Machine learning using a higher order correlation network
Lee, Y.C.; Doolen, G.; Chen, H.H.; Sun, G.Z.; Maxwell, T.; Lee, H.Y.
1986-01-01
A high-order correlation tensor formalism for neural networks is described. The model can simulate auto associative, heteroassociative, as well as multiassociative memory. For the autoassociative model, simulation results show a drastic increase in the memory capacity and speed over that of the standard Hopfield-like correlation matrix methods. The possibility of using multiassociative memory for a learning universal inference network is also discussed. 9 refs., 5 figs.
Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks
Jovanović, Stojan
2016-01-01
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. PMID:27271768
Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks.
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. PMID:27271768
Ganmor, Elad; Segev, Ronen; Schneidman, Elad
2011-01-01
Information is carried in the brain by the joint activity patterns of large groups of neurons. Understanding the structure and function of population neural codes is challenging because of the exponential number of possible activity patterns and dependencies among neurons. We report here that for groups of ~100 retinal neurons responding to natural stimuli, pairwise-based models, which were highly accurate for small networks, are no longer sufficient. We show that because of the sparse nature of the neural code, the higher-order interactions can be easily learned using a novel model and that a very sparse low-order interaction network underlies the code of large populations of neurons. Additionally, we show that the interaction network is organized in a hierarchical and modular manner, which hints at scalability. Our results suggest that learnability may be a key feature of the neural code. PMID:21602497
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.
Correlation in business networks
NASA Astrophysics Data System (ADS)
Souma, Wataru; Aoyama, Hideaki; Fujiwara, Yoshi; Ikeda, Yuichi; Iyetomi, Hiroshi; Kaizoji, Taisei
2006-10-01
This paper considers business networks. Through empirical study, we show that business networks display characteristics of small-world networks and scale-free networks. In this paper, we characterize firms as sales and bankruptcy probabilities. A correlation between sales and a correlation between bankruptcy probabilities in business networks are also considered. The results reveal that the correlation between sales depends strongly on the type of network, whereas the correlation between bankruptcy probabilities does so only weakly.
Correlational Neural Networks.
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. PMID:26654210
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
Correlation dimension of complex networks.
Lacasa, Lucas; Gómez-Gardeñes, Jesús
2013-04-19
We propose a new measure to characterize the dimension of complex networks based on the ergodic theory of dynamical systems. This measure is derived from the correlation sum of a trajectory generated by a random walker navigating the network, and extends the classical Grassberger-Procaccia algorithm to the context of complex networks. The method is validated with reliable results for both synthetic networks and real-world networks such as the world air-transportation network or urban networks, and provides a computationally fast way for estimating the dimensionality of networks which only relies on the local information provided by the walkers. PMID:23679650
Higher-order organization of complex networks.
Benson, Austin R; Gleich, David F; Leskovec, Jure
2016-07-01
Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges. However, higher-order organization of complex networks--at the level of small network subgraphs--remains largely unknown. Here, we develop a generalized framework for clustering networks on the basis of higher-order connectivity patterns. This framework provides mathematical guarantees on the optimality of obtained clusters and scales to networks with billions of edges. The framework reveals higher-order organization in a number of networks, including information propagation units in neuronal networks and hub structure in transportation networks. Results show that networks exhibit rich higher-order organizational structures that are exposed by clustering based on higher-order connectivity patterns. PMID:27387949
Theory of correlations in stochastic neural networks
NASA Astrophysics Data System (ADS)
Ginzburg, Iris; Sompolinsky, Haim
1994-10-01
One of the main experimental tools in probing the interactions between neurons has been the measurement of the correlations in their activity. In general, however, the interpretation of the observed correlations is difficult since the correlation between a pair of neurons is influenced not only by the direct interaction between them but also by the dynamic state of the entire network to which they belong. Thus a comparison between the observed correlations and the predictions from specific model networks is needed. In this paper we develop a theory of neuronal correlation functions in large networks comprising several highly connected subpopulations and obeying stochastic dynamic rules. When the networks are in asynchronous states, the cross correlations are relatively weak, i.e., their amplitude relative to that of the autocorrelations is of order of 1/N, N being the size of the interacting populations. Using the weakness of the cross correlations, general equations that express the matrix of cross correlations in terms of the mean neuronal activities and the effective interaction matrix are presented. The effective interactions are the synaptic efficacies multiplied by the gain of the postsynaptic neurons. The time-delayed cross-correlation matrix can be expressed as a sum of exponentially decaying modes that correspond to the (nonorthogonal) eigenvectors of the effective interaction matrix. The theory is extended to networks with random connectivity, such as randomly dilute networks. This allows for a comparison between the contribution from the internal common input and that from the direct interactions to the correlations of monosynaptically coupled pairs. A closely related quantity is the linear response of the neurons to external time-dependent perturbations. We derive the form of the dynamic linear response function of neurons in the above architecture in terms of the eigenmodes of the effective interaction matrix. The behavior of the correlations and the
Percolation on correlated random networks
NASA Astrophysics Data System (ADS)
Agliari, E.; Cioli, C.; Guadagnini, E.
2011-09-01
We consider a class of random, weighted networks, obtained through a redefinition of patterns in an Hopfield-like model, and, by performing percolation processes, we get information about topology and resilience properties of the networks themselves. Given the weighted nature of the graphs, different kinds of bond percolation can be studied: stochastic (deleting links randomly) and deterministic (deleting links based on rank weights), each mimicking a different physical process. The evolution of the network is accordingly different, as evidenced by the behavior of the largest component size and of the distribution of cluster sizes. In particular, we can derive that weak ties are crucial in order to maintain the graph connected and that, when they are the most prone to failure, the giant component typically shrinks without abruptly breaking apart; these results have been recently evidenced in several kinds of social networks.
Robustness of networks of networks with degree-degree correlation
NASA Astrophysics Data System (ADS)
Min, Byungjoon; Canals, Santiago; Makse, Hernan
Many real-world complex systems ranging from critical infrastructure and transportation networks to living systems including brain and cellular networks are not formed by an isolated network but by a network of networks. Randomly coupled networks with interdependency between different networks may easily result in abrupt collapse. Here, we seek a possible explanation of stable functioning in natural networks of networks including functional brain networks. Specifically, we analyze the robustness of networks of networks focused on one-to-many interconnections between different networks and degree-degree correlation. Implication of the network robustness on functional brain networks of rats is also discussed.
Quantum correlations with no causal order
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
Modeling Higher-Order Correlations within Cortical Microcolumns
Köster, Urs; Sohl-Dickstein, Jascha; Gray, Charles M.; Olshausen, Bruno A.
2014-01-01
We statistically characterize the population spiking activity obtained from simultaneous recordings of neurons across all layers of a cortical microcolumn. Three types of models are compared: an Ising model which captures pairwise correlations between units, a Restricted Boltzmann Machine (RBM) which allows for modeling of higher-order correlations, and a semi-Restricted Boltzmann Machine which is a combination of Ising and RBM models. Model parameters were estimated in a fast and efficient manner using minimum probability flow, and log likelihoods were compared using annealed importance sampling. The higher-order models reveal localized activity patterns which reflect the laminar organization of neurons within a cortical column. The higher-order models also outperformed the Ising model in log-likelihood: On populations of 20 cells, the RBM had 10% higher log-likelihood (relative to an independent model) than a pairwise model, increasing to 45% gain in a larger network with 100 spatiotemporal elements, consisting of 10 neurons over 10 time steps. We further removed the need to model stimulus-induced correlations by incorporating a peri-stimulus time histogram term, in which case the higher order models continued to perform best. These results demonstrate the importance of higher-order interactions to describe the structure of correlated activity in cortical networks. Boltzmann Machines with hidden units provide a succinct and effective way to capture these dependencies without increasing the difficulty of model estimation and evaluation. PMID:24991969
Correlated genotypes in friendship networks
Fowler, James H.; Settle, Jaime E.; Christakis, Nicholas A.
2011-01-01
It is well known that humans tend to associate with other humans who have similar characteristics, but it is unclear whether this tendency has consequences for the distribution of genotypes in a population. Although geneticists have shown that populations tend to stratify genetically, this process results from geographic sorting or assortative mating, and it is unknown whether genotypes may be correlated as a consequence of nonreproductive associations or other processes. Here, we study six available genotypes from the National Longitudinal Study of Adolescent Health to test for genetic similarity between friends. Maps of the friendship networks show clustering of genotypes and, after we apply strict controls for population stratification, the results show that one genotype is positively correlated (homophily) and one genotype is negatively correlated (heterophily). A replication study in an independent sample from the Framingham Heart Study verifies that DRD2 exhibits significant homophily and that CYP2A6 exhibits significant heterophily. These unique results show that homophily and heterophily obtain on a genetic (indeed, an allelic) level, which has implications for the study of population genetics and social behavior. In particular, the results suggest that association tests should include friends’ genes and that theories of evolution should take into account the fact that humans might, in some sense, be metagenomic with respect to the humans around them. PMID:21245293
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.
Representing higher-order dependencies in networks
Xu, Jian; Wickramarathne, Thanuka L.; Chawla, Nitesh V.
2016-01-01
To ensure the correctness of network analysis methods, the network (as the input) has to be a sufficiently accurate representation of the underlying data. However, when representing sequential data from complex systems, such as global shipping traffic or Web clickstream traffic as networks, conventional network representations that implicitly assume the Markov property (first-order dependency) can quickly become limiting. This assumption holds that, when movements are simulated on the network, the next movement depends only on the current node, discounting the fact that the movement may depend on several previous steps. However, we show that data derived from many complex systems can show up to fifth-order dependencies. In these cases, the oversimplifying assumption of the first-order network representation can lead to inaccurate network analysis results. To address this problem, we propose the higher-order network (HON) representation that can discover and embed variable orders of dependencies in a network representation. Through a comprehensive empirical evaluation and analysis, we establish several desirable characteristics of HON, including accuracy, scalability, and direct compatibility with the existing suite of network analysis methods. We illustrate how HON can be applied to a broad variety of tasks, such as random walking, clustering, and ranking, and we demonstrate that, by using it as input, HON yields more accurate results without any modification to these tasks. PMID:27386539
Entropy and order in urban street networks
NASA Astrophysics Data System (ADS)
Gudmundsson, Agust; Mohajeri, Nahid
2013-11-01
Many complex networks erase parts of their geometry as they develop, so that their evolution is difficult to quantify and trace. Here we introduce entropy measures for quantifying the complexity of street orientations and length variations within planar networks and apply them to the street networks of 41 British cities, whose geometric evolution over centuries can be explored. The results show that the street networks of the old central parts of the cities have lower orientation/length entropies - the streets are more tightly ordered and form denser networks - than the outer and more recent parts. Entropy and street length increase, because of spreading, with distance from the network centre. Tracing the 400-year evolution of one network indicates growth through densification (streets are added within the existing network) and expansion (streets are added at the margin of the network) and a gradual increase in entropy over time.
Measuring and modeling correlations in multiplex networks.
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. PMID:26465526
Measuring and modeling correlations in multiplex networks
NASA Astrophysics Data System (ADS)
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.
Ghost imaging with thermal light by third-order correlation
Bai Yanfeng; Han Shensheng
2007-10-15
Ghost imaging with classical incoherent light by third-order correlation is investigated. We discuss the similarities and the differences between ghost imaging by third-order correlation and by second-order correlation, and analyze the effect from each correlation part of the third-order correlation function on the imaging process. It is shown that the third-order correlated imaging includes richer correlated imaging effects than the second-order correlated one, while the imaging information originates mainly from the correlation of the intensity fluctuations between the test detector and each reference detector, as does ghost imaging by second-order correlation.
Earthquake correlations and networks: A comparative study
Krishna Mohan, T. R.; Revathi, P. G.
2011-04-15
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 69, 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.
Percolation of secret correlations in a network
Leverrier, Anthony; Garcia-Patron, Raul
2011-09-15
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.
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.
Correlated edge overlaps in multiplex networks.
Baxter, Gareth J; Bianconi, Ginestra; da Costa, Rui A; Dorogovtsev, Sergey N; Mendes, José F F
2016-07-01
We develop the theory of sparse multiplex networks with partially overlapping links based on their local treelikeness. This theory enables us to find the giant mutually connected component in a two-layer multiplex network with arbitrary correlations between connections of different types. We find that correlations between the overlapping and nonoverlapping links markedly change the phase diagram of the system, leading to multiple hybrid phase transitions. For assortative correlations we observe recurrent hybrid phase transitions. PMID:27575144
Correlated edge overlaps in multiplex networks
NASA Astrophysics Data System (ADS)
Baxter, Gareth J.; Bianconi, Ginestra; da Costa, Rui A.; Dorogovtsev, Sergey N.; Mendes, José F. F.
2016-07-01
We develop the theory of sparse multiplex networks with partially overlapping links based on their local treelikeness. This theory enables us to find the giant mutually connected component in a two-layer multiplex network with arbitrary correlations between connections of different types. We find that correlations between the overlapping and nonoverlapping links markedly change the phase diagram of the system, leading to multiple hybrid phase transitions. For assortative correlations we observe recurrent hybrid phase transitions.
Network robustness of multiplex networks with interlayer degree correlations
NASA Astrophysics Data System (ADS)
Min, Byungjoon; Yi, Su Do; Lee, Kyu-Min; Goh, K.-I.
2014-04-01
We study the robustness properties of multiplex networks consisting of multiple layers of distinct types of links, focusing on the role of correlations between degrees of a node in different layers. We use generating function formalism to address various notions of the network robustness relevant to multiplex networks, such as the resilience of ordinary and mutual connectivity under random or targeted node removals, as well as the biconnectivity. We found that correlated coupling can affect the structural robustness of multiplex networks in diverse fashion. For example, for maximally correlated duplex networks, all pairs of nodes in the giant component are connected via at least two independent paths and network structure is highly resilient to random failure. In contrast, anticorrelated duplex networks are on one hand robust against targeted attack on high-degree nodes, but on the other hand they can be vulnerable to random failure.
Change Point Detection in Correlation Networks
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. PMID:26739105
Synchronization from Second Order Network Connectivity Statistics
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
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.
Modular networks of word correlations on Twitter
Mathiesen, Joachim; Yde, Pernille; Jensen, Mogens H.
2012-01-01
Complex networks are important tools for analyzing the information flow in many aspects of nature and human society. Using data from the microblogging service Twitter, we study networks of correlations in the occurrence of words from three different categories, international brands, nouns and US major cities. We create networks where the strength of links is determined by a similarity measure based on the rate of co-occurrences of words. In comparison with the null model, where words are assumed to be uncorrelated, the heavy-tailed distribution of pair correlations is shown to be a consequence of groups of words representing similar entities. PMID:23139863
Order Parameters for Two-Dimensional Networks
NASA Astrophysics Data System (ADS)
Kaatz, Forrest; Bultheel, Adhemar; Egami, Takeshi
2007-10-01
We derive methods that explain how to quantify the amount of order in ``ordered'' and ``highly ordered'' porous arrays. Ordered arrays from bee honeycomb and several from the general field of nanoscience are compared. Accurate measures of the order in porous arrays are made using the discrete pair distribution function (PDF) and the Debye-Waller Factor (DWF) from 2-D discrete Fourier transforms calculated from the real-space data using MATLAB routines. An order parameter, OP3, is defined from the PDF to evaluate the total order in a given array such that an ideal network has the value of 1. When we compare PDFs of man-made arrays with that of our honeycomb we find OP3=0.399 for the honeycomb and OP3=0.572 for man's best hexagonal array. The DWF also scales with this order parameter with the least disorder from a computer-generated hexagonal array and the most disorder from a random array. An ideal hexagonal array normalizes a two-dimensional Fourier transform from which a Debye-Waller parameter is derived which describes the disorder in the arrays. An order parameter S, defined by the DWF, takes values from [0, 1] and for the analyzed man-made array is 0.90, while for the honeycomb it is 0.65. This presentation describes methods to quantify the order found in these arrays.
NASA Astrophysics Data System (ADS)
Scholtes, Ingo; Wider, Nicolas; Garas, Antonios
2016-03-01
Despite recent advances in the study of temporal networks, the analysis of time-stamped network data is still a fundamental challenge. In particular, recent studies have shown that correlations in the ordering of links crucially alter causal topologies of temporal networks, thus invalidating analyses based on static, time-aggregated representations of time-stamped data. These findings not only highlight an important dimension of complexity in temporal networks, but also call for new network-analytic methods suitable to analyze complex systems with time-varying topologies. Addressing this open challenge, here we introduce a novel framework for the study of path-based centralities in temporal networks. Studying betweenness, closeness and reach centrality, we first show than an application of these measures to time-aggregated, static representations of temporal networks yields misleading results about the actual importance of nodes. To overcome this problem, we define path-based centralities in higher-order aggregate networks, a recently proposed generalization of the commonly used static representation of time-stamped data. Using data on six empirical temporal networks, we show that the resulting higher-order measures better capture the true, temporal centralities of nodes. Our results demonstrate that higher-order aggregate networks constitute a powerful abstraction, with broad perspectives for the design of new, computationally efficient data mining techniques for time-stamped relational data.
Coevolution and Correlated Multiplexity in Multiplex Networks
NASA Astrophysics Data System (ADS)
Kim, Jung Yeol; Goh, K.-I.
2013-08-01
Distinct channels of interaction in a complex networked system define network layers, which coexist and cooperate for the system’s function. Towards understanding such multiplex systems, we propose a modeling framework based on coevolution of network layers, with a class of minimalistic growing network models as working examples. We examine how the entangled growth of coevolving layers can shape the network structure and show analytically and numerically that the coevolution can induce strong degree correlations across layers, as well as modulate degree distributions. We further show that such a coevolution-induced correlated multiplexity can alter the system’s response to the dynamical process, exemplified by the suppressed susceptibility to a social cascade process.
Node Survival in Networks under Correlated Attacks
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
Node Survival in Networks under Correlated Attacks.
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
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.
Competing Orders in Strongly Correlated Systems
NASA Astrophysics Data System (ADS)
Ramachandran, Ganesh
Systems with competing orders are of great interest in condensed matter physics. When two phases have comparable energies, novel interplay effects such can be induced by tuning an appropriate parameter. In this thesis, we study two problems of competing orders - (i) ultracold atom gases with competing superfluidity and Charge Density Wave (CDW) orders, and (ii) low dimensional antiferromagnets with Neel order competing against various disordered ground states. In the first part of the thesis, we study the attractive Hubbard model which could soon be realized in ultracold atom experiments. Close to half-filling, the superfluid ground state competes with a low-lying CDW phase. We study the collective excitations of the superfluid using the Generalized Random Phase Approximation (GRPA) and strong-coupling spin wave analysis. The competing CDW phase manifests as a roton-like excitation. We characterize the collective mode spectrum, setting benchmarks for experiments. We drive competition between orders by imposing superfluid flow. Superflow leads to various instabilities: in particular, we find a dynamical instability associated with CDW order. We also find a novel dynamical incommensurate instability analogous to exciton condensation in semiconductors. In the second part, inspired by experiments on Bi3Mn 4O12(NO3)(BMNO), we first study the interlayer dimer state in spin-S bilayer antiferromagnets. At a critical bilayer coupling strength, condensation of triplet excitations leads to Neel order. In describing this transition, bond operator mean field theory suffers from systematic deviations. We bridge these deviations by taking into account corrections arising from higher spin excitations. The interlayer dimer state shows a field induced Neel transition, as seen in BMNO. Our results are relevant to the quantitative modelling of spin-S dimerized systems. We then study the J1 - J2 model on the honeycomb lattice with frustrating next-nearest neighbour exchange. For J2 >J1
High-order correlation of chaotic bosons and fermions
NASA Astrophysics Data System (ADS)
Liu, Hong-Chao
2016-08-01
We theoretically study the high-order correlation functions of chaotic bosons and fermions. Based on the different parity of the Stirling number, the products of the first-order correlation functions are well classified and employed to represent the high-order correlation function. The correlation of bosons conduces a bunching effect, which will be enhanced as order N increases. Different from bosons, the anticommutation relation of fermions leads to the parity of the Stirling number, which thereby results in a mixture of bunching and antibunching behaviors in high-order correlation. By further investigating third-order ghost diffraction and ghost imaging, the differences between the high-order correlations of bosons and fermions are discussed in detail. A larger N will dramatically improve the ghost image quality for bosons, but a good strategy should be carefully chosen for the fermionic ghost imaging process due to its complex correlation components.
Dynamics on modular networks with heterogeneous correlations
Melnik, Sergey; Porter, Mason A.; Mucha, Peter J.; Gleeson, James P.
2014-06-15
We develop a new ensemble of modular random graphs in which degree-degree correlations can be different in each module, and the inter-module connections are defined by the joint degree-degree distribution of nodes for each pair of modules. We present an analytical approach that allows one to analyze several types of binary dynamics operating on such networks, and we illustrate our approach using bond percolation, site percolation, and the Watts threshold model. The new network ensemble generalizes existing models (e.g., the well-known configuration model and Lancichinetti-Fortunato-Radicchi networks) by allowing a heterogeneous distribution of degree-degree correlations across modules, which is important for the consideration of nonidentical interacting networks.
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.
Quantifying higher-order correlations in a neuronal pool
NASA Astrophysics Data System (ADS)
Montangie, Lisandro; Montani, Fernando
2015-03-01
Recent experiments involving a relatively large population of neurons have shown a very significant amount of higher-order correlations. However, little is known of how these affect the integration and firing behavior of a population of neurons beyond the second order statistics. To investigate how higher-order inputs statistics can shape beyond pairwise spike correlations and affect information coding in the brain, we consider a neuronal pool where each neuron fires stochastically. We develop a simple mathematically tractable model that makes it feasible to account for higher-order spike correlations in a neuronal pool with highly interconnected common inputs beyond second order statistics. In our model, correlations between neurons appear from q-Gaussian inputs into threshold neurons. The approach constitutes the natural extension of the Dichotomized Gaussian model, where the inputs to the model are just Gaussian distributed and therefore have no input interactions beyond second order. We obtain an exact analytical expression for the joint distribution of firing, quantifying the degree of higher-order spike correlations, truly emphasizing the functional aspects of higher-order statistics, as we account for beyond second order inputs correlations seen by each neuron within the pool. We determine how higher-order correlations depend on the interaction structure of the input, showing that the joint distribution of firing is skewed as the parameter q increases inducing larger excursions of synchronized spikes. We show how input nonlinearities can shape higher-order correlations and enhance coding performance by neural populations.
Characterizing the intrinsic correlations of scale-free networks
NASA Astrophysics Data System (ADS)
de Brito, J. B.; Sampaio Filho, C. I. N.; Moreira, A. A.; Andrade, J. S.
2016-08-01
When studying topological or dynamical properties of random scale-free networks, it is tacitly assumed that degree-degree correlations are not present. However, simple constraints, such as the absence of multiple edges and self-loops, can give rise to intrinsic correlations in these structures. In the same way that Fermionic correlations in thermodynamic systems are relevant only in the limit of low temperature, the intrinsic correlations in scale-free networks are relevant only when the extreme values for the degrees grow faster than the square root of the network size. In this situation, these correlations can significantly affect the dependence of the average degree of the nearest neighbors of a given vertex on this vertices degree. Here, we introduce an analytical approach that is capable to predict the functional form of this property. Moreover, our results indicate that random scale-free network models are not self-averaging, that is, the second moment of their degree distribution may vary orders of magnitude among different realizations. Finally, we argue that the intrinsic correlations investigated here may have profound impact on the critical properties of random scale-free networks.
Weak pairwise correlations imply strongly correlated network states in a neural population
Schneidman, Elad; Berry, Michael J.; Segev, Ronen; Bialek, William
2006-01-01
Biological networks have so many possible states that exhaustive sampling is impossible. Successful analysis thus depends on simplifying hypotheses, but experiments on many systems hint that complicated, higher-order interactions among large groups of elements have an important role. Here we show, in the vertebrate retina, that weak correlations between pairs of neurons coexist with strongly collective behaviour in the responses of ten or more neurons. We find that this collective behaviour is described quantitatively by models that capture the observed pairwise correlations but assume no higher-order interactions. These maximum entropy models are equivalent to Ising models, and predict that larger networks are completely dominated by correlation effects. This suggests that the neural code has associative or error-correcting properties, and we provide preliminary evidence for such behaviour. As a first test for the generality of these ideas, we show that similar results are obtained from networks of cultured cortical neurons. PMID:16625187
Degree correlations in signed social networks
NASA Astrophysics Data System (ADS)
Ciotti, Valerio; Bianconi, Ginestra; Capocci, Andrea; Colaiori, Francesca; Panzarasa, Pietro
2015-03-01
We investigate degree correlations in two online social networks where users are connected through different types of links. We find that, while subnetworks in which links have a positive connotation, such as endorsement and trust, are characterized by assortative mixing by degree, networks in which links have a negative connotation, such as disapproval and distrust, are characterized by disassortative patterns. We introduce a class of simple theoretical models to analyze the interplay between network topology and the superimposed structure based on the sign of links. Results uncover the conditions that underpin the emergence of the patterns observed in the data, namely the assortativity of positive subnetworks and the disassortativity of negative ones. We discuss the implications of our study for the analysis of signed complex networks.
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.
Learning Bayesian Networks from Correlated Data
Bae, Harold; Monti, Stefano; Montano, Monty; Steinberg, Martin H.; Perls, Thomas T.; Sebastiani, Paola
2016-01-01
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. PMID:27146517
Learning Bayesian Networks from Correlated Data.
Bae, Harold; Monti, Stefano; Montano, Monty; Steinberg, Martin H; Perls, Thomas T; Sebastiani, Paola
2016-01-01
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. PMID:27146517
Higher order correlation beams in atmosphere under strong turbulence conditions.
Avetisyan, H; Monken, C H
2016-02-01
Higher order correlation beams, that is, two-photon beams obtained from the process of spontaneous parametric down-conversion pumped by Hermite-Gauss or Laguerre-Gauss beams of any order, can be used to encode information in many modes, opening the possibility of quantum communication with large alphabets. In this paper we calculate, analytically, the fourth-order correlation function for the Hermite-Gauss and Laguerre-Gauss coherent and partially coherent correlation beams propagating through a strong turbulent medium. We show that fourth-order correlation functions for correlation beams have, under certain conditions, expressions similar to those of intensities of classical beams and are degraded by turbulence in a similar way as the classical beams. Our results can be useful in establishing limits for the use of two-photon beams in quantum communications with larger alphabets under atmospheric turbulence. PMID:26906808
Irreducible many-body correlations in topologically ordered systems
NASA Astrophysics Data System (ADS)
Liu, Yang; Zeng, Bei; Zhou, D. L.
2016-02-01
Topologically ordered systems exhibit large-scale correlation in their ground states, which may be characterized by quantities such as topological entanglement entropy. We propose that the concept of irreducible many-body correlation (IMC), the correlation that cannot be implied by all local correlations, may also be used as a signature of topological order. In a topologically ordered system, we demonstrate that for a part of the system with holes, the reduced density matrix exhibits IMCs which become reducible when the holes are removed. The appearance of these IMCs then represents a key feature of topological phase. We analyze the many-body correlation structures in the ground state of the toric code model in external magnetic fields, and show that the topological phase transition is signaled by the IMCs.
athena: Tree code for second-order correlation functions
NASA Astrophysics Data System (ADS)
Kilbinger, Martin; Bonnett, Christopher; Coupon, Jean
2014-02-01
athena is a 2d-tree code that estimates second-order correlation functions from input galaxy catalogues. These include shear-shear correlations (cosmic shear), position-shear (galaxy-galaxy lensing) and position-position (spatial angular correlation). Written in C, it includes a power-spectrum estimator implemented in Python; this script also calculates the aperture-mass dispersion. A test data set is available.
High-order resting-state functional connectivity network for MCI classification.
Chen, Xiaobo; Zhang, Han; Gao, Yue; Wee, Chong-Yaw; Li, Gang; Shen, Dinggang
2016-09-01
Brain functional connectivity (FC) network, estimated with resting-state functional magnetic resonance imaging (RS-fMRI) technique, has emerged as a promising approach for accurate diagnosis of neurodegenerative diseases. However, the conventional FC network is essentially low-order in the sense that only the correlations among brain regions (in terms of RS-fMRI time series) are taken into account. The features derived from this type of brain network may fail to serve as an effective disease biomarker. To overcome this drawback, we propose extraction of novel high-order FC correlations that characterize how the low-order correlations between different pairs of brain regions interact with each other. Specifically, for each brain region, a sliding window approach is first performed over the entire RS-fMRI time series to generate multiple short overlapping segments. For each segment, a low-order FC network is constructed, measuring the short-term correlation between brain regions. These low-order networks (obtained from all segments) describe the dynamics of short-term FC along the time, thus also forming the correlation time series for every pair of brain regions. To overcome the curse of dimensionality, we further group the correlation time series into a small number of different clusters according to their intrinsic common patterns. Then, the correlation between the respective mean correlation time series of different clusters is calculated to represent the high-order correlation among different pairs of brain regions. Finally, we design a pattern classifier, by combining features of both low-order and high-order FC networks. Experimental results verify the effectiveness of the high-order FC network on disease diagnosis. Hum Brain Mapp 37:3282-3296, 2016. © 2016 Wiley Periodicals, Inc. PMID:27144538
Clustering and information in correlation based financial networks
NASA Astrophysics Data System (ADS)
Onnela, J.-P.; Kaski, K.; Kertész, J.
2004-03-01
Networks of companies can be constructed by using return correlations. A crucial issue in this approach is to select the relevant correlations from the correlation matrix. In order to study this problem, we start from an empty graph with no edges where the vertices correspond to stocks. Then, one by one, we insert edges between the vertices according to the rank of their correlation strength, resulting in a network called asset graph. We study its properties, such as topologically different growth types, number and size of clusters and clustering coefficient. These properties, calculated from empirical data, are compared against those of a random graph. The growth of the graph can be classified according to the topological role of the newly inserted edge. We find that the type of growth which is responsible for creating cycles in the graph sets in much earlier for the empirical asset graph than for the random graph, and thus reflects the high degree of networking present in the market. We also find the number of clusters in the random graph to be one order of magnitude higher than for the asset graph. At a critical threshold, the random graph undergoes a radical change in topology related to percolation transition and forms a single giant cluster, a phenomenon which is not observed for the asset graph. Differences in mean clustering coefficient lead us to conclude that most information is contained roughly within 10% of the edges.
Dynamic functional network connectivity using distance correlation
NASA Astrophysics Data System (ADS)
Rudas, Jorge; Guaje, Javier; Demertzi, Athena; Heine, Lizette; Tshibanda, Luaba; Soddu, Andrea; Laureys, Steven; Gómez, Francisco
2015-01-01
Investigations about the intrinsic brain organization in resting-state are critical for the understanding of healthy, pathological and pharmacological cerebral states. Recent studies on fMRI suggest that resting state activity is organized on large scale networks of coordinated activity, in the so called, Resting State Networks (RSNs). The assessment of the interactions among these functional networks plays an important role for the understanding of different brain pathologies. Current methods to quantify these interactions commonly assume that the underlying coordination mechanisms are stationary and linear through the whole recording of the resting state phenomena. Nevertheless, recent evidence suggests that rather than stationary, these mechanisms may exhibit a rich set of time-varying repertoires. In addition, these approaches do not consider possible non-linear relationships maybe linked to feed-back communication mechanisms between RSNs. In this work, we introduce a novel approach for dynamical functional network connectivity for functional magnetic resonance imaging (fMRI) resting activity, which accounts for non-linear dynamic relationships between RSNs. The proposed method is based on a windowed distance correlations computed on resting state time-courses extracted at single subject level. We showed that this strategy is complementary to the current approaches for dynamic functional connectivity and will help to enhance the discrimination capacity of patients with disorder of consciousness.
Inferring Correlation Networks from Genomic Survey Data
Friedman, Jonathan; Alm, Eric J.
2012-01-01
High-throughput sequencing based techniques, such as 16S rRNA gene profiling, have the potential to elucidate the complex inner workings of natural microbial communities - be they from the world's oceans or the human gut. A key step in exploring such data is the identification of dependencies between members of these communities, which is commonly achieved by correlation analysis. However, it has been known since the days of Karl Pearson that the analysis of the type of data generated by such techniques (referred to as compositional data) can produce unreliable results since the observed data take the form of relative fractions of genes or species, rather than their absolute abundances. Using simulated and real data from the Human Microbiome Project, we show that such compositional effects can be widespread and severe: in some real data sets many of the correlations among taxa can be artifactual, and true correlations may even appear with opposite sign. Additionally, we show that community diversity is the key factor that modulates the acuteness of such compositional effects, and develop a new approach, called SparCC (available at https://bitbucket.org/yonatanf/sparcc), which is capable of estimating correlation values from compositional data. To illustrate a potential application of SparCC, we infer a rich ecological network connecting hundreds of interacting species across 18 sites on the human body. Using the SparCC network as a reference, we estimated that the standard approach yields 3 spurious species-species interactions for each true interaction and misses 60% of the true interactions in the human microbiome data, and, as predicted, most of the erroneous links are found in the samples with the lowest diversity. PMID:23028285
Inferring correlation networks from genomic survey data.
Friedman, Jonathan; Alm, Eric J
2012-01-01
High-throughput sequencing based techniques, such as 16S rRNA gene profiling, have the potential to elucidate the complex inner workings of natural microbial communities - be they from the world's oceans or the human gut. A key step in exploring such data is the identification of dependencies between members of these communities, which is commonly achieved by correlation analysis. However, it has been known since the days of Karl Pearson that the analysis of the type of data generated by such techniques (referred to as compositional data) can produce unreliable results since the observed data take the form of relative fractions of genes or species, rather than their absolute abundances. Using simulated and real data from the Human Microbiome Project, we show that such compositional effects can be widespread and severe: in some real data sets many of the correlations among taxa can be artifactual, and true correlations may even appear with opposite sign. Additionally, we show that community diversity is the key factor that modulates the acuteness of such compositional effects, and develop a new approach, called SparCC (available at https://bitbucket.org/yonatanf/sparcc), which is capable of estimating correlation values from compositional data. To illustrate a potential application of SparCC, we infer a rich ecological network connecting hundreds of interacting species across 18 sites on the human body. Using the SparCC network as a reference, we estimated that the standard approach yields 3 spurious species-species interactions for each true interaction and misses 60% of the true interactions in the human microbiome data, and, as predicted, most of the erroneous links are found in the samples with the lowest diversity. PMID:23028285
Accelerating coordination in temporal networks by engineering the link order
Masuda, Naoki
2016-01-01
Social dynamics on a network may be accelerated or decelerated depending on which pairs of individuals in the network communicate early and which pairs do later. The order with which the links in a given network are sequentially used, which we call the link order, may be a strong determinant of dynamical behaviour on networks, potentially adding a new dimension to effects of temporal networks relative to static networks. Here we study the effect of the link order on linear coordination (i.e., synchronisation) dynamics. We show that the coordination speed considerably depends on specific orders of links. In addition, applying each single link for a long time to ensure strong pairwise coordination before moving to a next pair of individuals does not often enhance coordination of the entire network. We also implement a simple greedy algorithm to optimise the link order in favour of fast coordination. PMID:26916093
Accelerating coordination in temporal networks by engineering the link order
NASA Astrophysics Data System (ADS)
Masuda, Naoki
2016-02-01
Social dynamics on a network may be accelerated or decelerated depending on which pairs of individuals in the network communicate early and which pairs do later. The order with which the links in a given network are sequentially used, which we call the link order, may be a strong determinant of dynamical behaviour on networks, potentially adding a new dimension to effects of temporal networks relative to static networks. Here we study the effect of the link order on linear coordination (i.e., synchronisation) dynamics. We show that the coordination speed considerably depends on specific orders of links. In addition, applying each single link for a long time to ensure strong pairwise coordination before moving to a next pair of individuals does not often enhance coordination of the entire network. We also implement a simple greedy algorithm to optimise the link order in favour of fast coordination.
Weak value amplification via second-order correlated technique
NASA Astrophysics Data System (ADS)
Ting, Cui; Jing-Zheng, Huang; Xiang, Liu; Gui-Hua, Zeng
2016-02-01
We propose a new framework combining weak measurement and second-order correlated technique. The theoretical analysis shows that weak value amplification (WVA) experiment can also be implemented by a second-order correlated system. We then build two-dimensional second-order correlated function patterns for achieving higher amplification factor and discuss the signal-to-noise ratio influence. Several advantages can be obtained by our proposal. For instance, detectors with high resolution are not necessary. Moreover, detectors with low saturation intensity are available in WVA setup. Finally, type-one technical noise can be effectively suppressed. Project supported by the Union Research Centre of Advanced Spaceflight Technology (Grant No. USCAST2013-05), the National Natural Science Foundation of China (Grant Nos. 61170228, 61332019, and 61471239), and the High-Tech Research and Development Program of China (Grant No. 2013AA122901).
Analyzing complex networks through correlations in centrality measurements
NASA Astrophysics Data System (ADS)
Furlan Ronqui, José Ricardo; Travieso, Gonzalo
2015-05-01
Many real world systems can be expressed as complex networks of interconnected nodes. It is frequently important to be able to quantify the relative importance of the various nodes in the network, a task accomplished by defining some centrality measures, with different centrality definitions stressing different aspects of the network. It is interesting to know to what extent these different centrality definitions are related for different networks. In this work, we study the correlation between pairs of a set of centrality measures for different real world networks and two network models. We show that the centralities are in general correlated, but with stronger correlations for network models than for real networks. We also show that the strength of the correlation of each pair of centralities varies from network to network. Taking this fact into account, we propose the use of a centrality correlation profile, consisting of the values of the correlation coefficients between all pairs of centralities of interest, as a way to characterize networks. Using the yeast protein interaction network as an example we show also that the centrality correlation profile can be used to assess the adequacy of a network model as a representation of a given real network.
Social networking profile correlates of schizotypy.
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. PMID:22796101
Social networking profile correlates of schizotypy
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
Bagging and boosting negatively correlated neural networks.
Islam, Md Monirul; Yao, Xin; Shahriar Nirjon, S M Shahriar; Islam, Muhammad Asiful; Murase, Kazuyuki
2008-06-01
In this paper, we propose two cooperative ensemble learning algorithms, i.e., NegBagg and NegBoost, for designing neural network (NN) ensembles. The proposed algorithms incrementally train different individual NNs in an ensemble using the negative correlation learning algorithm. Bagging and boosting algorithms are used in NegBagg and NegBoost, respectively, to create different training sets for different NNs in the ensemble. The idea behind using negative correlation learning in conjunction with the bagging/boosting algorithm is to facilitate interaction and cooperation among NNs during their training. Both NegBagg and NegBoost use a constructive approach to automatically determine the number of hidden neurons for NNs. NegBoost also uses the constructive approach to automatically determine the number of NNs for the ensemble. The two algorithms have been tested on a number of benchmark problems in machine learning and NNs, including Australian credit card assessment, breast cancer, diabetes, glass, heart disease, letter recognition, satellite, soybean, and waveform problems. The experimental results show that NegBagg and NegBoost require a small number of training epochs to produce compact NN ensembles with good generalization. PMID:18558541
Functional Cortical Network in Alpha Band Correlates with Social Bargaining
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
Theory-independent limits on correlations from generalized Bayesian networks
NASA Astrophysics Data System (ADS)
Henson, Joe; Lal, Raymond; Pusey, Matthew F.
2014-11-01
Bayesian networks provide a powerful tool for reasoning about probabilistic causation, used in many areas of science. They are, however, intrinsically classical. In particular, Bayesian networks naturally yield the Bell inequalities. Inspired by this connection, we generalize the formalism of classical Bayesian networks in order to investigate non-classical correlations in arbitrary causal structures. Our framework of ‘generalized Bayesian networks’ replaces latent variables with the resources of any generalized probabilistic theory, most importantly quantum theory, but also, for example, Popescu-Rohrlich boxes. We obtain three main sets of results. Firstly, we prove that all of the observable conditional independences required by the classical theory also hold in our generalization; to obtain this, we extend the classical d-separation theorem to our setting. Secondly, we find that the theory-independent constraints on probabilities can go beyond these conditional independences. For example we find that no probabilistic theory predicts perfect correlation between three parties using only bipartite common causes. Finally, we begin a classification of those causal structures, such as the Bell scenario, that may yield a separation between classical, quantum and general-probabilistic correlations.
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.
Pump power dependence of second order correlation in nondegenerate SPDC
NASA Astrophysics Data System (ADS)
Kim, Charles; Kanner, Gary
2009-08-01
We observed the second order correlation peak for nondegenerate spontaneous parametric down conversion (SPDC) of a pulsed pump at 532 nm into 810 nm and 1550 nm entangled beams. We used a Si avalanche photodiode (APD) to detect the 810 nm photons, and an InGaAs APD to detect those at 1550 nm. We defined both a visibility and signal-to-noise ratio (SNR) based on the data, which were obtained at various pump powers. In contrast to classical imaging systems, for which SNR increases monotonically with transmitted power, the SNR for the correlation peak in our setup exhibited a gradual decay as the pump power increased. We derived an empirical relation for the SNR, which was inversely proportional to the square root of pump power.
Collapse of ordered spatial pattern in neuronal network
NASA Astrophysics Data System (ADS)
Song, Xinlin; Wang, Chunni; Ma, Jun; Ren, Guodong
2016-06-01
Spatiotemporal systems can emerge some regular spatial patterns due to self organization or under external periodical pacing while external attack or intrinsic collapse can destroy the regularity in the spatial system. For an example, the electrical activities of neurons in nervous system show regular spatial distribution under appropriate coupling and connection. It is believed that distinct regularity could be induced in the media by appropriate forcing or feedback, while a diffusive collapse induced by continuous destruction can cause breakdown of the media. In this paper, the collapse of ordered spatial distribution is investigated in a regular network of neurons (Morris-Lecar, Hindmarsh-Rose) in two-dimensional array. A stable target wave is developed regular spatial distribution emerges by imposing appropriate external forcing with diversity, or generating heterogeneity (parameter diversity in space). The diffusive invasion could be produced by continuous parameter collapse or switch in local area, e.g, the diffusive poisoning in ion channels of potassium in Morris-Lecar neurons causes breakdown in conductance of channels. It is found that target wave-dominated regularity can be suppressed when the collapsed area is diffused in random. Statistical correlation functions for sampled nodes (neurons) are defined to detect the collapse of ordered state by series analysis.
Analysis of community structure in networks of correlated data
Gomez, S.; Jensen, P.; Arenas, A.
2008-12-25
We present a reformulation of modularity that allows the analysis of the community structure in networks of correlated data. The new modularity preserves the probabilistic semantics of the original definition even when the network is directed, weighted, signed, and has self-loops. This is the most general condition one can find in the study of any network, in particular those defined from correlated data. We apply our results to a real network of correlated data between stores in the city of Lyon (France).
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.
Frustration and chiral orderings in correlated electron systems.
Batista, Cristian D; Lin, Shi-Zeng; Hayami, Satoru; Kamiya, Yoshitomo
2016-08-01
The term frustration refers to lattice systems whose ground state cannot simultaneously satisfy all the interactions. Frustration is an important property of correlated electron systems, which stems from the sign of loop products (similar to Wilson products) of interactions on a lattice. It was early recognized that geometric frustration can produce rather exotic physical behaviors, such as macroscopic ground state degeneracy and helimagnetism. The interest in frustrated systems was renewed two decades later in the context of spin glasses and the emergence of magnetic superstructures. In particular, Phil Anderson's proposal of a quantum spin liquid ground state for a two-dimensional lattice S = 1/2 Heisenberg magnet generated a very active line of research that still continues. As a result of these early discoveries and conjectures, the study of frustrated models and materials exploded over the last two decades. Besides the large efforts triggered by the search of quantum spin liquids, it was also recognized that frustration plays a crucial role in a vast spectrum of physical phenomena arising from correlated electron materials. Here we review some of these phenomena with particular emphasis on the stabilization of chiral liquids and non-coplanar magnetic orderings. In particular, we focus on the ubiquitous interplay between magnetic and charge degrees of freedom in frustrated correlated electron systems and on the role of anisotropy. We demonstrate that these basic ingredients lead to exotic phenomena, such as, charge effects in Mott insulators, the stabilization of single magnetic vortices, as well as vortex and skyrmion crystals, and the emergence of different types of chiral liquids. In particular, these orderings appear more naturally in itinerant magnets with the potential of inducing a very large anomalous Hall effect. PMID:27376461
Frustration and chiral orderings in correlated electron systems
NASA Astrophysics Data System (ADS)
Batista, Cristian D.; Lin, Shi-Zeng; Hayami, Satoru; Kamiya, Yoshitomo
2016-08-01
The term frustration refers to lattice systems whose ground state cannot simultaneously satisfy all the interactions. Frustration is an important property of correlated electron systems, which stems from the sign of loop products (similar to Wilson products) of interactions on a lattice. It was early recognized that geometric frustration can produce rather exotic physical behaviors, such as macroscopic ground state degeneracy and helimagnetism. The interest in frustrated systems was renewed two decades later in the context of spin glasses and the emergence of magnetic superstructures. In particular, Phil Anderson’s proposal of a quantum spin liquid ground state for a two-dimensional lattice S = 1/2 Heisenberg magnet generated a very active line of research that still continues. As a result of these early discoveries and conjectures, the study of frustrated models and materials exploded over the last two decades. Besides the large efforts triggered by the search of quantum spin liquids, it was also recognized that frustration plays a crucial role in a vast spectrum of physical phenomena arising from correlated electron materials. Here we review some of these phenomena with particular emphasis on the stabilization of chiral liquids and non-coplanar magnetic orderings. In particular, we focus on the ubiquitous interplay between magnetic and charge degrees of freedom in frustrated correlated electron systems and on the role of anisotropy. We demonstrate that these basic ingredients lead to exotic phenomena, such as, charge effects in Mott insulators, the stabilization of single magnetic vortices, as well as vortex and skyrmion crystals, and the emergence of different types of chiral liquids. In particular, these orderings appear more naturally in itinerant magnets with the potential of inducing a very large anomalous Hall effect.
Binary higher order neural networks for realizing Boolean functions.
Zhang, Chao; Yang, Jie; Wu, Wei
2011-05-01
In order to more efficiently realize Boolean functions by using neural networks, we propose a binary product-unit neural network (BPUNN) and a binary π-ς neural network (BPSNN). The network weights can be determined by one-step training. It is shown that the addition " σ," the multiplication " π," and two kinds of special weighting operations in BPUNN and BPSNN can implement the logical operators " ∨," " ∧," and " ¬" on Boolean algebra 〈Z(2),∨,∧,¬,0,1〉 (Z(2)={0,1}), respectively. The proposed two neural networks enjoy the following advantages over the existing networks: 1) for a complete truth table of N variables with both truth and false assignments, the corresponding Boolean function can be realized by accordingly choosing a BPUNN or a BPSNN such that at most 2(N-1) hidden nodes are needed, while O(2(N)), precisely 2(N) or at most 2(N), hidden nodes are needed by existing networks; 2) a new network BPUPS based on a collaboration of BPUNN and BPSNN can be defined to deal with incomplete truth tables, while the existing networks can only deal with complete truth tables; and 3) the values of the weights are all simply -1 or 1, while the weights of all the existing networks are real numbers. Supporting numerical experiments are provided as well. Finally, we present the risk bounds of BPUNN, BPSNN, and BPUPS, and then analyze their probably approximately correct learnability. PMID:21427020
Quantifying the connectivity of a network: The network correlation function method
NASA Astrophysics Data System (ADS)
Barzel, Baruch; Biham, Ofer
2009-10-01
Networks are useful for describing systems of interacting objects, where the nodes represent the objects and the edges represent the interactions between them. The applications include chemical and metabolic systems, food webs as well as social networks. Lately, it was found that many of these networks display some common topological features, such as high clustering, small average path length (small-world networks), and a power-law degree distribution (scale-free networks). The topological features of a network are commonly related to the network’s functionality. However, the topology alone does not account for the nature of the interactions in the network and their strength. Here, we present a method for evaluating the correlations between pairs of nodes in the network. These correlations depend both on the topology and on the functionality of the network. A network with high connectivity displays strong correlations between its interacting nodes and thus features small-world functionality. We quantify the correlations between all pairs of nodes in the network, and express them as matrix elements in the correlation matrix. From this information, one can plot the correlation function for the network and to extract the correlation length. The connectivity of a network is then defined as the ratio between this correlation length and the average path length of the network. Using this method, we distinguish between a topological small world and a functional small world, where the latter is characterized by long-range correlations and high connectivity. Clearly, networks that share the same topology may have different connectivities, based on the nature and strength of their interactions. The method is demonstrated on metabolic networks, but can be readily generalized to other types of networks.
Order and correlation contributions to the entropy of hydrophobic solvation
NASA Astrophysics Data System (ADS)
Liu, Maoyuan; Besford, Quinn Alexander; Mulvaney, Thomas; Gray-Weale, Angus
2015-03-01
The entropy of hydrophobic solvation has been explained as the result of ordered solvation structures, of hydrogen bonds, of the small size of the water molecule, of dispersion forces, and of solvent density fluctuations. We report a new approach to the calculation of the entropy of hydrophobic solvation, along with tests of and comparisons to several other methods. The methods are assessed in the light of the available thermodynamic and spectroscopic information on the effects of temperature on hydrophobic solvation. Five model hydrophobes in SPC/E water give benchmark solvation entropies via Widom's test-particle insertion method, and other methods and models are tested against these particle-insertion results. Entropies associated with distributions of tetrahedral order, of electric field, and of solvent dipole orientations are examined. We find these contributions are small compared to the benchmark particle-insertion entropy. Competitive with or better than other theories in accuracy, but with no free parameters, is the new estimate of the entropy contributed by correlations between dipole moments. Dipole correlations account for most of the hydrophobic solvation entropy for all models studied and capture the distinctive temperature dependence seen in thermodynamic and spectroscopic experiments. Entropies based on pair and many-body correlations in number density approach the correct magnitudes but fail to describe temperature and size dependences, respectively. Hydrogen-bond definitions and free energies that best reproduce entropies from simulations are reported, but it is difficult to choose one hydrogen bond model that fits a variety of experiments. The use of information theory, scaled-particle theory, and related methods is discussed briefly. Our results provide a test of the Frank-Evans hypothesis that the negative solvation entropy is due to structured water near the solute, complement the spectroscopic detection of that solvation structure by
Order and correlation contributions to the entropy of hydrophobic solvation
Liu, Maoyuan; Besford, Quinn Alexander; Mulvaney, Thomas; Gray-Weale, Angus
2015-03-21
The entropy of hydrophobic solvation has been explained as the result of ordered solvation structures, of hydrogen bonds, of the small size of the water molecule, of dispersion forces, and of solvent density fluctuations. We report a new approach to the calculation of the entropy of hydrophobic solvation, along with tests of and comparisons to several other methods. The methods are assessed in the light of the available thermodynamic and spectroscopic information on the effects of temperature on hydrophobic solvation. Five model hydrophobes in SPC/E water give benchmark solvation entropies via Widom’s test-particle insertion method, and other methods and models are tested against these particle-insertion results. Entropies associated with distributions of tetrahedral order, of electric field, and of solvent dipole orientations are examined. We find these contributions are small compared to the benchmark particle-insertion entropy. Competitive with or better than other theories in accuracy, but with no free parameters, is the new estimate of the entropy contributed by correlations between dipole moments. Dipole correlations account for most of the hydrophobic solvation entropy for all models studied and capture the distinctive temperature dependence seen in thermodynamic and spectroscopic experiments. Entropies based on pair and many-body correlations in number density approach the correct magnitudes but fail to describe temperature and size dependences, respectively. Hydrogen-bond definitions and free energies that best reproduce entropies from simulations are reported, but it is difficult to choose one hydrogen bond model that fits a variety of experiments. The use of information theory, scaled-particle theory, and related methods is discussed briefly. Our results provide a test of the Frank-Evans hypothesis that the negative solvation entropy is due to structured water near the solute, complement the spectroscopic detection of that solvation structure by
Order and correlation contributions to the entropy of hydrophobic solvation.
Liu, Maoyuan; Besford, Quinn Alexander; Mulvaney, Thomas; Gray-Weale, Angus
2015-03-21
The entropy of hydrophobic solvation has been explained as the result of ordered solvation structures, of hydrogen bonds, of the small size of the water molecule, of dispersion forces, and of solvent density fluctuations. We report a new approach to the calculation of the entropy of hydrophobic solvation, along with tests of and comparisons to several other methods. The methods are assessed in the light of the available thermodynamic and spectroscopic information on the effects of temperature on hydrophobic solvation. Five model hydrophobes in SPC/E water give benchmark solvation entropies via Widom's test-particle insertion method, and other methods and models are tested against these particle-insertion results. Entropies associated with distributions of tetrahedral order, of electric field, and of solvent dipole orientations are examined. We find these contributions are small compared to the benchmark particle-insertion entropy. Competitive with or better than other theories in accuracy, but with no free parameters, is the new estimate of the entropy contributed by correlations between dipole moments. Dipole correlations account for most of the hydrophobic solvation entropy for all models studied and capture the distinctive temperature dependence seen in thermodynamic and spectroscopic experiments. Entropies based on pair and many-body correlations in number density approach the correct magnitudes but fail to describe temperature and size dependences, respectively. Hydrogen-bond definitions and free energies that best reproduce entropies from simulations are reported, but it is difficult to choose one hydrogen bond model that fits a variety of experiments. The use of information theory, scaled-particle theory, and related methods is discussed briefly. Our results provide a test of the Frank-Evans hypothesis that the negative solvation entropy is due to structured water near the solute, complement the spectroscopic detection of that solvation structure by
CORRELATION PROFILES AND MOTIFS IN COMPLEX NETWORKS.
MASLOV,S.SNEPPEN,K.ALON,U.
2004-01-16
Networks have recently emerged as a unifying theme in complex systems research [1]. It is in fact no coincidence that networks and complexity are so heavily intertwined. Any future definition of a complex system should reflect the fact that such systems consist of many mutually interacting components. These components are far from being identical as say electrons in systems studied by condensed matter physics. In a truly complex system each of them has a unique identity allowing one to separate it from the others. The very first question one may ask about such a system is which other components a given component interacts with? This information system wide can be visualized as a graph, whose nodes correspond to individual components of the complex system in question and edges to their mutual interactions. Such a network can be thought of as a backbone of the complex system. Of course, system's dynamics depends not only on the topology of an underlying network but also on the exact form of interaction of components with each other, which can be very different in various complex systems. However, the underlying network may contain clues about the basic design principles and/or evolutionary history of the complex system in question. The goal of this article is to provide readers with a set of useful tools that would help to decide which features of a complex network are there by pure chance alone, and which of them were possibly designed or evolved to their present state.
On degree-degree correlations in multilayer networks
NASA Astrophysics Data System (ADS)
de Arruda, Guilherme Ferraz; Cozzo, Emanuele; Moreno, Yamir; Rodrigues, Francisco A.
2016-06-01
We propose a generalization of the concept of assortativity based on the tensorial representation of multilayer networks, covering the definitions given in terms of Pearson and Spearman coefficients. Our approach can also be applied to weighted networks and provides information about correlations considering pairs of layers. By analyzing the multilayer representation of the airport transportation network, we show that contrasting results are obtained when the layers are analyzed independently or as an interconnected system. Finally, we study the impact of the level of assortativity and heterogeneity between layers on the spreading of diseases. Our results highlight the need of studying degree-degree correlations on multilayer systems, instead of on aggregated networks.
Reconstruction of evolved dynamic networks from degree correlations
NASA Astrophysics Data System (ADS)
Karalus, Steffen; Krug, Joachim
2016-06-01
We study the importance of local structural properties in networks which have been evolved for a power-law scaling in their Laplacian spectrum. To this end, the degree distribution, two-point degree correlations, and degree-dependent clustering are extracted from the evolved networks and used to construct random networks with the prescribed distributions. In the analysis of these reconstructed networks it turns out that the degree distribution alone is not sufficient to generate the spectral scaling and the degree-dependent clustering has only an indirect influence. The two-point correlations are found to be the dominant characteristic for the power-law scaling over a broader eigenvalue range.
Correlation between crystalline order and vitrification in colloidal monolayers
NASA Astrophysics Data System (ADS)
Tamborini, Elisa; Royall, C. Patrick; Cicuta, Pietro
2015-05-01
We investigate experimentally the relationship between local structure and dynamical arrest in a quasi-2d colloidal model system which approximates hard discs. We introduce polydispersity to the system to suppress crystallisation. Upon compression, the increase in structural relaxation time is accompanied by the emergence of local hexagonal symmetry. Examining the dynamical heterogeneity of the system, we identify three types of motion: ‘zero-dimensional’ corresponding to β-relaxation, ‘one-dimensional’ or stringlike motion and ‘2D’ motion. The dynamic heterogeneity is correlated with the local order, that is to say locally hexagonal regions are more likely to be dynamically slow. However, we find that lengthscales corresponding to dynamic heterogeneity and local structure do not appear to scale together approaching the glass transition.
Image Segmentation Using Higher-Order Correlation Clustering.
Kim, Sungwoong; Yoo, Chang D; Nowozin, Sebastian; Kohli, Pushmeet
2014-09-01
In this paper, a hypergraph-based image segmentation framework is formulated in a supervised manner for many high-level computer vision tasks. To consider short- and long-range dependency among various regions of an image and also to incorporate wider selection of features, a higher-order correlation clustering (HO-CC) is incorporated in the framework. Correlation clustering (CC), which is a graph-partitioning algorithm, was recently shown to be effective in a number of applications such as natural language processing, document clustering, and image segmentation. It derives its partitioning result from a pairwise graph by optimizing a global objective function such that it simultaneously maximizes both intra-cluster similarity and inter-cluster dissimilarity. In the HO-CC, the pairwise graph which is used in the CC is generalized to a hypergraph which can alleviate local boundary ambiguities that can occur in the CC. Fast inference is possible by linear programming relaxation, and effective parameter learning by structured support vector machine is also possible by incorporating a decomposable structured loss function. Experimental results on various data sets show that the proposed HO-CC outperforms other state-of-the-art image segmentation algorithms. The HO-CC framework is therefore an efficient and flexible image segmentation framework. PMID:26352230
Effect of correlations on controllability transition in network control
NASA Astrophysics Data System (ADS)
Nie, Sen; Wang, Xu-Wen; Wang, Bing-Hong; Jiang, Luo-Luo
2016-04-01
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.
Effect of correlations on controllability transition in network control
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
On-line lower-order modeling via neural networks.
Ho, H F; Rad, A B; Wong, Y K; Lo, W L
2003-10-01
This paper presents a novel method to determine the parameters of a first-order plus dead-time model using neural networks. The outputs of the neural networks are the gain, dominant time constant, and apparent time delay. By combining this algorithm with a conventional PI or PID controller, we also present an adaptive controller which requires very little a priori knowledge about the plant under control. The simplicity of the scheme for real-time control provides a new approach for implementing neural network applications for a variety of on-line industrial control problems. Simulation and experimental results demonstrate the feasibility and adaptive property of the proposed scheme. PMID:14582882
Fractional-order LβCα filter circuit network
NASA Astrophysics Data System (ADS)
Hong, Zheng; Qian, Jing; Chen, Di-Yi; Herbert, H. C. Iu
2015-08-01
In this paper we introduce the new fundamentals of the conventional LC filter circuit network in the fractional domain. First, we derive the general formulae of the impedances for the conventional and fractional-order filter circuit network. Based on this, the impedance characteristics and phase characteristics with respect to the system variables of the filter circuit network are studied in detail, which shows the greater flexibility of the fractional-order filter circuit network in design. Moreover, from the point of view of the filtering property, we systematically study the effects of the filter units and fractional orders on the amplitude-frequency characteristics and phase-frequency characteristics. In addition, numerical tables of the cut-off frequency are presented. Finally, two typical examples are presented to promote the industrial applications of the fractional-order filter circuit network. Numerical simulations are presented to verify the theoretical results introduced in this paper. Project supported by the National Natural Science Foundation of China (Grant No. 51469011).
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.
Manby, Frederick R; Werner, Hans-Joachim; Adler, Thomas B; May, Andrew J
2006-03-01
The recently introduced MP2-R122*A(loc) and LMP2-R122*A(loc) methods are modified to use a short-range correlation factor expanded as a fixed linear combination of Gaussian geminals. Density fitting is used to reduce the effort for integral evaluation, and local approximations are introduced to improve the scaling of the computational resources with molecular size. The MP2-F122*A(loc) correlation energies converge very rapidly with respect to the atomic orbital basis set size. Already with the aug-cc-pVTZ basis the correlation energies computed for a set of 21 small molecules are found to be within 0.5% of the MP2 basis set limit. Furthermore the short-range correlation factor leads to an improved convergence of the resolution of the identity, and eliminates problems with long-range errors in density fitting caused by the linear r12 factor. The DF-LMP2-F122*A(loc) method is applied to compute second-order correlation energies for molecules with up to 49 atoms and more than 1600 basis functions. PMID:16526841
Correlated Networks of Magnetic and Inertial Sensors to Study Transient Phenomena
NASA Astrophysics Data System (ADS)
Zhivun, Elena; Gnome Collaboration; Nose Collaboration; Urban Magnetometer Network Collaboration
2016-05-01
We describe several new collaborative efforts to develop networks of magnetometers (the GNOME and Urban Magnetometer Network collaborations), atom interferometers (NOSE), and other precision sensors. These networks use geographically separated, time-synchronized sensors to search for correlated transient signals. The Global Network of Optical Magnetometers to search for Exotic physics (GNOME) searches for nuclear and electron spin couplings to various exotic fields generated by astrophysical sources. The UC Network Of Sensors for Exotic physics (NOSE) searches for dark matter and dark energy by detecting the influence of a background field of ultra-light particles with a network of various sensors such as atom interferometers, novel solid-state acceleration sensors, and GNOME magnetometers. The Urban Magnetometer Network project characterizes and determines the origin of the ambient field fluctuations, in order to to improve magnetic anomalies detection and extract maximal information from magnetic signals in the city environment. Global Network of Optical Magnetometers.
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.
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.
Clarkson, Christopher G; Lovett, Joseph R; Madsen, Jeppe; Armes, Steven P; Geoghegan, Mark
2015-09-01
The temperature and pH-dependent diffusion of poly(glycerol monomethacrylate)-block-poly(2-hydroxypropyl methacrylate) nanoparticles prepared via polymerization-induced self-assembly in water is characterized using fluorescence correlation spectroscopy (FCS). Lowering the solution temperature or raising the solution pH induces a worm-to-sphere transition and hence an increase in diffusion coefficient by a factor of between four and eight. FCS enables morphological transitions to be monitored at relatively high copolymer concentrations (10% w/w) compared to those required for dynamic light scattering (0.1% w/w). This is important because such transitions are reversible at the former concentration, whereas they are irreversible at the latter. Furthermore, the FCS data suggest that the thermal transition takes place over a very narrow temperature range (less than 2 °C). These results demonstrate the application of FCS to characterize order-order transitions, as opposed to order-disorder transitions. PMID:26096738
Correlations between weights and overlap in ensembles of weighted multiplex networks
NASA Astrophysics Data System (ADS)
Menichetti, Giulia; Remondini, Daniel; Bianconi, Ginestra
2014-12-01
Multiplex networks describe a large number of systems ranging from social networks to the brain. These multilayer structure encode information in their structure. This information can be extracted by measuring the correlations present in the multiplex networks structure, such as the overlap of the links in different layers. Many multiplex networks are also weighted, and the weights of the links can be strongly correlated with the structural properties of the multiplex network. For example, in multiplex network formed by the citation and collaboration networks between PRE scientists it was found that the statistical properties of citations to coauthors differ from the one of citations to noncoauthors, i.e., the weights depend on the overlap of the links. Here we present a theoretical framework for modeling multiplex weighted networks with different types of correlations between weights and overlap. To this end, we use the framework of canonical network ensembles, and the recently introduced concept of multilinks, showing that null models of a large variety of network structures can be constructed in this way. In order to provide a concrete example of how this framework apply to real data we consider a multiplex constructed from gene expression data of healthy and cancer tissues.
Traffic-driven epidemic spreading in correlated networks
NASA Astrophysics Data System (ADS)
Yang, Han-Xin; Tang, Ming; Lai, Ying-Cheng
2015-06-01
In spite of the extensive previous efforts on traffic dynamics and epidemic spreading in complex networks, the problem of traffic-driven epidemic spreading on correlated networks has not been addressed. Interestingly, we find that the epidemic threshold, a fundamental quantity underlying the spreading dynamics, exhibits a nonmonotonic behavior in that it can be minimized for some critical value of the assortativity coefficient, a parameter characterizing the network correlation. To understand this phenomenon, we use the degree-based mean-field theory to calculate the traffic-driven epidemic threshold for correlated networks. The theory predicts that the threshold is inversely proportional to the packet-generation rate and the largest eigenvalue of the betweenness matrix. We obtain consistency between theory and numerics. Our results may provide insights into the important problem of controlling and/or harnessing real-world epidemic spreading dynamics driven by traffic flows.
Functional Alterations in Order Short-Term Memory Networks in Adults With Dyslexia.
Martinez Perez, Trecy; Poncelet, Martine; Salmon, Eric; Majerus, Steve
2015-01-01
Dyslexia is characterized not only by reading impairment but also by short-term memory (STM) deficits, and this particularly for the retention of serial order information. Here, we explored the functional neural correlates associated with serial order STM performance of adults with dyslexia for verbal and visual STM tasks. Relative to a group of age-matched controls, the dyslexic group showed abnormal activation in a network associated with order STM encompassing the right intraparietal and superior frontal sulcus, and this for both verbal and visual order STM conditions. This study highlights long-lasting alterations in non-language neural substrates and processes in dyslexia. PMID:27043828
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.
Flow distributions and spatial correlations in human brain capillary networks
NASA Astrophysics Data System (ADS)
Lorthois, Sylvie; Peyrounette, Myriam; Larue, Anne; Le Borgne, Tanguy
2015-11-01
The vascular system of the human brain cortex is composed of a space filling mesh-like capillary network connected upstream and downstream to branched quasi-fractal arterioles and venules. The distribution of blood flow rates in these networks may affect the efficiency of oxygen transfer processes. Here, we investigate the distribution and correlation properties of blood flow velocities from numerical simulations in large 3D human intra-cortical vascular network (10000 segments) obtained from an anatomical database. In each segment, flow is solved from a 1D non-linear model taking account of the complex rheological properties of blood flow in microcirculation to deduce blood pressure, blood flow and red blood cell volume fraction distributions throughout the network. The network structural complexity is found to impart broad and spatially correlated Lagrangian velocity distributions, leading to power law transit time distributions. The origins of this behavior (existence of velocity correlations in capillary networks, influence of the coupling with the feeding arterioles and draining veins, topological disorder, complex blood rheology) are studied by comparison with results obtained in various model capillary networks of controlled disorder. ERC BrainMicroFlow GA615102, ERC ReactiveFronts GA648377.
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…
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).
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.
Higher-Order Synaptic Interactions Coordinate Dynamics in Recurrent Networks
Chambers, Brendan; MacLean, Jason N.
2016-01-01
Linking synaptic connectivity to dynamics is key to understanding information processing in neocortex. Circuit dynamics emerge from complex interactions of interconnected neurons, necessitating that links between connectivity and dynamics be evaluated at the network level. Here we map propagating activity in large neuronal ensembles from mouse neocortex and compare it to a recurrent network model, where connectivity can be precisely measured and manipulated. We find that a dynamical feature dominates statistical descriptions of propagating activity for both neocortex and the model: convergent clusters comprised of fan-in triangle motifs, where two input neurons are themselves connected. Fan-in triangles coordinate the timing of presynaptic inputs during ongoing activity to effectively generate postsynaptic spiking. As a result, paradoxically, fan-in triangles dominate the statistics of spike propagation even in randomly connected recurrent networks. Interplay between higher-order synaptic connectivity and the integrative properties of neurons constrains the structure of network dynamics and shapes the routing of information in neocortex. PMID:27542093
Higher-Order Synaptic Interactions Coordinate Dynamics in Recurrent Networks.
Chambers, Brendan; MacLean, Jason N
2016-08-01
Linking synaptic connectivity to dynamics is key to understanding information processing in neocortex. Circuit dynamics emerge from complex interactions of interconnected neurons, necessitating that links between connectivity and dynamics be evaluated at the network level. Here we map propagating activity in large neuronal ensembles from mouse neocortex and compare it to a recurrent network model, where connectivity can be precisely measured and manipulated. We find that a dynamical feature dominates statistical descriptions of propagating activity for both neocortex and the model: convergent clusters comprised of fan-in triangle motifs, where two input neurons are themselves connected. Fan-in triangles coordinate the timing of presynaptic inputs during ongoing activity to effectively generate postsynaptic spiking. As a result, paradoxically, fan-in triangles dominate the statistics of spike propagation even in randomly connected recurrent networks. Interplay between higher-order synaptic connectivity and the integrative properties of neurons constrains the structure of network dynamics and shapes the routing of information in neocortex. PMID:27542093
A generative spike train model with time-structured higher order correlations
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. PMID:23908626
Correlations between Community Structure and Link Formation in Complex Networks
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
Correlated gene expression supports synchronous activity in brain networks
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.
2016-01-01
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. PMID:26068849
Probing non local order parameters in highly correlated Bose insulators
NASA Astrophysics Data System (ADS)
Altman, Ehud
2008-03-01
Ground states of integer spin chains are known since the late 80's to sustain highly non local order described by infinite string operators of the spins. Such states defy the usual Landau theory description and can be considered simple prototypes of topological order. Recently we showed that spinless Bose insulators with nearest neighbor or longer range repulsion in one dimension can exhibit similar string order in terms of the boson density [1]. The tunability of cold atomic systems would allow much more flexibility in probing the non local order than spin systems do. For example the bosons can be tuned across a quantum phase transition between the exotic insulator, which we term Haldane insulator, and the usual Mott insulator. Investigating how the transition responds to external perturbations lends direct access to properties of the string order parameter. I will demonstrate this with several new results obtained from a field theoretic description of the phases and confirmed by numerical calculations using DMRG. Particularly revealing of the unusual character of the string order is the prediction that any external perturbation, which breaks the lattice inversion symmetry, would eliminate the distinction between the Haldane and Mott phases and allow a fully gapped adiabatic connection between them. This is remarkable given that neither phase involves spontaneous breaking of lattice inversion symmetry. We also predict that inter-chain tunneling destroys the direct phase transition between the two insulators by establishing an intermediate superfluid phase. Finally I will discuss how the new phases and phase transitions may be realized and probed in actual experiments with ultra cold atoms or polar molecules. [1] E. G. Dalla Torre, E. Berg and E. Altman, Phys. Rev. Lett. 97, 260401 (2006)
Robustness of onionlike correlated networks against targeted attacks
NASA Astrophysics Data System (ADS)
Tanizawa, Toshihiro; Havlin, Shlomo; Stanley, H. Eugene
2012-04-01
Recently, it was found by Schneider [Proc. Natl. Acad. Sci. USAPNASA60027-842410.1073/pnas.1009440108 108, 3838 (2011)], using simulations, that scale-free networks with “onion structure” are very robust against targeted high degree attacks. The onion structure is a network where nodes with almost the same degree are connected. Motivated by this work, we propose and analyze, based on analytical considerations, an onionlike candidate for a nearly optimal structure against simultaneous random and targeted high degree node attacks. The nearly optimal structure can be viewed as a set of hierarchically interconnected random regular graphs,the degrees and populations of whose nodes are specified by the degree distribution. This network structure exhibits an extremely assortative degree-degree correlation and has a close relationship to the “onion structure.” After deriving a set of exact expressions that enable us to calculate the critical percolation threshold and the giant component of a correlated network for an arbitrary type of node removal, we apply the theory to the cases of random scale-free networks that are highly vulnerable against targeted high degree node removal. Our results show that this vulnerability can be significantly reduced by implementing this onionlike type of degree-degree correlation without much undermining the almost complete robustness against random node removal. We also investigate in detail the robustness enhancement due to assortative degree-degree correlation by introducing a joint degree-degree probability matrix that interpolates between an uncorrelated network structure and the onionlike structure proposed here by tuning a single control parameter. The optimal values of the control parameter that maximize the robustness against simultaneous random and targeted attacks are also determined. Our analytical calculations are supported by numerical simulations.
Theoretical scheme of thermal-light many-ghost imaging by Nth-order intensity correlation
Liu Yingchuan; 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 the 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.
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.
Synchronization, quantum correlations and entanglement in oscillator networks
Manzano, Gonzalo; Galve, Fernando; Giorgi, Gian Luca; Hernández-García, Emilio; Zambrini, Roberta
2013-01-01
Synchronization is one of the paradigmatic phenomena in the study of complex systems. It has been explored theoretically and experimentally mostly to understand natural phenomena, but also in view of technological applications. Although several mechanisms and conditions for synchronous behavior in spatially extended systems and networks have been identified, the emergence of this phenomenon has been largely unexplored in quantum systems until very recently. Here we discuss synchronization in quantum networks of different harmonic oscillators relaxing towards a stationary state, being essential the form of dissipation. By local tuning of one of the oscillators, we establish the conditions for synchronous dynamics, in the whole network or in a motif. Beyond the classical regime we show that synchronization between (even unlinked) nodes witnesses the presence of quantum correlations and entanglement. Furthermore, synchronization and entanglement can be induced between two different oscillators if properly linked to a random network. PMID:23486526
Synchronization of fractional-order linear complex networks.
Wang, Junwei; Zeng, Caibin
2015-03-01
In this paper, we concentrate on the synchronization problem of fractional-order complex networks with general linear dynamics under connected topology. By introducing a pseudo-state transformation, the problem is converted into an equivalent simultaneous stabilization problem of independent subsystems, which is characterized by nonzero eigenvalues of the Laplacian matrix. Then, sufficient conditions in terms of linear matrix inequalities (LMIs) for synchronization are established, which can be easily solved by efficient convex optimization algorithms. Finally, three examples are provided to illustrate the effectiveness of the proposed method. PMID:25467542
Default Mode and Executive Networks Areas: Association with the Serial Order in Divergent Thinking.
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
Effects of degree correlations on the explosive synchronization of scale-free networks.
Sendiña-Nadal, I; Leyva, I; Navas, A; Villacorta-Atienza, J A; Almendral, J A; Wang, Z; Boccaletti, S
2015-03-01
We study the organization of finite-size, large ensembles of phase oscillators networking via scale-free topologies in the presence of a positive correlation between the oscillators' natural frequencies and the network's degrees. Under those circumstances, abrupt transitions to synchronization are known to occur in growing scale-free networks, while the transition has a completely different nature for static random configurations preserving the same structure-dynamics correlation. We show that the further presence of degree-degree correlations in the network structure has important consequences on the nature of the phase transition characterizing the passage from the phase-incoherent to the phase-coherent network state. While high levels of positive and negative mixing consistently induce a second-order phase transition, moderate values of assortative mixing, such as those ubiquitously characterizing social networks in the real world, greatly enhance the irreversible nature of explosive synchronization in scale-free networks. The latter effect corresponds to a maximization of the area and of the width of the hysteretic loop that differentiates the forward and backward transitions to synchronization. PMID:25871161
NASA Astrophysics Data System (ADS)
Massobrio, C.
Disordered network-forming materials are characterized by structural order extending well beyond the first shell of neighbors. For these reasons, reliable atomic-scale modeling is ideally suited to complement experiments in the search of the microscopic origins of this behavior. A key to understand why these systems have specific structural properties is to focus on the nanostructural units by which they are composed. By analyzing the role played by these units, one is able to put forth a valuable rationale accounting for the occurrence of intermediate range order. In this review, we present recent results obtained via first-principles molecular dynamics on a set of disordered network-forming materials, with special emphasis on the prototypical system GeSe2. In a short introduction we begin with explicit examples of differences, at the structure factor and pair correlation level, between networks exhibiting intermediate range order and those purely disordered at any length scale. Concerning our theoretical approach, we rely on density functional theory and first-principles molecular dynamics to follow the time trajectories at finite temperature of these networks and obtain statistical averages to be compared with the experimental quantities. Specific methodological issues pertaining to the simulation of disordered materials are analyzed in detail (size of the computational cell, role of exchange-correlation functional, and production of an amorphous phase). Then, three specific points are addressed by considering both experimental and simulation results: first, the atomic-scale signature of intermediate range order as it manifests itself via the appearance of the first sharp diffraction peak in the total neutron structure factor; second, the correlation existing between fluctuations of concentration on the intermediate distances scale and the shape taken by the partial structure factors; and third, the establishment of the nanostructural units responsible for the
Disorder overtakes order in information concentration over quantum networks
Prabhu, R.; Pradhan, Saurabh; Sen, Aditi; Sen, Ujjwal
2011-10-15
We consider different classes of quenched disordered quantum XY spin chains, including a quantum XY spin glass and a quantum XY model with a random transverse field, and investigate the behavior of genuine multiparty entanglement in the ground states of these models. We find that there are distinct ranges of the disorder parameter that give rise to a higher genuine multiparty entanglement than in the corresponding systems without disorder: an order-from-disorder phenomenon in genuine multiparty entanglement. Moreover, we show that such a disorder-induced advantage in the genuine multiparty entanglement is useful: It is almost certainly accompanied by a order-from-disorder phenomenon for a multiport quantum dense coding capacity with the same ground state used as a multiport quantum network.
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.
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.
Clinical Risk Prediction by Exploring High-Order Feature Correlations
Wang, Fei; Zhang, Ping; Wang, Xiang; Hu, Jianying
2014-01-01
Clinical risk prediction is one important problem in medical informatics, and logistic regression is one of the most widely used approaches for clinical risk prediction. In many cases, the number of potential risk factors is fairly large and the actual set of factors that contribute to the risk is small. Therefore sparse logistic regression is proposed, which can not only predict the clinical risk but also identify the set of relevant risk factors. The inputs of logistic regression and sparse logistic regression are required to be in vector form. This limits the applicability of these models in the problems when the data cannot be naturally represented vectors (e.g., medical images are two-dimensional matrices). To handle the cases when the data are in the form of multi-dimensional arrays, we propose HOSLR: High-Order Sparse Logistic Regression, which can be viewed as a high order extension of sparse logistic regression. Instead of solving one classification vector as in conventional logistic regression, we solve for K classification vectors in HOSLR (K is the number of modes in the data). A block proximal descent approach is proposed to solve the problem and its convergence is guaranteed. Finally we validate the effectiveness of HOSLR on predicting the onset risk of patients with Alzheimer’s disease and heart failure. PMID:25954428
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.
Detecting Distributed Network Traffic Anomaly with Network-Wide Correlation Analysis
NASA Astrophysics Data System (ADS)
Zonglin, Li; Guangmin, Hu; Xingmiao, Yao; Dan, Yang
2008-12-01
Distributed network traffic anomaly refers to a traffic abnormal behavior involving many links of a network and caused by the same source (e.g., DDoS attack, worm propagation). The anomaly transiting in a single link might be unnoticeable and hard to detect, while the anomalous aggregation from many links can be prevailing, and does more harm to the networks. Aiming at the similar features of distributed traffic anomaly on many links, this paper proposes a network-wide detection method by performing anomalous correlation analysis of traffic signals' instantaneous parameters. In our method, traffic signals' instantaneous parameters are firstly computed, and their network-wide anomalous space is then extracted via traffic prediction. Finally, an anomaly is detected by a global correlation coefficient of anomalous space. Our evaluation using Abilene traffic traces demonstrates the excellent performance of this approach for distributed traffic anomaly detection.
Using higher-order Markov models to reveal flow-based communities in networks
NASA Astrophysics Data System (ADS)
Salnikov, Vsevolod; Schaub, Michael T.; Lambiotte, Renaud
2016-03-01
Complex systems made of interacting elements are commonly abstracted as networks, in which nodes are associated with dynamic state variables, whose evolution is driven by interactions mediated by the edges. Markov processes have been the prevailing paradigm to model such a network-based dynamics, for instance in the form of random walks or other types of diffusions. Despite the success of this modelling perspective for numerous applications, it represents an over-simplification of several real-world systems. Importantly, simple Markov models lack memory in their dynamics, an assumption often not realistic in practice. Here, we explore possibilities to enrich the system description by means of second-order Markov models, exploiting empirical pathway information. We focus on the problem of community detection and show that standard network algorithms can be generalized in order to extract novel temporal information about the system under investigation. We also apply our methodology to temporal networks, where we can uncover communities shaped by the temporal correlations in the system. Finally, we discuss relations of the framework of second order Markov processes and the recently proposed formalism of using non-backtracking matrices for community detection.
Using higher-order Markov models to reveal flow-based communities in networks
Salnikov, Vsevolod; Schaub, Michael T.; Lambiotte, Renaud
2016-01-01
Complex systems made of interacting elements are commonly abstracted as networks, in which nodes are associated with dynamic state variables, whose evolution is driven by interactions mediated by the edges. Markov processes have been the prevailing paradigm to model such a network-based dynamics, for instance in the form of random walks or other types of diffusions. Despite the success of this modelling perspective for numerous applications, it represents an over-simplification of several real-world systems. Importantly, simple Markov models lack memory in their dynamics, an assumption often not realistic in practice. Here, we explore possibilities to enrich the system description by means of second-order Markov models, exploiting empirical pathway information. We focus on the problem of community detection and show that standard network algorithms can be generalized in order to extract novel temporal information about the system under investigation. We also apply our methodology to temporal networks, where we can uncover communities shaped by the temporal correlations in the system. Finally, we discuss relations of the framework of second order Markov processes and the recently proposed formalism of using non-backtracking matrices for community detection. PMID:27029508
Using higher-order Markov models to reveal flow-based communities in networks.
Salnikov, Vsevolod; Schaub, Michael T; Lambiotte, Renaud
2016-01-01
Complex systems made of interacting elements are commonly abstracted as networks, in which nodes are associated with dynamic state variables, whose evolution is driven by interactions mediated by the edges. Markov processes have been the prevailing paradigm to model such a network-based dynamics, for instance in the form of random walks or other types of diffusions. Despite the success of this modelling perspective for numerous applications, it represents an over-simplification of several real-world systems. Importantly, simple Markov models lack memory in their dynamics, an assumption often not realistic in practice. Here, we explore possibilities to enrich the system description by means of second-order Markov models, exploiting empirical pathway information. We focus on the problem of community detection and show that standard network algorithms can be generalized in order to extract novel temporal information about the system under investigation. We also apply our methodology to temporal networks, where we can uncover communities shaped by the temporal correlations in the system. Finally, we discuss relations of the framework of second order Markov processes and the recently proposed formalism of using non-backtracking matrices for community detection. PMID:27029508
Yang, Jin-Ju; Kwon, Hunki; Lee, Jong-Min
2016-01-01
Morphometric correlation networks of cortical thickness, surface area, and gray matter volume have statistically different structural topology. However, there is no report directly describing their correlation patterns in view of interregional covariance. Here, we examined the characteristics of the correlation patterns in three morphometric networks of cortical thickness, surface area, and gray matter volume using a Venn diagram concept across 314 normal subjects. We found that over 60% of all nonoverlapping correlation patterns emerged with divergent unique patterns, while there were 10% of all common edges in ipsilateral and homotopic regions among the three morphometric correlation networks. It was also found that the network parameters of the three networks were different. Our findings showed that correlation patterns of the network itself can provide complementary information when compared with network properties. We demonstrate that morphometric correlation networks of distinct structural phenotypes have different correlation patterns and different network properties. This finding implies that the topology of each morphometric correlation network may reflect different aspects of each morphometric descriptor. PMID:27226000
Yang, Jin-Ju; Kwon, Hunki; Lee, Jong-Min
2016-01-01
Morphometric correlation networks of cortical thickness, surface area, and gray matter volume have statistically different structural topology. However, there is no report directly describing their correlation patterns in view of interregional covariance. Here, we examined the characteristics of the correlation patterns in three morphometric networks of cortical thickness, surface area, and gray matter volume using a Venn diagram concept across 314 normal subjects. We found that over 60% of all nonoverlapping correlation patterns emerged with divergent unique patterns, while there were 10% of all common edges in ipsilateral and homotopic regions among the three morphometric correlation networks. It was also found that the network parameters of the three networks were different. Our findings showed that correlation patterns of the network itself can provide complementary information when compared with network properties. We demonstrate that morphometric correlation networks of distinct structural phenotypes have different correlation patterns and different network properties. This finding implies that the topology of each morphometric correlation network may reflect different aspects of each morphometric descriptor. PMID:27226000
Dopaminergic correlates of metabolic network activity in Parkinson's disease.
Holtbernd, Florian; Ma, Yilong; Peng, Shichun; Schwartz, Frank; Timmermann, Lars; Kracht, Lutz; Fink, Gereon R; Tang, Chris C; Eidelberg, David; Eggers, Carsten
2015-09-01
Parkinson's disease (PD) is associated with distinct metabolic covariance patterns that relate to the motor and cognitive manifestations of the disorder. It is not known, however, how the expression of these patterns relates to measurements of nigrostriatal dopaminergic activity from the same individuals. To explore these associations, we studied 106 PD subjects who underwent cerebral PET with both (18) F-fluorodeoxyglucose (FDG) and (18) F-fluoro-L-dopa (FDOPA). Expression values for the PD motor- and cognition-related metabolic patterns (PDRP and PDCP, respectively) were computed for each subject; these measures were correlated with FDOPA uptake on a voxel-by-voxel basis. To explore the relationship between dopaminergic function and local metabolic activity, caudate and putamen FDOPA PET signal was correlated voxel-wise with FDG uptake over the entire brain. PDRP expression correlated with FDOPA uptake in caudate and putamen (P < 0.001), while PDCP expression correlated with uptake in the anterior striatum (P < 0.001). While statistically significant, the correlations were only of modest size, accounting for less than 20% of the overall variation in these measures. After controlling for PDCP expression, PDRP correlations were significant only in the posterior putamen. Of note, voxel-wise correlations between caudate/putamen FDOPA uptake and whole-brain FDG uptake were significant almost exclusively in PDRP regions. Overall, the data indicate that PDRP and PDCP expression correlates significantly with PET indices of presynaptic dopaminergic functioning obtained in the same individuals. Even so, the modest size of these correlations suggests that in PD patients, individual differences in network activity cannot be explained solely by nigrostriatal dopamine loss. PMID:26037537
Exploring the miRNA Regulatory Network Using Evolutionary Correlations
Obermayer, Benedikt; Levine, Erel
2014-01-01
Post-transcriptional regulation by miRNAs is a widespread and highly conserved phenomenon in metazoans, with several hundreds to thousands of conserved binding sites for each miRNA, and up to two thirds of all genes under miRNA regulation. At the same time, the effect of miRNA regulation on mRNA and protein levels is usually quite modest and associated phenotypes are often weak or subtle. This has given rise to the notion that the highly interconnected miRNA regulatory network exerts its function less through any individual link and more via collective effects that lead to a functional interdependence of network links. We present a Bayesian framework to quantify conservation of miRNA target sites using vertebrate whole-genome alignments. The increased statistical power of our phylogenetic model allows detection of evolutionary correlation in the conservation patterns of site pairs. Such correlations could result from collective functions in the regulatory network. For instance, co-conservation of target site pairs supports a selective benefit of combinatorial regulation by multiple miRNAs. We find that some miRNA families are under pronounced co-targeting constraints, indicating a high connectivity in the regulatory network, while others appear to function in a more isolated way. By analyzing coordinated targeting of different curated gene sets, we observe distinct evolutionary signatures for protein complexes and signaling pathways that could reflect differences in control strategies. Our method is easily scalable to analyze upcoming larger data sets, and readily adaptable to detect high-level selective constraints between other genomic loci. We thus provide a proof-of-principle method to understand regulatory networks from an evolutionary perspective. PMID:25299225
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.
Correlation and network analysis of global financial indices.
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. PMID:23005819
Innovation diffusion equations on correlated scale-free networks
NASA Astrophysics Data System (ADS)
Bertotti, M. L.; Brunner, J.; Modanese, G.
2016-07-01
We introduce a heterogeneous network structure into the Bass diffusion model, in order to study the diffusion times of innovation or information in networks with a scale-free structure, typical of regions where diffusion is sensitive to geographic and logistic influences (like for instance Alpine regions). We consider both the diffusion peak times of the total population and of the link classes. In the familiar trickle-down processes the adoption curve of the hubs is found to anticipate the total adoption in a predictable way. In a major departure from the standard model, we model a trickle-up process by introducing heterogeneous publicity coefficients (which can also be negative for the hubs, thus turning them into stiflers) and a stochastic term which represents the erratic generation of innovation at the periphery of the network. The results confirm the robustness of the Bass model and expand considerably its range of applicability.
Modeling network correlations in cortical tissue from juvenile human epileptics
NASA Astrophysics Data System (ADS)
Hobbs, Jonathan Paul
Models of neural tissue can make predictions about a real neural network, but these predictions rely on the data to determine parameters. Hence, the model is only as good as the data. I collected in vitro data removed from juvenile humans with refractory epilepsy, and found human-specific spatial and temporal dynamics that are not found in rats. I will first describe the general characteristics of the human data in comparison with rat data, and my attempts to model these differences with three popular models of neural networks: branching, pair-wise maximum entropy, and a forest fire model. I will describe three key discoveries from this exploration: first, spatial dynamics are more easily satisfied than temporal in both the rat and human tissue, second temporal correlations are not captured by the branching or the maximum entropy model, and thirdly, strong temporal correlations can be accounted for with the addition of a parameter in the forest fire model. Finally I will suggest new questions that this research has revealed about human tissue, and models of neural networks.
Bose-Einstein or HBT Correlation Signals of a Second Order QCD Phase Transition
Csoergo, T.; Hegyi, S.; Novak, T.; Zajc, W. A.
2006-04-11
For particles emerging from a second order QCD phase transition, we show that a recently introduced shape parameter of the Bose-Einstein correlation function, the Levy index of stability equals to the correlation exponent -- one of the critical exponents that characterize the behaviour of the matter in the vicinity of the second order phase transition point. Hence the shape of the Bose-Einstein / HBT correlation functions, when measured as a function of bombarding energy and centrality in various heavy ion reactions, can be utilized to locate experimentally the second order phase transition and the critical end point of the first order phase transition line in QCD.
NASF transposition network: A computing network for unscrambling p-ordered vectors
NASA Technical Reports Server (NTRS)
Lim, R. S.
1979-01-01
The viewpoints of design, programming, and application of the transportation network (TN) is presented. The TN is a programmable combinational logic network that connects 521 memory modules to 512 processors. The unscrambling of p-ordered vectors to 1-ordered vectors in one cycle is described. The TN design is based upon the concept of cyclic groups from abstract algebra and primitive roots and indices from number theory. The programming of the TN is very simple, requiring only 20 bits: 10 bits for offset control and 10 bits for barrel switch shift control. This simple control is executed by the control unit (CU), not the processors. Any memory access by a processor must be coordinated with the CU and wait for all other processors to come to a synchronization point. These wait and synchronization events can be a degradation in performance to a computation. The TN application is for multidimensional data manipulation, matrix processing, and data sorting, and can also perform a perfect shuffle. Unlike other more complicated and powerful permutation networks, the TN cannot, if possible at all, unscramble non-p-ordered vectors in one cycle.
Crespi, Catherine M.; Wong, Weng Kee; Mishra, Shiraz I.
2009-01-01
SUMMARY In cluster randomized trials, it is commonly assumed that the magnitude of the correlation among subjects within a cluster is constant across clusters. However, the correlation may in fact be heterogeneous and depend on cluster characteristics. Accurate modeling of the correlation has the potential to improve inference. We use second-order generalized estimating equations to model heterogeneous correlation in cluster randomized trials. Using simulation studies we show that accurate modeling of heterogeneous correlation can improve inference when the correlation is high or varies by cluster size. We apply the methods to a cluster randomized trial of an intervention to promote breast cancer screening. PMID:19109804
BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks.
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. PMID:26068849
Estimating individual contribution from group-based structural correlation networks.
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. PMID:26162553
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.
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.
Pulse transmission receiver with higher-order time derivative pulse correlator
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.
Medium range order and structural relaxation in As–Se network glasses through FSDP analysis
Golovchak, R.; Lucas, P.; Oelgoetz, J.; Kovalskiy, A.; York-Winegar, J.; Saiyasombat, Ch.; Shpotyuk, O.; Feygenson, M.; Neuefeind, J.; Jain, H.
2015-03-01
Synchrotron X-ray diffraction and neutron scattering studies are performed on As–Se glasses in two states: as-prepared (rejuvenated) and aged for ~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. The comparison of structural information shows that density fluctuations, which were thought previously to have a significant contribution to FSDP, have much smaller effect than the cation–cation correlations, presence of ordered structural fragments or cage molecules.
Staude, Benjamin; Grün, Sonja; Rotter, Stefan
2009-01-01
The extent to which groups of neurons exhibit higher-order correlations in their spiking activity is a controversial issue in current brain research. A major difficulty is that currently available tools for the analysis of massively parallel spike trains (N >10) for higher-order correlations typically require vast sample sizes. While multiple single-cell recordings become increasingly available, experimental approaches to investigate the role of higher-order correlations suffer from the limitations of available analysis techniques. We have recently presented a novel method for cumulant-based inference of higher-order correlations (CuBIC) that detects correlations of higher order even from relatively short data stretches of length T = 10–100 s. CuBIC employs the compound Poisson process (CPP) as a statistical model for the population spike counts, and assumes spike trains to be stationary in the analyzed data stretch. In the present study, we describe a non-stationary version of the CPP by decoupling the correlation structure from the spiking intensity of the population. This allows us to adapt CuBIC to time-varying firing rates. Numerical simulations reveal that the adaptation corrects for false positive inference of correlations in data with pure rate co-variation, while allowing for temporal variations of the firing rates has a surprisingly small effect on CuBICs sensitivity for correlations. PMID:20725510
A unified view on weakly correlated recurrent networks
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
Pulse shape measurement by a non-collinear third-order correlation technique
NASA Astrophysics Data System (ADS)
Priebe, G.; Janulewicz, K. A.; Redkorechev, V. I.; Tümmler, J.; Nickles, P. V.
2006-03-01
A third-order correlator suitable for detailed shape measurements of picosecond laser pulses has been developed. The working principle in both the single shot and the scanning mode is based on detection of the phase-matched difference frequency non-collinear generated signal in a non-linear crystal. This third-order OPA correlator was applied for the characterization of the specifically shaped picosecond laser pulses from the MBI CPA Nd: glass laser system.
A Maximum Entropy Test for Evaluating Higher-Order Correlations in Spike Counts
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
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…
The dynamic correlation between degree and betweenness of complex network under attack
NASA Astrophysics Data System (ADS)
Nie, Tingyuan; Guo, Zheng; Zhao, Kun; Lu, Zhe-Ming
2016-09-01
Complex networks are often subjected to failure and attack. Recent work has addressed the resilience of complex networks to either random or intentional deletion of nodes or links. Here we simulate the breakdown of the small-world network and the scale-free network under node failure or attacks. We analyze and discuss the dynamic correlation between degree and betweenness in the process of attack. The simulation results show that the correlation for scale-free network obeys a power law distribution until the network collapses, while it represents irregularly for small-world network.
Photoinduced melting of magnetic order in the correlated electron insulator NdNiO3
NASA Astrophysics Data System (ADS)
Caviglia, A. D.; Först, M.; Scherwitzl, R.; Khanna, V.; Bromberger, H.; Mankowsky, R.; Singla, R.; Chuang, Y.-D.; Lee, W. S.; Krupin, O.; Schlotter, W. F.; Turner, J. J.; Dakovski, G. L.; Minitti, M. P.; Robinson, J.; Scagnoli, V.; Wilkins, S. B.; Cavill, S. A.; Gibert, M.; Gariglio, S.; Zubko, P.; Triscone, J.-M.; Hill, J. P.; Dhesi, S. S.; Cavalleri, A.
2013-12-01
Using ultrafast resonant soft x-ray diffraction, we demonstrate photoinduced melting of antiferromagnetic order in the correlated electron insulator NdNiO3. Time-dependent analysis of the resonant diffraction spectra allows us to follow the temporal evolution of the charge imbalance between adjacent Ni sites. A direct correlation between the melting of magnetic order and charge rebalancing is found. Furthermore, we demonstrate that the magnetic ordering on the Ni and Nd sites, which are locked together in equilibrium, become decoupled during this nonthermal process.
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. PMID:27026862
Caballero-Águila, Raquel; Hermoso-Carazo, Aurora; Linares-Pérez, Josefa
2016-01-01
This paper is concerned with the distributed and centralized fusion filtering problems in sensor networked systems with random one-step delays in transmissions. The delays are described by Bernoulli variables correlated at consecutive sampling times, with different characteristics at each sensor. The measured outputs are subject to uncertainties modeled by random parameter matrices, thus providing a unified framework to describe a wide variety of network-induced phenomena; moreover, the additive noises are assumed to be one-step autocorrelated and cross-correlated. Under these conditions, without requiring the knowledge of the signal evolution model, but using only the first and second order moments of the processes involved in the observation model, recursive algorithms for the optimal linear distributed and centralized filters under the least-squares criterion are derived by an innovation approach. Firstly, local estimators based on the measurements received from each sensor are obtained and, after that, the distributed fusion filter is generated as the least-squares matrix-weighted linear combination of the local estimators. Also, a recursive algorithm for the optimal linear centralized filter is proposed. In order to compare the estimators performance, recursive formulas for the error covariance matrices are derived in all the algorithms. The effects of the delays in the filters accuracy are analyzed in a numerical example which also illustrates how some usual network-induced uncertainties can be dealt with using the current observation model described by random matrices. PMID:27338387
Caballero-Águila, Raquel; Hermoso-Carazo, Aurora; Linares-Pérez, Josefa
2016-01-01
This paper is concerned with the distributed and centralized fusion filtering problems in sensor networked systems with random one-step delays in transmissions. The delays are described by Bernoulli variables correlated at consecutive sampling times, with different characteristics at each sensor. The measured outputs are subject to uncertainties modeled by random parameter matrices, thus providing a unified framework to describe a wide variety of network-induced phenomena; moreover, the additive noises are assumed to be one-step autocorrelated and cross-correlated. Under these conditions, without requiring the knowledge of the signal evolution model, but using only the first and second order moments of the processes involved in the observation model, recursive algorithms for the optimal linear distributed and centralized filters under the least-squares criterion are derived by an innovation approach. Firstly, local estimators based on the measurements received from each sensor are obtained and, after that, the distributed fusion filter is generated as the least-squares matrix-weighted linear combination of the local estimators. Also, a recursive algorithm for the optimal linear centralized filter is proposed. In order to compare the estimators performance, recursive formulas for the error covariance matrices are derived in all the algorithms. The effects of the delays in the filters accuracy are analyzed in a numerical example which also illustrates how some usual network-induced uncertainties can be dealt with using the current observation model described by random matrices. PMID:27338387
Shimazaki, Hideaki; Amari, Shun-ichi; Brown, Emery N.; Grün, Sonja
2012-01-01
Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods
Shimazaki, Hideaki; Amari, Shun-Ichi; Brown, Emery N; Grün, Sonja
2012-01-01
Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods
Neural network guided search control in partial order planning
Zimmerman, T.
1996-12-31
The development of efficient search control methods is an active research topic in the field of planning. Investigation of a planning program integrated with a neural network (NN) that assists in search control is underway, and has produced promising preliminary results.
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
Ordering spatiotemporal chaos in complex thermosensitive neuron networks
NASA Astrophysics Data System (ADS)
Gong, Yubing; Xu, Bo; Xu, Qiang; Yang, Chuanlu; Ren, Tingqi; Hou, Zhonghuai; Xin, Houwen
2006-04-01
We have studied the effect of random long-range connections in chaotic thermosensitive neuron networks with each neuron being capable of exhibiting diverse bursting behaviors, and found stochastic synchronization and optimal spatiotemporal patterns. For a given coupling strength, the chaotic burst-firings of the neurons become more and more synchronized as the number of random connections (or randomness) is increased and, rather, the most pronounced spatiotemporal pattern appears for an optimal randomness. As the coupling strength is increased, the optimal randomness shifts towards a smaller strength. This result shows that random long-range connections can tame the chaos in the neural networks and make the neurons more effectively reach synchronization. Since the model studied can be used to account for hypothalamic neurons of dogfish, catfish, etc., this result may reflect the significant role of random connections in transferring biological information.
Use of the particle swarm optimization algorithm for second order design of levelling networks
NASA Astrophysics Data System (ADS)
Yetkin, Mevlut; Inal, Cevat; Yigit, Cemal Ozer
2009-08-01
The weight problem in geodetic networks can be dealt with as an optimization procedure. This classic problem of geodetic network optimization is also known as second-order design. The basic principles of geodetic network optimization are reviewed. Then the particle swarm optimization (PSO) algorithm is applied to a geodetic levelling network in order to solve the second-order design problem. PSO, which is an iterative-stochastic search algorithm in swarm intelligence, emulates the collective behaviour of bird flocking, fish schooling or bee swarming, to converge probabilistically to the global optimum. Furthermore, it is a powerful method because it is easy to implement and computationally efficient. Second-order design of a geodetic levelling network using PSO yields a practically realizable solution. It is also suitable for non-linear matrix functions that are very often encountered in geodetic network optimization. The fundamentals of the method and a numeric example are given.
The Network of Counterparty Risk: Analysing Correlations in OTC Derivatives
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
The Network of Counterparty Risk: Analysing Correlations in OTC Derivatives.
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
Non-local bias contribution to third-order galaxy correlations
NASA Astrophysics Data System (ADS)
Bel, J.; Hoffmann, K.; Gaztañaga, E.
2015-10-01
We study halo clustering bias with second- and third-order statistics of halo and matter density fields in the Marenostrum Institut de Ciències de l'Espai (MICE) Grand Challenge simulation. We verify that two-point correlations deliver reliable estimates of the linear bias parameters at large scales, while estimations from the variance can be significantly affected by non-linear and possibly non-local contributions to the bias function. Combining three-point auto- and cross-correlations we find, for the first time in configuration space, evidence for the presence of such non-local contributions. These contributions are consistent with predicted second-order non-local effects on the bias functions originating from the dark matter tidal field. Samples of massive haloes show indications of bias (local or non-local) beyond second order. Ignoring non-local bias causes 20-30 and 5-10 per cent overestimation of the linear bias from three-point auto- and cross-correlations, respectively. We study two third-order bias estimators that are not affected by second-order non-local contributions. One is a combination of three-point auto- and cross-correlations. The other is a combination of third-order one- and two-point cumulants. Both methods deliver accurate estimations of the linear bias. Ignoring non-local bias causes higher values of the second-order bias from three-point correlations. Our results demonstrate that third-order statistics can be employed for breaking the growth-bias degeneracy.
Correlation network analysis for multi-dimensional data in stocks market
NASA Astrophysics Data System (ADS)
Kazemilari, Mansooreh; Djauhari, Maman Abdurachman
2015-07-01
This paper shows how the concept of vector correlation can appropriately measure the similarity among multivariate time series in stocks network. The motivation of this paper is (i) to apply the RV coefficient to define the network among stocks where each of them is represented by a multivariate time series; (ii) to analyze that network in terms of topological structure of the stocks of all minimum spanning trees, and (iii) to compare the network topology between univariate correlation based on r and multivariate correlation network based on RV coefficient.
Noise Correlation Spectroscopy of the Broken Order of a Mott Insulating Phase
Guarrera, V.; Fabbri, N.; Fallani, L.; Fort, C.; Stam, K. M. R. van der; Inguscio, M.
2008-06-27
We use a two-color lattice to break the homogeneous site occupation of an atomic Mott insulator of bosonic {sup 87}Rb. We detect the disruption of the ordered Mott domains via noise correlation analysis of the atomic density distribution after time of flight. The appearance of additional correlation peaks evidences the redistribution of the atoms into a strongly inhomogeneous insulating state, in quantitative agreement with the predictions.
Correlation Network Analysis Applied to Complex Biofilm Communities
Duran-Pinedo, Ana E.; Paster, Bruce; Teles, Ricardo; Frias-Lopez, Jorge
2011-01-01
The complexity of the human microbiome makes it difficult to reveal organizational principles of the community and even more challenging to generate testable hypotheses. It has been suggested that in the gut microbiome species such as Bacteroides thetaiotaomicron are keystone in maintaining the stability and functional adaptability of the microbial community. In this study, we investigate the interspecies associations in a complex microbial biofilm applying systems biology principles. Using correlation network analysis we identified bacterial modules that represent important microbial associations within the oral community. We used dental plaque as a model community because of its high diversity and the well known species-species interactions that are common in the oral biofilm. We analyzed samples from healthy individuals as well as from patients with periodontitis, a polymicrobial disease. Using results obtained by checkerboard hybridization on cultivable bacteria we identified modules that correlated well with microbial complexes previously described. Furthermore, we extended our analysis using the Human Oral Microbe Identification Microarray (HOMIM), which includes a large number of bacterial species, among them uncultivated organisms present in the mouth. Two distinct microbial communities appeared in healthy individuals while there was one major type in disease. Bacterial modules in all communities did not overlap, indicating that bacteria were able to effectively re-associate with new partners depending on the environmental conditions. We then identified hubs that could act as keystone species in the bacterial modules. Based on those results we then cultured a not-yet-cultivated microorganism, Tannerella sp. OT286 (clone BU063). After two rounds of enrichment by a selected helper (Prevotella oris OT311) we obtained colonies of Tannerella sp. OT286 growing on blood agar plates. This system-level approach would open the possibility of manipulating microbial
Higher-order statistics correlation stacking for DC electrical data in the wavelet domain
NASA Astrophysics Data System (ADS)
Li, Jinghe; He, Zhanxiang; Liu, Qing Huo
2013-12-01
DC (direct current) electrical and shallow seismic methods are indispensable to the near surface geophysical exploration, but the near surface areas are very difficult environments for any geophysical exploration due to the random noise caused by near surface inhomogeneities. As a new algorithm based on higher-order statistics theory, the higher-order correlation stacking algorithm for seismic data smoothing in the wavelet domain has been developed and applied efficiently to filter some correlation noise that the conventional second-order correlation stacking could not inhibit. In this paper, this higher-order statistics correlation stacking technology is presented for DC electrical data in wavelet domain. Taking into account the single section and multiple section data, we present two new formulations of correlation stacking for DC electrical data. Synthetic examples with Gaussian noise are designed to analyze the overall efficiency of the new algorithm and to determine its efficacy. Meanwhile, comparison with the traditional least-squares optimization inversion method for field examples from electrical imaging surveys and time-domain IP measurement in China shows its significant advantages. The quality of the new algorithm also has been assessed by physical simulation experiments. This new technology in DC electrical exploration measurements provides a new application in engineering and mining investigation.
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.
Quantum Effects in Higher-Order Correlators of a Quantum-Dot Spin Qubit
NASA Astrophysics Data System (ADS)
Bechtold, A.; Li, F.; Müller, K.; Simmet, T.; Ardelt, P.-L.; Finley, J. J.; Sinitsyn, N. A.
2016-07-01
We measure time correlators of a spin qubit in an optically active quantum dot beyond the second order. Such higher-order correlators are shown to be directly sensitive to pure quantum effects that cannot be explained within the classical framework. They allow direct determination of ensemble and quantum dephasing times, T2* and T2, using only repeated projective measurements and without the need for coherent spin control. Our method enables studies of purely quantum behavior in solid state systems, including tests of the Leggett-Garg type of inequalities that rule out local hidden variable interpretation of the quantum-dot spin dynamics.
Quantum Effects in Higher-Order Correlators of a Quantum-Dot Spin Qubit.
Bechtold, A; Li, F; Müller, K; Simmet, T; Ardelt, P-L; Finley, J J; Sinitsyn, N A
2016-07-01
We measure time correlators of a spin qubit in an optically active quantum dot beyond the second order. Such higher-order correlators are shown to be directly sensitive to pure quantum effects that cannot be explained within the classical framework. They allow direct determination of ensemble and quantum dephasing times, T_{2}^{*} and T_{2}, using only repeated projective measurements and without the need for coherent spin control. Our method enables studies of purely quantum behavior in solid state systems, including tests of the Leggett-Garg type of inequalities that rule out local hidden variable interpretation of the quantum-dot spin dynamics. PMID:27447523
Hierarchical neural networks for the storage of correlated memories
Deshpande, V.; Dasgupta, C. )
1991-08-01
A class of hierarchical neural network models introduced by Dotsenko for the storage and associative recall of strongly correlated memories is studied analytically and numerically. In these models, patterns stored in higher levels of the hierarchy represent generalized categories and those stored in lower levels describe finer details. The authors first show that the models originally proposed by Dotsenko have a serious flaw: they are not able to detect or correct errors in categorization that may be present in the input. They then describe three different models that attempt to overcome this shortcoming of the original models. In the first model, the interaction between different levels of the hierarchy has the form of an external field conjugate to memories stored in the lower level. In the second model, a three-spin interaction term is included in addition to the usual binary interactions of the Hopfield type. The third model makes use of a time-delay mechanism to induce, if necessary, transitions between memory states and their complements. Detailed analytical and numerical studies of the performance of these models are presented. All three models are able to detect and also to correct, in varying degrees, any error in categorization that may be present in the input pattern.
Hierarchical neural networks for the storage of correlated memories
NASA Astrophysics Data System (ADS)
Deshpande, Varsha; Dasgupta, Chandan
1991-08-01
A class of hierarchical neural network models introduced by Dotsenko for the storage and associative recall of strongly correlated memories is studied analytically and numerically. In these models, patterns stored in higher levels of the hierarchy represent generalized categories and those stored in lower levels describe finer details. We first show that the models originally proposed by Dotsenko have a serious flaw: they are not able to detect or correct errors in categorization which may be present in the input. We then describe three different models which attempt to overcome this shortcoming of the original models. In the first model, the interaction between different levels of the hierarchy has the form of an external field conjugate to memories stored in the lower level. In the second model, a three-spin interaction term is included in addition to the usual binary interactions of the Hopfield type. The third model makes use of a time delay mechanism to induce, if necessary, transitions between memory states and their complements. Detailed analytical and numerical studies of the performance of these models are presented. Our analysis shows that all three models are able to detect and also to correct in varying degrees any error in categorization that may be present in the input pattern.
Effects of random rewiring on the degree correlation of scale-free networks
NASA Astrophysics Data System (ADS)
Qu, Jing; Wang, Sheng-Jun; Jusup, Marko; Wang, Zhen
2015-10-01
Random rewiring is used to generate null networks for the purpose of analyzing the topological properties of scale-free networks, yet the effects of random rewiring on the degree correlation are subject to contradicting interpretations in the literature. We comprehensively analyze the degree correlation of randomly rewired scale-free networks and show that random rewiring increases disassortativity by reducing the average degree of the nearest neighbors of high-degree nodes. The effect can be captured by the measures of the degree correlation that consider all links in the network, but not by analogous measures that consider only links between degree peers, hence the potential for contradicting interpretations. We furthermore find that random and directional rewiring affect the topology of a scale-free network differently, even if the degree correlation of the rewired networks is the same. Consequently, the network dynamics is changed, which is proven here by means of the biased random walk.
Effects of random rewiring on the degree correlation of scale-free networks
Qu, Jing; Wang, Sheng-Jun; Jusup, Marko; Wang, Zhen
2015-01-01
Random rewiring is used to generate null networks for the purpose of analyzing the topological properties of scale-free networks, yet the effects of random rewiring on the degree correlation are subject to contradicting interpretations in the literature. We comprehensively analyze the degree correlation of randomly rewired scale-free networks and show that random rewiring increases disassortativity by reducing the average degree of the nearest neighbors of high-degree nodes. The effect can be captured by the measures of the degree correlation that consider all links in the network, but not by analogous measures that consider only links between degree peers, hence the potential for contradicting interpretations. We furthermore find that random and directional rewiring affect the topology of a scale-free network differently, even if the degree correlation of the rewired networks is the same. Consequently, the network dynamics is changed, which is proven here by means of the biased random walk. PMID:26482005
Observation of correlated particle-hole pairs and string order in low-dimensional Mott insulators.
Endres, M; Cheneau, M; Fukuhara, T; Weitenberg, C; Schauss, P; Gross, C; Mazza, L; Bañuls, M C; Pollet, L; Bloch, I; Kuhr, S
2011-10-14
Quantum phases of matter are characterized by the underlying correlations of the many-body system. Although this is typically captured by a local order parameter, it has been shown that a broad class of many-body systems possesses a hidden nonlocal order. In the case of bosonic Mott insulators, the ground state properties are governed by quantum fluctuations in the form of correlated particle-hole pairs that lead to the emergence of a nonlocal string order in one dimension. By using high-resolution imaging of low-dimensional quantum gases in an optical lattice, we directly detect these pairs with single-site and single-particle sensitivity and observe string order in the one-dimensional case. PMID:21998381
A unified data representation theory for network visualization, ordering and coarse-graining
NASA Astrophysics Data System (ADS)
Kovács, István A.; Mizsei, Réka; Csermely, Péter
2015-09-01
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.
A unified data representation theory for network visualization, ordering and coarse-graining
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
Hidden topological order and its correlation with glass-forming ability in metallic glasses
NASA Astrophysics Data System (ADS)
Wu, Z. W.; Li, M. Z.; Wang, W. H.; Liu, K. X.
2015-01-01
Unlike the well-defined long-range periodic order that characterizes crystals, so far the inherent atomic packing mode in glassy solids remains mysterious. Based on molecular dynamics simulations, here we find medium-range atomic packing orders in metallic glasses, which are hidden in the diffraction data in terms of structure factors or pair correlation functions. The analysis of the hidden orders in various metallic glasses indicates that the glassy and crystalline solids share a nontrivial structural homology in short-to-medium range, and the hidden orders are formulated by inheriting partial crystalline orders during glass formation. As the number of chemical components increases, more hidden orders are often developed in a metallic glass and entangled topologically. We use this phenomenon to explain the geometric frustration in glass formation and the glass-forming ability of metallic alloys.
Hidden topological order and its correlation with glass-forming ability in metallic glasses.
Wu, Z W; Li, M Z; Wang, W H; Liu, K X
2015-01-01
Unlike the well-defined long-range periodic order that characterizes crystals, so far the inherent atomic packing mode in glassy solids remains mysterious. Based on molecular dynamics simulations, here we find medium-range atomic packing orders in metallic glasses, which are hidden in the diffraction data in terms of structure factors or pair correlation functions. The analysis of the hidden orders in various metallic glasses indicates that the glassy and crystalline solids share a nontrivial structural homology in short-to-medium range, and the hidden orders are formulated by inheriting partial crystalline orders during glass formation. As the number of chemical components increases, more hidden orders are often developed in a metallic glass and entangled topologically. We use this phenomenon to explain the geometric frustration in glass formation and the glass-forming ability of metallic alloys. PMID:25580857
NASA Astrophysics Data System (ADS)
Men, Ke-Pei; Zhao, Kai
2014-04-01
According to the statistical data, a total of 23 M ≥ 8 earthquakes occurred in Mainland China from 1303 to 2012. The seismic activity of M ≥ 8 earthquakes has showed an obvious self-organized orderliness. It should be remarked especially that there were three ordered pairs of M ≥8 earthquakes occurred in West China during 1902 - 2001, of which the time interval in each pair of two earthquakes was four years. This is a unique and rare earthquake example in earthquake history of China and the world. In the guidance of the information forecasting theory of Wen-Bo Weng, based on previous research results, combining ordered analysis with complex network technology, this paper focuses on the summary of the ordered network structure of M ≥ 8 earthquakes, supplements new information, constructs and further optimizes the 2D- and 3D-ordered network structure of M ≥ 8 earthquakes to make prediction research. At last, a new prediction opinion is presented that the future ordered pair of great earthquakes will probably occur around 2022 and 2026 in Mainland China.
Polyakov loop and correlator of Polyakov loops at next-to-next-to-leading order
Brambilla, Nora; Vairo, Antonio; Ghiglieri, Jacopo; Petreczky, Peter
2010-10-01
We study the Polyakov loop and the correlator of two Polyakov loops at finite temperature in the weak-coupling regime. We calculate the Polyakov loop at order g{sup 4}. The calculation of the correlator of two Polyakov loops is performed at distances shorter than the inverse of the temperature and for electric screening masses larger than the Coulomb potential. In this regime, it is accurate up to order g{sup 6}. We also evaluate the Polyakov-loop correlator in an effective field theory framework that takes advantage of the hierarchy of energy scales in the problem and makes explicit the bound-state dynamics. In the effective field theory framework, we show that the Polyakov-loop correlator is at leading order in the multipole expansion the sum of a color-singlet and a color-octet quark-antiquark correlator, which are gauge invariant, and compute the corresponding color-singlet and color-octet free energies.
Projective synchronization of fractional-order memristor-based neural networks.
Bao, Hai-Bo; Cao, Jin-De
2015-03-01
This paper investigates the projective synchronization of fractional-order memristor-based neural networks. Sufficient conditions are derived in the sense of Caputo's fractional derivation and by combining a fractional-order differential inequality. Two numerical examples are given to show the effectiveness of the main results. The results in this paper extend and improve some previous works on the synchronization of fractional-order neural networks. PMID:25463390
Analysis of Entanglement Length and Segmental Order Parameter in Polymer Networks
NASA Astrophysics Data System (ADS)
Lang, M.; Sommer, J.-U.
2010-04-01
The tube model of entangled chains is applied to compute segment fluctuations and segmental orientational order in polymer networks. The entanglement length Ne is extracted directly from monomer fluctuations without constructing a primitive path. Sliding motion of monomers along the tube axis leads to reduction of segmental order along the chain. For network strands of length N≫Ne, the average segmental order decreases ˜(NeN)-1/2 in marked contrast to the 1/Ne contribution of entanglements to network elasticity. As a consequence, network modulus is not proportional to segmental order in entangled polymer networks. Monte Carlo simulations over a wide range of molecular weights are in quantitative agreement with our theoretical predictions. The impact of entanglements on these properties is directly tested by comparing with simulations where entanglement constraints are switched off.
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…
Extension of local-type inequality for the higher order correlation functions
Suyama, Teruaki; Yokoyama, Shuichiro 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.
Non-local Coulomb correlations in metals close to a charge order insulator transition
NASA Astrophysics Data System (ADS)
Merino, Jaime
2008-03-01
Recent extensions of dynamical mean-field theory (DMFT) to clusters either in its real space (CDMFT) or momentum space versions (DCA) have become important tools for the description of electronic properties of low dimensional strongly correlated systems. In contrast to single site DMFT, short range correlation effects on electronic properties of systems close to the Mott transition can be analyzed. We have investigated the charge ordering transition induced by the nearest-neighbor Coulomb repulsion V in the 1/4-filled extended Hubbard model using CDMFT. We find a transition to a strongly renormalized charge ordered Fermi liquid at VCO and a metal-to- insulator transition at VMI>VCO. Short range antiferromagnetism occurs concomitantly with the CO transition. Approaching the charge ordered insulator, V
Hidden String Order in a Hole Superconductor with Extended Correlated Hopping
NASA Astrophysics Data System (ADS)
Chhajlany, Ravindra W.; Grzybowski, Przemysław R.; Stasińska, Julia; Lewenstein, Maciej; Dutta, Omjyoti
2016-06-01
Ultracold fermions in one-dimensional, spin-dependent nonoverlapping optical lattices are described by a nonstandard Hubbard model with next-nearest-neighbor correlated hopping. In the limit of a kinetically constraining value of the correlated hopping equal to the normal hopping, we map the invariant subspaces of the Hamiltonian exactly to free spinless fermion chains of varying lengths. As a result, the system exactly manifests spin-charge separation and we obtain the system properties for arbitrary filling: ground state collective order characterized by a spin gap, which can be ascribed to an unconventional critical hole superconductor associated with finite long range nonlocal string order. We study the system numerically away from the integrable point and show the persistence of both long range string order and spin gap for appropriate parameters as well as a transition to a ferromagnetic state.
Ni, Jianhua; Qian, Tianlu; Xi, Changbai; Rui, Yikang; Wang, Jiechen
2016-01-01
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. PMID:27548197
Ni, Jianhua; Qian, Tianlu; Xi, Changbai; Rui, Yikang; Wang, Jiechen
2016-01-01
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. PMID:27548197
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.
NASA Astrophysics Data System (ADS)
Salditt, T.; Koltover, I.; Rädler, J. O.; Safinya, C. R.
1998-07-01
We report a synchrotron small-angle x-ray scattering (SAXS) study of the mutilayered, self-assembled structure (complex) that is formed by mixing DNA with cationic liposomes. In these complexes the DNA is confined between charged lipid bilayers and orders as a two-dimensional (2D) smectic liquid crystal. The power-law bilayer-bilayer correlations of the 3D multilayer smectic liquid crystal, which are coupled to the 2D lattice of DNA chains, are found to deviate significantly from those described by the standard Caillé model of smectic-A phases. To model the DNA ordering, the 2D smectic correlation function and the corresponding structure factor are derived from the smectic Hamiltonian in harmonic approximation. The resulting line shape is then fitted to the DNA correlation peak. It is found that for samples of higher d, short-range correlations between the DNA in adjacent sheets have to be assumed to explain the data. From the least-square fitting, the 2D DNA interchain compressibility modulus B is extracted as a function of d and discussed in view of different possible microscopic interactions responsible for the ordering.
Charge-correlation effects in calculations of atomic short-range order in metallic alloys
NASA Astrophysics Data System (ADS)
Pinski, F. J.; Staunton, J. B.; Johnson, D. D.
1998-06-01
The ``local'' chemical environment that surrounds an atom directly influences its electronic charge density. These atomic charge correlations play an important role in describing the Coulomb and total energies for random substitutional alloys. Although the electronic structure may be well represented by a single-site theory, such as the coherent potential approximation, the electrostatic energy is not as well represented when these charge correlations are ignored. For metals, including the average effect from the charge correlation coming from only the nearest-neighbor shell has been shown to be sufficient to determine accurately the energy of formation. In this paper, we incorporate such charge correlations into the concentration-wave approach for calculating the atomic short-range order in random (substitutional) alloys. We present changes within the formalism, and apply the resulting equations to equiatomic nickel platinum. By including these effects, we obtain significantly better agreement with experimental data. In fact, particular to NiPt, a consequence of the charge correlation is a screening which cancels much of the electrostatic contribution to the energy and thus to the atomic short-range order, resulting in agreement with a picture originally outlined using only ``band-energy'' contributions.
Multilabel image classification via high-order label correlation driven active learning.
Zhang, Bang; Wang, Yang; Chen, Fang
2014-03-01
Supervised machine learning techniques have been applied to multilabel image classification problems with tremendous success. Despite disparate learning mechanisms, their performances heavily rely on the quality of training images. However, the acquisition of training images requires significant efforts from human annotators. This hinders the applications of supervised learning techniques to large scale problems. In this paper, we propose a high-order label correlation driven active learning (HoAL) approach that allows the iterative learning algorithm itself to select the informative example-label pairs from which it learns so as to learn an accurate classifier with less annotation efforts. Four crucial issues are considered by the proposed HoAL: 1) unlike binary cases, the selection granularity for multilabel active learning need to be fined from example to example-label pair; 2) different labels are seldom independent, and label correlations provide critical information for efficient learning; 3) in addition to pair-wise label correlations, high-order label correlations are also informative for multilabel active learning; and 4) since the number of label combinations increases exponentially with respect to the number of labels, an efficient mining method is required to discover informative label correlations. The proposed approach is tested on public data sets, and the empirical results demonstrate its effectiveness. PMID:24723538
Approach to the glass transition studied by higher order correlation functions
NASA Astrophysics Data System (ADS)
Lacevic, N.; Glotzer, S. C.
2003-08-01
We present a theoretical framework based on a higher order density correlation function, analogous to that used to investigate spin glasses, to describe dynamical heterogeneities in simulated glass-forming liquids. These higher order correlation functions are a four-point, time-dependent density correlation function g4(r,t) and a corresponding 'structure factor' S4(q,t) which measure the spatial correlations between the local liquid density at two points in space, each at two different times. g4(r,t) and S4(q,t) were extensively studied via molecular dynamics simulations of a binary Lennard-Jones mixture approaching the mode coupling temperature from above in Franz et al (1999 Phil. Mag. B 79 1827), Donati et al (2002 J. Non-Cryst. Solids 307 215), Glotzer et al (2000 J. Chem. Phys. 112 509), Lacevic et al (2002 Phys. Rev. E 66 030101), Lacevic et al (2003 J. Chem. Phys. submitted) and Lacevic (2003 Dissertation The Johns Hopkins University). Here, we examine the contribution to g4(r,t), S4(q,t) and the corresponding dynamical correlation length, as well as the corresponding order parameter Q(t) and generalized susceptibility chi4(t), from localized particles. We show that the dynamical correlation length xi4SS(t) of localized particles has a maximum as a function of time t, and the value of the maximum of xi4SS(t) increases steadily in the temperature range approaching the mode coupling temperature from above.
NASA Astrophysics Data System (ADS)
Sun, Xiaojuan; Perc, Matjaž; Lu, Qishao; Kurths, Jürgen
2010-09-01
In this paper, we examine the effects of correlated Gaussian noise on a two-dimensional neuronal network that is locally modeled by the Rulkov map. More precisely, we study the effects of the noise correlation on the variations of the mean firing rate and the correlations among neurons versus the noise intensity. Via numerical simulations, we show that the mean firing rate can always be optimized at an intermediate noise intensity, irrespective of the noise correlation. On the other hand, variations of the population coherence with respect to the noise intensity are strongly influenced by the ratio between local and global Gaussian noisy inputs. Biological implications of our findings are also discussed.
Kohn–Sham exchange-correlation potentials from second-order reduced density matrices
Cuevas-Saavedra, Rogelio; Staroverov, Viktor N.; Ayers, Paul W.
2015-12-28
We describe a practical algorithm for constructing the Kohn–Sham exchange-correlation potential corresponding to a given second-order reduced density matrix. Unlike conventional Kohn–Sham inversion methods in which such potentials are extracted from ground-state electron densities, the proposed technique delivers unambiguous results in finite basis sets. The approach can also be used to separate approximately the exchange and correlation potentials for a many-electron system for which the reduced density matrix is known. The algorithm is implemented for configuration-interaction wave functions and its performance is illustrated with numerical examples.
77 FR 36305 - Stream Communications Network & Media, Inc.; Order of Suspension of Trading
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Jung, Yousung; Lochan, Rohini C.; Dutoi, Anthony D.; Head-Gordon, Martin
2004-08-02
A simplified approach to treating the electron correlation energy is suggested in which only the alpha-beta component of the second order Moller-Plesset energy is evaluated, and then scaled by an empirical factor which is suggested to be 1.3. This scaled opposite spin second order energy (SOS-MP2) yields results for relative energies and derivative properties that are statistically improved over the conventional MP2 method. Furthermore, the SOS-MP2 energy can be evaluated without the 5th order computational steps associated with MP2 theory, even without exploiting any spatial locality. A 4th order algorithm is given for evaluating the opposite spin MP2 energy using auxiliary basis expansions, and a Laplace approach, and timing comparisons are given.
NASA Astrophysics Data System (ADS)
El Araby, Omar; Baeriswyl, Dionys
2014-04-01
The exact ground state of the reduced BCS Hamiltonian is investigated numerically for large system sizes and compared with the BCS ansatz. A "canonical" order parameter is found to be equal to the largest eigenvalue of Yang's reduced density matrix in the thermodynamic limit. Moreover, the limiting values of the exact analysis agree with those obtained for the BCS ground state. Exact results for the ground-state energy, level occupations, and a pseudospin-pseudospin correlation function are also found to converge to the BCS values already for relatively small system sizes. However, discrepancies persist for a pair-pair correlation function, for interlevel correlations of occupancies and for the fidelity susceptibility, even for large system sizes where these quantities have visibly converged to well-defined limits. Our results indicate that there exist nonperturbative corrections to the BCS predictions in the thermodynamic limit.
Transport on weighted networks: When the correlations are independent of the degree
NASA Astrophysics Data System (ADS)
Ramasco, José J.; Gonçalves, Bruno
2007-12-01
Most real-world networks are weighted graphs with the weight of the edges reflecting the relative importance of the connections. In this work, we study nondegree dependent correlations between edge weights, generalizing thus the correlations beyond the degree dependent case. We propose a simple method to introduce weight-weight correlations in topologically uncorrelated graphs. This allows us to test different measures to discriminate between the different correlation types and to quantify their intensity. We also discuss here the effect of weight correlations on the transport properties of the networks, showing that positive correlations dramatically improve transport. Finally, we give two examples of real-world networks (social and transport graphs) in which weight-weight correlations are present.
Yamaguchi, Takefumi; Negishi, Kazuno; Ohnuma, Kazuhiko; Tsubota, Kazuo
2011-01-01
Background The purpose of this study was to evaluate the correlation between contrast sensitivity and calculated higher-order aberrations based on individual natural pupil diameter after cataract surgery. Methods This prospective study included 120 eyes from 92 patients who were randomized to receive one of four lenses, including three aspheric lenses (Acrysof SN60WF, Tecnis ZA9000, and Hoya Py60AD) and one spherical lens (Acrysof SN60AT). Contrast sensitivity, higher-order aberrations of the whole eye, and pupil diameter under photopic and mesopic conditions were measured 1 month postoperatively. Higher-order aberrations were decomposed into Zernike coefficients, calculated according to individual pupil diameter. The correlation between higher-order aberrations and contrast sensitivity was evaluated. Results There were no significant differences in contrast sensitivity function between the four types of lenses under photopic conditions. However, the contrast sensitivity function and area under log contrast sensitivity function in the aspheric lenses were significantly better than in the spherical lens under mesopic conditions. Under mesopic conditions, spherical aberration in eyes with aspheric lenses was significantly lower than in eyes with spherical lenses (P < 0.05). Under photopic conditions, coma aberration had a significant negative correlation with contrast sensitivity at 12 cycles/degree. Under mesopic conditions, spherical aberration had a significant negative correlation with contrast sensitivity at 3, 6, and 12 cycles/degree with glare, and with contrast sensitivity at 6 and 18 cycles/degree without glare. Conclusion In terms of influence on visual function, coma aberration may be more significant under photopic conditions and spherical aberration under mesopic conditions. PMID:22205829
Seeing the unseen: Second-order correlation learning in 7- to 11-month-olds.
Yermolayeva, Yevdokiya; Rakison, David H
2016-07-01
We present four experiments with the object-examining procedure that investigated 7-, 9-, and 11-month-olds' ability to associate two object features that were never presented simultaneously. In each experiment, infants were familiarized with a number of 3D objects that incorporated different correlations among the features of those objects and the body of the objects (e.g., Part A and Body 1, and Part B and Body 1). Infants were then tested with objects with a novel body that either possessed both of the parts that were independently correlated with one body during familiarization (e.g., Part A and B on Body 3) or that were attached to two different bodies during familiarization. The experiments demonstrate that infants as young as 7months of age are capable of this kind of second-order correlation learning. Furthermore, by at least 11months of age infants develop a representation for the object that incorporates both of the features they experienced during training. We suggest that the ability to learn second-order correlations represents a powerful but as yet largely unexplored process for generalization in the first years of life. PMID:27038738
Correlation Between Channel Profile and Plan View Drainage Network Architecture
NASA Astrophysics Data System (ADS)
Shelef, E.; Hilley, G. E.
2011-12-01
This research explores the relationship between the plan-view network and profile geometry of channels using high-resolution digital topography and numerical models. In particular, we study the relations between plan-view morphometrics of the channel network and the mechanics of land-shaping processes as reflected by channel profile concavity. This analysis addresses one of the long-standing questions in geomorphology relating to the mechanistic significance of various plan-view channel network geometry measures. Statistically based studies suggest that Hortonian measures of channel network architecture (e.g. bifurcation ratio, area ratio, and length ratio) describe virtually all possible network geometries, and hence are not diagnostic when evaluating the origins of the geometry of a particular network. Our analyses of high resolution DEMs that capture different channel profile concavities (i.e debris flow vs. fluvial flows), as well as the topography of landscapes produced by process-based numerical models affirms this conclusion and indicates that Hortonian measures, as well as Hack exponent, are insensitive to channel concavity. In contrast, channel frequency (number of channel segments per area) appears to provide a measure that is sensitive to channel concavity. As such, channel frequency appears to discern between landscapes dominated by different land-shaping processes that produce different channel profile concavities. In the context of headword growing networks, the observed relations between concavity and channel frequency can be modeled through the coupled effect of concavity and surface roughness on the competition between headword growing channels. Our results suggest that the plan-view geometry of channel networks does not simply arise from random deflection of channels that once joined, cannot separate, but rather reflects the underlying processes that incise through rock and transport mass through the channel network
Hanbury Brown-Twiss interferometry and second-order correlations of inflaton quanta
Giovannini, Massimo
2011-01-15
The quantum theory of optical coherence is applied to the scrutiny of the statistical properties of the relic inflaton quanta. After adapting the description of the quantized scalar and tensor modes of the geometry to the analysis of intensity correlations, the normalized degrees of first-order and second-order coherence are computed in the concordance paradigm and are shown to encode faithfully the statistical properties of the initial quantum state. The strongly bunched curvature phonons are not only super-Poissonian but also superchaotic. Testable inequalities are derived in the limit of large-angular scales and can be physically interpreted in the light of the tenets of Hanbury Brown-Twiss interferometry. The quantum mechanical results are compared and contrasted with different situations including the one where intensity correlations are the result of a classical stochastic process. The survival of second-order correlations (not necessarily related to the purity of the initial quantum state) is addressed by defining a generalized ensemble where super-Poissonian statistics is an intrinsic property of the density matrix and turns out to be associated with finite volume effects which are expected to vanish in the thermodynamic limit.
Misra, Avijit; Biswas, Anindya; Pati, Arun K; Sen De, Aditi; Sen, Ujjwal
2015-05-01
Quantum discord is a measure of quantum correlations beyond the entanglement-separability paradigm. It is conceptualized by using the von Neumann entropy as a measure of disorder. We introduce a class of quantum correlation measures as differences between total and classical correlations, in a shared quantum state, in terms of the sandwiched relative Rényi and Tsallis entropies. We compare our results with those obtained by using the traditional relative entropies. We find that the measures satisfy all the plausible axioms for quantum correlations. We evaluate the measures for shared pure as well as paradigmatic classes of mixed states. We show that the measures can faithfully detect the quantum critical point in the transverse quantum Ising model and find that they can be used to remove an unquieting feature of nearest-neighbor quantum discord in this respect. Furthermore, the measures provide better finite-size scaling exponents of the quantum critical point than the ones for other known order parameters, including entanglement and information-theoretic measures of quantum correlations. PMID:26066137
Correlations, hierarchies and networks of the world’s automotive companies
NASA Astrophysics Data System (ADS)
Kocakaplan, Yusuf; Doğan, Şerafettin; Deviren, Bayram; Keskin, Mustafa
2013-06-01
We investigate, within the scope of econophysics, the correlations, hierarchies and networks of the world’s automotive companies over the 2003-2010 period by using the concept of a minimal spanning tree (MST) and hierarchical tree (HT). We derive a hierarchical organization and construct the MSTs and HTs for the 2003-2010 period and illustrate how the MSTs and their associated HTs developed over time. These periods are divided into two subperiods, such as 2003-2006 and 2007-2010, in order to test various time-windows and understand the temporal evolution of the correlation structure over time. We perform the bootstrap techniques to investigate a value of the statistical reliability to the links of the MSTs. We also use average linkage cluster analysis (ALCA) to observe the cluster structure more clearly in HTs. From the structural topologies of these trees, we identify different clusters of companies according to their geographical proximity and economic ties. Our results show that some companies are more important within the network, due to a tighter connection with other companies. We also find that these important companies play a predominant role in the world’s automotive industry.
Hu, Yanqing; Ksherim, Baruch; Cohen, Reuven; Havlin, Shlomo
2011-12-01
Robustness of two coupled networks systems has been studied separately only for dependency coupling [Buldyrev et al., Nature (London) 464, 1025 (2010)] and only for connectivity coupling [Leicht and D'Souza, e-print arXiv:0907.0894]. Here we study, using a percolation approach, a more realistic coupled networks system where both interdependent and interconnected links exist. We find rich and unusual phase-transition phenomena including hybrid transition of mixed first and second order, i.e., discontinuities like in a first-order transition of the giant component followed by a continuous decrease to zero like in a second-order transition. Moreover, we find unusual discontinuous changes from second-order to first-order transition as a function of the dependency coupling between the two networks. PMID:22304164
NASA Astrophysics Data System (ADS)
Hu, Yanqing; Ksherim, Baruch; Cohen, Reuven; Havlin, Shlomo
2011-12-01
Robustness of two coupled networks systems has been studied separately only for dependency coupling [Buldyrev , Nature (London)NATUAS0028-083610.1038/nature08932 464, 1025 (2010)] and only for connectivity coupling [Leicht and D’Souza, e-print arXiv:0907.0894]. Here we study, using a percolation approach, a more realistic coupled networks system where both interdependent and interconnected links exist. We find rich and unusual phase-transition phenomena including hybrid transition of mixed first and second order, i.e., discontinuities like in a first-order transition of the giant component followed by a continuous decrease to zero like in a second-order transition. Moreover, we find unusual discontinuous changes from second-order to first-order transition as a function of the dependency coupling between the two networks.
Design constraints for third-order PLL nodes in master-slave clock distribution networks
NASA Astrophysics Data System (ADS)
Bueno, A. M.; Rigon, A. G.; Ferreira, A. A.; Piqueira, José R. C.
2010-09-01
Clock signal distribution in telecommunication commercial systems usually adopts a master-slave architecture, with a precise time basis generator as a master and phase-locked loops (PLLs) as slaves. In the majority of the networks, second-order PLLs are adopted due to their simplicity and stability. Nevertheless, in some applications better transient responses are necessary and, consequently, greater order PLLs need to be used, in spite of the possibility of bifurcations and chaotic attractors. Here a master-slave network with third-order PLLs is analyzed and conditions for the stability of the synchronous state are derived, providing design constraints for the node parameters, in order to guarantee stability and reachability of the synchronous state for the whole network. Numerical simulations are carried out in order to confirm the analytical results.
NASA Astrophysics Data System (ADS)
Lemieux, Pierre-Anthony
This dissertation proceeds in two steps. It first extends traditional dynamic light scattering techniques by introducing intensity correlation of higher-order. It then investigates the intermittency transition in granular flow using this newly developed formalism. The intermittency transition occurs when a granular system relaxes intermittently despite being driven continuously. It is of practical importance as granular materials play a crucial role in geophysical phenomena and industry. It is of theoretical importance as similar behavior has been observed other systems such as colloidal glasses and foams near the onset of jamming. In order to study such flows we need to simultaneously capture the fast single-grain dynamics and the much slower collective intermittency. For this, we turn to dynamic light scattering techniques. In these techniques, the dynamic properties of the medium are extracted from a second-order quantity, the intensity auto-correlation g(2). This approach is limited to systems where the scattered electric field is a Gaussian random variable, and breaks down when the scattering sites are few or correlated. We first demonstrate that intensity correlations functions g (n) of higher-order can be used to both detect non-Gaussian scattering processes, and extract information not available in g (2) alone. The g(n) are experimentally measured by a combination of a commercial correlator and a custom-designed digital delay line. This approach is first tested in prototypical experimental situations, then specialized to the study of intermittent dynamics. We then introduce a model system for the study of granular flows near the intermittency transition, in the form of a granular heap by the steady addition of grains at its top. Using the higher-order light intensity framework we obtain the first continuous picture of granular dynamics across the intermittency transition. We find that microscopic gain dynamics during an avalanche are similar to those in the
Point model equations for neutron correlation counting: Extension of Böhnel's equations to any order
NASA Astrophysics Data System (ADS)
Favalli, Andrea; Croft, Stephen; Santi, Peter
2015-09-01
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 nuclear 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 work represents the first necessary step towards determining if higher order multiplets can add value to nondestructive measurement practice for nuclear materials control and accountancy.
Point model equations for neutron correlation counting: Extension of Böhnel's equations to any order
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 nuclear 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.
Point model equations for neutron correlation counting: Extension of Böhnel's equations to any order
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
McDermott, Jason E.; Costa, Michelle N.; Stevens, S.L.; Stenzel-Poore, Mary; Sanfilippo, Antonio P.
2011-01-20
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 that 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 evaluated by a
Correlation-driven charge order at the interface between a Mott and a band insulator.
Pentcheva, Rossitza; Pickett, Warren E
2007-07-01
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, 1
Optimization of thermal ghost imaging: high-order correlations vs. background subtraction.
Chan, Kam Wai C; O'Sullivan, Malcolm N; Boyd, Robert W
2010-03-15
We compare the performance of high-order thermal ghost imaging with that of conventional (that is, lowest-order) thermal ghost imaging for different data processing methods. Particular attention is given to high-order thermal ghost imaging with background normalization and conventional ghost imaging with background subtraction. The contrast-to-noise ratio (CNR) of the ghost image is used as the figure of merit for the comparison.We find analytically that the CNR of the normalized high-order ghost image is inversely proportional to the square root of the number of transmitting pixels of the object. This scaling law is independent of the exponents used in calculating the high-order correlation and is the same as that of conventional ghost imaging with background subtraction. We find that no data processing procedure performs better than lowest-order ghost imaging with background subtraction. Our results are found to be able to explain the observations of a recent experiment [Chen et al., arXiv:0902.3713v3 [quant-ph
Higher-order local and non-local correlations for 1D strongly interacting Bose gas
NASA Astrophysics Data System (ADS)
Nandani, EJKP; Römer, Rudolf A.; Tan, Shina; Guan, Xi-Wen
2016-05-01
The correlation function is an important quantity in the physics of ultracold quantum gases because it provides information about the quantum many-body wave function beyond the simple density profile. In this paper we first study the M-body local correlation functions, g M , of the one-dimensional (1D) strongly repulsive Bose gas within the Lieb–Liniger model using the analytical method proposed by Gangardt and Shlyapnikov (2003 Phys. Rev. Lett. 90 010401; 2003 New J. Phys. 5 79). In the strong repulsion regime the 1D Bose gas at low temperatures is equivalent to a gas of ideal particles obeying the non-mutual generalized exclusion statistics with a statistical parameter α =1-2/γ , i.e. the quasimomenta of N strongly interacting bosons map to the momenta of N free fermions via {k}i≈ α {k}iF with i=1,\\ldots ,N. Here γ is the dimensionless interaction strength within the Lieb–Liniger model. We rigorously prove that such a statistical parameter α solely determines the sub-leading order contribution to the M-body local correlation function of the gas at strong but finite interaction strengths. We explicitly calculate the correlation functions g M in terms of γ and α at zero, low, and intermediate temperatures. For M = 2 and 3 our results reproduce the known expressions for g 2 and g 3 with sub-leading terms (see for instance (Vadim et al 2006 Phys. Rev. A 73 051604(R); Kormos et al 2009 Phys. Rev. Lett. 103 210404; Wang et al 2013 Phys. Rev. A 87 043634). We also express the leading order of the short distance non-local correlation functions < {{{\\Psi }}}\\dagger ({x}1)\\cdots {{{\\Psi }}}\\dagger ({x}M){{\\Psi }}({y}M)\\cdots {{\\Psi }}({y}1)> of the strongly repulsive Bose gas in terms of the wave function of M bosons at zero collision energy and zero total momentum. Here {{\\Psi }}(x) is the boson annihilation operator. These general formulas of the higher-order local and non-local correlation functions of the 1D Bose gas provide new insights into the
Alparone, Andrea
2013-08-01
Dipole moments (μ), charge distributions, and static electronic first-order hyperpolarizabilities (β(μ)) of the two lowest-energy keto tautomers of guanine (7H and 9H) were determined in the gas phase using Hartree-Fock, Møller-Plesset perturbation theory (MP2 and MP4), and DFT (PBE1PBE, B97-1, B3LYP, CAM-B3LYP) methods with Dunning's correlation-consistent aug-cc-pVDZ and d-aug-cc-pVDZ basis sets. The most stable isomer 7H exhibits a μ value smaller than that of the 9H form by a factor of ca. 3.5. The β μ value of the 9H tautomer is strongly dependent on the computational method employed, as it dramatically influences the β(μ) (9H)/β(μ) (7H) ratio, which at the highest correlated MP4/aug-cc-pVDZ level is predicted to be ca. 5. The Coulomb-attenuating hybrid exchange-correlation CAM-B3LYP method is superior to the conventional PBE1PBE, B3LYP, and B97-1 functionals in predicting the β(μ) values. Differences between the largest diagonal hyperpolarizability components were clarified through hyperpolarizability density analyses. Dipole moment and first-order hyperpolarizability are molecular properties that are potentially useful for distinguishing the 7H from the 9H tautomer. PMID:23605138
Synchronization of fractional-order complex-valued neural networks with time delay.
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. PMID:27268259
Stability and synchronization of memristor-based fractional-order delayed neural networks.
Chen, Liping; Wu, Ranchao; Cao, Jinde; Liu, Jia-Bao
2015-11-01
Global asymptotic stability and synchronization of a class of fractional-order memristor-based delayed neural networks are investigated. For such problems in integer-order systems, Lyapunov-Krasovskii functional is usually constructed, whereas similar method has not been well developed for fractional-order nonlinear delayed systems. By employing a comparison theorem for a class of fractional-order linear systems with time delay, sufficient condition for global asymptotic stability of fractional memristor-based delayed neural networks is derived. Then, based on linear error feedback control, the synchronization criterion for such neural networks is also presented. Numerical simulations are given to demonstrate the effectiveness of the theoretical results. PMID:26282374
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.
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.
Relative ordering of square-norm distance correlations in open quantum systems
NASA Astrophysics Data System (ADS)
Wu, Tao; Song, Xue-Ke; Ye, Liu
2014-10-01
We investigate the square-norm distance correlation dynamics of the Bell-diagonal states under different local decoherence channels, including phase flip, bit flip, and bit-phase flip channels by employing the geometric discord (GD) and its modified geometric discord (MGD), as the measures of the square-norm distance correlations. Moreover, an explicit comparison between them is made in detail. The results show that there is no distinct dominant relative ordering between them. Furthermore, we obtain that the GD just gradually deceases to zero, while MGD initially has a large freezing interval, and then suddenly changes in evolution. The longer the freezing interval, the less the MGD is. Interestingly, it is shown that the dynamic behaviors of the two geometric discords under the three noisy environments for the Werner-type initial states are the same.
Accuracy of electronic wave functions in quantum Monte Carlo: The effect of high-order correlations
NASA Astrophysics Data System (ADS)
Huang, Chien-Jung; Umrigar, C. J.; Nightingale, M. P.
1997-08-01
Compact and accurate wave functions can be constructed by quantum Monte Carlo methods. Typically, these wave functions consist of a sum of a small number of Slater determinants multiplied by a Jastrow factor. In this paper we study the importance of including high-order, nucleus-three-electron correlations in the Jastrow factor. An efficient algorithm based on the theory of invariants is used to compute the high-body correlations. We observe significant improvements in the variational Monte Carlo energy and in the fluctuations of the local energies but not in the fixed-node diffusion Monte Carlo energies. Improvements for the ground states of physical, fermionic atoms are found to be smaller than those for the ground states of fictitious, bosonic atoms, indicating that errors in the nodal surfaces of the fermionic wave functions are a limiting factor.
Correlation Function Analysis of Fiber Networks: Implications for Thermal Conductivity
NASA Technical Reports Server (NTRS)
Martinez-Garcia, Jorge; Braginsky, Leonid; Shklover, Valery; Lawson, John W.
2011-01-01
The heat transport in highly porous fiber structures is investigated. The fibers are supposed to be thin, but long, so that the number of the inter-fiber connections along each fiber is large. We show that the effective conductivity of such structures can be found from the correlation length of the two-point correlation function of the local conductivities. Estimation of the parameters, determining the conductivity, from the 2D images of the structures is analyzed.
Default Network Deactivations Are Correlated with Psychopathic Personality Traits
Sheng, Tong; Gheytanchi, Anahita; Aziz-Zadeh, Lisa
2010-01-01
Background The posteromedial cortex (PMC) and medial prefrontal cortex (mPFC) are part of a network of brain regions that has been found to exhibit decreased activity during goal-oriented tasks. This network is thought to support a baseline of brain activity, and is commonly referred to as the “default network”. Although recent reports suggest that the PMC and mPFC are associated with affective, social, and self-referential processes, the relationship between these default network components and personality traits, especially those pertaining to social context, is poorly understood. Methodology/Principal Findings In the current investigation, we assessed the relationship between PMC and mPFC deactivations and psychopathic personality traits using fMRI and a self-report measure. We found that PMC deactivations predicted traits related to egocentricity and mPFC deactivations predicted traits related to decision-making. Conclusions/Significance These results suggest that the PMC and mPFC are associated with processes involving self-relevancy and affective decision-making, consistent with previous reports. More generally, these findings suggest a link between default network activity and personality traits. PMID:20830290
Orientational order of the lamellipodial actin network as demonstrated in living motile cells.
Verkhovsky, Alexander B; Chaga, Oleg Y; Schaub, Sébastien; Svitkina, Tatyana M; Meister, Jean-Jacques; Borisy, Gary G
2003-11-01
Lamellipodia of crawling cells represent both the motor for cell advance and the primary building site for the actin cytoskeleton. The organization of actin in the lamellipodium reflects actin dynamics and is of critical importance for the mechanism of cell motility. In previous structural studies, the lamellipodial actin network was analyzed primarily by electron microscopy (EM). An understanding of lamellipodial organization would benefit significantly if the EM data were complemented and put into a kinetic context by establishing correspondence with structural features observable at the light microscopic level in living cells. Here, we use an enhanced phase contrast microscopy technique to visualize an apparent long-range diagonal actin meshwork in the advancing lamellipodia of living cells. Visualization of this meshwork permitted a correlative light and electron microscopic approach that validated the underlying organization of lamellipodia. The linear features in the light microscopic meshwork corresponded to regions of greater actin filament density. Orientation of features was analyzed quantitatively and compared with the orientation of actin filaments at the EM level. We infer that the light microscopic meshwork reflects the orientational order of actin filaments which, in turn, is related to their branching angle. PMID:13679520
Orientational Order of the Lamellipodial Actin Network as Demonstrated in Living Motile CellsV⃞
Verkhovsky, Alexander B.; Chaga, Oleg Y.; Schaub, Sébastien; Svitkina, Tatyana M.; Meister, Jean-Jacques; Borisy, Gary G.
2003-01-01
Lamellipodia of crawling cells represent both the motor for cell advance and the primary building site for the actin cytoskeleton. The organization of actin in the lamellipodium reflects actin dynamics and is of critical importance for the mechanism of cell motility. In previous structural studies, the lamellipodial actin network was analyzed primarily by electron microscopy (EM). An understanding of lamellipodial organization would benefit significantly if the EM data were complemented and put into a kinetic context by establishing correspondence with structural features observable at the light microscopic level in living cells. Here, we use an enhanced phase contrast microscopy technique to visualize an apparent long-range diagonal actin meshwork in the advancing lamellipodia of living cells. Visualization of this meshwork permitted a correlative light and electron microscopic approach that validated the underlying organization of lamellipodia. The linear features in the light microscopic meshwork corresponded to regions of greater actin filament density. Orientation of features was analyzed quantitatively and compared with the orientation of actin filaments at the EM level. We infer that the light microscopic meshwork reflects the orientational order of actin filaments which, in turn, is related to their branching angle. PMID:13679520
Next-to-leading order perturbative QCD corrections to baryon correlators in matter
Groote, S.; Koerner, J. G.; Pivovarov, A. A.
2008-08-01
We compute the next-to-leading order (NLO) perturbative QCD corrections to the correlators of nucleon interpolating currents in relativistic nuclear matter. The main new result is the calculation of the O({alpha}{sub s}) perturbative corrections to the coefficient functions of the vector quark condensate in matter. This condensate appears in matter due to the violation of Lorentz invariance. The NLO perturbative QCD corrections turn out to be large which implies that the NLO corrections must be included in a sum rule analysis of the properties of both bound nucleons and relativistic nuclear matter.
Ten-no, Seiichiro; Yamaki, Daisuke
2012-10-01
We propose explicitly correlated Ansatz for four-component relativistic methods within the framework of the no-pair approximation. Kinetically balanced geminal basis is derived to satisfy the cusp conditions in the non-relativistic limit based on the Lévy-Leblend-like equation. Relativistic variants of strong-orthogonality projection operator (Ansätze 2α and 2β) suitable for practical calculations are introduced by exploiting the orthogonal complement of the large-component basis. A pilot implementation is performed for the second order Møller-Plesset perturbation theory. PMID:23039576
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…
Automatic angle measurement of a 2D object using optical correlator-neural networks hybrid system
NASA Astrophysics Data System (ADS)
Manivannan, N.; Neil, M. A. A.
2011-04-01
In this paper a novel method is proposed and demonstrated for automatic rotation angle measurement of a 2D object using a hybrid architecture, consisting of a 4f optical correlator with a binary phase only multiplexed matched filter and a single layer neural network. The hybrid set-up can be considered as a two-layer perceptron-like neural network; an optical correlator is the first layer and the standard single layer neural network is the second layer. The training scheme used to train the hybrid architecture is a combination of a Direct Binary Search algorithm, to train the optical correlator, and an Error Back Propagation algorithm, to train the neural network. The aim is to perform the major information processing by the optical correlator with a small additional processing by the neural network stage. This allows the system to be used for real-time applications as optics has the inherent ability to process information in a parallel manner at high speed. The neural network stage gives an extra dimension of freedom so that complicated tasks like automatic rotation angle measurement can be achieved. Results of both computer simulation and experimental set-up are presented for rotation angle measurement of an English alphabetic character as a 2D object. The experimental set-up consists of a real optical correlator using two spatial light modulators for both input and frequency plane representations and a PC based model of a single layer network.
Pinning synchronization of fractional-order complex networks with Lipschitz-type nonlinear dynamics.
Wang, Junwei; Ma, Qinghua; Chen, Aimin; Liang, Zhipeng
2015-07-01
This paper deals with pinning synchronization problem of fractional-order complex networks with Lipschitz-type nonlinear nodes and directed communication topology. We first reformulate the problem as a global asymptotic stability problem by describing network evolution in terms of error dynamics. Then, a novel frequency domain approach is developed by using Laplace transform, algebraic graph theory and generalized Gronwall inequality. We show that pinning synchronization can be ensured if the extended network topology contains a spanning tree and the coupling strength is large enough. Furthermore, we provide an easily testable criterion for global pinning synchronization depending on fractional-order, network topology, oscillator dynamics and state feedback. Numerical simulations are provided to illustrate the effectiveness of the theoretical analysis. PMID:25721408
Gallus, Susanne; Janke, Axel; Kumar, Vikas; Nilsson, Maria A
2015-04-01
The ancestors to the Australian marsupials entered Australia around 60 (54-72) Ma from Antarctica, and radiated into the four living orders Peramelemorphia, Dasyuromorphia, Diprotodontia, and Notoryctemorphia. The relationship between the four Australian marsupial orders has been a long-standing question, because different phylogenetic studies have not been able to consistently reconstruct the same topology. Initial in silico analysis of the Tasmanian devil genome and experimental screening in the seven marsupial orders revealed 20 informative transposable element insertions for resolving the inter- and intraordinal relationships of Australian and South American orders. However, the retrotransposon insertions support three conflicting topologies regarding Peramelemorphia, Dasyuromorphia, and Notoryctemorphia, indicating that the split between the three orders may be best understood as a network. This finding is supported by a phylogenetic reanalysis of nuclear gene sequences, using a consensus network approach that allows depicting hidden phylogenetic conflict, otherwise lost when forcing the data into a bifurcating tree. The consensus network analysis agrees with the transposable element analysis in that all possible topologies regarding Peramelemorphia, Dasyuromorphia, and Notoryctemorphia in a rooted four-taxon topology are equally well supported. In addition, retrotransposon insertion data support the South American order Didelphimorphia being the sistergroup to all other living marsupial orders. The four Australian orders originated within 3 Myr at the Cretaceous-Paleogene boundary. The rapid divergences left conflicting phylogenetic information in the genome possibly generated by incomplete lineage sorting or introgressive hybridization, leaving the relationship among Australian marsupial orders unresolvable as a bifurcating process millions of years later. PMID:25786431
Gallus, Susanne; Janke, Axel; Kumar, Vikas; Nilsson, Maria A.
2015-01-01
The ancestors to the Australian marsupials entered Australia around 60 (54–72) Ma from Antarctica, and radiated into the four living orders Peramelemorphia, Dasyuromorphia, Diprotodontia, and Notoryctemorphia. The relationship between the four Australian marsupial orders has been a long-standing question, because different phylogenetic studies have not been able to consistently reconstruct the same topology. Initial in silico analysis of the Tasmanian devil genome and experimental screening in the seven marsupial orders revealed 20 informative transposable element insertions for resolving the inter- and intraordinal relationships of Australian and South American orders. However, the retrotransposon insertions support three conflicting topologies regarding Peramelemorphia, Dasyuromorphia, and Notoryctemorphia, indicating that the split between the three orders may be best understood as a network. This finding is supported by a phylogenetic reanalysis of nuclear gene sequences, using a consensus network approach that allows depicting hidden phylogenetic conflict, otherwise lost when forcing the data into a bifurcating tree. The consensus network analysis agrees with the transposable element analysis in that all possible topologies regarding Peramelemorphia, Dasyuromorphia, and Notoryctemorphia in a rooted four-taxon topology are equally well supported. In addition, retrotransposon insertion data support the South American order Didelphimorphia being the sistergroup to all other living marsupial orders. The four Australian orders originated within 3 Myr at the Cretaceous–Paleogene boundary. The rapid divergences left conflicting phylogenetic information in the genome possibly generated by incomplete lineage sorting or introgressive hybridization, leaving the relationship among Australian marsupial orders unresolvable as a bifurcating process millions of years later. PMID:25786431
Effects of gender, birth order, and other correlates on childhood mortality in China.
Choe, M K; Hao, H; Wang, F
1995-01-01
Using data from the 1988 Two-Per-Thousand Survey of Fertility and Birth Control, this paper examines the effects of gender, birth order, and other correlates of childhood mortality in China. Controlling for family-level factors, childhood mortality is found to be associated with the child's gender and birth order. Among firstborn children the difference between male and female childhood mortality is not statistically significant, but among others, female children between ages 1 and 5 experience higher mortality than male children. Childhood mortality is slightly higher for children who have older brothers only than for those who have older sisters only, and it is highest for those who have both older brothers and sisters. Other factors affecting childhood mortality in China include mortality of older siblings, birth interval, urban/rural residence, mother's level of education, and mother's occupation. All interactive effects between gender and family-level characteristics are found to be statistically insignificant. PMID:7481920
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.
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. PMID:25597833
Dynamical analysis of memristor-based fractional-order neural networks with time delay
NASA Astrophysics Data System (ADS)
Cui, Xueli; Yu, Yongguang; Wang, Hu; Hu, Wei
2016-06-01
In this paper, the memristor-based fractional-order neural networks with time delay are analyzed. Based on the theories of set-value maps, differential inclusions and Filippov’s solution, some sufficient conditions for asymptotic stability of this neural network model are obtained when the external inputs are constants. Besides, uniform stability condition is derived when the external inputs are time-varying, and its attractive interval is estimated. Finally, numerical examples are given to verify our results.
Second-order Kohn-Sham perturbation theory: correlation potential for atoms in a cavity.
Jiang, Hong; Engel, Eberhard
2005-12-01
Second-order perturbation theory based on the Kohn-Sham Hamiltonian leads to an implicit density functional for the correlation energy E(c) (MP2), which is explicitly dependent on both occupied and unoccupied Kohn-Sham single-particle orbitals and energies. The corresponding correlation potential v(c) (MP2), which has to be evaluated by the optimized potential method, was found to be divergent in the asymptotic region of atoms, if positive-energy continuum states are included in the calculation [Facco Bonetti et al., Phys. Rev. Lett. 86, 2241 (2001)]. On the other hand, Niquet et al., [J. Chem. Phys. 118, 9504 (2003)] showed that v(c) (MP2) has the same asymptotic -alpha(2r(4)) behavior as the exact correlation potential, if the system under study has a discrete spectrum only. In this work we study v(c) (MP2) for atoms in a spherical cavity within a basis-set-free finite differences approach, ensuring a completely discrete spectrum by requiring hard-wall boundary conditions at the cavity radius. Choosing this radius sufficiently large, one can devise a numerical continuation procedure which allows to normalize v(c) (MP2) consistent with the standard choice v(c)(r-->infinity)=0 for free atoms, without modifying the potential in the chemically relevant region. An important prerequisite for the success of this scheme is the inclusion of very high-energy virtual states. Using this technique, we have calculated v(c) (MP2) for all closed-shell and spherical open-shell atoms up to argon. One finds that v(c) (MP2) reproduces the shell structure of the exact correlation potential very well but consistently overestimates the corresponding shell oscillations. In the case of spin-polarized atoms one observes a strong interrelation between the correlation potentials of the two spin channels, which is completely absent for standard density functionals. However, our results also demonstrate that E(c) (MP2) can only serve as a first step towards the construction of a suitable
Relational correlates of interpersonal citizenship behavior: a social network perspective.
Bowler, Wm Matthew; Brass, Daniel J
2006-01-01
This study examines the role of social network ties in the performance and receipt of interpersonal citizenship behavior (ICB), one form of organizational citizenship behavior (OCB). A field study involving 141 employees of a manufacturing firm provided evidence that social network ties are related to the performance and receipt of ICB. Results support hypothesized relationships, which are based on social exchange theory, suggesting strength of friendship is related to performance and receipt of ICB. Support was also found for impression management-based hypotheses suggesting that asymmetric influence and 3rd-party influence are related to the performance and receipt of ICB. These relationships were significant when controlling for job satisfaction, commitment, procedural justice, hierarchical level, demographic similarity, and job similarity. Implications and directions for future research are addressed. PMID:16435939
Ring Correlations in Two-Dimensional (2D) Random Networks
NASA Astrophysics Data System (ADS)
Sadjadi, Mahdi; Thorpe, M. F.
Amorphous materials can be characterized by their ring structure. Recently, two experimental groups imaged bilayers of vitreous silica at atomic resolution which provides a direct access to the ring structure of a 2D glass. It has been shown that experimental samples have various ring statistics, obey Aboav-Weaire law and have a distinct area law. In this work, we study correlations between rings as a function of their size and topological separation. We show that correlation is medium-range and vanishes when the separation is about three rings apart. We also present a generalization of the Aboav-Weaire law.
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
A generalized LSTM-like training algorithm for second-order recurrent neural networks
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
Rakkiyappan, R; Velmurugan, G; Cao, Jinde
2015-04-01
In this paper, the problem of the existence, uniqueness and uniform stability of memristor-based fractional-order neural networks (MFNNs) with two different types of memductance functions is extensively investigated. Moreover, we formulate the complex-valued memristor-based fractional-order neural networks (CVMFNNs) with two different types of memductance functions and analyze the existence, uniqueness and uniform stability of such networks. By using Banach contraction principle and analysis technique, some sufficient conditions are obtained to ensure the existence, uniqueness and uniform stability of the considered MFNNs and CVMFNNs with two different types of memductance functions. The analysis results establish from the theory of fractional-order differential equations with discontinuous right-hand sides. Finally, four numerical examples are presented to show the effectiveness of our theoretical results. PMID:25861402
Orientational ordering in hard rectangles: The role of three-body correlations.
Martínez-Ratón, Yuri; Velasco, Enrique; Mederos, Luis
2006-07-01
We investigate the effect of three-body correlations on the phase behavior of hard rectangle two-dimensional fluids. The third virial coefficient B3 is incorporated via an equation of state that recovers scaled particle theory for parallel hard rectangles. This coefficient, a functional of the orientational distribution function, is calculated by Monte Carlo integration, using an accurate parametrized distribution function, for various particle aspect ratios in the range of 1-25. A bifurcation analysis of the free energy calculated from the obtained equation of state is applied to find the isotropic (I)-uniaxial nematic (N(u)) and isotropic-tetratic nematic (N(t)) spinodals and to study the order of these phase transitions. We find that the relative stability of the N(t) phase with respect to the isotropic phase is enhanced by the introduction of B3. Finally, we have calculated the complete phase diagram using a variational procedure and compared the results with those obtained from scaled particle theory and with Monte Carlo simulations carried out for hard rectangles with various aspect ratios. The predictions of our proposed equation of state as regards the transition densities between the isotropic and orientationally ordered phases for small aspect ratios are in fair agreement with simulations. Also, the critical aspect ratio below which the N(t) phase becomes stable is predicted to increase due to three-body correlations, although the corresponding value is underestimated with respect to simulation. PMID:16863310
NASA Astrophysics Data System (ADS)
Schreiber, Tomasz
2010-08-01
We consider polygonal Markov fields originally introduced by Arak in 4th USSR-Japan Symposium on Probability Theory and Mathematical Statistics, Abstracts of Communications, 1982; Arak and Surgailis in Probab. Theory Relat. Fields 80:543-579, 1989. Our attention is focused on fields with nodes of order two, which can be regarded as continuum ensembles of non-intersecting contours in the plane, sharing a number of salient features with the two-dimensional Ising model. The purpose of this paper is to establish an explicit stochastic representation for the higher-order correlation functions of polygonal Markov fields in their consistency regime. The representation is given in terms of the so-called crop functionals (defined by a Möbius-type formula) of polygonal webs which arise in a graphical construction dual to that giving rise to polygonal fields. The proof of our representation formula goes by constructing a martingale interpolation between the correlation functions of polygonal fields and crop functionals of polygonal webs.
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.
N(th)-order correlation functions of galaxies from the Sloan Digital Sky Survey
NASA Astrophysics Data System (ADS)
Ross, Ashley Jacob
I present the correlation function measurements and analysis I have conducted with SDSS data. I have focused my measurements on angular N -point area- averaged correlation functions ( o N ([straight theta]) ) and auto-correlation functions (o 2 ([straight theta])) of galaxies. The measured o N ([straight theta]) are used to calculate the projected, s N , and real space, S N , hierarchical amplitudes. I have used SDSS DR3 data to show that the measurements are robust against the systematic effects of reddening and seeing, and to determine that large differences exist in the higher-order clustering of early- and late-type galaxies--quantified in terms of bias parameters. Using photometric redshift catalogs from SDSS DR5 data, I have created two volume limited samples of galaxies, allowing me to measure o N ([straight theta]) as a function of type, redshift, and luminosity. I have found that the higher-order bias of early-type galaxies does not vary significantly with changes in either redshift or luminosity, as c 2, early is consistent with 0.09 for all measurements. I have shown that the higher-order clustering of late-type galaxies shows dramatic differences for galaxies selected with redshifts above and below z = 0.3. Using LRGs photometrically selected from SDSS DR5, I have measured the 2nd-order bias tern, c 2 , using both o 2 ([straight theta]) and s 3 , and I have combined these measurements to determine that the normalization of the matter power spectrum at 8 h -1 Mpc, s 8 , is 0.79 ± 0.05 and c 2, LRG = 0.09 ± 0.04--consistent with the DR5 early-type results. I have calculated o 2 ([straight theta]) of galaxies from SDSS DR5 to constrain the HOD of galaxies as a function of type. I found that a new model that separated early- and late- type galaxies into different dark matter halos as much as possible was required to allow good fits to the measurements. Throughout, my findings are interpreted with respect to both the evolution of structure formation and
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
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
Lin, Naibo; Liu, Xiang Yang
2015-11-01
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
Yu, Sung-Nien; Liu, Fan-Tsen
2014-01-01
Regular electrocardiogram beat classification system usually based on single lead ECG signal. This study designated to add a second lead of ECG signal to the system and apply higher-order statistics and inter-lead cross-correlation features to study the influence of the second lead to the recognition rates and noise-tolerance of the classifier. Discrete wavelet transformation is employed to decompose the ECG signals into different subband components and higher order statistics is recruited to characterize the ECG signals as an attempt to elevate the accuracy and noise-resistibility of heartbeat discrimination. A feed-forward back-propagation neural network (FFBNN) is employed as classifier. When compared with the system that uses only one lead, the second lead raises the recognition rate from 97.74% to 98.25%. We also study the ability of the two-lead system in resisting different levels of white Gaussian noise. More than 97.8% accuracy can be retained with the two-lead system even when the SNR decreases to 10 dB. PMID:25569892
A novel joint sparse partial correlation method for estimating group functional networks.
Liang, Xiaoyun; Connelly, Alan; Calamante, Fernando
2016-03-01
Advances in graph theory have provided a powerful tool to characterize brain networks. In particular, functional networks at group-level have great appeal to gain further insight into complex brain function, and to assess changes across disease conditions. These group networks, however, often have two main limitations. First, they are popularly estimated by directly averaging individual networks that are compromised by confounding variations. Secondly, functional networks have been estimated mainly through Pearson cross-correlation, without taking into account the influence of other regions. In this study, we propose a sparse group partial correlation method for robust estimation of functional networks based on a joint graphical models approach. To circumvent the issue of choosing the optimal regularization parameters, a stability selection method is employed to extract networks. The proposed method is, therefore, denoted as JGMSS. By applying JGMSS across simulated datasets, the resulting networks show consistently higher accuracy and sensitivity than those estimated using an alternative approach (the elastic-net regularization with stability selection, ENSS). The robustness of the JGMSS is evidenced by the independence of the estimated networks to choices of the initial set of regularization parameters. The performance of JGMSS in estimating group networks is further demonstrated with in vivo fMRI data (ASL and BOLD), which show that JGMSS can more robustly estimate brain hub regions at group-level and can better control intersubject variability than it is achieved using ENSS. PMID:26859311
NASA Astrophysics Data System (ADS)
Chen, Diyi; Zhang, Runfan; Liu, Xinzhi; Ma, Xiaoyi
2014-12-01
In this paper, we bring attention to the existence of fractional order Lyapunov stability theorem which is strictly descripted by mathematic formulas. Firstly, we introduce fractional-order Lyapunov function based on the definition of fractional calculation and integer order Lyapunov theory. By using the classical stability theorem of linear fractional order systems, we demonstrate convincingly the existence of fractional-order Lyapunov function and present a mathematical description of fractional-order Lyapunov stability theorem. Furthermore, as an example, we apply the presented fractional order Lyapunov stability theorem to synchronization of both direct and undirected complex networks with fractional order equation nodes. Based on the proposed theorem, a novel T-S fuzzy model pining controller with minimum control nodes is designed. Finally, numerical simulations are agreement with theoretical analysis, which both confirm that the correctness of the presented theory.
Adaptive pinning synchronization in fractional-order uncertain complex dynamical networks with delay
NASA Astrophysics Data System (ADS)
Liang, Song; Wu, Ranchao; Chen, Liping
2016-02-01
Based on the stability theory of fractional-order systems, synchronization of general fractional-order uncertain complex networks with delay is investigated in this paper. By the inequality of the fractional derivative and the comparison principle of the linear fractional equation with delay, synchronization of complex networks with delay is realized under adaptive control. Some sufficient criteria ensuring local asymptotical synchronization under adaptive control and global asymptotical synchronization under adaptive pinning control are derived, respectively. Finally, numerical simulations are presented to demonstrate the validity and feasibility of the proposed synchronization criteria.
NASA Astrophysics Data System (ADS)
Jukić, Damir; Denić-Jukić, Vesna
2015-11-01
Time series of rainfall and karst-spring discharge are influenced by various space-time-variant processes involved in the transfer of water in hydrological cycle. The effects of these processes can be exhibited in auto-correlation and cross-correlation functions. Consequently, ambiguities with respect to the effects encoded in the correlation functions exist. To solve this problem, a new statistical method for investigating relationships between rainfall and karst-spring discharge is proposed. The method is based on the determination and analysis of higher-order partial correlation functions and their spectral representations. The study area is the catchment of the Jadro Spring in Croatia. The analyzed daily time series are the air temperature, relative humidity, spring discharge, and rainfall at seven rain-gauges over a period of 19 years, from 1995 to 2013. The application results show that the effects of spatial and temporal variations of hydrological time series and the space-time-variant behaviours of the karst system can be separated from the correlation functions. Specifically, the effect of evapotranspiration can be separated to obtain the forms of correlation functions that represent the hydrogeological characteristics of the karst system. Using the proposed method, it is also possible to separate the effects of the process of groundwater recharge that occurs in neighbouring parts of a catchment to identify the specific contribution of each part of the catchment to the karst-spring discharge. The main quantitative results obtained for the Jadro Spring show that the quick-flow duration is 14 days, the intermediate-flow duration is 80 days, and the pure base flow starts after 80 days. The base flow consists of an inter-catchment groundwater flow. The system memory of the spring is 80 days. The presented results indicate the far-reaching applicability of the proposed method in the analyses of relationships between rainfall and karst-spring discharge; e
Bias and high-order galaxy correlation functions in the APM galaxy survey
NASA Technical Reports Server (NTRS)
Gaztanaga, Enrique; Frieman, Joshua A.
1994-01-01
On large scales, the higher order moments of the mass distribution, S(sub J) = bar-zeta(sub J)/bar-zeta(sup J-1)(sub 2), e.g., the skewness S(sub 3) and kurtosis S(sub 4), can be predicted using nonlinear perturbation theory. Comparison of these predictions with moments of the observed galaxy distribution probes the bias between galaxies and mass. Applying this method to models with initially Gaussian fluctuations and power spectra P(k) similar to that of galaxies in the Automatic Plate Measuring (APM) survey, we find that the predicted higher order moments S(sub J)(R) are in good agreement with those directly inferred from the APM survey in the absence of bias. We use this result to place limits on the linear and nonlinear bias parameters. Models in which the extra power observed on large scales (with respect to the standard cold dark matter (CDM) model) is produced by scale-dependent bias match the APM higher order amplitudes only if nonlinear bias (rather than nonlinear gravity) generates the observed higher order moments. When normalized to Cosmic Background Explorer Differential Microwave Radiometer (COBE DMR), these models are siginificantly ruled out by the S(sub 3) observations. The cold plus hot dark matter model normalized to COBE can reproduce the APM higher order correlations if one introduces nonlinear bias terms, while the low-density CDM model with a cosmological constant does not require any bias to fit the large-scale amplitudes.
Chandrasekaran, Naresh; Gann, Eliot; Jain, Nakul; Kumar, Anshu; Gopinathan, Sreelekha; Sadhanala, Aditya; Friend, Richard H; Kumar, Anil; McNeill, Christopher R; Kabra, Dinesh
2016-08-10
In this paper we correlate the solar cell performance with bimolecular packing of donor:acceptor bulk heterojunction (BHJ) organic solar cells (OSCs), where interchain ordering of the donor molecule and its influence on morphology, optical properties, and charge carrier dynamics of BHJ solar cells are studied in detail. Solar cells that are fabricated using more ordered defect free 100% regioregular poly(3-hexylthiophene) (DF-P3HT) as the donor polymer show ca. 10% increase in the average power conversion efficiency (PCE) when compared to that of the solar cell fabricated using 92% regioregularity P3HT, referred to as rr-P3HT. EQE and UV-vis absorption spectrum show a clear increase in the 607 nm vibronic shoulder of the DF-P3HT blend suggesting better interchain ordering which was also reflected in the less Urbach energy (Eu) value for this system. The increase in ordering inside the blend has enhanced the hole-mobility which is calculated from the single carrier device J-V characteristics. Electroluminance (EL) studies on the DF-P3HT system showed a red-shifted peak when compared to rr-P3HT-based devices suggesting low CT energy states in DF-P3HT. The morphologies of the blend films are studied using AFM and grazing-incidence wide-angle X-ray scattering (GIWAXS) suggesting increase in the roughness and phase segregation which could enhance the internal scattering of the light inside the device and improvement in the crystallinity along alkyl and π-stacking direction. Hence, higher PCE, lower Eu, red-shifted EL emission, high hole-mobility, and better crystallinity suggest improved interchain ordering has facilitated a more delocalized HOMO state in DF-P3HT-based BHJ solar cells. PMID:27415029
Measuring mixing patterns in complex networks by Spearman rank correlation coefficient
NASA Astrophysics Data System (ADS)
Zhang, Wen-Yao; Wei, Zong-Wen; Wang, Bing-Hong; Han, Xiao-Pu
2016-06-01
In this paper, we utilize Spearman rank correlation coefficient to measure mixing patterns in complex networks. Compared with the widely used Pearson coefficient, Spearman coefficient is rank-based, nonparametric, and size-independent. Thus it is more effective to assess linking patterns of diverse networks, especially for large-size networks. We demonstrate this point by testing a variety of empirical and artificial networks. Moreover, we show that normalized Spearman ranks of stubs are subject to an interesting linear rule where the correlation coefficient is just the Spearman coefficient. This compelling linear relationship allows us to directly produce networks with any prescribed Spearman coefficient. Our method apparently has an edge over the well known uncorrelated configuration model.
Block correlated second order perturbation theory with a generalized valence bond reference function
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 Møller–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.
Weighted network analysis of high-frequency cross-correlation measures.
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. PMID:17500762
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.
Robust outer synchronization between two complex networks with fractional order dynamics.
Asheghan, Mohammad Mostafa; Míguez, Joaquín; Hamidi-Beheshti, Mohammad T; Tavazoei, Mohammad Saleh
2011-09-01
Synchronization between two coupled complex networks with fractional-order dynamics, hereafter referred to as outer synchronization, is investigated in this work. In particular, we consider two systems consisting of interconnected nodes. The state variables of each node evolve with time according to a set of (possibly nonlinear and chaotic) fractional-order differential equations. One of the networks plays the role of a master system and drives the second network by way of an open-plus-closed-loop (OPCL) scheme. Starting from a simple analysis of the synchronization error and a basic lemma on the eigenvalues of matrices resulting from Kronecker products, we establish various sets of conditions for outer synchronization, i.e., for ensuring that the errors between the state variables of the master and response systems can asymptotically vanish with time. Then, we address the problem of robust outer synchronization, i.e., how to guarantee that the states of the nodes converge to common values when the parameters of the master and response networks are not identical, but present some perturbations. Assuming that these perturbations are bounded, we also find conditions for outer synchronization, this time given in terms of sets of linear matrix inequalities (LMIs). Most of the analytical results in this paper are valid both for fractional-order and integer-order dynamics. The assumptions on the inner (coupling) structure of the networks are mild, involving, at most, symmetry and diffusivity. The analytical results are complemented with numerical examples. In particular, we show examples of generalized and robust outer synchronization for networks whose nodes are governed by fractional-order Lorenz dynamics. PMID:21974656
Dissociating the neural correlates of tactile temporal order and simultaneity judgements
Miyazaki, Makoto; Kadota, Hiroshi; Matsuzaki, Kozue S.; Takeuchi, Shigeki; Sekiguchi, Hirofumi; Aoyama, Takuo; Kochiyama, Takanori
2016-01-01
Perceiving temporal relationships between sensory events is a key process for recognising dynamic environments. Temporal order judgement (TOJ) and simultaneity judgement (SJ) are used for probing this perceptual process. TOJ and SJ exhibit identical psychometric parameters. However, there is accumulating psychophysical evidence that distinguishes TOJ from SJ. Some studies have proposed that the perceptual processes for SJ (e.g., detecting successive/simultaneity) are also included in TOJ, whereas TOJ requires more processes (e.g., determination of the temporal order). Other studies have proposed two independent processes for TOJ and SJ. To identify differences in the neural activity associated with TOJ versus SJ, we performed functional magnetic resonance imaging of participants during TOJ and SJ with identical tactile stimuli. TOJ-specific activity was observed in multiple regions (e.g., left ventral and bilateral dorsal premotor cortices and left posterior parietal cortex) that overlap the general temporal prediction network for perception and motor systems. SJ-specific activation was observed only in the posterior insular cortex. Our results suggest that TOJ requires more processes than SJ and that both TOJ and SJ implement specific process components. The neural differences between TOJ and SJ thus combine features described in previous psychophysical hypotheses that proposed different mechanisms. PMID:27064734
Dissociating the neural correlates of tactile temporal order and simultaneity judgements.
Miyazaki, Makoto; Kadota, Hiroshi; Matsuzaki, Kozue S; Takeuchi, Shigeki; Sekiguchi, Hirofumi; Aoyama, Takuo; Kochiyama, Takanori
2016-01-01
Perceiving temporal relationships between sensory events is a key process for recognising dynamic environments. Temporal order judgement (TOJ) and simultaneity judgement (SJ) are used for probing this perceptual process. TOJ and SJ exhibit identical psychometric parameters. However, there is accumulating psychophysical evidence that distinguishes TOJ from SJ. Some studies have proposed that the perceptual processes for SJ (e.g., detecting successive/simultaneity) are also included in TOJ, whereas TOJ requires more processes (e.g., determination of the temporal order). Other studies have proposed two independent processes for TOJ and SJ. To identify differences in the neural activity associated with TOJ versus SJ, we performed functional magnetic resonance imaging of participants during TOJ and SJ with identical tactile stimuli. TOJ-specific activity was observed in multiple regions (e.g., left ventral and bilateral dorsal premotor cortices and left posterior parietal cortex) that overlap the general temporal prediction network for perception and motor systems. SJ-specific activation was observed only in the posterior insular cortex. Our results suggest that TOJ requires more processes than SJ and that both TOJ and SJ implement specific process components. The neural differences between TOJ and SJ thus combine features described in previous psychophysical hypotheses that proposed different mechanisms. PMID:27064734
NASA Astrophysics Data System (ADS)
Ren, Xinguo; Rinke, Patrick; Scuseria, Gustavo E.; Scheffler, Matthias
2013-07-01
We present a renormalized second-order perturbation theory (rPT2), based on a Kohn-Sham (KS) reference state, for the electron correlation energy that includes the random-phase approximation (RPA), second-order screened exchange (SOSEX), and renormalized single excitations (rSE). These three terms all involve a summation of certain types of diagrams to infinite order, and can be viewed as ``renormalization'' of the second-order direct, exchange, and single-excitation (SE) terms of Rayleigh-Schrödinger perturbation theory based on a KS reference. In this work, we establish the concept of rPT2 and present the numerical details of our SOSEX and rSE implementations. A preliminary version of rPT2, in which the renormalized SE (rSE) contribution was treated approximately, has already been benchmarked for molecular atomization energies and chemical reaction barrier heights and shows a well-balanced performance [J. Paier , New J. Phys.1367-263010.1088/1367-2630/14/4/043002 14, 043002 (2012)]. In this work, we present a refined version of rPT2, in which we evaluate the rSE series of diagrams rigorously. We then extend the benchmark studies to noncovalent interactions, including the rare-gas dimers, and the S22 and S66 test sets, as well as the cohesive energy of small copper clusters, and the equilibrium geometry of 10 diatomic molecules. Despite some remaining shortcomings, we conclude that rPT2 gives an overall satisfactory performance across different electronic situations, and is a promising step towards a generally applicable electronic-structure approach.
The Open Host Network Packet Process Correlator for Windows
2014-06-17
The Hone sensors are packet-process correlation engines that log the relationships between applications and the communications they are responsible for. Hone sensors are available for a variety of platforms including Linux, Windows, and MacOSX. Hone sensors are designed to help analysts understand the meaning of communications on a deeper level by associating the origin or destination process to the communication. They do this by tracing communications on a per-packet basis, through the kernel of the operating system to determine their ultimate source/destination on the monitored machine.
The Open Host Network Packet Process Correlator for Windows
Energy Science and Technology Software Center (ESTSC)
2014-06-17
The Hone sensors are packet-process correlation engines that log the relationships between applications and the communications they are responsible for. Hone sensors are available for a variety of platforms including Linux, Windows, and MacOSX. Hone sensors are designed to help analysts understand the meaning of communications on a deeper level by associating the origin or destination process to the communication. They do this by tracing communications on a per-packet basis, through the kernel of themore » operating system to determine their ultimate source/destination on the monitored machine.« less
First-Order Melting of a Weak Spin-Orbit Mott Insulator into a Correlated Metal.
Hogan, Tom; Yamani, Z; Walkup, D; Chen, Xiang; Dally, Rebecca; Ward, Thomas Z; Dean, M P M; Hill, John; Islam, Z; Madhavan, Vidya; Wilson, Stephen D
2015-06-26
The electronic phase diagram of the weak spin-orbit Mott insulator (Sr(1-x)La(x))(3)Ir(2)O(7) is determined via an exhaustive experimental study. Upon doping electrons via La substitution, an immediate collapse in resistivity occurs along with a narrow regime of nanoscale phase separation comprised of antiferromagnetic, insulating regions and paramagnetic, metallic puddles persisting until x≈0.04. Continued electron doping results in an abrupt, first-order phase boundary where the Néel state is suppressed and a homogenous, correlated, metallic state appears with an enhanced spin susceptibility and local moments. As the metallic state is stabilized, a weak structural distortion develops and suggests a competing instability with the parent spin-orbit Mott state. PMID:26197142
First-order melting of a weak spin-orbit mott insulator into a correlated metal
Hogan, Tom; Yamani, Z.; Walkup, D.; Chen, Xiang; Dally, Rebecca; Ward, Thomas Zac; Dean, M. P. M.; Hill, John P.; Islam, Z.; Madhavan, Vidya; et al
2015-06-25
Herein, the electronic phase diagram of the weak spin-orbit Mott insulator (Sr1-xLax)3Ir2O7 is determined via an exhaustive experimental study. Upon doping electrons via La substitution, an immediate collapse in resistivity occurs along with a narrow regime of nanoscale phase separation comprised of antiferromagnetic, insulating regions and paramagnetic, metallic puddles persisting until x≈0.04. Continued electron doping results in an abrupt, first-order phase boundary where the Néel state is suppressed and a homogenous, correlated, metallic state appears with an enhanced spin susceptibility and local moments. In conclusion, as the metallic state is stabilized, a weak structural distortion develops and suggests a competingmore » instability with the parent spin-orbit Mott state.« less
First-order melting of a weak spin-orbit mott insulator into a correlated metal
Hogan, Tom; Yamani, Z.; Walkup, D.; Chen, Xiang; Dally, Rebecca; Ward, Thomas Zac; Dean, M. P. M.; Hill, John P.; Islam, Z.; Madhavan, Vidya; Wilson, Stephen D.
2015-06-25
Herein, the electronic phase diagram of the weak spin-orbit Mott insulator (Sr_{1-x}La_{x})_{3}Ir_{2}O_{7} is determined via an exhaustive experimental study. Upon doping electrons via La substitution, an immediate collapse in resistivity occurs along with a narrow regime of nanoscale phase separation comprised of antiferromagnetic, insulating regions and paramagnetic, metallic puddles persisting until x≈0.04. Continued electron doping results in an abrupt, first-order phase boundary where the Néel state is suppressed and a homogenous, correlated, metallic state appears with an enhanced spin susceptibility and local moments. In conclusion, as the metallic state is stabilized, a weak structural distortion develops and suggests a competing instability with the parent spin-orbit Mott state.
First-Order Melting of a Weak Spin-Orbit Mott Insulator into a Correlated Metal
NASA Astrophysics Data System (ADS)
Hogan, Tom; Yamani, Z.; Walkup, D.; Chen, Xiang; Dally, Rebecca; Ward, Thomas Z.; Dean, M. P. M.; Hill, John; Islam, Z.; Madhavan, Vidya; Wilson, Stephen D.
2015-06-01
The electronic phase diagram of the weak spin-orbit Mott insulator (Sr1 -xLax)3Ir2O7 is determined via an exhaustive experimental study. Upon doping electrons via La substitution, an immediate collapse in resistivity occurs along with a narrow regime of nanoscale phase separation comprised of antiferromagnetic, insulating regions and paramagnetic, metallic puddles persisting until x ≈0.04 . Continued electron doping results in an abrupt, first-order phase boundary where the Néel state is suppressed and a homogenous, correlated, metallic state appears with an enhanced spin susceptibility and local moments. As the metallic state is stabilized, a weak structural distortion develops and suggests a competing instability with the parent spin-orbit Mott state.
Doping-dependent charge order correlations in electron-doped cuprates.
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. PMID:27536726
Doping-dependent charge order correlations in electron-doped cuprates
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
Explicitly correlated atomic orbital basis second order Møller-Plesset theory
NASA Astrophysics Data System (ADS)
Hollman, David S.; Wilke, Jeremiah J.; Schaefer, Henry F.
2013-02-01
The scope of problems treatable by ab initio wavefunction methods has expanded greatly through the application of local approximations. In particular, atomic orbital (AO) based wavefunction methods have emerged as powerful techniques for exploiting sparsity and have been applied to biomolecules as large as 1707 atoms [S. A. Maurer, D. S. Lambrecht, D. Flaig, and C. Ochsenfeld, J. Chem. Phys. 136, 144107 (2012)], 10.1063/1.3693908. Correlated wavefunction methods, however, converge notoriously slowly to the basis set limit and, excepting the use of large basis sets, will suffer from a severe basis set incompleteness error (BSIE). The use of larger basis sets is prohibitively expensive for AO basis methods since, for example, second-order Møller-Plesset perturbation theory (MP2) scales linearly with the number of atoms, but still scales as O(N^5) in the number of functions per atom. Explicitly correlated F12 methods have been shown to drastically reduce BSIE for even modestly sized basis sets. In this work, we therefore explore an atomic orbital based formulation of explicitly correlated MP2-F12 theory. We present working equations for the new method, which produce results identical to the widely used molecular orbital (MO) version of MP2-F12 without resorting to a delocalized MO basis. We conclude with a discussion of several possible approaches to a priori screening of contraction terms in our method and the prospects for a linear scaling implementation of AO-MP2-F12. The discussion includes concrete examples involving noble gas dimers and linear alkane chains.
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
Davis, J C Séamus; Lee, Dung-Hai
2013-10-29
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
COSMO-RSC: Second-Order Quasi-Chemical Theory Recovering Local Surface Correlation Effects.
Klamt, A
2016-03-31
The conductor-like screening model for realistic solvation (COSMO-RS) was introduced 20 years ago and meanwhile has become an important tool for the prediction of fluid phase equilibrium properties. Starting from quantum chemical information about the surface polarity of solutes and solvents, it solves the statistical thermodynamics of molecules in liquid phases by the very efficient approximation of independently pairwise interacting surfaces, which meanwhile was shown to be equivalent to Guggenheim's quasi-chemical theory. One of the basic limitations of COSMO-RS, as of any quasi-chemical model, is the neglect of neighbor information, i.e., of local correlations of surface types on the molecular surface. In this paper we present the completely novel concept of using the first-order COSMO-RS contact probabilities for the construction of local surface correlation functions. These are fed as an entropic correction for the pair interactions into a second COSMO-RS self-consistency loop, which yields new contact probabilities, enthalpies, free energies and activity coefficients recovering much of the originally lost neighbor effects. By a novel analytic correction for concentration dependent interactions, the resulting activity coefficients remain exactly Gibbs-Duhem consistent. The theory is demonstrated on the example of a lattice Monte Carlo fluid of dimerizing pseudomolecules. In this showcase the strong deviations of the lattice Monte Carlo fluid from quasi-chemical theory are almost perfectly reproduced by COSMO-RSC. PMID:26963690
Chakraborty, Saikat; Das, Subir K
2016-03-01
The dynamics of ordering in the Ising model, following quench to zero temperature, has been studied via Glauber spin-flip Monte Carlo simulations in space dimensions d=2 and 3. One of the primary objectives has been to understand phenomena associated with the persistent spins, viz., time decay in the number of unaffected spins, growth of the corresponding pattern, and its fractal dimensionality for varying correlation length in the initial configurations, prepared at different temperatures, at and above the critical value. It is observed that the fractal dimensionality and the exponent describing the power-law decay of persistence probability are strongly dependent upon the relative values of nonequilibrium domain size and the initial equilibrium correlation length. Via appropriate scaling analyses, these quantities have been estimated for quenches from infinite and critical temperatures. The above-mentioned dependence is observed to be less pronounced in the higher dimension. In addition to these findings for the local persistence, we present results for the global persistence as well. Furthermore, important observations on the standard domain growth problem are reported. For the latter, a controversy in d=3, related to the value of the exponent for the power-law growth of the average domain size with time, has been resolved. PMID:27078324
Fluctuation/correlation effects in symmetric diblock copolymers: on the order-disorder transition.
Zong, Jing; Wang, Qiang
2013-09-28
Using fast off-lattice Monte Carlo simulations with experimentally accessible fluctuations, we reported the first systematic study unambiguously quantifying the shift of the order-disorder transition (ODT) χ* of symmetric diblock copolymers from the mean-field prediction χ(MF)*. Our simulations are performed in a canonical ensemble with variable box lengths to eliminate the restriction of periodic boundary conditions on the lamellar period, and give the most accurate data of χ* and bulk lamellar period reported to date. Exactly the same model system (Hamiltonian) is used in both our simulations and mean-field theory; the ODT shift is therefore due to the fluctuations/correlations neglected by the latter. While χ*/χ(MF)*-1∝N(-k) is found with N denoting the invariant degree of polymerization, k decreases around the N-value corresponding to the face-centered cubic close packing of polymer segments as hard spheres, indicating the short-range correlation effects. PMID:24089804
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
... From the Federal Register Online via the Government Publishing Office SECURITIES AND EXCHANGE COMMISSION 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...
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2013-03-05
... February 6, 2013 (78 FR 8592). In response to new information received during the investigation of petition... Verizon Business Network Services, Inc., Senior Analysts-Order Management, Voice Over Internet Protocol... Management, Voice Over Internet Protocol, Small And Medium Business, Tampa, Florida; Verizon...
Correlation Networks from Flows. The Case of Forced and Time-Dependent Advection-Diffusion Dynamics.
Tupikina, Liubov; Molkenthin, Nora; López, Cristóbal; Hernández-García, Emilio; Marwan, Norbert; Kurths, Jürgen
2016-01-01
Complex network theory provides an elegant and powerful framework to statistically investigate different types of systems such as society, brain or the structure of local and long-range dynamical interrelationships in the climate system. Network links in climate networks typically imply information, mass or energy exchange. However, the specific connection between oceanic or atmospheric flows and the climate network's structure is still unclear. We propose a theoretical approach for verifying relations between the correlation matrix and the climate network measures, generalizing previous studies and overcoming the restriction to stationary flows. Our methods are developed for correlations of a scalar quantity (temperature, for example) which satisfies an advection-diffusion dynamics in the presence of forcing and dissipation. Our approach reveals that correlation networks are not sensitive to steady sources and sinks and the profound impact of the signal decay rate on the network topology. We illustrate our results with calculations of degree and clustering for a meandering flow resembling a geophysical ocean jet. PMID:27128846
Spin-orbit correlated magnetic order in honeycomb α-RuCl3
NASA Astrophysics Data System (ADS)
Venkataraman, Vijay Shankar; Kim, Heung-Sik; Kee, Hae-Young
2015-03-01
There has been a lot of recent interest in the combined effects of spin-orbit coupling (SOC) and electronic correlations in transition metal compounds. RuCl3 with layered honeycomb structure was proposed as a candidate material, where SOC boosts the electronic interaction, leading to an insulating phase. However, the role of SOC is not clear in materials with 4d-orbitals, since SOC strength is weaker than 5d-orbital materials. Here we study electronic band structures of honeycomb RuCl3 using ab-initio and tight binding methods, and estimate its SOC strength. We find that SOC in RuCl3 is not strong enough to justify an effective jeff = 1 / 2 single band unlike in the iridates. However, when electronic interactions are introduced, a magnetic order develops, and upper- and lower-Hubbard bands are characterized by jeff = 1 / 2 and 3 / 2 , respectively. Within a mean field theory with multi-orbital bands, we find that a zig-zag magnetic order is a ground state. Experimental implications are also discussed.
Spin state ordering of strongly correlating LaCoO3 induced at ultrahigh magnetic fields
NASA Astrophysics Data System (ADS)
Ikeda, Akihiko; Nomura, Toshihiro; Matsuda, Yasuhiro H.; Matsuo, Akira; Kindo, Koichi; Sato, Keisuke
2016-06-01
Magnetization measurements of LaCoO3 have been carried out up to 133 T, generated with a destructive pulse magnet at a wide temperature range from 2 to 120 K. A novel magnetic transition was found at B >100 T and T >T*=32 ±5 K, which is characterized by its transition field increasing with increasing temperature. At T
Strange Dynamics in a Fractional Derivative of Complex-Order Network of Chaotic Oscillators
NASA Astrophysics Data System (ADS)
Pinto, Carla M. A.
We study the peculiar dynamical features of a fractional derivative of complex-order network. The network is composed of two unidirectional rings of cells, coupled through a "buffer" cell. The network has a Z3 × Z5 cyclic symmetry group. The complex derivative Dα±jβ, with α, β ∈ R+ is a generalization of the concept of integer order derivative, where α = 1, β = 0. Each cell is modeled by the Chen oscillator. Numerical simulations of the coupled cell system associated with the network expose patterns such as equilibria, periodic orbits, relaxation oscillations, quasiperiodic motion, and chaos, in one or in two rings of cells. In addition, fixing β = 0.8, we perceive differences in the qualitative behavior of the system, as the parameter c ∈ [13, 24] of the Chen oscillator and/or the real part of the fractional derivative, α ∈ {0.5, 0.6, 0.7, 0.8, 0.9, 1.0}, are varied. Some patterns produced by the coupled system are constrained by the network architecture, but other features are only understood in the light of the internal dynamics of each cell, in this case, the Chen oscillator. What is more important, architecture and/or internal dynamics?
Finite-time synchronization of fractional-order memristor-based neural networks with time delays.
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. PMID:26547242
A novel nonlinear adaptive filter using a pipelined second-order Volterra recurrent neural network.
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. PMID:19523787
Correlation between weighted spectral distribution and average path length in evolving networks.
Jiao, Bo; Shi, Jianmai; Wu, Xiaoqun; Nie, Yuanping; Huang, Chengdong; Du, Jing; Zhou, Ying; Guo, Ronghua; Tao, Yerong
2016-02-01
The weighted spectral distribution (WSD) is a metric defined on the normalized Laplacian spectrum. In this study, synchronic random graphs are first used to rigorously analyze the metric's scaling feature, which indicates that the metric grows sublinearly as the network size increases, and the metric's scaling feature is demonstrated to be common in networks with Gaussian, exponential, and power-law degree distributions. Furthermore, a deterministic model of diachronic graphs is developed to illustrate the correlation between the slope coefficient of the metric's asymptotic line and the average path length, and the similarities and differences between synchronic and diachronic random graphs are investigated to better understand the correlation. Finally, numerical analysis is presented based on simulated and real-world data of evolving networks, which shows that the ratio of the WSD to the network size is a good indicator of the average path length. PMID:26931591
Correlation between weighted spectral distribution and average path length in evolving networks
NASA Astrophysics Data System (ADS)
Jiao, Bo; Shi, Jianmai; Wu, Xiaoqun; Nie, Yuanping; Huang, Chengdong; Du, Jing; Zhou, Ying; Guo, Ronghua; Tao, Yerong
2016-02-01
The weighted spectral distribution (WSD) is a metric defined on the normalized Laplacian spectrum. In this study, synchronic random graphs are first used to rigorously analyze the metric's scaling feature, which indicates that the metric grows sublinearly as the network size increases, and the metric's scaling feature is demonstrated to be common in networks with Gaussian, exponential, and power-law degree distributions. Furthermore, a deterministic model of diachronic graphs is developed to illustrate the correlation between the slope coefficient of the metric's asymptotic line and the average path length, and the similarities and differences between synchronic and diachronic random graphs are investigated to better understand the correlation. Finally, numerical analysis is presented based on simulated and real-world data of evolving networks, which shows that the ratio of the WSD to the network size is a good indicator of the average path length.
Inferring Meaningful Communities from Topology-Constrained Correlation Networks
Hleap, Jose Sergio; Blouin, Christian
2014-01-01
Community structure detection is an important tool in graph analysis. This can be done, among other ways, by solving for the partition set which optimizes the modularity scores . Here it is shown that topological constraints in correlation graphs induce over-fragmentation of community structures. A refinement step to this optimization based on Linear Discriminant Analysis (LDA) and a statistical test for significance is proposed. In structured simulation constrained by topology, this novel approach performs better than the optimization of modularity alone. This method was also tested with two empirical datasets: the Roll-Call voting in the 110th US Senate constrained by geographic adjacency, and a biological dataset of 135 protein structures constrained by inter-residue contacts. The former dataset showed sub-structures in the communities that revealed a regional bias in the votes which transcend party affiliations. This is an interesting pattern given that the 110th Legislature was assumed to be a highly polarized government. The -amylase catalytic domain dataset (biological dataset) was analyzed with and without topological constraints (inter-residue contacts). The results without topological constraints showed differences with the topology constrained one, but the LDA filtering did not change the outcome of the latter. This suggests that the LDA filtering is a robust way to solve the possible over-fragmentation when present, and that this method will not affect the results where there is no evidence of over-fragmentation. PMID:25409022
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.
Probing Defects and Correlations in the Hydrogen-Bond Network of ab Initio Water.
Gasparotto, Piero; Hassanali, Ali A; Ceriotti, Michele
2016-04-12
The hydrogen-bond network of water is characterized by the presence of coordination defects relative to the ideal tetrahedral network of ice, whose fluctuations determine the static and time-dependent properties of the liquid. Because of topological constraints, such defects do not come alone but are highly correlated coming in a plethora of different pairs. Here we discuss in detail such correlations in the case of ab initio water models and show that they have interesting similarities to regular and defective solid phases of water. Although defect correlations involve deviations from idealized tetrahedrality, they can still be regarded as weaker hydrogen bonds that retain a high degree of directionality. We also investigate how the structure and population of coordination defects is affected by approximations to the interatomic potential, finding that, in most cases, the qualitative features of the hydrogen-bond network are remarkably robust. PMID:26881726
Kanesaki, Takuma; Edwards, Carina M; Schwarz, Ulrich S; Grosshans, Jörg
2011-11-01
In syncytial embryos nuclei undergo cycles of division and rearrangement within a common cytoplasm. It is presently unclear to what degree and how the nuclear array maintains positional order in the face of rapid cell divisions. Here we establish a quantitative assay, based on image processing, for analysing the dynamics of the nuclear array. By tracking nuclear trajectories in Drosophila melanogaster embryos, we are able to define and evaluate local and time-dependent measures for the level of geometrical order in the array. We find that after division, order is re-established in a biphasic manner, indicating the competition of different ordering processes. Using mutants and drug injections, we show that the order of the nuclear array depends on cytoskeletal networks organised by centrosomes. While both f-actin and microtubules are required for re-establishing order after mitosis, only f-actin is required to maintain the stability of this arrangement. Furthermore, f-actin function relies on myosin-independent non-contractile filaments that suppress individual nuclear mobility, whereas microtubules promote mobility and attract adjacent nuclei. Actin caps are shown to act to prevent nuclear incorporation into adjacent microtubule baskets. Our data demonstrate that two principal ordering mechanisms thus simultaneously contribute: (1) a passive crowding mechanism in which nuclei and actin caps act as spacers and (2) an active self-organisation mechanism based on a microtubule network. PMID:22001900
Spatial and time correlation of thermometers and pluviometers in a weather network database
NASA Astrophysics Data System (ADS)
Tardivo, Gianmarco
2015-04-01
A basic issue that arises when analysing data bases from weather networks is the correlation system that characterizes the set of weather stations. Some statistical models being used for simulating temperature and precipitation or estimating missing data often exploit the Pearson's correlation coefficient, whereby a selection of predictors is carried out. In this paper, a specific analysis was made to understand the relationship between the distances (between the stations) and the correlation structure (of the network) and to assess the evolution of the stations ranking over the time from the network establishment, given that they were ranked on the basis of their correlation coefficient values with a target station. This study was first carried out over the whole of the Veneto region in Northeast Italy, and subsequently, it was repeated, subdividing the area into three main climatic zones: mountain, plain and coast. The variables that are involved in this study are the following: daily precipitation and daily maximum, mean and minimum temperature. Generally, the correlation coefficients of the database of precipitation are, on average, inversely proportional to the mean distances from the target station. Considering that the same behaviour was not detected on analysing the temperature database, the main results of this work can be summarized as follows: (1) the most correlated stations of precipitation are generally closer to a target station than the most correlated stations of temperature (entire area); (2) starting from 5.5 years after the network was established, the temperature variable is characterized by a high stability (over time) of the correlation rankings, up to a wide radius from the target station; (3) this trend is not so clear in precipitation data. However, when taking into account the first result, (4) generally, the most correlated stations are placed within the radius of stability, more frequently so for precipitation than for temperature.
Correlation Networks from Flows. The Case of Forced and Time-Dependent Advection-Diffusion Dynamics
Tupikina, Liubov; Molkenthin, Nora; López, Cristóbal; Hernández-García, Emilio; Marwan, Norbert; Kurths, Jürgen
2016-01-01
Complex network theory provides an elegant and powerful framework to statistically investigate different types of systems such as society, brain or the structure of local and long-range dynamical interrelationships in the climate system. Network links in climate networks typically imply information, mass or energy exchange. However, the specific connection between oceanic or atmospheric flows and the climate network’s structure is still unclear. We propose a theoretical approach for verifying relations between the correlation matrix and the climate network measures, generalizing previous studies and overcoming the restriction to stationary flows. Our methods are developed for correlations of a scalar quantity (temperature, for example) which satisfies an advection-diffusion dynamics in the presence of forcing and dissipation. Our approach reveals that correlation networks are not sensitive to steady sources and sinks and the profound impact of the signal decay rate on the network topology. We illustrate our results with calculations of degree and clustering for a meandering flow resembling a geophysical ocean jet. PMID:27128846
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.
Pressure-driven transformation of the ordering in amorphous network-forming materials
NASA Astrophysics Data System (ADS)
Zeidler, Anita; Salmon, Philip S.
2016-06-01
The pressure-induced changes to the structure of disordered oxide and chalcogenide network-forming materials are investigated on the length scales associated with the first three peaks in measured diffraction patterns. The density dependence of a given peak position does not yield the network dimensionality, in contrast to metallic glasses where the results indicate a fractal geometry with a local dimensionality of ≃5 /2 . For oxides, a common relation is found between the intermediate-range ordering, as described by the position of the first sharp diffraction peak, and the oxygen-packing fraction, a parameter that plays a key role in driving changes to the coordination number of local motifs. The first sharp diffraction peak can therefore be used to gauge when topological changes are likely to occur, events that transform network structures and their related physical properties.
Chu, Tianjiang; Sheng, Qiang; Wang, Sikai; Wu, Jihua
2014-01-01
Dendritic tidal creek networks are important habitats for sustaining biodiversity and ecosystem functioning in salt marsh wetlands. To evaluate the importance of creek heterogeneity in supporting benthic secondary production, we assess the spatial distribution and secondary production of a representative polychaete species (Dentinephtys glabra) in creek networks along a stream-order gradient in a Yangtze River estuarine marsh. Density, biomass, and secondary production of polychaetes were found to be highest in intermediate order creeks. In high order (3rd and 4th) creeks, the density and biomass of D. glabra were higher in creek edge sites than in creek bottom sites, whereas the reverse was true for low order (1st and 2nd) creeks. Secondary production was highest in 2nd order creeks (559.7 mg AFDM m−2 year−1) and was ca. 2 folds higher than in 1st and 4th order creeks. Top fitting AIC models indicated that the secondary production of D. glabra was mainly associated with geomorphological characters including cross-sectional area and bank slope. This suggests that hydrodynamic forces are essential factors influencing secondary production of macrobenthos in salt marshes. This study emphasizes the importance of microhabitat variability when evaluating secondary production and ecosystem functions. PMID:24817092
Stability Switches of Arbitrary High-Order Consensus in Multiagent Networks with Time Delays
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. PMID:24109207
NASA Astrophysics Data System (ADS)
Nazaripour, Hamid; Mansouri Daneshvar, Mohammad Reza
2016-06-01
The present study is aimed at evaluation of a rain gauge network in order to optimize a network design. In this regard, point rainfall estimations were assessed using a spatial correlation approach in the Kerman region, Iran. This approach was implemented based on monthly rainfall data for existing 117 rain gauge stations in the study area. The results revealed that the regular arrangement of rain gauges could provide the reliable values for accurate rainfall estimation. Low density of rain gauge combined with the low rainfall values may result in strong increase of the interpolation errors. Based on the existing rain gauge network, the relative mean error of observed rainfalls (E a ) is less than 5 % over the study area. The spatial interpolation errors (E i ) were considered to optimize the design of rain gauge network at the confidence level of 85 %, where the mean errors were exhibited from 8.5 to 14 % in districts A and B, respectively. On this basis, about 46 locations were proposed for allocation of new stations. Therefore, it was suggested to relocate about 20 existing stations in order to achieve an accurate design.
Magnetic reversal of an artificial square ice: dipolar correlation and charge ordering
NASA Astrophysics Data System (ADS)
Morgan, Jason; Stein, Aaron; Langridge, Sean; Marrows, Christopher
2012-02-01
Artificial spin ices are lithographically patterned arrays of single domain nanomagnets [1-4]. The elongated elements form a 2D system of interlinked vertices at which Ising-like dipole moments meet with incompatible interactions. They are directly analogous to 3D bulk spin ice materials [5]. We report on the magnetic reversal of an athermal artificial square ice pattern subject to a sequence of magnetic fields applied slightly off the diagonal symmetry axis, investigated via magnetic force microscopy of the remanent states that result [1]. From an initial diagonally polarised state, sublattice independent reversal is observed via bulk-nucleated incrementally-pinned flipped moment chains along parallel channels of magnetic elements, as evident from analysis of vertex populations and dipolar correlation functions. Weak dipolar interactions between adjacent chains favour antialignment and give rise to weak charge ordering of ``monopole'' vertices during reversal. [4pt] [1] J. P. Morgan, A. Stein, S. Langridge & C.H. Marrows, New Journal of Physics (2011), 13, 105002.[0pt] [2] R. F. Wang et al., Nature (2006), 439, 303-306.[0pt] [3] E. Mengotti et al., Nature Physics (2011), 7, 68-74.[0pt] [4] J. P. Morgan et al., Nature Physics (2011), 7, 75-79.[0pt] [5] M. J. Harris et al., PRL (1997), 79, 2554-255
Wang, Xiao; Broch, Katharina; Scholz, Reinhard; Schreiber, Frank; Meixner, Alfred J; Zhang, Dai
2014-04-01
Cylindrical vector beams, such as radially or azimuthally polarized doughnut beams, are combined with topography studies of pentacene thin films, allowing us to correlate Raman spectroscopy with intermolecular interactions depending on the particular pentacene polymorph. Polarization-dependent Raman spectra of the C-H bending vibrations are resolved layer by layer within a thin film of ∼20 nm thickness. The variation of the Raman peak positions indicates changes in the molecular orientation and in the local environment at different heights of the pentacene film. With the assistance of a theoretical model based on harmonic oscillator and perturbation theory, our method reveals the local structural order and the polymorph at different locations within the same pentacene thin film, depending mainly on its thickness. In good agreement with the crystallographic structures reported in the literature, our observations demonstrate that the first few monolayers grown in a structure are closer to the thin-film phase, but for larger film thicknesses, the morphology evolves toward the crystal-bulk phase with a larger tilting angle of the pentacene molecules against the substrate normal. PMID:26274447
The influence of age-age correlations on epidemic spreading in social network
NASA Astrophysics Data System (ADS)
Grabowski, Andrzej
2014-07-01
On the basis of the experimental data concerning interactions between humans the process of epidemic spreading in a social network was investigated. It was found that number of contact and average age of nearest neighbors are highly correlated with age of an individual. The influence of those correlations on the process of epidemic spreading and effectiveness of control measures like mass immunizations campaigns was investigated. It occurs that the magnitude of epidemic is decreased and the effectiveness of target vaccination is increased.
Co-occurrence correlations of heavy metals in sediments revealed using network analysis.
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. PMID:24559934
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.
On correlated reaction sets and coupled reaction sets in metabolic networks.
Marashi, Sayed-Amir; Hosseini, Zhaleh
2015-08-01
Two reactions are in the same "correlated reaction set" (or "Co-Set") if their fluxes are linearly correlated. On the other hand, two reactions are "coupled" if nonzero flux through one reaction implies nonzero flux through the other reaction. Flux correlation analysis has been previously used in the analysis of enzyme dysregulation and enzymopathy, while flux coupling analysis has been used to predict co-expression of genes and to model network evolution. The goal of this paper is to emphasize, through a few examples, that these two concepts are inherently different. In other words, except for the case of full coupling, which implies perfect correlation between two fluxes (R(2) = 1), there are no constraints on Pearson correlation coefficients (CC) in case of any other type of (un)coupling relations. In other words, Pearson CC can take any value between 0 and 1 in other cases. Furthermore, by analyzing genome-scale metabolic networks, we confirm that there are some examples in real networks of bacteria, yeast and human, which approve that flux coupling and flux correlation cannot be used interchangeably. PMID:25747383
Searchlight Correlation Detectors: Optimal Seismic Monitoring Using Regional and Global Networks
NASA Astrophysics Data System (ADS)
Gibbons, Steven J.; Kværna, Tormod; Näsholm, Sven Peter
2015-04-01
The sensitivity of correlation detectors increases greatly when the outputs from multiple seismic traces are considered. For single-array monitoring, a zero-offset stack of individual correlation traces will provide significant noise suppression and enhanced sensitivity for a source region surrounding the hypocenter of the master event. The extent of this region is limited only by the decrease in waveform similarity with increasing hypocenter separation. When a regional or global network of arrays and/or 3-component stations is employed, the zero-offset approach is only optimal when the master and detected events are co-located exactly. In many monitoring situations, including nuclear test sites and geothermal fields, events may be separated by up to many hundreds of meters while still retaining sufficient waveform similarity for correlation detection on single channels. However, the traveltime differences resulting from the hypocenter separation may result in significant beam loss on the zero-offset stack and a deployment of many beams for different hypothetical source locations in geographical space is required. The beam deployment necessary for optimal performance of the correlation detectors is determined by an empirical network response function which is most easily evaluated using the auto-correlation functions of the waveform templates from the master event. The correlation detector beam deployments for providing optimal network sensitivity for the North Korea nuclear test site are demonstrated for both regional and teleseismic monitoring configurations.
Ordering spatiotemporal chaos in discrete neural networks with small-world connections
NASA Astrophysics Data System (ADS)
Wei, Du Qu; Shu Luo, Xiao
2007-06-01
We investigate ordering of spatiotemporal chaos in two-dimensional map neuron (2DMN) networks with small-world (SW) connections, in which each neuron exhibits chaotic spiking-bursting behavior, focusing on the dependence of the spatiotemporal evolution on the topological randomness p. It is found that as the randomness p is increased, the chaotic spiking bursts become appreciably and more and more synchronized in space and coherent in time, and the maximal spatiotemporal order appears at a particular value of randomness p. However, if the randomness p is further increased, the temporal regularity is apparently destroyed, although spatial synchronization is enhanced. These phenomena imply that topological randomness can tame the spatiotemporal chaos in the 2DMN networks with SW connections. The comparison between this work and previous studies is made and it is found that the 2DMN network with small-world connections captures well the maximal spatiotemporal order. Our results may provide a useful tip for understanding the properties of collective dynamics in coupled real neurons.
Edge-pixel-based stereo correspondence through ordering-oriented neural networks
NASA Astrophysics Data System (ADS)
Siy, Pepe; Hu, Joe-E.
1993-09-01
This paper describes a fast and robust artificial neural network algorithm for solving the stereo correspondence problem in binocular vision. In this algorithm, the stereo correspondence problem is modelled as a cost minimization problem where the cost is the value of matching function between the edge pixels along the same epipolar line. A multiple-constraint energy minimization neural network is implemented for this matching process. This algorithm differs from previous works in that it integrates ordering, and geometry constraints in addition to uniqueness, continuity, and epipolar line constraint into a neural network implementation. The processing procedures are similar to that of human vision process. The edge pixels are divided into different clusters according to their orientation and contrast polarity. The matching is performed only between the edge pixels in the same clusters and at the same epipolar line. By following the epipolar line, the ordering constraint (the left-right relation between pixels) can be specified easily without building extra relational graph as in the earlier works. The algorithm thus assigns artificial neurons which follow the same order of the pixels along an epipolar line to represent the matching candidate pairs.
Choi, Wonshik; Lee, Moonjoo; Lee, Ye-Ryoung; Park, Changsoon; Lee, Jai-Hyung; An, Kyungwon; Fang-Yen, C.; Dasari, R. R.; Feld, M. S.
2005-08-15
A novel high-throughput second-order correlation measurement system is developed that records and makes use of all the arrival times of photons detected at both start and stop detectors. This system is suitable, particularly for a light source having a high photon flux and a long coherence time, since it is more efficient than conventional methods by an amount equal to the product of the count rate and the correlation time of the light source. We have used this system in carefully investigating the dead time effects of detectors and photon counters on the second-order correlation function in the two-detector configuration. For a nonstationary light source, a distortion of the original signal was observed at high photon flux. A systematic way of calibrating the second-order correlation function has been devised by introducing the concept of an effective dead time of the entire measurement system.
Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome
Technology Transfer Automated Retrieval System (TEKTRAN)
One of the challenges for systems biology approaches is that hundreds to thousands of variables are often measured for treatments with low replication, thus creating a multiple testing problem. Principal component analysis (PCA) and weighted correlation network analysis (WGCNA) are two complementary...
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…
Understanding the shift of correlation matrices during financial crisis: A network topology
NASA Astrophysics Data System (ADS)
Yusoff, Nur Syahidah; Sharif, Shamshuritawati
2015-02-01
Understanding the shift of correlation matrices in any process is not an easy task. From the literatures, the most popular and widely used test for correlation shift is Jennrich's test. This motivates us to use it in this paper. However, if after hypothesis testing the hypothesis of stable process correlation is rejected, then the next problem is to find out the root causes of that situation. In this paper, network topology approach will be used to understand the shift. A case study will be discussed and presented to illustrate the advantage of this approach.
NASA Astrophysics Data System (ADS)
Ross, Ashley J.; Brunner, Robert J.; Myers, Adam D.
2008-08-01
We present a novel technique with which to measure σ8. It relies on measuring the dependence of the second-order bias of a density field on σ8, using two separate techniques. Each technique employs area-averaged angular correlation functions (bar omegaN), one relying on the shape of bar omega2, the other relying on the amplitude of s3 (s3 = bar omega3/bar omega22). We confirm the validity of this method by testing it on a mock catalog drawn from Millennium Simulation data and finding a value of σ8 - σtrue8 = - 0.002 +/- 0.062. We create a catalog of photometrically selected LRGs from SDSS DR5 and separate it into three distinct data sets by photometric redshift, with median redshifts of 0.47, 0.53, and 0.61. Measurements of c2 and σ8 are made for each data set, with the assumption of a flat geometry and WMAP3 best-fit priors on Ωm, h, and Γ. We find, with increasing redshift, that c2 = 0.09 +/- 0.04, 0.09 +/- 0.05, and 0.09 +/- 0.03, and σ8 = 0.78 +/- 0.08, 0.80 +/- 0.09, and 0.80 +/- 0.09. We combine these three consistent σ8 measurements to produce σ8 = 0.79 +/- 0.05. Allowing the parameters Ωm, h, and Γ to vary within their WMAP3 1 σ error, we find that the best-fit value of σ8 does not change by more than 8%, and we are thus confident that our measurement is accurate to within 10%. We anticipate that future surveys, such as Pan-STARRS, DES, and LSST, will be able to employ this method in order to measure σ8 to great precision, and this will serve as an important check, complementarily, on the values determined via more established methods.
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
Zhang, Jinmai; Luo, Huajie; Liu, Hao; Ye, Wei; Luo, Ray; Chen, Hai-Feng
2016-01-01
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. PMID:27079666
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.
Dynamics Correlation Network for Allosteric Switching of PreQ1 Riboswitch.
Wang, Wei; Jiang, Cheng; Zhang, Jinmai; Ye, Wei; Luo, Ray; Chen, Hai-Feng
2016-01-01
Riboswitches are a class of metabolism control elements mostly found in bacteria. Due to their fundamental importance in bacteria gene regulation, riboswitches have been proposed as antibacterial drug targets. Prequeuosine (preQ1) is the last free precursor in the biosynthetic pathway of queuosine that is crucial for translation efficiency and fidelity. However, the regulation mechanism for the preQ1 riboswitch remains unclear. Here we constructed fluctuation correlation network based on all-atom molecular dynamics simulations to reveal the regulation mechanism. The results suggest that the correlation network in the bound riboswitch is distinctly different from that in the apo riboswitch. The community network indicates that the information freely transfers from the binding site of preQ1 to the expression platform of the P3 helix in the bound riboswitch and the P3 helix is a bottleneck in the apo riboswitch. Thus, a hypothesis of "preQ1-binding induced allosteric switching" is proposed to link riboswitch and translation regulation. The community networks of mutants support this hypothesis. Finally, a possible allosteric pathway of A50-A51-A52-U10-A11-G12-G56 was also identified based on the shortest path algorithm and confirmed by mutations and network perturbation. The novel fluctuation network analysis method can be used as a general strategy in studies of riboswitch structure-function relationship. PMID:27484311
Dynamics Correlation Network for Allosteric Switching of PreQ1 Riboswitch
Wang, Wei; Jiang, Cheng; Zhang, Jinmai; Ye, Wei; Luo, Ray; Chen, Hai-Feng
2016-01-01
Riboswitches are a class of metabolism control elements mostly found in bacteria. Due to their fundamental importance in bacteria gene regulation, riboswitches have been proposed as antibacterial drug targets. Prequeuosine (preQ1) is the last free precursor in the biosynthetic pathway of queuosine that is crucial for translation efficiency and fidelity. However, the regulation mechanism for the preQ1 riboswitch remains unclear. Here we constructed fluctuation correlation network based on all-atom molecular dynamics simulations to reveal the regulation mechanism. The results suggest that the correlation network in the bound riboswitch is distinctly different from that in the apo riboswitch. The community network indicates that the information freely transfers from the binding site of preQ1 to the expression platform of the P3 helix in the bound riboswitch and the P3 helix is a bottleneck in the apo riboswitch. Thus, a hypothesis of “preQ1-binding induced allosteric switching” is proposed to link riboswitch and translation regulation. The community networks of mutants support this hypothesis. Finally, a possible allosteric pathway of A50-A51-A52-U10-A11-G12-G56 was also identified based on the shortest path algorithm and confirmed by mutations and network perturbation. The novel fluctuation network analysis method can be used as a general strategy in studies of riboswitch structure-function relationship. PMID:27484311
NASA Astrophysics Data System (ADS)
Larios, Edgar; Pitera, Jed W.; Swope, William C.; Gruebele, Martin
2006-03-01
Experimental and computational Φ-value analysis of two-state helical proteins has shown that definite interactions among helix-forming segments build up in the transition state ensemble, but this type of analysis is not applicable to downhill folders. Here, we ask whether orientational ordering of helix-forming segments occurs early on during folding of a downhill λ 6-85 mutant, and how much it correlates with the thermodynamics and kinetics of various λ 6-85 mutants that do have folding barriers. From a grand total of 5 μs of implicit solvent replica-exchange molecular dynamics, we conclude that under folding conditions segments 1 and 4 form more helical structure and orient correctly relative to the native structure more often than do segments 2 and 3. Helices 1 and 2 retain the most residual structure and orientation at high temperatures. This is further supported by experimental data showing that perturbations in helices 1 and 4 of this well-designed folder affect folding kinetics and stability more sensitively than elsewhere in the protein, and that the helix 1-2 only bundle retains a cooperative melting transition and helical CD spectrum. The correct orientational propensity of helices 1 and 4 at low temperature is in agreement with the work by Takada, Portman and Wolynes proposing initial structure formation during folding in helices 1 and 4 of the wild-type λ 6-85 protein, a two-state folder. Thus, the absence of a large barrier in the downhill mutant does not fundamentally alter the steps the wild-type protein takes to fold.
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. PMID:27151776
Spectral and network methods in the analysis of correlation matrices of stock returns
NASA Astrophysics Data System (ADS)
Heimo, Tapio; Saramäki, Jari; Onnela, Jukka-Pekka; Kaski, Kimmo
2007-09-01
Correlation matrices inferred from stock return time series contain information on the behaviour of the market, especially on clusters of highly correlating stocks. Here we study a subset of New York Stock Exchange (NYSE) traded stocks and compare three different methods of analysis: (i) spectral analysis, i.e. investigation of the eigenvalue-eigenvector pairs of the correlation matrix, (ii) asset trees, obtained by constructing the maximal spanning tree of the correlation matrix, and (iii) asset graphs, which are networks in which the strongest correlations are depicted as edges. We illustrate and discuss the localisation of the most significant modes of fluctuation, i.e. eigenvectors corresponding to the largest eigenvalues, on the asset trees and graphs.
NASA Astrophysics Data System (ADS)
Pathak, Anand; Sinha, Sitabhra
2015-09-01
Many complex systems can be represented as networks of dynamical elements whose states evolve in response to interactions with neighboring elements, noise and external stimuli. The collective behavior of such systems can exhibit remarkable ordering phenomena such as chimera order corresponding to coexistence of ordered and disordered regions. Often, the interactions in such systems can also evolve over time responding to changes in the dynamical states of the elements. Link adaptation inspired by Hebbian learning, the dominant paradigm for neuronal plasticity, has been earlier shown to result in structural balance by removing any initial frustration in a system that arises through conflicting interactions. Here we show that the rate of the adaptive dynamics for the interactions is crucial in deciding the emergence of different ordering behavior (including chimera) and frustration in networks of Ising spins. In particular, we observe that small changes in the link adaptation rate about a critical value result in the system exhibiting radically different energy landscapes, viz., smooth landscape corresponding to balanced systems seen for fast learning, and rugged landscapes corresponding to frustrated systems seen for slow learning.
Curvature-induced crosshatched order in two-dimensional semiflexible polymer networks
NASA Astrophysics Data System (ADS)
Vrusch, Cyril; Storm, Cornelis
2015-12-01
A recurring motif in the organization of biological tissues are networks of long, fibrillar protein strands effectively confined to cylindrical surfaces. Often, the fibers in such curved, quasi-two-dimensional (2D) geometries adopt a characteristic order: the fibers wrap around the central axis at an angle which varies with radius and, in several cases, is strongly bimodally distributed. In this Rapid Communication, we investigate the general problem of a 2D crosslinked network of semiflexible fibers confined to a cylindrical substrate, and demonstrate that in such systems the trade-off between bending and stretching energies, very generically, gives rise to crosshatched order. We discuss its general dependency on the radius of the confining cylinder, and present an intuitive model that illustrates the basic physical principle of curvature-induced order. Our findings shed new light on the potential origin of some curiously universal fiber orientational distributions in tissue biology, and suggests novel ways in which synthetic polymeric soft materials may be instructed or programmed to exhibit preselected macromolecular ordering.
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.
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.
Correlation enhanced modularity-based belief propagation method for community detection in networks
NASA Astrophysics Data System (ADS)
Lai, Darong; Shu, Xin; Nardini, Christine
2016-05-01
Community structure is an important feature of networks, and the correct detection of communities is a fundamental problem in network analysis. Statistical inference has recently been proposed for successful detection, provided the number of communities can be appropriately estimated a priori. In the absence of such information, model selection by determination of the number of communities remains an issue. We show here that correlation between communities from a highly parceled partition can be used to estimate a narrow range of variation for the real number of communities. This range, further elaborated by modularity-based belief propagation, correctly identifies communities. Testing on synthetic networks generated by a stochastic block model and a set of real-world networks shows that our method can alleviate the effects of modularity fluctuations well and enhance the ability of community detection of the bare modularity-based belief propagation method.
Triangular Alignment (TAME). A Tensor-based Approach for Higher-order Network Alignment
Mohammadi, Shahin; Gleich, David F.; Kolda, Tamara G.; Grama, Ananth
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 Triangular 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.
Wrinkles of graphene on Ir(111): Macroscopic network ordering and internal multi-lobed structure
Petrovic, Marin; Sadowski, Jerzy T.; Siber, Antonio; Kralj, Marko
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
Wrinkles of graphene on Ir(111): Macroscopic network ordering and internal multi-lobed structure
Petrovic, Marin; Sadowski, Jerzy T.; Siber, Antonio; Kralj, Marko
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 most 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.
NASA Astrophysics Data System (ADS)
Huang, Xuan; An, Haizhong; Gao, Xiangyun; Hao, Xiaoqing; Liu, Pengpeng
2015-06-01
This study introduces an approach to study the multiscale transmission characteristics of the correlation modes between bivariate time series. The correlation between the bivariate time series fluctuates over time. The transmission among the correlation modes exhibits a multiscale phenomenon, which provides richer information. To investigate the multiscale transmission of the correlation modes, this paper describes a hybrid model integrating wavelet analysis and complex network theory to decompose and reconstruct the original bivariate time series into sequences in a joint time-frequency domain and defined the correlation modes at each time-frequency domain. We chose the crude oil spot and futures prices as the sample data. The empirical results indicate that the main duration of volatility (32-64 days) for the strongly positive correlation between the crude oil spot price and the futures price provides more useful information for investors. Moreover, the weighted degree, weighted indegree and weighted outdegree of the correlation modes follow power-law distributions. The correlation fluctuation strengthens the extent of persistence over the long term, whereas persistence weakens over the short and medium term. The primary correlation modes dominating the transmission process and the major intermediary modes in the transmission process are clustered both in the short and long term.
Percolation of spatially constrained Erdős-Rényi networks with degree correlations
NASA Astrophysics Data System (ADS)
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), 10.1073/pnas.0707492105], 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.
Geier, Christian; Lehnertz, Klaus; Bialonski, Stephan
2015-01-01
We investigate the long-term evolution of degree-degree correlations (assortativity) in functional brain networks from epilepsy patients. Functional networks are derived from continuous multi-day, multi-channel electroencephalographic data, which capture a wide range of physiological and pathophysiological activities. In contrast to previous studies which all reported functional brain networks to be assortative on average, even in case of various neurological and neurodegenerative disorders, we observe large fluctuations in time-resolved degree-degree correlations ranging from assortative to dissortative mixing. Moreover, in some patients these fluctuations exhibit some periodic temporal structure which can be attributed, to a large extent, to daily rhythms. Relevant aspects of the epileptic process, particularly possible pre-seizure alterations, contribute marginally to the observed long-term fluctuations. Our findings suggest that physiological and pathophysiological activity may modify functional brain networks in a different and process-specific way. We evaluate factors that possibly influence the long-term evolution of degree-degree correlations. PMID:26347641
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. PMID:18262579
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. PMID:25040229
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
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.
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.
Template-directed assembly of metal-chalcogenide nanocrystals into ordered mesoporous networks.
Vamvasakis, Ioannis; Subrahmanyam, Kota S.; Kanatzidis, Mercouri G.; Armatas, Gerasimos S.
2015-04-01
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 pore 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.
2016-01-01
Network controllability is an important topic in wide-ranging research fields. However, the relationship between controllability and network structure is poorly understood, although degree heterogeneity is known to determine the controllability. We focus on the size of a minimum dominating set (MDS), a measure of network controllability, and investigate the effect of degree-degree correlation, which is universally observed in real-world networks, on the size of an MDS. We show that disassortativity or negative degree-degree correlation reduces the size of an MDS using analytical treatments and numerical simulation, whereas positive correlations hardly affect the size of an MDS. This result suggests that disassortativity enhances network controllability. Furthermore, apart from the controllability issue, the developed techniques provide new ways of analyzing complex networks with degree-degree correlations. PMID:27327273
Correlated noise in networks of gravitational-wave detectors: Subtraction and mitigation
NASA Astrophysics Data System (ADS)
Thrane, E.; Christensen, N.; Schofield, R. M. S.; Effler, A.
2014-07-01
One of the key science goals of advanced gravitational-wave detectors is to observe a stochastic gravitational-wave background. However, recent work demonstrates that correlated magnetic fields from Schumann resonances can produce correlated strain noise over global distances, potentially limiting the sensitivity of stochastic background searches with advanced detectors. In this paper, we estimate the correlated noise budget for the worldwide advanced detector network and conclude that correlated noise may affect upcoming measurements. We investigate the possibility of a Wiener filtering scheme to subtract correlated noise from Advanced LIGO searches, and estimate the required specifications. We also consider the possibility that residual correlated noise remains following subtraction, and we devise an optimal strategy for measuring astronomical parameters in the presence of correlated noise. Using this new formalism, we estimate the loss of sensitivity for a broadband, isotropic stochastic background search using 1 yr of LIGO data at design sensitivity. Given our current noise budget, the uncertainty with which LIGO can estimate energy density will likely increase by a factor of ≈12—if it is impossible to achieve significant subtraction. Additionally, narrow band cross-correlation searches may be severely affected at low frequencies f ≲70 Hz without effective subtraction.
HIERtalker: A default hierarchy of high order neural networks that learns to read English aloud
An, Z.G.; Mniszewski, S.M.; Lee, Y.C.; Papcun, G.; Doolen, G.D.
1988-01-01
A new learning algorithm based on a default hierarchy of high order neural networks has been developed that is able to generalize as well as handle exceptions. It learns the ''building blocks'' or clusters of symbols in a stream that appear repeatedly and convey certain messages. The default hierarchy prevents a combinatoric explosion of rules. A simulator of such a hierarchy, HIERtalker, has been applied to the conversion of English words to phonemes. Achieved accuracy is 99% for trained words and ranges from 76% to 96% for sets of new words. 8 refs., 4 figs., 1 tab.
Correlation structure analysis for distributed video compression over wireless video sensor networks
NASA Astrophysics Data System (ADS)
He, Zhihai; Chen, Xi
2006-01-01
From the information-theoretic perspective, as stated by the Wyner-Ziv theorem, the distributed source encoder doesn't need any knowledge about its side information in achieving the R-D performance limit. However, from the system design and performance analysis perspective, correlation modeling plays an important role in analysis, control, and optimization of the R-D behavior of the Wyner-Ziv video coding In this work, we observe that videos captured from a wireless video sensor network (WVSN) are uniquely correlated under the multi-view geometry. We propose to utilize this computer vision principal, as well as other existing information, which is already available or can be easily obtained from the encoder, to estimate the source correlation structure. The source correlation determines the R-D behavior of the Wyner-Ziv encoder, and provide useful information for rate control and performance optimization of the Wyner-Ziv encoder.
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.
Moslonka-Lefebvre, Mathieu; Pautasso, Marco; Jeger, Mike J
2009-10-01
Network epidemiology has mainly focused on large-scale complex networks. It is unclear whether findings of these investigations also apply to networks of small size. This knowledge gap is of relevance for many biological applications, including meta-communities, plant-pollinator interactions and the spread of the oomycete pathogen Phytophthora ramorum in networks of plant nurseries. Moreover, many small-size biological networks are inherently asymmetrical and thus cannot be realistically modelled with undirected networks. We modelled disease spread and establishment in directed networks of 100 and 500 nodes at four levels of connectance in six network structures (local, small-world, random, one-way, uncorrelated, and two-way scale-free networks). The model was based on the probability of infection persistence in a node and of infection transmission between connected nodes. Regardless of the size of the network, the epidemic threshold did not depend on the starting node of infection but was negatively related to the correlation coefficient between in- and out-degree for all structures, unless networks were sparsely connected. In this case clustering played a significant role. For small-size scale-free directed networks to have a lower epidemic threshold than other network structures, there needs to be a positive correlation between number of links to and from nodes. When this correlation is negative (one-way scale-free networks), the epidemic threshold for small-size networks can be higher than in non-scale-free networks. Clustering does not necessarily have an influence on the epidemic threshold if connectance is kept constant. Analyses of the influence of the clustering on the epidemic threshold in directed networks can also be spurious if they do not consider simultaneously the effect of the correlation coefficient between in- and out-degree. PMID:19545575
Parallel access alignment network with barrel switch implementation for d-ordered vector elements
NASA Technical Reports Server (NTRS)
Barnes, George H. (Inventor)
1980-01-01
An alignment network between N parallel data input ports and N parallel data outputs includes a first and a second barrel switch. The first barrel switch fed by the N parallel input ports shifts the N outputs thereof and in turn feeds the N-1 input data paths of the second barrel switch according to the relationship X=k.sup.y modulo N wherein x represents the output data path ordering of the first barrel switch, y represents the input data path ordering of the second barrel switch, and k equals a primitive root of the number N. The zero (0) ordered output data path of the first barrel switch is fed directly to the zero ordered output port. The N-1 output data paths of the second barrel switch are connected to the N output ports in the reverse ordering of the connections between the output data paths of the first barrel switch and the input data paths of the second barrel switch. The second switch is controlled by a value m, which in the preferred embodiment is produced at the output of a ROM addressed by the value d wherein d represents the incremental spacing or distance between data elements to be accessed from the N input ports, and m is generated therefrom according to the relationship d=k.sup.m modulo N.
Effects of correlation among stored patterns on associative dynamics of chaotic neural network
NASA Astrophysics Data System (ADS)
Iwai, Toshiya; Matsuzaki, Fuminari; Kuroiwa, Jousuke; Miyake, Shogo
2005-12-01
We numerically investigate the effects of correlation among stored patterns on the associative dynamics in a chaotic neural network model. In the model, there are two kinds of parameters: one is a measure of the Hopfield like behavior of the retrieval process and another controls the chaotic behavior. The parameter dependence of the associative dynamics is also examined. The following results are found. (i) Two dimensional parameter space is divided into two kinds of associative states by a distinct boundary. One is the retrieval state of the association such as the Hopfield like retrieval state, and another is the wandering state of the associative dynamics where the network retrieves stored patterns and their reverse patterns. (ii) The area of the wandering state becomes larger as the degree of correlation becomes larger. (iii) As the degree of correlation becomes larger, both the recall ratio of correlated patterns and the transition frequency between correlated patterns becomes larger in the wandering state. (iv) The whole region of the wandering state in the parameter space is not necessarily chaotic from the view point of the Lyapunov dimension, but most of the region of the wandering state is chaotic.
2010-01-01
Background For large-scale biological networks represented as signed graphs, the index of frustration measures how far a network is from a monotone system, i.e., how incoherently the system responds to perturbations. Results In this paper we find that the frustration is systematically lower in transcriptional networks (modeled at functional level) than in signaling and metabolic networks (modeled at stoichiometric level). A possible interpretation of this result is in terms of energetic cost of an interaction: an erroneous or contradictory transcriptional action costs much more than a signaling/metabolic error, and therefore must be avoided as much as possible. Averaging over all possible perturbations, however, we also find that unlike for transcriptional networks, in the signaling/metabolic networks the probability of finding the system in its least frustrated configuration tends to be high also in correspondence of a moderate energetic regime, meaning that, in spite of the higher frustration, these networks can achieve a globally ordered response to perturbations even for moderate values of the strength of the interactions. Furthermore, an analysis of the energy landscape shows that signaling and metabolic networks lack energetic barriers around their global optima, a property also favouring global order. Conclusion In conclusion, transcriptional and signaling/metabolic networks appear to have systematic differences in both the index of frustration and the transition to global order. These differences are interpretable in terms of the different functions of the various classes of networks. PMID:20537143
Adler, Thomas B; Werner, Hans-Joachim; Manby, Frederick R
2009-02-01
A local explicitly correlated LMP2-F12 method is described that can be applied to large molecules. The steep scaling of computer time with molecular size is reduced by the use of local approximations, the scaling with respect to the basis set size per atom is improved by density fitting, and the slow convergence of the correlation energy with orbital basis size is much accelerated by the introduction of terms into the wave function that explicitly depend on the interelectronic distance. The local approximations lead to almost linear scaling of the computational effort with molecular size without much affecting the accuracy. At the same time, the domain error of conventional LMP2 is removed in LMP2-F12. LMP2-F12 calculations on molecules of chemical interest involving up to 80 atoms, 200 correlated electrons, and 2600 contracted Gaussian-type orbitals, as well as several reactions of large biochemical molecules are reported. PMID:19206957
Grafting of higher-order correlations of real financial markets into herding models
NASA Astrophysics Data System (ADS)
Ahn, Sanghyun; Lim, Gyuchang; Kim, Sooyong; Kim, Kyungsik
2009-08-01
In this work, we graft the volatility clustering observed in empirical financial time series into the Equiluz and Zimmermann (EZ) model, which was introduced to reproduce the herding behaviors of a financial time series. The original EZ model failed to reproduce the empirically observed power-law exponents of real financial data. The EZ model ordinarily produces a more fat-tailed distribution compared to real data, and a long-range correlation of absolute returns that underlie the volatility clustering. As it is not appropriate to capture the empirically observed correlations in a modified EZ model, we apply a sorting method to incorporate the nonlinear correlation structure of a real financial time series into the generated returns. By doing so, we observe that the slow convergence of distribution of returns is well established for returns generated from the EZ model and its modified version. It is also found that the modified EZ model leads to a less fat-tailed distribution.
The Second Order Approximation to Sample Influence Curve in Canonical Correlation Analysis.
ERIC Educational Resources Information Center
Fung, Wing K.; Gu, Hong
1998-01-01
A second order approximation to the sample influence curve (SIC) has been derived in the literature. This paper presents a more accurate second order approximation, which is exact for the SIC of the squared multiple correction coefficient. An example is presented. (SLD)
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. PMID:26874968
Tureau, Maëva S.; Kuan, Wei-Fan; Rong, Lixia; Hsiao, Benjamin S.; Epps, III, Thomas H.
2015-10-15
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 variety of triblock copolymer systems for nanotemplating and nanoscale separation applications requiring nanoscale feature sizes and/or high areal feature densities.
Solving the Dynamic Correlation Problem of the Susceptible-Infected-Susceptible Model on Networks.
Cai, Chao-Ran; Wu, Zhi-Xi; Chen, Michael Z Q; Holme, Petter; Guan, Jian-Yue
2016-06-24
The susceptible-infected-susceptible (SIS) model is a canonical model for emerging disease outbreaks. Such outbreaks are naturally modeled as taking place on networks. A theoretical challenge in network epidemiology is the dynamic correlations coming from that if one node is infected, then its neighbors are likely to be infected. By combining two theoretical approaches-the heterogeneous mean-field theory and the effective degree method-we are able to include these correlations in an analytical solution of the SIS model. We derive accurate expressions for the average prevalence (fraction of infected) and epidemic threshold. We also discuss how to generalize the approach to a larger class of stochastic population models. PMID:27391759
Solving the Dynamic Correlation Problem of the Susceptible-Infected-Susceptible Model on Networks
NASA Astrophysics Data System (ADS)
Cai, Chao-Ran; Wu, Zhi-Xi; Chen, Michael Z. Q.; Holme, Petter; Guan, Jian-Yue
2016-06-01
The susceptible-infected-susceptible (SIS) model is a canonical model for emerging disease outbreaks. Such outbreaks are naturally modeled as taking place on networks. A theoretical challenge in network epidemiology is the dynamic correlations coming from that if one node is infected, then its neighbors are likely to be infected. By combining two theoretical approaches—the heterogeneous mean-field theory and the effective degree method—we are able to include these correlations in an analytical solution of the SIS model. We derive accurate expressions for the average prevalence (fraction of infected) and epidemic threshold. We also discuss how to generalize the approach to a larger class of stochastic population models.
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. PMID:26655372
Word sense disambiguation via high order of learning in complex networks
NASA Astrophysics Data System (ADS)
Silva, Thiago C.; Amancio, Diego R.
2012-06-01
Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model.
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.
“Pure chaotic” orbits of one-dimensional maps have third-order correlation
NASA Astrophysics Data System (ADS)
Tsuchiya, Takashi; Ichimura, Atsushi
1984-04-01
The triple-time-correlation function for the logistic map and the tent map, both for the uppermost values of their parameters, are calculated analytically and orbits of these systems are shown to be different from a pseudo-random-number sequence generated on a computer. Violation of the time-reversal invariance by these orbits is also discussed.
Sabri, Mohammad Mahdi; Adibi, Mehdi; Arabzadeh, Ehsan
2016-01-01
To study the spatiotemporal dynamics of neural activity in a cortical population, we implanted a 10 × 10 microelectrode array in the vibrissal cortex of urethane-anesthetized rats. We recorded spontaneous neuronal activity as well as activity evoked in response to sustained and brief sensory stimulation. To quantify the temporal dynamics of activity, we computed the probability distribution function (PDF) of spiking on one electrode given the observation of a spike on another. The spike-triggered PDFs quantified the strength, temporal delay, and temporal precision of correlated activity across electrodes. Nearby cells showed higher levels of correlation at short delays, whereas distant cells showed lower levels of correlation, which tended to occur at longer delays. We found that functional space built based on the strength of pairwise correlations predicted the anatomical arrangement of electrodes. Moreover, the correlation profile of electrode pairs during spontaneous activity predicted the “signal” and “noise” correlations during sensory stimulation. Finally, mutual information analyses revealed that neurons with stronger correlations to the network during spontaneous activity, conveyed higher information about the sensory stimuli in their evoked response. Given the 400-μm-distance between adjacent electrodes, our functional quantifications unravel the spatiotemporal dynamics of activity among nearby and distant cortical columns. PMID:27458347
Sabri, Mohammad Mahdi; Adibi, Mehdi; Arabzadeh, Ehsan
2016-01-01
To study the spatiotemporal dynamics of neural activity in a cortical population, we implanted a 10 × 10 microelectrode array in the vibrissal cortex of urethane-anesthetized rats. We recorded spontaneous neuronal activity as well as activity evoked in response to sustained and brief sensory stimulation. To quantify the temporal dynamics of activity, we computed the probability distribution function (PDF) of spiking on one electrode given the observation of a spike on another. The spike-triggered PDFs quantified the strength, temporal delay, and temporal precision of correlated activity across electrodes. Nearby cells showed higher levels of correlation at short delays, whereas distant cells showed lower levels of correlation, which tended to occur at longer delays. We found that functional space built based on the strength of pairwise correlations predicted the anatomical arrangement of electrodes. Moreover, the correlation profile of electrode pairs during spontaneous activity predicted the "signal" and "noise" correlations during sensory stimulation. Finally, mutual information analyses revealed that neurons with stronger correlations to the network during spontaneous activity, conveyed higher information about the sensory stimuli in their evoked response. Given the 400-μm-distance between adjacent electrodes, our functional quantifications unravel the spatiotemporal dynamics of activity among nearby and distant cortical columns. PMID:27458347
Effects of temporal correlations on cascades: Threshold models on temporal networks
NASA Astrophysics Data System (ADS)
Backlund, Ville-Pekka; Saramäki, Jari; Pan, Raj Kumar
2014-06-01
A person's decision to adopt an idea or product is often driven by the decisions of peers, mediated through a network of social ties. A common way of modeling adoption dynamics is to use threshold models, where a node may become an adopter given a high enough rate of contacts with adopted neighbors. We study the dynamics of threshold models that take both the network topology and the timings of contacts into account, using empirical contact sequences as substrates. The models are designed such that adoption is driven by the number of contacts with different adopted neighbors within a chosen time. We find that while some networks support cascades leading to network-level adoption, some do not: the propagation of adoption depends on several factors from the frequency of contacts to burstiness and timing correlations of contact sequences. More specifically, burstiness is seen to suppress cascade sizes when compared to randomized contact timings, while timing correlations between contacts on adjacent links facilitate cascades.
Nonlocal Coulomb Correlations in Metals Close to a Charge Order Insulator Transition
NASA Astrophysics Data System (ADS)
Merino, Jaime
2007-07-01
The charge ordering transition induced by the nearest-neighbor Coulomb repulsion V in the 1/4-filled extended Hubbard model is investigated using cellular dynamical mean-field theory. We find a transition to a strongly renormalized charge ordered Fermi liquid at VCO and a metal-to-insulator transition at VMI>VCO. Short range antiferromagnetism occurs concomitantly with the CO transition. Approaching the charge ordered insulator, V≲VMI, the Fermi surface deforms and the scattering rate of electrons develops momentum dependence on the Fermi surface.
A second-order learning algorithm for multilayer networks based on block Hessian matrix.
Wang, Yi Jen; Lin, Chin Teng
1998-12-01
This article proposes a new second-order learning algorithm for training the multilayer perceptron (MLP) networks. The proposed algorithm is a revised Newton's method. A forward-backward propagation scheme is first proposed for network computation of the Hessian matrix, H, of the output error function of the MLP. A block Hessian matrix, H(b), is then defined to approximate and simplify H. Several lemmas and theorems are proved to uncover the important properties of H and H(b), and verify the good approximation of H(b) to H; H(b) preserves the major properties of H. The theoretic analysis leads to the development of an efficient way for computing the inverse of H(b) recursively. In the proposed second-order learning algorithm, the least squares estimation technique is adopted to further lessen the local minimum problems. The proposed algorithm overcomes not only the drawbacks of the standard backpropagation algorithm (i.e. slow asymptotic convergence rate, bad controllability of convergence accuracy, local minimum problems, and high sensitivity to learning constant), but also the shortcomings of normal Newton's method used on the MLP, such as the lack of network implementation of H, ill representability of the diagonal terms of H, the heavy computation load of the inverse of H, and the requirement of a good initial estimate of the solution (weights). Several example problems are used to demonstrate the efficiency of the proposed learning algorithm. Extensive performance (convergence rate and accuracy) comparisons of the proposed algorithm with other learning schemes (including the standard backpropagation algorithm) are also made. PMID:12662732
Granado, E; Lynn, J W; Jardim, R F; Torikachvili, M S
2013-01-01
Neutron diffraction on the double perovskite Sr(2)YRuO(6) with a quasi-fcc lattice of Ru moments reveals planar magnetic correlations that condense into a partial long-range ordered state with coupled alternate antiferromagnetic (AFM) YRuO(4) square layers coexisting with the short-range correlations below T(N1) = 32 K. A second transition to a fully ordered AFM state below T(N2) = 24 K is observed. The reduced dimensionality of the spin correlations is arguably due to a cancellation of the magnetic coupling between consecutive AFM square layers in fcc antiferromagnets, which is the simplest three-dimensional frustrated magnet model system. PMID:23383833
Barzegaran, Elham; Joudaki, Amir; Jalili, Mahdi; Rossetti, Andrea O; Frackowiak, Richard S; Knyazeva, Maria G
2012-01-01
Abnormalities in the topology of brain networks may be an important feature and etiological factor for psychogenic non-epileptic seizures (PNES). To explore this possibility, we applied a graph theoretical approach to functional networks based on resting state EEGs from 13 PNES patients and 13 age- and gender-matched controls. The networks were extracted from Laplacian-transformed time-series by a cross-correlation method. PNES patients showed close to normal local and global connectivity and small-world structure, estimated with clustering coefficient, modularity, global efficiency, and small-worldness (SW) metrics, respectively. Yet the number of PNES attacks per month correlated with a weakness of local connectedness and a skewed balance between local and global connectedness quantified with SW, all in EEG alpha band. In beta band, patients demonstrated above-normal resiliency, measured with assortativity coefficient, which also correlated with the frequency of PNES attacks. This interictal EEG phenotype may help improve differentiation between PNES and epilepsy. The results also suggest that local connectivity could be a target for therapeutic interventions in PNES. Selective modulation (strengthening) of local connectivity might improve the skewed balance between local and global connectivity and so prevent PNES events. PMID:23267325
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
Daraselia, Nikolai; Yuryev, Anton; Egorov, Sergei; Mazo, Ilya; Ispolatov, Iaroslav
2007-01-01
and size of GO groups without any noticeable decrease of the link density within the groups indicated that this expansion significantly broadens the public GO annotation without diluting its quality. We revealed that functional GO annotation correlates mostly with clustering in a physical interaction protein network, while its overlap with indirect regulatory network communities is two to three times smaller. Conclusion Protein functional annotations extracted by the NLP technology expand and enrich the existing GO annotation system. The GO functional modularity correlates mostly with the clustering in the physical interaction network, suggesting that the essential role of structural organization maintained by these interactions. Reciprocally, clustering of proteins in physical interaction networks can serve as an evidence for their functional similarity. PMID:17620146
Federal Register 2010, 2011, 2012, 2013, 2014
2012-05-08
... From the Federal Register Online via the Government Publishing Office ] SECURITIES AND EXCHANGE COMMISSION Order of Suspension of Trading; In the Matter of Anthracite Capital, Inc., Auto Data Network Inc... lack of current and accurate information concerning the securities of Auto Data Network Inc. because...
Berkovich-Ohana, Aviva; Harel, Michal; Hahamy, Avital; Arieli, Amos; Malach, Rafael
2016-09-01
FMRI data described here was recorded during resting-state in Mindfulness Meditators (MM) and control participants (see "Task-induced activity and resting-state fluctuations undergo similar alterations in visual and DMN areas of long-term meditators" Berkovich-Ohana et al. (2016) [1] for details). MM participants were also scanned during meditation. Analyses focused on functional connectivity within and between the default mode network (DMN) and visual network (Vis). Here we show data demonstrating that: 1) Functional connectivity within the DMN and the Visual networks were higher in the control group than in the meditators; 2) Data show an increase for the functional connectivity between the DMN and the Visual networks in the meditators compared to controls; 3) Data demonstrate that functional connectivity both within and between networks reduces during meditation, compared to the resting-state; and 4) A significant negative correlation was found between DMN functional connectivity and meditation expertise. The reader is referred to Berkovich-Ohana et al. (2016) [1] for further interpretation and discussion. PMID:27508242
Cosplicing network analysis of mammalian brain RNA-Seq data utilizing WGCNA and Mantel correlations.
Iancu, Ovidiu D; Colville, Alexandre; Oberbeck, Denesa; Darakjian, Priscila; McWeeney, Shannon K; Hitzemann, Robert
2015-01-01
Across species and tissues and especially in the mammalian brain, production of gene isoforms is widespread. While gene expression coordination has been previously described as a scale-free coexpression network, the properties of transcriptome-wide isoform production coordination have been less studied. Here we evaluate the system-level properties of cosplicing in mouse, macaque, and human brain gene expression data using a novel network inference procedure. Genes are represented as vectors/lists of exon counts and distance measures sensitive to exon inclusion rates quantifies differences across samples. For all gene pairs, distance matrices are correlated across samples, resulting in cosplicing or cotranscriptional network matrices. We show that networks including cosplicing information are scale-free and distinct from coexpression. In the networks capturing cosplicing we find a set of novel hubs with unique characteristics distinguishing them from coexpression hubs: heavy representation in neurobiological functional pathways, strong overlap with markers of neurons and neuroglia, long coding lengths, and high number of both exons and annotated transcripts. Further, the cosplicing hubs are enriched in genes associated with autism spectrum disorders. Cosplicing hub homologs across eukaryotes show dramatically increasing intronic lengths but stable coding region lengths. Shared transcription factor binding sites increase coexpression but not cosplicing; the reverse is true for splicing-factor binding sites. Genes with protein-protein interactions have strong coexpression and cosplicing. Additional factors affecting the networks include shared microRNA binding sites, spatial colocalization within the striatum, and sharing a chromosomal folding domain. Cosplicing network patterns remain relatively stable across species. PMID:26029240
Combined topological and Landau order from strong correlations in Chern bands
NASA Astrophysics Data System (ADS)
Daghofer, Maria; Kourtis, Stefanos
2014-03-01
In recent years, topologically nontrivial and nearly dispersionless bands have attracted attention as hosts for states analogous to fractional quantum-Hall states, but without a magnetic field. Indeed, such fractional Chern insulators were found and connections to fractional quantum-Hall states in Landau levels were established. We discuss here aspects where fractional Chern insulators differ from Landau levels. In particular, we present a class of states where both topological order and symmetry breaking arise spontaneously: the states show both fractional Hall conductivity and charge order. This coexistence of topological and conventional Landau order relies on the geometric frustration of the underlying lattice and consequently goes qualitatively beyond physics found in continuous Landau levels with their weak lattice. Supported by the Emmy-Noether program of the Deutsche Forschungsgemeinschaft (DFG).
Symmetry and correlations underlying hidden order in URu2Si2
NASA Astrophysics Data System (ADS)
Butch, Nicholas P.; Manley, Michael E.; Jeffries, Jason R.; Janoschek, Marc; Huang, Kevin; Maple, M. Brian; Said, Ayman H.; Leu, Bogdan M.; Lynn, Jeffrey W.
2015-01-01
We experimentally investigate the symmetry in the hidden order (HO) phase of intermetallic URu2Si2 by mapping the lattice and magnetic excitations via inelastic neutron and x-ray scattering measurements in the HO and high-temperature paramagnetic phases. At all temperatures, the excitations respect the zone edges of the body-centered tetragonal paramagnetic phase, showing no signs of reduced spatial symmetry, even in the HO phase. The magnetic excitations originate from transitions between hybridized bands and track the Fermi surface, whose features are corroborated by the phonon measurements. Due to a large hybridization energy scale, a full uranium moment persists in the HO phase, consistent with a lack of observed crystal-field-split states. Our results are inconsistent with local order-parameter models and the behavior of typical density waves. We suggest that an order parameter that does not break spatial symmetry would naturally explain these characteristics.
In Vivo Flow Mapping in Complex Vessel Networks by Single Image Correlation
Sironi, Laura; Bouzin, Margaux; Inverso, Donato; D'Alfonso, Laura; Pozzi, Paolo; Cotelli, Franco; Guidotti, Luca G.; Iannacone, Matteo; Collini, Maddalena; Chirico, Giuseppe
2014-01-01
We describe a novel method (FLICS, FLow Image Correlation Spectroscopy) to extract flow speeds in complex vessel networks from a single raster-scanned optical xy-image, acquired in vivo by confocal or two-photon excitation microscopy. Fluorescent flowing objects produce diagonal lines in the raster-scanned image superimposed to static morphological details. The flow velocity is obtained by computing the Cross Correlation Function (CCF) of the intensity fluctuations detected in pairs of columns of the image. The analytical expression of the CCF has been derived by applying scanning fluorescence correlation concepts to drifting optically resolved objects and the theoretical framework has been validated in systems of increasing complexity. The power of the technique is revealed by its application to the intricate murine hepatic microcirculatory system where blood flow speed has been mapped simultaneously in several capillaries from a single xy-image and followed in time at high spatial and temporal resolution. PMID:25475129
In Vivo Flow Mapping in Complex Vessel Networks by Single Image Correlation
NASA Astrophysics Data System (ADS)
Sironi, Laura; Bouzin, Margaux; Inverso, Donato; D'Alfonso, Laura; Pozzi, Paolo; Cotelli, Franco; Guidotti, Luca G.; Iannacone, Matteo; Collini, Maddalena; Chirico, Giuseppe
2014-12-01
We describe a novel method (FLICS, FLow Image Correlation Spectroscopy) to extract flow speeds in complex vessel networks from a single raster-scanned optical xy-image, acquired in vivo by confocal or two-photon excitation microscopy. Fluorescent flowing objects produce diagonal lines in the raster-scanned image superimposed to static morphological details. The flow velocity is obtained by computing the Cross Correlation Function (CCF) of the intensity fluctuations detected in pairs of columns of the image. The analytical expression of the CCF has been derived by applying scanning fluorescence correlation concepts to drifting optically resolved objects and the theoretical framework has been validated in systems of increasing complexity. The power of the technique is revealed by its application to the intricate murine hepatic microcirculatory system where blood flow speed has been mapped simultaneously in several capillaries from a single xy-image and followed in time at high spatial and temporal resolution.
NASA Astrophysics Data System (ADS)
Thrane, E.; Christensen, N.; Schofield, R. M. S.
2013-06-01
One of the most ambitious goals of gravitational-wave astronomy is to observe the stochastic gravitational-wave background. Correlated noise in two or more detectors can introduce a systematic error, which limits the sensitivity of stochastic searches. We report on measurements of correlated magnetic noise from Schumann resonances at the widely separated LIGO and Virgo detectors. We investigate the effect of this noise on a global network of gravitational-wave detectors and derive a constraint on the allowable coupling of environmental magnetic fields to test mass motion in gravitational-wave detectors. We find that while correlated noise from global electromagnetic fields could be safely ignored for initial LIGO stochastic searches, it could severely impact Advanced LIGO, Advanced Virgo, KAGRA, as well as third-generation detectors.
On the structure of Si(100) surface: importance of higher order correlations for buckled dimer.
Back, Seoin; Schmidt, Johan A; Ji, Hyunjun; Heo, Jiyoung; Shao, Yihan; Jung, Yousung
2013-05-28
We revisit a dangling theoretical question of whether the surface reconstruction of the Si(100) surface would energetically favor the symmetric or buckled dimers on the intrinsic potential energy surfaces at 0 K. This seemingly simple question is still unanswered definitively since all existing density functional based calculations predict the dimers to be buckled, while most wavefunction based correlated treatments prefer the symmetric configurations. Here, we use the doubly hybrid density functional (DHDF) geometry optimizations, in particular, XYGJ-OS, complete active space self-consistent field theory, multi-reference perturbation theory, multi-reference configuration interaction (MRCI), MRCI with the Davidson correction (MRCI + Q), multi-reference average quadratic CC (MRAQCC), and multi-reference average coupled pair functional (MRACPF) methods to address this question. The symmetric dimers are still shown to be lower in energy than the buckled dimers when using the CASPT2 method on the DHDF optimized geometries, consistent with the previous results using B3LYP geometries [Y. Jung, Y. Shao, M. S. Gordon, D. J. Doren, and M. Head-Gordon, J. Chem. Phys. 119, 10917 (2003)]. Interestingly, however, the MRCI + Q, MRAQCC, and MRACPF results (which give a more refined description of electron correlation effects) suggest that the buckled dimer is marginally more stable than its symmetric counterpart. The present study underlines the significance of having an accurate description of the electron-electron correlation as well as proper multi-reference wave functions when exploring the extremely delicate potential energy surfaces of the reconstructed Si(100) surface. PMID:23742502
On the structure of Si(100) surface: Importance of higher order correlations for buckled dimer
NASA Astrophysics Data System (ADS)
Back, Seoin; Schmidt, Johan A.; Ji, Hyunjun; Heo, Jiyoung; Shao, Yihan; Jung, Yousung
2013-05-01
We revisit a dangling theoretical question of whether the surface reconstruction of the Si(100) surface would energetically favor the symmetric or buckled dimers on the intrinsic potential energy surfaces at 0 K. This seemingly simple question is still unanswered definitively since all existing density functional based calculations predict the dimers to be buckled, while most wavefunction based correlated treatments prefer the symmetric configurations. Here, we use the doubly hybrid density functional (DHDF) geometry optimizations, in particular, XYGJ-OS, complete active space self-consistent field theory, multi-reference perturbation theory, multi-reference configuration interaction (MRCI), MRCI with the Davidson correction (MRCI + Q), multi-reference average quadratic CC (MRAQCC), and multi-reference average coupled pair functional (MRACPF) methods to address this question. The symmetric dimers are still shown to be lower in energy than the buckled dimers when using the CASPT2 method on the DHDF optimized geometries, consistent with the previous results using B3LYP geometries [Y. Jung, Y. Shao, M. S. Gordon, D. J. Doren, and M. Head-Gordon, J. Chem. Phys. 119, 10917 (2003), 10.1063/1.1620994]. Interestingly, however, the MRCI + Q, MRAQCC, and MRACPF results (which give a more refined description of electron correlation effects) suggest that the buckled dimer is marginally more stable than its symmetric counterpart. The present study underlines the significance of having an accurate description of the electron-electron correlation as well as proper multi-reference wave functions when exploring the extremely delicate potential energy surfaces of the reconstructed Si(100) surface.
Measuring Gaussian Quantum Information and Correlations Using the Rényi Entropy of Order 2
NASA Astrophysics Data System (ADS)
Adesso, Gerardo; Girolami, Davide; Serafini, Alessio
2012-11-01
We demonstrate that the Rényi-2 entropy provides a natural measure of information for any multimode Gaussian state of quantum harmonic systems, operationally linked to the phase-space Shannon sampling entropy of the Wigner distribution of the state. We prove that, in the Gaussian scenario, such an entropy satisfies the strong subadditivity inequality, a key requirement for quantum information theory. This allows us to define and analyze measures of Gaussian entanglement and more general quantum correlations based on such an entropy, which are shown to satisfy relevant properties such as monogamy.
Measuring Gaussian quantum information and correlations using the Rényi entropy of order 2.
Adesso, Gerardo; Girolami, Davide; Serafini, Alessio
2012-11-01
We demonstrate that the Rényi-2 entropy provides a natural measure of information for any multimode Gaussian state of quantum harmonic systems, operationally linked to the phase-space Shannon sampling entropy of the Wigner distribution of the state. We prove that, in the Gaussian scenario, such an entropy satisfies the strong subadditivity inequality, a key requirement for quantum information theory. This allows us to define and analyze measures of Gaussian entanglement and more general quantum correlations based on such an entropy, which are shown to satisfy relevant properties such as monogamy. PMID:23215368
Dai, Lingyun; Prokudin, Alexei; Kang, Zhong-Bo; Vitev, Ivan
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.
Simultaneous first- and second-order percolation transitions in interdependent networks
NASA Astrophysics Data System (ADS)
Zhou, Dong; Bashan, Amir; Cohen, Reuven; Berezin, Yehiel; Shnerb, Nadav; Havlin, Shlomo
2014-07-01
In a system of interdependent networks, an initial failure of nodes invokes a cascade of iterative failures that may lead to a total collapse of the whole system in the form of an abrupt first-order transition. When the fraction of initial failed nodes 1-p reaches criticality p =pc, the abrupt collapse occurs by spontaneous cascading failures. At this stage, the giant component decreases slowly in a plateau form and the number of iterations in the cascade τ diverges. The origin of this plateau and its increasing with the size of the system have been unclear. Here we find that, simultaneously with the abrupt first-order transition, a spontaneous second-order percolation occurs during the cascade of iterative failures. This sheds light on the origin of the plateau and how its length scales with the size of the system. Understanding the critical nature of the dynamical process of cascading failures may be useful for designing strategies for preventing and mitigating catastrophic collapses.
Tomic, Slavisa; Beko, Marko; Dinis, Rui
2014-01-01
In this paper, we propose a new approach based on convex optimization to address the received signal strength (RSS)-based cooperative localization problem in wireless sensor networks (WSNs). By using iterative procedures and measurements between two adjacent nodes in the network exclusively, each target node determines its own position locally. The localization problem is formulated using the maximum likelihood (ML) criterion, since ML-based solutions have the property of being asymptotically efficient. To overcome the non-convexity of the ML optimization problem, we employ the appropriate convex relaxation technique leading to second-order cone programming (SOCP). Additionally, a simple heuristic approach for improving the convergence of the proposed scheme for the case when the transmit power is known is introduced. Furthermore, we provide details about the computational complexity and energy consumption of the considered approaches. Our simulation results show that the proposed approach outperforms the existing ones in terms of the estimation accuracy for more than 1.5 m. Moreover, the new approach requires a lower number of iterations to converge, and consequently, it is likely to preserve energy in all presented scenarios, in comparison to the state-of-the-art approaches. PMID:25275350
NASA Astrophysics Data System (ADS)
Azaria, P.; Konik, R. M.; Lecheminant, P.; Pálmai, T.; Takács, G.; Tsvelik, A. M.
2016-08-01
In this paper we study a (1 +1 )-dimensional version of the famous Nambu-Jona-Lasinio model of quantum chromodynamics (QCD2) both at zero and at finite baryon density. We use nonperturbative techniques (non-Abelian bosonization and the truncated conformal spectrum approach). When the baryon chemical potential, μ , is zero, we describe the formation of fermion three-quark (nucleons and Δ baryons) and boson (two-quark mesons, six-quark deuterons) bound states. We also study at μ =0 the formation of a topologically nontrivial phase. When the chemical potential exceeds the critical value and a finite baryon density appears, the model has a rich phase diagram which includes phases with a density wave and superfluid quasi-long-range (QLR) order, as well as a phase of a baryon Tomonaga-Luttinger liquid (strange metal). The QLR order results in either a condensation of scalar mesons (the density wave) or six-quark bound states (deuterons).
The post-mitotic state in neurons correlates with a stable nuclear higher-order structure.
Aranda-Anzaldo, Armando
2012-03-01
Neurons become terminally differentiated (TD) post-mitotic cells very early during development yet they may remain alive and functional for decades. TD neurons preserve the molecular machinery necessary for DNA synthesis that may be reactivated by different stimuli but they never complete a successful mitosis. The non-reversible nature of the post-mitotic state in neurons suggests a non-genetic basis for it since no set of mutations has been able to revert it. Comparative studies of the nuclear higher-order structure in neurons and cells with proliferating potential suggest that the non-reversible nature of the post-mitotic state in neurons has a structural basis in the stability of the nuclear higher-order structure. PMID:22808316
Ding, Zhixia; Shen, Yi; Wang, Leimin
2016-01-01
This paper is concerned with the global Mittag-Leffler synchronization for a class of fractional-order neural networks with discontinuous activations (FNNDAs). We give the concept of Filippov solution for FNNDAs in the sense of Caputo's fractional derivation. By using a singular Gronwall inequality and the properties of fractional calculus, the existence of global solution under the framework of Filippov for FNNDAs is proved. Based on the nonsmooth analysis and control theory, some sufficient criteria for the global Mittag-Leffler synchronization of FNNDAs are derived by designing a suitable controller. The proposed results enrich and enhance the previous reports. Finally, one numerical example is given to demonstrate the effectiveness of the theoretical results. PMID:26562442
From explosive to infinite-order transitions on a hyperbolic network
NASA Astrophysics Data System (ADS)
Singh, Vijay; Brunson, C. T.; Boettcher, Stefan
2014-11-01
We analyze the phase transitions that emerge from the recursive design of certain hyperbolic networks that includes, for instance, a discontinuous ("explosive") transition in ordinary percolation. To this end, we solve the q -state Potts model in the analytic continuation for noninteger q with the real-space renormalization group. We find exact expressions for this one-parameter family of models that describe the dramatic transformation of the transition. In particular, this variation in q shows that the discontinuous transition is generic in the regime q <2 that includes percolation. A continuous ferromagnetic transition is recovered in a singular manner only for the Ising model, q =2 . For q >2 the transition immediately transforms into an infinitely smooth order parameter of the Berezinskii-Kosterlitz-Thouless type.
Ordered Patterns of Cell Shape and Orientational Correlation during Spontaneous Cell Migration
Iwaya, Suguru; Sano, Masaki
2008-01-01
Background In the absence of stimuli, most motile eukaryotic cells move by spontaneously coordinating cell deformation with cell movement in the absence of stimuli. Yet little is known about how cells change their own shape and how cells coordinate the deformation and movement. Here, we investigated the mechanism of spontaneous cell migration by using computational analyses. Methodology We observed spontaneously migrating Dictyostelium cells in both a vegetative state (round cell shape and slow motion) and starved one (elongated cell shape and fast motion). We then extracted regular patterns of morphological dynamics and the pattern-dependent systematic coordination with filamentous actin (F-actin) and cell movement by statistical dynamic analyses. Conclusions/Significance We found that Dictyostelium cells in both vegetative and starved states commonly organize their own shape into three ordered patterns, elongation, rotation, and oscillation, in the absence of external stimuli. Further, cells inactivated for PI3-kinase (PI3K) and/or PTEN did not show ordered patterns due to the lack of spatial control in pseudopodial formation in both the vegetative and starved states. We also found that spontaneous polarization was achieved in starved cells by asymmetric localization of PTEN and F-actin. This breaking of the symmetry of protein localization maintained the leading edge and considerably enhanced the persistence of directed migration, and overall random exploration was ensured by switching among the different ordered patterns. Our findings suggest that Dictyostelium cells spontaneously create the ordered patterns of cell shape mediated by PI3K/PTEN/F-actin and control the direction of cell movement by coordination with these patterns even in the absence of external stimuli. PMID:19011688
Symmetry and correlations underlying hidden order in URu2Si2
Butch, Nicholas P.; Manley, Michael E.; Jeffries, Jason R.; Janoschek, Marc; Huang, Kevin; Maple, M. Brian; Said, Ayman H.; Leu, Bogdan M.; Lynn, Jeffrey W.
2015-01-26
In this paper, we experimentally investigate the symmetry in the hidden order (HO) phase of intermetallic URu2Si2 by mapping the lattice and magnetic excitations via inelastic neutron and x-ray scattering measurements in the HO and high-temperature paramagnetic phases. At all temperatures, the excitations respect the zone edges of the body-centered tetragonal paramagnetic phase, showing no signs of reduced spatial symmetry, even in the HO phase. The magnetic excitations originate from transitions between hybridized bands and track the Fermi surface, whose features are corroborated by the phonon measurements. Due to a large hybridization energy scale, a full uranium moment persists inmore » the HO phase, consistent with a lack of observed crystal-field-split states. Our results are inconsistent with local order-parameter models and the behavior of typical density waves. Finally, we suggest that an order parameter that does not break spatial symmetry would naturally explain these characteristics.« less
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
NASA Astrophysics Data System (ADS)
Spica, Zack; Perton, Mathieu; Calò, Marco; Legrand, Denis; Córdoba Montiel, Francisco; Iglesias, Arturo
2016-07-01
This work presents an innovative strategy to enhance the resolution of surface wave tomography obtained from ambient noise cross-correlation (C1) by bridging asynchronous seismic networks through the correlation of coda of correlations (C3). Rayleigh wave group dispersion curves show consistent results between synchronous and asynchronous stations. Rayleigh wave group travel times are inverted to construct velocity-period maps with unprecedented resolution for a region covering Mexico and the southern United States. The resulting period maps are then used to regionalize dispersion curves in order to obtain local 1-D shear velocity models (VS) of the crust and uppermost mantle in every cell of a grid of 0.4°. The 1-D structures are obtained by iteratively adding layers until reaching a given misfit, and a global tomography model is considered as an input for depths below 150 km. Finally, a high-resolution 3-D VS model is obtained from these inversions. The major structures observed in the 3-D model are in agreement with the tectonic-geodynamic features and with previous regional and local studies. It also offers new insights to understand the present and past tectonic evolution of the region.
NASA Astrophysics Data System (ADS)
Spica, Zack; Perton, Mathieu; Calò, Marco; Legrand, Denis; Córdoba-Montiel, Francisco; Iglesias, Arturo
2016-09-01
This work presents an innovative strategy to enhance the resolution of surface wave tomography obtained from ambient noise cross-correlation (C1) by bridging asynchronous seismic networks through the correlation of coda of correlations (C3). Rayleigh wave group dispersion curves show consistent results between synchronous and asynchronous stations. Rayleigh wave group traveltimes are inverted to construct velocity-period maps with unprecedented resolution for a region covering Mexico and the southern United States. The resulting period maps are then used to regionalize dispersion curves in order to obtain local 1-D shear velocity models (VS) of the crust and uppermost mantle in every cell of a grid of 0.4°. The 1-D structures are obtained by iteratively adding layers until reaching a given misfit, and a global tomography model is considered as an input for depths below 150 km. Finally, a high-resolution 3-D VS model is obtained from these inversions. The major structures observed in the 3-D model are in agreement with the tectonic-geodynamic features and with previous regional and local studies. It also offers new insights to understand the present and past tectonic evolution of the region.
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.
Olsen, Aaron M
2015-11-01
Herbivory is rare among birds and is usually thought to have evolved predominately among large, flightless birds due to energetic constraints or an association with increased body mass. Nearly all members of the bird order Anseriformes, which includes ducks, geese, and swans, are flighted and many are predominately herbivorous. However, it is unknown whether herbivory represents a derived state for the order and how many times a predominately herbivorous diet may have evolved. Compiling data from over 200 published diet studies to create a continuous character for herbivory, models of trait evolution support at least five independent transitions toward a predominately herbivorous diet in Anseriformes. Although a nonphylogenetic correlation test recovers a significant positive correlation between herbivory and body mass, this correlation is not significant when accounting for phylogeny. These results indicate a lack of support for the hypothesis that a larger body mass confers an advantage in the digestion of low-quality diets but does not exclude the possibility that shifts to a more abundant food source have driven shifts toward herbivory in other bird lineages. The exceptional number of transitions toward a more herbivorous diet in Anseriformes and lack of correlation with body mass prompts a reinterpretation of the relatively infrequent origination of herbivory among flighted birds. PMID:26640679
NASA Astrophysics Data System (ADS)
Texier, Christophe; Montambaux, Gilles
2016-01-01
We consider the electronic transport in multi-terminal mesoscopic networks of weakly disordered metallic wires. After a brief description of the classical transport, we analyse the weak localisation (WL) correction to the four-terminal resistances, which involves an integration of the Cooperon over the wires with proper weights. We provide an interpretation of these weights in terms of classical transport properties. We illustrate the formalism on examples and show that weak localisation to four-terminal conductances may become large in some situations. In a second part, we study the correlations of four-terminal resistances and show that integration of Diffuson and Cooperon inside the network involves the same weights as the WL. The formulae are applied to multiconnected wire geometries.
NASA Astrophysics Data System (ADS)
Texier, Christophe; Montambaux, Gilles
2016-08-01
We consider the electronic transport in multi-terminal mesoscopic networks of weakly disordered metallic wires. After a brief description of the classical transport, we analyse the weak localisation (WL) correction to the four-terminal resistances, which involves an integration of the Cooperon over the wires with proper weights. We provide an interpretation of these weights in terms of classical transport properties. We illustrate the formalism on examples and show that weak localisation to four-terminal conductances may become large in some situations. In a second part, we study the correlations of four-terminal resistances and show that integration of Diffuson and Cooperon inside the network involves the same weights as the WL. The formulae are applied to multiconnected wire geometries.
NASA Astrophysics Data System (ADS)
Temleitner, L.; Pusztai, L.
2010-04-01
The liquid, plastic crystalline and ordered crystalline phases of CBr4 were studied using neutron-powder diffraction. The measured total scattering differential cross sections were modeled by reverse Monte Carlo simulation techniques ( RMC++ and RMCPOW). Following successful simulations, the single-crystal-diffraction pattern of the plastic phase as well as partial radial distribution functions and orientational correlations for all the three phases have been calculated from the atomic coordinates (particle configurations). The single-crystal pattern, calculated from a configuration that had been obtained from modeling the powder pattern, shows identical behavior to the recent single-crystal data of Folmer [Phys. Rev. B 77, 144205 (2008)]. The BrBr partial radial-distribution functions of the liquid and plastic crystalline phases are almost the same while CC correlations clearly display long-range ordering in the latter phase. Orientational correlations also suggest strong similarities between liquid and plastic crystalline phases whereas the monoclinic phase behaves very differently. Orientations of the molecules are distinct in the ordered phase whereas in the plastic crystal their distribution seems to be isotropic.
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.
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
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
Young, C.J.; Beiriger, J.I.; Moore, S.G.
1998-04-01
Further improvements to the Waveform Correlation Event Detection System (WCEDS) developed by Sandia Laboratory have made it possible to test the system on the accepted Comprehensive Test Ban Treaty (CTBT) seismic monitoring network. For our test interval we selected a 24-hour period from December 1996, and chose to use the Reviewed Event Bulletin (REB) produced by the Prototype International Data Center (PIDC) as ground truth for evaluating the results. The network is heterogeneous, consisting of array and three-component sites, and as a result requires more flexible waveform processing algorithms than were available in the first version of the system. For simplicity and superior performance, we opted to use the spatial coherency algorithm of Wagner and Owens (1996) for both types of sites. Preliminary tests indicated that the existing version of WCEDS, which ignored directional information, could not achieve satisfactory detection or location performance for many of the smaller events in the REB, particularly those in the south Pacific where the network coverage is unusually sparse. To achieve an acceptable level of performance, we made modifications to include directional consistency checks for the correlations, making the regions of high correlation much less ambiguous. These checks require the production of continuous azimuth and slowness streams for each station, which is accomplished by means of FK processing for the arrays and power polarization processing for the three-component sites. In addition, we added the capability to use multiple frequency-banded data streams for each site to increase sensitivity to phases whose frequency content changes as a function of distance.
Zhang, Kai; Du, Kai; Liu, Hao; Zhang, X.-G.; Lan, Fanli; Lin, Hanxuan; Wei, Wengang; Zhu, Yinyan; Kou, Yunfang; Shao, Jian; Niu, Jiebin; Wang, Wenbin; Wu, Ruqian; Yin, Lifeng; Plummer, E. W.; Shen, Jian
2015-01-01
The interesting transport and magnetic properties in manganites depend sensitively on the nucleation and growth of electronic phase-separated domains. By fabricating antidot arrays in La0.325Pr0.3Ca0.375MnO3 (LPCMO) epitaxial thin films, we create ordered arrays of micrometer-sized ferromagnetic metallic (FMM) rings in the LPCMO films that lead to dramatically increased metal–insulator transition temperatures and reduced resistances. The FMM rings emerge from the edges of the antidots where the lattice symmetry is broken. Based on our Monte Carlo simulation, these FMM rings assist the nucleation and growth of FMM phase domains increasing the metal–insulator transition with decreasing temperature or increasing magnetic field. This study points to a way in which electronic phase separation in manganites can be artificially controlled without changing chemical composition or applying external field. PMID:26195791
Approximated optimum condition of second order response surface model with correlated observations
NASA Astrophysics Data System (ADS)
Somayasa, Wayan
2016-06-01
In the present paper we establish an inference procedure for the eigenvalues of the model matrix of the second-order response surface model (RSM). In contrast to the classical treatment where the sample are assumed to be independently distributed, in this work we do not need such distributional simplification. The confidence region for the unknown vector of the eigenvalues is derived by means of delta method. The finite sample behavior of the convergence result is discussed by Monte Carlo Simulation. We get the approximated distribution of the pivotal quantity of the population eigenvalues as a chi-square distribution model. Next we attempt to apply the method to a real data provided by a mining industry. The data represents the percentage of cobalt (Co) observed over the exploration region.
Tagoshi, Hideyuki; Mukhopadhyay, Himan; Dhurandhar, Sanjeev; Sago, Norichika; Takahashi, Hirotaka; Kanda, Nobuyuki
2007-04-15
We discuss the coherent search strategy to detect gravitational waves from inspiraling compact binaries by a network of correlated laser interferometric detectors. From the maximum likelihood ratio statistic, we obtain a coherent statistic which is slightly different from and generally better than what we obtained in our previous work. In the special case when the cross spectrum of two detectors normalized by the power spectrum density is constant, the new statistic agrees with the old one. The quantitative difference of the detection probability for a given false alarm rate is also evaluated in a simple case.
First order phase transformations: scaling relations for grain self-correlation functions
Axe, J.D.; Shapiro, S.M.; Yamada, Y.; Hamaya, N.
1985-06-01
At high pressure many alkali halides transform from the NaCl (B1) structure to the CsCl (B2) structure. We have recently studied this transformation in polycrystalline RbI, which transforms at a critical pressure, P/sub c/ = 3.5 kbar. By observing the time development of the neutron diffraction pattern after sudden increase of hydrostatic pressure from P
P/sub c/ we directly deduced X(t), the fraction of the sample converted from metastable to stable phase, as a function of time. We showed that X(t) taken at different P could be approximately scaled onto a universal growth curve by introducing an adjustable characteristic time tau(P) for each curve. The success of the Kolmogorov in fitting X(t) suggests that comparisons of model predictions with other experimental observables be made on the system. For example, by a trivial (in principle) extension of the neutron diffraction techniques described above, one might determine the broadening of the powder diffraction peaks due to finite grain size as a function of time throughout growth. This particle size broadening is related by Fourier transformation to the grain autocorrelation function, G/sub s/(r,t), which measures the ensemble average of the overlap of grains with themselves upon translation of the grain pattern by an amount r. We present some results of a study of the scaling properties of G/sub s/(r,t) for the Kolmogorov model for d=1 and d=2. Although the model is highly idealized, it is perhaps the simplest conceivable one which obeys correlation function scaling in early stages of growth and undergoes nontrivial saturation due to volume fraction effects in the late stages. 4 refs., 6 figs.
Schmidt, J. A.; Olsen, J. M. H.
2014-11-14
The photodissociation of carbonyl sulfide (OCS) was investigated theoretically in a series of studies by Schmidt and co-workers. Initial studies [J. A. Schmidt, M. S. Johnson, G. C. McBane, and R. Schinke, J. Chem. Phys. 136, 131101 (2012); J. A. Schmidt, M. S. Johnson, G. C. McBane, and R. Schinke, J. Chem. Phys. 137, 054313 (2012)] found photodissociation in the first UV-band to occur mainly by excitation of the 2{sup 1}A{sup ′} (A) excited state. However, in a later study [G. C. McBane, J. A. Schmidt, M. S. Johnson, and R. Schinke, J. Chem. Phys. 138, 094314 (2013)] it was found that a significant fraction of photodissociation must occur by excitation of 1{sup 1}A{sup ″} (B) excited state to explain the product angular distribution. The branching between excitation of the A and B excited states is determined by the magnitude of the transition dipole moment vectors in the Franck-Condon region. This study examines the sensitivity of these quantities to changes in the employed electronic structure methodology. This study benchmarks the methodology employed in previous studies against highly correlated electronic structure methods (CC3 and MRAQCC) and provide evidence in support of the picture of the OCS photodissociation process presented in [G. C. McBane, J. A. Schmidt, M. S. Johnson, and R. Schinke, J. Chem. Phys. 138, 094314 (2013)] showing that excitation of A and B electronic states both contribute significantly to the first UV absorption band of OCS. In addition, this study presents evidence in support of the assertion that the A state potential energy surface employed in previous studies underestimates the energy at highly bent geometries (γ ∼ 70°) leading to overestimated rotational energy in the product CO.
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.
Meeks, E.; Chou, C. -P.; Garratt, T.
2013-03-31
Engineering simulations of coal gasifiers are typically performed using computational fluid dynamics (CFD) software, where a 3-D representation of the gasifier equipment is used to model the fluid flow in the gasifier and source terms from the coal gasification process are captured using discrete-phase model source terms. Simulations using this approach can be very time consuming, making it difficult to imbed such models into overall system simulations for plant design and optimization. For such system-level designs, process flowsheet software is typically used, such as Aspen Plus® [1], where each component where each component is modeled using a reduced-order model. For advanced power-generation systems, such as integrated gasifier/gas-turbine combined-cycle systems (IGCC), the critical components determining overall process efficiency and emissions are usually the gasifier and combustor. Providing more accurate and more computationally efficient reduced-order models for these components, then, enables much more effective plant-level design optimization and design for control. Based on the CHEMKIN-PRO and ENERGICO software, we have developed an automated methodology for generating an advanced form of reduced-order model for gasifiers and combustors. The reducedorder model offers representation of key unit operations in flowsheet simulations, while allowing simulation that is fast enough to be used in iterative flowsheet calculations. Using high-fidelity fluiddynamics models as input, Reaction Design’s ENERGICO® [2] software can automatically extract equivalent reactor networks (ERNs) from a CFD solution. For the advanced reduced-order concept, we introduce into the ERN a much more detailed kinetics model than can be included practically in the CFD simulation. The state-of-the-art chemistry solver technology within CHEMKIN-PRO allows that to be accomplished while still maintaining a very fast model turn-around time. In this way, the ERN becomes the basis for
Liu, Qing; Shi, Chaowei; Yu, Lu; Zhang, Longhua; Xiong, Ying; Tian, Changlin
2015-02-13
Internal backbone dynamic motions are essential for different protein functions and occur on a wide range of time scales, from femtoseconds to seconds. Molecular dynamic (MD) simulations and nuclear magnetic resonance (NMR) spin relaxation measurements are valuable tools to gain access to fast (nanosecond) internal motions. However, there exist few reports on correlation analysis between MD and NMR relaxation data. Here, backbone relaxation measurements of {sup 15}N-labeled SH3 (Src homology 3) domain proteins in aqueous buffer were used to generate general order parameters (S{sup 2}) using a model-free approach. Simultaneously, 80 ns MD simulations of SH3 domain proteins in a defined hydrated box at neutral pH were conducted and the general order parameters (S{sup 2}) were derived from the MD trajectory. Correlation analysis using the Gromos force field indicated that S{sup 2} values from NMR relaxation measurements and MD simulations were significantly different. MD simulations were performed on models with different charge states for three histidine residues, and with different water models, which were SPC (simple point charge) water model and SPC/E (extended simple point charge) water model. S{sup 2} parameters from MD simulations with charges for all three histidines and with the SPC/E water model correlated well with S{sup 2} calculated from the experimental NMR relaxation measurements, in a site-specific manner. - Highlights: • Correlation analysis between NMR relaxation measurements and MD simulations. • General order parameter (S{sup 2}) as common reference between the two methods. • Different protein dynamics with different Histidine charge states in neutral pH. • Different protein dynamics with different water models.
Avershina, E; Storrø, O; Øien, T; Johnsen, R; Wilson, R; Egeland, T; Rudi, K
2013-01-01
Bifidobacteria are a major microbial component of infant gut microbiota, which is believed to promote health benefits for the host and stimulate maturation of the immune system. Despite their perceived importance, very little is known about the natural development of and possible correlations between bifidobacteria in human populations. To address this knowledge gap, we analyzed stool samples from a randomly selected healthy cohort of 87 infants and their mothers with >90% of vaginal delivery and nearly 100% breast-feeding at 4 months. Fecal material was sampled during pregnancy, at 3 and 10 days, at 4 months, and at 1 and 2 years after birth. Stool samples were predicted to be rich in the species Bifidobacterium adolescentis, B. bifidum, B. dentium, B. breve, and B. longum. Due to high variation, we did not identify a clear age-related structure at the individual level. Within the population as a whole, however, there were clear age-related successions. Negative correlations between the B. longum group and B. adolescentis were detected in adults and in 1- and 2-year-old children, whereas negative correlations between B. longum and B. breve were characteristic for newborns and 4-month-old infants. The highly structured age-related development of and correlation networks between bifidobacterial species during the first 2 years of life mirrors their different or competing nutritional requirements, which in turn may be associated with specific biological functions in the development of healthy gut. PMID:23124244
NASA Astrophysics Data System (ADS)
Messé, Arnaud; Hütt, Marc-Thorsten; König, Peter; Hilgetag, Claus C.
2015-01-01
The relationship between the structural connectivity (SC) and functional connectivity (FC) of neural systems is a central focus in brain network science. It is an open question, however, how strongly the SC-FC relationship depends on specific topological features of brain networks or the models used for describing excitable dynamics. Using a basic model of discrete excitable units that follow a susceptible - excited - refractory dynamic cycle (SER model), we here analyze how functional connectivity is shaped by the topological features of a neural network, in particular its modularity. We compared the results obtained by the SER model with corresponding simulations by another well established dynamic mechanism, the Fitzhugh-Nagumo model, in order to explore general features of the SC-FC relationship. We showed that apparent discrepancies between the results produced by the two models can be resolved by adjusting the time window of integration of co-activations from which the FC is derived, providing a clearer distinction between co-activations and sequential activations. Thus, network modularity appears as an important factor shaping the FC-SC relationship across different dynamic models.
Influence of baryons on the spatial distribution of matter: higher order correlation functions
NASA Astrophysics Data System (ADS)
Zhu, Xiao-Jun; Pan, Jun
2012-12-01
Physical processes involving baryons could leave a non-negligible imprint on the distribution of cosmic matter. A series of simulated data sets at high resolution with identical initial conditions are employed for count-in-cell analysis, including one N-body pure dark matter run, one with only adiabatic gas and one with dissipative processes. Variances and higher order cumulants Sn of dark matter and gas are estimated. It is found that physical processes with baryons mainly affect distributions of dark matter at scales less than 1 h-1 Mpc. In comparison with the pure dark matter run, adiabatic processes alone strengthen the variance of dark matter by ~ 10% at a scale of 0.1 h-1 Mpc, while the Sn parameters of dark matter only mildly deviate by a few percent. The dissipative gas run does not differ much from the adiabatic run in terms of variance for dark matter, but renders significantly different Sn parameters describing the dark matter, bringing about a more than 10% enhancement to S3 at 0.1 h-1 Mpc and z = 0 and being even larger at a higher redshift. Distribution patterns of gas in two hydrodynamical simulations are quite different. Variance of gas at z = 0 decreases by ~ 30% in the adiabatic simulation but by ~ 60% in the nonadiabatic simulation at 0.1 h-1 Mpc. The attenuation is weaker at larger scales but is still obvious at ~ 10 h-1 Mpc. Sn parameters of gas are biased upward at scales < ~ 4 h-1 Mpc, and dissipative processes show an ~ 84% promotion at z = 0 to S3 at 0.1 h-1 Mpc in contrast with the ~ 7% change in the adiabatic run. The segregation in clustering between gas and dark matter could have dramatic implications on modeling distributions of galaxies and relevant cosmological applications demanding fine details of matter distribution in a strongly nonlinear regime.
Lewicki, J P; Mayer, B P; Wilson, T S; Chinn, S C; Maxwell, R S
2010-12-09
This work is at a relatively early stage, however it has been demonstrated that we can reliably probe basic network architectures using the MQ-NMR technique. The initial results are in good agreement with what is known from standard network theory and will serve as a basis for the study of progressively increasing structural complexity in Siloxane network systems.
NASA Astrophysics Data System (ADS)
Wierschem, K.; Beach, K. S. D.
2016-06-01
The strange correlator [Phys. Rev. Lett. 112, 247202 (2014), 10.1103/PhysRevLett.112.247202] has been proposed as a measure of symmetry protected topological order in one- and two-dimensional systems. It takes the form of a spin-spin correlation function, computed as a mixed overlap between the state of interest and a trivial local product state. We demonstrate that it can be computed exactly (asymptotically, in the Monte Carlo sense) for various Affleck-Kennedy-Lieb-Tasaki states by direct evaluation of the wave function within the valence bond loop gas framework. We present results for lattices with chain, square, honeycomb, cube, diamond, and hyperhoneycomb geometries. In each case, the spin quantum number S is varied such that 2 S (the number of valence bonds emerging from each site) achieves various integer multiples of the lattice coordination number. We introduce the concept of strange correlator loop winding number and point to its utility in testing for the presence of symmetry protected topological order.
NASA Astrophysics Data System (ADS)
Manivannan, Nadarajah; Neil, Mark A. A.; Balachandran, Wamadeva
2012-05-01
A new interpolation algorithm is proposed and demonstrated to perform automatic angle measurement of two-dimensional (2D) objects. The proposed algorithm works in conjunction with optical correlator neural network hybrid architecture (OCNN). The OCNN is trained with a combined algorithm of direct binary search and error back propagation. Input of the OCNN is presented with an image whose angle of rotation is to be measured, and output from the OCNN is fed into the proposed interpolation algorithm, which finally produces the rotation angle of the input image. Results of both computer simulation and experimental set-up are presented for an English alphabetic character as a 2D object. The experimental set-up consists of a real optical correlator using two spatial light modulators for both input and frequency plane representations and a PC based model of a single layer neural network. We obtained very low experimental mean absolute error of 3.18 deg with standard deviation of 2.9 deg.
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.
Vogt, Brent A.; Laureys, Steven
2008-01-01
Neuronal aggregates involved in conscious awareness are not evenly distributed throughout the CNS but are comprised of key components referred to as the neural network correlates of consciousness (NNCC). A critical node in this network is the retrosplenial, posterior cingulate, and precuneal cortices (RSC/PCC/PrCC). The cytological and neurochemical composition of this region is reviewed in relation to the Brodmann map. This region has the highest level of brain glucose metabolism and cytochrome c oxidase activity. Monkey studies suggest that the anterior thalamic projection likely drives RSC and PCC metabolism and that the midbrain projection to the anteroventral thalamic nucleus is a key coupling site between the brainstem system for arousal and cortical systems for cognitive processing and awareness. The pivotal role of RSC/PCC/PrCC in consciousness is demonstrated with posterior cingulate epilepsy cases, midcingulate lesions that de-afferent this region and are associated with unilateral sensory neglect, observations from stroke and vegetative state patients, alterations in blood flow during sleep, and the actions of anesthetics. Since this region is critically involved in self reflection, it is not surprising that it is similarly a site for the NNCC. Interestingly, information processing during complex cognitive tasks and during aversive sensations such as pain induces efforts to terminate self reflection and result in decreased processing in PCC/PrCC. Finally, anatomical relations between the neural correlates of mind and NNCC in the cingulate gyrus do not appear to overlap and suggests that mental function and conscious awareness may be mediated by two neural networks. PMID:16186025
Fehr, T; Achtziger, A; Roth, G; Strüber, D
2014-10-01
The neural processing of impulsive behavior is a central topic in various clinical and non-clinical contexts. To investigate neural and behavioral correlates of the empathic processing of complex social scenarios, especially considering ecological validity of the experimental procedure, we developed and investigated a video stimulus inventory. It includes realistic neutral, social-positive, and reactive-aggressive action scenarios. Short video-clips showing these social scenarios from a first-person perspective triggering different emotional states were presented to a non-clinical sample of 20 young adult male participants during fMRI measurements. Both affective interaction conditions (social-positive and reactive-aggressive) were contrasted against a neutral baseline condition and against each other. Behavioral evaluation data largely confirmed the validity of the emotion-inducing stimulus material. Reactive-aggressive and social-positive interaction scenarios produced widely overlapping fMRI activation patterns in hetero-modal association cortices, but also in subcortical regions, such as the peri-aqueductal gray. Reactive-aggressive compared to social-positive scenarios yielded a more anterior distribution of activations in pre-motor and inferior frontal brain regions associated to motor-preparation and inhibitory control processing as well as in the insula associated to pain- and/or aversion-processing. We argue that there are both principally common neural networks recruited for the processing of reactive-aggressive and social-positive scenarios, but also exclusive network parts in particular involved depending on individual socialization. PMID:24814646
NASA Astrophysics Data System (ADS)
Hima Nagamanasa, K.; Gokhale, Shreyas; Sood, A. K.; Ganapathy, Rajesh
2015-05-01
The transformation of flowing liquids into rigid glasses is thought to involve increasingly cooperative relaxation dynamics as the temperature approaches that of the glass transition. However, the precise nature of this motion is unclear, and a complete understanding of vitrification thus remains elusive. Of the numerous theoretical perspectives devised to explain the process, random first-order theory (RFOT; refs , ) is a well-developed thermodynamic approach, which predicts a change in the shape of relaxing regions as the temperature is lowered. However, the existence of an underlying `ideal’ glass transition predicted by RFOT remains debatable, largely because the key microscopic predictions concerning the growth of amorphous order and the nature of dynamic correlations lack experimental verification. Here, using holographic optical tweezers, we freeze a wall of particles in a two-dimensional colloidal glass-forming liquid and provide direct evidence for growing amorphous order in the form of a static point-to-set length. We uncover the non-monotonic dependence of dynamic correlations on area fraction and show that this non-monotonicity follows directly from the change in morphology and internal structure of cooperatively rearranging regions. Our findings support RFOT and thereby constitute a crucial step in distinguishing between competing theories of glass formation.
Systems biology beyond networks: generating order from disorder through self-organization
Saetzler, K.; Sonnenschein, C.; Soto, A.M.
2011-01-01
Erwin Schrödinger pointed out in his 1944 book “What is Life” that one defining attribute of biological systems seems to be their tendency to generate order from disorder defying the second law of thermodynamics. Almost parallel to his findings, the science of complex systems was founded based on observations on physical and chemical systems showing that inanimate matter can exhibit complex structures although their interacting parts follow simple rules. This is explained by a process known as self-organization and it is now widely accepted that multi-cellular biological organisms are themselves self-organizing complex systems in which the relations among their parts are dynamic, contextual and interdependent. In order to fully understand such systems, we are required to computationally and mathematically model their interactions as promulgated in systems biology. The preponderance of network models in the practice of systems biology inspired by a reductionist, bottom-up view, seems to neglect, however, the importance of bidirectional interactions across spatial scales and domains. This approach introduces a shortcoming that may hinder research on emergent phenomena such as those of tissue morphogenesis and related diseases, such as cancer. Another hindrance of current modeling attempts is that those systems operate in a parameter space that seems far removed from biological reality. This misperception calls for more tightly coupled mathematical and computational models to biological experiments by creating and designing biological model systems that are accessible to a wide range of experimental manipulations. In this way, a comprehensive understanding of fundamental processes in normal development or of aberrations, like cancer, will be generated. PMID:21569848
Persi, Erez; Horn, David
2013-01-01
We present a novel analysis of compositional order (CO) based on the occurrence of Frequent amino-acid Triplets (FTs) that appear much more than random in protein sequences. The method captures all types of proteomic compositional order including single amino-acid runs, tandem repeats, periodic structure of motifs and otherwise low complexity amino-acid regions. We introduce new order measures, distinguishing between ‘regularity’, ‘periodicity’ and ‘vocabulary’, to quantify these phenomena and to facilitate the identification of evolutionary effects. Detailed analysis of representative species across the tree-of-life demonstrates that CO proteins exhibit numerous functional enrichments, including a wide repertoire of particular patterns of dependencies on regularity and periodicity. Comparison between human and mouse proteomes further reveals the interplay of CO with evolutionary trends, such as faster substitution rate in mouse leading to decrease of periodicity, while innovation along the human lineage leads to larger regularity. Large-scale analysis of 94 proteomes leads to systematic ordering of all major taxonomic groups according to FT-vocabulary size. This is measured by the count of Different Frequent Triplets (DFT) in proteomes. The latter provides a clear hierarchical delineation of vertebrates, invertebrates, plants, fungi and prokaryotes, with thermophiles showing the lowest level of FT-vocabulary. Among eukaryotes, this ordering correlates with phylogenetic proximity. Interestingly, in all kingdoms CO accumulation in the proteome has universal characteristics. We suggest that CO is a genomic-information correlate of both macroevolution and various protein functions. The results indicate a mechanism of genomic ‘innovation’ at the peptide level, involved in protein elongation, shaped in a universal manner by mutational and selective forces. PMID:24278003
Zhou Yu; Simon, Jason; Liu Jianbin; Shih, Yanhua
2010-04-15
In a near-field three-photon correlation measurement, we observed the third-order temporal and spatial correlation functions of chaotic thermal light in the single-photon counting regime. In the study, we found that the probability of jointly detecting three randomly radiated photons from a chaotic thermal source by three individual detectors is 6 times greater if the photodetection events fall in the coherence time and coherence area of the radiation field than if they do not. From the viewpoint of quantum mechanics, the observed phenomenon is the result of three-photon interference. By making use of this property, we measured the three-photon thermal light lensless ghost image of a double spot and achieved higher visibility compared with the two-photon thermal light ghost image.
Well-ordered ZnO nanotube arrays and networks grown by atomic layer deposition
NASA Astrophysics Data System (ADS)
Zhang, Yijun; Liu, Ming; Ren, Wei; Ye, Zuo-Guang
2015-06-01
Semiconductor ZnO, possessing a large exciton binding energy and wide band gap, has received a great deal of attention because it shows great potential for applications in optoelectronics. Precisely controlling the growth of three-dimensional ZnO nanotube structures with a uniform morphology constitutes an important step forward toward integrating ZnO nanostructures into microelectronic devices. Atomic layer deposition (ALD) technique, featured with self-limiting surface reactions, is an ideal approach to the fabrication of ZnO nanostructures, because it allows for accurate control of the thickness at atomic level and conformal coverage in complex 3D structures. In this work, well-ordered ZnO nanotube arrays and networks are prepared by ALD. The morphology, crystallinity and wall thickness of these nanotube structures are examined for different growth conditions. The microstructure of the ZnO nanotubes is investigated by transmission electron microscopy and X-ray diffraction. The high aspect ratio of ZnO nanotubes provides a large specific area which could enhance the kinetics of chemical reactions taking place between the ZnO and its surroundings, making the potential devices more efficient and compact.
NASA Astrophysics Data System (ADS)
Yang, D.-M.; Stronach, A. F.; MacConnell, P.; Penman, J.
2002-03-01
This paper addresses the development of a novel condition monitoring procedure for rolling element bearings which involves a combination of signal processing, signal analysis and artificial intelligence methods. Seven approaches based on power spectrum, bispectral and bicoherence vibration analyses are investigated as signal pre-processing techniques for application in the diagnosis of a number of induction motor rolling element bearing conditions. The bearing conditions considered are a normal bearing and bearings with cage and inner and outer race faults. The vibration analysis methods investigated are based on the power spectrum, the bispectrum, the bicoherence, the bispectrum diagonal slice, the bicoherence diagonal slice, the summed bispectrum and the summed bicoherence. Selected features are extracted from the vibration signatures so obtained and these are used as inputs to an artificial neural network trained to identify the bearing conditions. Quadratic phase coupling (QPC), examined using the magnitude of bispectrum and bicoherence and biphase, is shown to be absent from the bearing system and it is therefore concluded that the structure of the bearing vibration signatures results from inter-modulation effects. In order to test the proposed procedure, experimental data from a bearing test rig are used to develop an example diagnostic system. Results show that the bearing conditions examined can be diagnosed with a high success rate, particularly when using the summed bispectrum signatures.
NASA Astrophysics Data System (ADS)
Yu, Dan; Yang, Jun-Zhong
2014-02-01
A recent study has found an explosive synchronization in a Kurammoto model on scale-free networks when the natural frequencies of oscillators are equal to their degrees. In this work, we introduce a quantity to characterize the correlation between the structural and the dynamical properties and investigate the impacts of the correlation on the synchronization transition in the Kuramoto model on scale-free networks. We find that the synchronization transition may be either a continuous one or a discontinuous one depending on the correlation and that strong correlation always postpones both the transitions from the incoherent state to a synchronous one and the transition from a synchronous state to the incoherent one. We find that the dependence of the synchronization transition on the correlation is also valid for other types of distributions of natural frequency.
Xu, Yue Quan; Wu, Hui; Chen, Si Si; Guo, Ji Jun
2013-01-01
Background 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. Objective 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. Methods 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. Results 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
Kohn, Kurt W; Zeeberg, Barry M; Reinhold, William C; Pommier, Yves
2014-01-01
Using gene expression data to enhance our knowledge of control networks relevant to cancer biology and therapy is a challenging but urgent task. Based on the premise that genes that are expressed together in a variety of cell types are likely to functions together, we derived mutually correlated genes that function together in various processes in epithelial-like tumor cells. Expression-correlated genes were derived from data for the NCI-60 human tumor cell lines, as well as data from the Broad Institute's CCLE cell lines. NCI-60 cell lines that selectively expressed a mutually correlated subset of tight junction genes served as a signature for epithelial-like cancer cells. Those signature cell lines served as a seed to derive other correlated genes, many of which had various other epithelial-related functions. Literature survey yielded molecular interaction and function information about those genes, from which molecular interaction maps were assembled. Many of the genes had epithelial functions unrelated to tight junctions, demonstrating that new function categories were elicited. The most highly correlated genes were implicated in the following epithelial functions: interactions at tight junctions (CLDN7, CLDN4, CLDN3, MARVELD3, MARVELD2, TJP3, CGN, CRB3, LLGL2, EPCAM, LNX1); interactions at adherens junctions (CDH1, ADAP1, CAMSAP3); interactions at desmosomes (PPL, PKP3, JUP); transcription regulation of cell-cell junction complexes (GRHL1 and 2); epithelial RNA splicing regulators (ESRP1 and 2); epithelial vesicle traffic (RAB25, EPN3, GRHL2, EHF, ADAP1, MYO5B); epithelial Ca(+2) signaling (ATP2C2, S100A14, BSPRY); terminal differentiation of epithelial cells (OVOL1 and 2, ST14, PRSS8, SPINT1 and 2); maintenance of apico-basal polarity (RAB25, LLGL2, EPN3). The findings provide a foundation for future studies to elucidate the functions of regulatory networks specific to epithelial-like cancer cells and to probe for anti-cancer drug targets. PMID:24940735
NASA Astrophysics Data System (ADS)
Dumez, Jean-Nicolas; Butler, Mark C.; Emsley, Lyndon
2010-12-01
The design of simulations of free evolution in dipolar-coupled nuclear-spin systems using low-order correlations in Liouville space (LCL) is discussed, and a computational scheme relying on the Suzuki-Trotter algorithm and involving minimal memory requirements is described. The unusual nature of the approximation introduced by Liouville-space reduction in a spinning solid is highlighted by considering the accuracy of LCL simulations at different spinning frequencies, the quasiequilibria achieved by spin systems in LCL simulations, and the growth of high-order coherences in the exact dynamics. In particular, it is shown that accurate LCL simulations of proton spin diffusion occur in a regime where the reduced space excludes the coherences that make the dominant contribution to Vert σ Vert ^2, the norm-squared of the density matrix.
Ligeti, Balázs; Pénzváltó, Zsófia; Vera, Roberto; Győrffy, Balázs; Pongor, Sándor
2015-01-01
Drug combinations are highly efficient in systemic treatment of complex multigene diseases such as cancer, diabetes, arthritis and hypertension. Most currently used combinations were found in empirical ways, which limits the speed of discovery for new and more effective combinations. Therefore, there is a substantial need for efficient and fast computational methods. Here, we present a principle that is based on the assumption that perturbations generated by multiple pharmaceutical agents propagate through an interaction network and can cause unexpected amplification at targets not immediately affected by the original drugs. In order to capture this phenomenon, we introduce a novel Target Overlap Score (TOS) that is defined for two pharmaceutical agents as the number of jointly perturbed targets divided by the number of all targets potentially affected by the two agents. We show that this measure is correlated with the known effects of beneficial and deleterious drug combinations taken from the DCDB, TTD and Drugs.com databases. We demonstrate the utility of TOS by correlating the score to the outcome of recent clinical trials evaluating trastuzumab, an effective anticancer agent utilized in combination with anthracycline- and taxane- based systemic chemotherapy in HER2-receptor (erb-b2 receptor tyrosine kinase 2) positive breast cancer. PMID:26047322
NASA Astrophysics Data System (ADS)
Ayala, Helon Vicente Hultmann; Coelho, Leandro dos Santos
2016-02-01
The present work introduces a procedure for input selection and parameter estimation for system identification based on Radial Basis Functions Neural Networks (RBFNNs) models with an improved objective function based on the residuals and its correlation function coefficients. We show the results when the proposed methodology is applied to model a magnetorheological damper, with real acquired data, and other two well-known benchmarks. The canonical genetic and differential evolution algorithms are used in cascade to decompose the problem of defining the lags taken as the inputs of the model and its related parameters based on the simultaneous minimization of the residuals and higher orders correlation functions. The inner layer of the cascaded approach is composed of a population which represents the lags on the inputs and outputs of the system and an outer layer represents the corresponding parameters of the RBFNN. The approach is able to define both the inputs of the model and its parameters. This is interesting as it frees the designer of manual procedures, which are time consuming and prone to error, usually done to define the model inputs. We compare the proposed methodology with other works found in the literature, showing overall better results for the cascaded approach.
Shang, Yu; Yu, Guoqiang
2014-09-29
Conventional semi-infinite analytical solutions of correlation diffusion equation may lead to errors when calculating blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements in tissues with irregular geometries. Very recently, we created an algorithm integrating a Nth-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in homogenous tissues with arbitrary geometries for extraction of BFI (i.e., αD{sub B}). The purpose of this study is to extend the capability of the Nth-order linear algorithm for extracting BFI in heterogeneous tissues with arbitrary geometries. The previous linear algorithm was modified to extract BFIs in different types of tissues simultaneously through utilizing DCS data at multiple source-detector separations. We compared the proposed linear algorithm with the semi-infinite homogenous solution in a computer model of adult head with heterogeneous tissue layers of scalp, skull, cerebrospinal fluid, and brain. To test the capability of the linear algorithm for extracting relative changes of cerebral blood flow (rCBF) in deep brain, we assigned ten levels of αD{sub B} in the brain layer with a step decrement of 10% while maintaining αD{sub B} values constant in other layers. Simulation results demonstrate the accuracy (errors < 3%) of high-order (N ≥ 5) linear algorithm in extracting BFIs in different tissue layers and rCBF in deep brain. By contrast, the semi-infinite homogenous solution resulted in substantial errors in rCBF (34.5% ≤ errors ≤ 60.2%) and BFIs in different layers. The Nth-order linear model simplifies data analysis, thus allowing for online data processing and displaying. Future study will test this linear algorithm in heterogeneous tissues with different levels of blood flow variations and noises.
NASA Astrophysics Data System (ADS)
Shang, Yu; Yu, Guoqiang
2014-09-01
Conventional semi-infinite analytical solutions of correlation diffusion equation may lead to errors when calculating blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements in tissues with irregular geometries. Very recently, we created an algorithm integrating a Nth-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in homogenous tissues with arbitrary geometries for extraction of BFI (i.e., αDB). The purpose of this study is to extend the capability of the Nth-order linear algorithm for extracting BFI in heterogeneous tissues with arbitrary geometries. The previous linear algorithm was modified to extract BFIs in different types of tissues simultaneously through utilizing DCS data at multiple source-detector separations. We compared the proposed linear algorithm with the semi-infinite homogenous solution in a computer model of adult head with heterogeneous tissue layers of scalp, skull, cerebrospinal fluid, and brain. To test the capability of the linear algorithm for extracting relative changes of cerebral blood flow (rCBF) in deep brain, we assigned ten levels of αDB in the brain layer with a step decrement of 10% while maintaining αDB values constant in other layers. Simulation results demonstrate the accuracy (errors < 3%) of high-order (N ≥ 5) linear algorithm in extracting BFIs in different tissue layers and rCBF in deep brain. By contrast, the semi-infinite homogenous solution resulted in substantial errors in rCBF (34.5% ≤ errors ≤ 60.2%) and BFIs in different layers. The Nth-order linear model simplifies data analysis, thus allowing for online data processing and displaying. Future study will test this linear algorithm in heterogeneous tissues with different levels of blood flow variations and noises.
Ordered cyclic motifs contribute to dynamic stability in biological and engineered networks
Ma'ayan, Avi; Cecchi, Guillermo A.; Wagner, John; Rao, A. Ravi; Iyengar, Ravi; Stolovitzky, Gustavo
2008-01-01
Representation and analysis of complex biological and engineered systems as directed networks is useful for understanding their global structure/function organization. Enrichment of network motifs, which are over-represented subgraphs in real networks, can be used for topological analysis. Because counting network motifs is computationally expensive, only characterization of 3- to 5-node motifs has been previously reported. In this study we used a supercomputer to analyze cyclic motifs made of 3–20 nodes for 6 biological and 3 technological networks. Using tools from statistical physics, we developed a theoretical framework for characterizing the ensemble of cyclic motifs in real networks. We have identified a generic property of real complex networks, antiferromagnetic organization, which is characterized by minimal directional coherence of edges along cyclic subgraphs, such that consecutive links tend to have opposing direction. As a consequence, we find that the lack of directional coherence in cyclic motifs leads to depletion in feedback loops, where the number of nodes affected by feedback loops appears to be at a local minimum compared with surrogate shuffled networks. This topology provides more dynamic stability in large networks. PMID:19033453
Feng, Yu; Tran, Karen; Bale, Shridhar; Kumar, Shailendra; Guenaga, Javier; Wilson, Richard; de Val, Natalia; Arendt, Heather; DeStefano, Joanne; Ward, Andrew B; Wyatt, Richard T
2016-08-01
In the context of HIV vaccine design and development, HIV-1 spike mimetics displaying a range of stabilities were evaluated to determine whether more stable, well-ordered trimers would more efficiently elicit neutralizing antibodies. To begin, in vitro analysis of trimers derived from the cysteine-stabilized SOSIP platform or the uncleaved, covalently linked NFL platform were evaluated. These native-like trimers, derived from HIV subtypes A, B, and C, displayed a range of thermostabilities, and were "stress-tested" at varying temperatures as a prelude to in vivo immunogenicity. Analysis was performed both in the absence and in the presence of two different adjuvants. Since partial trimer degradation was detected at 37°C before or after formulation with adjuvant, we sought to remedy such an undesirable outcome. Cross-linking (fixing) of the well-ordered trimers with glutaraldehyde increased overall thermostability, maintenance of well-ordered trimer integrity without or with adjuvant, and increased resistance to solid phase-associated trimer unfolding. Immunization of unfixed and fixed well-ordered trimers into animals revealed that the elicited tier 2 autologous neutralizing activity correlated with overall trimer thermostability, or melting temperature (Tm). Glutaraldehyde fixation also led to higher tier 2 autologous neutralization titers. These results link retention of trimer quaternary packing with elicitation of tier 2 autologous neutralizing activity, providing important insights for HIV-1 vaccine design. PMID:27487086
Bale, Shridhar; Kumar, Shailendra; Guenaga, Javier; Wilson, Richard; de Val, Natalia; Arendt, Heather; DeStefano, Joanne; Ward, Andrew B.; Wyatt, Richard T.
2016-01-01
In the context of HIV vaccine design and development, HIV-1 spike mimetics displaying a range of stabilities were evaluated to determine whether more stable, well-ordered trimers would more efficiently elicit neutralizing antibodies. To begin, in vitro analysis of trimers derived from the cysteine-stabilized SOSIP platform or the uncleaved, covalently linked NFL platform were evaluated. These native-like trimers, derived from HIV subtypes A, B, and C, displayed a range of thermostabilities, and were “stress-tested” at varying temperatures as a prelude to in vivo immunogenicity. Analysis was performed both in the absence and in the presence of two different adjuvants. Since partial trimer degradation was detected at 37°C before or after formulation with adjuvant, we sought to remedy such an undesirable outcome. Cross-linking (fixing) of the well-ordered trimers with glutaraldehyde increased overall thermostability, maintenance of well-ordered trimer integrity without or with adjuvant, and increased resistance to solid phase-associated trimer unfolding. Immunization of unfixed and fixed well-ordered trimers into animals revealed that the elicited tier 2 autologous neutralizing activity correlated with overall trimer thermostability, or melting temperature (Tm). Glutaraldehyde fixation also led to higher tier 2 autologous neutralization titers. These results link retention of trimer quaternary packing with elicitation of tier 2 autologous neutralizing activity, providing important insights for HIV-1 vaccine design. PMID:27487086
Grabowski, Ireneusz Śmiga, Szymon; Buksztel, Adam; Fabiano, Eduardo; Teale, Andrew M.; Sala, Fabio Della
2014-07-14
The performance of correlated optimized effective potential (OEP) functionals based on the spin-resolved second-order correlation energy is analysed. The relative importance of singly- and doubly- excited contributions as well as the effect of scaling the same- and opposite- spin components is investigated in detail comparing OEP results with Kohn–Sham (KS) quantities determined via an inversion procedure using accurate ab initio electronic densities. Special attention is dedicated in particular to the recently proposed scaled-opposite–spin OEP functional [I. Grabowski, E. Fabiano, and F. Della Sala, Phys. Rev. B 87, 075103 (2013)] which is the most advantageous from a computational point of view. We find that for high accuracy, a careful, system dependent, selection of the scaling coefficient is required. We analyse several size-extensive approaches for this selection. Finally, we find that a composite approach, named OEP2-SOSh, based on a post-SCF rescaling of the correlation energy can yield high accuracy for many properties, being comparable with the most accurate OEP procedures previously reported in the literature but at substantially reduced computational effort.
Wang, Yang Min; Hättig, Christof; Reine, Simen; Valeev, Edward; Kjærgaard, Thomas; Kristensen, Kasper
2016-05-28
We present the DEC-RIMP2-F12 method where we have augmented the Divide Expand-Consolidate resolution-of-the-identity second-order Møller-Plesset perturbation theory method (DEC-RIMP2) [P. Baudin et al., J. Chem. Phys. 144, 054102 (2016)] with an explicitly correlated (F12) correction. The new method is linear-scaling, massively parallel, and it corrects for the basis set incompleteness error in an efficient manner. In addition, we observe that the F12 contribution decreases the domain error of the DEC-RIMP2 correlation energy by roughly an order of magnitude. An important feature of the DEC scheme is the inherent error control defined by a single parameter, and this feature is also retained for the DEC-RIMP2-F12 method. In this paper we present the working equations for the DEC-RIMP2-F12 method and proof of concept numerical results for a set of test molecules. PMID:27250284
Wang, Jing; Zhu, Shijun; Wang, Haiyan; Cai, Yangjian; Li, Zhenhua
2016-05-30
Recently, we introduced a new class of radially polarized cosine-Gaussian correlated Schell-model (CGCSM) beams of rectangular symmetry based on the partially coherent electromagnetic theory [Opt. Express23, 33099 (2015)]. In this paper, we extend the work to study the second-order statistics such as the average intensity, the spectral degree of coherence, the spectral degree of polarization and the state of polarization in anisotropic turbulence based on an extended von Karman power spectrum with a non-Kolmogorov power law α and an effective anisotropic parameter. Analytical formulas for the cross-spectral density matrix elements of a radially polarized CGCSM beam in anisotropic turbulence are derived. It is found that the second-order statistics are greatly affected by the source correlation function, and the change in the turbulent statistics induces relatively small effect. The significant effect of anisotropic turbulence on the beam parameters mainly appears nearα=3.1, and decreases with the increase of the anisotropic parameter. Furthermore, the polarization state exhibits self-splitting property and each beamlet evolves into a radially polarized structure in the far field. Our work enriches the classical coherence theory and may be important for free-space optical communications. PMID:27410089
Single image correlation for blood flow mapping in complex vessel networks
NASA Astrophysics Data System (ADS)
Chirico, Giuseppe; Sironi, Laura; Bouzin, Margaux; D'Alfonso, Laura; Collini, Maddalena; Ceffa, Nicolo'G.; Marquezin, Cassia
2015-05-01
Microcirculation plays a key role in the maintenance and hemodynamics of tissues and organs also due to its extensive interaction with the immune system. A critical limitation of state-of-the-art clinical techniques to characterize the blood flow is their lack of the spatial resolution required to scale down to individual capillaries. On the other hand the study of the blood flow through auto- or cross-correlation methods fail to correlate the flow speed values with the morphological details required to describe an intricate network of capillaries. Here we propose to use a newly developed technique (FLICS, FLow Image Correlation Spectroscopy) that, by employing a single raster-scanned xy-image acquired in vivo by confocal or multi-photon excitation fluorescence microscopy, allows the quantitative measurement of the blood flow velocity in the whole vessel pattern within the field of view, while simultaneously maintaining the morphological information on the immobile structures of the explored circulatory system. Fluorescent flowing objects produce diagonal lines in the raster-scanned image superimposed to static morphological details. The flow velocity is obtained by computing the Cross Correlation Function (CCF) of the intensity fluctuations detected in pairs of columns of the image. The whole analytical dependence of the CCFs on the flow speed amplitude and the flow direction has been reported recently. We report here the derivation of approximated analytical relations that allows to use the CCF peak lag time and the corresponding CCF value, to directly estimate the flow speed amplitude and the flow direction. The validation has been performed on Zebrafish embryos for which the flow direction was changed systematically by rotating the embryos on the microscope stage. The results indicate that also from the CCF peak lag time it is possible to recover the flow speed amplitude within 13% of uncertainty (overestimation) in a wide range of angles between the flow and
Efficient evaluation of high-order moments and cumulants in tensor network states
NASA Astrophysics Data System (ADS)
West, Colin G.; Garcia-Saez, Artur; Wei, Tzu-Chieh
2015-09-01
We present a numerical scheme for efficiently extracting the higher-order moments and cumulants of various operators on spin systems represented as tensor product states, for both finite and infinite systems, and present several applications for such quantities. For example, the second cumulant of the energy of a state, <Δ H2> , gives a straightforward method to check the convergence of numerical ground-state approximation algorithms. Additionally, we discuss the use of moments and cumulants in the study of phase transitions. Of particular interest is the application of our method to calculate the so-called Binder cumulant, which we use to detect critical points and study the critical exponent of the correlation length with only small finite numerical calculations. We apply these methods to study the behavior of a family of one-dimensional models (the transverse Ising model, the spin-1 Ising model, and the spin-1 Ising model in a crystal field), as well as the two-dimensional Ising model on a square lattice. Our results show that in one dimension, cumulant-based methods can produce precise estimates of the critical points at a low computational cost. The method shows promise for two-dimensional systems as well.
Piao, Xinglin; Hu, Yongli; Sun, Yanfeng; Yin, Baocai; Gao, Junbin
2014-01-01
The emerging low rank matrix approximation (LRMA) method provides an energy efficient scheme for data collection in wireless sensor networks (WSNs) by randomly sampling a subset of sensor nodes for data sensing. However, the existing LRMA based methods generally underutilize the spatial or temporal correlation of the sensing data, resulting in uneven energy consumption and thus shortening the network lifetime. In this paper, we propose a correlated spatio-temporal data collection method for WSNs based on LRMA. In the proposed method, both the temporal consistence and the spatial correlation of the sensing data are simultaneously integrated under a new LRMA model. Moreover, the network energy consumption issue is considered in the node sampling procedure. We use Gini index to measure both the spatial distribution of the selected nodes and the evenness of the network energy status, then formulate and resolve an optimization problem to achieve optimized node sampling. The proposed method is evaluated on both the simulated and real wireless networks and compared with state-of-the-art methods. The experimental results show the proposed method efficiently reduces the energy consumption of network and prolongs the network lifetime with high data recovery accuracy and good stability. PMID:25490583
Automatic tremor detection with a combined cross-correlation and neural network approach
NASA Astrophysics Data System (ADS)
Horstmann, T.; Harrington, R. M.; Cochran, E. S.
2011-12-01
Low-amplitude, long-duration, and ambiguous phase arrivals associated with crustal tremor make automatic detection difficult. We present a new detection method that combines cross-correlation with a neural network clustering algorithm. The approach is independent of any a priori assumptions regarding tremor event duration; instead, it examines frequency content, amplitude, and motion products of continuous data to distinguish tremor from earthquakes and background noise in an automated fashion. Because no assumptions regarding event duration are required, the clustering algorithm is therefore able to detect short, burst-like events which may be missed by many current methods. We detect roughly 130 seismic events occurring over 100 minutes, including earthquakes and tremor, in a three-week long test data set of waveforms recorded near Cholame, California. The detection has a success rate of over 90% when compared to visually selected events. We use continuous broadband data from 13 STS-2 seismometers deployed from May 2010 to July 2011 along the Cholame segment of the San Andreas Fault, as well as stations from the HRSN network. The large volume of waveforms requires first reducing the amount of data before applying the neural network algorithm. First, we filter the data between 2 Hz and 8 Hz, calculate envelopes, and decimate them to 0.2 Hz. We cross-correlate signals at each station with two master stations using a moving 520-second time window with a 5-sec time step. We calculate a mean cross-correlation coefficient value between all station pairs for each time window and each master station, and select the master station with the highest mean value. Time windows with mean coefficients exceeding 0.3 are used in the neural network approach, and windows separated by less than 300 seconds are grouped together. In the second step, we apply the neural network algorithm, i.e., Self Organized Map (SOM), to classify the reduced data set. We first calculate feature
Minimum average correlation energy (MACE) prefilter networks for automatic target recognition
NASA Astrophysics Data System (ADS)
Hobson, Gregory L.; Sims, S. Richard F.; Gader, Paul D.; Keller, James M.
1994-07-01
Minimum average correlation energy (MACE) filters have been shown to be an effective generalization of the synthetic discriminant function (SDF) approach to automatic target recognition. The MACE filter has the advantage of having a very low false alarm rate, but suffers from a diminished probability of detection. Several generalizations have recently been proposed to maintain the low false alarm rate while increasing the probability of detection. The mathematical formulation of the MACE filter can be decomposed into a linear `prefilter' followed by an SDF-like operation. It is the prefiltering portion of the MACE which accounts for the low false alarm rate. In this paper, we insert a nonlinearity in this process by replacing the SDF portion of the operation by a neural network and show that we can increase the probability of detection without sacrificing low false alarm rates. This approach is demonstrated on a standard multiaspect image set and compared to the MACE and its generalizations.
ERIC Educational Resources Information Center
Putnik, Goran; Costa, Eric; Alves, Cátia; Castro, Hélio; Varela, Leonilde; Shah, Vaibhav
2016-01-01
Social network-based engineering education (SNEE) is designed and implemented as a model of Education 3.0 paradigm. SNEE represents a new learning methodology, which is based on the concept of social networks and represents an extended model of project-led education. The concept of social networks was applied in the real-life experiment,…
Dispersion Curve Analyis with Ambient Noise Cross Correlation in Case of Onshore-Offshore Networks
NASA Astrophysics Data System (ADS)
Schmidt, Andreas
2010-05-01
The determination of surface wave Green's functions between two seismic stations by cross correlating seismic noise has become a promising approach to tomography. In this study we emphasise the special application of this method for the case of onshore-offshore networks. The seismic records of 65 three-component land stations (Guralp, Mark, STS-2) and 22 ocean bottom seismographs (OBS, only Hydrophone component) are available for this study. The EGELADOS network was deployed in the southern Aegean from October 2005 to April 2007. Dispersion analysis of resulting seismograms provides group velocity curves which give information about the crustal and uppermost mantle structure that cannot be obtained from earthquake data. For the Aegean region considerable regional differences in crustal structure can be expected. Particular attention is paid to the determination and analysis of dispersive signals in case of Ocean Bottom Seismographs and hydrophones. At land stations we extract clear dispersion curves but OBS seismograms show multiple signals of different velocities. The frequency range and characteristics are dependent on surface structures between the two stations as well as their location. Ray path profiles between two hydrophones with no elevation have a strong acoustic phase with a faint surface wave signal.
Fidelak, Jeremy; Ferrer, Silvia; Oberlin, Michael; Moras, Dino; Dejaegere, Annick; Stote, Roland H
2010-10-01
Peroxisome proliferator-activated receptor-γ nuclear receptor (PPAR-γ) belongs to the superfamily of nuclear receptor proteins that function as ligand-dependent transcription factors and plays a specific physiological role as a regulator of lipid metabolism. A number of experimental studies have suggested that allostery plays an important role in the functioning of PPAR-γ. Here we use normal-mode analysis of PPAR-γ to characterize a network of dynamically coupled amino acids that link physiologically relevant binding surfaces such as the ligand-dependent activation domain AF-2 with the ligand binding site and the heterodimer interface. Multiple calculations were done in both the presence and absence of the agonist rosiglitazone, and the differences in dynamics were characterized. The global dynamics of the ligand binding domain were affected by the ligand, and in particular, changes to the network of dynamically correlated amino acids were observed with only small changes in conformation. These results suggest that changes in dynamic couplings can be functionally significant with respect to the transmission of allosteric signals. PMID:20496064
Fujimoto, Kayo; Valente, Thomas W
2015-01-01
Adolescents interact with their peers in multiple social settings and form various types of peer relationships that affect drinking behavior. Friendship and popularity perceptions constitute critical relationships during adolescence. These two relations are commonly measured by asking students to name their friends, and this network is used to construct drinking exposure and peer status variables. This study takes a multiplex network approach by examining the congruity between friendships and popularity as correlates of adolescent drinking. Using data on friendship and popularity nominations among high school adolescents in Los Angeles, California (N = 1707; five schools), we examined the associations between an adolescent's drinking and drinking by (a) their friends only; (b) multiplexed friendships, friends also perceived as popular; and (c) congruent, multiplexed-friends, close friends perceived as popular. Logistic regression results indicated that friend-only drinking, but not multiplexed-friend drinking, was significantly associated with self-drinking (AOR = 3.51, p < 0.05). However, congruent, multiplexed-friend drinking also was associated with self-drinking (AOR = 3.10, p < 0.05). This study provides insight into how adolescent health behavior is predicated on the multiplexed nature of peer relationships. The results have implications for the design of health promotion interventions for adolescent drinking. PMID:24913275
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
Magnetic order, magnetic correlations, and spin dynamics in the pyrochlore antiferromagnet Er2Ti2O7
NASA Astrophysics Data System (ADS)
Dalmas de Réotier, P.; Yaouanc, A.; Chapuis, Y.; Curnoe, S. H.; Grenier, B.; Ressouche, E.; Marin, C.; Lago, J.; Baines, C.; Giblin, S. R.
2012-09-01
Er2Ti2O7 is believed to be a realization of an XY antiferromagnet on a frustrated lattice of corner-sharing regular tetrahedra. It is presented as an example of the order-by-disorder mechanism in which fluctuations lift the degeneracy of the ground state, leading to an ordered state. Here we report detailed measurements of the low-temperature magnetic properties of Er2Ti2O7, which displays a second-order phase transition at TN≃1.2 K with coexisting short- and long-range orders. Magnetic susceptibility studies show that there is no spin-glass-like irreversible effect. Heat capacity measurements reveal that the paramagnetic critical exponent is typical of a 3-dimensional XY magnet while the low-temperature specific heat sets an upper limit on the possible spin-gap value and provides an estimate for the spin-wave velocity. Muon spin relaxation measurements show the presence of spin dynamics in the nanosecond time scale down to 21 mK. This time range is intermediate between the shorter time characterizing the spin dynamics in Tb2Sn2O7, which also displays long- and short-range magnetic order, and the time scale typical of conventional magnets. Hence the ground state is characterized by exotic spin dynamics. We determine the parameters of a symmetry-dictated Hamiltonian restricted to the spins in a tetrahedron, by fitting the paramagnetic diffuse neutron scattering intensity for two reciprocal lattice planes. These data are recorded in a temperature region where the assumption that the correlations are limited to nearest neighbors is fair.
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
Occurrence of magnetoelectric effect correlated to the Dy order in Dy2NiMnO6 double perovskite
NASA Astrophysics Data System (ADS)
Masud, Md G.; Dey, K.; Ghosh, A.; Majumdar, S.; Giri, S.
2015-08-01
Magnetic, dielectric, and ac conductivity as well as room temperature structural and Raman studies are performed on double perovskite Dy2NiMnO6. The crystal structure of the compound adopts monoclinic P21/n space group, where alternate Mn and Ni distorted octahedral are arranged in anti-phase a- a- b+ order in Glazer notation. Magnetization studies show two magnetic transitions around 100 K and 20 K which are related to the ordering of transition and rare earth cations moment, respectively. Temperature dependent dielectric permittivity shows Havriliak-Negami type thermally activated dielectric relaxation. The ac conductivity at different temperature is found to follow Jonscher power law behavior. Time-temperature scaling of the conductivity spectra reveals that the charge transport dynamics is independent of temperature. Intriguingly, an anomaly in the dielectric constant is observed close to the order of Dy moment which indicates intrinsic magnetoelectric coupling. The hybridization between Dy and Ni/Mn is suggested to be correlated with the magnetoelectric coupling.
Wang, Heping; Dai, Wenkui; Qiu, Chuangzhao; Li, Shuaicheng; Wang, Wenjian; Xu, Jianqiang; Li, Zhichuan; Wang, Hongmei; Li, Yuzheng; Yang, Zhenyu; Feng, Xin; Zhou, Qian; Han, Lijuan; Li, Yinhu
2016-01-01
Pneumonia is one of the most serious diseases for children, with which lung microbiota are proved to be associated. We performed 16S rDNA analysis on broncho-alveolar lavage fluid (BALF) for 32 children with tracheomalacia (C group), pneumonia infected with Streptococcus pneumoniae (S. pneumoniae) (D1 group) or Mycoplasma pneumoniae (M. pneumoniae) (D2 group). Children with tracheomalacia held lower microbial diversity and accumulated Lactococcus (mean ± SD, 45.21%±5.07%, P value <0.05), Porphyromonas (0.12%±0.31%, P value <0.05). D1 and D2 group were enriched by Streptococcus (7.57%±11.61%, P value <0.01 when compared with D2 group) and Mycoplasma (0.67%±1.25%, P value <0.01) respectively. Bacterial correlation in C group was mainly intermediated by Pseudomonas and Arthrobacter. Whilst, D1 group harbored simplest microbial correlation in three groups, and D2 group held the most complicated network, involving enriched Staphylococcus (0.26%±0.71%), Massilia (0.81%±2.42%). This will be of significance for understanding pneumonia incidence and progression more comprehensively, and discerning between bacterial infection and carriage. PMID:27293852
Wang, Heping; Dai, Wenkui; Qiu, Chuangzhao; Li, Shuaicheng; Wang, Wenjian; Xu, Jianqiang; Li, Zhichuan; Wang, Hongmei; Li, Yuzheng; Yang, Zhenyu; Feng, Xin; Zhou, Qian; Han, Lijuan; Li, Yinhu; Zheng, Yuejie
2016-06-01
Pneumonia is one of the most serious diseases for children, with which lung microbiota are proved to be associated. We performed 16S rDNA analysis on broncho-alveolar lavage fluid (BALF) for 32 children with tracheomalacia (C group), pneumonia infected with Streptococcus pneumoniae (S. pneumoniae) (D1 group) or Mycoplasma pneumoniae (M. pneumoniae) (D2 group). Children with tracheomalacia held lower microbial diversity and accumulated Lactococcus (mean ± SD, 45.21%±5.07%, P value <0.05), Porphyromonas (0.12%±0.31%, P value <0.05). D1 and D2 group were enriched by Streptococcus (7.57%±11.61%, P value <0.01 when compared with D2 group) and Mycoplasma (0.67%±1.25%, P value <0.01) respectively. Bacterial correlation in C group was mainly intermediated by Pseudomonas and Arthrobacter. Whilst, D1 group harbored simplest microbial correlation in three groups, and D2 group held the most complicated network, involving enriched Staphylococcus (0.26%±0.71%), Massilia (0.81%±2.42%). This will be of significance for understanding pneumonia incidence and progression more comprehensively, and discerning between bacterial infection and carriage. PMID:27293852
Parallel retrieval of correlated patterns: from Hopfield networks to Boltzmann machines.
Agliari, Elena; Barra, Adriano; De Antoni, Andrea; Galluzzi, Andrea
2013-02-01
In this work, we first revise some extensions of the standard Hopfield model in the low storage limit, namely the correlated attractor case and the multitasking case recently introduced by the authors. The former case is based on a modification of the Hebbian prescription, which induces a coupling between consecutive patterns and this effect is tuned by a parameter a. In the latter case, dilution is introduced in pattern entries, in such a way that a fraction d of them is blank. Then, we merge these two extensions to obtain a system able to retrieve several patterns in parallel and the quality of retrieval, encoded by the set of Mattis magnetizations {m(μ)}, is reminiscent of the correlation among patterns. By tuning the parameters d and a, qualitatively different outputs emerge, ranging from highly hierarchical to symmetric. The investigations are accomplished by means of both numerical simulations and statistical mechanics analysis, properly adapting a novel technique originally developed for spin glasses, i.e. the Hamilton-Jacobi interpolation, with excellent agreement. Finally, we show the thermodynamical equivalence of this associative network with a (restricted) Boltzmann machine and study its stochastic dynamics to obtain even a dynamical picture, perfectly consistent with the static scenario earlier discussed. PMID:23246601
NASA Astrophysics Data System (ADS)
Payne, Joshua L.; Harris, Kameron Decker; Dodds, Peter Sheridan
2011-07-01
We derive analytic expressions for the possibility, probability, and expected size of global spreading events starting from a single infected seed for a broad collection of contagion processes acting on random networks with both directed and undirected edges and arbitrary degree-degree correlations. Our work extends previous theoretical developments for the undirected case, and we provide numerical support for our findings by investigating an example class of networks for which we are able to obtain closed-form expressions.
NASA Astrophysics Data System (ADS)
Carpi, Laura; Masoller, Cristina; Díaz-Guilera, Albert; Ravetti, Martín G.
2015-04-01
During the period between the mid-1990s and late 2000s Australia had suffered one of the worst droughts on record. Severe rainfall deficits affected great part of southeast Australia, causing widespread drought conditions and catastrophic bushfires. The "Millennium Drought", as it was called, was unusual in terms of its severity, duration and extent, leaving important environmental and financial damages. One of the most important drivers of Australia climate variability is the Indian Ocean dipole (IOD), that is a coupled ocean and atmosphere phenomenon in the equatorial Indian Ocean. The IOD is measured by an index (DMI) that is the difference between sea surface temperature (SST) anomalies in the western and eastern equatorial Indian Ocean. Its positive phase is characterized by lower than normal sea surface temperatures in the tropical eastern coast, and higher than normal in the tropical western Indian Ocean. Extreme positive IOD (pIOD) events are associated to severe droughts in countries located over the eastern Indian Ocean, and to severe floods in the western tropical ones. Recent research works projected that the frequency of extreme pIOD events will increase significantly over the twenty-first century and consequently, the frequency of extreme climate conditions in the zones affected by it. In this work we study the dynamics of the Indian Ocean for the period of 1979-2014, by using climate networks of skin temperature and humidity (reanalysis data). Annual networks are constructed by creating links when the Pearson correlation coefficient between two nodes is greater than a specific value. The distance distribution Pd(k), that indicates the fraction of pairs of nodes at distance k, is computed to characterize the dynamics of the network by using Information Theory quantifiers. We found a clear change in the Indian Ocean dynamics and an increment in the network's similarities quantified by the Jensen-Shannon divergence in the late 1990s. We speculate that
Distinct behavioural and network correlates of two interneuron types in prefrontal cortex
Kvitsiani, D.; Ranade, S.; Hangya, B.; Taniguchi, H.; Huang, JZ.; Kepecs, A.
2013-01-01
Neurons in prefrontal cortex exhibit diverse behavioural correlates1–4, an observation that has been attributed to cell-type diversity. To link identified neuron types with network and behavioural functions, we recorded from the two largest genetically-defined inhibitory interneuron classes, the perisomatically-targeting parvalbumin (Pv) and the dendritically-targeting somatostatin (Som) neurons5–8 in anterior cingulate cortex (ACC) of mice performing a reward foraging task. Here we show that Pv and a subtype of Som neurons form functionally homogeneous populations showing a double dissociation between both their inhibitory impact and behavioural correlates. Out of a number of events pertaining to behaviour, a subtype of Som neurons selectively responded at reward approach, while Pv neurons responded at reward leaving encoding preceding stay duration. These behavioural correlates of Pv and Som neurons defined a behavioural epoch and a decision variable important for foraging (whether to stay or to leave), a crucial function attributed to ACC9–11. Furthermore, Pv neurons could fire in millisecond synchrony exerting fast and powerful inhibition on principal cell firing, while the inhibitory impact of Som neurons on firing output was weak and more variable, consistent with the idea that they respectively control the outputs of and inputs to principal neurons12–16. These results suggest a connection between the circuit-level function of different interneuron-types in regulating the flow of information, and the behavioural functions served by the cortical circuits. Moreover these observations bolster the hope that functional response diversity during behaviour can in part be explained by cell-type diversity. PMID:23708967
Mental disorder recovery correlated with centralities and interactions on an online social network.
Ma, Xinpei; Sayama, Hiroki
2015-01-01
Recent research has established both a theoretical basis and strong empirical evidence that effective social behavior plays a beneficial role in the maintenance of physical and psychological well-being of people. To test whether social behavior and well-being are also associated in online communities, we studied the correlations between the recovery of patients with mental disorders and their behaviors in online social media. As the source of the data related to the social behavior and progress of mental recovery, we used PatientsLikeMe (PLM), the world's first open-participation research platform for the development of patient-centered health outcome measures. We first constructed an online social network structure based on patient-to-patient ties among 200 patients obtained from PLM. We then characterized patients' online social activities by measuring the numbers of "posts and views" and "helpful marks" each patient obtained. The patients' recovery data were obtained from their self-reported status information that was also available on PLM. We found that some node properties (in-degree, eigenvector centrality and PageRank) and the two online social activity measures were significantly correlated with patients' recovery. Furthermore, we re-collected the patients' recovery data two months after the first data collection. We found significant correlations between the patients' social behaviors and the second recovery data, which were collected two months apart. Our results indicated that social interactions in online communities such as PLM were significantly associated with the current and future recoveries of patients with mental disorders. PMID:26312174
Mental disorder recovery correlated with centralities and interactions on an online social network
Sayama, Hiroki
2015-01-01
Recent research has established both a theoretical basis and strong empirical evidence that effective social behavior plays a beneficial role in the maintenance of physical and psychological well-being of people. To test whether social behavior and well-being are also associated in online communities, we studied the correlations between the recovery of patients with mental disorders and their behaviors in online social media. As the source of the data related to the social behavior and progress of mental recovery, we used PatientsLikeMe (PLM), the world’s first open-participation research platform for the development of patient-centered health outcome measures. We first constructed an online social network structure based on patient-to-patient ties among 200 patients obtained from PLM. We then characterized patients’ online social activities by measuring the numbers of “posts and views” and “helpful marks” each patient obtained. The patients’ recovery data were obtained from their self-reported status information that was also available on PLM. We found that some node properties (in-degree, eigenvector centrality and PageRank) and the two online social activity measures were significantly correlated with patients’ recovery. Furthermore, we re-collected the patients’ recovery data two months after the first data collection. We found significant correlations between the patients’ social behaviors and the second recovery data, which were collected two months apart. Our results indicated that social interactions in online communities such as PLM were significantly associated with the current and future recoveries of patients with mental disorders. PMID:26312174
Tomasi, D.; Fowler, J.; Tomasi, D.; Volkow, N.D.; Wang, R.L.; Telang, F.; Wang, Chang, L.; Ernst, T.; /Fowler, J.S.
2009-06-01
Dopamine and dopamine transporters (DAT, which regulate extracellular dopamine in the brain) are implicated in the modulation of attention but their specific roles are not well understood. Here we hypothesized that dopamine modulates attention by facilitation of brain deactivation in the default mode network (DMN). Thus, higher striatal DAT levels, which would result in an enhanced clearance of dopamine and hence weaker dopamine signals, would be associated to lower deactivation in the DMN during an attention task. For this purpose we assessed the relationship between DAT in striatum (measured with positron emission tomography and [{sup 11}C]cocaine used as DAT radiotracer) and brain activation and deactivation during a parametric visual attention task (measured with blood oxygenation level dependent functional magnetic resonance imaging) in healthy controls. We show that DAT availability in caudate and putamen had a negative correlation with deactivation in ventral parietal regions of the DMN (precuneus, BA 7) and a positive correlation with deactivation in a small region in the ventral anterior cingulate gyrus (BA 24/32). With increasing attentional load, DAT in caudate showed a negative correlation with load-related deactivation increases in precuneus. These findings provide evidence that dopamine transporters modulate neural activity in the DMN and anterior cingulate gyrus during visuospatial attention. Our findings suggest that dopamine modulates attention in part by regulating neuronal activity in posterior parietal cortex including precuneus (region involved in alertness) and cingulate gyrus (region deactivated in proportion to emotional interference). These findings suggest that the beneficial effects of stimulant medications (increase dopamine by blocking DAT) in inattention reflect in part their ability to facilitate the deactivation of the DMN.
Supekar, Kaustubh; Menon, Vinod
2012-02-01
Cognitive skills undergo protracted developmental changes resulting in proficiencies that are a hallmark of human cognition. One skill that develops over time is the ability to problem solve, which in turn relies on cognitive control and attention abilities. Here we use a novel multimodal neurocognitive network-based approach combining task-related fMRI, resting-state fMRI and diffusion tensor imaging (DTI) to investigate the maturation of control processes underlying problem solving skills in 7-9 year-old children. Our analysis focused on two key neurocognitive networks implicated in a wide range of cognitive tasks including control: the insula-cingulate salience network, anchored in anterior insula (AI), ventrolateral prefrontal cortex and anterior cingulate cortex, and the fronto-parietal central executive network, anchored in dorsolateral prefrontal cortex and posterior parietal cortex (PPC). We found that, by age 9, the AI node of the salience network is a major causal hub initiating control signals during problem solving. Critically, despite stronger AI activation, the strength of causal regulatory influences from AI to the PPC node of the central executive network was significantly weaker and contributed to lower levels of behavioral performance in children compared to adults. These results were validated using two different analytic methods for estimating causal interactions in fMRI data. In parallel, DTI-based tractography revealed weaker AI-PPC structural connectivity in children. Our findings point to a crucial role of AI connectivity, and its causal cross-network influences, in the maturation of dynamic top-down control signals underlying cognitive development. Overall, our study demonstrates how a unified neurocognitive network model when combined with multimodal imaging enhances our ability to generalize beyond individual task-activated foci and provides a common framework for elucidating key features of brain and cognitive development. The
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. PMID:22570412
Finley, Anna J.; Tang, David; Schmeichel, Brandon J.
2015-01-01
Prior research has found that persons who favor more analytic modes of thought are less religious. We propose that individual differences in analytic thought are associated with reduced religious beliefs particularly when analytic thought is measured (hence, primed) first. The current study provides a direct replication of prior evidence that individual differences in analytic thinking are negatively related to religious beliefs when analytic thought is measured before religious beliefs. When religious belief is measured before analytic thinking, however, the negative relationship is reduced to non-significance, suggesting that the link between analytic thought and religious belief is more tenuous than previously reported. The current study suggests that whereas inducing analytic processing may reduce religious belief, more analytic thinkers are not necessarily less religious. The potential for measurement order to inflate the inverse correlation between analytic thinking and religious beliefs deserves additional consideration. PMID:26402334
NASA Astrophysics Data System (ADS)
Räth, Christoph; Müller, Dirk; Sidorenko, Irina; Monetti, Roberto; Bauer, Jan
2010-03-01
The quantitative characterization of images showing tissue probes being visualized by e.g. CT or MR is of great interest in many fields of medical image analysis. A proper quantification of the information content in such images can be realized by calculating well-suited texture measures, which are able to capture the main characteristics of the image structures under study. Using test images showing the complex trabecular structure of the inner bone of a healthy and osteoporotic patient we propose and apply a novel statistical framework, with which one can systematically assess the sensitivity of texture measures to scale-dependent higher order correlations (HOCs). To this end, so-called surrogate images are generated, in which the linear properties are exactly preserved, while parts of the higher order correlations (if present) are wiped out in a scale dependent manner. This is achieved by dedicated Fourier phase shuffling techniques. We compare three commonly used classes of texture measures, namely spherical Mexican hat wavelets (SMHW), Minkowski functionals (MF) and scaling indices (SIM). While the SMHW were sensitive to HOCs on small scales (Significance S=19-23), the MF and SIM could detect the HOCs very well for the larger scales (S = 39 (MF) and S = 29 (SIM)). Thus the three classes of texture measures are complimentary with respect to their ability to detect scaledependent HOCs. The MF and SIM are, however, slightly preferable, because they are more sensitive to HOCs on length scales, which the important structural elements, i.e. the trabeculae, are considered to have.
NASA Astrophysics Data System (ADS)
Phillips, Jordan J.; Zgid, Dominika
2014-06-01
We report an implementation of self-consistent Green's function many-body theory within a second-order approximation (GF2) for application with molecular systems. This is done by iterative solution of the Dyson equation expressed in matrix form in an atomic orbital basis, where the Green's function and self-energy are built on the imaginary frequency and imaginary time domain, respectively, and fast Fourier transform is used to efficiently transform these quantities as needed. We apply this method to several archetypical examples of strong correlation, such as a H32 finite lattice that displays a highly multireference electronic ground state even at equilibrium lattice spacing. In all cases, GF2 gives a physically meaningful description of the metal to insulator transition in these systems, without resorting to spin-symmetry breaking. Our results show that self-consistent Green's function many-body theory offers a viable route to describing strong correlations while remaining within a computationally tractable single-particle formalism.
Phillips, Jordan J. Zgid, Dominika
2014-06-28
We report an implementation of self-consistent Green's function many-body theory within a second-order approximation (GF2) for application with molecular systems. This is done by iterative solution of the Dyson equation expressed in matrix form in an atomic orbital basis, where the Green's function and self-energy are built on the imaginary frequency and imaginary time domain, respectively, and fast Fourier transform is used to efficiently transform these quantities as needed. We apply this method to several archetypical examples of strong correlation, such as a H{sub 32} finite lattice that displays a highly multireference electronic ground state even at equilibrium lattice spacing. In all cases, GF2 gives a physically meaningful description of the metal to insulator transition in these systems, without resorting to spin-symmetry breaking. Our results show that self-consistent Green's function many-body theory offers a viable route to describing strong correlations while remaining within a computationally tractable single-particle formalism.
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
Liu, Rong; Lv, Qiao-Li; Yu, Jing; Hu, Lei; Zhang, Li-Hua; Cheng, Yu; Zhou, Hong-Hao
2015-06-01
We aimed to investigate the association between gene co-expression modules and responses to neoadjuvant chemotherapy in breast cancer by using a systematic biological approach. The gene expression profiles and clinico-pathological data of 508 (discovery set) and 740 (validation set) patients with breast cancer who received neoadjuvant chemotherapy were analyzed. Weighted gene co-expression network analysis was performed and identified seven co-regulated gene modules. Each module and gene signature were evaluated with logistic regression models for pathological complete response (pCR). The association between modules and pCR in each intrinsic molecular subtype was also investigated. Two transcriptional modules were correlated with tumor grade, estrogen receptor status, progesterone receptor status, and chemotherapy response in breast cancer. One module that constitutes upregulated cell proliferation genes was associated with a high probability for pCR in the whole (odds ratio (OR) = 5.20 and 3.45 in the discovery and validation datasets, respectively), luminal B, and basal-like subtypes. The prognostic potentials of novel genes, such as MELK, and pCR-related genes, such as ESR1 and TOP2A, were identified. The upregulation of another gene co-expression module was associated with weak chemotherapy responses (OR = 0.19 and 0.33 in the discovery and validation datasets, respectively). The novel gene CA12 was identified as a potential prognostic indicator in this module. A systems biology network-based approach may facilitate the discovery of biomarkers for predicting chemotherapy responses in breast cancer and contribute in developing personalized medicines. PMID:25981901
Charge and Spin Ordering in Insulator Na0.5CoO2: Effects of Correlation and Symmetry
NASA Astrophysics Data System (ADS)
Lee, Kwan-Woo; Pickett, Warren
2006-03-01
The discovery by Takada and coworkers of superconductivity in Na0.3CoO21.3 H2O near 5K has led to extensive studies of the rich variation of properties in the NaxCoO2 system (0.2 <=x <=1), which has a triangular lattice of Co sites and a layered structure. In addition, specifically at x=0.5, the system has been observed to undergo a charge disproportionation (2Co^3.5+ -> Co^3++Co^4+) and metal-insulator transition at 50 K, while the rest of the phase diagram is metallic. We will present results of studies of charge disproportionation and charge- and spin-ordering in insulating in Na0.5CoO2, applying ab initio band theory including correlations due to intra-atomic repulsion. Various ordering patterns (zigzag and two striped) for four-Co supercells are analyzed before focusing on the observed ``out-of-phase stripe'' pattern of antiferromagnetic Co^4+ spins along charge-ordered stripes. This pattern relieves frustration and shows distinct analogies with the cuprate layers: a bipartite lattice of antialigned spins, with axes at 90^o angles. Substantial distinctions with cuprates are also discussed, including the tiny gap of a new variant of ``charge transfer'' type within the Co 3d system. [References] [1] K. Takada et al., Nature 422, 53 (2003). [2] M. L. Foo et al., Phys. Rev. Lett. 92,247001 (2004). [3] K.-W. Lee, J. Kunes, P. Novak, and W. E. Pickett, Phys. Rev. Lett. 94, 026403 (2005). [4] K.-W. Lee and W. E. Pickett, cond-mat/0510555.
Shang, Yu; Lin, Yu; Yu, Guoqiang; Li, Ting; Chen, Lei; Toborek, Michal
2014-05-12
Conventional semi-infinite solution for extracting blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements may cause errors in estimation of BFI (αD{sub B}) in tissues with small volume and large curvature. We proposed an algorithm integrating Nth-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in tissue for the extraction of αD{sub B}. The volume and geometry of the measured tissue were incorporated in the Monte Carlo simulation, which overcome the semi-infinite restrictions. The algorithm was tested using computer simulations on four tissue models with varied volumes/geometries and applied on an in vivo stroke model of mouse. Computer simulations shows that the high-order (N ≥ 5) linear algorithm was more accurate in extracting αD{sub B} (errors < ±2%) from the noise-free DCS data than the semi-infinite solution (errors: −5.3% to −18.0%) for different tissue models. Although adding random noises to DCS data resulted in αD{sub B} variations, the mean values of errors in extracting αD{sub B} were similar to those reconstructed from the noise-free DCS data. In addition, the errors in extracting the relative changes of αD{sub B} using both linear algorithm and semi-infinite solution were fairly small (errors < ±2.0%) and did not rely on the tissue volume/geometry. The experimental results from the in vivo stroke mice agreed with those in simulations, demonstrating the robustness of the linear algorithm. DCS with the high-order linear algorithm shows the potential for the inter-subject comparison and longitudinal monitoring of absolute BFI in a variety of tissues/organs with different volumes/geometries.
NASA Astrophysics Data System (ADS)
Gardezi, A.; Umer, T.; Butt, F.; Young, R. C. D.; Chatwin, C. R.
2016-04-01
A spatial domain optimal trade-off Maximum Average Correlation Height (SPOT-MACH) filter has been previously developed and shown to have advantages over frequency domain implementations in that it can be made locally adaptive to spatial variations in the input image background clutter and normalised for local intensity changes. The main concern for using the SPOT-MACH is its computationally intensive nature. However in the past enhancements techniques were proposed for the SPOT-MACH to make its execution time comparable to its frequency domain counterpart. In this paper a novel approach is discussed which uses VANET parameters coupled with the SPOT-MACH in order to minimise the extensive processing of the large video dataset acquired from the Pakistan motorways surveillance system. The use of VANET parameters gives us an estimation criterion of the flow of traffic on the Pakistan motorway network and acts as a precursor to the training algorithm. The use of VANET in this scenario would contribute heavily towards the computational complexity minimization of the proposed monitoring system.
Learning invariant object recognition from temporal correlation in a hierarchical network.
Lessmann, Markus; Würtz, Rolf P
2014-06-01
Invariant object recognition, which means the recognition of object categories independent of conditions like viewing angle, scale and illumination, is a task of great interest that humans can fulfill much better than artificial systems. During the last years several basic principles were derived from neurophysiological observations and careful consideration: (1) Developing invariance to possible transformations of the object by learning temporal sequences of visual features that occur during the respective alterations. (2) Learning in a hierarchical structure, so basic level (visual) knowledge can be reused for different kinds of objects. (3) Using feedback to compare predicted input with the current one for choosing an interpretation in the case of ambiguous signals. In this paper we propose a network which implements all of these concepts in a computationally efficient manner which gives very good results on standard object datasets. By dynamically switching off weakly active neurons and pruning weights computation is sped up and thus handling of large databases with several thousands of images and a number of categories in a similar order becomes possible. The involved parameters allow flexible adaptation to the information content of training data and allow tuning to different databases relatively easily. Precondition for successful learning is that training images are presented in an order assuring that images of the same object under similar viewing conditions follow each other. Through an implementation with sparse data structures the system has moderate memory demands and still yields very good recognition rates. PMID:24657573
Ossandón, Tomas; Jerbi, Karim; Vidal, Juan R; Bayle, Dimitri J; Henaff, Marie-Anne; Jung, Julien; Minotti, Lorella; Bertrand, Olivier; Kahane, Philippe; Lachaux, Jean-Philippe
2011-10-12
Task performance is associated with increased brain metabolism but also with prominent deactivation in specific brain structures known as the default-mode network (DMN). The role of DMN deactivation remains enigmatic in part because its electrophysiological correlates, temporal dynamics, and link to behavior are poorly understood. Using extensive depth electrode recordings in humans, we provide first electrophysiological evidence for a direct correlation between the dynamics of power decreases in the DMN and individual subject behavior. We found that all DMN areas displayed transient suppressions of broadband gamma (60-140 Hz) power during performance of a visual search task and, critically, we show for the first time that the millisecond range duration and extent of the transient gamma suppressions are correlated with task complexity and subject performance. In addition, trial-by-trial correlations revealed that spatially distributed gamma power increases and decreases formed distinct anticorrelated large-scale networks. Beyond unraveling the electrophysiological basis of DMN dynamics, our results suggest that, rather than indicating a mere switch to a global exteroceptive mode, DMN deactivation encodes the extent and efficiency of our engagement with the external world. Furthermore, our findings reveal a pivotal role for broadband gamma modulations in the interplay between task-positive and task-negative networks mediating efficient goal-directed behavior and facilitate our understanding of the relationship between electrophysiology and neuroimaging studies of intrinsic brain networks. PMID:21994368
Ordering Subjects: Actor-Networks and Intellectual Technologies in Lifelong Learning.
ERIC Educational Resources Information Center
Edwards, Richard
2003-01-01
Argues that discourses of lifelong learning act as intellectual technologies that construct individuals as subjects in a learning society. Discuses three discourses using actor-network theory: (1) economics/human capital (individuals as accumulators of skills for competitiveness); (2) humanistic psychology (individuals seeking fulfilment through…
A taxonomy of health networks and systems: bringing order out of chaos.
Bazzoli, G J; Shortell, S M; Dubbs, N; Chan, C; Kralovec, P
1999-01-01
OBJECTIVE: To use existing theory and data for empirical development of a taxonomy that identifies clusters of organizations sharing common strategic/structural features. DATA SOURCES: Data from the 1994 and 1995 American Hospital Association Annual Surveys, which provide extensive data on hospital involvement in hospital-led health networks and systems. STUDY DESIGN: Theories of organization behavior and industrial organization economics were used to identify three strategic/structural dimensions: differentiation, which refers to the number of different products/services along a healthcare continuum; integration, which refers to mechanisms used to achieve unity of effort across organizational components; and centralization, which relates to the extent to which activities take place at centralized versus dispersed locations. These dimensions were applied to three components of the health service/product continuum: hospital services, physician arrangements, and provider-based insurance activities. DATA EXTRACTION METHODS: We identified 295 health systems and 274 health networks across the United States in 1994, and 297 health systems and 306 health networks in 1995 using AHA data. Empirical measures aggregated individual hospital data to the health network and system level. PRINCIPAL FINDINGS: We identified a reliable, internally valid, and stable four-cluster solution for health networks and a five-cluster solution for health systems. We found that differentiation and centralization were particularly important in distinguishing unique clusters of organizations. High differentiation typically occurred with low centralization, which suggests that a broader scope of activity is more difficult to centrally coordinate. Integration was also important, but we found that health networks and systems typically engaged in both ownership-based and contractual-based integration or they were not integrated at all. CONCLUSIONS: Overall, we were able to classify approximately 70
Zheng, Xue-Li; Song, Ji-Peng; Ling, Tao; Hu, Zhen Peng; Yin, Peng-Fei; Davey, Kenneth; Du, Xi-Wen; Qiao, Shi-Zhang
2016-06-01
Strongly coupled Nafion molecules and ordered porous CdS networks are fabricated for visible-light photoelectrochemical (PEC) hydrogen evolution. The Nafion layer coating shifts the band position of CdS upward and accelerates charge transfer in the photoelectrode/electrolyte interface. It is highly expected that the strong coupling effect between organic and inorganic materials will provide new routes to advance PEC water splitting. PMID:27038367
NASA Astrophysics Data System (ADS)
Wang, Xingyuan; Xu, Bing; Luo, Chao
2012-11-01
This paper proposes a novel asynchronous communication scheme. Based on this scheme, a model using the hyperchaotic system of 6th-order Cellular Neural Network (CNN) is designed. This scheme enhances the security of asynchronous communication compared to the conventional ones. It is noteworthy that the proposed communication scheme does not depend on synchronization, and almost all chaotic systems can be involved in this scheme. Numerical simulations show the effectiveness of this scheme.
FlowerNet: A Gene Expression Correlation Network for Anther and Pollen Development1[OPEN
Pearce, Simon; Ferguson, Alison; King, John; Wilson, Zoe A.
2015-01-01
Floral formation, in particular anther and pollen development, is a complex biological process with critical importance for seed set and for targeted plant breeding. Many key transcription factors regulating this process have been identified; however, their direct role remains largely unknown. Using publicly available gene expression data from Arabidopsis (Arabidopsis thaliana), focusing on those studies that analyze stamen-, pollen-, or flower-specific expression, we generated a network model of the global transcriptional interactions (FlowerNet). FlowerNet highlights clusters of genes that are transcriptionally coregulated and therefore likely to have interacting roles. Focusing on four clusters, and using a number of data sets not included in the generation of FlowerNet, we show that there is a close correlation in how the genes are expressed across a variety of conditions, including male-sterile mutants. This highlights the important role that FlowerNet can play in identifying new players in anther and pollen development. However, due to the use of general floral expression data in FlowerNet, it also has broad application in the characterization of genes associated with all aspects of floral development and reproduction. To aid the dissection of genes of interest, we have made FlowerNet available as a community resource (http://www.cpib.ac.uk/anther). For this resource, we also have generated plots showing anther/flower expression from a variety of experiments: These are normalized together where possible to allow further dissection of the resource. PMID:25667314
Reproductive correlates of social network variation in plurally breeding degus (Octodon degus)
Wey, Tina W.; Burger, Joseph R.; Ebensperger, Luis A.; Hayes, Loren D.
2013-01-01
Studying the causes and reproductive consequences of social variation can provide insight into the evolutionary basis of sociality. Individuals are expected to behave adaptively to maximize reproductive success, but reproductive outcomes can also depend on group structure. Degus (Octodon degus) are plurally breeding rodents, in which females allonurse indiscriminately. However, communal rearing does not appear to enhance female reproductive success, and larger group sizes are correlated with decreasing per capita pup production. To further investigate mechanisms underlying these patterns, we asked how differences in sex, season and average group reproductive success are related to degu association networks. We hypothesized that if reproductive differences mirror social relationships, then females (core group members) should show stronger and more stable associations than males, and female association strength should be strongest during lactation. We also hypothesized that, at the group level, social cohesion would increase reproductive output, while social conflict would decrease it. Females did have higher association strength and more preferred partners than males, but only during lactation, when overall female associations increased. Females also had more stable preferred social partnerships between seasons. A measure of social cohesion (average association strength) was not related to per capita pup production of female group members, but potential social conflict (heterogeneity of association strengths) was negatively related to per capita pup production of female group members. Our results highlight temporal and multilevel patterns of social structure that may reflect reproductive costs and benefits to females. PMID:24511149
NASA Technical Reports Server (NTRS)
Cassady, K.; Ruitenberg, M.; Koppelmans, V.; DeDios, Y.; Gadd, N.; Wood, S.; Reuter-Lorenz, P.; Riascos, R.; Kofman, I.; Bloomberg, J.; Mulavara, A.; Seidler, R.
2016-01-01
Sensorimotor adaptation is a type of procedural motor learning that enables individuals to preserve accurate movements in the presence of external or internal perturbations. Adaptation learning can be divided into an early, more cognitively demanding stage, and a later, more automatic stage. In recent years, several investigations have identified significant associations between sensorimotor adaptation and brain structure and function. However, the question of whether individual variability in functional connectivity strength is predictive of sensorimotor adaptation performance has been largely unaddressed. In the present study, we investigate whether such variability in early sensorimotor adaptation is associated with individual differences in resting-state functional connectivity. We used resting state functional magnetic resonance imaging (rs-fMRI) to estimate functional connectivity strength using hypothesis-driven (seed-to-voxel) and hypothesis-free (voxel-to-voxel) approaches. For the hypothesis-driven analysis, we selected several regions of interest (ROIs) from sensorimotor and default mode networks of the brain. We then correlated these connectivity measures with the rate of early learning during a visuomotor adaptation task in 16 healthy participants. For this task, participants lay supine in the MRI scanner and moved an MRI-compatible dual axis joystick with their right hand to hit targets presented on a screen. Each movement was initiated from the central position on the display screen. Participants were instructed to move the cursor to the target as quickly as possible by moving the joystick, and to hold the cursor within the target until it disappeared. They were then instructed to release the joystick handle after target disappearance, allowing the cursor to re-center for the next trial. Performance was assessed by measuring direction error (DE), defined as the angle between the line from the start to the target position, and the line from the start
On Precision of Recurrent Higher-Order Neural Network that Simulates Turing Machines
NASA Astrophysics Data System (ADS)
Tanaka, Ken
When a neural network simulates a Turing machine, the states of finite state controller and the symbols on infinite tape are encoded in continuous numbers of neuron's outputs. The precision of outputs is regarded as a space resource in neural computations. We show a sufficient condition about the precision to guarantee the correctness of computations. Linear precision suffice in regard to nT, where n is the number of neurons and T is the iteration count of state updates.
Wu, N. C.; Yan, M.; Zuo, L.; Wang, J. Q.
2014-01-28
To clarify the correlation of medium-range order (MRO) structure with glass forming ability (GFA) of Al-based metallic glasses, Al{sub 86}Ni{sub 14-a}Y{sub a} (a = 2∼9 at. %) metallic glasses were analyzed by x-ray diffraction in detail and further verified by synchrotron high-energy x-ray diffraction. The prepeak that reflects the MRO structural evolution was found to be much sensitive to alloy composition. We have proposed an icosahedral supercluster MRO structure model in Al-TM (transition metal)-RE (rare earth metal) system, which consists of 12 RE(TM)-centered clusters on the vertex of icosahedral supercluster, one RE(TM)-centered clusters in the center, and TM(RE) atoms located at RE(TM)-centered cluster tetrahedral interstices in the icosahedral supercluster. It was indicated that the MRO structural stability mainly depends on the interaction of efficient dense packing and electrochemical potential equalization principle. The Al{sub 86}Ni{sub 9}Y(La){sub 5} alloys present good GFA due to the combination of the two structural factors.
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.
Local network structure of a-SiC:H and its correlation with dielectric function
NASA Astrophysics Data System (ADS)
Kageyama, Shota; Matsuki, Nobuyuki; Fujiwara, Hiroyuki
2013-12-01
The microscopic disordered structures of hydrogenated amorphous silicon carbide (a-Si1-xCx:H) layers with different carbon contents have been determined based on the correlations between the dielectric function in the ultraviolet/visible region and the local bonding states studied by high-sensitivity infrared attenuated total reflection spectroscopy. We find that the microscopic structure of the a-Si1-xCx:H layers fabricated by plasma-enhanced chemical vapor deposition shows a sharp structural transition at a boundary of x = 6.3 at. %. In the regime of x ≤ 6.3 at. %, (i) the amplitude of the a-SiC:H dielectric function reduces and (ii) the SiH2 content increases drastically with x, even though most of the C atoms are introduced into the tetrahedral sites without bonding with H. In the regime of x > 6.3 at. %, on the other hand, (i) the amplitude of the dielectric function reduces further and (ii) the concentration of the sp3 CHn (n = 2,3) groups increases. Moreover, we obtained the direct evidence that the sp2 C bonding state in the a-SiC matrix exists in the configuration of C = CH2 and the generation of the graphite-like C = CH2 unit suppresses the band gap widening significantly. At high C contents of x > 6.3 at. %, the a-SiC:H layers show quite porous structures due to the formation of microvoids terminated with the SiH2/CHn groups. By taking the SiH2/CHn microvoid generation in the network and the high-energy shift of the dielectric function by the local bonding states into account, the a-SiC:H dielectric function model has been established. From the analysis using this model, we have confirmed that the a-SiC:H optical properties in the ultraviolet/visible region are determined almost completely by the local network structures.
Reaching a consensus in networks of high-order integral agents under switching directed topologies
NASA Astrophysics Data System (ADS)
Cheng, Long; Wang, Hanlei; Hou, Zeng-Guang; Tan, Min
2016-06-01
Consensus problem of high-order integral multi-agent systems under switching directed topology is considered in this study. Depending on whether the agent's full state is available or not, two distributed protocols are proposed to ensure that states of all agents can be convergent to a same stationary value. In the proposed protocols, the gain vector associated with the agent's (estimated) state and the gain vector associated with the relative (estimated) states between agents are designed in a sophisticated way. By this particular design, the high-order integral multi-agent system can be transformed into a first-order integral multi-agent system. Also, the convergence of the transformed first-order integral agent's state indicates the convergence of the original high-order integral agent's state, if and only if all roots of the polynomial, whose coefficients are the entries of the gain vector associated with the relative (estimated) states between agents, are in the open left-half complex plane. Therefore, many analysis techniques in the first-order integral multi-agent system can be directly borrowed to solve the problems in the high-order integral multi-agent system. Due to this property, it is proved that to reach a consensus, the switching directed topology of multi-agent system is only required to be 'uniformly jointly quasi-strongly connected', which seems the mildest connectivity condition in the literature. In addition, the consensus problem of discrete-time high-order integral multi-agent systems is studied. The corresponding consensus protocol and performance analysis are presented. Finally, three simulation examples are provided to show the effectiveness of the proposed approach.
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. PMID:27330970
NASA Astrophysics Data System (ADS)
Fischer, Elmar; Grinberg, Farida; Kimmich, Rainer; Hafner, Siegfried
1998-07-01
Chain dynamics in a series of styrene-butadiene rubbers (SBR) was studied with the aid of the dipolar correlation effect (DCE) and field-cycling NMR relaxometry (FCR). The typical time scales of the two techniques are t>10-4 s and t<10-3 s, respectively, and therefore complementary. The crosslink density of the polymer networks was varied in a wide range. In order to prevent sinusoidal undulations of the stimulated-echo attenuation curves due to spin exchange between groups with different chemical-shift offsets, the DCE of the samples was examined using a modified radio frequency pulse sequence with additional π pulses inserted in the free-evolution intervals. Residual dipolar couplings can thus be probed in samples where chemical-shift and dipolar interactions are of the same order. The dipolar correlations probed with the DCE in SBR networks turned out to exist on a time scale exceeding 300 ms. The short-time fluctuations (probed by FCR) and the long-time dynamics (probed by DCE) can be approached by power-law dipolar correlation functions with exponents -0.78±0.02 and -1.5±0.1, respectively. The crossover time is in the order of 1 ms. In contrast to FCR, the DCE data strongly depend on the crosslink density but not on the temperature in a range from 30 to 80 °C. On this basis determinations of the crosslink density may be possible as an alternative to the usual mechanical torsion modulus measurements.
Peeters, S C T; van Bronswijk, S; van de Ven, V; Gronenschild, E H B M; Goebel, R; van Os, J; Marcelis, M
2015-11-01
Altered frontoparietal network functional connectivity (FPN-fc) has been associated with neurocognitive dysfunction in individuals with (risk for) psychotic disorder. Cannabis use is associated with cognitive and FPN-fc alterations in healthy individuals, but it is not known whether cannabis exposure moderates the FPN-fc-cognition association. We studied FPN-fc in relation to psychosis risk, as well as the moderating effects of psychosis risk and cannabis use on the association between FPN-fc and (social) cognition. This was done by collecting resting-state fMRI scans and (social) cognitive test results from 63 patients with psychotic disorder, 73 unaffected siblings and 59 controls. Dorsolateral prefrontal cortex (DLPFC) seed-based correlation analyses were used to estimate FPN-fc group differences. Additionally, group×FPN-fc and cannabis×FPN-fc interactions in models of cognition were assessed with regression models. Results showed that DLPFC-fc with the left precuneus, right inferior parietal lobule, right middle temporal gyrus (MTG), inferior frontal gyrus (IFG) regions and right insula was decreased in patients compared to controls. Siblings had reduced DLPFC-fc with the right MTG, left middle frontal gyrus, right superior frontal gyrus, IFG regions, and right insula compared to controls, with an intermediate position between patients and controls for DLPFC-IFG/MTG and insula-fc. There were no significant FPN-fc×group or FPN-fc×cannabis interactions in models of cognition. Reduced DLPFC-insula-fc was associated with worse social cognition in the total sample. In conclusion, besides patient- and sibling-specific FPN-fc alterations, there was evidence for trait-related alterations. FPN-fc-cognition associations were not conditional on familial liability or cannabis use. Lower FPN-fc was associated with lower emotion processing in the total group. PMID:26411531
Well-balanced high-order solver for blood flow in networks of vessels with variable properties.
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. PMID:23913466
NASA Technical Reports Server (NTRS)
Moore, Craig E.; Cardelino, Beatriz H.; Frazier, Donald O.; Niles, Julian; Wang, Xian-Qiang
1997-01-01
Calculations were performed on the valence contribution to the static molecular third-order polarizabilities (gamma) of thirty carbon-cage fullerenes (C60, C70, five isomers of C78, and twenty-three isomers of C84). The molecular structures were obtained from B3LYP/STO-3G calculations. The values of the tensor elements and an associated numerical uncertainty were obtained using the finite-field approach and polynomial expansions of orders four to eighteen of polarization versus static electric field data. The latter information was obtained from semiempirical calculations using the AM1 hamiltonian.
Canals, Isaac; Soriano, Jordi; Orlandi, Javier G.; Torrent, Roger; Richaud-Patin, Yvonne; Jiménez-Delgado, Senda; Merlin, Simone; Follenzi, Antonia; Consiglio, Antonella; Vilageliu, Lluïsa; Grinberg, Daniel; Raya, Angel
2015-01-01
Summary Induced pluripotent stem cell (iPSC) technology has been successfully used to recapitulate phenotypic traits of several human diseases in vitro. Patient-specific iPSC-based disease models are also expected to reveal early functional phenotypes, although this remains to be proved. Here, we generated iPSC lines from two patients with Sanfilippo type C syndrome, a lysosomal storage disorder with inheritable progressive neurodegeneration. Mature neurons obtained from patient-specific iPSC lines recapitulated the main known phenotypes of the disease, not present in genetically corrected patient-specific iPSC-derived cultures. Moreover, neuronal networks organized in vitro from mature patient-derived neurons showed early defects in neuronal activity, network-wide degradation, and altered effective connectivity. Our findings establish the importance of iPSC-based technology to identify early functional phenotypes, which can in turn shed light on the pathological mechanisms occurring in Sanfilippo syndrome. This technology also has the potential to provide valuable readouts to screen compounds, which can prevent the onset of neurodegeneration. PMID:26411903
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. PMID:26278974
Bowers, Jeffrey S; Vankov, Ivan I; Damian, Markus F; Davis, Colin J
2014-04-01
A key insight from 50 years of neurophysiology is that some neurons in cortex respond to information in a highly selective manner. Why is this? We argue that selective representations support the coactivation of multiple "things" (e.g., words, objects, faces) in short-term memory, whereas nonselective codes are often unsuitable for this purpose. That is, the coactivation of nonselective codes often results in a blend pattern that is ambiguous; the so-called superposition catastrophe. We show that a recurrent parallel distributed processing network trained to code for multiple words at the same time over the same set of units learns localist letter and word codes, and the number of localist codes scales with the level of the superposition. Given that many cortical systems are required to coactivate multiple things in short-term memory, we suggest that the superposition constraint plays a role in explaining the existence of selective codes in cortex. PMID:24564411
Short-Term Memory for Serial Order: A Recurrent Neural Network Model
ERIC Educational Resources Information Center
Botvinick, Matthew M.; Plaut, David C.
2006-01-01
Despite a century of research, the mechanisms underlying short-term or working memory for serial order remain uncertain. Recent theoretical models have converged on a particular account, based on transient associations between independent item and context representations. In the present article, the authors present an alternative model, according…
Wang, Yongmei Michelle; Xia, Jing
2011-01-01
There is a rapidly growing interest in the neuroimaging field to use functional magnetic resonance imaging (fMRI) to explore brain networks, i.e., how regions of the brain communicate with one another. This paper presents a general and novel statistical framework for robust and more complete estimation of brain functional connectivity from fMRI based on correlation analyses and hypothesis testing. In addition to the ability of examining the correlations with each individual seed as in the standard and existing methods, the proposed framework can detect functional interactions by simultaneously examining multiseed correlations via multiple correlation coefficients. Spatially structured noise in fMRI is also taken into account during the identification of functional interconnection networks through noncentral F hypothesis tests. The associated issues for the multiple testing and the effective degrees-of-freedom are considered as well. Furthermore, partial multiple correlations are introduced and formulated to measure any additional task-induced but not stimulus-locked relation over brain regions so that we can take the analysis of functional connectivity closer to the characterization of direct functional interactions of the brain. Evaluation for accuracy and advantages, and comparisons of the new approaches in the presented general framework are performed using both realistic synthetic data and in vivo fMRI data. PMID:19237342
NASA Astrophysics Data System (ADS)
Gligor, M.; Ausloos, M.
2008-06-01
GDP/capita correlations are investigated in various time windows (TW), for the time interval 1990 2005. The target group of countries is the set of 25 EU members, 15 till 2004 plus the 10 countries which joined EU later on. The TW-means of the statistical correlation coefficients are taken as the weights (links) of a fully connected network having the countries as nodes. Thereafter we define and introduce the overlapping index of weighted network nodes. A cluster structure of EU countries is derived from the statistically relevant eigenvalues and eigenvectors of the adjacency matrix. This may be considered to yield some information about the structure, stability and evolution of the EU country clusters in a macroeconomic sense.
Place-Based Social Network Quality and Correlates of Substance Use among Urban Adolescents
ERIC Educational Resources Information Center
Mason, Michael J.; Valente, Thomas W.; Coatsworth, J. Douglas; Mennis, Jeremy; Lawrence, Frank; Zelenak, Patricia
2010-01-01
A sample of 301 Philadelphia adolescents were assessed for substance use and place-based social network quality, a weighted variable based upon risky and protective behaviors of alters. The network measure was anchored in routine locations identified as safe, risky, important, or favorite. Results show young females' (13-16) substance use was…
ERIC Educational Resources Information Center
Liu, Wei
2011-01-01
Correlation is often present among observations in a distributed system. This thesis deals with various design issues when correlated data are observed at distributed terminals, including: communicating correlated sources over interference channels, characterizing the common information among dependent random variables, and testing the presence of…
Dyskova, Tereza; Fillerova, Regina; Novosad, Tomas; Kudelka, Milos; Zurkova, Monika; Gajdos, Petr; Kolek, Vitezslav; Kriegova, Eva
2015-01-01
Sarcoidosis is an inflammatory granulomatous disease with unknown etiology driven by cytokines and chemokines. There is limited information regarding the regulation of cytokine/chemokine-receptor network in bronchoalveolar lavage (BAL) cells in pulmonary sarcoidosis, suggesting contribution of miRNAs and transcription factors. We therefore investigated gene expression of 25 inflammation-related miRNAs, 27 cytokines/chemokines/receptors, and a Th1-transcription factor T-bet in unseparated BAL cells obtained from 48 sarcoidosis patients and 14 control subjects using quantitative RT-PCR. We then examined both miRNA-mRNA expressions to enrich relevant relationships. This first study on miRNAs in sarcoid BAL cells detected deregulation of miR-146a, miR-150, miR-202, miR-204, and miR-222 expression comparing to controls. Subanalysis revealed higher number of miR-155, let-7c transcripts in progressing (n = 20) comparing to regressing (n = 28) disease as assessed by 2-year follow-up. Correlation network analysis revealed relationships between microRNAs, transcription factor T-bet, and deregulated cytokine/chemokine-receptor network in sarcoid BAL cells. Furthermore, T-bet showed more pronounced regulatory capability to sarcoidosis-associated cytokines/chemokines/receptors than miRNAs, which may function rather as “fine-tuners” of cytokine/chemokine expression. Our correlation network study implies contribution of both microRNAs and Th1-transcription factor T-bet to the regulation of cytokine/chemokine-receptor network in BAL cells in sarcoidosis. Functional studies are needed to confirm biological relevance of the obtained relationships. PMID:26696750
Pereira, Vanessa Helena; Gama, Maria Carolina Traina; Sousa, Filipe Antônio Barros; Lewis, Theodore Gyle; Gobatto, Claudio Alexandre; Manchado - Gobatto, Fúlvia Barros
2015-01-01
The aims of the present study were analyze the fatigue process at distinct intensity efforts and to investigate its occurrence as interactions at distinct body changes during exercise, using complex network models. For this, participants were submitted to four different running intensities until exhaustion, accomplished in a non-motorized treadmill using a tethered system. The intensities were selected according to critical power model. Mechanical (force, peak power, mean power, velocity and work) and physiological related parameters (heart rate, blood lactate, time until peak blood lactate concentration (lactate time), lean mass, anaerobic and aerobic capacities) and IPAQ score were obtained during exercises and it was used to construction of four complex network models. Such models have both, theoretical and mathematical value, and enables us to perceive new insights that go beyond conventional analysis. From these, we ranked the influences of each node at the fatigue process. Our results shows that nodes, links and network metrics are sensibility according to increase of efforts intensities, been the velocity a key factor to exercise maintenance at models/intensities 1 and 2 (higher time efforts) and force and power at models 3 and 4, highlighting mechanical variables in the exhaustion occurrence and even training prescription applications. PMID:25994386