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.
Multi-omics approach for estimating metabolic networks using low-order partial correlations.
Kayano, Mitsunori; Imoto, Seiya; Yamaguchi, Rui; Miyano, Satoru
2013-08-01
Two typical purposes of metabolome analysis are to estimate metabolic pathways and to understand the regulatory systems underlying the metabolism. A powerful source of information for these analyses is a set of multi-omics data for RNA, proteins, and metabolites. However, integrated methods that analyze multi-omics data simultaneously and unravel the systems behind metabolisms have not been well established. We developed a statistical method based on low-order partial correlations with a robust correlation coefficient for estimating metabolic networks from metabolome, proteome, and transcriptome data. Our method is defined by the maximum of low-order, particularly first-order, partial correlations (MF-PCor) in order to assign a correct edge with the highest correlation and to detect the factors that strongly affect the correlation coefficient. First, through numerical experiments with real and synthetic data, we showed that the use of protein and transcript data of enzymes improved the accuracy of the estimated metabolic networks in MF-PCor. In these experiments, the effectiveness of the proposed method was also demonstrated by comparison with a correlation network (Cor) and a Gaussian graphical model (GGM). Our theoretical investigation confirmed that the performance of MF-PCor could be superior to that of the competing methods. In addition, in the real data analysis, we investigated the role of metabolites, enzymes, and enzyme genes that were identified as important factors in the network established by MF-PCor. We then found that some of them corresponded to specific reactions between metabolites mediated by catalytic enzymes that were difficult to be identified by analysis based on metabolite data alone.
Murg, V; Verstraete, F; Schneider, R; Nagy, P R; Legeza, Ö
2015-03-10
We study the tree-tensor-network-state (TTNS) method with variable tensor orders for quantum chemistry. TTNS is a variational method to efficiently approximate complete active space (CAS) configuration interaction (CI) wave functions in a tensor product form. TTNS can be considered as a higher order generalization of the matrix product state (MPS) method. The MPS wave function is formulated as products of matrices in a multiparticle basis spanning a truncated Hilbert space of the original CAS-CI problem. These matrices belong to active orbitals organized in a one-dimensional array, while tensors in TTNS are defined upon a tree-like arrangement of the same orbitals. The tree-structure is advantageous since the distance between two arbitrary orbitals in the tree scales only logarithmically with the number of orbitals N, whereas the scaling is linear in the MPS array. It is found to be beneficial from the computational costs point of view to keep strongly correlated orbitals in close vicinity in both arrangements; therefore, the TTNS ansatz is better suited for multireference problems with numerous highly correlated orbitals. To exploit the advantages of TTNS a novel algorithm is designed to optimize the tree tensor network topology based on quantum information theory and entanglement. The superior performance of the TTNS method is illustrated on the ionic-neutral avoided crossing of LiF. It is also shown that the avoided crossing of LiF can be localized using only ground state properties, namely one-orbital entanglement.
Correlation of structural order, anomalous density, and hydrogen bonding network of liquid water.
Bandyopadhyay, Dibyendu; Mohan, S; Ghosh, S K; Choudhury, Niharendu
2013-07-25
We use extensive molecular dynamics simulations employing different state-of-the-art force fields to find a common framework for comparing structural orders and density anomalies as obtained from different water models. It is found that the average number of hydrogen bonds correlates well with various order parameters as well as the temperature of maximum densities across the different models, unifying apparently disparate results from different models and emphasizing the importance of hydrogen bonding in determining anomalous properties and the structure of water. A deeper insight into the hydrogen bond network of water reveals that the solvation shell of a water molecule can be defined by considering only those neighbors that are hydrogen-bonded to it. On the basis of this view, the origin of the appearance of a non-tetrahedral peak at a higher temperature in the distribution of tetrahedral order parameters has been explained. It is found that a neighbor that is hydrogen-bonded to the central molecule is tetrahedrally coordinated even at higher temperatures. The non-tetrahedral peak at a higher temperature arises due to the strained orientation of the neighbors that are non-hydrogen-bonded to the central molecule. With the new definition of the solvation shell, liquid water can be viewed as an instantaneously changing random hydrogen-bonded network consisting of differently coordinated hydrogen-bonded molecules with their distinct solvation shells. The variation of the composition of these hydrogen-bonded molecules against temperature accounts for the density anomaly without introducing the concept of large-scale structural polyamorphism in water.
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.
Higher-order artificial neural networks
Bengtsson, M.
1990-12-01
The report investigates the storage capacity of an artificial neural network where the state of each neuron depends on quadratic correlations of all other neurons, i.e. a third order network. This is in contrast to a standard Hopfield network where the state of each single neuron depends on the state on every other neuron, without any correlations. The storage capacity of a third order network is larger than that for standard Hopfield by one order of N. However, the number of connections is also larger by an order of N. It is shown that the storage capacity per connection is identical for standard Hopfield and for this third order network.
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.
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.
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
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
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.
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
Representing higher-order dependencies in networks.
Xu, Jian; Wickramarathne, Thanuka L; Chawla, Nitesh V
2016-05-01
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
Gudmundsson, Agust; Mohajeri, Nahid
2013-01-01
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. PMID:24281305
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.
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.
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.
Earthquake correlations and networks: a comparative study.
Krishna Mohan, T R; Revathi, P G
2011-04-01
We quantify the correlation between earthquakes and use the same to extract causally connected earthquake pairs. Our correlation metric is a variation on the one introduced by Baiesi and Paczuski [M. Baiesi and M. Paczuski, Phys. Rev. E 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.
Earthquake correlations and networks: A comparative study
NASA Astrophysics Data System (ADS)
Krishna Mohan, T. R.; Revathi, P. G.
2011-04-01
We quantify the correlation between earthquakes and use the same to extract causally connected earthquake pairs. Our correlation metric is a variation on the one introduced by Baiesi and Paczuski [M. Baiesi and M. Paczuski, Phys. Rev. E EULEEJ1539-375510.1103/PhysRevE.69.06610669, 066106 (2004)]. A network of earthquakes is then constructed from the time-ordered catalog and with links between the more correlated ones. A list of recurrences to each of the earthquakes is identified employing correlation thresholds to demarcate the most meaningful ones in each cluster. Data pertaining to three different seismic regions (viz., California, Japan, and the Himalayas) are comparatively analyzed using such a network model. The distribution of recurrence lengths and recurrence times are two of the key features analyzed to draw conclusions about the universal aspects of such a network model. We find that the unimodal feature of recurrence length distribution, which helps to associate typical rupture lengths with different magnitude earthquakes, is robust across the different seismic regions. The out-degree of the networks shows a hub structure rooted on the large magnitude earthquakes. In-degree distribution is seen to be dependent on the density of events in the neighborhood. Power laws, with two regimes having different exponents, are obtained with recurrence time distribution. The first regime confirms the Omori law for aftershocks while the second regime, with a faster falloff for the larger recurrence times, establishes that pure spatial recurrences also follow a power-law distribution. The crossover to the second power-law regime can be taken to be signaling the end of the aftershock regime in an objective fashion.
Partially ordered sets in complex networks
NASA Astrophysics Data System (ADS)
Xuan, Qi; Du, Fang; Wu, Tie-Jun
2010-05-01
In this paper, a partial-order relation is defined among vertices of a network to describe which vertex is more important than another on its contribution to the connectivity of the network. A maximum linearly ordered subset of vertices is defined as a chain and the chains sharing the same end-vertex are grouped as a family. Through combining the same vertices appearing in different chains, a directed chain graph is obtained. Based on these definitions, a series of new network measurements, such as chain length distribution, family diversity distribution, as well as the centrality of families, are proposed. By studying the partially ordered sets in three kinds of real-world networks, many interesting results are revealed. For instance, the similar approximately power-law chain length distribution may be attributed to a chain-based positive feedback mechanism, i.e. new vertices prefer to participate in longer chains, which can be inferred by combining the notable preferential attachment rule with a well-ordered recommendation manner. Moreover, the relatively large average incoming degree of the chain graphs may indicate an efficient substitution mechanism in these networks. Most of the partially ordered set-based properties cannot be explained by the current well-known scale-free network models; therefore, we are required to propose more appropriate network models in the future.
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.
Consistency analysis of metabolic correlation networks
Müller-Linow, Mark; Weckwerth, Wolfram; Hütt, Marc-Thorsten
2007-01-01
Background Metabolic correlation networks are derived from the covariance of metabolites in replicates of metabolomics experiments. They constitute an interesting intermediate between topology (i.e. the system's architecture defined by the set of reactions between metabolites) and dynamics (i.e. the metabolic concentrations observed as fluctuations around steady-state values in the metabolic network). Results Here we analyze, how such a correlation network changes over time, and compare the relative positions of metabolites in the correlation networks with those in established metabolic networks derived from genome databases. We find that network similarity indeed decreases with an increasing time difference between these networks during a day/night course and, counter intuitively, that proximity of metabolites in the correlation network is no indicator of proximity of the metabolites in the metabolic network. Conclusion The organizing principles of correlation networks are distinct from those of metabolic reaction maps. Time courses of correlation networks may in the future prove an important data source for understanding these organizing principles. PMID:17892579
Random interactions in higher order neural networks
NASA Technical Reports Server (NTRS)
Baldi, Pierre; Venkatesh, Santosh S.
1993-01-01
Recurrent networks of polynomial threshold elements with random symmetric interactions are studied. Precise asymptotic estimates are derived for the expected number of fixed points as a function of the margin of stability. In particular, it is shown that there is a critical range of margins of stability (depending on the degree of polynomial interaction) such that the expected number of fixed points with margins below the critical range grows exponentially with the number of nodes in the network, while the expected number of fixed points with margins above the critical range decreases exponentially with the number of nodes in the network. The random energy model is also briefly examined and links with higher order neural networks and higher order spin glass models made explicit.
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.
Correlated measurement error hampers association network inference.
Kaduk, Mateusz; Hoefsloot, Huub C J; Vis, Daniel J; Reijmers, Theo; van der Greef, Jan; Smilde, Age K; Hendriks, Margriet M W B
2014-09-01
Modern chromatography-based metabolomics measurements generate large amounts of data in the form of abundances of metabolites. An increasingly popular way of representing and analyzing such data is by means of association networks. Ideally, such a network can be interpreted in terms of the underlying biology. A property of chromatography-based metabolomics data is that the measurement error structure is complex: apart from the usual (random) instrumental error there is also correlated measurement error. This is intrinsic to the way the samples are prepared and the analyses are performed and cannot be avoided. The impact of correlated measurement errors on (partial) correlation networks can be large and is not always predictable. The interplay between relative amounts of uncorrelated measurement error, correlated measurement error and biological variation defines this impact. Using chromatography-based time-resolved lipidomics data obtained from a human intervention study we show how partial correlation based association networks are influenced by correlated measurement error. We show how the effect of correlated measurement error on partial correlations is different for direct and indirect associations. For direct associations the correlated measurement error usually has no negative effect on the results, while for indirect associations, depending on the relative size of the correlated measurement error, results can become unreliable. The aim of this paper is to generate awareness of the existence of correlated measurement errors and their influence on association networks. Time series lipidomics data is used for this purpose, as it makes it possible to visually distinguish the correlated measurement error from a biological response. Underestimating the phenomenon of correlated measurement error will result in the suggestion of biologically meaningful results that in reality rest solely on complicated error structures. Using proper experimental designs that allow
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
2016-01-07
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.
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.
Temporal correlation coefficient for directed networks.
Büttner, Kathrin; Salau, Jennifer; Krieter, Joachim
2016-01-01
Previous studies dealing with network theory focused mainly on the static aggregation of edges over specific time window lengths. Thus, most of the dynamic information gets lost. To assess the quality of such a static aggregation the temporal correlation coefficient can be calculated. It measures the overall possibility for an edge to persist between two consecutive snapshots. Up to now, this measure is only defined for undirected networks. Therefore, we introduce the adaption of the temporal correlation coefficient to directed networks. This new methodology enables the distinction between ingoing and outgoing edges. Besides a small example network presenting the single calculation steps, we also calculated the proposed measurements for a real pig trade network to emphasize the importance of considering the edge direction. The farm types at the beginning of the pork supply chain showed clearly higher values for the outgoing temporal correlation coefficient compared to the farm types at the end of the pork supply chain. These farm types showed higher values for the ingoing temporal correlation coefficient. The temporal correlation coefficient is a valuable tool to understand the structural dynamics of these systems, as it assesses the consistency of the edge configuration. The adaption of this measure for directed networks may help to preserve meaningful additional information about the investigated network that might get lost if the edge directions are ignored. PMID:27516936
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.
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.
Using neural networks to describe tracer correlations
NASA Astrophysics Data System (ADS)
Lary, D. J.; Müller, M. D.; Mussa, H. Y.
2003-11-01
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 5 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 10 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
Using neural networks to describe tracer correlations
NASA Astrophysics Data System (ADS)
Lary, D. J.; Müller, M. D.; Mussa, H. Y.
2004-01-01
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 methane 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 coefficient between simulated and training values 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.
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
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.
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.
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.
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.
Correlations in complex networks under attack.
Srivastava, Animesh; Mitra, Bivas; Ganguly, Niloy; Peruani, Fernando
2012-09-01
For any initially correlated network after any kind of attack where either nodes or edges are removed, we obtain general expressions for the degree-degree probability matrix and degree distribution. We show that the proposed analytical approach predicts the correct topological changes after the attack by comparing the evolution of the assortativity coefficient for different attack strategies and intensities in theory and simulations. We find that it is possible to turn an initially assortative network into a disassortative one, and vice versa, by fine-tuning removal of either nodes or edges. For an initially uncorrelated network, on the other hand, we discover that only a targeted edge-removal attack can induce such correlations. PMID:23030979
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.
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.
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.
Spectral methods and cluster structure in correlation-based networks
NASA Astrophysics Data System (ADS)
Heimo, Tapio; Tibély, Gergely; Saramäki, Jari; Kaski, Kimmo; Kertész, János
2008-10-01
We investigate how in complex systems the eigenpairs of the matrices derived from the correlations of multichannel observations reflect the cluster structure of the underlying networks. For this we use daily return data from the NYSE and focus specifically on the spectral properties of weight W=|-δ and diffusion matrices D=W/sj-δ, where C is the correlation matrix and si=∑jW the strength of node j. The eigenvalues (and corresponding eigenvectors) of the weight matrix are ranked in descending order. As in the earlier observations, the first eigenvector stands for a measure of the market correlations. Its components are, to first approximation, equal to the strengths of the nodes and there is a second order, roughly linear, correction. The high ranking eigenvectors, excluding the highest ranking one, are usually assigned to market sectors and industrial branches. Our study shows that both for weight and diffusion matrices the eigenpair analysis is not capable of easily deducing the cluster structure of the network without a priori knowledge. In addition we have studied the clustering of stocks using the asset graph approach with and without spectrum based noise filtering. It turns out that asset graphs are quite insensitive to noise and there is no sharp percolation transition as a function of the ratio of bonds included, thus no natural threshold value for that ratio seems to exist. We suggest that these observations can be of use for other correlation based networks as well.
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.
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).
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.
Higher-order photon correlations in pulsed photonic crystal nanolasers
Elvira, D.; Hachair, X.; Braive, R.; Beaudoin, G.; Robert-Philip, I.; Sagnes, I.; Abram, I.; Beveratos, A.; Verma, V. B.; Baek, B.; Nam, S. W.; Stevens, M. J.; Dauler, E. A.
2011-12-15
We report on the higher-order photon correlations of a high-{beta} nanolaser under pulsed excitation at room temperature. Using a multiplexed four-element superconducting single-photon detector we measured g{sup (n)}(0-vector) with n=2,3,4. All orders of correlation display partially chaotic statistics, even at four times the threshold excitation power. We show that this departure from coherence and Poisson statistics is due to the quantum fluctuations associated with the small number of photons at the lasing threshold.
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
Tensor Spectral Clustering for Partitioning Higher-order Network Structures
Benson, Austin R.; Gleich, David F.; Leskovec, Jure
2016-01-01
Spectral graph theory-based methods represent an important class of tools for studying the structure of networks. Spectral methods are based on a first-order Markov chain derived from a random walk on the graph and thus they cannot take advantage of important higher-order network substructures such as triangles, cycles, and feed-forward loops. Here we propose a Tensor Spectral Clustering (TSC) algorithm that allows for modeling higher-order network structures in a graph partitioning framework. Our TSC algorithm allows the user to specify which higher-order network structures (cycles, feed-forward loops, etc.) should be preserved by the network clustering. Higher-order network structures of interest are represented using a tensor, which we then partition by developing a multilinear spectral method. Our framework can be applied to discovering layered flows in networks as well as graph anomaly detection, which we illustrate on synthetic networks. In directed networks, a higher-order structure of particular interest is the directed 3-cycle, which captures feedback loops in networks. We demonstrate that our TSC algorithm produces large partitions that cut fewer directed 3-cycles than standard spectral clustering algorithms.
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.
Investigation of higher order correlations in swirling pipe flows
NASA Astrophysics Data System (ADS)
Acrivlellis, M.; Jungbluth, H.; Cantrak, S.
1982-04-01
Statistical quantities of swirling pipe flows generated by radial guide vanes were studied by a triple hot-wire probe and digital data reduction at two cross-sections of the pipe, one directly behind the swirl generator and the other some distance downstream from the vanes. The influence of swirl intensity on the axial pipe flow was investigated with the measured second and third order correlations as well as the third and fourth order central moments. The probability-density distribution shows the significance of the turbulence transfer mechanism in the complicated process of swirling flows.
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.
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
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
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.
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.
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
Existence of high-order correlations in cortical activity
NASA Astrophysics Data System (ADS)
Benucci, Andrea; Verschure, Paul F.; König, Peter
2003-10-01
Neurons collect signals originating from a large number of other cells. The variability of this integrated population activity at the millisecond time scale is a critical constraint on the degree of signal integration and processing performed by single neurons. Optical imaging, EEG, and fMRI studies have indicated that cortical activity shows a high degree of variability at a time scale of hundreds of ms. However, currently no experimental methods are available to directly assess the variability in the activity of populations of neurons at a time scale closer to that of the characteristic time constants of neurons, i.e., around 10 ms. Here we integrate pertinent experimental data in one rigorous mathematical framework to demonstrate that (1) the high temporal variability in the spiking activity of individual neurons, (2) the second-order correlation properties of the spiking activity of cortical neurons, and (3) the correlations of the subthreshold dynamics, all impose high amplitude, fast variability in the population activity of cortical neurons. This implies that higher order correlations, a necessary condition for temporal coding models, must be a central feature of cortical dynamics.
Percolation transition in networks with degree-degree correlation
NASA Astrophysics Data System (ADS)
Noh, Jae Dong
2007-08-01
We introduce an exponential random graph model for networks with a fixed degree distribution and a tunable degree-degree correlation. We then investigate the nature of the percolation transition in a correlated network with a Poisson degree distribution. It is found that negative correlation is irrelevant in that the percolation transition in the disassortative network belongs to the same universality class as in the uncorrelated network. Positive correlation turns out to be relevant. The percolation transition in the assortative network is characterized by the nondiverging mean size of finite clusters and power-law scalings of the density of the largest cluster and the cluster size distribution in the nonpercolating phase as well as at the critical point. Our results suggest that the unusual type of percolation transition in the growing network models reported recently may be inherited from the assortative degree-degree correlation.
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.
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.
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.
Influence of network topology on the abnormal phase order
NASA Astrophysics Data System (ADS)
Zhou, Yinzuo; Zhou, Jie; Liu, Zonghua
2008-12-01
The abnormal phase order of coupled logistic maps, i.e., the ratio of two sequential "up phases" in the total iterations, can be characterized by the direction phase (Phys. Rev. Lett., 84 (2000) 2610). We here consider the case of coupled logistic maps on complex networks and study how the network topology influences the abnormal phase order. Our numerical simulations reveal that the critical point for the appearance of abnormal phase order increases with the coupling strength but decreases with the degree of heterogeneity of complex networks. Moreover, we find that unlike in the case of normal phase order, it is possible for the system to show a periodic window in the case of abnormal phase order, but only within an appropriate range of coupling strengths, and finally, that the heterogeneity can reduce the maximum number of the phase clusters in a given periodic window.
Design of order statistics filters using feedforward neural networks
NASA Astrophysics Data System (ADS)
Maslennikova, Yu. S.; Bochkarev, V. V.
2016-08-01
In recent years significant progress have been made in the development of nonlinear data processing techniques. Such techniques are widely used in digital data filtering and image enhancement. Many of the most effective nonlinear filters based on order statistics. The widely used median filter is the best known order statistic filter. Generalized form of these filters could be presented based on Lloyd's statistics. Filters based on order statistics have excellent robustness properties in the presence of impulsive noise. In this paper, we present special approach for synthesis of order statistics filters using artificial neural networks. Optimal Lloyd's statistics are used for selecting of initial weights for the neural network. Adaptive properties of neural networks provide opportunities to optimize order statistics filters for data with asymmetric distribution function. Different examples demonstrate the properties and performance of presented approach.
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
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
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).
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.
Correlation between crystalline order and vitrification in colloidal monolayers.
Tamborini, Elisa; Royall, C Patrick; Cicuta, Pietro
2015-05-20
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. PMID:25923174
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
ICA model order selection of task co-activation networks
Ray, Kimberly L.; McKay, D. Reese; Fox, Peter M.; Riedel, Michael C.; Uecker, Angela M.; Beckmann, Christian F.; Smith, Stephen M.; Fox, Peter T.; Laird, Angela R.
2013-01-01
Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders. PMID:24339802
Analysis and perturbation of degree correlation in complex networks
NASA Astrophysics Data System (ADS)
Xiang, Ju; Hu, Ke; Zhang, Yan; Hu, Tao; Li, Jian-Ming
2015-08-01
Degree correlation is an important topological property common to many real-world networks such as the protein-protein interactions and the metabolic networks. In the letter, the statistical measures for characterizing the degree correlation in networks are investigated analytically. We give an exact proof of the consistency for the statistical measures, and reveal the general linear relation in the degree correlation, which provides a simple and interesting perspective on the analysis of the degree correlation in complex networks. By using the general linear analysis, we investigate the perturbation of the degree correlation in complex networks caused by the simple structural variation such as the “rich club”. The results show that the assortativity of homogeneous networks such as the Erdős-Rényi graphs is easily strongly affected by the simple structural changes, while it has only a slight variation for heterogeneous networks with broad degree distribution such as the scale-free networks. Clearly, the homogeneous networks are more sensitive to the perturbation than the heterogeneous networks.
A Frank mixture copula family for modeling higher-order correlations of neural spike counts
NASA Astrophysics Data System (ADS)
Onken, Arno; Obermayer, Klaus
2009-12-01
In order to evaluate the importance of higher-order correlations in neural spike count codes, flexible statistical models of dependent multivariate spike counts are required. Copula families, parametric multivariate distributions that represent dependencies, can be applied to construct such models. We introduce the Frank mixture family as a new copula family that has separate parameters for all pairwise and higher-order correlations. In contrast to the Farlie-Gumbel-Morgenstern copula family that shares this property, the Frank mixture copula can model strong correlations. We apply spike count models based on the Frank mixture copula to data generated by a network of leaky integrate-and-fire neurons and compare the goodness of fit to distributions based on the Farlie-Gumbel-Morgenstern family. Finally, we evaluate the importance of using proper single neuron spike count distributions on the Shannon information. We find notable deviations in the entropy that increase with decreasing firing rates. Moreover, we find that the Frank mixture family increases the log likelihood of the fit significantly compared to the Farlie-Gumbel-Morgenstern family. This shows that the Frank mixture copula is a useful tool to assess the importance of higher-order correlations in spike count codes.
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.
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.
Neural population densities shape network correlations
NASA Astrophysics Data System (ADS)
Lefebvre, Jérémie; Perkins, Theodore J.
2012-02-01
The way sensory microcircuits manage cellular response correlations is a crucial question in understanding how such systems integrate external stimuli and encode information. Most sensory systems exhibit heterogeneities in terms of population sizes and features, which all impact their dynamics. This work addresses how correlations between the dynamics of neural ensembles depend on the relative size or density of excitatory and inhibitory populations. To do so, we study an apparently symmetric system of coupled stochastic differential equations that model the evolution of the populations’ activities. Excitatory and inhibitory populations are connected by reciprocal recurrent connections, and both receive different stimuli exhibiting a certain level of correlation with each other. A stability analysis is performed, which reveals an intrinsic asymmetry in the distribution of the fixed points with respect to the threshold of the nonlinearities. Based on this, we show how the cross correlation between the population responses depends on the density of the inhibitory population, and that a specific ratio between both population sizes leads to a state of zero correlation. We show that this so-called asynchronous state subsists, despite the presence of stimulus correlation, and most importantly, that it occurs only in asymmetrical systems where one population outnumbers the other. Using linear approximations, we derive analytical expressions for the root of the cross-correlation function and study how the asynchronous state is impacted by the model's parameters. This work suggests a possible explanation for why inhibitory cells outnumber excitatory cells in the visual system.
Correlation between structure and temperature in prokaryotic metabolic networks
Takemoto, Kazuhiro; Nacher, Jose C; Akutsu, Tatsuya
2007-01-01
Background In recent years, an extensive characterization of network structures has been made in an effort to elucidate design principles of metabolic networks, providing valuable insights into the functional organization and the evolutionary history of organisms. However, previous analyses have not discussed the effects of environmental factors (i.e., exogenous forces) in shaping network structures. In this work, we investigate the effect of temperature, which is one of the environmental factors that may have contributed to shaping structures of metabolic networks. Results For this, we investigate the correlations between several structural properties characterized by graph metrics like the edge density, the degree exponent, the clustering coefficient, and the subgraph concentration in the metabolic networks of 113 prokaryotes and optimal growth temperature. As a result, we find that these structural properties are correlated with the optimal growth temperature. With increasing temperature, the edge density, the clustering coefficient and the subgraph concentration decrease and the degree exponent becomes large. Conclusion This result implies that the metabolic networks transit with temperature as follows. The density of chemical reactions becomes low, the connectivity of the networks becomes homogeneous such as random networks and both the network modularity, based on the graph-theoretic clustering coefficient, and the frequency of recurring subgraphs decay. In short, metabolic networks undergo a change from heterogeneous and high-modular structures to homogeneous and low-modular structures, such as random networks, with temperature. This finding may suggest that the temperature plays an important role in the design principles of metabolic networks. PMID:17711568
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.
Influence of Choice of Null Network on Small-World Parameters of Structural Correlation Networks
Hosseini, S. M. Hadi; Kesler, Shelli R.
2013-01-01
In recent years, coordinated variations in brain morphology (e.g., volume, thickness) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1) networks constructed by topology randomization (TOP), 2) networks matched to the distributional properties of the observed covariance matrix (HQS), and 3) networks generated from correlation of randomized input data (COR). The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures. PMID:23840672
Influence of choice of null network on small-world parameters of structural correlation networks.
Hosseini, S M Hadi; Kesler, Shelli R
2013-01-01
In recent years, coordinated variations in brain morphology (e.g., volume, thickness) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1) networks constructed by topology randomization (TOP), 2) networks matched to the distributional properties of the observed covariance matrix (HQS), and 3) networks generated from correlation of randomized input data (COR). The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures.
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.
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.
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
Network clustering coefficient without degree-correlation biases.
Soffer, Sara Nadiv; Vázquez, Alexei
2005-05-01
The clustering coefficient quantifies how well connected are the neighbors of a vertex in a graph. In real networks it decreases with the vertex degree, which has been taken as a signature of the network hierarchical structure. Here we show that this signature of hierarchical structure is a consequence of degree-correlation biases in the clustering coefficient definition. We introduce a definition in which the degree-correlation biases are filtered out, and provide evidence that in real networks the clustering coefficient is constant or decays logarithmically with vertex degree.
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.
Strong correlations between text quality and complex networks features
NASA Astrophysics Data System (ADS)
Antiqueira, L.; Nunes, M. G. V.; Oliveira, O. N., Jr.; F. Costa, L. da
2007-01-01
Concepts of complex networks have been used to obtain metrics that were correlated to text quality established by scores assigned by human judges. Texts produced by high-school students in Portuguese were represented as scale-free networks (word adjacency model), from which typical network features such as the in/outdegree, clustering coefficient and shortest path were obtained. Another metric was derived from the dynamics of the network growth, based on the variation of the number of connected components. The scores assigned by the human judges according to three text quality criteria (coherence and cohesion, adherence to standard writing conventions and theme adequacy/development) were correlated with the network measurements. Text quality for all three criteria was found to decrease with increasing average values of outdegrees, clustering coefficient and deviation from the dynamics of network growth. Among the criteria employed, cohesion and coherence showed the strongest correlation, which probably indicates that the network measurements are able to capture how the text is developed in terms of the concepts represented by the nodes in the networks. Though based on a particular set of texts and specific language, the results presented here point to potential applications in other instances of text analysis.
Degree correlations in directed scale-free networks.
Williams, Oliver; Del Genio, Charo I
2014-01-01
Scale-free networks, in which the distribution of the degrees obeys a power-law, are ubiquitous in the study of complex systems. One basic network property that relates to the structure of the links found is the degree assortativity, which is a measure of the correlation between the degrees of the nodes at the end of the links. Degree correlations are known to affect both the structure of a network and the dynamics of the processes supported thereon, including the resilience to damage, the spread of information and epidemics, and the efficiency of defence mechanisms. Nonetheless, while many studies focus on undirected scale-free networks, the interactions in real-world systems often have a directionality. Here, we investigate the dependence of the degree correlations on the power-law exponents in directed scale-free networks. To perform our study, we consider the problem of building directed networks with a prescribed degree distribution, providing a method for proper generation of power-law-distributed directed degree sequences. Applying this new method, we perform extensive numerical simulations, generating ensembles of directed scale-free networks with exponents between 2 and 3, and measuring ensemble averages of the Pearson correlation coefficients. Our results show that scale-free networks are on average uncorrelated across directed links for three of the four possible degree-degree correlations, namely in-degree to in-degree, in-degree to out-degree, and out-degree to out-degree. However, they exhibit anticorrelation between the number of outgoing connections and the number of incoming ones. The findings are consistent with an entropic origin for the observed disassortativity in biological and technological networks.
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.
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).
Network topology and correlation features affiliated with European airline companies
NASA Astrophysics Data System (ADS)
Han, Ding-Ding; Qian, Jiang-Hai; Liu, Jin-Gao
2009-01-01
The physics information of four specific airline flight networks in European Continent, namely the Austrian airline, the British airline, the France-Holland airline and the Lufthhansa airline, was quantitatively analyzed by the concepts of a complex network. It displays some features of small-world networks, namely a large clustering coefficient and small average shortest-path length for these specific airline networks. The degree distributions for the small degree branch reveal power law behavior with an exponent value of 2-3 for the Austrian and the British flight networks, and that of 1-2 for the France-Holland and the Lufthhansa airline flight networks. So the studied four airlines are sorted into two classes according to the topology structure. Similarly, the flight weight distributions show two kinds of different decay behavior with the flight weight: one for the Austrian and the British airlines and another for the France-Holland airline and the Lufthhansa airlines. In addition, the degree-degree correlation analysis shows that the network has disassortative behavior for all the value of degree k, and this phenomenon is different from the international airline network and US airline network. Analysis of the clustering coefficient ( C(k)) versus k, indicates that the flight networks of the Austrian Airline and the British Airline reveal a hierarchical organization for all airports, however, the France-Holland Airline and the Lufthhansa Airline show a hierarchical organization mostly for larger airports. The correlation of node strength ( S(k)) and degree is also analyzed, and a power-law fit S(k)∼k1.1 can roughly fit all data of these four airline companies. Furthermore, we mention seasonal changes and holidays may cause the flight network to form a different topology. An example of the Austrian Airline during Christmas was studied and analyzed.
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)
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
Predictability and reduced order modeling in stochastic reaction networks.
Najm, Habib N.; Debusschere, Bert J.; Sargsyan, Khachik
2008-10-01
Many systems involving chemical reactions between small numbers of molecules exhibit inherent stochastic variability. Such stochastic reaction networks are at the heart of processes such as gene transcription, cell signaling or surface catalytic reactions, which are critical to bioenergy, biomedical, and electrical storage applications. The underlying molecular reactions are commonly modeled with chemical master equations (CMEs), representing jump Markov processes, or stochastic differential equations (SDEs), rather than ordinary differential equations (ODEs). As such reaction networks are often inferred from noisy experimental data, it is not uncommon to encounter large parametric uncertainties in these systems. Further, a wide range of time scales introduces the need for reduced order representations. Despite the availability of mature tools for uncertainty/sensitivity analysis and reduced order modeling in deterministic systems, there is a lack of robust algorithms for such analyses in stochastic systems. In this talk, we present advances in algorithms for predictability and reduced order representations for stochastic reaction networks and apply them to bistable systems of biochemical interest. To study the predictability of a stochastic reaction network in the presence of both parametric uncertainty and intrinsic variability, an algorithm was developed to represent the system state with a spectral polynomial chaos (PC) expansion in the stochastic space representing parametric uncertainty and intrinsic variability. Rather than relying on a non-intrusive collocation-based Galerkin projection [1], this PC expansion is obtained using Bayesian inference, which is ideally suited to handle noisy systems through its probabilistic formulation. To accommodate state variables with multimodal distributions, an adaptive multiresolution representation is used [2]. As the PC expansion directly relates the state variables to the uncertain parameters, the formulation lends
Correlation and network topologies in global and local stock indices
NASA Astrophysics Data System (ADS)
Nobi, Ashadun; Lee, Sungmin; Kim, Doo Hwan; Lee, Jae Woo
2014-07-01
We examined how the correlation and network structure of the global indices and local Korean indices have changed during years 2000-2012. The average correlations of the global indices increased with time, while the local indices showed a decreasing trend except for drastic changes during the crises. A significant change in the network topologies was observed due to the financial crises in both markets. The Jaccard similarities identified the change in the market state due to a crisis in both markets. The dynamic change of the Jaccard index can be used as an indicator of systemic risk or precursors of the crisis.
Universal quantum computation with ordered spin-chain networks
NASA Astrophysics Data System (ADS)
Tserkovnyak, Yaroslav; Loss, Daniel
2011-09-01
It is shown that anisotropic spin chains with gapped bulk excitations and magnetically ordered ground states offer a promising platform for quantum computation, which bridges the conventional single-spin-based qubit concept with recently developed topological Majorana-based proposals. We show how to realize the single-qubit Hadamard, phase, and π/8 gates as well as the two-qubit controlled-not (cnot) gate, which together form a fault-tolerant universal set of quantum gates. The gates are implemented by judiciously controlling Ising exchange and magnetic fields along a network of spin chains, with each individual qubit furnished by a spin-chain segment. A subset of single-qubit operations is geometric in nature, relying on control of anisotropy of spin interactions rather than their strength. We contrast topological aspects of the anisotropic spin-chain networks to those of p-wave superconducting wires discussed in the literature.
Dissociable networks involved in spatial and temporal order source retrieval.
Ekstrom, Arne D; Copara, Milagros S; Isham, Eve A; Wang, Wei-chun; Yonelinas, Andrew P
2011-06-01
Space and time are important components of our episodic memories. Without this information, we cannot determine the "where and when" of our recent memories, rendering it difficult to disambiguate individual episodes from each other. The neural underpinnings of spatial and temporal order memory in humans remain unclear, in part because of difficulties in disentangling the contributions of these two types of source information. To address this issue, we conducted an experiment in which participants first navigated a virtual city, experiencing unique routes in a specific temporal order and learning about the spatial layout of the city. Spatial and temporal order information were dissociated in our task such that learning one type of information did not facilitate the other behaviorally. This allowed us to then address the extent to which the two types of information involved functionally distinct or overlapping brain areas. During functional magnetic resonance imaging (fMRI), participants retrieved information about the relative distance of stores within the city (spatial task) and the temporal order of stores from each other (temporal task). Comparable hippocampal activity was observed during these two tasks, but greater prefrontal activity was seen during temporal order retrieval whereas greater parahippocampal activity was seen during spatial retrieval. We suggest that while the brain possesses dissociable networks for maintaining and representing spatial layout and temporal order components of episodic memory, this information may converge into a common representation for source memory in areas such as the hippocampus.
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.
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…
A stochastic analysis of first-order reaction networks.
Gadgil, Chetan; Lee, Chang Hyeong; Othmer, Hans G
2005-09-01
A stochastic model for a general system of first-order reactions in which each reaction may be either a conversion reaction or a catalytic reaction is derived. The governing master equation is formulated in a manner that explicitly separates the effects of network topology from other aspects, and the evolution equations for the first two moments are derived. We find the surprising, and apparently unknown, result that the time evolution of the second moments can be represented explicitly in terms of the eigenvalues and projections of the matrix that governs the evolution of the means. The model is used to analyze the effects of network topology and the reaction type on the moments of the probability distribution. In particular, it is shown that for an open system of first-order conversion reactions, the distribution of all the system components is a Poisson distribution at steady state. Two different measures of the noise have been used previously, and it is shown that different qualitative and quantitative conclusions can result, depending on which measure is used. The effect of catalytic reactions on the variance of the system components is also analyzed, and the master equation for a coupled system of first-order reactions and diffusion is derived.
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.
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
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
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
Correlates of viral richness in bats (order Chiroptera).
Turmelle, Amy S; Olival, Kevin J
2009-12-01
Historic and contemporary host ecology and evolutionary dynamics have profound impacts on viral diversity, virulence, and associated disease emergence. Bats have been recognized as reservoirs for several emerging viral pathogens, and are unique among mammals in their vagility, potential for long-distance dispersal, and often very large, colonial populations. We investigate the relative influences of host ecology and population genetic structure for predictions of viral richness in relevant reservoir species. We test the hypothesis that host geographic range area, distribution, population genetic structure, migratory behavior, International Union for Conservation of Nature and Natural Resources (IUCN) threat status, body mass, and colony size, are associated with known viral richness in bats. We analyze host traits and viral richness in a generalized linear regression model framework, and include a correction for sampling effort and phylogeny. We find evidence that sampling effort, IUCN status, and population genetic structure correlate with observed viral species richness in bats, and that these associations are independent of phylogeny. This study is an important first step in understanding the mechanisms that promote viral richness in reservoir species, and may aid in predicting the emergence of viral zoonoses from bats.
Spam Source Clustering by Constructing Spammer Network with Correlation Measure
NASA Astrophysics Data System (ADS)
Shin, Jeongkyu; Kim, Seunghwan
Spam filtering is one of the most challenging problems in electric message systems. In general, recent studies on specifying real spam source are based on content filtering because spammers usually falsify their origin. We propose a method to specify spam source based on structural analysis with complex network. We assume that each spam sources either has the same victim list or uses the same spam-hosting program. We treat spam source - target relationship as a bipartite network and construct weighted spam source network by network projection using correlation measure. We find that community clustering methods are inappropriate with spammer network. We group spammers with gradient-based grouping, which uses correlations between nodes as gradient between nodes. We convert them into local minima, which helps to cluster spammers into a few spam source groups. We investigate the weblog spam data with the proposed method and validate it. The method that we propose can be applied to diverse categorization problems, such as multiple text categorization and network subunit clustering.
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.
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.
Robustness of onionlike correlated networks against targeted attacks.
Tanizawa, Toshihiro; Havlin, Shlomo; Stanley, H Eugene
2012-04-01
Recently, it was found by Schneider et al. [Proc. Natl. Acad. Sci. USA 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.
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.
Supercooperation in evolutionary games on correlated weighted networks
NASA Astrophysics Data System (ADS)
Buesser, Pierre; Tomassini, Marco
2012-01-01
In this work we study the behavior of classical two-person, two-strategies evolutionary games on a class of weighted networks derived from Barabási-Albert and random scale-free unweighted graphs. Using customary imitative dynamics, our numerical simulation results show that the presence of link weights that are correlated in a particular manner with the degree of the link end points leads to unprecedented levels of cooperation in the whole games' phase space, well above those found for the corresponding unweighted complex networks. We provide intuitive explanations for this favorable behavior by transforming the weighted networks into unweighted ones with particular topological properties. The resulting structures help us to understand why cooperation can thrive and also give ideas as to how such supercooperative networks might be built.
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.
Effects of degree correlations on the explosive synchronization of scale-free networks
NASA Astrophysics Data System (ADS)
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.
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.
Correlated loss of ecosystem services in coupled mutualistic networks.
Albrecht, Jörg; Berens, Dana Gertrud; Jaroszewicz, Bogdan; Selva, Nuria; Brandl, Roland; Farwig, Nina
2014-05-08
Networks of species interactions promote biodiversity and provide important ecosystem services. These networks have traditionally been studied in isolation, but species are commonly involved in multiple, diverse types of interaction. Therefore, whether different types of species interaction networks coupled through shared species show idiosyncratic or correlated responses to habitat degradation is unresolved. Here we study the collective response of coupled mutualistic networks of plants and their pollinators and seed dispersers to the degradation of Europe's last relict of old-growth lowland forest (Białowieża, Poland). We show that logging of old-growth forests has correlated effects on the number of partners and interactions of plants in both mutualisms, and that these effects are mediated by shifts in plant densities on logged sites. These results suggest bottom-up-controlled effects of habitat degradation on plant-animal mutualistic networks, and predict that the conversion of primary old-growth forests to secondary habitats may cause a parallel loss of multiple animal-mediated ecosystem services.
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.
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
A canonical correlation neural network for multicollinearity and functional data.
Gou, Zhenkun; Fyfe, Colin
2004-03-01
We review a recent neural implementation of Canonical Correlation Analysis and show, using ideas suggested by Ridge Regression, how to make the algorithm robust. The network is shown to operate on data sets which exhibit multicollinearity. We develop a second model which not only performs as well on multicollinear data but also on general data sets. This model allows us to vary a single parameter so that the network is capable of performing Partial Least Squares regression (at one extreme) to Canonical Correlation Analysis (at the other)and every intermediate operation between the two. On multicollinear data, the parameter setting is shown to be important but on more general data no particular parameter setting is required. Finally, we develop a second penalty term which acts on such data as a smoother in that the resulting weight vectors are much smoother and more interpretable than the weights without the robustification term. We illustrate our algorithms on both artificial and real data.
NASA Astrophysics Data System (ADS)
OświÈ©cimka, Paweł; Livi, Lorenzo; DroŻdŻ, Stanisław
2016-10-01
We investigate the scaling of the cross-correlations calculated for two-variable time series containing vertex properties in the context of complex networks. Time series of such observables are obtained by means of stationary, unbiased random walks. We consider three vertex properties that provide, respectively, short-, medium-, and long-range information regarding the topological role of vertices in a given network. In order to reveal the relation between these quantities, we applied the multifractal cross-correlation analysis technique, which provides information about the nonlinear effects in coupling of time series. We show that the considered network models are characterized by unique multifractal properties of the cross-correlation. In particular, it is possible to distinguish between Erdös-Rényi, Barabási-Albert, and Watts-Strogatz networks on the basis of fractal cross-correlation. Moreover, the analysis of protein contact networks reveals characteristics shared with both scale-free and small-world models.
Correlation between eigenvalue spectra and dynamics of neural networks.
Zhou, Qingguo; Jin, Tao; Zhao, Hong
2009-10-01
This letter presents a study of the correlation between the eigenvalue spectra of synaptic matrices and the dynamical properties of asymmetric neural networks with associative memories. For this type of neural network, it was found that there are essentially two different dynamical phases: the chaos phase, with almost all trajectories converging to a single chaotic attractor, and the memory phase, with almost all trajectories being attracted toward fixed-point attractors acting as memories. We found that if a neural network is designed in the chaos phase, the eigenvalue spectrum of its synaptic matrix behaves like that of a random matrix (i.e., all eigenvalues lie uniformly distributed within a circle in the complex plan), and if it is designed in the memory phase, the eigenvalue spectrum will split into two parts: one part corresponds to a random background, the other part equal in number to the memory attractors. The mechanism for these phenomena is discussed in this letter.
The thalamo-cortical complex network correlates of chronic pain
Zippo, Antonio G.; Valente, Maurizio; Caramenti, Gian Carlo; Biella, Gabriele E. M.
2016-01-01
Chronic pain (CP) is a condition with a large repertory of clinical signs and symptoms with diverse expressions. Though widely analyzed, an appraisal at the level of single neuron and neuronal networks in CP is however missing. The present research proposes an empirical and theoretic framework which identifies a complex network correlate nested in the somatosensory thalamocortical (TC) circuit in diverse CP models. In vivo simultaneous extracellular neuronal electrophysiological high-density recordings have been performed from the TC circuit in rats. Wide functional network statistics neatly discriminated CP from control animals identifying collective dynamical traits. In particular, a collapsed functional connectivity and an altered modular architecture of the thalamocortical circuit have been evidenced. These results envisage CP as a functional connectivity disorder and give the clue for unveiling innovative therapeutic strategies. PMID:27734895
Parallel calculation of multi-electrode array correlation networks.
Ribeiro, Pedro; Simonotto, Jennifer; Kaiser, Marcus; Silva, Fernando
2009-11-15
When calculating correlation networks from multi-electrode array (MEA) data, one works with extensive computations. Unfortunately, as the MEAs grow bigger, the time needed for the computation grows even more: calculating pair-wise correlations for current 60 channel systems can take hours on normal commodity computers whereas for future 1000 channel systems it would take almost 280 times as long, given that the number of pairs increases with the square of the number of channels. Even taking into account the increase of speed in processors, soon it can be unfeasible to compute correlations in a single computer. Parallel computing is a way to sustain reasonable calculation times in the future. We provide a general tool for rapid computation of correlation networks which was tested for: (a) a single computer cluster with 16 cores, (b) the Newcastle Condor System utilizing idle processors of university computers and (c) the inter-cluster, with 192 cores. Our reusable tool provides a simple interface for neuroscientists, automating data partition and job submission, and also allowing coding in any programming language. It is also sufficiently flexible to be used in other high-performance computing environments. PMID:19666054
Role of intensity fluctuations in third-order correlation double-slit interference of thermal light.
Chen, Xi-Hao; Chen, Wen; Meng, Shao-Ying; Wu, Wei; Wu, Ling-An; Zhai, Guang-Jie
2013-07-01
A third-order double-slit interference experiment with a pseudothermal light source in the high-intensity limit has been performed by actually recording the intensities in three optical paths. It is shown that not only can the visibility be dramatically enhanced compared to the second-order case as previously theoretically predicted and shown experimentally, but also that the higher visibility is a consequence of the contribution of third-order correlation interaction terms, which is equal to the sum of all contributions from second-order correlation. It is interesting that, when the two reference detectors are scanned in opposite directions, negative values for the third-order correlation term of the intensity fluctuations may appear. The phenomenon can be completely explained by the theory of classical statistical optics and is the first concrete demonstration of the influence of the third-order correlation terms.
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.
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.
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.
Identifying module biomarkers from gastric cancer by differential correlation network
Liu, Xiaoping; Chang, Xiao
2016-01-01
Gastric cancer (stomach cancer) is a severe disease caused by dysregulation of many functionally correlated genes or pathways instead of the mutation of individual genes. Systematic identification of gastric cancer biomarkers can provide insights into the mechanisms underlying this deadly disease and help in the development of new drugs. In this paper, we present a novel network-based approach to predict module biomarkers of gastric cancer that can effectively distinguish the disease from normal samples. Specifically, by assuming that gastric cancer has mainly resulted from dysfunction of biomolecular networks rather than individual genes in an organism, the genes in the module biomarkers are potentially related to gastric cancer. Finally, we identified a module biomarker with 27 genes, and by comparing the module biomarker with known gastric cancer biomarkers, we found that our module biomarker exhibited a greater ability to diagnose the samples with gastric cancer. PMID:27703371
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.
Xiao, Min; Zheng, Wei Xing; Jiang, Guoping; Cao, Jinde
2015-12-01
In this paper, a fractional-order recurrent neural network is proposed and several topics related to the dynamics of such a network are investigated, such as the stability, Hopf bifurcations, and undamped oscillations. The stability domain of the trivial steady state is completely characterized with respect to network parameters and orders of the commensurate-order neural network. Based on the stability analysis, the critical values of the fractional order are identified, where Hopf bifurcations occur and a family of oscillations bifurcate from the trivial steady state. Then, the parametric range of undamped oscillations is also estimated and the frequency and amplitude of oscillations are determined analytically and numerically for such commensurate-order networks. Meanwhile, it is shown that the incommensurate-order neural network can also exhibit a Hopf bifurcation as the network parameter passes through a critical value which can be determined exactly. The frequency and amplitude of bifurcated oscillations are determined.
A framework for network-wide semantic event correlation
NASA Astrophysics Data System (ADS)
Hall, Robert T.; Taylor, Joshua
2013-05-01
An increasing need for situational awareness within network-deployed Systems Under Test has increased desire for frameworks that facilitate system-wide data correlation and analysis. Massive event streams are generated from heterogeneous sensors which require tedious manual analysis. We present a framework for sensor data integration and event correlation based on Linked Data principles, Semantic Web reasoning technology, complex event processing, and blackboard architectures. Sensor data are encoded as RDF models, then processed by complex event processing agents (which incorporate domain specific reasoners, as well as general purpose Semantic Web reasoning techniques). Agents can publish inferences on shared blackboards and generate new semantic events that are fed back into the system. We present AIS, Inc.'s Cyber Battlefield Training and Effectiveness Environment to demonstrate use of the framework.
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.
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
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.
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
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.
NASA Astrophysics Data System (ADS)
Bonneau, François; Caumon, Guillaume; Renard, Philippe
2016-08-01
Stochastic discrete fracture networks (DFNs) are classically simulated using stochastic point processes which neglect mechanical interactions between fractures and yield a low spatial correlation in a network. We propose a sequential parent-daughter Poisson point process that organizes fracture objects according to mechanical interactions while honoring statistical characterization data. The hierarchical organization of the resulting DFNs has been investigated in 3-D by computing their correlation dimension. Sensitivity analysis on the input simulation parameters shows that various degrees of spatial correlation emerge from this process. A large number of realizations have been performed in order to statistically validate the method. The connectivity of these correlated fracture networks has been investigated at several scales and compared to those described in the literature. Our study quantitatively confirms that spatial correlations can affect the percolation threshold and the connectivity at a particular scale.
Sadhukhan, Debasis; Prabhu, R; Sen De, Aditi; Sen, Ujjwal
2016-03-01
We investigate the behavior of quantum correlations of paradigmatic quenched disordered quantum spin models, viz., the XY spin glass and random-field XY models. We show that quenched averaged quantum correlations can exhibit the order-from-disorder phenomenon for finite-size systems as well as in the thermodynamic limit. Moreover, we find that the order-from-disorder can become more pronounced in the presence of temperature by suitable tuning of the system parameters. The effects are found for entanglement measures as well as for information-theoretic quantum correlation ones, although the former show them more prominently. We also observe that the equivalence between the quenched averages and their self-averaged cousins--for classical and quantum correlations--is related to the quantum critical point in the corresponding ordered system. PMID:27078300
Correlated Fluctuations in Strongly Coupled Binary Networks Beyond Equilibrium
NASA Astrophysics Data System (ADS)
Dahmen, David; Bos, Hannah; Helias, Moritz
2016-07-01
Randomly coupled Ising spins constitute the classical model of collective phenomena in disordered systems, with applications covering glassy magnetism and frustration, combinatorial optimization, protein folding, stock market dynamics, and social dynamics. The phase diagram of these systems is obtained in the thermodynamic limit by averaging over the quenched randomness of the couplings. However, many applications require the statistics of activity for a single realization of the possibly asymmetric couplings in finite-sized networks. Examples include reconstruction of couplings from the observed dynamics, representation of probability distributions for sampling-based inference, and learning in the central nervous system based on the dynamic and correlation-dependent modification of synaptic connections. The systematic cumulant expansion for kinetic binary (Ising) threshold units with strong, random, and asymmetric couplings presented here goes beyond mean-field theory and is applicable outside thermodynamic equilibrium; a system of approximate nonlinear equations predicts average activities and pairwise covariances in quantitative agreement with full simulations down to hundreds of units. The linearized theory yields an expansion of the correlation and response functions in collective eigenmodes, leads to an efficient algorithm solving the inverse problem, and shows that correlations are invariant under scaling of the interaction strengths.
NASA Astrophysics Data System (ADS)
Johnson, Aaron P.; Kingdom, Frederick A. A.; Baker, Curtis L.
2005-10-01
Spatial filters that mimic receptive fields of visual cortex neurons provide an efficient representation of achromatic image structure, but the extension of this idea to chromatic information is at an early stage. Relatively few studies have looked at the statistical relationships between the modeled responses to natural scenes of the luminance (LUM), red-green (RG), and blue-yellow (BY) postreceptoral channels of the primate visual system. Here we consider the correlations among these channel responses in terms of pixel, first-order, and second-order information. First-order linear filtering was implemented by convolving the cosine-windowed images with oriented Gabor functions, whose gains were scaled to give equal amplitude response across spatial frequency to random fractal images. Second-order filtering was implemented via a filter-rectify-filter cascade, with Gabor functions for both first- and second-stage filters. Both signed and unsigned filter responses were obtained across a range of filter parameters (spatial frequency, 2-64 cycles/image orientation, 0-135°). The filter responses to the LUM channel images were larger than those for either RG or BY channel images. Cross correlations between the first-order channel responses and between the first- and second-order channel responses were measured. Results showed that the unsigned correlations between first-order channel responses were higher than expected on the basis of previous studies and that first-order channel responses were highly correlated with LUM, but not with RG or BY, second-order responses. These findings imply that course-scale color information correlates well with course-scale changes of fine-scale texture.
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.
Third-order optical intensity correlation measurements of pseudo-thermal light
NASA Astrophysics Data System (ADS)
Chen, Xi-Hao; Wu, Wei; Meng, Shao-Ying; Li, Ming-Fei
2014-09-01
Third-order Hanbrury Brown—Twiss and double-slit interference experiments with a pseudo-thermal light are performed by recording intensities in single, double and triple optical paths, respectively. The experimental results verifies the theoretical prediction that the indispensable condition for achieving a interference pattern or ghost image in Nth-order intensity correlation measurements is the synchronous detection of the same light field by each reference detector, no matter the intensities recorded in one, or two, or N optical paths. It is shown that, when the reference detectors are scanned in the opposite directions, the visibility and resolution of the third-order spatial correlation function of thermal light is much better than that scanned in the same direction, but it is no use for obtaining the Nth-order interference pattern or ghost image in the thermal Nth-order interference or ghost imaging.
Global asymptotical ω-periodicity of a fractional-order non-autonomous neural networks.
Chen, Boshan; Chen, Jiejie
2015-08-01
We study the global asymptotic ω-periodicity for a fractional-order non-autonomous neural networks. Firstly, based on the Caputo fractional-order derivative it is shown that ω-periodic or autonomous fractional-order neural networks cannot generate exactly ω-periodic signals. Next, by using the contraction mapping principle we discuss the existence and uniqueness of S-asymptotically ω-periodic solution for a class of fractional-order non-autonomous neural networks. Then by using a fractional-order differential and integral inequality technique, we study global Mittag-Leffler stability and global asymptotical periodicity of the fractional-order non-autonomous neural networks, which shows that all paths of the networks, starting from arbitrary points and responding to persistent, nonconstant ω-periodic external inputs, asymptotically converge to the same nonconstant ω-periodic function that may be not a solution.
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.
NASA Astrophysics Data System (ADS)
Sadhukhan, Debasis; Prabhu, R.; SenDe, Aditi; Sen, Ujjwal
2016-03-01
We investigate the behavior of quantum correlations of paradigmatic quenched disordered quantum spin models, viz., the X Y spin glass and random-field X Y models. We show that quenched averaged quantum correlations can exhibit the order-from-disorder phenomenon for finite-size systems as well as in the thermodynamic limit. Moreover, we find that the order-from-disorder can become more pronounced in the presence of temperature by suitable tuning of the system parameters. The effects are found for entanglement measures as well as for information-theoretic quantum correlation ones, although the former show them more prominently. We also observe that the equivalence between the quenched averages and their self-averaged cousins—for classical and quantum correlations—is related to the quantum critical point in the corresponding ordered system.
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.
Knowledge extraction from evolving spiking neural networks with rank order population coding.
Soltic, Snjezana; Kasabov, Nikola
2010-12-01
This paper demonstrates how knowledge can be extracted from evolving spiking neural networks with rank order population coding. Knowledge discovery is a very important feature of intelligent systems. Yet, a disproportionally small amount of research is centered on the issue of knowledge extraction from spiking neural networks which are considered to be the third generation of artificial neural networks. The lack of knowledge representation compatibility is becoming a major detriment to end users of these networks. We show that a high-level knowledge can be obtained from evolving spiking neural networks. More specifically, we propose a method for fuzzy rule extraction from an evolving spiking network with rank order population coding. The proposed method was used for knowledge discovery on two benchmark taste recognition problems where the knowledge learnt by an evolving spiking neural network was extracted in the form of zero-order Takagi-Sugeno fuzzy IF-THEN rules.
A new method to infer higher-order spike correlations from membrane potentials.
Reimer, Imke C G; Staude, Benjamin; Boucsein, Clemens; Rotter, Stefan
2013-10-01
What is the role of higher-order spike correlations for neuronal information processing? Common data analysis methods to address this question are devised for the application to spike recordings from multiple single neurons. Here, we present a new method which evaluates the subthreshold membrane potential fluctuations of one neuron, and infers higher-order correlations among the neurons that constitute its presynaptic population. This has two important advantages: Very large populations of up to several thousands of neurons can be studied, and the spike sorting is obsolete. Moreover, this new approach truly emphasizes the functional aspects of higher-order statistics, since we infer exactly those correlations which are seen by a neuron. Our approach is to represent the subthreshold membrane potential fluctuations as presynaptic activity filtered with a fixed kernel, as it would be the case for a leaky integrator neuron model. This allows us to adapt the recently proposed method CuBIC (cumulant based inference of higher-order correlations from the population spike count; Staude et al., J Comput Neurosci 29(1-2):327-350, 2010c) with which the maximal order of correlation can be inferred. By numerical simulation we show that our new method is reasonably sensitive to weak higher-order correlations, and that only short stretches of membrane potential are required for their reliable inference. Finally, we demonstrate its remarkable robustness against violations of the simplifying assumptions made for its construction, and discuss how it can be employed to analyze in vivo intracellular recordings of membrane potentials.
NASA Astrophysics Data System (ADS)
Ji, Xingpei; Wang, Bo; Liu, Dichen; Dong, Zhaoyang; Chen, Guo; Zhu, Zhenshan; Zhu, Xuedong; Wang, Xunting
2016-10-01
Whether the realistic electrical cyber-physical interdependent networks will undergo first-order transition under random failures still remains a question. To reflect the reality of Chinese electrical cyber-physical system, the "partial one-to-one correspondence" interdependent networks model is proposed and the connectivity vulnerabilities of three realistic electrical cyber-physical interdependent networks are analyzed. The simulation results show that due to the service demands of power system the topologies of power grid and its cyber network are highly inter-similar which can effectively avoid the first-order transition. By comparing the vulnerability curves between electrical cyber-physical interdependent networks and its single-layer network, we find that complex network theory is still useful in the vulnerability analysis of electrical cyber-physical interdependent networks.
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.
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
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.
Stability analysis of fractional-order Hopfield neural networks with time delays.
Wang, Hu; Yu, Yongguang; Wen, Guoguang
2014-07-01
This paper investigates the stability for fractional-order Hopfield neural networks with time delays. Firstly, the fractional-order Hopfield neural networks with hub structure and time delays are studied. Some sufficient conditions for stability of the systems are obtained. Next, two fractional-order Hopfield neural networks with different ring structures and time delays are developed. By studying the developed neural networks, the corresponding sufficient conditions for stability of the systems are also derived. It is shown that the stability conditions are independent of time delays. Finally, numerical simulations are given to illustrate the effectiveness of the theoretical results obtained in this paper.
NASA Astrophysics Data System (ADS)
Atkinson, Steven; Stillinger, Frank H.; Torquato, Salvatore
2016-09-01
The nonequilibrium process by which hard-particle systems may be compressed into disordered, jammed states has received much attention because of its wide utility in describing a broad class of amorphous materials. While dynamical signatures are known to precede jamming, the task of identifying static structural signatures indicating the onset of jamming have proven more elusive. The observation that compressing hard-particle packings towards jamming is accompanied by an anomalous suppression of density fluctuations (termed "hyperuniformity") has paved the way for the analysis of jamming as an "inverted critical point" in which the direct correlation function c (r ) , rather than the total correlation function h (r ) , diverges. We expand on the notion that c (r ) provides both universal and protocol-specific information as packings approach jamming. By considering the degree and position of singularities (discontinuities in the n th derivative) as well as how they are changed by the convolutions found in the Ornstein-Zernike equation, we establish quantitative statements about the structure of c (r ) with regards to singularities it inherits from h (r ) . These relations provide a concrete means of identifying features that must be expressed in c (r ) if one hopes to reproduce various details in the pair correlation function accurately and provide stringent tests on the associated numerics. We also analyze the evolution of systems of three-dimensional monodisperse hard spheres of diameter D as they approach ordered and disordered jammed configurations. For the latter, we use the Lubachevsky-Stillinger (LS) molecular dynamics and Torquato-Jiao (TJ) sequential linear programming algorithms, which both generate disordered packings, but can show perceptible structural differences. We identify a short-ranged scaling c (r )∝-1 /r as r →0 that accompanies the formation of the delta function at c (D ) that indicates the formation of contacts in all cases, and show
Chen, Jiejie; Zeng, Zhigang; Jiang, Ping
2014-03-01
The present paper introduces memristor-based fractional-order neural networks. The conditions on the global Mittag-Leffler stability and synchronization are established by using Lyapunov method for these networks. The analysis in the paper employs results from the theory of fractional-order differential equations with discontinuous right-hand sides. The obtained results extend and improve some previous works on conventional memristor-based recurrent neural networks.
A general fractional-order dynamical network: Synchronization behavior and state tuning
NASA Astrophysics Data System (ADS)
Wang, Junwei; Xiong, Xiaohua
2012-06-01
A general fractional-order dynamical network model for synchronization behavior is proposed. Different from previous integer-order dynamical networks, the model is made up of coupled units described by fractional differential equations, where the connections between individual units are nondiffusive and nonlinear. We show that the synchronous behavior of such a network cannot only occur, but also be dramatically different from the behavior of its constituent units. In particular, we find that simple behavior can emerge as synchronized dynamics although the isolated units evolve chaotically. Conversely, individually simple units can display chaotic attractors when the network synchronizes. We also present an easily checked criterion for synchronization depending only on the eigenvalues distribution of a decomposition matrix and the fractional orders. The analytic results are complemented with numerical simulations for two networks whose nodes are governed by fractional-order Lorenz dynamics and fractional-order Rössler dynamics, respectively.
Measuring the growth of matter fluctuations with third-order galaxy correlations
NASA Astrophysics Data System (ADS)
Hoffmann, K.; Bel, J.; Gaztañaga, E.; Crocce, M.; Fosalba, P.; Castander, F. J.
2015-02-01
Measurements of the linear growth factor D at different redshifts z are key to distinguish among cosmological models. One can estimate the derivative dD(z)/dln (1 + z) from redshift space measurements of the 3D anisotropic galaxy two-point correlation ξ(z), but the degeneracy of its transverse (or projected) component with galaxy bias b, i.e. ξ⊥(z) ∝ D2(z)b2(z), introduces large errors in the growth measurement. Here, we present a comparison between two methods which breaks this degeneracy by combining second- and third-order statistics. One uses the shape of the reduced three-point correlation and the other a combination of third-order one- and two-point cumulants. These methods use the fact that, for Gaussian initial conditions and scales larger than 20 h-1 Mpc, the reduced third-order matter correlations are independent of redshift (and therefore of the growth factor), while the third-order galaxy correlations depend on b. We use matter and halo catalogues from the MICE-GC simulation to test how well we can recover b(z) and therefore D(z) with these methods in 3D real space. We also present a new approach, which enables us to measure D directly from the redshift evolution of the second- and third-order galaxy correlations without the need of modelling matter correlations. For haloes with masses lower than 1014 h-1 M⊙, we find 10 per cent deviations between the different estimates of D, which are comparable to current observational errors. At higher masses, we find larger differences that can probably be attributed to the breakdown of the bias model and non-Poissonian shot noise.
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.
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.
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.
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.
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
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…
Palmer, A G; Thompson, N L
1987-01-01
The use of high order autocorrelation in fluorescence correlation spectroscopy for investigating aggregation in a sample that contains fluorescent molecules is described. Theoretical expressions for the fluorescence fluctuation autocorrelation functions defined by gm,n(tau) = [(delta fm(t + tau)delta fm(t] - (delta Fm(t] (delta Fn(t
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.
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.
NASA Astrophysics Data System (ADS)
Ikuta, Akira; Orimoto, Hisako; Ogawa, Hitoshi
In this study, a stochastic detection method of failure of machines based on the changing information of not only a linear correlation but also the higher order nonlinear correlation is proposed in a form suitable for on-line signal processing in time domain by using a personal computer, especially in order to find minutely the mutual relationship between sound and vibration emitted from rotational machines. More specifically, a conditional probability hierarchically reflecting various types of correlation information is theoretically derived by introducing an expression on the multi-dimensional probability distribution in orthogonal expansion series form. The effectiveness of the proposed theory is experimentally confirmed by applying it to the observed data emitted from a rotational machine driven by an electric motor.
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.
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.
Motif analysis in directed ordered networks and applications to food webs
Paulau, Pavel V.; Feenders, Christoph; Blasius, Bernd
2015-01-01
The analysis of small recurrent substructures, so called network motifs, has become a standard tool of complex network science to unveil the design principles underlying the structure of empirical networks. In many natural systems network nodes are associated with an intrinsic property according to which they can be ordered and compared against each other. Here, we expand standard motif analysis to be able to capture the hierarchical structure in such ordered networks. Our new approach is based on the identification of all ordered 3-node substructures and the visualization of their significance profile. We present a technique to calculate the fine grained motif spectrum by resolving the individual members of isomorphism classes (sets of substructures formed by permuting node-order). We apply this technique to computer generated ensembles of ordered networks and to empirical food web data, demonstrating the importance of considering node order for food-web analysis. Our approach may not only be helpful to identify hierarchical patterns in empirical food webs and other natural networks, it may also provide the base for extending motif analysis to other types of multi-layered networks. PMID:26144248
Motif analysis in directed ordered networks and applications to food webs.
Paulau, Pavel V; Feenders, Christoph; Blasius, Bernd
2015-01-01
The analysis of small recurrent substructures, so called network motifs, has become a standard tool of complex network science to unveil the design principles underlying the structure of empirical networks. In many natural systems network nodes are associated with an intrinsic property according to which they can be ordered and compared against each other. Here, we expand standard motif analysis to be able to capture the hierarchical structure in such ordered networks. Our new approach is based on the identification of all ordered 3-node substructures and the visualization of their significance profile. We present a technique to calculate the fine grained motif spectrum by resolving the individual members of isomorphism classes (sets of substructures formed by permuting node-order). We apply this technique to computer generated ensembles of ordered networks and to empirical food web data, demonstrating the importance of considering node order for food-web analysis. Our approach may not only be helpful to identify hierarchical patterns in empirical food webs and other natural networks, it may also provide the base for extending motif analysis to other types of multi-layered networks.
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.
Hidden String Order in a Hole Superconductor with Extended Correlated Hopping.
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.
Perez, Amanda G M; Rodrigues, Ana A; Luzo, Angela C M; Lana, José F S D; Belangero, William D; Santana, Maria H A
2014-08-01
Fibrin networks are obtained through activation of platelet-rich plasma (PRP) for use in tissue regeneration. The importance of fibrin networks relies on mediation of release of growth factors, proliferation of tissue cells and rheological properties of the fibrin gels. Activation of PRP usually involves the decomposition of fibrinogen by agonists, in a wide range of concentrations. Therefore fibrin networks with a large structural diversity are formed, making comparative evaluations difficult. In order to standardize the fibrin networks, we used the statistical techniques central composite rotatable design and response-surface analysis, to correlate the radius of the fibers with the ratios between the agonists (autologous serum/calcium chloride) and agonist/PRP. From an individual and interactive analysis of the variables, architectures characterized by thick, medium and thin fibers were delineated on the response-surface. Furthermore, the architectures were correlated with coagulation time. This approach is valuable for standardizing the PRP preparation for clinical applications.
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.
NASA Astrophysics Data System (ADS)
Shchukin, E.; van Loock, P.
2016-03-01
We derive two types of sets of higher-order conditions for bipartite entanglement in terms of continuous variables. One corresponds to an extension of the well-known Duan inequalities from second to higher moments describing a kind of higher-order Einstein-Podolsky-Rosen (EPR) correlations. Only the second type, however, expressed by powers of the mode operators leads to tight conditions with a hierarchical structure. We start with a minimization problem for the single-partite case and, using the results obtained, establish relevant inequalities for higher-order moments satisfied by all bipartite separable states. We give an explicit example of a non-Gaussian state that exhibits fourth-order but no second-order EPR correlations. Similarly, a certain fourth-order condition cannot be violated by any Gaussian state and we present non-Gaussian states whose entanglement is detected by that condition. Violations of all our conditions are provided, so they can all be used as entanglement tests.
Xu, Enhua; Zhao, Dongbo; Li, Shuhua
2015-10-13
A multireference second order perturbation theory based on a complete active space configuration interaction (CASCI) function or density matrix renormalized group (DMRG) function has been proposed. This method may be considered as an approximation to the CAS/A approach with the same reference, in which the dynamical correlation is simplified with blocked correlated second order perturbation theory based on the generalized valence bond (GVB) reference (GVB-BCPT2). This method, denoted as CASCI-BCPT2/GVB or DMRG-BCPT2/GVB, is size consistent and has a similar computational cost as the conventional second order perturbation theory (MP2). We have applied it to investigate a number of problems of chemical interest. These problems include bond-breaking potential energy surfaces in four molecules, the spectroscopic constants of six diatomic molecules, the reaction barrier for the automerization of cyclobutadiene, and the energy difference between the monocyclic and bicyclic forms of 2,6-pyridyne. Our test applications demonstrate that CASCI-BCPT2/GVB can provide comparable results with CASPT2 (second order perturbation theory based on the complete active space self-consistent-field wave function) for systems under study. Furthermore, the DMRG-BCPT2/GVB method is applicable to treat strongly correlated systems with large active spaces, which are beyond the capability of CASPT2.
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.
Parameter estimation and topology identification of uncertain fractional order complex networks
NASA Astrophysics Data System (ADS)
Si, Gangquan; Sun, Zhiyong; Zhang, Hongying; Zhang, Yanbin
2012-12-01
This paper focuses on a significant issue in the research of fractional order complex network, i.e., the identification problem of unknown system parameters and network topologies in uncertain complex networks with fractional-order node dynamics. Based on the stability analysis of fractional order systems and the adaptive control method, we propose a novel and general approach to address this challenge. The theoretical results in this paper have generalized the synchronization-based identification method that has been reported in several literatures on identifying integer order complex networks. We further derive the sufficient condition that ensures successful network identification. An uncertain complex network with four fractional-order Lorenz systems is employed to verify the effectiveness of the proposed approach. The numerical results show that this approach is applicable for online monitoring of the static or changing network topology. In addition, we present a discussion to explore which factor would influence the identification process. Certain interesting conclusions from the discussion are obtained, which reveal that large coupling strengths and small fractional orders are both harmful for a successful identification.
NASA Astrophysics Data System (ADS)
Pokharel, Rajendra K.
Relativistic heavy ion collision experiments show clear evidence of creation of a very short-lived phase of nuclear matter consisting of color-deconfined quarks and gluons. This matter is known as the quark-gluon plasma (QGP). Fluctuation and correlation measurements of the detected particles have played a very important role in revealing the properties of QGP. In particular, these measurements have shown that the QGP behaves like a nearly perfect liquid. Relativistic hydrodynamics has been successfully used to study how the QGP evolves before the system hadronizes and ultimately produces the final state particles. Transport properties like shear viscosity constitute an important part in such studies. This work is focused on developing a second order hydrodynamic theory for the evolution of two-particle transverse momentum correlations. We use general temperature dependent transport and relaxation coefficients as well as the latest information on equations of state and use both first and second order relativistic viscous hydrodynamics to compute experimentally measurable observables. We will show that our computations using the second order viscous hydrodynamics are in good agreement with experimental data. We also highlight some features that distinguish the second order viscous hydrodynamic evolution of QGP from the first order.
NASA Astrophysics Data System (ADS)
Noda, Isao
2016-11-01
Modified forms of two-dimensional (2D) correlation spectra, i.e., sign-adjusted asynchronous spectrum and merged correlation spectrum, are discussed. They are developed for the streamlined determination of the sequential order of spectral intensity variations using only one 2D map by combining the pertinent information of synchronous and asynchronous spectra. Development of small side lobe artifacts near the peripheral of a cross peak is sometimes noted, especially for highly overlapped bands which are changing intensities in the opposite directions. The merit of the ease of interpretation afforded by the modification of correlation spectra probably outweighs the introduction minor artifacts, but some care certainly is required to avoid misinterpretation. Modified spectrum provides additional characteristic signature to the butterfly pattern cluster of cross peaks for the unambiguous identification of the presence of a band with position shift.
NASA Astrophysics Data System (ADS)
Buranasiri, Prathan
2005-04-01
Using barium titanate as the photorefractive material, we demonstrate phase conjugation, beam coupling, higher diffraction order generation. At small incident angles less than 0.015 radian, both codirectional isotropic self-diffraction (CODIS) and contradirectional isotropic self-diffraction (CONDIS) are generated simultaneously. At bigger incident angles approximately more than 0.2094 radian, only codirectional anisotropic-self diffraction (CODAS) are generated. On going imaging correlation is also showing.
77 FR 36305 - Stream Communications Network & Media, Inc.; Order of Suspension of Trading
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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.
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
Interplay between electron correlations and quantum orders in the Hubbard model
NASA Astrophysics Data System (ADS)
Witczak-Kremp, William
We discuss the appearance of quantum orders in the Hubbard model for interacting electrons, at half-filling. Such phases do not have local order parameters and need to be characterized by the quantum mechanical properties of their ground state. On one hand, we study the Mott transition from a metal to a spin liquid insulator in two dimensions, of potential relevance to some layered organic compounds. The correlation-driven transition occurs at fixed filling and involves fractionalization of the electron: upon entering the insulator, a Fermi surface of neutral spinons coupled to an internal gauge field emerges. We focus on the transport properties near the quantum critical point and find that the emergent gauge uctuations play a key role in determining the universal scaling. Second, motivated by a class of three-dimensional transition metal oxides, the pyrochlore iridates, we study the interplay of non-trivial band topology and correlations. Building on the strong spin orbit coupling in these compounds, we construct a general microscopic Hubbard model and determine its mean-field phase diagram, which contains topological insulators, Weyl semimetals, axion insulators and various antiferromagnets. We also discuss the effects many-body correlations on theses phases. We close by examining a fractionalized topological insulator that combines the two main themes of the thesis: fractionalization and non-trivial band topology. Specifically, we study how the twodimensional protected surface states of a topological Mott insulator interact with a threedimensional emergent gauge field. Various correlation effects on observables are identified.
Construction and repair of highly ordered 2D covalent networks by chemical equilibrium regulation.
Guan, Cui-Zhong; Wang, Dong; Wan, Li-Jun
2012-03-21
The construction of well-ordered 2D covalent networks via the dehydration of di-borate aromatic molecules was successfully realized through introducing a small amount of water into a closed reaction system to regulate the chemical equilibrium.
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
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.
Hanbury Brown-Twiss interferometry and second-order correlations of inflaton quanta
NASA Astrophysics Data System (ADS)
Giovannini, Massimo
2011-01-01
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.
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.
Vascular networks due to dynamically arrested crystalline ordering of elongated cells
NASA Astrophysics Data System (ADS)
Palm, Margriet M.; Merks, Roeland M. H.
2013-01-01
Recent experimental and theoretical studies suggest that crystallization and glass-like solidification are useful analogies for understanding cell ordering in confluent biological tissues. It remains unexplored how cellular ordering contributes to pattern formation during morphogenesis. With a computational model we show that a system of elongated, cohering biological cells can get dynamically arrested in a network pattern. Our model provides an explanation for the formation of cellular networks in culture systems that exclude intercellular interaction via chemotaxis or mechanical traction.
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
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.
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
Precision Measurements of Higher-Order Angular Galaxy. Correlations Using 10 Million SDSS Galaxies
NASA Astrophysics Data System (ADS)
Ross, Ashley J.; Brunner, R. J.; Myers, A. D.
2006-06-01
We present estimates of the galaxy, N-point, area-averaged angular correlation functions, ωN(θ),; for N = 2,...,7 from the third data release (DR3) of the Sloan Digital Sky Survey. Our sample was constructed from galaxies with r magnitude between 18 and 21, and is currently the largest study of galaxy higher-order correlations. The calculated angular correlation functions are used to measure the projected, sN, and real space, SN, hierarchical amplitudes. Our measurements of the real space amplitudes are remarkably precise over the physical scales 0.2-10 h-1 Mpc, and are consistent with Gaussian primordial density fluctuations. Our measurements also suggest that higher-order galaxy bias is non-negligible. By defining b1 = 1, we find that c2 = -0.26 ± 0.10 and c3 = 1.0 ± 0.9. This is the first reported measurement of a marginally significant third-order bias, and it hints at the importance of even higher-order bias terms. We find early-type galaxies exhibit significantly different clustering than late-types at both small and large scales. At large scales (r > 1 h-1 Mpc), we find the SN for late-type galaxies are lower than for early-types, implying a difference between the higher-order bias of the respective samples. We find b1,early = 1.38 ± 0.10, c2,early = 0.29 ± 0.12, b1,late = 0.81 ± 0.03, and c2,late = -0.68 ± 0.09. This supports recent measurements of the higher-order correlations of infrared-selected galaxies, which found a positive c2, presumably due to the dominance of early-type galaxies in the 2MASS sample (Frith et al. 2005). We have extended our analysis to photometrically-selected quasars in the SDSS DR3, and are planning to leverage future SDSS data releases to make even tighter constraints on primordial non-Gaussianity and non-linear bias components. We acknowledge support from NASA grants NAG5-12578 and NAG5-12580, Microsoft Research, and the NSF PACI Project.
High-order behaviour in learning gate networks with lateral inhibition.
Blanzieri, E; Grandi, F; Maio, D
1996-01-01
In this work we present a neural network model incorporating activity-dependent presynaptic facilitation with multidimensional inputs. The processing unit used is based on a slightly simplified version of the Learning Gate Model proposed by Ciaccia et al. (1992). The network topology integrates a well-known biological neural circuit with a lateral inhibition connection subnet. By means of simulation experiments, we show that the proposed networks exhibit basic and high-order features of associative learning. In particular, overshadowing and blocking are reproduced in the presence of both noise-free and noisy inputs. The role of noise in the development of high-order learning capabilities is also discussed.
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)
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.
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
Robustness analysis of bimodal networks in the whole range of degree correlation
NASA Astrophysics Data System (ADS)
Mizutaka, Shogo; Tanizawa, Toshihiro
2016-08-01
We present an exact analysis of the physical properties of bimodal networks specified by the two peak degree distribution fully incorporating the degree-degree correlation between node connections. The structure of the correlated bimodal network is uniquely determined by the Pearson coefficient of the degree correlation, keeping its degree distribution fixed. The percolation threshold and the giant component fraction of the correlated bimodal network are analytically calculated in the whole range of the Pearson coefficient from -1 to 1 against two major types of node removal, which are the random failure and the degree-based targeted attack. The Pearson coefficient for next-nearest-neighbor pairs is also calculated, which always takes a positive value even when the correlation between nearest-neighbor pairs is negative. From the results, it is confirmed that the percolation threshold is a monotonically decreasing function of the Pearson coefficient for the degrees of nearest-neighbor pairs increasing from -1 and 1 regardless of the types of node removal. In contrast, the node fraction of the giant component for bimodal networks with positive degree correlation rapidly decreases in the early stage of random failure, while that for bimodal networks with negative degree correlation remains relatively large until the removed node fraction reaches the threshold. In this sense, bimodal networks with negative degree correlation are more robust against random failure than those with positive degree correlation.
Robustness analysis of bimodal networks in the whole range of degree correlation.
Mizutaka, Shogo; Tanizawa, Toshihiro
2016-08-01
We present an exact analysis of the physical properties of bimodal networks specified by the two peak degree distribution fully incorporating the degree-degree correlation between node connections. The structure of the correlated bimodal network is uniquely determined by the Pearson coefficient of the degree correlation, keeping its degree distribution fixed. The percolation threshold and the giant component fraction of the correlated bimodal network are analytically calculated in the whole range of the Pearson coefficient from -1 to 1 against two major types of node removal, which are the random failure and the degree-based targeted attack. The Pearson coefficient for next-nearest-neighbor pairs is also calculated, which always takes a positive value even when the correlation between nearest-neighbor pairs is negative. From the results, it is confirmed that the percolation threshold is a monotonically decreasing function of the Pearson coefficient for the degrees of nearest-neighbor pairs increasing from -1 and 1 regardless of the types of node removal. In contrast, the node fraction of the giant component for bimodal networks with positive degree correlation rapidly decreases in the early stage of random failure, while that for bimodal networks with negative degree correlation remains relatively large until the removed node fraction reaches the threshold. In this sense, bimodal networks with negative degree correlation are more robust against random failure than those with positive degree correlation. PMID:27627318
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.
Oberer, R.B.
2002-11-12
The current practice of nondestructive assay (NDA) of fissile materials using neutrons is dominated by the {sup 3}He detector. This has been the case since the mid 1980s when Fission Multiplicity Detection (FMD) was replaced with thermal well counters and neutron multiplicity counting (NMC). The thermal well counters detect neutrons by neutron capture in the {sup 3}He detector subsequent to moderation. The process of detection requires from 30 to 60 {micro}s. As will be explained in Section 3.3 the rate of detecting correlated neutrons (signal) from the same fission are independent of this time but the rate of accidental correlations (noise) are proportional to this time. The well counters are at a distinct disadvantage when there is a large source of uncorrelated neutrons present from ({alpha}, n) reactions for example. Plastic scintillating detectors, as were used in FMD, require only about 20 ns to detect neutrons from fission. One thousandth as many accidental coincidences are therefore accumulated. The major problem with the use of fast-plastic scintillation detectors, however, is that both neutrons and gamma rays are detected. The pulses from the two are indistinguishable in these detectors. For this thesis, a new technique was developed to use higher-order time correlation statistics to distinguish combinations of neutron and gamma ray detections in fast-plastic scintillation detectors. A system of analysis to describe these correlations was developed based on simple physical principles. Other sources of correlations from non-fission events are identified and integrated into the analysis developed for fission events. A number of ratios and metric are identified to determine physical properties of the source from the correlations. It is possible to determine both the quantity being measured and detection efficiency from these ratios from a single measurement without a separate calibration. To account for detector dead-time, an alternative analytical technique
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
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.
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
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.
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.
McDermott, Jason E; Archuleta, Michelle; Stevens, Susan L; Stenzel-Poore, Mary P; Sanfilippo, Antonio
2011-01-01
Determining biological network dependencies that can help predict the behavior of a system given prior observations from high-throughput data is a very valuable but difficult task, especially in the light of the ever-increasing volume of experimental data. Such an endeavor can be greatly enhanced by considering regulatory influences on co-expressed groups of genes representing functional modules, thus constraining the number of parameters in the system. This allows development of network models that are predictive of system dynamics. We first develop a predictive network model of the transcriptomics of whole blood from a mouse model of neuroprotection in ischemic stroke, and show that it can accurately predict system behavior under novel conditions. We then use a network topology approach to expand the set of regulators considered and show that addition of topological bottlenecks improves the performance of the predictive model. Finally, we explore how improvements in definition of functional modules may be achieved through an integration of inferred network relationships and functional relationships defined using Gene Ontology similarity. We show that appropriate integration of these two types of relationships can result in models with improved 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.
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
2015-01-19
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.
Synchronization of fractional-order colored dynamical networks via open-plus-closed-loop control
NASA Astrophysics Data System (ADS)
Yang, Lixin; Jiang, Jun; Liu, Xiaojun
2016-02-01
In this paper, the synchronization of a fractional-order colored complex dynamical network model is studied for the first time. In this network model, color edges imply that both the outer coupling topology and the inner interactions between any pair of nodes may be different, and color nodes mean that local dynamics may be different. Based on the stability theory of fractional-order systems, the scheme of synchronization for fractional-order colored complex dynamical networks is presented. To achieve the synchronization of a complex fractional-order edge-colored network, the open-plus-closed-loop (OPCL) strategy is adopted and effective controllers for synchronization are designed. The open-plus-closed-loop (OPCL) strategy avoids the need for computation of eigenvalues of a very large matrix. Then, a synchronization method for a class of fractional-order colored complex network, containing both colored edges and colored nodes, is developed and some effective synchronization conditions via close-loop control are presented. Two examples of numerical simulations are presented to show the effectiveness of the proposed control strategies.
Exploiting multi-lead electrocardiogram correlations using robust third-order tensor decomposition.
Padhy, Sibasankar; Dandapat, Samarendra
2015-10-01
In this Letter, a robust third-order tensor decomposition of multi-lead electrocardiogram (MECG) comprising of 12-leads is proposed to reduce the dimension of the storage data. An order-3 tensor structure is employed to represent the MECG data by rearranging the MECG information in three dimensions. The three-dimensions of the formed tensor represent the number of leads, beats and samples of some fixed ECG duration. Dimension reduction of such an arrangement exploits correlations present among the successive beats (intra-beat and inter-beat) and across the leads (inter-lead). The higher-order singular value decomposition is used to decompose the tensor data. In addition, multiscale analysis has been added for effective care of ECG information. It grossly segments the ECG characteristic waves (P-wave, QRS-complex, ST-segment and T-wave etc.) into different sub-bands. In the meantime, it separates high-frequency noise components into lower-order sub-bands which helps in removing noise from the original data. For evaluation purposes, we have used the publicly available PTB diagnostic database. The proposed method outperforms the existing algorithms where compression ratio is under 10 for MECG data. Results show that the original MECG data volume can be reduced by more than 45 times with acceptable diagnostic distortion level. PMID:26609416
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
Gallus, Susanne; Janke, Axel; Kumar, Vikas; Nilsson, Maria A
2015-03-18
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.
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…
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)
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.
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.
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.
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
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.
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
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.
Optical implementation of a second-order translation-invariant network algorithm
NASA Astrophysics Data System (ADS)
Horan, Paul; Jennings, Andrew; Kelly, Brian; Hegarty, John
1993-03-01
Higher-order networks, particularly second-order translation-invariant networks, are introduced, and their suitability for optical implementation is outlined. The algorithm is implemented with a conventional liquid-crystal display, permitting on-line learning and updating of weights. The basic operation of the optical system is demonstrated, and the ability of the system to adapt to system nonuniformities is illustrated. The implementation with an integrated optoelectronic array of asymmetric Fabry-Perot modulators containing a GaAs/AlGaAs multiple-quantum-well active region is described. The principles of operation and operating characteristics of the device array are outlined. The use of the array in an optical system to calculate the autocorrelation matrix necessary for a second-order network is demonstrated.
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
Evidence of an odd-parity hidden order in a spin-orbit coupled correlated iridate
NASA Astrophysics Data System (ADS)
Zhao, L.; Torchinsky, D. H.; Chu, H.; Ivanov, V.; Lifshitz, R.; Flint, R.; Qi, T.; Cao, G.; Hsieh, D.
2016-01-01
A rare combination of strong spin-orbit coupling and electron-electron correlations makes the iridate Mott insulator Sr2IrO4 a promising host for novel electronic phases of matter. The resemblance of its crystallographic, magnetic and electronic structures to La2CuO4, as well as the emergence on doping of a pseudogap region and a low-temperature d-wave gap, has particularly strengthened analogies to cuprate high-Tc superconductors. However, unlike the cuprate phase diagram, which features a plethora of broken symmetry phases in a pseudogap region that includes charge density wave, stripe, nematic and possibly intra-unit-cell loop-current orders, no broken symmetry phases proximate to the parent antiferromagnetic Mott insulating phase in Sr2IrO4 have been observed so far, making the comparison of iridate to cuprate phenomenology incomplete. Using optical second-harmonic generation, we report evidence of a hidden non-dipolar magnetic order in Sr2IrO4 that breaks both the spatial inversion and rotational symmetries of the underlying tetragonal lattice. Four distinct domain types corresponding to discrete 90°-rotated orientations of a pseudovector order parameter are identified using nonlinear optical microscopy, which is expected from an electronic phase that possesses the symmetries of a magneto-electric loop-current order. The onset temperature of this phase is monotonically suppressed with bulk hole doping, albeit much more weakly than the Néel temperature, revealing an extended region of the phase diagram with purely hidden order. Driving this hidden phase to its quantum critical point may be a path to realizing superconductivity in Sr2IrO4.
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
Correlations with projectile-like fragments and emission order of light charged particles
NASA Astrophysics Data System (ADS)
Kohley, Z.; Bonasera, A.; Galanopoulos, S.; Hagel, K.; May, L. W.; McIntosh, A. B.; Stein, B. C.; Souliotis, G. A.; Tripathi, R.; Wuenschel, S.; Yennello, S. J.
2012-10-01
Correlations of midrapidity light charged particles (LCPs) and intermediate mass fragments (IMFs) with projectile-like fragments (PLFs) have been examined from the 35 MeV/u 70Zn+70Zn, 64Zn+64Zn, and 64Ni+64Ni reaction systems. A new method was developed to examine the flow of the particles with respect to the PLF. The invariant PLF-scaled flow allowed for the dynamics of the midrapidity Z=1-4 particles to be studied. Strong differences in the PLF-scaled flow were observed between the different isotopes. In particular, the most n-rich LCPs exhibited a negative PLF-scaled flow in comparison to the other LCPs. A classical molecular dynamics model and a three-body Coulomb trajectory simulation were both used to show that the PLF-scaled flow observable could be connected to the average order of emission of the LCPs. The experimental results suggest that the midrapidity region is preferentially populated with neutron-rich LCPs and Z=3-4 IMFs at a relatively early stage in the collision. The deuteron and 3He particles are emitted later followed, lastly, by protons and alphas. The average order of emission of the midrapidity LCPs was extracted from the constrained molecular dynamics simulations and showed good agreement with the emission order suggested by the experimental PLF-scaled flow results.
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
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)
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.
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.
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.
Willow, Soohaeng Yoo; Zhang, Jinmei; Valeev, Edward F.; Hirata, So
2014-01-21
A stochastic algorithm is proposed that can compute the basis-set-incompleteness correction to the second-order many-body perturbation (MP2) energy of a polyatomic molecule. It evaluates the sum of two-, three-, and four-electron integrals over an explicit function of electron-electron distances by a Monte Carlo (MC) integration at an operation cost per MC step increasing only quadratically with size. The method can reproduce the corrections to the MP2/cc-pVTZ energies of H{sub 2}O, CH{sub 4}, and C{sub 6}H{sub 6} within a few mE{sub h} after several million MC steps. It circumvents the resolution-of-the-identity approximation to the nonfactorable three-electron integrals usually necessary in the conventional explicitly correlated (R12 or F12) methods.
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
2016-08-01
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.
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.
Hollman, David S; Wilke, Jeremiah J; Schaefer, Henry F
2013-02-14
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)]. 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
2013-10-29
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.
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.
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
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
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.
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
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
Sikkink, Kristin L; Reynolds, Rose M; Cresko, William A; Phillips, Patrick C
2015-05-01
Selection in novel environments can lead to a coordinated evolutionary response across a suite of characters. Environmental conditions can also potentially induce changes in the genetic architecture of complex traits, which in turn could alter the pattern of the multivariate response to selection. We describe a factorial selection experiment using the nematode Caenorhabditis remanei in which two different stress-related phenotypes (heat and oxidative stress resistance) were selected under three different environmental conditions. The pattern of covariation in the evolutionary response between phenotypes or across environments differed depending on the environment in which selection occurred, including asymmetrical responses to selection in some cases. These results indicate that variation in pleiotropy across the stress response network is highly sensitive to the external environment. Our findings highlight the complexity of the interaction between genes and environment that influences the ability of organisms to acclimate to novel environments. They also make clear the need to identify the underlying genetic basis of genetic correlations in order understand how patterns of pleiotropy are distributed across complex genetic networks.
Tracer dispersion in a percolation network with spatial correlations
Makse; Andrade; Eugene Stanley H
2000-01-01
We analyze the transport properties of a neutral tracer in a carrier fluid flowing through percolationlike porous media with spatial correlations. We model convection in the mass transport process using the velocity field obtained by the numerical solution of the Navier-Stokes and continuity equations in the pore space. We find that the resulting statistical properties of the tracer show a transition from a subdiffusion regime at low Peclet number to an enhanced diffusion regime at high Peclet number.
NASA Astrophysics Data System (ADS)
Deberry, John W.
1989-05-01
The primary goal of this research was to determine if artificial Neural Networks (ANNs) can be trained to classify the correlation signatures of direct sequence and frequency-hopped spread-spectrum signals. Secondary goals were to determine: (1) if network classification performance can be modeled with a conditional probability matrix, (2) if the symmetry of the matrices can be controlled, and (3) if using a majority vote rule over independently trained networks improves classification performance. Correlation signatures of the spread-spectrum signals were obtained from United States Army Harry Diamond Laboratories. The signatures were preprocessed and separated into various training and testing data sets. Thirty samples of network responses for several sets of training conditions were gathered using a neural network simulator.
Goldberg, Ariel M; Rapp, Brenda
2008-03-01
Although considerable progress has been made in determining the cognitive architecture of spelling, less is known about the serial-order mechanism of spelling: the process(es) involved in producing each letter in the proper order. In this study, we investigate compound chaining as a theory of the serial-order mechanism of spelling. Chaining theories posit that the retrieval from memory of each element in a sequence is dependent upon the retrieval of previous elements. We examine this issue by comparing the performance of simple recurrent networks (a class of neural networks that we show can operate by chaining) with that of two individuals with acquired dysgraphia affecting the serial-order mechanism of spelling-the graphemic buffer. We compare their performance in terms of the effects of serial position, the effect of length on overall letter accuracy, and the effect of length on the accuracy of specific positions within the word. We find that the networks produce significantly different patterns of performance from those of the dysgraphics, indicating that compound chaining is not an appropriate theory of the serial-order mechanism of spelling.
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 themore » operating system to determine their ultimate source/destination on the monitored machine.« less
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.
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.
Neural networks for short-term memory for order differentiate high and low proficiency bilinguals.
Majerus, S; Belayachi, S; De Smedt, B; Leclercq, A L; Martinez, T; Schmidt, C; Weekes, B; Maquet, P
2008-10-01
Short-term memory (STM) for order information, as compared to STM for item information, has been shown to be a critical determinant of language learning capacity. The present fMRI study asked whether the neural substrates of order STM can serve as markers for bilingual language achievement. Two groups of German-French bilinguals differing in second language proficiency were presented STM tasks probing serial order or item information. During order STM but not item STM tasks, the high proficiency group showed increased activation in the lateral orbito-frontal and the superior frontal gyri associated with updating and grouped rehearsal of serial order information. Functional connectivity analyses for order encoding showed a functional network involving the left IPS, the right IPS and the right superior cerebellum in the high proficiency group while the low proficiency group showed enhanced connectivity between the left IPS and bilateral superior temporal and temporo-parietal areas involved in item processing. The present data suggest that low proficiency bilinguals activate STM networks for order in a less efficient and differentiated way, and this may explain their poorer storage and learning capacity for verbal sequences.
Generalized friendship paradox in networks with tunable degree-attribute correlation.
Jo, Hang-Hyun; Eom, Young-Ho
2014-08-01
One of the interesting phenomena due to topological heterogeneities in complex networks is the friendship paradox: Your friends have on average more friends than you do. Recently, this paradox has been generalized for arbitrary node attributes, called the generalized friendship paradox (GFP). The origin of GFP at the network level has been shown to be rooted in positive correlations between degrees and attributes. However, how the GFP holds for individual nodes needs to be understood in more detail. For this, we first analyze a solvable model to characterize the paradox holding probability of nodes for the uncorrelated case. Then we numerically study the correlated model of networks with tunable degree-degree and degree-attribute correlations. In contrast to the network level, we find at the individual level that the relevance of degree-attribute correlation to the paradox holding probability may depend on whether the network is assortative or dissortative. These findings help us to understand the interplay between topological structure and node attributes in complex networks.
Generalized friendship paradox in networks with tunable degree-attribute correlation
NASA Astrophysics Data System (ADS)
Jo, Hang-Hyun; Eom, Young-Ho
2014-08-01
One of the interesting phenomena due to topological heterogeneities in complex networks is the friendship paradox: Your friends have on average more friends than you do. Recently, this paradox has been generalized for arbitrary node attributes, called the generalized friendship paradox (GFP). The origin of GFP at the network level has been shown to be rooted in positive correlations between degrees and attributes. However, how the GFP holds for individual nodes needs to be understood in more detail. For this, we first analyze a solvable model to characterize the paradox holding probability of nodes for the uncorrelated case. Then we numerically study the correlated model of networks with tunable degree-degree and degree-attribute correlations. In contrast to the network level, we find at the individual level that the relevance of degree-attribute correlation to the paradox holding probability may depend on whether the network is assortative or dissortative. These findings help us to understand the interplay between topological structure and node attributes in complex networks.
Gao, Wei; Alcauter, Sarael; Elton, Amanda; Hernandez-Castillo, Carlos R; Smith, J Keith; Ramirez, Juanita; Lin, Weili
2015-09-01
The first postnatal year is characterized by the most dramatic functional network development of the human lifespan. Yet, the relative sequence of the maturation of different networks and the impact of socioeconomic status (SES) on their development during this critical period remains poorly characterized. Leveraging a large, normally developing infant sample with multiple longitudinal resting-state functional magnetic resonance imaging scans during the first year (N = 65, scanned every 3 months), we aimed to delineate the relative maturation sequence of 9 key brain functional networks and examine their SES correlations. Our results revealed a maturation sequence from primary sensorimotor/auditory to visual to attention/default-mode, and finally to executive control networks. Network-specific critical growth periods were also identified. Finally, marginally significant positive SES-brain correlations were observed at 6 months of age for both the sensorimotor and default-mode networks, indicating interesting SES effects on functional brain maturation. To the best of our knowledge, this is the first study delineating detailed longitudinal growth trajectories of all major functional networks during the first year of life and their SES correlations. Insights from this study not only improve our understanding of early brain development, but may also inform the critical periods for SES expression during infancy.
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.
Li, Yifeng; Chen, Haifen; Zheng, Jie; Ngom, Alioune
2016-01-01
Accurately reconstructing gene regulatory network (GRN) from gene expression data is a challenging task in systems biology. Although some progresses have been made, the performance of GRN reconstruction still has much room for improvement. Because many regulatory events are asynchronous, learning gene interactions with multiple time delays is an effective way to improve the accuracy of GRN reconstruction. Here, we propose a new approach, called Max-Min high-order dynamic Bayesian network (MMHO-DBN) by extending the Max-Min hill-climbing Bayesian network technique originally devised for learning a Bayesian network's structure from static data. Our MMHO-DBN can explicitly model the time lags between regulators and targets in an efficient manner. It first uses constraint-based ideas to limit the space of potential structures, and then applies search-and-score ideas to search for an optimal HO-DBN structure. The performance of MMHO-DBN to GRN reconstruction was evaluated using both synthetic and real gene expression time-series data. Results show that MMHO-DBN is more accurate than current time-delayed GRN learning methods, and has an intermediate computing performance. Furthermore, it is able to learn long time-delayed relationships between genes. We applied sensitivity analysis on our model to study the performance variation along different parameter settings. The result provides hints on the setting of parameters of MMHO-DBN.
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.
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.
Critical correlations and dynamics of force networks in a model of jammed granular solids
NASA Astrophysics Data System (ADS)
Turitsyn, Konstantin; Gruzberg, Ilya; Stern, Julie; Nienhuis, Bernard
2010-03-01
Distribution and correlations of contact forces in two-dimensional static granular packings is studied within a framework of (Edwards) Force Network Ensemble. A two-dimensional ``snooker'' model without friction is shown to be critical. An explicit expression for the force correlation function is derived using Bethe Ansatz. The force correlation function decays algebraically with universal exponent -2. This result implies that the system satisfies (extended) Harris criterion, and that the scale invariant force networks (studied by Ostojic et. al. Nature, 439, 2006) belong to the universality class of the usual percolation transition. These analytical results are confirmed by direct numerical sampling from the ensemble. It is also shown that the microscopic dynamics of forces experiences critical slowdown: the force network relaxation time grows like the sixth power of the system size. This result explains the slow convergence of standard Monte Carlo algorithms, and suggests novel approaches for studying the model.
Huang, Yue; Zhang, Jun; Huang, Yunying
2012-09-01
Changes in retinal vascular morphology are well known as predictive clinical signs of many diseases such as hypertension, diabetes and so on. Computer-aid image processing and analysis for retinal vessels in fundus images are effective and efficient in clinical diagnosis instead of tedious manual labeling and measurement. An automated computational framework for retinal vascular network labeling and analysis is presented in this work. The framework includes 1) detecting and locating the optic disc; 2) tracking the vessel centerline from detected seed points and linking the breaks after tracing; 3) extracting all the retinal vascular trees and identifying all the significant points; and 4) classifying terminal points into starting points and ending points based on the information of optic disc location, and finally assigning branch order for each extracted vascular tree in the image. All the modules in the framework are fully automated. Based on the results, morphological analysis is then applied to achieve geometrical and topological features based on branching order for one individual vascular tree or for the vascular network through the retinal vascular network in the images. Validation and experiments on the public DRIVE database have demonstrated that the proposed framework is a novel approach to analyze and study the vascular network pattern, and may offer new insights to the diagnosis of retinopathy.
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.
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.
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
Hybrid optical CDMA-FSO communications network under spatially correlated gamma-gamma scintillation.
Jurado-Navas, Antonio; Raddo, Thiago R; Garrido-Balsells, José María; Borges, Ben-Hur V; Olmos, Juan José Vegas; Monroy, Idelfonso Tafur
2016-07-25
In this paper, we propose a new hybrid network solution based on asynchronous optical code-division multiple-access (OCDMA) and free-space optical (FSO) technologies for last-mile access networks, where fiber deployment is impractical. The architecture of the proposed hybrid OCDMA-FSO network is thoroughly described. The users access the network in a fully asynchronous manner by means of assigned fast frequency hopping (FFH)-based codes. In the FSO receiver, an equal gain-combining technique is employed along with intensity modulation and direct detection. New analytical formalisms for evaluating the average bit error rate (ABER) performance are also proposed. These formalisms, based on the spatially correlated gamma-gamma statistical model, are derived considering three distinct scenarios, namely, uncorrelated, totally correlated, and partially correlated channels. Numerical results show that users can successfully achieve error-free ABER levels for the three scenarios considered as long as forward error correction (FEC) algorithms are employed. Therefore, OCDMA-FSO networks can be a prospective alternative to deliver high-speed communication services to access networks with deficient fiber infrastructure.
Hybrid optical CDMA-FSO communications network under spatially correlated gamma-gamma scintillation.
Jurado-Navas, Antonio; Raddo, Thiago R; Garrido-Balsells, José María; Borges, Ben-Hur V; Olmos, Juan José Vegas; Monroy, Idelfonso Tafur
2016-07-25
In this paper, we propose a new hybrid network solution based on asynchronous optical code-division multiple-access (OCDMA) and free-space optical (FSO) technologies for last-mile access networks, where fiber deployment is impractical. The architecture of the proposed hybrid OCDMA-FSO network is thoroughly described. The users access the network in a fully asynchronous manner by means of assigned fast frequency hopping (FFH)-based codes. In the FSO receiver, an equal gain-combining technique is employed along with intensity modulation and direct detection. New analytical formalisms for evaluating the average bit error rate (ABER) performance are also proposed. These formalisms, based on the spatially correlated gamma-gamma statistical model, are derived considering three distinct scenarios, namely, uncorrelated, totally correlated, and partially correlated channels. Numerical results show that users can successfully achieve error-free ABER levels for the three scenarios considered as long as forward error correction (FEC) algorithms are employed. Therefore, OCDMA-FSO networks can be a prospective alternative to deliver high-speed communication services to access networks with deficient fiber infrastructure. PMID:27464133
Liu, Ling; Wu, Ailong; Song, Xingguo
2016-01-01
This article is concerned with the global [Formula: see text] stabilization for a class of fractional-order memristive neural networks with time delays (FMDNNs). Two kinds of control scheme (i.e., state feedback control law and output feedback control law) are employed to stabilize a class of FMDNNs. Several stabilization conditions in form of algebraic criteria are presented based on a new fractional-order Lyapunov function method and Leibniz rule. Some examples are given to substantiate the effectiveness of the presented theoretical results.
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.
Baek, Yongjoo; Ha, Meesoon; Jeong, Hawoong
2014-12-01
We investigate the totally asymmetric simple exclusion process on closed and directed random regular networks, which is a simple model of active transport in the one-dimensional segments coupled by junctions. By a pair mean-field theory and detailed numerical analyses, it is found that the correlations at junctions induce two notable deviations from the simple mean-field theory, which neglects these correlations: (1) the narrower range of particle density for phase coexistence and (2) the algebraic decay of density profile with exponent 1/2 even outside the maximal-current phase. We show that these anomalies are attributable to the effective slow bonds formed by the network junctions.
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.
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.
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
NASA Astrophysics Data System (ADS)
Montangie, Lisandro; Montani, Fernando
2016-10-01
Spike correlations among neurons are widely encountered in the brain. Although models accounting for pairwise interactions have proved able to capture some of the most important features of population activity at the level of the retina, the evidence shows that pairwise neuronal correlation analysis does not resolve cooperative population dynamics by itself. By means of a series expansion for short time scales of the mutual information conveyed by a population of neurons, the information transmission can be broken down into firing rate and correlational components. In a proposed extension of this framework, we investigate the information components considering both second- and higher-order correlations. We show that the existence of a mixed stimulus-dependent correlation term defines a new scenario for the interplay between pairwise and higher-than-pairwise interactions in noise and signal correlations that would lead either to redundancy or synergy in the information-theoretic sense.
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.
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.
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.
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
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.
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.
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.
Xiao, Min; Ge, Haitao; Khundrakpam, Budhachandra S.; Xu, Junhai; Bezgin, Gleb; Leng, Yuan; Zhao, Lu; Tang, Yuchun; Ge, Xinting; Jeon, Seun; Xu, Wenjian; Evans, Alan C.; Liu, Shuwei
2016-01-01
Functional neuroimaging studies have indicated the involvement of separate brain areas in three distinct attention systems: alerting, orienting, and executive control (EC). However, the structural correlates underlying attention remains unexplored. Here, we utilized graph theory to examine the neuroanatomical substrates of the three attention systems measured by attention network test (ANT) in 65 healthy subjects. White matter connectivity, assessed with diffusion tensor imaging deterministic tractography was modeled as a structural network comprising 90 nodes defined by the automated anatomical labeling (AAL) template. Linear regression analyses were conducted to explore the relationship between topological parameters and the three attentional effects. We found a significant positive correlation between EC function and global efficiency of the whole brain network. At the regional level, node-specific correlations were discovered between regional efficiency and all three ANT components, including dorsolateral superior frontal gyrus, thalamus and parahippocampal gyrus for EC, thalamus and inferior parietal gyrus for alerting, and paracentral lobule and inferior occipital gyrus for orienting. Our findings highlight the fundamental architecture of interregional structural connectivity involved in attention and could provide new insights into the anatomical basis underlying human behavior. PMID:27777556
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.
Nguyen, Thi-Kieu-Oanh; Jamali, Arash; Lanoue, Arnaud; Gontier, Eric; Dauwe, Rebecca
2015-08-01
The tropane alkaloid spectrum in Solanaceae is highly variable within and between species. Little is known about the topology and the coordination of the biosynthetic pathways leading to the variety of tropine and pseudotropine derived esters in the alkaloid spectrum, or about the metabolic dynamics induced by tropane alkaloid biosynthesis stimulating conditions. A good understanding of the metabolism, including all ramifications, is however necessary for the development of strategies to increase the abundance of pharmacologically interesting compounds such as hyoscyamine and scopolamine. The present study explores the tropane alkaloid metabolic pathways in an untargeted approach involving a correlation-based network analysis. Using GC-MS metabolite profiling, the variation and co-variation among tropane alkaloids and primary metabolites was monitored in 60 Datura innoxia Mill. individuals, of which half were exposed to tropane alkaloid biosynthesis stimulating conditions by co-culture with Agrobacterium rhizogenes. Considerable variation was evident in the relative proportions of the tropane alkaloids. Remodeling of the tropane alkaloid spectrum under co-culture with A. rhizogenes involved a specific and strong increase of hyoscyamine production and revealed that the accumulation of hyoscyamine, 3-tigloyloxy-6,7-epoxytropane, and 3-methylbutyryloxytropane was controlled independently of the majority of tropane alkaloids. Based on correlations between metabolites, we propose a biosynthetic origin of hygrine, the order of esterification of certain di-oxygenated tropanes, and that the rate of acetoxylation contributes to control of hyoscyamine production. Overall, this study shows that the biosynthesis of tropane alkaloids may be far more complex and finely controlled than previously expected.
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.
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
Nie, Xiaobing; Cao, Jinde
2011-11-01
In this paper, second-order interactions are introduced into competitive neural networks (NNs) and the multistability is discussed for second-order competitive NNs (SOCNNs) with nondecreasing saturated activation functions. Firstly, based on decomposition of state space, Cauchy convergence principle, and inequality technique, some sufficient conditions ensuring the local exponential stability of 2N equilibrium points are derived. Secondly, some conditions are obtained for ascertaining equilibrium points to be locally exponentially stable and to be located in any designated region. Thirdly, the theory is extended to more general saturated activation functions with 2r corner points and a sufficient criterion is given under which the SOCNNs can have (r+1)N locally exponentially stable equilibrium points. Even if there is no second-order interactions, the obtained results are less restrictive than those in some recent works. Finally, three examples with their simulations are presented to verify the theoretical analysis.
Higher-order-statistics-based radial basis function networks for signal enhancement.
Lin, Bor-Shyh; Lin, Bor-Shing; Chong, Fok-Ching; Lai, Feipei
2007-05-01
In this paper, a higher-order-statistics (HOS)-based radial basis function (RBF) network for signal enhancement is introduced. In the proposed scheme, higher order cumulants of the reference signal were used as the input of HOS-based RBF. An HOS-based supervised learning algorithm, with mean square error obtained from higher order cumulants of the desired input and the system output as the learning criterion, was used to adapt weights. The motivation is that the HOS can effectively suppress Gaussian and symmetrically distributed non-Gaussian noise. The influence of a Gaussian noise on the input of HOS-based RBF and the HOS-based learning algorithm can be mitigated. Simulated results indicate that HOS-based RBF can provide better performance for signal enhancement under different noise levels, and its performance is insensitive to the selection of learning rates. Moreover, the efficiency of HOS-based RBF under the nonstationary Gaussian noise is stable.
Spreading dynamics on small-world networks with connectivity fluctuations and correlations.
Vazquez, Alexei
2006-11-01
Infectious diseases and computer malwares spread among humans and computers through the network of contacts among them. These networks are characterized by wide connectivity fluctuations, connectivity correlations, and the small-world property. In a previous work [Phys. Rev. Lett. 96, 038702 (2006)] I have shown that the connectivity fluctuations together with the small-world property lead to a spreading law characterized by an initial power law growth with an exponent determined by the average node distance on the network. Here I extend these results to consider the influence of connectivity correlations which are generally observed in real networks. I show that assortative and disassortative connectivity correlations enhance and diminish, respectively, the range of validity of this spreading law. As a corollary I obtain the region of connectivity fluctuations and degree correlations characterized by the absence of an epidemic threshold. These results are relevant for the spreading of infectious diseases, rumors, and information among humans and the spreading of computer viruses, email worms, and hoaxes among computer users. PMID:17279962
Spreading dynamics on small-world networks with connectivity fluctuations and correlations
NASA Astrophysics Data System (ADS)
Vazquez, Alexei
2006-11-01
Infectious diseases and computer malwares spread among humans and computers through the network of contacts among them. These networks are characterized by wide connectivity fluctuations, connectivity correlations, and the small-world property. In a previous work [Phys. Rev. Lett. 96, 038702 (2006)] I have shown that the connectivity fluctuations together with the small-world property lead to a spreading law characterized by an initial power law growth with an exponent determined by the average node distance on the network. Here I extend these results to consider the influence of connectivity correlations which are generally observed in real networks. I show that assortative and disassortative connectivity correlations enhance and diminish, respectively, the range of validity of this spreading law. As a corollary I obtain the region of connectivity fluctuations and degree correlations characterized by the absence of an epidemic threshold. These results are relevant for the spreading of infectious diseases, rumors, and information among humans and the spreading of computer viruses, email worms, and hoaxes among computer users.
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)
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
Robust cooperative output tracking of networked high-order power integrators systems
NASA Astrophysics Data System (ADS)
Peng, J. M.; Wang, J. N.; Shan, J. Y.
2016-02-01
This paper focuses on a robust cooperative output tracking problem of networked power integrator systems. The dynamics of each system is considered as a nonlinear high-order power integrator whose linearised model is uncontrollable around its origin. It is proven via Lyapunov Theory that under some mild assumptions and graph structural properties, all agents' outputs in the network can be synchronised to a desired trajectory with a bounded error in the presence of external disturbances as well as model uncertainties. Moreover, the tracking performance can be tuned by appropriately choosing parameters within the controller. The proposed controller for each agent is in the essence constructed via backstepping technique consisting of three components: the state feedback of its own, the outputs of its neighbours and the information of the desired trajectory if connected, and thus in a distributed manner.
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.
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.
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.
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.
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)
Noniterative local second order Møller-Plesset theory: Convergence with local correlation space
NASA Astrophysics Data System (ADS)
Maslen, P. E.; Head-Gordon, M.
1998-11-01
We extend our noniterative local correlation method [P. E. Maslen and M. Head-Gordon, Chem. Phys. Lett., 283, 102 (1998)] by defining a hierarchy of local spaces, ranging from small to large. The accuracy of the local method is then examined as a function of the size of the local space. A medium size local space recovers 98% of the MP2 correlation energy, and reproduces fine details of the potential energy surface such as rotational barriers with an RMS error of 0.2 kcal/mol and a maximum error of 0.4 kcal/mol. A large local space recovers 99.5% of the correlation energy and yields rotational barriers with a RMS error of 0.05 kcal/mol and a maximum error of 0.1 kcal/mol, at significantly increased computational cost.
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.
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.
Kolind, Jens; Hounsgaard, Jørn; Berg, Rune W.
2012-01-01
Neurons often receive massive concurrent bombardment of synaptic inhibition and excitation during functional network activity. This increases membrane conductance and causes fluctuations in membrane potential (Vm) and spike timing. The conductance increase is commonly attributed to synaptic conductance, but also includes the intrinsic conductances recruited during network activity. These two sources of conductance have contrasting dynamic properties at sub-threshold membrane potentials. Synaptic transmitter gated conductance changes abruptly and briefly with each presynaptic action potential. If the spikes arrive at random times the changes in synaptic conductance are therefore stochastic and rapid during intense network activity. In comparison, sub-threshold intrinsic conductances vary smoothly in time. In the present study this discrepancy is investigated using two conductance-based models: a (1) compartment model and a (2) compartment with realistic slow intrinsic conductances. We examine the effects of varying the relative contributions of non-fluctuating intrinsic conductance with fluctuating concurrent inhibitory and excitatory synaptic conductance. For given levels of correlation in the synaptic input we find that the magnitude of the membrane fluctuations uniquely determines the relative contribution of synaptic and intrinsic conductance. We also quantify how Vm-fluctuations vary with synaptic correlations for fixed ratios of synaptic and intrinsic conductance. Interestingly, the levels of Vm -fluctuations and conductance observed experimentally during functional network activity leave little room for intrinsic conductance to contribute. Even without intrinsic conductances the variance in Vm -fluctuations can only be explained by a high degree of correlated firing among presynaptic neurons. PMID:22783184
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.
Ordering of self-assembled nanobiominerals in correlation to mechanical properties of hard tissues
Jiang Huaidong; Liu Xiangyang; Lim, Chwee T.; Hsu, Chin Y.
2005-04-18
Biominerals in the hard tissues of many organisms exhibit superior mechanical properties due to their unique hierarchical nanostructures. In this article, we show the microstructure of human tooth enamel examined by position-resolved small-angle x-ray scattering and electron microscopy. It is found that the degree of ordering of the biominerals varies strikingly within the dental sample. Combined with nanoindentation, our results show that both the hardness and the elastic modulus increase predominantly with the ordering of the biomineral crystallites. This can be attributed to the fact that the ordered structure helps sustain a more complex mechanical stress.
“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.
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
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.
Shock-Induced Ordering in a Nano-segregated Network-Forming Ionic Liquid.
Yang, Ke; Lee, Jaejun; Sottos, Nancy R; Moore, Jeffrey S
2015-12-30
Understanding shockwave-induced physical and chemical changes of impact-absorbing materials is an important step toward the rational design of materials that mitigate the damage. In this work, we report a series of network-forming ionic liquids (NILs) that possess an intriguing shockwave absorption property upon laser-induced shockwave. Microstructure analysis by X-ray scattering suggests nano-segregation of alkyl side chains and charged head groups in NILs. Further post-shock observations indicate changes in the low-Q region, implying that the soft alkyl domain in NILs plays an important role in absorbing shockwaves. Interestingly, we observe a shock-induced ordering in the NIL with the longest (hexyl) side chain, indicating that both nano-segregated structure and shock-induced ordering contribute to NIL's shockwave absorption performance.
The effect of cross-link distributions in axially-ordered, cross-linked networks
Bennett, C. Brad; Kruczek, James; Rabson, D. A.; Matthews, W. Garrett; Pandit, Sagar A.
2013-01-01
Cross-linking between the constituent chains of biopolymers has a marked effect on their materials properties. In certain of these materials, such as fibrillar collagen, increases in cross-linking lead to an increase in the melting temperature. Fibrillar collagen is an axially-ordered network of cross-linked polymer chains exhibiting a broadened denaturation transition, which has been explained in terms of the successive denaturation with temperature of multiple species. We model axially-ordered cross-linked materials as stiff chains with distinct arrangements of cross-link-forming sites. Simulations suggest that systems composed of chains with identical arrangements of cross-link-forming sites exhibit critical behavior. In contrast, systems composed of non-identical chains undergo a crossover. This model suggests that the arrangement of cross-link-forming sites may contribute to the broadening of the denaturation transition in fibrillar collagen. PMID:23751928
Cluster synchronization in colored community network with different order node dynamics
NASA Astrophysics Data System (ADS)
Wu, Zhaoyan
2014-04-01
In this paper, a colored community network model with different order nodes dynamics is introduced for the first time. The color of nodes or edges are assumed to be identical if they belong to the same community and nonidentical if they belong to different communities. The color of edges between any pair of communities are assumed to be identical if they connect the same pair of communities and nonidentical if they connect different pair of communities. Further, the order of the node dynamics in different communities can be totally different. Firstly, based on the Lyapunov stability theory, adaptive feedback controllers are designed for achieving cluster synchronization. Secondly, periodically intermittent controllers are designed for achieving cluster synchronization and the synchronization conditions are derived by using mathematical induction method and the analysis technique. Finally, several numerical examples are provided to illustrate the effectiveness of the derived theoretical results.
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.
Higher-Order Neural Networks Applied to 2D and 3D Object Recognition
NASA Technical Reports Server (NTRS)
Spirkovska, Lilly; Reid, Max B.
1994-01-01
A Higher-Order Neural Network (HONN) can be designed to be invariant to geometric transformations such as scale, translation, and in-plane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Thus, for 2D object recognition, the network needs to be trained on just one view of each object class, not numerous scaled, translated, and rotated views. Because the 2D object recognition task is a component of the 3D object recognition task, built-in 2D invariance also decreases the size of the training set required for 3D object recognition. We present results for 2D object recognition both in simulation and within a robotic vision experiment and for 3D object recognition in simulation. We also compare our method to other approaches and show that HONNs have distinct advantages for position, scale, and rotation-invariant object recognition. The major drawback of HONNs is that the size of the input field is limited due to the memory required for the large number of interconnections in a fully connected network. We present partial connectivity strategies and a coarse-coding technique for overcoming this limitation and increasing the input field to that required by practical object recognition problems.
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
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.
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.
NASA Astrophysics Data System (ADS)
Buranasiri, Prathan; Banerjee, Partha P.; Polejaev, Vladimir; Sun, Ching-Cherng
2003-10-01
Using two beam coupling geometry, high order copropagating and contrapropagating isotropic and copropagating anisotropic self-diffraction are demonstrated using photorefractive cerium doped barium titanate. At small incident angles, typically less than 0.015 radians, both codirectional isotropic self-diffraction (CODIS) and contradirectional isotropic self-diffraction (CONDIS) orders are generated simultaneously. At larger incident angles, typically approximately more than 0.2094 radians, only codirectional anisotropic-self diffraction (CODAS) orders are generated. Ongoing work on image auto/cross correlation results are also shown.
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.
Takemoto, Kazuhiro; Akutsu, Tatsuya
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.
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
Correlation analysis of a ground-water level monitoring network, Miami-Dade County, Florida
Prinos, Scott T.
2005-01-01
The U.S. Geological Survey cooperative ground-water monitoring program in Miami-Dade County, Florida, expanded from 4 to 98 continuously recording water-level monitoring wells during the 1939-2001 period. Network design was based on area specific assessments; however, no countywide statistical assessments of network coverage had been performed for the purpose of assessing network redundancy. To aid in the assessment of network redundancy, correlation analyses were performed using S-PLUS 2000 statistical analysis software for daily maximum water-level data from 98 monitoring wells for the November 1, 1973, to October 31, 2000 period. Because of the complexities of the hydrologic, water-supply, and water-management systems in Miami-Dade County and the changes that have occurred to these systems through time, spatial and temporal variations in the degree of correlation had to be considered. To assess temporal variation in correlation, water-level data from each well were subdivided by year and by wet and dry seasons. For each well, year, and season, correlation analyses were performed on the data from those wells that had available data. For selected wells, the resulting correlation coefficients from each year and season were plotted with respect to time. To assess spatial variation in correlation, the coefficients determined from the correlation analysis were averaged. These average wet- and dry-season correlation coefficients were plotted spatially using geographic information system software. Wells with water-level data that correlated with a coefficient of 0.95 or greater were almost always located in relatively close proximity to each other. Five areas were identified where the water-level data from wells within the area remained correlated with that of other wells in the area during the wet and dry seasons. These areas are located in or near the C-1 and C-102 basins (2 wells), in or near the C-6 and C-7 basins (2 wells), near the Florida Keys Aqueduct Authority
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.
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.
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.
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.
Charge ordering, charge fluctuations and lattice effects in strongly correlated electron systems
NASA Astrophysics Data System (ADS)
Goto, Terutaka; Lüthi, Bruno
2003-02-01
Charge fluctuation and charge-ordering phenomena in compounds based on the 3d electrons of transition-metal ions and 4f electrons of rare-earth ions are reviewed with particular emphasis on the mutual coupling of charge and lattice degrees of freedom. For the description of charge ordering in inhomogeneously mixed-valence compounds of localized 3d or 4f-electron systems, we employ Landau's phenomenological theory for second-order phase transitions. By use of the group-theoretical method, the charge fluctuation mode corresponding to the active representation for the second-order transition is determined. The localization of 3d and 4f electrons makes the valence for the specific ions an integer number of charge units e in the ordered phases at low temperatures. The elastic soft mode observed by the ultrasonic method is often a useful indication for the charge fluctuation mode that is frozen below the charge-ordering point TC. The transverse c44 mode exhibiting a considerable softening in the rare-earth compound Yb4As3 couples to the Γ5 triplet of the charge fluctuation mode, giving rise to a linear chain of magnetic Yb3+ ions along [111] below TC = 292 K. Magnetite Fe3O4 and the substitution system Fe3-xZnxO4 exhibit softening of the c44 mode that couples to the charge fluctuation mode, which freezes below the Verwey transition temperature TV = 124 K. The soft c66 mode of the transition-metal compound NaV2O5 gives evidence for a zigzag structure of V4+ ions in the a-b plane below TC = 34 K and is the precursor for the orthorhombic-monoclinic phase transition. The charge glass compounds of Sm3X4 (X = Se or Te) show ultrasonic dispersion due to thermal hopping of 4f electrons between Sm2+ and Sm3+ ions. The ln T decrease in elastic constants of Sm3X4 is described in terms of a two-level system of the 4f-electron tunnelling in the random potential. The characteristic ultrasonic dispersion for the copper oxide compound Sr12Ca2Cu24O41 is also presented. The elastic
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.
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.
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.
Lang, Xing‐You; Liu, Bo‐Tian; Shi, Xiang‐Mei; Li, Ying‐Qi; Wen, Zi
2016-01-01
Nanostructured transition‐metal oxides can store high‐density energy in fast surface redox reactions, but their poor conductivity causes remarkable reductions in the energy storage of most pseudocapacitors at high power delivery (fast charge/discharge rates). Here it is shown that electron‐correlated oxide hybrid electrodes made of nanocrystalline vanadium sesquioxide and manganese dioxide with 3D and bicontinuous nanoporous architecture (NP V2O3/MnO2) have enhanced conductivity because of metallization of electron‐correlated V2O3 skeleton via insulator‐to‐metal transition. The conductive V2O3 skeleton at ambient temperature enables fast electron and ion transports in the entire electrode and facilitates charge transfer at abundant V2O3/MnO2 interface. These merits significantly improve the pseudocapacitive behavior and rate capability of the constituent MnO2. Symmetric pseudocapacitors assembled with binder‐free NP V2O3/MnO2 electrodes deliver ultrahigh electrical powers (up to ≈422 W cm23) while maintaining the high volumetric energy of thin‐film lithium battery with excellent stability. PMID:27812465
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.
Structure and evolution of a European Parliament via a network and correlation analysis
NASA Astrophysics Data System (ADS)
Puccio, Elena; Pajala, Antti; Piilo, Jyrki; Tumminello, Michele
2016-11-01
We present a study of the network of relationships among elected members of the Finnish parliament, based on a quantitative analysis of initiative co-signatures, and its evolution over 16 years. To understand the structure of the parliament, we constructed a statistically validated network of members, based on the similarity between the patterns of initiatives they signed. We looked for communities within the network and characterized them in terms of members' attributes, such as electoral district and party. To gain insight on the nested structure of communities, we constructed a hierarchical tree of members from the correlation matrix. Afterwards, we studied parliament dynamics yearly, with a focus on correlations within and between parties, by also distinguishing between government and opposition. Finally, we investigated the role played by specific individuals, at a local level. In particular, whether they act as proponents who gather consensus, or as signers. Our results provide a quantitative background to current theories in political science. From a methodological point of view, our network approach has proven able to highlight both local and global features of a complex social system.
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.
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
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
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).
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
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
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.
Jung, Yousung; Lochan, Rohini C; Dutoi, Anthony D; Head-Gordon, Martin
2004-11-22
A simplified approach to treating the electron correlation energy is suggested in which only the alpha-beta component of the second order Møller-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), where MP2 is Møller-Plesset theory, 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 fifth order computational steps associated with MP2 theory, even without exploiting any spatial locality. A fourth order algorithm is given for evaluating the opposite spin MP2 energy using auxiliary basis expansions, and a Laplace approach, and timing comparisons are given.
Tekpinar, Mustafa; Zheng, Wenjun
2010-08-15
The decryption of sequence of structural events during protein conformational transitions is essential to a detailed understanding of molecular functions of various biological nanomachines. Coarse-grained models have proven useful by allowing highly efficient simulations of protein conformational dynamics. By combining two coarse-grained elastic network models constructed based on the beginning and end conformations of a transition, we have developed an interpolated elastic network model to generate a transition pathway between the two protein conformations. For validation, we have predicted the order of local and global conformational changes during key ATP-driven transitions in three important biological nanomachines (myosin, F(1) ATPase and chaperonin GroEL). We have found that the local conformational change associated with the closing of active site precedes the global conformational change leading to mechanical motions. Our finding is in good agreement with the distribution of intermediate experimental structures, and it supports the importance of local motions at active site to drive or gate various conformational transitions underlying the workings of a diverse range of biological nanomachines.
Prescott, Thomas P; Papachristodoulou, Antonis
2014-09-01
Biochemical reaction networks tend to exhibit behaviour on more than one timescale and they are inevitably modelled by stiff systems of ordinary differential equations. Singular perturbation is a well-established method for approximating stiff systems at a given timescale. Standard applications of singular perturbation partition the state variable into fast and slow modules and assume a quasi-steady state behaviour in the fast module. In biochemical reaction networks, many reactants may take part in both fast and slow reactions; it is not necessarily the case that the reactants themselves are fast or slow. Transformations of the state space are often required in order to create fast and slow modules, which thus no longer model the original species concentrations. This paper introduces a layered decomposition, which is a natural choice when reaction speeds are separated in scale. The new framework ensures that model reduction can be carried out without seeking state space transformations, and that the effect of the fast dynamics on the slow timescale can be described directly in terms of the original species.
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
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
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.
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
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.
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
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.
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
NASA Astrophysics Data System (ADS)
Guan, Jian-Yue; Wu, Zhi-Xi; Huang, Zi-Gang; Wang, Ying-Hai
2010-02-01
An evolutionary prisoner's dilemma game is investigated on two-layered complex networks respectively representing interaction and learning networks in one and two dimensions. A parameter q is introduced to denote the correlation degree between the two-layered networks. Using Monte Carlo simulations we studied the effects of the correlation degree on cooperative behaviour and found that the cooperator density nontrivially changes with q for different payoff parameter values depending on the detailed strategy updating and network dimension. An explanation for the obtained results is provided.
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.
Fast and efficient second-order method for training radial basis function networks.
Xie, Tiantian; Yu, Hao; Hewlett, Joel; Rózycki, Paweł; Wilamowski, Bogdan
2012-04-01
This paper proposes an improved second order (ISO) algorithm for training radial basis function (RBF) networks. Besides the traditional parameters, including centers, widths and output weights, the input weights on the connections between input layer and hidden layer are also adjusted during the training process. More accurate results can be obtained by increasing variable dimensions. Initial centers are chosen from training patterns and other parameters are generated randomly in limited range. Taking the advantages of fast convergence and powerful search ability of second order algorithms, the proposed ISO algorithm can normally reach smaller training/testing error with much less number of RBF units. During the computation process, quasi Hessian matrix and gradient vector are accumulated as the sum of related sub matrices and vectors, respectively. Only one Jacobian row is stored and used for multiplication, instead of the entire Jacobian matrix storage and multiplication. Memory reduction benefits the computation speed and allows the training of problems with basically unlimited number of patterns. Several practical discrete and continuous classification problems are applied to test the properties of the proposed ISO training algorithm.
NASA Astrophysics Data System (ADS)
Koutselos, Andreas D.
1996-06-01
The dynamic and transport properties of swarms of ions in a uniform electrostatic field are studied by using a molecular dynamics method. For a representative system, K+ in Ar, using a universal interaction model potential, second and third order ion-velocity correlation functions are determined at various field strengths. From them, Fickian diffusion coefficients parallel and perpendicular to the field, as well as higher order diffusion coefficients, Qzzz, are obtained within estimated overall accuracy 5% and 7%, respectively. Comparisons of the Fickian diffusion coefficients against results of the moment solution of Boltzmann kinetic equation and a Monte Carlo simulation method using the same interaction potential as well as against experimental data, reveal consistency among all calculation procedures and in addition agreement with drift tube measurements. These comparisons provide new tests for the accuracy of the employed interaction potential. The method has been applied for up to third order velocity correlations and diffusion coefficients but it is extendible to higher order dynamic and transport properties.
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.
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.
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 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
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
Pairwise and edge-based models of epidemic dynamics on correlated weighted networks
Rattana, P.; Miller, J.C.; Kiss, I.Z.
2014-01-01
In this paper we explore the potential of the pairwise-type modelling approach to be extended to weighted networks where nodal degree and weights are not independent. As a baseline or null model for weighted networks, we consider undirected, heterogenous networks where edge weights are randomly distributed. We show that the pairwise model successfully captures the extra complexity of the network, but does this at the cost of limited analytical tractability due the high number of equations. To circumvent this problem, we employ the edge-based modelling approach to derive models corresponding to two different cases, namely for degree-dependent and randomly distributed weights. These models are more amenable to compute important epidemic descriptors, such as early growth rate and final epidemic size, and produce similarly excellent agreement with simulation. Using a branching process approach we compute the basic reproductive ratio for both models and discuss the implication of random and correlated weight distributions on this as well as on the time evolution and final outcome of epidemics. Finally, we illustrate that the two seemingly different modelling approaches, pairwsie and edge-based, operate on similar assumptions and it is possible to formally link the two. PMID:25580064
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.
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 (15)N-labeled SH3 (Src homology 3) domain proteins in aqueous buffer were used to generate general order parameters (S(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(2)) were derived from the MD trajectory. Correlation analysis using the Gromos force field indicated that S(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(2) parameters from MD simulations with charges for all three histidines and with the SPC/E water model correlated well with S(2) calculated from the experimental NMR relaxation measurements, in a site-specific manner.
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)
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.
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.
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.
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.
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
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.
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.
Subtraction of correlated noise in global networks of gravitational-wave interferometers
NASA Astrophysics Data System (ADS)
Coughlin, Michael W.; Christensen, Nelson L.; De Rosa, Rosario; Fiori, Irene; Gołkowski, Mark; Guidry, Melissa; Harms, Jan; Kubisz, Jerzy; Kulak, Andrzej; Mlynarczyk, Janusz; Paoletti, Federico; Thrane, Eric
2016-11-01
The recent discovery of merging black holes suggests that a stochastic gravitational-wave background is within reach of the advanced detector network operating at design sensitivity. However, correlated magnetic noise from Schumann resonances threatens to contaminate observation of a stochastic background. In this paper, we report on the first effort to eliminate intercontinental correlated noise from Schumann resonances using Wiener filtering. Using magnetometers as proxies for gravitational-wave detectors, we demonstrate as much as a factor of two reduction in the coherence between magnetometers on different continents. While much work remains to be done, our results constitute a proof-of-principle and motivate follow-up studies with a dedicated array of magnetometers.
Move ordering and communities in complex networks describing the game of go
NASA Astrophysics Data System (ADS)
Kandiah, Vivek; Georgeot, Bertrand; Giraud, Olivier
2014-10-01
We analyze the game of go from the point of view of complex networks. We construct three different directed networks of increasing complexity, defining nodes as local patterns on plaquettes of increasing sizes, and links as actual successions of these patterns in databases of real games. We discuss the peculiarities of these networks compared to other types of networks. We explore the ranking vectors and community structure of the networks and show that this approach enables to extract groups of moves with common strategic properties. We also investigate different networks built from games with players of different levels or from different phases of the game. We discuss how the study of the community structure of these networks may help to improve the computer simulations of the game. More generally, we believe such studies may help to improve the understanding of human decision process.
Formation of geometrically complex lipid nanotube-vesicle networks of higher-order topologies
Karlsson, Mattias; Sott, Kristin; Davidson, Maximillian; Cans, Ann-Sofie; Linderholm, Pontus; Chiu, Daniel; Orwar, Owe
2002-01-01
We present a microelectrofusion method for construction of fluid-state lipid bilayer networks of high geometrical complexity up to fully connected networks with genus = 3 topology. Within networks, self-organizing branching nanotube architectures could be produced where intersections spontaneously arrange themselves into three-way junctions with an angle of 120° between each nanotube. Formation of branching nanotube networks appears to follow a minimum-bending energy algorithm that solves for pathway minimization. It is also demonstrated that materials can be injected into specific containers within a network by nanotube-mediated transport of satellite vesicles having defined contents. Using a combination of microelectrofusion, spontaneous nanotube pattern formation, and satellite-vesicle injection, complex networks of containers and nanotubes can be produced for a range of applications in, for example, nanofluidics and artificial cell design. In addition, this electrofusion method allows integration of biological cells into lipid nanotube-vesicle networks. PMID:12185244
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.
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.
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
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
Mangum, Benjamin D; Ghosh, Yagnaseni; Hollingsworth, Jennifer A; Htoon, Han
2013-03-25
In single particle spectroscopy, the degree of observed fluorescence anti-bunching in a second-order cross correlation experiment is indicative of its bi-exciton quantum yield and whether or not a particle is well isolated. Advances in quantum dot synthesis have produced single particles with bi-exciton quantum yields approaching unity. Consequently, this creates uncertainty as to whether a particle has a high bi-exciton quantum yield or if it exists as a cluster. We report on a time-gated anti-bunching technique capable of determining the relative contributions of both multi-exciton emission and clustering effects. In this way, we can now unambiguously determine if a particle is single. Additionally, this time-gated anti-bunching approach provides an accurate way for the determination of bi-exciton lifetime with minimal contribution from higher order multi-exciton states.
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.
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.
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
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.
Kang, Jian; Bowman, F DuBois; Mayberg, Helen; Liu, Han
2016-11-01
To establish brain network properties associated with major depressive disorder (MDD) using resting-state functional magnetic resonance imaging (Rs-fMRI) data, we develop a multi-attribute graph model to construct a region-level functional connectivity network that uses all voxel level information. For each region pair, we define the strength of the connectivity as the kernel canonical correlation coefficient between voxels in the two regions; and we develop a permutation test to assess the statistical significance. We also construct a network based classifier for making predictions on the risk of MDD. We apply our method to Rs-fMRI data from 20 MDD patients and 20 healthy control subjects in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study. Using this method, MDD patients can be distinguished from healthy control subjects based on significant differences in the strength of regional connectivity. We also demonstrate the performance of the proposed method using simulationstudies. PMID:27474522
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
NASA Astrophysics Data System (ADS)
Wang, Yang Min; Hättig, Christof; Reine, Simen; Valeev, Edward; Kjærgaard, Thomas; Kristensen, Kasper
2016-05-01
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.
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.
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
2008-12-01
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.
NASA Astrophysics Data System (ADS)
Watanabe, Shunsuke; Kabashima, Yoshiyuki
2014-01-01
We develop a methodology for analyzing the percolation phenomena of two mutually coupled (interdependent) networks based on the cavity method of statistical mechanics. In particular, we take into account the influence of degree-degree correlations inside and between the networks on the network robustness against targeted (random degree-dependent) attacks and random failures. We show that the developed methodology is reduced to the well-known generating function formalism in the absence of degree-degree correlations. The validity of the developed methodology is confirmed by a comparison with the results of numerical experiments. Our analytical results indicate that the robustness of the interdependent networks depends on both the intranetwork and internetwork degree-degree correlations in a nontrivial way for both cases of random failures and targeted attacks.
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.
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)
Massimiliano Capisani, Luca; Facchinetti, Tullio; Ferrara, Antonella
2010-08-01
This article presents the networked control of a robotic anthropomorphic manipulator based on a second-order sliding mode technique, where the control objective is to track a desired trajectory for the manipulator. The adopted control scheme allows an easy and effective distribution of the control algorithm over two networked machines. While the predictability of real-time tasks execution is achieved by the Soft Hard Real-Time Kernel (S.Ha.R.K.) real-time operating system, the communication is established via a standard Ethernet network. The performances of the control system are evaluated under different experimental system configurations using, to perform the experiments, a COMAU SMART3-S2 industrial robot, and the results are analysed to put into evidence the robustness of the proposed approach against possible network delays, packet losses and unmodelled effects.
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.
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.
Yu, Zhaoxia; Wang, Leiwei; Hildebrandt, Michelle A.T.; Schaid, Daniel J.
2009-01-01
SUMMARY Genetic networks for gene expression data are often built by graphical models, which in turn are built from pairwise correlations of gene expression levels. A key feature of building graphical models is evaluation of conditional independence of two traits, given other traits. When conditional independence can be assumed, the traits that are conditioned on are considered to “explain” the correlation of a pair of traits, allowing efficient building and interpretation of a network. Overlaying genetic polymorphisms, such as single nucleotide polymorphisms (SNPs), on quantitative measures of gene expression provides a much richer set of data to build a genetic network, because it is possible to evaluate whether sets of SNPs “explain” the correlation of gene expression levels. However, there is strong evidence that gene expression levels are controlled by multiple interacting genes, suggesting that it will be difficult to reduce the partial correlation completely to zero. Ignoring the fact that some set of SNPs can explain at least part of the correlation between gene expression levels, if not all, might miss important clues on the genetic control of gene expression. To enrich the assessment of the causes of correlation between gene expression levels, we develop methods to evaluate whether a set of covariates (e.g., SNPs, or even a set of quantitative expression transcripts), explains at least some of the correlation of gene expression levels. These methods can be used to assist the interpretation of regulation of gene expression and the construction of gene regulation networks. PMID:18444230
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
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
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
Scalar field correlator in de Sitter space at next-to-leading order in a 1 /N expansion
NASA Astrophysics Data System (ADS)
Gautier, F.; Serreau, J.
2015-11-01
We study the dynamics of light quantum scalar fields in de Sitter space on superhorizon scales. We compute the self-energy of an O (N ) symmetric theory at next-to-leading order in a 1 /N expansion in the regime of superhorizon momenta, and we obtain an exact analytical solution of the corresponding Dyson-Schwinger equations for the two-point correlator. This amounts to resumming the infinite series of nonlocal self-energy insertions, which typically generate spurious infrared and/or secular divergences. The potentially large de Sitter logarithms resum into well-behaved power laws from which we extract the field strength and mass renormalization. The nonperturbative 1 /N expansion allows us to discuss the case of vanishing and negative tree-level square mass, which both correspond to strongly coupled effective theories in the infrared.
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
Yilmaz, Bülent; Cunedioğlu, Uğur
2007-10-01
Catheter-based approaches used in the localization and treatment of the source of heart rhythm disturbances (arrhythmias) have become popular, because they do not require highly invasive and risky open-chest operations. In most of the existing approaches, mapping of the outer surface (epicardium) is not possible even though arrhythmic substrates involving epicardial and subepicardial layers account for about 15% of the ventricular tachycardias. In this study, we report a feasibility study of a novel mapping approach targeting the epicardium which is based on the measurements of multielectrode catheters placed in the coronary veins. We investigated three methods in determining the most probable region of early activation, i.e., the region that contains the source of the abnormal activation on the heart, using only a set of sparse venous catheter recordings. The methods we proposed here were the linear estimation, correlation, and the back propagation networks. The linear estimation technique hypothesized the relationship between venous catheter measurements and unmeasured epicardial sites based on a previously recorded training data set. The correlation method included a comparative analysis between test and training epicardial activation time maps based on the measured values from the venous sites. In the back propagation method, the input layer consisted of the source data in the form of 42 nodes which were the activation time values from the intravenous catheter leads. We used two hidden layers with 600 and 500 nodes, respectively. The output layer consisted of 28 nodes in the output layer that corresponded to the manually selected early activation regions on the epicardium. The results of the linear estimation and the correlation methods showed that they could be used as a good predictor for the region of early activation, and thus, these approaches may be employed to direct a subsequent more focused electrophysiological study and curative radiofrequency (RF
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.
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.
NASA Astrophysics Data System (ADS)
McClanahan, T. P.; Mazarico, E.; Droege, G.; Lunar Reconnaissance Orbiter (LRO) LEND Team; LOLA Team
2011-12-01
The Moon's polar permanent shadow regions (PSR) have long been considered the unique repository for volatile Hydrogen (H). This postulate was due to the extreme and persistently cold environment that has been maintained over eons of lunar history. However, we have recently reported two results that have challenged the PSR hypothesis. 1) that higher lunar H distributions are only weakly correlated to the PSR condition. 2) We suggest a relationship between higher H in the context of pole-facing slopes relative to equator facing slopes. Correlated observations by the Lunar Exploration Neutron Detector (LEND) onboard the Lunar Reconnaissance Orbiter (LRO) and the Lunar Orbiting Laser Altimeter (LOLA) have been performed indicating pole-facing slopes have 0.01 to 0.02 cps lower epithermal count rates than their equivalent equator-facing slopes. These bulk observations were hypothesis tested and indicate a significant and consistent relationship between topographically modulated insolation effects derived from specialized transformations of LOLA digital elevation models (DEM)'s and LEND maps for latitudes > 60 deg latitude. In this presentation we review the techniques and results from the recent high latitude analysis and apply similar techniques to equatorial regions. We also compare results to second order modeling results derived from long-term LOLA numerical modeling of insolation conditions on the Moon. Results from our low latitude analysis will be reported. We discuss interpretations and implications for Lunar Hydrogen studies
NASA Astrophysics Data System (ADS)
Nilsen, G. J.; Coomer, F. C.; de Vries, M. A.; Stewart, J. R.; Deen, P. P.; Harrison, A.; Rønnow, H. M.
2011-11-01
We present spatial and dynamic information on the s=1/2 distorted kagome antiferromagnet volborthite, Cu3V2O7(OD)2·2D2O, obtained by polarized and inelastic neutron scattering. The instantaneous structure factor, S(Q), is dominated by nearest-neighbor pair correlations, with short-range order at wave vectors Q1=0.65(3) Å-1 and Q2=1.15(5) Å-1 emerging below 5 K. The excitation spectrum, S(Q,ω), reveals two steep branches dispersing from Q1 and Q2, and a flat mode at ωf=5.0(2) meV. The results allow us to identify the crossover at T*˜1 K in 51V NMR and specific-heat measurements as the buildup of correlations at Q1. We compare our data to theoretical models proposed for volborthite, and also demonstrate that the excitation spectrum can be explained by spin-wave-like excitations with anisotropic exchange parameters, as suggested by recent local-density calculations.
STARD13-correlated ceRNA network inhibits EMT and metastasis of breast cancer
Zhang, Feng; Hu, Jinhang; Chou, Jinjiang; Liu, Yu; Xing, Yingying; Xi, Tao
2016-01-01
Competing endogenous RNAs (ceRNAs) network has been correlated with the initiation and development of cancer. Here, we identify CDH5, HOXD1, and HOXD10 as putative STARD13 ceRNAs and they display concordant patterns with STARD13 in different metastatic potential breast cancer cell lines and tissues. Notably, 3’UTRs of these genes suppress breast cancer metastasis via inhibiting epithelial-mesenchymal transition (EMT) in vitro and in vivo, which are activated through the crosstalk between STARD13 and its ceRNAs in 3’UTR- and miRNA-dependent manners. In addition, Kaplan-Meier survival analysis reveals that mRNA level of STARD13 and its ceRNAs is remarkably associated with survival of breast cancer patients. These results suggest that 3’UTRs of CDH5, HOXD1, and HOXD10 inhibit breast cancer metastasis via serving as STARD13 ceRNAs. PMID:26985770
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,…
Gilson, Matthieu; Burkitt, Anthony N; Grayden, David B; Thomas, Doreen A; van Hemmen, J Leo
2009-08-01
Spike-timing-dependent plasticity (STDP) determines the evolution of the synaptic weights according to their pre- and post-synaptic activity, which in turn changes the neuronal activity. In this paper, we extend previous studies of input selectivity induced by (STDP) for single neurons to the biologically interesting case of a neuronal network with fixed recurrent connections and plastic connections from external pools of input neurons. We use a theoretical framework based on the Poisson neuron model to analytically describe the network dynamics (firing rates and spike-time correlations) and thus the evolution of the synaptic weights. This framework incorporates the time course of the post-synaptic potentials and synaptic delays. Our analysis focuses on the asymptotic states of a network stimulated by two homogeneous pools of "steady" inputs, namely Poisson spike trains which have fixed firing rates and spike-time correlations. The (STDP) model extends rate-based learning in that it can implement, at the same time, both a stabilization of the individual neuron firing rates and a slower weight specialization depending on the input spike-time correlations. When one input pathway has stronger within-pool correlations, the resulting synaptic dynamics induced by (STDP) are shown to be similar to those arising in the case of a purely feed-forward network: the weights from the more correlated inputs are potentiated at the expense of the remaining input connections. PMID:19536560
The effects of connectivity on metapopulation persistence: network symmetry and degree correlations.
Shtilerman, Elad; Stone, Lewi
2015-05-01
A spatial metapopulation is a mosaic of interconnected patch populations. The complex routes of colonization between the patches are governed by the metapopulation's dispersal network. Over the past two decades, there has been considerable interest in uncovering the effects of dispersal network topology and its symmetry on metapopulation persistence. While most studies find that the level of symmetry in dispersal pattern enhances persistence, some have reached the conclusion that symmetry has at most a minor effect. In this work, we present a new perspective on the debate. We study properties of the in- and out-degree distribution of patches in the metapopulation which define the number of dispersal routes into and out of a particular patch, respectively. By analysing the spectral radius of the dispersal matrices, we confirm that a higher level of symmetry has only a marginal impact on persistence. We continue to analyse different properties of the in-out degree distribution, namely the 'in-out degree correlation' (IODC) and degree heterogeneity, and find their relationship to metapopulation persistence. Our analysis shows that, in contrast to symmetry, the in-out degree distribution and particularly, the IODC are dominant factors controlling persistence.
The effects of connectivity on metapopulation persistence: network symmetry and degree correlations.
Shtilerman, Elad; Stone, Lewi
2015-05-01
A spatial metapopulation is a mosaic of interconnected patch populations. The complex routes of colonization between the patches are governed by the metapopulation's dispersal network. Over the past two decades, there has been considerable interest in uncovering the effects of dispersal network topology and its symmetry on metapopulation persistence. While most studies find that the level of symmetry in dispersal pattern enhances persistence, some have reached the conclusion that symmetry has at most a minor effect. In this work, we present a new perspective on the debate. We study properties of the in- and out-degree distribution of patches in the metapopulation which define the number of dispersal routes into and out of a particular patch, respectively. By analysing the spectral radius of the dispersal matrices, we confirm that a higher level of symmetry has only a marginal impact on persistence. We continue to analyse different properties of the in-out degree distribution, namely the 'in-out degree correlation' (IODC) and degree heterogeneity, and find their relationship to metapopulation persistence. Our analysis shows that, in contrast to symmetry, the in-out degree distribution and particularly, the IODC are dominant factors controlling persistence. PMID:25833858
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.
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
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
NASA Astrophysics Data System (ADS)
Heflin, James Randolph, Jr.
1990-01-01
Comprehensive theoretical and experimental studies of the magnitude, sign, dispersion, and length dependence of the third order molecular susceptibility gamma _{ijkl}(-omega_4 ;omega_1, omega_2, omega_3 ) demonstrate that the microscopic origin of the nonresonant third order nonlinear optical properties of conjugated linear chains is determined by the effects of electron correlation due to electron-electron repulsion. Multiple -excited configuration interaction calculations of gamma_{ijkl}(-omega _4;omega_1, omega_2, omega _3) for the archetypal class of quasi-one dimensional conjugated structures known as polyenes reveal for the first time the principal role of strongly correlated, energetically high-lying, two photon ^1 A_{rm g} virtual states in the largest of the two dominant, competing virtual excitation processes that determine gamma _{ijkl}(-omega_4 ;omega_1,omega _2,omega_3). It is also found in studies of the effects of conformation on gamma_{ijkl}( -omega_4;omega _1,omega_2, omega_3) that the origin of the third order optical properties remains basically the same for the all-trans and cis-transoid polyenes, and the results for the two conformations are unified by a common power law dependence of the dominant tensor component gamma_{xxxx}(- omega_4;omega_1 ,omega_2,omega _3) on the physical end-to-end length L of the chain with an exponent of 3.5. Calculations for a noncentrosymmetric conjugated chain demonstrate that virtual excitation processes involving diagonal transition moments that are forbidden in centrosymmetric structures lead to a more than an order of magnitude enhancement in gamma_{xxxx}(-omega _4;omega_1, omega_2,omega _3) compared to the analog centrosymmetric structure. Experimental measurements of the dispersion in the isotropically averaged dc-induced second harmonic susceptibility
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.
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.
NASA Astrophysics Data System (ADS)
Shang, Yu; Li, Ting; Chen, Lei; Lin, Yu; Toborek, Michal; Yu, Guoqiang
2014-05-01
Conventional semi-infinite solution for extracting blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements may cause errors in estimation of BFI (αDB) 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 αDB. 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 αDB (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 αDB variations, the mean values of errors in extracting αDB were similar to those reconstructed from the noise-free DCS data. In addition, the errors in extracting the relative changes of αDB 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.
Mediated entanglement and correlations in a star network of interacting spins
Hutton, A.; Bose, S.
2004-04-01
We investigate analytically a star network of spins, in which all spins interact exclusively and continuously with a central spin through Heisenberg XX couplings of equal strength. We find that the central spin correlates and entangles the other spins at zero temperature to a degree that depends on the total number of spins. We find that the entanglement mediating capability of the central spin depends on the evenness or oddness of this number. In the limit of an infinite collection of spins, the difference between entanglement and correlations in terms of divisibility among multiple parties is clearly demonstrated. We also show that with a significant probability one can maximally entangle any two noncentral spins by measuring all the other spins (a process related to the recently introduced notion of localizable entanglement). This probability depends on the evenness and oddness of the total number of spins and remains substantial even for an infinite collection of spins. We show how symmetric multiparty states for optimal sharing and splitting of entanglement can be obtained as ground states of this system using a magnetic field. These states can then be mapped on to flying qubits for transmission to distant parties. We discuss a number of advantages of this mode of generation and distribution of entanglement over other standard methods.
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.
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
Yamazawa, Akira; Date, Yasuhiro; Ito, Keijiro; Kikuchi, Jun
2014-03-01
Microbial ecosystems are typified by diverse microbial interactions and competition. Consequently, the microbial networks and metabolic dynamics of bioprocesses catalyzed by these ecosystems are highly complex, and their visualization is regarded as essential to bioengineering technology and innovation. Here we describe a means of visualizing the variants in a microbial community and their metabolic profiles. The approach enables previously unidentified bacterial functions in the ecosystems to be elucidated. We investigated the anaerobic bioremediation of chlorinated ethene in a soil column experiment as a case study. Microbial community and dechlorination profiles in the ecosystem were evaluated by denaturing gradient gel electrophoresis (DGGE) fingerprinting and gas chromatography, respectively. Dechlorination profiles were obtained from changes in dechlorination by microbial community (evaluated by data mining methods). Individual microbes were then associated with their dechlorination profiles by heterogenous correlation analysis. Our correlation-based visualization approach enables deduction of the roles and functions of bacteria in the dechlorination of chlorinated ethenes. Because it estimates functions and relationships between unidentified microbes and metabolites in microbial ecosystems, this approach is proposed as a control-logic tool by which to understand complex microbial processes. PMID:24095212
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
Patel, Prianka V; Gianoulis, Tara A; Bjornson, Robert D; Yip, Kevin Y; Engelman, Donald M; Gerstein, Mark B
2010-07-01
Recent metagenomics studies have begun to sample the genomic diversity among disparate habitats and relate this variation to features of the environment. Membrane proteins are an intuitive, but thus far overlooked, choice in this type of analysis as they directly interact with the environment, receiving signals from the outside and transporting nutrients. Using global ocean sampling (GOS) data, we found nearly approximately 900,000 membrane proteins in large-scale metagenomic sequence, approximately a fifth of which are completely novel, suggesting a large space of hitherto unexplored protein diversity. Using GPS coordinates for the GOS sites, we extracted additional environmental features via interpolation from the World Ocean Database, the National Center for Ecological Analysis and Synthesis, and empirical models of dust occurrence. This allowed us to study membrane protein variation in terms of natural features, such as phosphate and nitrate concentrations, and also in terms of human impacts, such as pollution and climate change. We show that there is widespread variation in membrane protein content across marine sites, which is correlated with changes in both oceanographic variables and human factors. Furthermore, using these data, we developed an approach, protein families and environment features network (PEN), to quantify and visualize the correlations. PEN identifies small groups of covarying environmental features and membrane protein families, which we call "bimodules." Using this approach, we find that the affinity of phosphate transporters is related to the concentration of phosphate and that the occurrence of iron transporters is connected to the amount of shipping, pollution, and iron-containing dust.
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
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.
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.
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.
NASA Astrophysics Data System (ADS)
Zenil, Hector; Soler-Toscano, Fernando; Dingle, Kamaludin; Louis, Ard A.
2014-06-01
We show that numerical approximations of Kolmogorov complexity (K) of graphs and networks capture some group-theoretic and topological properties of empirical networks, ranging from metabolic to social networks, and of small synthetic networks that we have produced. That K and the size of the group of automorphisms of a graph are correlated opens up interesting connections to problems in computational geometry, and thus connects several measures and concepts from complexity science. We derive these results via two different Kolmogorov complexity approximation methods applied to the adjacency matrices of the graphs and networks. The methods used are the traditional lossless compression approach to Kolmogorov complexity, and a normalised version of a Block Decomposition Method (BDM) based on algorithmic probability theory.
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
A network model of correlated growth of tissue stiffening in pulmonary fibrosis
Oliveira, Cláudio L N; Bates, Jason H T; Suki, Béla
2014-01-01
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) ≈ B0exp(2c)[1 + cβ ln(NaI)], where β = 0.994 ± 0.024 and aI = 0.54 ± 0.026. For intermediate concentrations of stiffening, 0.3 ≤ c ≤ 0.8, another exponential rule describes the bulk modulus as B(N, c) = 4B0exp[aII (c − cc)], where aII and cc are parameters that depend on N. For c > 0.8, B(N, c) is linear in c and independent of N, such that B(N, c) = 100B0 − 100a
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
Fairbank, Michael; Alonso, Eduardo; Prokhorov, Danil
2012-10-01
We derive an algorithm to exactly calculate the mixed second-order derivatives of a neural network's output with respect to its input vector and weight vector. This is necessary for the adaptive dynamic programming (ADP) algorithms globalized dual heuristic programming (GDHP) and value-gradient learning. The algorithm calculates the inner product of this second-order matrix with a given fixed vector in a time that is linear in the number of weights in the neural network. We use a "forward accumulation" of the derivative calculations which produces a much more elegant and easy-to-implement solution than has previously been published for this task. In doing so, the algorithm makes GDHP simple to implement and efficient, bridging the gap between the widely used DHP and GDHP ADP methods.
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
2013-01-01
Background Dysregulation of genetic factors such as microRNAs (miRNAs) and mRNAs has been widely shown to be associated with cancer progression and development. In particular, miRNAs and mRNAs cooperate to affect biological processes, including tumorigenesis. The complexity of miRNA-mRNA interactions presents a major barrier to identifying their co-regulatory roles and functional effects. Thus, by computationally modeling these complex relationships, it may be possible to infer the gene interaction networks underlying complicated biological processes. Results We propose a data-driven, hypergraph structural method for constructing higher-order miRNA-mRNA interaction networks from cancer genomic profiles. The proposed model explicitly characterizes higher-order relationships among genetic factors, from which cooperative gene activities in biological processes may be identified. The proposed model is learned by iteration of structure and parameter learning. The structure learning efficiently constructs a hypergraph structure by generating putative hyperedges representing complex miRNA-mRNA modules. It adopts an evolutionary method based on information-theoretic criteria. In the parameter learning phase, the constructed hypergraph is refined by updating the hyperedge weights using the gradient descent method. From the model, we produce biologically relevant higher-order interaction networks showing the properties of primary and metastatic prostate cancer, as candidates of potential miRNA-mRNA regulatory circuits. Conclusions Our approach focuses on potential cancer-specific interactions reflecting higher-order relationships between miRNAs and mRNAs from expression profiles. The constructed miRNA-mRNA interaction networks show oncogenic or tumor suppression characteristics, which are known to be directly associated with prostate cancer progression. Therefore, the hypergraph-based model can assist hypothesis formulation for the molecular pathogenesis of cancer. PMID
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.
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)
Wu, N. C.; Yan, M.; Zuo, L.; Wang, J. Q.
2014-01-01
To clarify the correlation of medium-range order (MRO) structure with glass forming ability (GFA) of Al-based metallic glasses, Al86Ni14-aYa (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 Al86Ni9Y(La)5 alloys present good GFA due to the combination of the two structural factors.
Federal Register 2010, 2011, 2012, 2013, 2014
2013-03-05
... Management, Voice Over Internet Protocol, Small And Medium Business, Tampa, Florida; Verizon Business... Analysts-Order Management, Voice Over Internet Protocol, Small and Medium Business, Tampa, Florida. The workers supplied order management services to small and medium business customers relating to the...
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
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.
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.
Vidal-Piñeiro, Didac; Valls-Pedret, Cinta; Fernández-Cabello, Sara; Arenaza-Urquijo, Eider M.; Sala-Llonch, Roser; Solana, Elisabeth; Bargalló, Núria; Junqué, Carme; Ros, Emilio; Bartrés-Faz, David
2014-01-01
Ageing entails cognitive and motor decline as well as brain changes such as loss of gray (GM) and white matter (WM) integrity, neurovascular and functional connectivity alterations. Regarding connectivity, reduced resting-state fMRI connectivity between anterior and posterior nodes of the Default Mode Network (DMN) relates to cognitive function and has been postulated to be a hallmark of ageing. However, the relationship between age-related connectivity changes and other neuroimaging-based measures in ageing is fragmentarily investigated. In a sample of 116 healthy elders we aimed to study the relationship between antero-posterior DMN connectivity and measures of WM integrity, GM integrity and cerebral blood flow (CBF), assessed with an arterial spin labeling sequence. First, we replicated previous findings demonstrating DMN connectivity decreases in ageing and an association between antero-posterior DMN connectivity and memory scores. The results showed that the functional connectivity between posterior midline structures and the medial prefrontal cortex was related to measures of WM and GM integrity but not to CBF. Gray and WM correlates of anterio-posterior DMN connectivity included, but were not limited to, DMN areas and cingulum bundle. These results resembled patterns of age-related vulnerability which was studied by comparing the correlates of antero-posterior DMN with age-effect maps. These age-effect maps were obtained after performing an independent analysis with a second sample including both young and old subjects. We argue that antero-posterior connectivity might be a sensitive measure of brain ageing over the brain. By using a comprehensive approach, the results provide valuable knowledge that may shed further light on DMN connectivity dysfunctions in ageing. PMID:25309433
Vidal-Piñeiro, Didac; Valls-Pedret, Cinta; Fernández-Cabello, Sara; Arenaza-Urquijo, Eider M; Sala-Llonch, Roser; Solana, Elisabeth; Bargalló, Núria; Junqué, Carme; Ros, Emilio; Bartrés-Faz, David
2014-01-01
Ageing entails cognitive and motor decline as well as brain changes such as loss of gray (GM) and white matter (WM) integrity, neurovascular and functional connectivity alterations. Regarding connectivity, reduced resting-state fMRI connectivity between anterior and posterior nodes of the Default Mode Network (DMN) relates to cognitive function and has been postulated to be a hallmark of ageing. However, the relationship between age-related connectivity changes and other neuroimaging-based measures in ageing is fragmentarily investigated. In a sample of 116 healthy elders we aimed to study the relationship between antero-posterior DMN connectivity and measures of WM integrity, GM integrity and cerebral blood flow (CBF), assessed with an arterial spin labeling sequence. First, we replicated previous findings demonstrating DMN connectivity decreases in ageing and an association between antero-posterior DMN connectivity and memory scores. The results showed that the functional connectivity between posterior midline structures and the medial prefrontal cortex was related to measures of WM and GM integrity but not to CBF. Gray and WM correlates of anterio-posterior DMN connectivity included, but were not limited to, DMN areas and cingulum bundle. These results resembled patterns of age-related vulnerability which was studied by comparing the correlates of antero-posterior DMN with age-effect maps. These age-effect maps were obtained after performing an independent analysis with a second sample including both young and old subjects. We argue that antero-posterior connectivity might be a sensitive measure of brain ageing over the brain. By using a comprehensive approach, the results provide valuable knowledge that may shed further light on DMN connectivity dysfunctions in ageing.
NASA Astrophysics Data System (ADS)
Timm, Wolfram H. W.; Gluer, Claus C.; Sommer, Gerald
2004-05-01
Trabecular networks of cancellous bone show complex and stochastic characteristics, which have to be modelled in an adequate way to determine pathological changes of the network induced by osteoporosis. The analysis of complexity may be handled by the use of the Markov Theory, which is based on local interactions of a set of elements and thus can be applied to trabecular networks. The conditional entropy which is investigated as a potential measure of complexity, estimates the order of a structure and thus provides a means for classification of healthy versus osteoporotic bone structures. Since the conditional entropy is based on transition probabilities, stochastic characteristics are modelled, too. From a set of 29 female human vertebra T12, classified into two groups of 18 non-osteoporotic and 11 osteoporotic vertebrae axial biopsies were excised from the centre of the vertebral body. A digital model of the trabecular network was extracted with a Micro-CT device (FanBeam Microscope, Stratec, Pforzheim, Germany). Transition probabilities between neighboured voxels were coded as a set of 18 symbols describing the local dimension of a voxel and its relationship to its neighbours within a certain distance. A tree graph of the symbolic transitions coded the transition probabilities and founded a basis for the calculation of the local conditional entropy as a measure of order. The estimated local entropy for a distance at and above 10 voxels showed significantly higher values for the non-osteoporotic subjects than for the osteoporotic ones. This difference indicates, that non-osteoporotic trabecular networks show a higher degree of disorder compared to the osteoporotic ones.
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.
Williamson, Amanda K; Masuda, Koichi; Thonar, Eugene J-M A; Sah, Robert L
2003-08-01
Articular cartilage biochemical composition and mechanical properties evolve during in utero and in vivo growth, with marked differences between fetus, newborn, and young adult. The objectives of this study were to test whether in vitro growth of bovine fetal and newborn calf articular cartilage explants resulted in changes in biochemical and tensile properties during up to 6 weeks of free-swelling culture in serum-supplemented medium. During this culture period, both fetal and calf cartilage grew markedly in size, increasing in dry and wet mass by 150-270%. This was due in part to increases in sulfated glycosaminoglycan (+248%), collagen (+96%), and pyridinoline cross-link (+133%). This was accompanied by an increase in water content so that the concentration of matrix components decreased, despite the overall net increase in mass. The ratio of pyridinoline cross-link to collagen remained low and characteristic of immature tissue. The equilibrium and dynamic tensile moduli and strength of both fetal and calf cartilage decreased during the culture period. The biochemical and biomechanical properties of the cartilage explants were correlated, such that the low values of modulus and strength were associated with low concentrations of collagen and pyridinoline. Thus, the tested culture conditions supported growth and maintenance cartilage in an immature state, but did not induce biomechanical or collagen network maturation.
Topology of the correlation networks among major currencies using hierarchical structure methods
NASA Astrophysics Data System (ADS)
Keskin, Mustafa; Deviren, Bayram; Kocakaplan, Yusuf
2011-02-01
We studied the topology of correlation networks among 34 major currencies using the concept of a minimal spanning tree and hierarchical tree for the full years of 2007-2008 when major economic turbulence occurred. We used the USD (US Dollar) and the TL (Turkish Lira) as numeraires in which the USD was the major currency and the TL was the minor currency. We derived a hierarchical organization and constructed minimal spanning trees (MSTs) and hierarchical trees (HTs) for the full years of 2007, 2008 and for the 2007-2008 period. We performed a technique to associate a value of reliability to the links of MSTs and HTs by using bootstrap replicas of data. We also used the average linkage cluster analysis for obtaining the hierarchical trees in the case of the TL as the numeraire. These trees are useful tools for understanding and detecting the global structure, taxonomy and hierarchy in financial data. We illustrated how the minimal spanning trees and their related hierarchical trees developed over a period of time. From these trees we identified different clusters of currencies according to their proximity and economic ties. The clustered structure of the currencies and the key currency in each cluster were obtained and we found that the clusters matched nicely with the geographical regions of corresponding countries in the world such as Asia or Europe. As expected the key currencies were generally those showing major economic activity.
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
Synchronous activation within the default mode network correlates with perceived social support.
Che, Xianwei; Zhang, Qinglin; Zhao, Jizheng; Wei, Dongtao; Li, Bingbing; Guo, Yanan; Qiu, Jiang; Liu, Yijun
2014-10-01
Perceived social support emphasizes subjective feeling of provisions offered by family, friends and significant others. In consideration of the great significance of perceived social support to health outcomes, attempt to reveal the neural substrates of perceived social support will facilitate its application in a series of mental disorders. Perceived social support potentially relies on healthy interpersonal relationships calling for cognitive processes like perspective taking, empathy and theory of mind. Interestingly, functional activations and connectivity within the default mode network (DMN) are extensively involved in these interpersonal skills. As a result, it is proposed that synchronous activities among brain regions within the DMN will correlate with self-report of perceived social support. In the present study, we tried to investigate the associations between coherence among the DMN regions and perceived social support at resting state. A total of 333 (145 men) participants were directed to fulfill the Multidimensional Scale of Perceived Social Support (MSPSS) after a 484-s functional magnetic resonance imaging (fMRI) scanning without any task. As a result, seed-based functional connectivity and power spectrum analyses revealed that heightened synchronicity among the DMN regions was associated with better performance on perceived social support. Moreover, results in the present study were independent of different methods, structural changes, and general cognitive performance.
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.
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
NASA Astrophysics Data System (ADS)
Ghosh, S. K.; Ganguly, M.; Rout, S. K.; Sinha, T. P.
2015-04-01
Rare earth lanthanum (La) doped barium zirconate titanate, Ba1 - x La2 x/3Zr0.3Ti0.7O3 (BLZT) ceramics, with x = 0.00, 0.02, 0.04, 0.06, 0.08 and 0.10 were prepared using solid state reaction route. Structural characterizations of the materials were done by using X-ray diffraction and Raman spectroscopy. The Rietveld refinement technique employed to investigate the details of the crystal structure revealed a single phase cubic perovskite structure for all the compositions, belonging to the space group Pm-3m. Raman spectroscopy was used to probe the order-disorder correlation in local symmetry and it was verified that the presence of disorder in cubic structure is increased due to La3+ ion substitution at A-site. In addition, the signature of relaxor behavior and diffuse types of phase transition can be detected by monitoring the relative intensity of Raman features. Room temperature photo-electronic properties were investigated by using ultraviolet-visible (UV-Vis) and photoluminescence (PL) spectroscopy. Heterovalent doping (La3+) is accompanied by creation of ionic defects to maintain the charge neutrality; as a result the intermediate energy levels are formed within the band gap. These intermediate energy levels play a significant role in electronic band transitions in higher La concentration, x ≥ 0.08; enhancing the self-trapping mechanism leads to slightly decreasing in band gap values and shifting the PL emission spectra towards violet-blue regions. The temperature dependence of the dielectric constant was investigated and relaxor type of phase transition was observed in the material. The degree of relaxor behavior was enhanced with increase in La3+ ion concentration.
NASA Astrophysics Data System (ADS)
Zheng, Mingwen; Li, Lixiang; Peng, Haipeng; Xiao, Jinghua; Yang, Yixian; Zhao, Hui
2016-09-01
In this paper, we study the finite-time stability and synchronization problem of a class of memristor-based fractional-order Cohen-Grossberg neural network (MFCGNN) with the fractional order α ∈ (0,1 ]. We utilize the set-valued map and Filippov differential inclusion to treat MFCGNN because it has discontinuous right-hand sides. By using the definition of Caputo fractional-order derivative, the definitions of finite-time stability and synchronization, Gronwall's inequality and linear feedback controller, two new sufficient conditions are derived to ensure the finite-time stability of our proposed MFCGNN and achieve the finite-time synchronization of drive-response systems which are constituted by MFCGNNs. Finally, two numerical simulations are presented to verify the rightness of our proposed theorems.
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
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
2015-10-13
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.
Local network structure of a-SiC:H and its correlation with dielectric function
Kageyama, Shota; Matsuki, Nobuyuki; Fujiwara, Hiroyuki
2013-12-21
The microscopic disordered structures of hydrogenated amorphous silicon carbide (a-Si{sub 1−x}C{sub x}: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-Si{sub 1−x}C{sub x}: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 SiH{sub 2} 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 sp{sup 3} CH{sub n} (n = 2,3) groups increases. Moreover, we obtained the direct evidence that the sp{sup 2} C bonding state in the a-SiC matrix exists in the configuration of C = CH{sub 2} and the generation of the graphite-like C = CH{sub 2} 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 SiH{sub 2}/CH{sub n} groups. By taking the SiH{sub 2}/CH{sub n} 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.
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…
Clustering, coding, switching, hierarchical ordering, and control in a network of chaotic elements
NASA Astrophysics Data System (ADS)
Kaneko, Kunihiko
1990-03-01
A Network of chaotic elements is investigated with the use of globally coupled maps. A simple coding of many attractors with clustering is shown. Through the coding, the attractors are organized so that their change exhibits bifurcation-like phenomena. A precision-dependent tree is constructed which leads to the similarity of our attractor with those of spin-glasses. Hierarchical dynamics is constructed on the tree, which leads to the dynamical change of trees and the temporal change of effective degrees of freedom. By a simple input on a site, we can switch among attractors and tune the strength of chaos. A threshold on a cluster size is found, beyond which a peculiar “posi-nega” switch occurs. Possible application to biological information processing is discussed with the emphasis on the fuzzy switch (chaotic search) and hierarchical code (categorization).
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
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.
Labrecque, Rémi; Fournier, Eric; Sirard, Marc-André
2016-06-01
Follicle size is recognized as a predictor of the potential for the enclosed oocyte to yield an embryo following in vitro maturation and in vitro fertilization. Oocytes from larger follicles are more likely to reach the blastocyst stage than those from smaller follicles. A growing oocyte accumulates all the transcripts needed to ensure development until the maternal embryonic transition, and this accumulation must be completed before the period of transcriptional arrest. Accordingly, the transcriptomes of bovine germinal-vesicle-stage oocytes collected from follicles of increasing sizes (<3, 3-5, >5-8, and >8 mm) were evaluated, using the EmbryoGENE bovine transcriptomic platform (custom Agilent 4 × 44 K), to better understand transcriptional modulation in the oocyte as the follicle becomes larger. Microarray analyses revealed very few differences between oocytes from small follicles (<3 vs. 3-5 mm), whereas an important number of differences were detected at the mRNA level between oocytes from larger follicles. Weighted gene correlation network analysis allowed for the identification of several hub genes involved in crucial functions such as transcriptional regulation (TAF2), chromatin remodeling (PPP1CB), energy production (SLC25A31), as well as transport of key molecules within the cell (NAGPA, CYHR1, and SLC3A12). The results presented here thus reinforce the hypothesis that developmental competence acquisition cannot be seen as a simple one-step process, especially in regards to the modulation of mRNA. Mol. Reprod. Dev. 83: 558-569, 2016. © 2016 Wiley Periodicals, Inc. PMID:27127921
Finding New Order in Biological Functions from the Network Structure of Gene Annotations
Glass, Kimberly; Girvan, Michelle
2015-01-01
The Gene Ontology (GO) provides biologists with a controlled terminology that describes how genes are associated with functions and how functional terms are related to one another. These term-term relationships encode how scientists conceive the organization of biological functions, and they take the form of a directed acyclic graph (DAG). Here, we propose that the network structure of gene-term annotations made using GO can be employed to establish an alternative approach for grouping functional terms that captures intrinsic functional relationships that are not evident in the hierarchical structure established in the GO DAG. Instead of relying on an externally defined organization for biological functions, our approach connects biological functions together if they are performed by the same genes, as indicated in a compendium of gene annotation data from numerous different sources. We show that grouping terms by this alternate scheme provides a new framework with which to describe and predict the functions of experimentally identified sets of genes. PMID:26588252
Hill, J Grant
2011-07-28
Auxiliary basis sets specifically matched to the correlation consistent cc-pVnZ-PP, cc-pwCVnZ-PP, aug-cc-pVnZ-PP, and aug-cc-pwCVnZ-PP orbital basis sets (used in conjunction with pseudopotentials) for the 5d transition metal elements Hf-Pt have been optimized for use in density fitting second-order Møller-Plesset perturbation theory and other correlated ab initio methods. Calculations of the second-order Møller-Plesset perturbation theory correlation energy, for a test set of small to medium sized molecules, indicate that the density fitting error when utilizing these sets is negligible at three to four orders of magnitude smaller than the orbital basis set incompleteness error.
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.
NASA Astrophysics Data System (ADS)
Müller, Lucas O.; Blanco, Pablo J.
2015-11-01
We present a methodology for the high order approximation of hyperbolic conservation laws in networks by using the Dumbser-Enaux-Toro solver and exact solvers for the classical Riemann problem at junctions. The proposed strategy can be applied to any hyperbolic system, conservative or non-conservative, and possibly with flux functions containing discontinuous parameters, as long as an exact or approximate Riemann problem solver is available. The methodology is implemented for a one-dimensional blood flow model that considers discontinuous variations of mechanical and geometrical properties of vessels. The achievement of formal order of accuracy, as well as the robustness of the resulting numerical scheme, is verified through the simulation of both, academic tests and physiological flows.
Kwiat, Moria; Elnathan, Roey; Pevzner, Alexander; Peretz, Asher; Barak, Boaz; Peretz, Hagit; Ducobni, Tamir; Stein, Daniel; Mittelman, Leonid; Ashery, Uri; Patolsky, Fernando
2012-07-25
The use of artificial, prepatterned neuronal networks in vitro is a promising approach for studying the development and dynamics of small neural systems in order to understand the basic functionality of neurons and later on of the brain. The present work presents a high fidelity and robust procedure for controlling neuronal growth on substrates such as silicon wafers and glass, enabling us to obtain mature and durable neural networks of individual cells at designed geometries. It offers several advantages compared to other related techniques that have been reported in recent years mainly because of its high yield and reproducibility. The procedure is based on surface chemistry that allows the formation of functional, tailormade neural architectures with a micrometer high-resolution partition, that has the ability to promote or repel cells attachment. The main achievements of this work are deemed to be the creation of a large scale neuronal network at low density down to individual cells, that develop intact typical neurites and synapses without any glia-supportive cells straight from the plating stage and with a relatively long term survival rate, up to 4 weeks. An important application of this method is its use on 3D nanopillars and 3D nanowire-device arrays, enabling not only the cell bodies, but also their neurites to be positioned directly on electrical devices and grow with registration to the recording elements underneath.
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