Asymmetric Networked Systems with Discrete Components

    PubMed Central

    Rao, Nageswara S. V.; Ma, Chris Y. T.; Hausken, Kjell; He, Fei; Yau, David K. Y.

    2018-01-01

    We consider infrastructures consisting of a network of systems, each composed of discrete components. The network provides the vital connectivity between the systems and hence plays a critical, asymmetric role in the infrastructure operations. The individual components of the systems can be attacked by cyber and physical means and can be appropriately reinforced to withstand these attacks. We formulate the problem of ensuring the infrastructure performance as a game between an attacker and a provider, who choose the numbers of the components of the systems and network to attack and reinforce, respectively. The costs and benefits of attacks and reinforcements are characterized using the sum-form, product-form and composite utility functions, each composed of a survival probability term and a component cost term. We present a two-level characterization of the correlations within the infrastructure: (i) the aggregate failure correlation function specifies the infrastructure failure probability given the failure of an individual system or network, and (ii) the survival probabilities of the systems and network satisfy first-order differential conditions that capture the component-level correlations using multiplier functions. We derive Nash equilibrium conditions that provide expressions for individual system survival probabilities and also the expected infrastructure capacity specified by the total number of operational components. We apply these results to derive and analyze defense strategies for distributed cloud computing infrastructures using cyber-physical models. PMID:29751588

  13. Percolation of a general network of networks.

    PubMed

    Gao, Jianxi; Buldyrev, Sergey V; Stanley, H Eugene; Xu, Xiaoming; Havlin, Shlomo

    2013-12-01

    Percolation theory is an approach to study the vulnerability of a system. We develop an analytical framework and analyze the percolation properties of a network composed of interdependent networks (NetONet). Typically, percolation of a single network shows that the damage in the network due to a failure is a continuous function of the size of the failure, i.e., the fraction of failed nodes. In sharp contrast, in NetONet, due to the cascading failures, the percolation transition may be discontinuous and even a single node failure may lead to an abrupt collapse of the system. We demonstrate our general framework for a NetONet composed of n classic Erdős-Rényi (ER) networks, where each network depends on the same number m of other networks, i.e., for a random regular network (RR) formed of interdependent ER networks. The dependency between nodes of different networks is taken as one-to-one correspondence, i.e., a node in one network can depend only on one node in the other network (no-feedback condition). In contrast to a treelike NetONet in which the size of the largest connected cluster (mutual component) depends on n, the loops in the RR NetONet cause the largest connected cluster to depend only on m and the topology of each network but not on n. We also analyzed the extremely vulnerable feedback condition of coupling, where the coupling between nodes of different networks is not one-to-one correspondence. In the case of NetONet formed of ER networks, percolation only exhibits two phases, a second order phase transition and collapse, and no first order percolation transition regime is found in the case of the no-feedback condition. In the case of NetONet composed of RR networks, there exists a first order phase transition when the coupling strength q (fraction of interdependency links) is large and a second order phase transition when q is small. Our insight on the resilience of coupled networks might help in designing robust interdependent systems.

  14. DEFINING THE PLAYERS IN HIGHER-ORDER NETWORKS: PREDICTIVE MODELING FOR REVERSE ENGINEERING FUNCTIONAL INFLUENCE NETWORKS

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    McDermott, Jason E.; Costa, Michelle N.; Stevens, S.L.

    A difficult problem that is currently growing rapidly due to the sharp increase in the amount of high-throughput data available for many systems is that of determining useful and informative causative influence networks. These networks can be used to predict behavior given observation of a small number of components, predict behavior at a future time point, or identify components that are critical to the functioning of the system under particular conditions. In these endeavors incorporating observations of systems from a wide variety of viewpoints can be particularly beneficial, but has often been undertaken with the objective of inferring networks thatmore » are generally applicable. The focus of the current work is to integrate both general observations and measurements taken for a particular pathology, that of ischemic stroke, to provide improved ability to produce useful predictions of systems behavior. A number of hybrid approaches have recently been proposed for network generation in which the Gene Ontology is used to filter or enrich network links inferred from gene expression data through reverse engineering methods. These approaches have been shown to improve the biological plausibility of the inferred relationships determined, but still treat knowledge-based and machine-learning inferences as incommensurable inputs. In this paper, we explore how further improvements may be achieved through a full integration of network inference insights achieved through application of the Gene Ontology and reverse engineering methods with specific reference to the construction of dynamic models of transcriptional regulatory networks. We show that integrating two approaches to network construction, one based on reverse-engineering from conditional transcriptional data, one based on reverse-engineering from in situ hybridization data, and another based on functional associations derived from Gene Ontology, using probabilities can improve results of clustering as

  15. Geometric Assortative Growth Model for Small-World Networks

    PubMed Central

    2014-01-01

    It has been shown that both humanly constructed and natural networks are often characterized by small-world phenomenon and assortative mixing. In this paper, we propose a geometrically growing model for small-world networks. The model displays both tunable small-world phenomenon and tunable assortativity. We obtain analytical solutions of relevant topological properties such as order, size, degree distribution, degree correlation, clustering, transitivity, and diameter. It is also worth noting that the model can be viewed as a generalization for an iterative construction of Farey graphs. PMID:24578661

  16. PLAU inferred from a correlation network is critical for suppressor function of regulatory T cells

    PubMed Central

    He, Feng; Chen, Hairong; Probst-Kepper, Michael; Geffers, Robert; Eifes, Serge; del Sol, Antonio; Schughart, Klaus; Zeng, An-Ping; Balling, Rudi

    2012-01-01

    Human FOXP3+CD25+CD4+ regulatory T cells (Tregs) are essential to the maintenance of immune homeostasis. Several genes are known to be important for murine Tregs, but for human Tregs the genes and underlying molecular networks controlling the suppressor function still largely remain unclear. Here, we describe a strategy to identify the key genes directly from an undirected correlation network which we reconstruct from a very high time-resolution (HTR) transcriptome during the activation of human Tregs/CD4+ T-effector cells. We show that a predicted top-ranked new key gene PLAU (the plasminogen activator urokinase) is important for the suppressor function of both human and murine Tregs. Further analysis unveils that PLAU is particularly important for memory Tregs and that PLAU mediates Treg suppressor function via STAT5 and ERK signaling pathways. Our study demonstrates the potential for identifying novel key genes for complex dynamic biological processes using a network strategy based on HTR data, and reveals a critical role for PLAU in Treg suppressor function. PMID:23169000

  17. Diversity of charge orderings in correlated systems

    NASA Astrophysics Data System (ADS)

    Kapcia, Konrad Jerzy; Barański, Jan; Ptok, Andrzej

    2017-10-01

    The phenomenon associated with inhomogeneous distribution of electron density is known as a charge ordering. In this work, we study the zero-bandwidth limit of the extended Hubbard model, which can be considered as a simple effective model of charge ordered insulators. It consists of the on-site interaction U and the intersite density-density interactions W1 and W2 between nearest neighbors and next-nearest neighbors, respectively. We derived the exact ground state diagrams for different lattice dimensionalities and discuss effects of small finite temperatures in the limit of high dimensions. In particular, we estimated the critical interactions for which new ordered phases emerge (laminar or stripe and four-sublattice-type). Our analysis show that the ground state of the model is highly degenerated. One of the most intriguing finding is that the nonzero temperature removes these degenerations.

  18. B B ¯ angular correlations at the LHC in the parton Reggeization approach merged with higher-order matrix elements

    NASA Astrophysics Data System (ADS)

    Karpishkov, A. V.; Nefedov, M. A.; Saleev, V. A.

    2017-11-01

    We calculate the angular distribution spectra between beauty (B ) and antibeauty (B ¯) mesons in proton-proton collisions in the leading order approximation of the parton Reggeization approach consistently merged with the next-to-leading order corrections from the emission of an additional hard gluon. To describe b-quark hadronization we use the universal scale-dependent parton-to-meson fragmentation functions extracted from the world e+e- annihilation data. We have obtained good agreement between our predictions and data from the CMS Collaboration at the energy √{S }=7 TeV for B B ¯ angular correlations within uncertainties and without free parameters. Predictions for analogous correlation observables at √{S }=13 TeV are provided.

  19. Well-balanced high-order solver for blood flow in networks of vessels with variable properties.

    PubMed

    Müller, Lucas O; Toro, Eleuterio F

    2013-12-01

    We present a well-balanced, high-order non-linear numerical scheme for solving a hyperbolic system that models one-dimensional flow in blood vessels with variable mechanical and geometrical properties along their length. Using a suitable set of test problems with exact solution, we rigorously assess the performance of the scheme. In particular, we assess the well-balanced property and the effective order of accuracy through an empirical convergence rate study. Schemes of up to fifth order of accuracy in both space and time are implemented and assessed. The numerical methodology is then extended to realistic networks of elastic vessels and is validated against published state-of-the-art numerical solutions and experimental measurements. It is envisaged that the present scheme will constitute the building block for a closed, global model for the human circulation system involving arteries, veins, capillaries and cerebrospinal fluid. Copyright © 2013 John Wiley & Sons, Ltd.

  20. Block correlated second order perturbation theory with a generalized valence bond reference function.

    PubMed

    Xu, Enhua; Li, Shuhua

    2013-11-07

    The block correlated second-order perturbation theory with a generalized valence bond (GVB) reference (GVB-BCPT2) is proposed. In this approach, each geminal in the GVB reference is considered as a "multi-orbital" block (a subset of spin orbitals), and each occupied or virtual spin orbital is also taken as a single block. The zeroth-order Hamiltonian is set to be the summation of the individual Hamiltonians of all blocks (with explicit two-electron operators within each geminal) so that the GVB reference function and all excited configuration functions are its eigenfunctions. The GVB-BCPT2 energy can be directly obtained without iteration, just like the second order Mo̸ller-Plesset perturbation method (MP2), both of which are size consistent. We have applied this GVB-BCPT2 method to investigate the equilibrium distances and spectroscopic constants of 7 diatomic molecules, conformational energy differences of 8 small molecules, and bond-breaking potential energy profiles in 3 systems. GVB-BCPT2 is demonstrated to have noticeably better performance than MP2 for systems with significant multi-reference character, and provide reasonably accurate results for some systems with large active spaces, which are beyond the capability of all CASSCF-based methods.

  1. Spread of information and infection on finite random networks

    NASA Astrophysics Data System (ADS)

    Isham, Valerie; Kaczmarska, Joanna; Nekovee, Maziar

    2011-04-01

    The modeling of epidemic-like processes on random networks has received considerable attention in recent years. While these processes are inherently stochastic, most previous work has been focused on deterministic models that ignore important fluctuations that may persist even in the infinite network size limit. In a previous paper, for a class of epidemic and rumor processes, we derived approximate models for the full probability distribution of the final size of the epidemic, as opposed to only mean values. In this paper we examine via direct simulations the adequacy of the approximate model to describe stochastic epidemics and rumors on several random network topologies: homogeneous networks, Erdös-Rényi (ER) random graphs, Barabasi-Albert scale-free networks, and random geometric graphs. We find that the approximate model is reasonably accurate in predicting the probability of spread. However, the position of the threshold and the conditional mean of the final size for processes near the threshold are not well described by the approximate model even in the case of homogeneous networks. We attribute this failure to the presence of other structural properties beyond degree-degree correlations, and in particular clustering, which are present in any finite network but are not incorporated in the approximate model. In order to test this “hypothesis” we perform additional simulations on a set of ER random graphs where degree-degree correlations and clustering are separately and independently introduced using recently proposed algorithms from the literature. Our results show that even strong degree-degree correlations have only weak effects on the position of the threshold and the conditional mean of the final size. On the other hand, the introduction of clustering greatly affects both the position of the threshold and the conditional mean. Similar analysis for the Barabasi-Albert scale-free network confirms the significance of clustering on the dynamics of

  2. General solution of the chemical master equation and modality of marginal distributions for hierarchic first-order reaction networks.

    PubMed

    Reis, Matthias; Kromer, Justus A; Klipp, Edda

    2018-01-20

    Multimodality is a phenomenon which complicates the analysis of statistical data based exclusively on mean and variance. Here, we present criteria for multimodality in hierarchic first-order reaction networks, consisting of catalytic and splitting reactions. Those networks are characterized by independent and dependent subnetworks. First, we prove the general solvability of the Chemical Master Equation (CME) for this type of reaction network and thereby extend the class of solvable CME's. Our general solution is analytical in the sense that it allows for a detailed analysis of its statistical properties. Given Poisson/deterministic initial conditions, we then prove the independent species to be Poisson/binomially distributed, while the dependent species exhibit generalized Poisson/Khatri Type B distributions. Generalized Poisson/Khatri Type B distributions are multimodal for an appropriate choice of parameters. We illustrate our criteria for multimodality by several basic models, as well as the well-known two-stage transcription-translation network and Bateman's model from nuclear physics. For both examples, multimodality was previously not reported.

  3. O(t-α)-synchronization and Mittag-Leffler synchronization for the fractional-order memristive neural networks with delays and discontinuous neuron activations.

    PubMed

    Chen, Jiejie; Chen, Boshan; Zeng, Zhigang

    2018-04-01

    This paper investigates O(t -α )-synchronization and adaptive Mittag-Leffler synchronization for the fractional-order memristive neural networks with delays and discontinuous neuron activations. Firstly, based on the framework of Filippov solution and differential inclusion theory, using a Razumikhin-type method, some sufficient conditions ensuring the global O(t -α )-synchronization of considered networks are established via a linear-type discontinuous control. Next, a new fractional differential inequality is established and two new discontinuous adaptive controller is designed to achieve Mittag-Leffler synchronization between the drive system and the response systems using this inequality. Finally, two numerical simulations are given to show the effectiveness of the theoretical results. Our approach and theoretical results have a leading significance in the design of synchronized fractional-order memristive neural networks circuits involving discontinuous activations and time-varying delays. Copyright © 2018 Elsevier Ltd. All rights reserved.

  4. The data-driven null models for information dissemination tree in social networks

    NASA Astrophysics Data System (ADS)

    Zhang, Zhiwei; Wang, Zhenyu

    2017-10-01

    For the purpose of detecting relatedness and co-occurrence between users, as well as the distribution features of nodes in spreading path of a social network, this paper explores topological characteristics of information dissemination trees (IDT) that can be employed indirectly to probe the information dissemination laws within social networks. Hence, three different null models of IDT are presented in this article, including the statistical-constrained 0-order IDT null model, the random-rewire-broken-edge 0-order IDT null model and the random-rewire-broken-edge 2-order IDT null model. These null models firstly generate the corresponding randomized copy of an actual IDT; then the extended significance profile, which is developed by adding the cascade ratio of information dissemination path, is exploited not only to evaluate degree correlation of two nodes associated with an edge, but also to assess the cascade ratio of different length of information dissemination paths. The experimental correspondences of the empirical analysis for several SinaWeibo IDTs and Twitter IDTs indicate that the IDT null models presented in this paper perform well in terms of degree correlation of nodes and dissemination path cascade ratio, which can be better to reveal the features of information dissemination and to fit the situation of real social networks.

  5. An evaluation of potential sampling locations in a reservoir with emphasis on conserved spatial correlation structure.

    PubMed

    Yenilmez, Firdes; Düzgün, Sebnem; Aksoy, Aysegül

    2015-01-01

    In this study, kernel density estimation (KDE) was coupled with ordinary two-dimensional kriging (OK) to reduce the number of sampling locations in measurement and kriging of dissolved oxygen (DO) concentrations in Porsuk Dam Reservoir (PDR). Conservation of the spatial correlation structure in the DO distribution was a target. KDE was used as a tool to aid in identification of the sampling locations that would be removed from the sampling network in order to decrease the total number of samples. Accordingly, several networks were generated in which sampling locations were reduced from 65 to 10 in increments of 4 or 5 points at a time based on kernel density maps. DO variograms were constructed, and DO values in PDR were kriged. Performance of the networks in DO estimations were evaluated through various error metrics, standard error maps (SEM), and whether the spatial correlation structure was conserved or not. Results indicated that smaller number of sampling points resulted in loss of information in regard to spatial correlation structure in DO. The minimum representative sampling points for PDR was 35. Efficacy of the sampling location selection method was tested against the networks generated by experts. It was shown that the evaluation approach proposed in this study provided a better sampling network design in which the spatial correlation structure of DO was sustained for kriging.

  6. What can we learn from the network approach in finance?

    NASA Astrophysics Data System (ADS)

    Janos, Kertesz

    2005-03-01

    Correlations between variations of stock prices reveal information about relationships between companies. Different methods of analysis have been applied to such data in order to uncover the taxonomy of the market. We use Mantegna's miminum spanning tree (MST) method for daily data in a dynamic way: By introducing a moving window we study the temporal changes in the structure of the network defined by this ``asset tree.'' The MST is scale free with a significantly changing exponent of the degree distribution for crash periods, which demonstrates the restructuring of the network due to the enhancement of correlations. This approach is compared to that based on what we call ``asset graphs:'' We start from an empty graph with no edges where the vertices correspond to stocks and then, one by one, we insert edges between the vertices according to the rank of their correlation strength. We study the properties of the creatred (weighted) networks, such as topologically different growth types, number and size of clusters and clustering coefficient. Furthermore, we define new tools like subgraph intensity and coherence to describe the role of the weights. We also investigate the time shifted cross correlation functions for high frequency data and find a characteristic time delay in many cases representing that some stocks lead the price changes while others follow them. These data can be used to construct a directed network of influence.

  7. How structure sculpts function: Unveiling the contribution of anatomical connectivity to the brain's spontaneous correlation structure

    NASA Astrophysics Data System (ADS)

    Bettinardi, R. G.; Deco, G.; Karlaftis, V. M.; Van Hartevelt, T. J.; Fernandes, H. M.; Kourtzi, Z.; Kringelbach, M. L.; Zamora-López, G.

    2017-04-01

    Intrinsic brain activity is characterized by highly organized co-activations between different regions, forming clustered spatial patterns referred to as resting-state networks. The observed co-activation patterns are sustained by the intricate fabric of millions of interconnected neurons constituting the brain's wiring diagram. However, as for other real networks, the relationship between the connectional structure and the emergent collective dynamics still evades complete understanding. Here, we show that it is possible to estimate the expected pair-wise correlations that a network tends to generate thanks to the underlying path structure. We start from the assumption that in order for two nodes to exhibit correlated activity, they must be exposed to similar input patterns from the entire network. We then acknowledge that information rarely spreads only along a unique route but rather travels along all possible paths. In real networks, the strength of local perturbations tends to decay as they propagate away from the sources, leading to a progressive attenuation of the original information content and, thus, of their influence. Accordingly, we define a novel graph measure, topological similarity, which quantifies the propensity of two nodes to dynamically correlate as a function of the resemblance of the overall influences they are expected to receive due to the underlying structure of the network. Applied to the human brain, we find that the similarity of whole-network inputs, estimated from the topology of the anatomical connectome, plays an important role in sculpting the backbone pattern of time-average correlations observed at rest.

  8. Monte Carlo explicitly correlated second-order many-body perturbation theory

    NASA Astrophysics Data System (ADS)

    Johnson, Cole M.; Doran, Alexander E.; Zhang, Jinmei; Valeev, Edward F.; Hirata, So

    2016-10-01

    A stochastic algorithm is proposed and implemented that computes a basis-set-incompleteness (F12) correction to an ab initio second-order many-body perturbation energy as a short sum of 6- to 15-dimensional integrals of Gaussian-type orbitals, an explicit function of the electron-electron distance (geminal), and its associated excitation amplitudes held fixed at the values suggested by Ten-no. The integrals are directly evaluated (without a resolution-of-the-identity approximation or an auxiliary basis set) by the Metropolis Monte Carlo method. Applications of this method to 17 molecular correlation energies and 12 gas-phase reaction energies reveal that both the nonvariational and variational formulas for the correction give reliable correlation energies (98% or higher) and reaction energies (within 2 kJ mol-1 with a smaller statistical uncertainty) near the complete-basis-set limits by using just the aug-cc-pVDZ basis set. The nonvariational formula is found to be 2-10 times less expensive to evaluate than the variational one, though the latter yields energies that are bounded from below and is, therefore, slightly but systematically more accurate for energy differences. Being capable of using virtually any geminal form, the method confirms the best overall performance of the Slater-type geminal among 6 forms satisfying the same cusp conditions. Not having to precompute lower-dimensional integrals analytically, to store them on disk, or to transform them in a nonscalable dense-matrix-multiplication algorithm, the method scales favorably with both system size and computer size; the cost increases only as O(n4) with the number of orbitals (n), and its parallel efficiency reaches 99.9% of the ideal case on going from 16 to 4096 computer processors.

  9. Statistical performance evaluation of ECG transmission using wireless networks.

    PubMed

    Shakhatreh, Walid; Gharaibeh, Khaled; Al-Zaben, Awad

    2013-07-01

    This paper presents simulation of the transmission of biomedical signals (using ECG signal as an example) over wireless networks. Investigation of the effect of channel impairments including SNR, pathloss exponent, path delay and network impairments such as packet loss probability; on the diagnosability of the received ECG signal are presented. The ECG signal is transmitted through a wireless network system composed of two communication protocols; an 802.15.4- ZigBee protocol and an 802.11b protocol. The performance of the transmission is evaluated using higher order statistics parameters such as kurtosis and Negative Entropy in addition to the common techniques such as the PRD, RMS and Cross Correlation.

  10. Systematic studies of correlations between different order flow harmonics in Pb-Pb collisions at s NN = 2.76 TeV

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Acharya, S.; Adam, J.; Adamová, D.

    The correlations between event-by-event fluctuations of anisotropic flow harmonic amplitudes have been measured in Pb-Pb collisions atmore » $$\\sqrt{s}$$$_ {NN}$$=2.76 TeV with the ALICE detector at the Large Hadron Collider. The results are reported in terms of multiparticle correlation observables dubbed symmetric cumulants. These observables are robust against biases originating from nonflow effects. The centrality dependence of correlations between the higher order harmonics (the quadrangular v 4 and pentagonal v 5 flow) and the lower order harmonics (the elliptic v 2 and triangular v 3 flow) is presented. The transverse momentum dependences of correlations between v 3 and v 2 and between v 4 and v 2 are also reported. The results are compared to calculations from viscous hydrodynamics and a multiphase transport (AMPT) model calculations. The comparisons to viscous hydrodynamic models demonstrate that the different order harmonic correlations respond differently to the initial conditions and the temperature dependence of the ratio of shear viscosity to entropy density (η/s). A small average value of η/s is favored independent of the specific choice of initial conditions in the models. The calculations with the AMPT initial conditions yield results closest to the measurements. Correlations among the magnitudes of v 2, v 3, and v 4 show moderate p T dependence in midcentral collisions. This might be an indication of possible viscous corrections to the equilibrium distribution at hadronic freeze-out, which might help to understand the possible contribution of bulk viscosity in the hadronic phase of the system. Lastly, together with existing measurements of individual flow harmonics, the presented results provide further constraints on the initial conditions and the transport properties of the system produced in heavy-ion collisions.« less

  11. Systematic studies of correlations between different order flow harmonics in Pb-Pb collisions at s NN = 2.76 TeV

    DOE PAGES

    Acharya, S.; Adam, J.; Adamová, D.; ...

    2018-02-12

    The correlations between event-by-event fluctuations of anisotropic flow harmonic amplitudes have been measured in Pb-Pb collisions atmore » $$\\sqrt{s}$$$_ {NN}$$=2.76 TeV with the ALICE detector at the Large Hadron Collider. The results are reported in terms of multiparticle correlation observables dubbed symmetric cumulants. These observables are robust against biases originating from nonflow effects. The centrality dependence of correlations between the higher order harmonics (the quadrangular v 4 and pentagonal v 5 flow) and the lower order harmonics (the elliptic v 2 and triangular v 3 flow) is presented. The transverse momentum dependences of correlations between v 3 and v 2 and between v 4 and v 2 are also reported. The results are compared to calculations from viscous hydrodynamics and a multiphase transport (AMPT) model calculations. The comparisons to viscous hydrodynamic models demonstrate that the different order harmonic correlations respond differently to the initial conditions and the temperature dependence of the ratio of shear viscosity to entropy density (η/s). A small average value of η/s is favored independent of the specific choice of initial conditions in the models. The calculations with the AMPT initial conditions yield results closest to the measurements. Correlations among the magnitudes of v 2, v 3, and v 4 show moderate p T dependence in midcentral collisions. This might be an indication of possible viscous corrections to the equilibrium distribution at hadronic freeze-out, which might help to understand the possible contribution of bulk viscosity in the hadronic phase of the system. Lastly, together with existing measurements of individual flow harmonics, the presented results provide further constraints on the initial conditions and the transport properties of the system produced in heavy-ion collisions.« less

  12. Neural correlates of consciousness during general anesthesia using functional magnetic resonance imaging (fMRI).

    PubMed

    Bonhomme, V; Boveroux, P; Brichant, J F; Laureys, S; Boly, M

    2012-01-01

    This paper reviews the current knowledge about the mechanisms of anesthesia-induced alteration of consciousness. It is now evident that hypnotic anesthetic agents have specific brain targets whose function is hierarchically altered in a dose-dependent manner. Higher order networks, thought to be involved in mental content generation, as well as sub-cortical networks involved in thalamic activity regulation seems to be affected first by increasing concentrations of hypnotic agents that enhance inhibitory neurotransmission. Lower order sensory networks are preserved, including thalamo-cortical connectivity into those networks, even at concentrations that suppress responsiveness, but cross-modal sensory interactions are inhibited. Thalamo-cortical connectivity into the consciousness networks decreases with increasing concentrations of those agents, and is transformed into an anti-correlated activity between the thalamus and the cortex for the deepest levels of sedation, when the subject is non responsive. Future will tell us whether these brain function alterations are also observed with hypnotic agents that mainly inhibit excitatory neurotransmission. The link between the observations made using fMRI and the identified biochemical targets of hypnotic anesthetic agents still remains to be identified.

  13. Age-related differences in brain network activation and co-activation during multiple object tracking.

    PubMed

    Dørum, Erlend S; Alnæs, Dag; Kaufmann, Tobias; Richard, Geneviève; Lund, Martina J; Tønnesen, Siren; Sneve, Markus H; Mathiesen, Nina C; Rustan, Øyvind G; Gjertsen, Øivind; Vatn, Sigurd; Fure, Brynjar; Andreassen, Ole A; Nordvik, Jan Egil; Westlye, Lars T

    2016-11-01

    Multiple object tracking (MOT) is a powerful paradigm for measuring sustained attention. Although previous fMRI studies have delineated the brain activation patterns associated with tracking and documented reduced tracking performance in aging, age-related effects on brain activation during MOT have not been characterized. In particular, it is unclear if the task-related activation of different brain networks is correlated, and also if this coordination between activations within brain networks shows differential effects of age. We obtained fMRI data during MOT at two load conditions from a group of younger ( n  = 25, mean age = 24.4 ± 5.1 years) and older ( n  = 21, mean age = 64.7 ± 7.4 years) healthy adults. Using a combination of voxel-wise and independent component analysis, we investigated age-related differences in the brain network activation. In order to explore to which degree activation of the various brain networks reflect unique and common mechanisms, we assessed the correlations between the brain networks' activations. Behavioral performance revealed an age-related reduction in MOT accuracy. Voxel and brain network level analyses converged on decreased load-dependent activations of the dorsal attention network (DAN) and decreased load-dependent deactivations of the default mode networks (DMN) in the old group. Lastly, we found stronger correlations in the task-related activations within DAN and within DMN components for younger adults, and stronger correlations between DAN and DMN components for older adults. Using MOT as means for measuring attentional performance, we have demonstrated an age-related attentional decline. Network-level analysis revealed age-related alterations in network recruitment consisting of diminished activations of DAN and diminished deactivations of DMN in older relative to younger adults. We found stronger correlations within DMN and within DAN components for younger adults and stronger correlations between

  14. Good Features to Correlate for Visual Tracking

    NASA Astrophysics Data System (ADS)

    Gundogdu, Erhan; Alatan, A. Aydin

    2018-05-01

    During the recent years, correlation filters have shown dominant and spectacular results for visual object tracking. The types of the features that are employed in these family of trackers significantly affect the performance of visual tracking. The ultimate goal is to utilize robust features invariant to any kind of appearance change of the object, while predicting the object location as properly as in the case of no appearance change. As the deep learning based methods have emerged, the study of learning features for specific tasks has accelerated. For instance, discriminative visual tracking methods based on deep architectures have been studied with promising performance. Nevertheless, correlation filter based (CFB) trackers confine themselves to use the pre-trained networks which are trained for object classification problem. To this end, in this manuscript the problem of learning deep fully convolutional features for the CFB visual tracking is formulated. In order to learn the proposed model, a novel and efficient backpropagation algorithm is presented based on the loss function of the network. The proposed learning framework enables the network model to be flexible for a custom design. Moreover, it alleviates the dependency on the network trained for classification. Extensive performance analysis shows the efficacy of the proposed custom design in the CFB tracking framework. By fine-tuning the convolutional parts of a state-of-the-art network and integrating this model to a CFB tracker, which is the top performing one of VOT2016, 18% increase is achieved in terms of expected average overlap, and tracking failures are decreased by 25%, while maintaining the superiority over the state-of-the-art methods in OTB-2013 and OTB-2015 tracking datasets.

  15. The Network of Counterparty Risk: Analysing Correlations in OTC Derivatives.

    PubMed

    Nanumyan, Vahan; Garas, Antonios; Schweitzer, Frank

    2015-01-01

    Counterparty risk denotes the risk that a party defaults in a bilateral contract. This risk not only depends on the two parties involved, but also on the risk from various other contracts each of these parties holds. In rather informal markets, such as the OTC (over-the-counter) derivative market, institutions only report their aggregated quarterly risk exposure, but no details about their counterparties. Hence, little is known about the diversification of counterparty risk. In this paper, we reconstruct the weighted and time-dependent network of counterparty risk in the OTC derivatives market of the United States between 1998 and 2012. To proxy unknown bilateral exposures, we first study the co-occurrence patterns of institutions based on their quarterly activity and ranking in the official report. The network obtained this way is further analysed by a weighted k-core decomposition, to reveal a core-periphery structure. This allows us to compare the activity-based ranking with a topology-based ranking, to identify the most important institutions and their mutual dependencies. We also analyse correlations in these activities, to show strong similarities in the behavior of the core institutions. Our analysis clearly demonstrates the clustering of counterparty risk in a small set of about a dozen US banks. This not only increases the default risk of the central institutions, but also the default risk of peripheral institutions which have contracts with the central ones. Hence, all institutions indirectly have to bear (part of) the counterparty risk of all others, which needs to be better reflected in the price of OTC derivatives.

  16. The Network of Counterparty Risk: Analysing Correlations in OTC Derivatives

    PubMed Central

    Nanumyan, Vahan; Garas, Antonios; Schweitzer, Frank

    2015-01-01

    Counterparty risk denotes the risk that a party defaults in a bilateral contract. This risk not only depends on the two parties involved, but also on the risk from various other contracts each of these parties holds. In rather informal markets, such as the OTC (over-the-counter) derivative market, institutions only report their aggregated quarterly risk exposure, but no details about their counterparties. Hence, little is known about the diversification of counterparty risk. In this paper, we reconstruct the weighted and time-dependent network of counterparty risk in the OTC derivatives market of the United States between 1998 and 2012. To proxy unknown bilateral exposures, we first study the co-occurrence patterns of institutions based on their quarterly activity and ranking in the official report. The network obtained this way is further analysed by a weighted k-core decomposition, to reveal a core-periphery structure. This allows us to compare the activity-based ranking with a topology-based ranking, to identify the most important institutions and their mutual dependencies. We also analyse correlations in these activities, to show strong similarities in the behavior of the core institutions. Our analysis clearly demonstrates the clustering of counterparty risk in a small set of about a dozen US banks. This not only increases the default risk of the central institutions, but also the default risk of peripheral institutions which have contracts with the central ones. Hence, all institutions indirectly have to bear (part of) the counterparty risk of all others, which needs to be better reflected in the price of OTC derivatives. PMID:26335223

  17. Multilayer network decoding versatility and trust

    NASA Astrophysics Data System (ADS)

    Sarkar, Camellia; Yadav, Alok; Jalan, Sarika

    2016-01-01

    In the recent years, the multilayer networks have increasingly been realized as a more realistic framework to understand emergent physical phenomena in complex real-world systems. We analyze massive time-varying social data drawn from the largest film industry of the world under a multilayer network framework. The framework enables us to evaluate the versatility of actors, which turns out to be an intrinsic property of lead actors. Versatility in dimers suggests that working with different types of nodes are more beneficial than with similar ones. However, the triangles yield a different relation between type of co-actor and the success of lead nodes indicating the importance of higher-order motifs in understanding the properties of the underlying system. Furthermore, despite the degree-degree correlations of entire networks being neutral, multilayering picks up different values of correlation indicating positive connotations like trust, in the recent years. The analysis of weak ties of the industry uncovers nodes from a lower-degree regime being important in linking Bollywood clusters. The framework and the tools used herein may be used for unraveling the complexity of other real-world systems.

  18. Credit networks and systemic risk of Chinese local financing platforms: Too central or too big to fail?. -based on different credit correlations using hierarchical methods

    NASA Astrophysics Data System (ADS)

    He, Fang; Chen, Xi

    2016-11-01

    The accelerating accumulation and risk concentration of Chinese local financing platforms debts have attracted wide attention throughout the world. Due to the network of financial exposures among institutions, the failure of several platforms or regions of systemic importance will probably trigger systemic risk and destabilize the financial system. However, the complex network of credit relationships in Chinese local financing platforms at the state level remains unknown. To fill this gap, we presented the first complex networks and hierarchical cluster analysis of the credit market of Chinese local financing platforms using the ;bottom up; method from firm-level data. Based on balance-sheet channel, we analyzed the topology and taxonomy by applying the analysis paradigm of subdominant ultra-metric space to an empirical data in 2013. It is remarked that we chose to extract the network of co-financed financing platforms in order to evaluate the effect of risk contagion from platforms to bank system. We used the new credit similarity measure by combining the factor of connectivity and size, to extract minimal spanning trees (MSTs) and hierarchical trees (HTs). We found that: (1) the degree distributions of credit correlation backbone structure of Chinese local financing platforms are fat tailed, and the structure is unstable with respect to targeted failures; (2) the backbone is highly hierarchical, and largely explained by the geographic region; (3) the credit correlation backbone structure based on connectivity and size is significantly heterogeneous; (4) key platforms and regions of systemic importance, and contagion path of systemic risk are obtained, which are contributed to preventing systemic risk and regional risk of Chinese local financing platforms and preserving financial stability under the framework of macro prudential supervision. Our approach of credit similarity measure provides a means of recognizing ;systemically important; institutions and regions

  19. A Novel Group-Fused Sparse Partial Correlation Method for Simultaneous Estimation of Functional Networks in Group Comparison Studies.

    PubMed

    Liang, Xiaoyun; Vaughan, David N; Connelly, Alan; Calamante, Fernando

    2018-05-01

    The conventional way to estimate functional networks is primarily based on Pearson correlation along with classic Fisher Z test. In general, networks are usually calculated at the individual-level and subsequently aggregated to obtain group-level networks. However, such estimated networks are inevitably affected by the inherent large inter-subject variability. A joint graphical model with Stability Selection (JGMSS) method was recently shown to effectively reduce inter-subject variability, mainly caused by confounding variations, by simultaneously estimating individual-level networks from a group. However, its benefits might be compromised when two groups are being compared, given that JGMSS is blinded to other groups when it is applied to estimate networks from a given group. We propose a novel method for robustly estimating networks from two groups by using group-fused multiple graphical-lasso combined with stability selection, named GMGLASS. Specifically, by simultaneously estimating similar within-group networks and between-group difference, it is possible to address inter-subject variability of estimated individual networks inherently related with existing methods such as Fisher Z test, and issues related to JGMSS ignoring between-group information in group comparisons. To evaluate the performance of GMGLASS in terms of a few key network metrics, as well as to compare with JGMSS and Fisher Z test, they are applied to both simulated and in vivo data. As a method aiming for group comparison studies, our study involves two groups for each case, i.e., normal control and patient groups; for in vivo data, we focus on a group of patients with right mesial temporal lobe epilepsy.

  20. Substrate stiffness-modulated registry phase correlations in cardiomyocytes map structural order to coherent beating

    NASA Astrophysics Data System (ADS)

    Dasbiswas, K.; Majkut, S.; Discher, D. E.; Safran, Samuel A.

    2015-01-01

    Recent experiments show that both striation, an indication of the structural registry in muscle fibres, as well as the contractile strains produced by beating cardiac muscle cells can be optimized by substrate stiffness. Here we show theoretically how the substrate rigidity dependence of the registry data can be mapped onto that of the strain measurements. We express the elasticity-mediated structural registry as a phase-order parameter using a statistical physics approach that takes the noise and disorder inherent in biological systems into account. By assuming that structurally registered myofibrils also tend to beat in phase, we explain the observed dependence of both striation and strain measurements of cardiomyocytes on substrate stiffness in a unified manner. The agreement of our ideas with experiment suggests that the correlated beating of heart cells may be limited by the structural order of the myofibrils, which in turn is regulated by their elastic environment.