Second-order centrality correlation in scale-free networks
NASA Astrophysics Data System (ADS)
Lv, Meilei; Guo, Xinling; Chen, Jiaquan; Lu, Zhe-Ming; Nie, Tingyuan
2015-02-01
Scale-free networks in which the degree displays a power-law distribution can be classified into assortative, disassortative, and neutral networks according to their degree-degree correlation. The second-order centrality proposed in a distributed computation manner is quick-calculated and accurate to identify critical nodes. We explore the second-order centrality correlation (SOC) for each type of the scale-free networks. The SOC-SOC correlation in assortative network and neutral network behaves similarly to the degree-degree correlation, while it behaves an apparent difference in disassortative networks. Experiments show that the invulnerability of most of scale-free networks behaves similarly under the node removal ordering by SOC centrality and degree centrality, respectively. The netscience network and the Yeast network behave a little differently because they are native disconnecting networks.
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.
Biological network inference using low order partial correlation.
Zuo, Yiming; Yu, Guoqiang; Tadesse, Mahlet G; Ressom, Habtom W
2014-10-01
Biological network inference is a major challenge in systems biology. Traditional correlation-based network analysis results in too many spurious edges since correlation cannot distinguish between direct and indirect associations. To address this issue, Gaussian graphical models (GGM) were proposed and have been widely used. Though they can significantly reduce the number of spurious edges, GGM are insufficient to uncover a network structure faithfully due to the fact that they only consider the full order partial correlation. Moreover, when the number of samples is smaller than the number of variables, further technique based on sparse regularization needs to be incorporated into GGM to solve the singular covariance inversion problem. In this paper, we propose an efficient and mathematically solid algorithm that infers biological networks by computing low order partial correlation (LOPC) up to the second order. The bias introduced by the low order constraint is minimal compared to the more reliable approximation of the network structure achieved. In addition, the algorithm is suitable for a dataset with small sample size but large number of variables. Simulation results show that LOPC yields far less spurious edges and works well under various conditions commonly seen in practice. The application to a real metabolomics dataset further validates the performance of LOPC and suggests its potential power in detecting novel biomarkers for complex disease.
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.
Reconstruction of noise-driven nonlinear networks from node outputs by using high-order correlations
Chen, Yang; Zhang, Zhaoyang; Chen, Tianyu; Wang, Shihong; Hu, Gang
2017-01-01
Many practical systems can be described by dynamic networks, for which modern technique can measure their outputs, and accumulate extremely rich data. Nevertheless, the network structures producing these data are often deeply hidden in the data. The problem of inferring network structures by analyzing the available data, turns to be of great significance. On one hand, networks are often driven by various unknown facts, such as noises. On the other hand, network structures of practical systems are commonly nonlinear, and different nonlinearities can provide rich dynamic features and meaningful functions of realistic networks. Although many works have considered each fact in studying network reconstructions, much less papers have been found to systematically treat both difficulties together. Here we propose to use high-order correlation computations (HOCC) to treat nonlinear dynamics; use two-time correlations to decorrelate effects of network dynamics and noise driving; and use suitable basis and correlator vectors to unifiedly infer all dynamic nonlinearities, topological interaction links and noise statistical structures. All the above theoretical frameworks are constructed in a closed form and numerical simulations fully verify the validity of theoretical predictions. PMID:28322230
Reconstruction of noise-driven nonlinear networks from node outputs by using high-order correlations
NASA Astrophysics Data System (ADS)
Chen, Yang; Zhang, Zhaoyang; Chen, Tianyu; Wang, Shihong; Hu, Gang
2017-03-01
Many practical systems can be described by dynamic networks, for which modern technique can measure their outputs, and accumulate extremely rich data. Nevertheless, the network structures producing these data are often deeply hidden in the data. The problem of inferring network structures by analyzing the available data, turns to be of great significance. On one hand, networks are often driven by various unknown facts, such as noises. On the other hand, network structures of practical systems are commonly nonlinear, and different nonlinearities can provide rich dynamic features and meaningful functions of realistic networks. Although many works have considered each fact in studying network reconstructions, much less papers have been found to systematically treat both difficulties together. Here we propose to use high-order correlation computations (HOCC) to treat nonlinear dynamics; use two-time correlations to decorrelate effects of network dynamics and noise driving; and use suitable basis and correlator vectors to unifiedly infer all dynamic nonlinearities, topological interaction links and noise statistical structures. All the above theoretical frameworks are constructed in a closed form and numerical simulations fully verify the validity of theoretical predictions.
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.
2015-01-01
We study the tree-tensor-network-state (TTNS) method with variable tensor orders for quantum chemistry. TTNS is a variational method to efficiently approximate complete active space (CAS) configuration interaction (CI) wave functions in a tensor product form. TTNS can be considered as a higher order generalization of the matrix product state (MPS) method. The MPS wave function is formulated as products of matrices in a multiparticle basis spanning a truncated Hilbert space of the original CAS-CI problem. These matrices belong to active orbitals organized in a one-dimensional array, while tensors in TTNS are defined upon a tree-like arrangement of the same orbitals. The tree-structure is advantageous since the distance between two arbitrary orbitals in the tree scales only logarithmically with the number of orbitals N, whereas the scaling is linear in the MPS array. It is found to be beneficial from the computational costs point of view to keep strongly correlated orbitals in close vicinity in both arrangements; therefore, the TTNS ansatz is better suited for multireference problems with numerous highly correlated orbitals. To exploit the advantages of TTNS a novel algorithm is designed to optimize the tree tensor network topology based on quantum information theory and entanglement. The superior performance of the TTNS method is illustrated on the ionic-neutral avoided crossing of LiF. It is also shown that the avoided crossing of LiF can be localized using only ground state properties, namely one-orbital entanglement. PMID:25844072
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.
NASA Astrophysics Data System (ADS)
Dash, S.; Chen, P.; Boolchand, P.
2017-06-01
Glass transition width W of pure Se narrows from 7.1(3) °C to 1.5(2) °C and the non-reversing enthalpy of relaxation (Δ Hnr) at Tg increases from 0.23(5) cal/g to 0.90(5) cal/g upon room temperature aging for 4 months in the dark as examined in modulated differential scanning colorimetry (MDSC) at low scan rates. In Raman scattering, such aging leads the A1 mode of Sen-chains (near 250 cm-1) to narrow by 26% and its scattering strength to decrease as the strength of modes of correlated chains (near 235 cm-1) and of Se8 rings (near 264 cm-1) systematically grows. These calorimetric and Raman scattering results are consistent with the "molecular" chains of Sen, predominant in the fresh glass, reconstructing with each other to compact and partially order the network. Consequences of the aging induced reconstruction of the long super-flexible and uncorrelated Sen-chains are also manifested upon alloying up to 4 mol. % of Ge as revealed by a qualitative narrowing (by 25%) of the Raman vibrational mode of the corner-sharing GeSe4 tetrahedra and a blue-shift of the said mode by nearly 1 cm-1 in 194 cm-1. But, at higher Ge content (x > 6%), as the length of Sen chain-segments across Ge cross-links decreases qualitatively (⟨n ⟩ < 8), these aging induced chain-reconstruction effects are suppressed. The width of Tg increases beyond 15 °C in binary GexSe100-x glasses as x > 10% to acquire values observed earlier as alloying concentration approaches 20% and networks become spontaneously rigid.
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
2016-01-01
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
Network Routing Using the Network Tasking Order, a Chron Approach
2015-03-26
some advanced prediction techniques that are utilized for traffic routing and management as well as some synchronization techniques are presented...NETWORK ROUTING USING THE NETWORK TASKING ORDER, A CHRON APPROACH THESIS Nicholas J. Paltzer...MS-15-M-059 NETWORK ROUTING USING THE NETWORK TASKING ORDER, A CHRON APPROACH THESIS Presented to the Faculty Department of Electrical
Ring correlations in random networks
NASA Astrophysics Data System (ADS)
Sadjadi, Mahdi; Thorpe, M. F.
2016-12-01
We examine the correlations between rings in random network glasses in two dimensions as a function of their separation. Initially, we use the topological separation (measured by the number of intervening rings), but this leads to pseudo-long-range correlations due to a lack of topological charge neutrality in the shells surrounding a central ring. This effect is associated with the noncircular nature of the shells. It is, therefore, necessary to use the geometrical distance between ring centers. Hence we find a generalization of the Aboav-Weaire law out to larger distances, with the correlations between rings decaying away when two rings are more than about three rings apart.
Synchronization of fractional order complex dynamical networks
NASA Astrophysics Data System (ADS)
Wang, Yu; Li, Tianzeng
2015-06-01
In this letter the synchronization of complex dynamical networks with fractional order chaotic nodes is studied. A fractional order controller for synchronization of complex network is presented. Some new sufficient synchronization criteria are proposed based on the Lyapunov stability theory and the LaSalle invariance principle. These synchronization criteria can apply to an arbitrary fractional order complex network in which the coupling-configuration matrix and the inner-coupling matrix are not assumed to be symmetric or irreducible. It means that this method is more general and effective. Numerical simulations of two fractional order complex networks demonstrate the universality and the effectiveness of the proposed method.
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
Theory of correlations in stochastic neural networks
NASA Astrophysics Data System (ADS)
Ginzburg, Iris; Sompolinsky, Haim
1994-10-01
One of the main experimental tools in probing the interactions between neurons has been the measurement of the correlations in their activity. In general, however, the interpretation of the observed correlations is difficult since the correlation between a pair of neurons is influenced not only by the direct interaction between them but also by the dynamic state of the entire network to which they belong. Thus a comparison between the observed correlations and the predictions from specific model networks is needed. In this paper we develop a theory of neuronal correlation functions in large networks comprising several highly connected subpopulations and obeying stochastic dynamic rules. When the networks are in asynchronous states, the cross correlations are relatively weak, i.e., their amplitude relative to that of the autocorrelations is of order of 1/N, N being the size of the interacting populations. Using the weakness of the cross correlations, general equations that express the matrix of cross correlations in terms of the mean neuronal activities and the effective interaction matrix are presented. The effective interactions are the synaptic efficacies multiplied by the gain of the postsynaptic neurons. The time-delayed cross-correlation matrix can be expressed as a sum of exponentially decaying modes that correspond to the (nonorthogonal) eigenvectors of the effective interaction matrix. The theory is extended to networks with random connectivity, such as randomly dilute networks. This allows for a comparison between the contribution from the internal common input and that from the direct interactions to the correlations of monosynaptically coupled pairs. A closely related quantity is the linear response of the neurons to external time-dependent perturbations. We derive the form of the dynamic linear response function of neurons in the above architecture in terms of the eigenmodes of the effective interaction matrix. The behavior of the correlations and the
Networks with given two-point correlations: Hidden correlations from degree correlations
NASA Astrophysics Data System (ADS)
Fronczak, Agata; Fronczak, Piotr
2006-08-01
This paper orders certain important issues related to both uncorrelated and correlated networks with hidden variables, in which hidden variables correspond to desired node degrees. In particular, we show that networks being uncorrelated at the hidden level are also lacking in correlations between node degrees. The observation supported by the depoissonization idea allows us to extract a distribution of hidden variables from a given node degree distribution. It completes the algorithm for generating uncorrelated networks that was suggested by other authors. In this paper we also carefully analyze the interplay between hidden attributes and node degrees. We show how to extract hidden correlations from degree correlations. Our derivations provide a mathematical background for the algorithm for generating correlated networks that was proposed by Boguñá and Pastor-Satorras.
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.
Higher order correlations of IRAS galaxies
NASA Technical Reports Server (NTRS)
Meiksin, Avery; Szapudi, Istvan; Szalay, Alexander
1992-01-01
The higher order irreducible angular correlation functions are derived up to the eight-point function, for a sample of 4654 IRAS galaxies, flux-limited at 1.2 Jy in the 60 microns band. The correlations are generally found to be somewhat weaker than those for the optically selected galaxies, consistent with the visual impression of looser clusters in the IRAS sample. It is found that the N-point correlation functions can be expressed as the symmetric sum of products of N - 1 two-point functions, although the correlations above the four-point function are consistent with zero. The coefficients are consistent with the hierarchical clustering scenario as modeled by Hamilton and by Schaeffer.
Higher-Order Neural Networks Recognize Patterns
NASA Technical Reports Server (NTRS)
Reid, Max B.; Spirkovska, Lilly; Ochoa, Ellen
1996-01-01
Networks of higher order have enhanced capabilities to distinguish between different two-dimensional patterns and to recognize those patterns. Also enhanced capabilities to "learn" patterns to be recognized: "trained" with far fewer examples and, therefore, in less time than necessary to train comparable first-order neural networks.
Representing higher-order dependencies in networks
Xu, Jian; Wickramarathne, Thanuka L.; Chawla, Nitesh V.
2016-01-01
To ensure the correctness of network analysis methods, the network (as the input) has to be a sufficiently accurate representation of the underlying data. However, when representing sequential data from complex systems, such as global shipping traffic or Web clickstream traffic as networks, conventional network representations that implicitly assume the Markov property (first-order dependency) can quickly become limiting. This assumption holds that, when movements are simulated on the network, the next movement depends only on the current node, discounting the fact that the movement may depend on several previous steps. However, we show that data derived from many complex systems can show up to fifth-order dependencies. In these cases, the oversimplifying assumption of the first-order network representation can lead to inaccurate network analysis results. To address this problem, we propose the higher-order network (HON) representation that can discover and embed variable orders of dependencies in a network representation. Through a comprehensive empirical evaluation and analysis, we establish several desirable characteristics of HON, including accuracy, scalability, and direct compatibility with the existing suite of network analysis methods. We illustrate how HON can be applied to a broad variety of tasks, such as random walking, clustering, and ranking, and we demonstrate that, by using it as input, HON yields more accurate results without any modification to these tasks. PMID:27386539
Entropy and order in urban street networks
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
Modeling Higher-Order Correlations within Cortical Microcolumns
Köster, Urs; Sohl-Dickstein, Jascha; Gray, Charles M.; Olshausen, Bruno A.
2014-01-01
We statistically characterize the population spiking activity obtained from simultaneous recordings of neurons across all layers of a cortical microcolumn. Three types of models are compared: an Ising model which captures pairwise correlations between units, a Restricted Boltzmann Machine (RBM) which allows for modeling of higher-order correlations, and a semi-Restricted Boltzmann Machine which is a combination of Ising and RBM models. Model parameters were estimated in a fast and efficient manner using minimum probability flow, and log likelihoods were compared using annealed importance sampling. The higher-order models reveal localized activity patterns which reflect the laminar organization of neurons within a cortical column. The higher-order models also outperformed the Ising model in log-likelihood: On populations of 20 cells, the RBM had 10% higher log-likelihood (relative to an independent model) than a pairwise model, increasing to 45% gain in a larger network with 100 spatiotemporal elements, consisting of 10 neurons over 10 time steps. We further removed the need to model stimulus-induced correlations by incorporating a peri-stimulus time histogram term, in which case the higher order models continued to perform best. These results demonstrate the importance of higher-order interactions to describe the structure of correlated activity in cortical networks. Boltzmann Machines with hidden units provide a succinct and effective way to capture these dependencies without increasing the difficulty of model estimation and evaluation. PMID:24991969
Modeling higher-order correlations within cortical microcolumns.
Köster, Urs; Sohl-Dickstein, Jascha; Gray, Charles M; Olshausen, Bruno A
2014-07-01
We statistically characterize the population spiking activity obtained from simultaneous recordings of neurons across all layers of a cortical microcolumn. Three types of models are compared: an Ising model which captures pairwise correlations between units, a Restricted Boltzmann Machine (RBM) which allows for modeling of higher-order correlations, and a semi-Restricted Boltzmann Machine which is a combination of Ising and RBM models. Model parameters were estimated in a fast and efficient manner using minimum probability flow, and log likelihoods were compared using annealed importance sampling. The higher-order models reveal localized activity patterns which reflect the laminar organization of neurons within a cortical column. The higher-order models also outperformed the Ising model in log-likelihood: On populations of 20 cells, the RBM had 10% higher log-likelihood (relative to an independent model) than a pairwise model, increasing to 45% gain in a larger network with 100 spatiotemporal elements, consisting of 10 neurons over 10 time steps. We further removed the need to model stimulus-induced correlations by incorporating a peri-stimulus time histogram term, in which case the higher order models continued to perform best. These results demonstrate the importance of higher-order interactions to describe the structure of correlated activity in cortical networks. Boltzmann Machines with hidden units provide a succinct and effective way to capture these dependencies without increasing the difficulty of model estimation and evaluation.
Optical velocimeter by second order correlation
NASA Astrophysics Data System (ADS)
Suo, Yaji; Feng, Yaming; Xu, Da-xiao; Wan, Wenjie
2017-06-01
Motional information can be buried inside the temporal statistics of scattering light. Here we explore the second order correlation of scattering light to trace back the transverse velocity of objects, verifying the inverse relationship between the coherence time and the velocity. Based on this principle, a new type of optical velocimeter is demonstrated, which has been further applied to study the fluid flow inside a microfluidic channel. This new noninvasive and easy-to-implement velocimeter may offer a new avenue in many applications.
Correlated genotypes in friendship networks
Fowler, James H.; Settle, Jaime E.; Christakis, Nicholas A.
2011-01-01
It is well known that humans tend to associate with other humans who have similar characteristics, but it is unclear whether this tendency has consequences for the distribution of genotypes in a population. Although geneticists have shown that populations tend to stratify genetically, this process results from geographic sorting or assortative mating, and it is unknown whether genotypes may be correlated as a consequence of nonreproductive associations or other processes. Here, we study six available genotypes from the National Longitudinal Study of Adolescent Health to test for genetic similarity between friends. Maps of the friendship networks show clustering of genotypes and, after we apply strict controls for population stratification, the results show that one genotype is positively correlated (homophily) and one genotype is negatively correlated (heterophily). A replication study in an independent sample from the Framingham Heart Study verifies that DRD2 exhibits significant homophily and that CYP2A6 exhibits significant heterophily. These unique results show that homophily and heterophily obtain on a genetic (indeed, an allelic) level, which has implications for the study of population genetics and social behavior. In particular, the results suggest that association tests should include friends’ genes and that theories of evolution should take into account the fact that humans might, in some sense, be metagenomic with respect to the humans around them. PMID:21245293
Robustness of Oscillatory Behavior in Correlated Networks
Sasai, Takeyuki; Morino, Kai; Tanaka, Gouhei; Almendral, Juan A.; Aihara, Kazuyuki
2015-01-01
Understanding network robustness against failures of network units is useful for preventing large-scale breakdowns and damages in real-world networked systems. The tolerance of networked systems whose functions are maintained by collective dynamical behavior of the network units has recently been analyzed in the framework called dynamical robustness of complex networks. The effect of network structure on the dynamical robustness has been examined with various types of network topology, but the role of network assortativity, or degree–degree correlations, is still unclear. Here we study the dynamical robustness of correlated (assortative and disassortative) networks consisting of diffusively coupled oscillators. Numerical analyses for the correlated networks with Poisson and power-law degree distributions show that network assortativity enhances the dynamical robustness of the oscillator networks but the impact of network disassortativity depends on the detailed network connectivity. Furthermore, we theoretically analyze the dynamical robustness of correlated bimodal networks with two-peak degree distributions and show the positive impact of the network assortativity. PMID:25894574
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.
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.
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.
Inferring phylogenetic networks from gene order data.
Morozov, Alexey Anatolievich; Galachyants, Yuri Pavlovich; Likhoshway, Yelena Valentinovna
2013-01-01
Existing algorithms allow us to infer phylogenetic networks from sequences (DNA, protein or binary), sets of trees, and distance matrices, but there are no methods to build them using the gene order data as an input. Here we describe several methods to build split networks from the gene order data, perform simulation studies, and use our methods for analyzing and interpreting different real gene order datasets. All proposed methods are based on intermediate data, which can be generated from genome structures under study and used as an input for network construction algorithms. Three intermediates are used: set of jackknife trees, distance matrix, and binary encoding. According to simulations and case studies, the best intermediates are jackknife trees and distance matrix (when used with Neighbor-Net algorithm). Binary encoding can also be useful, but only when the methods mentioned above cannot be used.
Earthquake correlations and networks: A comparative study
Krishna Mohan, T. R.; Revathi, P. G.
2011-04-15
We quantify the correlation between earthquakes and use the same to extract causally connected earthquake pairs. Our correlation metric is a variation on the one introduced by Baiesi and Paczuski [M. Baiesi and M. Paczuski, Phys. Rev. E 69, 066106 (2004)]. A network of earthquakes is then constructed from the time-ordered catalog and with links between the more correlated ones. A list of recurrences to each of the earthquakes is identified employing correlation thresholds to demarcate the most meaningful ones in each cluster. Data pertaining to three different seismic regions (viz., California, Japan, and the Himalayas) are comparatively analyzed using such a network model. The distribution of recurrence lengths and recurrence times are two of the key features analyzed to draw conclusions about the universal aspects of such a network model. We find that the unimodal feature of recurrence length distribution, which helps to associate typical rupture lengths with different magnitude earthquakes, is robust across the different seismic regions. The out-degree of the networks shows a hub structure rooted on the large magnitude earthquakes. In-degree distribution is seen to be dependent on the density of events in the neighborhood. Power laws, with two regimes having different exponents, are obtained with recurrence time distribution. The first regime confirms the Omori law for aftershocks while the second regime, with a faster falloff for the larger recurrence times, establishes that pure spatial recurrences also follow a power-law distribution. The crossover to the second power-law regime can be taken to be signaling the end of the aftershock regime in an objective fashion.
Percolation of secret correlations in a network
Leverrier, Anthony; Garcia-Patron, Raul
2011-09-15
In this work, we explore the analogy between entanglement and secret classical correlations in the context of large networks--more precisely, the question of percolation of secret correlations in a network. It is known that entanglement percolation in quantum networks can display a highly nontrivial behavior depending on the topology of the network and on the presence of entanglement between the nodes. Here we show that this behavior, thought to be of a genuine quantum nature, also occurs in a classical context.
The Correlation Fractal Dimension of Complex Networks
NASA Astrophysics Data System (ADS)
Wang, Xingyuan; Liu, Zhenzhen; Wang, Mogei
2013-05-01
The fractality of complex networks is studied by estimating the correlation dimensions of the networks. Comparing with the previous algorithms of estimating the box dimension, our algorithm achieves a significant reduction in time complexity. For four benchmark cases tested, that is, the Escherichia coli (E. Coli) metabolic network, the Homo sapiens protein interaction network (H. Sapiens PIN), the Saccharomyces cerevisiae protein interaction network (S. Cerevisiae PIN) and the World Wide Web (WWW), experiments are provided to demonstrate the validity of our algorithm.
Jamming in complex networks with degree correlation
NASA Astrophysics Data System (ADS)
Pastore Y Piontti, Ana; Braunstein, Lidia; Macri, Pablo
2012-02-01
We study the effects of the degree-degree correlations on the pressure congestion J for a diffusive transport process on scale free complex networks. Using the gradient network approach we find that the pressure congestion for disassortative (assortative) networks is lower (bigger) than the one for uncorrelated networks which allow us to affirm that disassortative networks enhance transport through them. This result agree with the fact that many real world transportation networks naturally evolve to this kind of correlation. We explain our results showing that for the disassortative case the clusters in the gradient network turn out to be as much elongated as possible, reducing the pressure congestion J and observing the opposite behavior for the assortative case. Finally, we apply our transportation process to real world networks, and the results agree with our findings for model networks.
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.
Betweenness centrality correlation in social networks
NASA Astrophysics Data System (ADS)
Goh, K.-I.; Oh, E.; Kahng, B.; Kim, D.
2003-01-01
Scale-free (SF) networks exhibiting a power-law degree distribution can be grouped into the assortative, dissortative, and neutral networks according to the behavior of the degree-degree correlation coefficient. Here we investigate the betweenness centrality (BC) correlation for each type of SF networks. While the BC-BC correlation coefficients behave similarly to the degree-degree correlation coefficients for the dissortative and neutral networks, the BC correlation is nontrivial for the assortative ones found mainly in social networks. The mean BC of neighbors of a vertex with BC gi is almost independent of gi, implying that each person is surrounded by almost the same influential environments of people no matter how influential the person may be.
Jamming in complex networks with degree correlation
NASA Astrophysics Data System (ADS)
Pastore y Piontti, Ana L.; Braunstein, Lidia A.; Macri, Pablo A.
2010-10-01
We study the effects of the degree-degree correlations on the pressure congestion J when we apply a dynamical process on scale free complex networks using the gradient network approach. We find that the pressure congestion for disassortative (assortative) networks is lower (bigger) than the one for uncorrelated networks which allow us to affirm that disassortative networks enhance transport through them. This result agree with the fact that many real world transportation networks naturally evolve to this kind of correlation. We explain our results showing that for the disassortative case the clusters in the gradient network turn out to be as much elongated as possible, reducing the pressure congestion J and observing the opposite behavior for the assortative case. Finally we apply our model to real world networks, and the results agree with our theoretical model.
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.
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
Triple point in correlated interdependent networks.
Valdez, L D; Macri, P A; Stanley, H E; Braunstein, L A
2013-11-01
Many real-world networks depend on other networks, often in nontrivial ways, to maintain their functionality. These interdependent "networks of networks" are often extremely fragile. When a fraction 1-p of nodes in one network randomly fails, the damage propagates to nodes in networks that are interdependent and a dynamic failure cascade occurs that affects the entire system. We present dynamic equations for two interdependent networks that allow us to reproduce the failure cascade for an arbitrary pattern of interdependency. We study the "rich club" effect found in many real interdependent network systems in which the high-degree nodes are extremely interdependent, correlating a fraction α of the higher-degree nodes on each network. We find a rich phase diagram in the plane p-α, with a triple point reminiscent of the triple point of liquids that separates a nonfunctional phase from two functional phases.
Tensor network states with three-site correlators
NASA Astrophysics Data System (ADS)
Kovyrshin, Arseny; Reiher, Markus
2016-11-01
We present a detailed analysis of various tensor network parameterizations within the complete graph tensor network states (CGTNS) approach. We extend our 2-site CGTNS scheme by introducing 3-site correlators. For this we devise three different strategies. The first relies solely on 3-site correlators and the second on 3-site correlators added on top of the 2-site correlator ansatz. To avoid an inflation of the variational space introduced by higher-order correlators, we limit the number of higher-order correlators to the most significant ones in the third strategy. Approaches for the selection of these most significant correlators are discussed. The sextet and doublet spin states of the spin-crossover complex manganocene serve as a numerical test case. In general, the CGTNS scheme achieves a remarkable accuracy for a significantly reduced size of the variational space. The advantages, drawbacks, and limitations of all CGTNS parameterizations investigated are rigorously discussed.
Change Point Detection in Correlation Networks
NASA Astrophysics Data System (ADS)
Barnett, Ian; Onnela, Jukka-Pekka
2016-01-01
Many systems of interacting elements can be conceptualized as networks, where network nodes represent the elements and network ties represent interactions between the elements. In systems where the underlying network evolves, it is useful to determine the points in time where the network structure changes significantly as these may correspond to functional change points. We propose a method for detecting change points in correlation networks that, unlike previous change point detection methods designed for time series data, requires minimal distributional assumptions. We investigate the difficulty of change point detection near the boundaries of the time series in correlation networks and study the power of our method and competing methods through simulation. We also show the generalizable nature of the method by applying it to stock price data as well as fMRI data.
Change Point Detection in Correlation Networks
Barnett, Ian; Onnela, Jukka-Pekka
2016-01-01
Many systems of interacting elements can be conceptualized as networks, where network nodes represent the elements and network ties represent interactions between the elements. In systems where the underlying network evolves, it is useful to determine the points in time where the network structure changes significantly as these may correspond to functional change points. We propose a method for detecting change points in correlation networks that, unlike previous change point detection methods designed for time series data, requires minimal distributional assumptions. We investigate the difficulty of change point detection near the boundaries of the time series in correlation networks and study the power of our method and competing methods through simulation. We also show the generalizable nature of the method by applying it to stock price data as well as fMRI data. PMID:26739105
How Structure Determines Correlations in Neuronal Networks
Pernice, Volker; Staude, Benjamin; Cardanobile, Stefano; Rotter, Stefan
2011-01-01
Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, we face an intricate interplay between the topology of the network and the dynamics of the system, which is generally very hard to disentangle. A dynamical feature that has been subject of intense research in various fields are correlations between the noisy activity of nodes in a network. We consider a class of systems, where discrete signals are sent along the links of the network. Such systems are of particular relevance in neuroscience, because they provide models for networks of neurons that use action potentials for communication. We study correlations in dynamic networks with arbitrary topology, assuming linear pulse coupling. With our novel approach, we are able to understand in detail how specific structural motifs affect pairwise correlations. Based on a power series decomposition of the covariance matrix, we describe the conditions under which very indirect interactions will have a pronounced effect on correlations and population dynamics. In random networks, we find that indirect interactions may lead to a broad distribution of activation levels with low average but highly variable correlations. This phenomenon is even more pronounced in networks with distance dependent connectivity. In contrast, networks with highly connected hubs or patchy connections often exhibit strong average correlations. Our results are particularly relevant in view of new experimental techniques that enable the parallel recording of spiking activity from a large number of neurons, an appropriate interpretation of which is hampered by the currently limited understanding of structure-dynamics relations in complex networks. PMID:21625580
A Novel Higher Order Artificial Neural Networks
NASA Astrophysics Data System (ADS)
Xu, Shuxiang
2010-05-01
In this paper a new Higher Order Neural Network (HONN) model is introduced and applied in several data mining tasks. Data Mining extracts hidden patterns and valuable information from large databases. A hyperbolic tangent function is used as the neuron activation function for the new HONN model. Experiments are conducted to demonstrate the advantages and disadvantages of the new HONN model, when compared with several conventional Artificial Neural Network (ANN) models: Feedforward ANN with the sigmoid activation function; Feedforward ANN with the hyperbolic tangent activation function; and Radial Basis Function (RBF) ANN with the Gaussian activation function. The experimental results seem to suggest that the new HONN holds higher generalization capability as well as abilities in handling missing data.
Triple point in correlated interdependent networks
NASA Astrophysics Data System (ADS)
Valdez, L. D.; Macri, P. A.; Stanley, H. E.; Braunstein, L. A.
2013-11-01
Many real-world networks depend on other networks, often in nontrivial ways, to maintain their functionality. These interdependent “networks of networks” are often extremely fragile. When a fraction 1-p of nodes in one network randomly fails, the damage propagates to nodes in networks that are interdependent and a dynamic failure cascade occurs that affects the entire system. We present dynamic equations for two interdependent networks that allow us to reproduce the failure cascade for an arbitrary pattern of interdependency. We study the “rich club” effect found in many real interdependent network systems in which the high-degree nodes are extremely interdependent, correlating a fraction α of the higher-degree nodes on each network. We find a rich phase diagram in the plane p-α, with a triple point reminiscent of the triple point of liquids that separates a nonfunctional phase from two functional phases.
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.
Negative Correlations in Visual Cortical Networks
Chelaru, Mircea I.; Dragoi, Valentin
2016-01-01
The amount of information encoded by cortical circuits depends critically on the capacity of nearby neurons to exhibit trial-to-trial (noise) correlations in their responses. Depending on their sign and relationship to signal correlations, noise correlations can either increase or decrease the population code accuracy relative to uncorrelated neuronal firing. Whereas positive noise correlations have been extensively studied using experimental and theoretical tools, the functional role of negative correlations in cortical circuits has remained elusive. We addressed this issue by performing multiple-electrode recording in the superficial layers of the primary visual cortex (V1) of alert monkey. Despite the fact that positive noise correlations decayed exponentially with the difference in the orientation preference between cells, negative correlations were uniformly distributed across the population. Using a statistical model for Fisher Information estimation, we found that a mild increase in negative correlations causes a sharp increase in network accuracy even when mean correlations were held constant. To examine the variables controlling the strength of negative correlations, we implemented a recurrent spiking network model of V1. We found that increasing local inhibition and reducing excitation causes a decrease in the firing rates of neurons while increasing the negative noise correlations, which in turn increase the population signal-to-noise ratio and network accuracy. Altogether, these results contribute to our understanding of the neuronal mechanism involved in the generation of negative correlations and their beneficial impact on cortical circuit function. PMID:25217468
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.
Effect of degree correlations on networked traffic dynamics
NASA Astrophysics Data System (ADS)
Sun, Jin-Tu; Wang, Sheng-Jun; Huang, Zi-Gang; Wang, Ying-Hai
2009-08-01
In order to enhance the transport capacity of scale-free networks, we study the relation between the degree correlation and the transport capacity of the network. We calculate the degree-degree correlation coefficient, the maximal betweenness and the critical value of the generating rate Rc (traffic congestion occurs for R>Rc). Numerical experiments indicate that both assortative mixing and disassortative mixing can enhance the transport capacity. We also reveal how the network structure affects the transport capacity. Assortative (disassortative) mixing changes distributions of nodes’ betweennesses, and as a result, the traffic decreases through nodes with the highest degree while it increases through the initially idle nodes.
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. PMID:25932635
Node Survival in Networks under Correlated Attacks.
Hao, Yan; Armbruster, Dieter; Hütt, Marc-Thorsten
2015-01-01
We study the interplay between correlations, dynamics, and networks for repeated attacks on a socio-economic network. As a model system we consider an insurance scheme against disasters that randomly hit nodes, where a node in need receives support from its network neighbors. The model is motivated by gift giving among the Maasai called Osotua. Survival of nodes under different disaster scenarios (uncorrelated, spatially, temporally and spatio-temporally correlated) and for different network architectures are studied with agent-based numerical simulations. We find that the survival rate of a node depends dramatically on the type of correlation of the disasters: Spatially and spatio-temporally correlated disasters increase the survival rate; purely temporally correlated disasters decrease it. The type of correlation also leads to strong inequality among the surviving nodes. We introduce the concept of disaster masking to explain some of the results of our simulations. We also analyze the subsets of the networks that were activated to provide support after fifty years of random disasters. They show qualitative differences for the different disaster scenarios measured by path length, degree, clustering coefficient, and number of cycles.
Fourth-Order Spatial Correlation of Thermal Light
NASA Astrophysics Data System (ADS)
Wen, Feng; Zhang, Xun; Xue, Xin-Xin; Sun, Jia; Song, Jian-Ping; Zhang, Yan-Peng
2014-11-01
We investigate the fourth-order spatial correlation properties of pseudo-thermal light in the photon counting regime, and apply the Klyshko advanced-wave picture to describe the process of four-photon coincidence counting measurement. We deduce the theory of a proof-of-principle four-photon coincidence counting configuration, and find that if the four randomly radiated photons come from the same radiation area and are indistinguishable in principle, the fourth-order correlation of them is 24 times larger than that when four photons come from different radiation areas. In addition, we also show that the higher-order spatial correlation function can be decomposed into multiple lower-order correlation functions, and the contrast and visibility of low-order correlation peaks are less than those of higher orders, while the resolutions all are identical. This study may be useful for better understanding the four-photon interference and multi-channel correlation imaging.
Hidden geometric correlations in real multiplex networks
NASA Astrophysics Data System (ADS)
Kleineberg, Kaj-Kolja; Boguñá, Marián; Ángeles Serrano, M.; Papadopoulos, Fragkiskos
2016-11-01
Real networks often form interacting parts of larger and more complex systems. Examples can be found in different domains, ranging from the Internet to structural and functional brain networks. Here, we show that these multiplex systems are not random combinations of single network layers. Instead, they are organized in specific ways dictated by hidden geometric correlations between the layers. We find that these correlations are significant in different real multiplexes, and form a key framework for answering many important questions. Specifically, we show that these geometric correlations facilitate the definition and detection of multidimensional communities, which are sets of nodes that are simultaneously similar in multiple layers. They also enable accurate trans-layer link prediction, meaning that connections in one layer can be predicted by observing the hidden geometric space of another layer. And they allow efficient targeted navigation in the multilayer system using only local knowledge, outperforming navigation in the single layers only if the geometric correlations are sufficiently strong.
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.
Hidden Neuronal Correlations in Cultured Networks
NASA Astrophysics Data System (ADS)
Segev, Ronen; Baruchi, Itay; Hulata, Eyal; Ben-Jacob, Eshel
2004-03-01
Utilization of a clustering algorithm on neuronal spatiotemporal correlation matrices recorded during a spontaneous activity of in vitro networks revealed the existence of hidden correlations: the sequence of synchronized bursting events (SBEs) is composed of statistically distinguishable subgroups each with its own distinct pattern of interneuron spatiotemporal correlations. These findings hint that each of the SBE subgroups can serve as a template for coding, storage, and retrieval of a specific information.
Structural correlations in bacterial metabolic networks
2011-01-01
Background Evolution of metabolism occurs through the acquisition and loss of genes whose products acts as enzymes in metabolic reactions, and from a presumably simple primordial metabolism the organisms living today have evolved complex and highly variable metabolisms. We have studied this phenomenon by comparing the metabolic networks of 134 bacterial species with known phylogenetic relationships, and by studying a neutral model of metabolic network evolution. Results We consider the 'union-network' of 134 bacterial metabolisms, and also the union of two smaller subsets of closely related species. Each reaction-node is tagged with the number of organisms it belongs to, which we denote organism degree (OD), a key concept in our study. Network analysis shows that common reactions are found at the centre of the network and that the average OD decreases as we move to the periphery. Nodes of the same OD are also more likely to be connected to each other compared to a random OD relabelling based on their occurrence in the real data. This trend persists up to a distance of around five reactions. A simple growth model of metabolic networks is used to investigate the biochemical constraints put on metabolic-network evolution. Despite this seemingly drastic simplification, a 'union-network' of a collection of unrelated model networks, free of any selective pressure, still exhibit similar structural features as their bacterial counterpart. Conclusions The OD distribution quantifies topological properties of the evolutionary history of bacterial metabolic networks, and lends additional support to the importance of horizontal gene transfer during bacterial metabolic evolution where new reactions are attached at the periphery of the network. The neutral model of metabolic network growth can reproduce the main features of real networks, but we observe that the real networks contain a smaller common core, while they are more similar at the periphery of the network. This suggests
Correlated neural variability in persistent state networks.
Polk, Amber; Litwin-Kumar, Ashok; Doiron, Brent
2012-04-17
Neural activity that persists long after stimulus presentation is a biological correlate of short-term memory. Variability in spiking activity causes persistent states to drift over time, ultimately degrading memory. Models of short-term memory often assume that the input fluctuations to neural populations are independent across cells, a feature that attenuates population-level variability and stabilizes persistent activity. However, this assumption is at odds with experimental recordings from pairs of cortical neurons showing that both the input currents and output spike trains are correlated. It remains unclear how correlated variability affects the stability of persistent activity and the performance of cognitive tasks that it supports. We consider the stochastic long-timescale attractor dynamics of pairs of mutually inhibitory populations of spiking neurons. In these networks, persistent activity was less variable when correlated variability was globally distributed across both populations compared with the case when correlations were locally distributed only within each population. Using a reduced firing rate model with a continuum of persistent states, we show that, when input fluctuations are correlated across both populations, they drive firing rate fluctuations orthogonal to the persistent state attractor, thereby causing minimal stochastic drift. Using these insights, we establish that distributing correlated fluctuations globally as opposed to locally improves network's performance on a two-interval, delayed response discrimination task. Our work shows that the correlation structure of input fluctuations to a network is an important factor when determining long-timescale, persistent population spiking activity.
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.
Dynamics on modular networks with heterogeneous correlations
NASA Astrophysics Data System (ADS)
Melnik, Sergey; Porter, Mason A.; Mucha, Peter J.; Gleeson, James P.
2014-06-01
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.
Negative Correlations in Visual Cortical Networks.
Chelaru, Mircea I; Dragoi, Valentin
2016-01-01
The amount of information encoded by cortical circuits depends critically on the capacity of nearby neurons to exhibit trial-to-trial (noise) correlations in their responses. Depending on their sign and relationship to signal correlations, noise correlations can either increase or decrease the population code accuracy relative to uncorrelated neuronal firing. Whereas positive noise correlations have been extensively studied using experimental and theoretical tools, the functional role of negative correlations in cortical circuits has remained elusive. We addressed this issue by performing multiple-electrode recording in the superficial layers of the primary visual cortex (V1) of alert monkey. Despite the fact that positive noise correlations decayed exponentially with the difference in the orientation preference between cells, negative correlations were uniformly distributed across the population. Using a statistical model for Fisher Information estimation, we found that a mild increase in negative correlations causes a sharp increase in network accuracy even when mean correlations were held constant. To examine the variables controlling the strength of negative correlations, we implemented a recurrent spiking network model of V1. We found that increasing local inhibition and reducing excitation causes a decrease in the firing rates of neurons while increasing the negative noise correlations, which in turn increase the population signal-to-noise ratio and network accuracy. Altogether, these results contribute to our understanding of the neuronal mechanism involved in the generation of negative correlations and their beneficial impact on cortical circuit function. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Correlations in star networks: from Bell inequalities to network inequalities
NASA Astrophysics Data System (ADS)
Tavakoli, Armin; Olivier Renou, Marc; Gisin, Nicolas; Brunner, Nicolas
2017-07-01
The problem of characterizing classical and quantum correlations in networks is considered. Contrary to the usual Bell scenario, where distant observers share a physical system emitted by one common source, a network features several independent sources, each distributing a physical system to a subset of observers. In the quantum setting, the observers can perform joint measurements on initially independent systems, which may lead to strong correlations across the whole network. In this work, we introduce a technique to systematically map a Bell inequality to a family of Bell-type inequalities bounding classical correlations on networks in a star-configuration. Also, we show that whenever a given Bell inequality can be violated by some entangled state ρ, then all the corresponding network inequalities can be violated by considering many copies of ρ distributed in the star network. The relevance of these ideas is illustrated by applying our method to a specific multi-setting Bell inequality. We derive the corresponding network inequalities, and study their quantum violations.
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.
Higher order correlation beams in atmosphere under strong turbulence conditions.
Avetisyan, H; Monken, C H
2016-02-08
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.
Degree correlations in signed social networks
NASA Astrophysics Data System (ADS)
Ciotti, Valerio; Bianconi, Ginestra; Capocci, Andrea; Colaiori, Francesca; Panzarasa, Pietro
2015-03-01
We investigate degree correlations in two online social networks where users are connected through different types of links. We find that, while subnetworks in which links have a positive connotation, such as endorsement and trust, are characterized by assortative mixing by degree, networks in which links have a negative connotation, such as disapproval and distrust, are characterized by disassortative patterns. We introduce a class of simple theoretical models to analyze the interplay between network topology and the superimposed structure based on the sign of links. Results uncover the conditions that underpin the emergence of the patterns observed in the data, namely the assortativity of positive subnetworks and the disassortativity of negative ones. We discuss the implications of our study for the analysis of signed complex networks.
AutoCorrel: a neural network event correlation approach
NASA Astrophysics Data System (ADS)
Dondo, Maxwell G.; Japkowicz, Nathalie; Smith, Reuben
2006-04-01
Intrusion detection analysts are often swamped by multitudes of alerts originating from installed intrusion detection systems (IDS) as well as logs from routers and firewalls on the networks. Properly managing these alerts and correlating them to previously seen threats is critical in the ability to effectively protect a network from attacks. Manually correlating events can be a slow tedious task prone to human error. We present a two-stage alert correlation approach involving an artificial neural network (ANN) autoassociator and a single parameter decision threshold-setting unit. By clustering closely matched alerts together, this approach would be beneficial to the analyst. In this approach, alert attributes are extracted from each alert content and used to train an autoassociator. Based on the reconstruction error determined by the autoassociator, closely matched alerts are grouped together. Whenever a new alert is received, it is automatically categorised into one of the alert clusters which identify the type of attack and its severity level as previously known by the analyst. If the attack is entirely new and there is no match to the existing clusters, this would be appropriately reflected to the analyst. There are several advantages to using an ANN based approach. First, ANNs acquire knowledge straight from the data without the need for a human expert to build sets of domain rules and facts. Second, once trained, ANNs can be very fast, accurate and have high precision for near real-time applications. Finally, while learning, ANNs perform a type of dimensionality reduction allowing a user to input large amounts of information without fearing an effciency bottleneck. Thus, rather than storing the data in TCP Quad format (which stores only seven event attributes) and performing a multi-stage query on reduced information, the user can input all the relevant information available and instead allow the neural network to organise and reduce this knowledge in an
Accelerating coordination in temporal networks by engineering the link order
Masuda, Naoki
2016-01-01
Social dynamics on a network may be accelerated or decelerated depending on which pairs of individuals in the network communicate early and which pairs do later. The order with which the links in a given network are sequentially used, which we call the link order, may be a strong determinant of dynamical behaviour on networks, potentially adding a new dimension to effects of temporal networks relative to static networks. Here we study the effect of the link order on linear coordination (i.e., synchronisation) dynamics. We show that the coordination speed considerably depends on specific orders of links. In addition, applying each single link for a long time to ensure strong pairwise coordination before moving to a next pair of individuals does not often enhance coordination of the entire network. We also implement a simple greedy algorithm to optimise the link order in favour of fast coordination. PMID:26916093
Percolation of interdependent networks with degree-correlated inter-connections
NASA Astrophysics Data System (ADS)
Igarashi, Akito; Kuse, Tomohiro
2015-01-01
In interdependent networks, failures of nodes in one constituent network lead nodes in another network to fail. This happens recursively and leads to a cascade of failures. It is known that the interdependent networks with random inter-connections have weaker robustness than the individual networks. However, if the interdependent networks have degree correlations between the networks constructing them as in the actual cases, the robustness of the interdependent networks may be changed. In this paper, we perform numerical simulations on interdependent networks and obtain the giant cluster sizes after the cascade of failures in order to evaluate the robustness. We show that when a interdependent network has a positive degree inter-correlation, it has the stronger robustness than that for the networks with no degree correlation. We show not only the numerical simulation results but theoretical ones for the robustness of the interdependent networks.
NASA Astrophysics Data System (ADS)
Li, Wei; Bashan, Amir; Buldyrev, Sergey; Stanley, Eugene; Havlin, Shlomo
2012-02-01
We study a system composed of two interdependent lattice networks A and B, where nodes in network A depend on a node within a certain shuffling distance r of its corresponding counterpart in network B and vice versa. We find, using numerical simulation that percolation in the two interdependent lattice networks system shows that for small r the phase transition is second order while for larger r it is a first order.
Correlation Filter Synthesis Using Neural Networks.
1993-12-01
distortions, and this approach has clear advantages compared to searching stored filters. I)jL.,i.A I £EJTCMD 3 14. SUBJECT TERMS I NUMBER OF...distortions. They also indicate possible significant advantages compared to searching stored filters. ii 1. INTRODUCTION This section briefly...possible significant advantages compared to searching stored filters. The technical effort on correlation filter synthesis using neural networks was
Tensor Spectral Clustering for Partitioning Higher-order Network Structures.
Benson, Austin R; Gleich, David F; Leskovec, Jure
2015-01-01
Spectral graph theory-based methods represent an important class of tools for studying the structure of networks. Spectral methods are based on a first-order Markov chain derived from a random walk on the graph and thus they cannot take advantage of important higher-order network substructures such as triangles, cycles, and feed-forward loops. Here we propose a Tensor Spectral Clustering (TSC) algorithm that allows for modeling higher-order network structures in a graph partitioning framework. Our TSC algorithm allows the user to specify which higher-order network structures (cycles, feed-forward loops, etc.) should be preserved by the network clustering. Higher-order network structures of interest are represented using a tensor, which we then partition by developing a multilinear spectral method. Our framework can be applied to discovering layered flows in networks as well as graph anomaly detection, which we illustrate on synthetic networks. In directed networks, a higher-order structure of particular interest is the directed 3-cycle, which captures feedback loops in networks. We demonstrate that our TSC algorithm produces large partitions that cut fewer directed 3-cycles than standard spectral clustering algorithms.
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. PMID:27812399
Kinetics of first-order phase transitions with correlated nuclei
NASA Astrophysics Data System (ADS)
Rickman, J. M.; Barmak, K.
2017-02-01
We demonstrate that the time evolution of a first-order phase transition may be described quite generally in terms of the statistics of point processes, thereby providing an intuitive framework for visualizing transition kinetics. A number of attractive and repulsive nucleation scenarios is examined followed by isotropic domain growth at a constant rate This description holds for both uncorrelated and correlated nuclei, and may be employed to calculate the nonequilibrium, n -point spatiotemporal correlations that characterize the transition. Furthermore, it is shown that the interpretation of the one-point function in terms of a stretched-exponential, Kolmogorov-Johnson-Mehl-Avrami result is problematic in the case of correlated nuclei, but that the calculation of higher-order correlation functions permits one to distinguish among various nucleation scenarios.
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).
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.
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.
Dynamic functional network connectivity using distance correlation
NASA Astrophysics Data System (ADS)
Rudas, Jorge; Guaje, Javier; Demertzi, Athena; Heine, Lizette; Tshibanda, Luaba; Soddu, Andrea; Laureys, Steven; Gómez, Francisco
2015-01-01
Investigations about the intrinsic brain organization in resting-state are critical for the understanding of healthy, pathological and pharmacological cerebral states. Recent studies on fMRI suggest that resting state activity is organized on large scale networks of coordinated activity, in the so called, Resting State Networks (RSNs). The assessment of the interactions among these functional networks plays an important role for the understanding of different brain pathologies. Current methods to quantify these interactions commonly assume that the underlying coordination mechanisms are stationary and linear through the whole recording of the resting state phenomena. Nevertheless, recent evidence suggests that rather than stationary, these mechanisms may exhibit a rich set of time-varying repertoires. In addition, these approaches do not consider possible non-linear relationships maybe linked to feed-back communication mechanisms between RSNs. In this work, we introduce a novel approach for dynamical functional network connectivity for functional magnetic resonance imaging (fMRI) resting activity, which accounts for non-linear dynamic relationships between RSNs. The proposed method is based on a windowed distance correlations computed on resting state time-courses extracted at single subject level. We showed that this strategy is complementary to the current approaches for dynamic functional connectivity and will help to enhance the discrimination capacity of patients with disorder of consciousness.
Particulate templates and ordered liquid bridge networks in evaporative lithography.
Vakarelski, Ivan U; Kwek, Jin W; Tang, Xiaosong; O'Shea, Sean J; Chan, Derek Y C
2009-12-01
We investigate the properties of latex particle templates required to optimize the development of ordered liquid bridge networks in evaporative lithography. These networks are key precursors in the assembly of solutions of conducting nanoparticles into large, optically transparent, and conducting microwire networks on substrates (Vakarelski, I. U.; Chan, D. Y. C.; Nonoguchi, T.; Shinto, H.; Higashitani, K. Phys. Rev. Lett., 2009, 102, 058303). An appropriate combination of heat treatment and oxygen plasma etching of a close-packed latex particle monolayer is shown to create open-spaced particle templates which facilitates the formation of ordered fully connected liquid bridge networks that are critical to the formation of ordered microwire networks. Similar results can also be achieved if non-close-packed latex particle templates with square or honeycomb geometries are used. The present results have important implications for the development of the particulate templates to control the morphology of functional microwire networks by evaporative lithography.
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.
Electronic correlations and magnetic ordering in CsC60
NASA Astrophysics Data System (ADS)
Thier, K.-F.; Mehring, M.; Rachdi, F.
1998-08-01
We investigate the spin arrangement in the magnetically ordered orthorhombic phase of CsC60 by a comparison of 133Cs NMR data and simulations of a 3D ordered anisotropic antiferromagnet. Consistent results are obtained for two different magnetic ordering vectors. The agreement between simulation and experiment can be further improved by including a substantial amount of magnetic disorder. In addition, the metastable low temperature cubic phase is investigated using 13C NMR. We find a strongly correlated metallic system that transforms to the semiconducting dimer phase at T=140 K.
Charge ordering and correlation effects in the extended Hubbard model
NASA Astrophysics Data System (ADS)
Terletska, Hanna; Chen, Tianran; Gull, Emanuel
2017-03-01
We study the half-filled extended Hubbard model on a two-dimensional square lattice using cluster dynamical mean-field theory on clusters of size 8-20. We show that the model exhibits metallic, Mott-insulating, and charge-ordered phases, and determine the location of the charge-ordering phase-transition line and the properties of the phases as a function of temperature, local interaction, and nearest-neighbor interaction. We find strong nonlocal correlations outside the charge-ordered phase and a pronounced screening effect in the vicinity of the phase transition, where nonlocal interactions push the system towards metallic behavior. In contrast, correlations in the charge-ordered phase are mostly local to the unit cell. Finally, we demonstrate how strong nonlocal electron-electron interactions can increase electron mobility by turning a charge-ordered insulator into a metal. We analyze finite-size effects and the convergence of our data to the thermodynamic limit. Control of all sources of errors allows us to assess the regime of applicability of simpler approximation schemes for systems with nonlocal interactions.
Correlated stopping, proton clusters and higher order proton cumulants
NASA Astrophysics Data System (ADS)
Bzdak, Adam; Koch, Volker; Skokov, Vladimir
2017-05-01
We investigate possible effects of correlations between stopped nucleons on higher order proton cumulants at low energy heavy-ion collisions. We find that fluctuations of the number of wounded nucleons N_{part} lead to rather nontrivial dependence of the correlations on the centrality; however, this effect is too small to explain the large and positive four-proton correlations found in the preliminary data collected by the STAR collaboration at √{s}=7.7 GeV. We further demonstrate that, by taking into account additional proton clustering, we are able to qualitatively reproduce the preliminary experimental data. We speculate that this clustering may originate either from collective/multi-collision stopping which is expected to be effective at lower energies or from a possible first-order phase transition, or from (attractive) final state interactions. To test these ideas we propose to measure a mixed multi-particle correlation between stopped protons and a produced particle (e.g. pion, antiproton).
Correlated stopping, proton clusters and higher order proton cumulants
Bzdak, Adam; Koch, Volker; Skokov, Vladimir
2017-05-05
Here, we investigate possible effects of correlations between stopped nucleons on higher order proton cumulants at low energy heavy-ion collisions. We find that fluctuations of the number of wounded nucleons Npart lead to rather nontrivial dependence of the correlations on the centrality; however, this effect is too small to explain the large and positive four-proton correlations found in the preliminary data collected by the STAR collaboration at √s = 7.7 GeV. We further demonstrate that, by taking into account additional proton clustering, we are able to qualitatively reproduce the preliminary experimental data. We speculate that this clustering may originate eithermore » from collective/multi-collision stopping which is expected to be effective at lower energies or from a possible first-order phase transition, or from (attractive) final state interactions. To test these ideas we propose to measure a mixed multi-particle correlation between stopped protons and a produced particle (e.g. pion, antiproton).« less
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.
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
NASA Astrophysics Data System (ADS)
Piretzidis, D.; Sra, G.; Sideris, M. G.
2016-12-01
This study explores new methods for identifying correlation errors in harmonic coefficients derived from monthly solutions of the Gravity Recovery and Climate Experiment (GRACE) satellite mission using pattern recognition and neural network algorithms. These correlation errors are evidenced in the differences between monthly solutions and can be suppressed using a de-correlation filter. In all studies so far, the implementation of the de-correlation filter starts from a specific minimum order (i.e., 11 for RL04 and 38 for RL05) until the maximum order of the monthly solution examined. This implementation method has two disadvantages, namely, the omission of filtering correlated coefficients of order less than the minimum order and the filtering of uncorrelated coefficients of order higher than the minimum order. In the first case, the filtered solution is not completely free of correlated errors, whereas the second case results in a monthly solution that suffers from loss of geophysical signal. In the present study, a new method of implementing the de-correlation filter is suggested, by identifying and filtering only the coefficients that show indications of high correlation. Several numerical and geometric properties of the harmonic coefficient series of all orders are examined. Extreme cases of both correlated and uncorrelated coefficients are selected, and their corresponding properties are used to train a two-layer feed-forward neural network. The objective of the neural network is to identify and quantify the correlation by providing the probability of an order of coefficients to be correlated. Results show good performance of the neural network, both in the validation stage of the training procedure and in the subsequent use of the trained network to classify independent coefficients. The neural network is also capable of identifying correlated coefficients even when a small number of training samples and neurons are used (e.g.,100 and 10, respectively).
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.
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
Correlated Dynamics in Egocentric Communication Networks
Karsai, Márton; Kaski, Kimmo; Kertész, János
2012-01-01
We investigate the communication sequences of millions of people through two different channels and analyse the fine grained temporal structure of correlated event trains induced by single individuals. By focusing on correlations between the heterogeneous dynamics and the topology of egocentric networks we find that the bursty trains usually evolve for pairs of individuals rather than for the ego and his/her several neighbours, thus burstiness is a property of the links rather than of the nodes. We compare the directional balance of calls and short messages within bursty trains to the average on the actual link and show that for the trains of voice calls the imbalance is significantly enhanced, while for short messages the balance within the trains increases. These effects can be partly traced back to the technological constraints (for short messages) and partly to the human behavioural features (voice calls). We define a model that is able to reproduce the empirical results and may help us to understand better the mechanisms driving technology mediated human communication dynamics. PMID:22866176
The Dichotomy in Degree Correlation of Biological Networks
Hao, Dapeng; Li, Chuanxing
2011-01-01
Most complex networks from different areas such as biology, sociology or technology, show a correlation on node degree where the possibility of a link between two nodes depends on their connectivity. It is widely believed that complex networks are either disassortative (links between hubs are systematically suppressed) or assortative (links between hubs are enhanced). In this paper, we analyze a variety of biological networks and find that they generally show a dichotomous degree correlation. We find that many properties of biological networks can be explained by this dichotomy in degree correlation, including the neighborhood connectivity, the sickle-shaped clustering coefficient distribution and the modularity structure. This dichotomy distinguishes biological networks from real disassortative networks or assortative networks such as the Internet and social networks. We suggest that the modular structure of networks accounts for the dichotomy in degree correlation and vice versa, shedding light on the source of modularity in biological networks. We further show that a robust and well connected network necessitates the dichotomy of degree correlation, suggestive of an evolutionary motivation for its existence. Finally, we suggest that a dichotomous degree correlation favors a centrally connected modular network, by which the integrity of network and specificity of modules might be reconciled. PMID:22164269
Charge Ordered Insulator without Magnetic Order Studied by Correlator Projection Method
NASA Astrophysics Data System (ADS)
Hanasaki, Kota; Imada, Masatoshi
2005-10-01
The Hubbard model with additional intersite interaction ‘V’ (the extended Hubbard model) is investigated by the correlator projection method (CPM). CPM is a newly developed numerical method that combines the equation-of-motion approach and the dynamical mean-field theory. Using this method, properties of the extended Hubbard Model at quarter filling are discussed with special emphasis on the metal-insulator transition induced by electron-electron correlations. As we increase the interaction, a metal-insulator transition to a charge ordered insulator with antiferromagnetic order occurs at low temperatures, but a metal-insulator transition to a charge ordered insulator without magnetic symmetry breaking occurs at intermediate temperatures. Here, the magnetic order is found to be confined to low temperatures because of the smallness of the exchange coupling Jeff. The present results are in sharp contrast to the Hatree--Fock approximation whereas they are in agreement with the experimental results on quarter-filled materials with strong correlations such as organic BEDT-TTF conductors.
Bayesian model evidence for order selection and correlation testing.
Johnston, Leigh A; Mareels, Iven M Y; Egan, Gary F
2011-01-01
Model selection is a critical component of data analysis procedures, and is particularly difficult for small numbers of observations such as is typical of functional MRI datasets. In this paper we derive two Bayesian evidence-based model selection procedures that exploit the existence of an analytic form for the linear Gaussian model class. Firstly, an evidence information criterion is proposed as a model order selection procedure for auto-regressive models, outperforming the commonly employed Akaike and Bayesian information criteria in simulated data. Secondly, an evidence-based method for testing change in linear correlation between datasets is proposed, which is demonstrated to outperform both the traditional statistical test of the null hypothesis of no correlation change and the likelihood ratio test.
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.
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
Effects of degree correlation on scale-free gradient networks
NASA Astrophysics Data System (ADS)
Pan, Gui-Jun; Yan, Xiao-Qing; Ma, Wei-Chuan; Luo, Yi-Hui; Huang, Zhong-Bing
2010-05-01
We have studied the effects of degree correlation on congestion pressure in scale-free gradient networks. It is observed that the jamming coefficient J is insensitive to the degree correlation coefficient r for assortative and strongly disassortative scale-free networks, and J markedly decreases with an increase in r for weakly disassortative scale-free networks. We have also investigated the effects of degree correlation on the topology structure of scale-free gradient networks, and discussed the relation between the topology structure properties and transport efficiency of gradient networks.
Asymptotic properties of degree-correlated scale-free networks.
Menche, Jörg; Valleriani, Angelo; Lipowsky, Reinhard
2010-04-01
The possible correlation profiles of networks with a given scale-free degree distribution are restricted and bounded by maximally correlated configurations. Dissortative networks consist of nested bilayers, in which low-degree vertices are connected to high-degree vertices. The number of these bilayers attains a constant value for large network size N. Assortative networks exhibit monolayers of low-degree vertices, the number of which grows monotonously with N. Analytical relations for the Pearson correlation coefficient r of these extremal configurations are derived and shown to provide lower and upper bounds on the possible r values. Both bounds are found to vanish for large networks.
Linear correlates in the speech signal: the orderly output constraint.
Sussman, H M; Fruchter, D; Hilbert, J; Sirosh, J
1998-04-01
Neuroethological investigations of mammalian and avian auditory systems have documented species-specific specializations for processing complex acoustic signals that could, if viewed in abstract terms, have an intriguing and striking relevance for human speech sound categorization and representation. Each species forms biologically relevant categories based on combinatorial analysis of information-bearing parameters within the complex input signal. This target article uses known neural models from the mustached bat and barn owl to develop, by analogy, a conceptualization of human processing of consonant plus vowel sequences that offers a partial solution to the noninvariance dilemma--the nontransparent relationship between the acoustic waveform and the phonetic segment. Critical input sound parameters used to establish species-specific categories in the mustached bat and barn owl exhibit high correlation and linearity due to physical laws. A cue long known to be relevant to the perception of stop place of articulation is the second formant (F2) transition. This article describes an empirical phenomenon--the locus equations--that describes the relationship between the F2 of a vowel and the F2 measured at the onset of a consonant-vowel (CV) transition. These variables, F2 onset and F2 vowel within a given place category, are consistently and robustly linearly correlated across diverse speakers and languages, and even under perturbation conditions as imposed by bite blocks. A functional role for this category-level extreme correlation and linearity (the "orderly output constraint") is hypothesized based on the notion of an evolutionarily conserved auditory-processing strategy. High correlation and linearity between critical parameters in the speech signal that help to cue place of articulation categories might have evolved to satisfy a preadaptation by mammalian auditory systems for representing tightly correlated, linearly related components of acoustic signals.
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.
Correlated EEG Signals Simulation Based on Artificial Neural Networks.
Tomasevic, Nikola M; Neskovic, Aleksandar M; Neskovic, Natasa J
2016-09-30
In recent years, simulation of the human electroencephalogram (EEG) data found its important role in medical domain and neuropsychology. In this paper, a novel approach to simulation of two cross-correlated EEG signals is proposed. The proposed method is based on the principles of artificial neural networks (ANN). Contrary to the existing EEG data simulators, the ANN-based approach was leveraged solely on the experimentally acquired EEG data. More precisely, measured EEG data were utilized to optimize the simulator which consisted of two ANN models (each model responsible for generation of one EEG sequence). In order to acquire the EEG recordings, the measurement campaign was carried out on a healthy awake adult having no cognitive, physical or mental load. For the evaluation of the proposed approach, comprehensive quantitative and qualitative statistical analysis was performed considering probability distribution, correlation properties and spectral characteristics of generated EEG processes. The obtained results clearly indicated the satisfactory agreement with the measurement data.
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
Testing quantum mechanics using third-order correlations
NASA Astrophysics Data System (ADS)
Kinsler, Paul
1996-04-01
Semiclassical theories similar to stochastic electrodynamics are widely used in optics. The distinguishing feature of such theories is that the quantum uncertainty is represented by random statistical fluctuations. They can successfully predict some quantum-mechanical phenomena; for example, the squeezing of the quantum uncertainty in the parametric oscillator. However, since such theories are not equivalent to quantum mechanics, they will not always be useful. Complex number representations can be used to exactly model the quantum uncertainty, but care has to be taken that approximations do not reduce the description to a hidden variable one. This paper helps show the limitations of ``semiclassical theories,'' and helps show where a true quantum-mechanical treatment needs to be used. Third-order correlations are a test that provides a clear distinction between quantum and hidden variable theories in a way analogous to that provided by the ``all or nothing'' Greenberger-Horne-Zeilinger test of local hidden variable theories.
Optical-Correlator Neural Network Based On Neocognitron
NASA Technical Reports Server (NTRS)
Chao, Tien-Hsin; Stoner, William W.
1994-01-01
Multichannel optical correlator implements shift-invariant, high-discrimination pattern-recognizing neural network based on paradigm of neocognitron. Selected as basic building block of this neural network because invariance under shifts is inherent advantage of Fourier optics included in optical correlators in general. Neocognitron is conceptual electronic neural-network model for recognition of visual patterns. Multilayer processing achieved by iteratively feeding back output of feature correlator to input spatial light modulator and updating Fourier filters. Neural network trained by use of characteristic features extracted from target images. Multichannel implementation enables parallel processing of large number of selected features.
Optical-Correlator Neural Network Based On Neocognitron
NASA Technical Reports Server (NTRS)
Chao, Tien-Hsin; Stoner, William W.
1994-01-01
Multichannel optical correlator implements shift-invariant, high-discrimination pattern-recognizing neural network based on paradigm of neocognitron. Selected as basic building block of this neural network because invariance under shifts is inherent advantage of Fourier optics included in optical correlators in general. Neocognitron is conceptual electronic neural-network model for recognition of visual patterns. Multilayer processing achieved by iteratively feeding back output of feature correlator to input spatial light modulator and updating Fourier filters. Neural network trained by use of characteristic features extracted from target images. Multichannel implementation enables parallel processing of large number of selected features.
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).
Linear relation on the correlation in complex networks
NASA Astrophysics Data System (ADS)
Ma, C. W.; Szeto, K. Y.
2006-04-01
Correlation in complex networks follows a linear relation between the degree of a node and the total degrees of its neighbors for six different classes of real networks. This general linear relation is an extension of the Aboav-Weaire law in two-dimensional cellular structures and provides a simple and different perspective on the correlation in complex networks, which is complementary to an existing description using Pearson correlation coefficients and a power law fit. Analytical expression for this linear relation for three standard models of complex networks: the Erdos-Renyi, Watts-Strogatz, and Barabasi-Albert networks is provided. The slope and intercept of this linear relation are described by a single parameter a together with the first and second moment of the degree distribution of the network. The assortivity of the network can be related to the sign of the intercept.
Order-Based Representation in Random Networks of Cortical Neurons
Kermany, Einat; Lyakhov, Vladimir; Zrenner, Christoph; Marom, Shimon
2008-01-01
The wide range of time scales involved in neural excitability and synaptic transmission might lead to ongoing change in the temporal structure of responses to recurring stimulus presentations on a trial-to-trial basis. This is probably the most severe biophysical constraint on putative time-based primitives of stimulus representation in neuronal networks. Here we show that in spontaneously developing large-scale random networks of cortical neurons in vitro the order in which neurons are recruited following each stimulus is a naturally emerging representation primitive that is invariant to significant temporal changes in spike times. With a relatively small number of randomly sampled neurons, the information about stimulus position is fully retrievable from the recruitment order. The effective connectivity that makes order-based representation invariant to time warping is characterized by the existence of stations through which activity is required to pass in order to propagate further into the network. This study uncovers a simple invariant in a noisy biological network in vitro; its applicability under in vivo constraints remains to be seen. PMID:19023409
Fractality and degree correlations in scale-free networks
NASA Astrophysics Data System (ADS)
Fujiki, Yuka; Mizutaka, Shogo; Yakubo, Kousuke
2017-07-01
Fractal scale-free networks are empirically known to exhibit disassortative degree mixing. It is, however, not obvious whether a negative degree correlation between nearest neighbor nodes makes a scale-free network fractal. Here we examine the possibility that disassortativity in complex networks is the origin of fractality. To this end, maximally disassortative (MD) networks are prepared by rewiring edges while keeping the degree sequence of an initial uncorrelated scale-free network. We show that there are many MD networks with different topologies if the degree sequence is the same with that of the (u,v)-flower but most of them are not fractal. These results demonstrate that disassortativity does not cause the fractal property of networks. In addition, we suggest that fractality of scale-free networks requires a long-range repulsive correlation, in the sense of the shortest path distance, in similar degrees.
Statistical modelling of higher-order correlations in pools of neural activity
NASA Astrophysics Data System (ADS)
Montani, Fernando; Phoka, Elena; Portesi, Mariela; Schultz, Simon R.
2013-07-01
Simultaneous recordings from multiple neural units allow us to investigate the activity of very large neural ensembles. To understand how large ensembles of neurons process sensory information, it is necessary to develop suitable statistical models to describe the response variability of the recorded spike trains. Using the information geometry framework, it is possible to estimate higher-order correlations by assigning one interaction parameter to each degree of correlation, leading to a (2N-1)-dimensional model for a population with N neurons. However, this model suffers greatly from a combinatorial explosion, and the number of parameters to be estimated from the available sample size constitutes the main intractability reason of this approach. To quantify the extent of higher than pairwise spike correlations in pools of multiunit activity, we use an information-geometric approach within the framework of the extended central limit theorem considering all possible contributions from higher-order spike correlations. The identification of a deformation parameter allows us to provide a statistical characterisation of the amount of higher-order correlations in the case of a very large neural ensemble, significantly reducing the number of parameters, avoiding the sampling problem, and inferring the underlying dynamical properties of the network within pools of multiunit neural activity.
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.
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.
Second-order spatial correlation in the far-field: Comparing entangled and classical light sources
NASA Astrophysics Data System (ADS)
Zhang, Erfeng; Liu, Weitao; Lin, Huizu; Chen, Pingxing
2016-02-01
We consider second-order spatial correlation with entangled and classical light in the far-field. The quantum theory of second-order spatial correlation is analyzed, and the role of photon statistics and detection mode in the second-order spatial correlation are discussed. Meanwhile, the difference of second-order spatial correlation with entangled and classical light sources is deduced.
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.
Comparison of voter and Glauber ordering dynamics on networks.
Castellano, Claudio; Loreto, Vittorio; Barrat, Alain; Cecconi, Federico; Parisi, Domenico
2005-06-01
We study numerically the ordering process of two very simple dynamical models for a two-state variable on several topologies with increasing levels of heterogeneity in the degree distribution. We find that the zero-temperature Glauber dynamics for the Ising model may get trapped in sets of partially ordered metastable states even for finite system size, and this becomes more probable as the size increases. Voter dynamics instead always converges to full order on finite networks, even if this does not occur via coherent growth of domains. The time needed for order to be reached diverges with the system size. In both cases the ordering process is rather insensitive to the variation of the degree distribution from sharply peaked to scale free.
Out-of-Time-Ordered Density Correlators in Luttinger Liquids
NASA Astrophysics Data System (ADS)
Dóra, Balázs; Moessner, Roderich
2017-07-01
Information scrambling and the butterfly effect in chaotic quantum systems can be diagnosed by out-of-time-ordered (OTO) commutators through an exponential growth and large late time value. We show that the latter feature shows up in a strongly correlated many-body system, a Luttinger liquid, whose density fluctuations we study at long and short wavelengths, both in equilibrium and after a quantum quench. We find rich behavior combining robustly universal and nonuniversal features. The OTO commutators display temperature- and initial-state-independent behavior and grow as t2 for short times. For the short-wavelength density operator, they reach a sizable value after the light cone only in an interacting Luttinger liquid, where the bare excitations break up into collective modes. This challenges the common interpretation of the OTO commutator in chaotic systems. We benchmark our findings numerically on an interacting spinless fermion model in 1D and find persistence of central features even in the nonintegrable case. As a nonuniversal feature, the short-time growth exhibits a distance-dependent power.
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.
2010-09-01
designs for Net-Centric Warfare/Net-Centric Operations include directional wireless technology such as free space optical (FSO) or directed radio... Simulating and Testing Dynamic Wireless Networks. Graduate Research Project, AFIT/ENG/IC4-06J. Graduate School of Engineering and Management, Air...Core System 21 TCNO Time Compliance Network Order 14 TCP Transmission Control Protocol 3 TELNET TELetype
Living ordered neural networks as model systems for signal processing
NASA Astrophysics Data System (ADS)
Villard, C.; Amblard, P. O.; Becq, G.; Gory-Fauré, S.; Brocard, J.; Roth, S.
2007-06-01
Neural circuit architecture is a fundamental characteristic of the brain, and how architecture is bound to biological functions is still an open question. Some neuronal geometries seen in the retina or the cochlea are intriguing: information is processed in parallel by several entities like in "pooling" networks which have recently drawn the attention of signal processing scientists. These systems indeed exhibit the noise-enhanced processing effect, which is also actively discussed in the neuroscience community at the neuron scale. The aim of our project is to use in-vitro ordered neuron networks as living paradigms to test ideas coming from the computational science. The different technological bolts that have to be solved are enumerated and the first results are presented. A neuron is a polarised cell, with an excitatory axon and a receiving dendritic tree. We present how soma confinement and axon differentiation can be induced by surface functionalization techniques. The recording of large neuron networks, ordered or not, is also detailed and biological signals shown. The main difficulty to access neural noise in the case of weakly connected networks grown on micro electrode arrays is explained. This open the door to a new detection technology suitable for sub-cellular analysis and stimulation, whose development will constitute the next step of this project.
Effect of correlations on controllability transition in network control
NASA Astrophysics Data System (ADS)
Nie, Sen; Wang, Xu-Wen; Wang, Bing-Hong; Jiang, Luo-Luo
2016-04-01
The network control problem has recently attracted an increasing amount of attention, owing to concerns including the avoidance of cascading failures of power-grids and the management of ecological networks. It has been proven that numerical control can be achieved if the number of control inputs exceeds a certain transition point. In the present study, we investigate the effect of degree correlation on the numerical controllability in networks whose topological structures are reconstructed from both real and modeling systems, and we find that the transition point of the number of control inputs depends strongly on the degree correlation in both undirected and directed networks with moderately sparse links. More interestingly, the effect of the degree correlation on the transition point cannot be observed in dense networks for numerical controllability, which contrasts with the corresponding result for structural controllability. In particular, for directed random networks and scale-free networks, the influence of the degree correlation is determined by the types of correlations. Our approach provides an understanding of control problems in complex sparse networks.
Generalization of Clustering Coefficients to Signed Correlation Networks
Costantini, Giulio; Perugini, Marco
2014-01-01
The recent interest in network analysis applications in personality psychology and psychopathology has put forward new methodological challenges. Personality and psychopathology networks are typically based on correlation matrices and therefore include both positive and negative edge signs. However, some applications of network analysis disregard negative edges, such as computing clustering coefficients. In this contribution, we illustrate the importance of the distinction between positive and negative edges in networks based on correlation matrices. The clustering coefficient is generalized to signed correlation networks: three new indices are introduced that take edge signs into account, each derived from an existing and widely used formula. The performances of the new indices are illustrated and compared with the performances of the unsigned indices, both on a signed simulated network and on a signed network based on actual personality psychology data. The results show that the new indices are more resistant to sample variations in correlation networks and therefore have higher convergence compared with the unsigned indices both in simulated networks and with real data. PMID:24586367
Badger social networks correlate with tuberculosis infection.
Weber, Nicola; Carter, Stephen P; Dall, Sasha R X; Delahay, Richard J; McDonald, Jennifer L; Bearhop, Stuart; McDonald, Robbie A
2013-10-21
Although disease hosts are classically assumed to interact randomly [1], infection is likely to spread across structured and dynamic contact networks [2]. We used social network analyses to investigate contact patterns of group-living European badgers, Meles meles, which are an important wildlife reservoir of bovine tuberculosis (TB). We found that TB test-positive badgers were socially isolated from their own groups but were more important for flow, potentially of infection, between social groups. The distinctive social position of infected badgers may help explain how social stability mitigates, and social perturbation increases, the spread of infection in badgers.
Finding Associations among Histone Modifications Using Sparse Partial Correlation Networks
Lasserre, Julia; Chung, Ho-Ryun; Vingron, Martin
2013-01-01
Histone modifications are known to play an important role in the regulation of transcription. While individual modifications have received much attention in genome-wide analyses, little is known about their relationships. Some authors have built Bayesian networks of modifications, however most often they have used discretized data, and relied on unrealistic assumptions such as the absence of feedback mechanisms or hidden confounding factors. Here, we propose to infer undirected networks based on partial correlations between histone modifications. Within the partial correlation framework, correlations among two variables are controlled for associations induced by the other variables. Partial correlation networks thus focus on direct associations of histone modifications. We apply this methodology to data in CD4+ cells. The resulting network is well supported by common knowledge. When pairs of modifications show a large difference between their correlation and their partial correlation, a potential confounding factor is identified and provided as explanation. Data from different cell types (IMR90, H1) is also exploited in the analysis to assess the stability of the networks. The results are remarkably similar across cell types. Based on this observation, the networks from the three cell types are integrated into a consensus network to increase robustness. The data and the results discussed in the manuscript can be found, together with code, on http://spcn.molgen.mpg.de/index.html. PMID:24039558
Strong correlations and topological order in one-dimensional systems
NASA Astrophysics Data System (ADS)
De Gottardi, Wade Wells
This thesis presents theoretical studies of strongly correlated systems as well as topologically ordered systems in 1D. Non-Fermi liquid behavior characteristic of interacting 1D electron systems is investigated with an emphasis on experimentally relevant setups and observables. The existence of end Majorana fermions in a 1D p-wave superconductor subject to periodic, incommensurate and disordered potentials is studied. The Tomonaga-Luttinger liquid (TLL), a model of interacting electrons in one spatial dimension, is considered in the context of two systems of experimental interest. First, a study of the electronic properties of single-walled armchair carbon nanotubes in the presence of transverse electric and magnetic fields is presented. As a result of their effect on the band structure and electron wave functions, fields alter the nature of the (effective) Coulomb interaction in tubes. In particular, it is found that fields couple to nanotube bands (or valleys), a quantum degree of freedom inherited from the underlying graphene lattice. As revealed by a detailed TLL calculation, it is predicted that fields induce electrons to disperse into their spin, band, and charge components. Fields also provide a means of tuning the shell-filling behavior associated with short tubes. The phenomenon of charge fractionalization is investigated in a one-dimensional ring. TLL theory predicts that momentum-resolved electrons injected into the ring will fractionalize into clockwise- and counterclockwise-moving quasiparticles. As a complement to transport measurements in quantum wires connected to leads, non-invasive measures involving the magnetic field profiles around the ring are proposed. Topological aspects of 1D p-wave superconductors are explored. The intimate connection between non-trivial topology (fermions) and spontaneous symmetry breaking (spins) in one-dimension is investigated. Building on this connection, a spin ladder system endowed with vortex degrees of freedom is
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.
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.
Effective information spreading based on local information in correlated networks
NASA Astrophysics Data System (ADS)
Gao, Lei; Wang, Wei; Pan, Liming; Tang, Ming; Zhang, Hai-Feng
2016-12-01
Using network-based information to facilitate information spreading is an essential task for spreading dynamics in complex networks. Focusing on degree correlated networks, we propose a preferential contact strategy based on the local network structure and local informed density to promote the information spreading. During the spreading process, an informed node will preferentially select a contact target among its neighbors, basing on their degrees or local informed densities. By extensively implementing numerical simulations in synthetic and empirical networks, we find that when only consider the local structure information, the convergence time of information spreading will be remarkably reduced if low-degree neighbors are favored as contact targets. Meanwhile, the minimum convergence time depends non-monotonically on degree-degree correlation, and a moderate correlation coefficient results in the most efficient information spreading. Incorporating the local informed density information into contact strategy, the convergence time of information spreading can be further reduced, and be minimized by an moderately preferential selection.
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.
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
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).
When do correlations increase with firing rates in recurrent networks?
2017-01-01
A central question in neuroscience is to understand how noisy firing patterns are used to transmit information. Because neural spiking is noisy, spiking patterns are often quantified via pairwise correlations, or the probability that two cells will spike coincidentally, above and beyond their baseline firing rate. One observation frequently made in experiments, is that correlations can increase systematically with firing rate. Theoretical studies have determined that stimulus-dependent correlations that increase with firing rate can have beneficial effects on information coding; however, we still have an incomplete understanding of what circuit mechanisms do, or do not, produce this correlation-firing rate relationship. Here, we studied the relationship between pairwise correlations and firing rates in recurrently coupled excitatory-inhibitory spiking networks with conductance-based synapses. We found that with stronger excitatory coupling, a positive relationship emerged between pairwise correlations and firing rates. To explain these findings, we used linear response theory to predict the full correlation matrix and to decompose correlations in terms of graph motifs. We then used this decomposition to explain why covariation of correlations with firing rate—a relationship previously explained in feedforward networks driven by correlated input—emerges in some recurrent networks but not in others. Furthermore, when correlations covary with firing rate, this relationship is reflected in low-rank structure in the correlation matrix. PMID:28448499
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.
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.
Pairwise network information and nonlinear correlations
NASA Astrophysics Data System (ADS)
Martin, Elliot A.; Hlinka, Jaroslav; Davidsen, Jörn
2016-10-01
Reconstructing the structural connectivity between interacting units from observed activity is a challenge across many different disciplines. The fundamental first step is to establish whether or to what extent the interactions between the units can be considered pairwise and, thus, can be modeled as an interaction network with simple links corresponding to pairwise interactions. In principle, this can be determined by comparing the maximum entropy given the bivariate probability distributions to the true joint entropy. In many practical cases, this is not an option since the bivariate distributions needed may not be reliably estimated or the optimization is too computationally expensive. Here we present an approach that allows one to use mutual informations as a proxy for the bivariate probability distributions. This has the advantage of being less computationally expensive and easier to estimate. We achieve this by introducing a novel entropy maximization scheme that is based on conditioning on entropies and mutual informations. This renders our approach typically superior to other methods based on linear approximations. The advantages of the proposed method are documented using oscillator networks and a resting-state human brain network as generic relevant examples.
Pairwise network information and nonlinear correlations.
Martin, Elliot A; Hlinka, Jaroslav; Davidsen, Jörn
2016-10-01
Reconstructing the structural connectivity between interacting units from observed activity is a challenge across many different disciplines. The fundamental first step is to establish whether or to what extent the interactions between the units can be considered pairwise and, thus, can be modeled as an interaction network with simple links corresponding to pairwise interactions. In principle, this can be determined by comparing the maximum entropy given the bivariate probability distributions to the true joint entropy. In many practical cases, this is not an option since the bivariate distributions needed may not be reliably estimated or the optimization is too computationally expensive. Here we present an approach that allows one to use mutual informations as a proxy for the bivariate probability distributions. This has the advantage of being less computationally expensive and easier to estimate. We achieve this by introducing a novel entropy maximization scheme that is based on conditioning on entropies and mutual informations. This renders our approach typically superior to other methods based on linear approximations. The advantages of the proposed method are documented using oscillator networks and a resting-state human brain network as generic relevant examples.
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
The correlation of metrics in complex networks with applications in functional brain networks
NASA Astrophysics Data System (ADS)
Li, C.; Wang, H.; de Haan, W.; Stam, C. J.; Van Mieghem, P.
2011-11-01
An increasing number of network metrics have been applied in network analysis. If metric relations were known better, we could more effectively characterize networks by a small set of metrics to discover the association between network properties/metrics and network functioning. In this paper, we investigate the linear correlation coefficients between widely studied network metrics in three network models (Bárabasi-Albert graphs, Erdös-Rényi random graphs and Watts-Strogatz small-world graphs) as well as in functional brain networks of healthy subjects. The metric correlations, which we have observed and theoretically explained, motivate us to propose a small representative set of metrics by including only one metric from each subset of mutually strongly dependent metrics. The following contributions are considered important. (a) A network with a given degree distribution can indeed be characterized by a small representative set of metrics. (b) Unweighted networks, which are obtained from weighted functional brain networks with a fixed threshold, and Erdös-Rényi random graphs follow a similar degree distribution. Moreover, their metric correlations and the resultant representative metrics are similar as well. This verifies the influence of degree distribution on metric correlations. (c) Most metric correlations can be explained analytically. (d) Interestingly, the most studied metrics so far, the average shortest path length and the clustering coefficient, are strongly correlated and, thus, redundant. Whereas spectral metrics, though only studied recently in the context of complex networks, seem to be essential in network characterizations. This representative set of metrics tends to both sufficiently and effectively characterize networks with a given degree distribution. In the study of a specific network, however, we have to at least consider the representative set so that important network properties will not be neglected.
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.
Universal quantum computation with ordered spin-chain networks
Tserkovnyak, Yaroslav; Loss, Daniel
2011-09-15
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 {pi}/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.
Network motifs come in sets: correlations in the randomization process.
Ginoza, Reid; Mugler, Andrew
2010-07-01
The identification of motifs--subgraphs that appear significantly more often in a particular network than in an ensemble of randomized networks--has become a ubiquitous method for uncovering potentially important subunits within networks drawn from a wide variety of fields. We find that the most common algorithms used to generate the ensemble from the real network change subgraph counts in a highly correlated manner, such that one subgraph's status as a motif may not be independent from the statuses of the other subgraphs. We demonstrate this effect for the problem of three- and four-node motif identification in the transcriptional regulatory networks of E. coli and S. cerevisiae in which randomized networks are generated via an edge-swapping algorithm. We find strong correlations among subgraph counts; for three-node subgraphs these correlations are easily interpreted, and we present an information-theoretic tool that may be used to identify correlations among subgraphs of any size. Our results suggest that single-feature statistics such as Z scores that implicitly assume independence among subgraph counts constitute an insufficient summary of the network.
Traffic-driven epidemic spreading in correlated networks
NASA Astrophysics Data System (ADS)
Yang, Han-Xin; Tang, Ming; Lai, Ying-Cheng
2015-06-01
In spite of the extensive previous efforts on traffic dynamics and epidemic spreading in complex networks, the problem of traffic-driven epidemic spreading on correlated networks has not been addressed. Interestingly, we find that the epidemic threshold, a fundamental quantity underlying the spreading dynamics, exhibits a nonmonotonic behavior in that it can be minimized for some critical value of the assortativity coefficient, a parameter characterizing the network correlation. To understand this phenomenon, we use the degree-based mean-field theory to calculate the traffic-driven epidemic threshold for correlated networks. The theory predicts that the threshold is inversely proportional to the packet-generation rate and the largest eigenvalue of the betweenness matrix. We obtain consistency between theory and numerics. Our results may provide insights into the important problem of controlling and/or harnessing real-world epidemic spreading dynamics driven by traffic flows.
Neighbor-Neighbor Correlations Explain Measurement Bias in Networks.
Wu, Xin-Zeng; Percus, Allon G; Lerman, Kristina
2017-07-17
In numerous physical models on networks, dynamics are based on interactions that exclusively involve properties of a node's nearest neighbors. However, a node's local view of its neighbors may systematically bias perceptions of network connectivity or the prevalence of certain traits. We investigate the strong friendship paradox, which occurs when the majority of a node's neighbors have more neighbors than does the node itself. We develop a model to predict the magnitude of the paradox, showing that it is enhanced by negative correlations between degrees of neighboring nodes. We then show that by including neighbor-neighbor correlations, which are degree correlations one step beyond those of neighboring nodes, we accurately predict the impact of the strong friendship paradox in real-world networks. Understanding how the paradox biases local observations can inform better measurements of network structure and our understanding of collective phenomena.
Covariance, correlation matrix, and the multiscale community structure of networks.
Shen, Hua-Wei; Cheng, Xue-Qi; Fang, Bin-Xing
2010-07-01
Empirical studies show that real world networks often exhibit multiple scales of topological descriptions. However, it is still an open problem how to identify the intrinsic multiple scales of networks. In this paper, we consider detecting the multiscale community structure of network from the perspective of dimension reduction. According to this perspective, a covariance matrix of network is defined to uncover the multiscale community structure through the translation and rotation transformations. It is proved that the covariance matrix is the unbiased version of the well-known modularity matrix. We then point out that the translation and rotation transformations fail to deal with the heterogeneous network, which is very common in nature and society. To address this problem, a correlation matrix is proposed through introducing the rescaling transformation into the covariance matrix. Extensive tests on real world and artificial networks demonstrate that the correlation matrix significantly outperforms the covariance matrix, identically the modularity matrix, as regards identifying the multiscale community structure of network. This work provides a novel perspective to the identification of community structure and thus various dimension reduction methods might be used for the identification of community structure. Through introducing the correlation matrix, we further conclude that the rescaling transformation is crucial to identify the multiscale community structure of network, as well as the translation and rotation transformations.
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. PMID:25310101
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.
Nonlocal correlations between separated neural networks
NASA Astrophysics Data System (ADS)
Pizzi, Rita; Fantasia, Andrea; Gelain, Fabrizio; Rossetti, Danilo; Vescovi, Angelo
2004-08-01
In recent times the interest for quantum models of brain activity has rapidly grown. The Penrose-Hameroff model assumes that microtubules inside neurons are responsible for quantum computation inside brain. Several experiments seem to indicate that EPR-like correlations are possible at the biological level. In the past year , a very intensive experimental work about this subject has been done at DiBit Labs in Milan, Italy by our research group. Our experimental set-up is made by two separated and completely shielded basins where two parts of a common human DNA neuronal culture are monitored by EEG. Our main experimental result is that, under stimulation of one culture by means of a 630 nm laser beam at 300 ms, the cross-correlation between the two cultures grows up at maximum levels. Despite at this level of understanding it is impossible to tell if the origin of this non-locality is a genuine quantum effect, our experimental data seem to strongly suggest that biological systems present non-local properties not explainable by classical models.
Dynamics on networks: competition of temporal and topological correlations
NASA Astrophysics Data System (ADS)
Artime, Oriol; Ramasco, José J.; San Miguel, Maxi
2017-02-01
Links in many real-world networks activate and deactivate in correspondence to the sporadic interactions between the elements of the system. The activation patterns may be irregular or bursty and play an important role on the dynamics of processes taking place in the network. Information or disease spreading in networks are paradigmatic examples of this situation. Besides burstiness, several correlations may appear in the process of link activation: memory effects imply temporal correlations, but also the existence of communities in the network may mediate the activation patterns of internal an external links. Here we study the competition of topological and temporal correlations in link activation and how they affect the dynamics of systems running on the network. Interestingly, both types of correlations by separate have opposite effects: one (topological) delays the dynamics of processes on the network, while the other (temporal) accelerates it. When they occur together, our results show that the direction and intensity of the final outcome depends on the competition in a non trivial way.
Dynamics on networks: competition of temporal and topological correlations
Artime, Oriol; Ramasco, José J.; San Miguel, Maxi
2017-01-01
Links in many real-world networks activate and deactivate in correspondence to the sporadic interactions between the elements of the system. The activation patterns may be irregular or bursty and play an important role on the dynamics of processes taking place in the network. Information or disease spreading in networks are paradigmatic examples of this situation. Besides burstiness, several correlations may appear in the process of link activation: memory effects imply temporal correlations, but also the existence of communities in the network may mediate the activation patterns of internal an external links. Here we study the competition of topological and temporal correlations in link activation and how they affect the dynamics of systems running on the network. Interestingly, both types of correlations by separate have opposite effects: one (topological) delays the dynamics of processes on the network, while the other (temporal) accelerates it. When they occur together, our results show that the direction and intensity of the final outcome depends on the competition in a non trivial way. PMID:28150708
Network motifs come in sets: Correlations in the randomization process
NASA Astrophysics Data System (ADS)
Ginoza, Reid; Mugler, Andrew
2010-07-01
The identification of motifs—subgraphs that appear significantly more often in a particular network than in an ensemble of randomized networks—has become a ubiquitous method for uncovering potentially important subunits within networks drawn from a wide variety of fields. We find that the most common algorithms used to generate the ensemble from the real network change subgraph counts in a highly correlated manner, such that one subgraph’s status as a motif may not be independent from the statuses of the other subgraphs. We demonstrate this effect for the problem of three- and four-node motif identification in the transcriptional regulatory networks of E. coli and S. cerevisiae in which randomized networks are generated via an edge-swapping algorithm. We find strong correlations among subgraph counts; for three-node subgraphs these correlations are easily interpreted, and we present an information-theoretic tool that may be used to identify correlations among subgraphs of any size. Our results suggest that single-feature statistics such as Z scores that implicitly assume independence among subgraph counts constitute an insufficient summary of the network.
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.
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
NASA Astrophysics Data System (ADS)
Sugimoto, Toshiki; Aiga, Norihiro; Otsuki, Yuji; Watanabe, Kazuya; Matsumoto, Yoshiyasu
2016-11-01
Materials containing strong correlation and frustration have the potential to respond to external perturbations in an unusual way. In the case of common water ice, protons in the hydrogen-bond network are strongly correlated and highly frustrated under Pauling's ice rules. At low temperature, the strongly correlated protons lose ergodicity, and little is understood about the cooperative thermodynamic and electric response to external stimuli. Here, using a model platinum substrate, we demonstrate emergent high-Tc ferroelectric proton ordering in a heteroepitaxial ice film. Such proton ordering is thermodynamically stable and has an extremely high critical temperature of ~175 K. We found that anisotropy and protolysis driven by the electrostatistics at the heterointerface are key factors in stimulating this novel exotic ordering in the many-body correlated proton system. The significant increase in Tc due to the heterointerface suggests the ubiquity of ferroelectric ice in nature--specifically, in space and the polar stratosphere.
A generative spike train model with time-structured higher order correlations.
Trousdale, James; Hu, Yu; Shea-Brown, Eric; Josić, Krešimir
2013-01-01
Emerging technologies are revealing the spiking activity in ever larger neural ensembles. Frequently, this spiking is far from independent, with correlations in the spike times of different cells. Understanding how such correlations impact the dynamics and function of neural ensembles remains an important open problem. Here we describe a new, generative model for correlated spike trains that can exhibit many of the features observed in data. Extending prior work in mathematical finance, this generalized thinning and shift (GTaS) model creates marginally Poisson spike trains with diverse temporal correlation structures. We give several examples which highlight the model's flexibility and utility. For instance, we use it to examine how a neural network responds to highly structured patterns of inputs. We then show that the GTaS model is analytically tractable, and derive cumulant densities of all orders in terms of model parameters. The GTaS framework can therefore be an important tool in the experimental and theoretical exploration of neural dynamics.
A generative spike train model with time-structured higher order correlations
Trousdale, James; Hu, Yu; Shea-Brown, Eric; Josić, Krešimir
2013-01-01
Emerging technologies are revealing the spiking activity in ever larger neural ensembles. Frequently, this spiking is far from independent, with correlations in the spike times of different cells. Understanding how such correlations impact the dynamics and function of neural ensembles remains an important open problem. Here we describe a new, generative model for correlated spike trains that can exhibit many of the features observed in data. Extending prior work in mathematical finance, this generalized thinning and shift (GTaS) model creates marginally Poisson spike trains with diverse temporal correlation structures. We give several examples which highlight the model's flexibility and utility. For instance, we use it to examine how a neural network responds to highly structured patterns of inputs. We then show that the GTaS model is analytically tractable, and derive cumulant densities of all orders in terms of model parameters. The GTaS framework can therefore be an important tool in the experimental and theoretical exploration of neural dynamics. PMID:23908626
Nonlinear Transfer of Signal and Noise Correlations in Cortical Networks
Lyamzin, Dmitry R.; Barnes, Samuel J.; Donato, Roberta; Garcia-Lazaro, Jose A.; Keck, Tara
2015-01-01
Signal and noise correlations, a prominent feature of cortical activity, reflect the structure and function of networks during sensory processing. However, in addition to reflecting network properties, correlations are also shaped by intrinsic neuronal mechanisms. Here we show that spike threshold transforms correlations by creating nonlinear interactions between signal and noise inputs; even when input noise correlation is constant, spiking noise correlation varies with both the strength and correlation of signal inputs. We characterize these effects systematically in vitro in mice and demonstrate their impact on sensory processing in vivo in gerbils. We also find that the effects of nonlinear correlation transfer on cortical responses are stronger in the synchronized state than in the desynchronized state, and show that they can be reproduced and understood in a model with a simple threshold nonlinearity. Since these effects arise from an intrinsic neuronal property, they are likely to be present across sensory systems and, thus, our results are a critical step toward a general understanding of how correlated spiking relates to the structure and function of cortical networks. PMID:26019325
Neural correlates of serial order effect in verbal divergent thinking.
Wang, Meijuan; Hao, Ning; Ku, Yixuan; Grabner, Roland H; Fink, Andreas
2017-05-01
During the course of divergent thinking (DT), the number of generated ideas decreases while the originality of ideas increases. This phenomenon is labeled as serial order effect in DT. The present study investigated whether different executive processes (i.e., updating, shifting, and inhibition) specifically contribute to the serial order effect in DT. Participants' executive functions were measured by corresponding experimental tasks outside of the EEG lab. They were required to generate original uses of conventional objects (alternative uses task) during EEG recording. The behavioral results revealed that the originality of ideas was higher in later stage of DT (i.e., Epoch 2) than in its earlier stage (i.e., Epoch 1) for higher-shifting individuals, but showed no difference between two epochs for lower-shifting individuals. The EEG results revealed that lower-inhibition individuals showed stronger upper alpha (10-13Hz) synchronization in left frontal areas during Epoch 1 compared to during Epoch 2. For higher-inhibition individuals, no changes in upper alpha activity from Epoch 1 to Epoch 2 were found. These findings indicated that shifting and inhibition contributed to create a serial order effect in DT, perhaps because individuals suppress interference from obvious ideas and switch to new idea categories during DT, thus more original ideas appear as time passes by. Copyright © 2017 Elsevier Ltd. All rights reserved.
Reducing Network Depth in the Cascade-Correlation Learning Architecture,
1994-10-17
all of these experiments are shown in Table 2. The percentage of correct values for the vowel set are computed in a non-standard manner. The percent...34 in Neural Networks, Vol. 1. p 75-89. Hertz , J., Krogh, A, & Palmer, G. (1993) Introduction to the Theory of Neural Computation , Addison-Wesley... Computer Science Reducing Network Depth in the Cascade- Correlation Learning Architecture Shumeet Baluja & Scott E. FahIman October 17, 1994 CMU-CS
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.
Canonical correlation between LFP network and spike network during working memory task in rat.
Yi, Hu; Zhang, Xiaofan; Bai, Wenwen; Liu, Tiaotiao; Tian, Xin
2015-08-01
Working memory refers to a system to temporary holding and manipulation of information. Previous studies suggested that local field potentials (LFPs) and spikes as well as their coordination provide potential mechanism of working memory. Popular methods for LFP-spike coordination only focus on the two modality signals, isolating each channel from multi-channel data, ignoring the entirety of the networked brain. Therefore, we investigated the coordination between the LFP network and spike network to achieve a better understanding of working memory. Multi-channel LFPs and spikes were simultaneously recorded in rat prefrontal cortex via microelectrode array during a Y-maze working memory task. Functional connectivity in the LFP network and spike network was respectively estimated by the directed transfer function (DTF) and maximum likelihood estimation (MLE). Then the coordination between the two networks was quantified via canonical correlation analysis (CCA). The results show that the canonical correlation (CC) varied during the working memory task. The CC-curve peaked before the choice point, describing the coordination between LFP network and spike network enhanced greatly. The CC value in working memory showed a significant higher level than inter-trial interval. Our results indicate that the enhanced canonical correlation between the LFP network and spike network may provide a potential network integration mechanism for working memory.
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…
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…
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.
Optimal gene partition into operons correlates with gene functional order
NASA Astrophysics Data System (ADS)
Zaslaver, Alon; Mayo, Avi; Ronen, Michal; Alon, Uri
2006-09-01
Gene arrangement into operons varies between bacterial species. Genes in a given system can be on one operon in some organisms and on several operons in other organisms. Existing theories explain why genes that work together should be on the same operon, since this allows for advantageous lateral gene transfer and accurate stoichiometry. But what causes the frequent separation into multiple operons of co-regulated genes that act together in a pathway? Here we suggest that separation is due to benefits made possible by differential regulation of each operon. We present a simple mathematical model for the optimal distribution of genes into operons based on a balance of the cost of operons and the benefit of regulation that provides 'just-when-needed' temporal order. The analysis predicts that genes are arranged such that genes on the same operon do not skip functional steps in the pathway. This prediction is supported by genomic data from 137 bacterial genomes. Our work suggests that gene arrangement is not only the result of random historical drift, genome re-arrangement and gene transfer, but has elements that are solutions of an evolutionary optimization problem. Thus gene functional order may be inferred by analyzing the operon structure across different genomes.
Inhibitory control of correlated intrinsic variability in cortical networks
Stringer, Carsen; Pachitariu, Marius; Steinmetz, Nicholas A; Okun, Michael; Bartho, Peter; Harris, Kenneth D; Sahani, Maneesh; Lesica, Nicholas A
2016-01-01
Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different rodent species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the diverse activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations. DOI: http://dx.doi.org/10.7554/eLife.19695.001 PMID:27926356
Regularized negative correlation learning for neural network ensembles.
Chen, Huanhuan; Yao, Xin
2009-12-01
Negative correlation learning (NCL) is a neural network ensemble learning algorithm that introduces a correlation penalty term to the cost function of each individual network so that each neural network minimizes its mean square error (MSE) together with the correlation of the ensemble. This paper analyzes NCL and reveals that the training of NCL (when lambda = 1) corresponds to training the entire ensemble as a single learning machine that only minimizes the MSE without regularization. This analysis explains the reason why NCL is prone to overfitting the noise in the training set. This paper also demonstrates that tuning the correlation parameter lambda in NCL by cross validation cannot overcome the overfitting problem. The paper analyzes this problem and proposes the regularized negative correlation learning (RNCL) algorithm which incorporates an additional regularization term for the whole ensemble. RNCL decomposes the ensemble's training objectives, including MSE and regularization, into a set of sub-objectives, and each sub-objective is implemented by an individual neural network. In this paper, we also provide a Bayesian interpretation for RNCL and provide an automatic algorithm to optimize regularization parameters based on Bayesian inference. The RNCL formulation is applicable to any nonlinear estimator minimizing the MSE. The experiments on synthetic as well as real-world data sets demonstrate that RNCL achieves better performance than NCL, especially when the noise level is nontrivial in the data set.
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.
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.
Correlations between Community Structure and Link Formation in Complex Networks
Liu, Zhen; He, Jia-Lin; Kapoor, Komal; Srivastava, Jaideep
2013-01-01
Background Links in complex networks commonly represent specific ties between pairs of nodes, such as protein-protein interactions in biological networks or friendships in social networks. However, understanding the mechanism of link formation in complex networks is a long standing challenge for network analysis and data mining. Methodology/Principal Findings Links in complex networks have a tendency to cluster locally and form so-called communities. This widely existed phenomenon reflects some underlying mechanism of link formation. To study the correlations between community structure and link formation, we present a general computational framework including a theory for network partitioning and link probability estimation. Our approach enables us to accurately identify missing links in partially observed networks in an efficient way. The links having high connection likelihoods in the communities reveal that links are formed preferentially to create cliques and accordingly promote the clustering level of the communities. The experimental results verify that such a mechanism can be well captured by our approach. Conclusions/Significance Our findings provide a new insight into understanding how links are created in the communities. The computational framework opens a wide range of possibilities to develop new approaches and applications, such as community detection and missing link prediction. PMID:24039818
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.
Effective information spreading based on local information in correlated networks
Gao, Lei; Wang, Wei; Pan, Liming; Tang, Ming; Zhang, Hai-Feng
2016-01-01
Using network-based information to facilitate information spreading is an essential task for spreading dynamics in complex networks. Focusing on degree correlated networks, we propose a preferential contact strategy based on the local network structure and local informed density to promote the information spreading. During the spreading process, an informed node will preferentially select a contact target among its neighbors, basing on their degrees or local informed densities. By extensively implementing numerical simulations in synthetic and empirical networks, we find that when only consider the local structure information, the convergence time of information spreading will be remarkably reduced if low-degree neighbors are favored as contact targets. Meanwhile, the minimum convergence time depends non-monotonically on degree-degree correlation, and a moderate correlation coefficient results in the most efficient information spreading. Incorporating the local informed density information into contact strategy, the convergence time of information spreading can be further reduced, and be minimized by an moderately preferential selection. PMID:27910882
Correlated gene expression supports synchronous activity in brain networks
Richiardi, Jonas; Altmann, Andre; Milazzo, Anna-Clare; Chang, Catie; Chakravarty, M. Mallar; Banaschewski, Tobias; Barker, Gareth J.; Bokde, Arun L.W.; Bromberg, Uli; Büchel, Christian; Conrod, Patricia; Fauth-Bühler, Mira; Flor, Herta; Frouin, Vincent; Gallinat, Jürgen; Garavan, Hugh; Gowland, Penny; Heinz, Andreas; Lemaître, Hervé; Mann, Karl F.; Martinot, Jean-Luc; Nees, Frauke; Paus, Tomáš; Pausova, Zdenka; Rietschel, Marcella; Robbins, Trevor W.; Smolka, Michael N.; Spanagel, Rainer; Ströhle, Andreas; Schumann, Gunter; Hawrylycz, Mike; Poline, Jean-Baptiste; Greicius, Michael D.
2016-01-01
During rest, brain activity is synchronized between different regions widely distributed throughout the brain, forming functional networks. However, the molecular mechanisms supporting functional connectivity remain undefined. We show that functional brain networks defined with resting-state functional magnetic resonance imaging can be recapitulated by using measures of correlated gene expression in a post mortem brain tissue data set. The set of 136 genes we identify is significantly enriched for ion channels. Polymorphisms in this set of genes significantly affect resting-state functional connectivity in a large sample of healthy adolescents. Expression levels of these genes are also significantly associated with axonal connectivity in the mouse. The results provide convergent, multimodal evidence that resting-state functional networks correlate with the orchestrated activity of dozens of genes linked to ion channel activity and synaptic function. PMID:26068849
Evolutionary optimization of network reconstruction from derivative-variable correlations
NASA Astrophysics Data System (ADS)
Leguia, Marc G.; Andrzejak, Ralph G.; Levnajić, Zoran
2017-08-01
Topologies of real-world complex networks are rarely accessible, but can often be reconstructed from experimentally obtained time series via suitable network reconstruction methods. Extending our earlier work on methods based on statistics of derivative-variable correlations, we here present a new method built on integrating an evolutionary optimization algorithm into the derivative-variable correlation method. Results obtained from our modification of the method in general outperform the original results, demonstrating the suitability of evolutionary optimization logic in network reconstruction problems. We show the method’s usefulness in realistic scenarios where the reconstruction precision can be limited by the nature of the time series. We also discuss important limitations coming from various dynamical regimes that time series can belong to.
Experimental characterization of a quantum many-body system via higher-order correlations
NASA Astrophysics Data System (ADS)
Schweigler, Thomas; Kasper, Valentin; Erne, Sebastian; Mazets, Igor; Rauer, Bernhard; Cataldini, Federica; Langen, Tim; Gasenzer, Thomas; Berges, Jürgen; Schmiedmayer, Jörg
2017-05-01
Quantum systems can be characterized by their correlations. Higher-order (larger than second order) correlations, and the ways in which they can be decomposed into correlations of lower order, provide important information about the system, its structure, its interactions and its complexity. The measurement of such correlation functions is therefore an essential tool for reading, verifying and characterizing quantum simulations. Although higher-order correlation functions are frequently used in theoretical calculations, so far mainly correlations up to second order have been studied experimentally. Here we study a pair of tunnel-coupled one-dimensional atomic superfluids and characterize the corresponding quantum many-body problem by measuring correlation functions. We extract phase correlation functions up to tenth order from interference patterns and analyse whether, and under what conditions, these functions factorize into correlations of lower order. This analysis characterizes the essential features of our system, the relevant quasiparticles, their interactions and topologically distinct vacua. From our data we conclude that in thermal equilibrium our system can be seen as a quantum simulator of the sine-Gordon model, relevant for diverse disciplines ranging from particle physics to condensed matter. The measurement and evaluation of higher-order correlation functions can easily be generalized to other systems and to study correlations of any other observable such as density, spin and magnetization. It therefore represents a general method for analysing quantum many-body systems from experimental data.
NASA Astrophysics Data System (ADS)
Donev, Aleksandar; Torquato, Salvatore; Stillinger, Frank H.
2005-01-01
We study the approach to jamming in hard-sphere packings and, in particular, the pair correlation function g2(r) around contact, both theoretically and computationally. Our computational data unambiguously separate the narrowing δ -function contribution to g2 due to emerging interparticle contacts from the background contribution due to near contacts. The data also show with unprecedented accuracy that disordered hard-sphere packings are strictly isostatic: i.e., the number of exact contacts in the jamming limit is exactly equal to the number of degrees of freedom, once rattlers are removed. For such isostatic packings, we derive a theoretical connection between the probability distribution of interparticle forces Pf(f) , which we measure computationally, and the contact contribution to g2 . We verify this relation for computationally generated isostatic packings that are representative of the maximally random jammed state. We clearly observe a maximum in Pf and a nonzero probability of zero force, shedding light on long-standing questions in the granular-media literature. We computationally observe an unusual power-law divergence in the near-contact contribution to g2 , persistent even in the jamming limit, with exponent -0.4 clearly distinguishable from previously proposed inverse-square-root divergence. Additionally, we present high-quality numerical data on the two discontinuities in the split-second peak of g2 and use a shared-neighbor analysis of the graph representing the contact network to study the local particle clusters responsible for the peculiar features. Finally, we present the computational data on the contact contribution to g2 for vacancy-diluted fcc crystal packings and also investigate partially crystallized packings along the transition from maximally disordered to fully ordered packings. We find that the contact network remains isostatic even when ordering is present. Unlike previous studies, we find that ordering has a significant impact on
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.
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.
Synchronization, quantum correlations and entanglement in oscillator networks
Manzano, Gonzalo; Galve, Fernando; Giorgi, Gian Luca; Hernández-García, Emilio; Zambrini, Roberta
2013-01-01
Synchronization is one of the paradigmatic phenomena in the study of complex systems. It has been explored theoretically and experimentally mostly to understand natural phenomena, but also in view of technological applications. Although several mechanisms and conditions for synchronous behavior in spatially extended systems and networks have been identified, the emergence of this phenomenon has been largely unexplored in quantum systems until very recently. Here we discuss synchronization in quantum networks of different harmonic oscillators relaxing towards a stationary state, being essential the form of dissipation. By local tuning of one of the oscillators, we establish the conditions for synchronous dynamics, in the whole network or in a motif. Beyond the classical regime we show that synchronization between (even unlinked) nodes witnesses the presence of quantum correlations and entanglement. Furthermore, synchronization and entanglement can be induced between two different oscillators if properly linked to a random network. PMID:23486526
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.
Effects of degree correlations on the explosive synchronization of scale-free networks.
Sendiña-Nadal, I; Leyva, I; Navas, A; Villacorta-Atienza, J A; Almendral, J A; Wang, Z; Boccaletti, S
2015-03-01
We study the organization of finite-size, large ensembles of phase oscillators networking via scale-free topologies in the presence of a positive correlation between the oscillators' natural frequencies and the network's degrees. Under those circumstances, abrupt transitions to synchronization are known to occur in growing scale-free networks, while the transition has a completely different nature for static random configurations preserving the same structure-dynamics correlation. We show that the further presence of degree-degree correlations in the network structure has important consequences on the nature of the phase transition characterizing the passage from the phase-incoherent to the phase-coherent network state. While high levels of positive and negative mixing consistently induce a second-order phase transition, moderate values of assortative mixing, such as those ubiquitously characterizing social networks in the real world, greatly enhance the irreversible nature of explosive synchronization in scale-free networks. The latter effect corresponds to a maximization of the area and of the width of the hysteretic loop that differentiates the forward and backward transitions to synchronization.
Dynes, J F; Yuan, Z L; Sharpe, A W; Thomas, O; Shields, A J
2011-07-04
We demonstrate the use of two high speed avalanche photodiodes in exploring higher order photon correlations. By employing the photon number resolving capability of the photodiodes the response to higher order photon coincidences can be measured. As an example we show experimentally the sensitivity to higher order correlations for three types of photon sources with distinct photon statistics. This higher order correlation technique could be used as a low cost and compact tool for quantifying the degree of correlation of photon sources employed in quantum information science.
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.
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.
Detecting Distributed Network Traffic Anomaly with Network-Wide Correlation Analysis
NASA Astrophysics Data System (ADS)
Zonglin, Li; Guangmin, Hu; Xingmiao, Yao; Dan, Yang
2008-12-01
Distributed network traffic anomaly refers to a traffic abnormal behavior involving many links of a network and caused by the same source (e.g., DDoS attack, worm propagation). The anomaly transiting in a single link might be unnoticeable and hard to detect, while the anomalous aggregation from many links can be prevailing, and does more harm to the networks. Aiming at the similar features of distributed traffic anomaly on many links, this paper proposes a network-wide detection method by performing anomalous correlation analysis of traffic signals' instantaneous parameters. In our method, traffic signals' instantaneous parameters are firstly computed, and their network-wide anomalous space is then extracted via traffic prediction. Finally, an anomaly is detected by a global correlation coefficient of anomalous space. Our evaluation using Abilene traffic traces demonstrates the excellent performance of this approach for distributed traffic anomaly detection.
Rangaprakash, D; Hu, Xiaoping; Deshpande, Gopikrishna
2013-04-01
It is increasingly being recognized that resting state brain connectivity derived from functional magnetic resonance imaging (fMRI) data is an important marker of brain function both in healthy and clinical populations. Though linear correlation has been extensively used to characterize brain connectivity, it is limited to detecting first order dependencies. In this study, we propose a framework where in phase synchronization (PS) between brain regions is characterized using a new metric "correlation between probabilities of recurrence" (CPR) and subsequent graph-theoretic analysis of the ensuing networks. We applied this method to resting state fMRI data obtained from human subjects with and without administration of propofol anesthetic. Our results showed decreased PS during anesthesia and a biologically more plausible community structure using CPR rather than linear correlation. We conclude that CPR provides an attractive nonparametric method for modeling interactions in brain networks as compared to standard correlation for obtaining physiologically meaningful insights about brain function.
Shimazaki, Hideaki; Amari, Shun-Ichi; Brown, Emery N; Grün, Sonja
2012-01-01
Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods
Time-dependent cross-correlations between different stock returns: a directed network of influence.
Kullmann, L; Kertész, J; Kaski, K
2002-08-01
We study the time-dependent cross-correlations of stock returns, i.e., we measure the correlation as the function of the time shift between pairs of stock return time series using tick-by-tick data. We find a weak but significant effect showing that in many cases the maximum correlation appears at nonzero time shift, indicating directions of influence between the companies. Due to the weakness of this effect and the shortness of the characteristic time (of the order of a few minutes), our findings are compatible with market efficiency. The interaction of companies defines a directed network of influence.
Early Age-Related Functional Connectivity Decline in High-Order Cognitive Networks
Siman-Tov, Tali; Bosak, Noam; Sprecher, Elliot; Paz, Rotem; Eran, Ayelet; Aharon-Peretz, Judith; Kahn, Itamar
2017-01-01
As the world ages, it becomes urgent to unravel the mechanisms underlying brain aging and find ways of intervening with them. While for decades cognitive aging has been related to localized brain changes, growing attention is now being paid to alterations in distributed brain networks. Functional connectivity magnetic resonance imaging (fcMRI) has become a particularly useful tool to explore large-scale brain networks; yet, the temporal course of connectivity lifetime changes has not been established. Here, an extensive cross-sectional sample (21–85 years old, N = 887) from a public fcMRI database was used to characterize adult lifespan connectivity dynamics within and between seven brain networks: the default mode, salience, dorsal attention, fronto-parietal control, auditory, visual and motor networks. The entire cohort was divided into young (21–40 years, mean ± SD: 25.5 ± 4.8, n = 543); middle-aged (41–60 years, 50.6 ± 5.4, n = 238); and old (61 years and above, 69.0 ± 6.3, n = 106) subgroups. Correlation matrices as well as a mixed model analysis of covariance indicated that within high-order cognitive networks a considerable connectivity decline is already evident by middle adulthood. In contrast, a motor network shows increased connectivity in middle adulthood and a subsequent decline. Additionally, alterations in inter-network interactions are noticeable primarily in the transition between young and middle adulthood. These results provide evidence that aging-related neural changes start early in adult life. PMID:28119599
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.
Inferring gene correlation networks from transcription factor binding sites.
Mahdevar, Ghasem; Nowzari-Dalini, Abbas; Sadeghi, Mehdi
2013-01-01
Gene expression is a highly regulated biological process that is fundamental to the existence of phenotypes of any living organism. The regulatory relations are usually modeled as a network; simply, every gene is modeled as a node and relations are shown as edges between two related genes. This paper presents a novel method for inferring correlation networks, networks constructed by connecting co-expressed genes, through predicting co-expression level from genes promoter's sequences. According to the results, this method works well on biological data and its outcome is comparable to the methods that use microarray as input. The method is written in C++ language and is available upon request from the corresponding author.
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
Cortical Networks Involved in Memory for Temporal Order.
Manelis, Anna; Popov, Vencislav; Paynter, Christopher; Walsh, Matthew; Wheeler, Mark E; Vogt, Keith M; Reder, Lynne M
2017-03-15
We examined the neurobiological basis of temporal resetting, an aspect of temporal order memory, using a version of the delayed-match-to-multiple-sample task. While in an fMRI scanner, participants evaluated whether an item was novel or whether it had appeared before or after a reset event that signified the start of a new block of trials. Participants responded "old" to items that were repeated within the current block and "new" to both novel items and items that had last appeared before the reset event (pseudonew items). Medial-temporal, prefrontal, and occipital regions responded to absolute novelty of the stimulus-they differentiated between novel items and previously seen items, but not between old and pseudonew items. Activation for pseudonew items in the frontopolar and parietal regions, in contrast, was intermediate between old and new items. The posterior cingulate cortex extending to precuneus was the only region that showed complete temporal resetting, and its activation reflected whether an item was new or old according to the task instructions regardless of its familiarity. There was also a significant Condition (old/pseudonew) × Familiarity (second/third presentations) interaction effect on behavioral and neural measures. For pseudonew items, greater familiarity decreased response accuracy, increased RTs, increased ACC activation, and increased functional connectivity between ACC and the left frontal pole. The reverse was observed for old items. On the basis of these results, we propose a theoretical framework in which temporal resetting relies on an episodic retrieval network that is modulated by cognitive control and conflict resolution.
Response of degree-correlated scale-free networks to stimuli.
Wang, Sheng-Jun; Wu, An-Cai; Wu, Zhi-Xi; Xu, Xin-Jian; Wang, Ying-Hai
2007-04-01
The response of degree-correlated scale-free attractor networks to stimuli is studied. We show that degree-correlated scale-free networks are robust to random stimuli as well as the uncorrelated scale-free networks, while assortative (disassortative) scale-free networks are more (less) sensitive to directed stimuli than uncorrelated networks. We find that the degree correlation of scale-free networks makes the dynamics of attractor systems different from uncorrelated ones. The dynamics of correlated scale-free attractor networks results in the effects of degree correlation on the response to stimuli.
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.
Yang, Jin-Ju; Kwon, Hunki; Lee, Jong-Min
2016-05-26
Morphometric correlation networks of cortical thickness, surface area, and gray matter volume have statistically different structural topology. However, there is no report directly describing their correlation patterns in view of interregional covariance. Here, we examined the characteristics of the correlation patterns in three morphometric networks of cortical thickness, surface area, and gray matter volume using a Venn diagram concept across 314 normal subjects. We found that over 60% of all nonoverlapping correlation patterns emerged with divergent unique patterns, while there were 10% of all common edges in ipsilateral and homotopic regions among the three morphometric correlation networks. It was also found that the network parameters of the three networks were different. Our findings showed that correlation patterns of the network itself can provide complementary information when compared with network properties. We demonstrate that morphometric correlation networks of distinct structural phenotypes have different correlation patterns and different network properties. This finding implies that the topology of each morphometric correlation network may reflect different aspects of each morphometric descriptor.
Electrophysiological correlates of attention networks in childhood and early adulthood.
Abundis-Gutiérrez, Alicia; Checa, Purificación; Castellanos, Concepción; Rosario Rueda, M
2014-05-01
Attention has been related to functions of alerting, orienting, and executive control, which are associated with distinct brain networks. This study aimed at understanding the neural mechanisms underlying the development of attention functions during childhood. A total of 46 healthy 4-13-year-old children and 15 adults performed an adapted version of the Attention Network Task (ANT) while brain activation was registered with a high-density EEG system. Performance of the ANT revealed changes in the efficiency of attention networks across ages. While no differences were observed on the alerting score, both orienting and executive attention scores showed a more protracted developmental curve. Further, age-related differences in brain activity were mostly observed in early ERP components. Young children had poorer early processing of warning cues compared to 10-13-year-olds and adults, as shown by an immature auditory-evoked potential complex elicited by warning tones. Also, 4-6-year-olds exhibited a poorer processing of orienting cues as indexed by lack of modulation of the N1. Finally, flanker congruency produced earlier modulation of ERPs amplitude with age. Flanker congruency effects were delayed and more anteriorly distributed for young children, compared to adults who showed a clear modulation of the N2 in fronto-parietal channels. Additionally, interactions among attention networks were examined. Both alerting and orienting conditions modulated the effectiveness of conflict processing by the executive attention network. The Orienting×Executive networks interactions was only observed after about age 7. Results are informative of the neural correlates of the development of attention networks in childhood.
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.
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.
2015-01-13
We performed synchrotron X-ray diffraction and neutron scattering studies on As-Se glasses in two states: as-prepared (rejuvenated) and aged for similar to 27 years. The first sharp diffraction peak (FSDP) obtained from the structure factor data as a function of composition and temperature indicates that the cooperative processes that are responsible for structural relaxation do not affect FSDP. The results are correlated with the composition dependence of the complex heat capacity of the glasses and concentration of different structural fragments in the glass network. Furthermore, the comparison of structural information shows that density fluctuations, which were thought previously to have a significant contribution to FSDP, have much smaller effect than the cation-cation correlations, presence of ordered structural fragments or cage molecules.
Medium range order and structural relaxation in As–Se network glasses through FSDP analysis
Golovchak, R.; Lucas, P.; Oelgoetz, J.; ...
2015-01-13
We performed synchrotron X-ray diffraction and neutron scattering studies on As-Se glasses in two states: as-prepared (rejuvenated) and aged for similar to 27 years. The first sharp diffraction peak (FSDP) obtained from the structure factor data as a function of composition and temperature indicates that the cooperative processes that are responsible for structural relaxation do not affect FSDP. The results are correlated with the composition dependence of the complex heat capacity of the glasses and concentration of different structural fragments in the glass network. Furthermore, the comparison of structural information shows that density fluctuations, which were thought previously to havemore » a significant contribution to FSDP, have much smaller effect than the cation-cation correlations, presence of ordered structural fragments or cage molecules.« less
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.
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.
Weak Higher-order Interactions in Macroscopic Functional Networks of the Resting Brain.
Huang, Xuhui; Xu, Kaibin; Chu, Congying; Jiang, Tianzi; Yu, Shan
2017-09-26
Interactions among different brain regions are usually examined through functional connectivity (FC) analysis, which is exclusively based on measuring pairwise correlations in activities. However, interactions beyond the pairwise level, i.e., higher-order interactions (HOIs), are vital in understanding the behavior of many complex systems. So far whether HOIs exist among brain regions and how they can affect brain's activities remain largely elusive. To address these issues, here we analyzed blood oxygenation level-dependent (BOLD) signals recorded from six typical macroscopic functional networks of the brain in 100 human subjects (46 males and 54 females) during the resting state. Through examining the binarized BOLD signals, we found that HOIs within and across individual networks were both very weak, regardless of the network size, topology, degree of spatial proximity, spatial scales and whether the global signal was regressed or not. To investigate the potential mechanisms underlying the weak HOIs, we analyzed the dynamics of a network model, and also found that HOIs were generally weak within a wide range of key parameters, provided that the overall dynamic feature of the model was similar to the empirical data and it was operating close to a linear fluctuation regime. Taken together, our results suggest that weak HOI may be a general property of brain's macroscopic functional networks, which implies the dominance of pairwise interactions in shaping brain activities at such a scale and warrants the validity of widely used pairwise-based FC approaches.SIGNIFICANCE STATEMENTTo explain how activities of different brain areas are coordinated through interactions is essential to reveal the mechanisms underlying various brain functions. Traditionally, such an interaction structure is commonly studied by using pairwise-based functional network analyses. It is unclear whether the interactions beyond the pairwise level (higher-order interactions or HOIs) play any role
Correlation and network analysis of global financial indices.
Kumar, Sunil; Deo, Nivedita
2012-08-01
Random matrix theory (RMT) and network methods are applied to investigate the correlation and network properties of 20 financial indices. The results are compared before and during the financial crisis of 2008. In the RMT method, the components of eigenvectors corresponding to the second largest eigenvalue form two clusters of indices in the positive and negative directions. The components of these two clusters switch in opposite directions during the crisis. The network analysis uses the Fruchterman-Reingold layout to find clusters in the network of indices at different thresholds. At a threshold of 0.6, before the crisis, financial indices corresponding to the Americas, Europe, and Asia-Pacific form separate clusters. On the other hand, during the crisis at the same threshold, the American and European indices combine together to form a strongly linked cluster while the Asia-Pacific indices form a separate weakly linked cluster. If the value of the threshold is further increased to 0.9 then the European indices (France, Germany, and the United Kingdom) are found to be the most tightly linked indices. The structure of the minimum spanning tree of financial indices is more starlike before the crisis and it changes to become more chainlike during the crisis. The average linkage hierarchical clustering algorithm is used to find a clearer cluster structure in the network of financial indices. The cophenetic correlation coefficients are calculated and found to increase significantly, which indicates that the hierarchy increases during the financial crisis. These results show that there is substantial change in the structure of the organization of financial indices during a financial crisis.
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.
Out-of-time-order correlation at a quantum phase transition
NASA Astrophysics Data System (ADS)
Shen, Huitao; Zhang, Pengfei; Fan, Ruihua; Zhai, Hui
2017-08-01
Motivated by the recent studies of out-of-time-order correlation functions and the holographic duality, we propose the quantum critical point conjecture, which is stated as: For a many-body quantum system with a quantum phase transition, the Lyapunov exponent extracted from the out-of-time-order correlators will exhibit a maximum around the quantum critical region. We first demonstrate that the Lyapunov exponent is well defined in the one-dimensional Bose-Hubbard model with the help of the out-of-time-order correlation-Rényi-entropy theorem. We then support the conjecture by numerically computing the out-of-time-order correlators. We also compute the butterfly velocity, and propose an experiment protocol of measuring this correlator without inverting the Hamiltonian.
A universal order parameter for synchrony in networks of limit cycle oscillators
NASA Astrophysics Data System (ADS)
Schröder, Malte; Timme, Marc; Witthaut, Dirk
2017-07-01
We analyze the properties of order parameters measuring synchronization and phase locking in complex oscillator networks. First, we review network order parameters previously introduced and reveal several shortcomings: none of the introduced order parameters capture all transitions from incoherence over phase locking to full synchrony for arbitrary, finite networks. We then introduce an alternative, universal order parameter that accurately tracks the degree of partial phase locking and synchronization, adapting the traditional definition to account for the network topology and its influence on the phase coherence of the oscillators. We rigorously prove that this order parameter is strictly monotonously increasing with the coupling strength in the phase locked state, directly reflecting the dynamic stability of the network. Furthermore, it indicates the onset of full phase locking by a diverging slope at the critical coupling strength. The order parameter may find applications across systems where different types of synchrony are possible, including biological networks and power grids.
Spin correlations in percolating networks with fractal geometry
Ikeda, H.; Iwasa, K.; Fernandez-Baca, J.A.; Nicklow, R.M.
1994-07-28
Using neutron scattering techniques, the authors investigated the magnetic correlations in diluted antiferromagnets close to the percolation threshold in which the magnetic connectivity takes a fractal form. Recent experimental results concerning the self-similarity of the magnetic order, and magnetic excitations in two-dimensional Ising and three-dimensional Heisenberg antiferromagnets are presented.
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.
Environmental Response Laboratory Network (ERLN) Basic Ordering Agreement Example
A BOA is a written instrument of understanding between EPA and a laboratory that contains terms and clauses applying to all future orders, a description of services to be provided, and methods for pricing, issuing, and delivering future orders.
An Analytical Model for the Three-Point Third-Order Velocity Correlation in Isotropic Turbulence
NASA Astrophysics Data System (ADS)
Chang, Henry; Moser, Robert
2006-11-01
In turbulent flows, the three-point third-order velocity correlation Tijk(r,r') =
Staude, Benjamin; Grün, Sonja; Rotter, Stefan
2009-01-01
The extent to which groups of neurons exhibit higher-order correlations in their spiking activity is a controversial issue in current brain research. A major difficulty is that currently available tools for the analysis of massively parallel spike trains (N >10) for higher-order correlations typically require vast sample sizes. While multiple single-cell recordings become increasingly available, experimental approaches to investigate the role of higher-order correlations suffer from the limitations of available analysis techniques. We have recently presented a novel method for cumulant-based inference of higher-order correlations (CuBIC) that detects correlations of higher order even from relatively short data stretches of length T = 10–100 s. CuBIC employs the compound Poisson process (CPP) as a statistical model for the population spike counts, and assumes spike trains to be stationary in the analyzed data stretch. In the present study, we describe a non-stationary version of the CPP by decoupling the correlation structure from the spiking intensity of the population. This allows us to adapt CuBIC to time-varying firing rates. Numerical simulations reveal that the adaptation corrects for false positive inference of correlations in data with pure rate co-variation, while allowing for temporal variations of the firing rates has a surprisingly small effect on CuBICs sensitivity for correlations. PMID:20725510
Socioeconomic correlations and stratification in social-communication networks.
Leo, Yannick; Fleury, Eric; Alvarez-Hamelin, J Ignacio; Sarraute, Carlos; Karsai, Márton
2016-12-01
The uneven distribution of wealth and individual economic capacities are among the main forces, which shape modern societies and arguably bias the emerging social structures. However, the study of correlations between the social network and economic status of individuals is difficult due to the lack of large-scale multimodal data disclosing both the social ties and economic indicators of the same population. Here, we close this gap through the analysis of coupled datasets recording the mobile phone communications and bank transaction history of one million anonymized individuals living in a Latin American country. We show that wealth and debt are unevenly distributed among people in agreement with the Pareto principle; the observed social structure is strongly stratified, with people being better connected to others of their own socioeconomic class rather than to others of different classes; the social network appears to have assortative socioeconomic correlations and tightly connected 'rich clubs'; and that individuals from the same class live closer to each other but commute further if they are wealthier. These results are based on a representative, society-large population, and empirically demonstrate some long-lasting hypotheses on socioeconomic correlations, which potentially lay behind social segregation, and induce differences in human mobility.
Correlated grain-boundary distributions in two-dimensional networks.
Mason, Jeremy K; Schuh, Christopher A
2007-07-01
In polycrystals, there are spatial correlations in grain-boundary species, even in the absence of correlations in the grain orientations, due to the need for crystallographic consistency among misorientations. Although this consistency requirement substantially influences the connectivity of grain-boundary networks, the nature of the resulting correlations are generally only appreciated in an empirical sense. Here a rigorous treatment of this problem is presented for a model two-dimensional polycrystal with uncorrelated grain orientations or, equivalently, a cross section through a three-dimensional polycrystal in which each grain shares a common crystallographic direction normal to the plane of the network. The distribution of misorientations theta, boundary inclinations phi and the joint distribution of misorientations about a triple junction are derived for arbitrary crystal symmetry and orientation distribution functions of the grains. From these, general analytical solutions for the fraction of low-angle boundaries and the triple-junction distributions within the same subset of systems are found. The results agree with existing analysis of a few specific cases in the literature but present a significant generalization.
CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains
Rotter, Stefan; Grün, Sonja
2009-01-01
Recent developments in electrophysiological and optical recording techniques enable the simultaneous observation of large numbers of neurons. A meaningful interpretation of the resulting multivariate data, however, presents a serious challenge. In particular, the estimation of higher-order correlations that characterize the cooperative dynamics of groups of neurons is impeded by the combinatorial explosion of the parameter space. The resulting requirements with respect to sample size and recording time has rendered the detection of coordinated neuronal groups exceedingly difficult. Here we describe a novel approach to infer higher-order correlations in massively parallel spike trains that is less susceptible to these problems. Based on the superimposed activity of all recorded neurons, the cumulant-based inference of higher-order correlations (CuBIC) presented here exploits the fact that the absence of higher-order correlations imposes also strong constraints on correlations of lower order. Thus, estimates of only few lower-order cumulants suffice to infer higher-order correlations in the population. As a consequence, CuBIC is much better compatible with the constraints of in vivo recordings than previous approaches, which is shown by a systematic analysis of its parameter dependence. PMID:19862611
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.
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.
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. Copyright © 2015 Elsevier Ltd. All rights reserved.
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.
On the accuracy of second-order Møller-Plesset correlation energies
NASA Astrophysics Data System (ADS)
Flores, J. R.
1997-05-01
Accurate second-order Møller-Plesset correlation energies are computed and compared with several semi-empirical estimates of the total correlation energies including those provided by Clementi, Anno and Teruya, and the recent results of Davidson, Froese and co-workers, for atoms with ten, twelve and eighteen electrons. Somewhat surprisingly, the MP2 correlation energies present what is considered to be in good agreement with the newest estimates, especially when the behaviour with the nuclear charge is examined.
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.
Ordering spatiotemporal chaos in complex thermosensitive neuron networks
NASA Astrophysics Data System (ADS)
Gong, Yubing; Xu, Bo; Xu, Qiang; Yang, Chuanlu; Ren, Tingqi; Hou, Zhonghuai; Xin, Houwen
2006-04-01
We have studied the effect of random long-range connections in chaotic thermosensitive neuron networks with each neuron being capable of exhibiting diverse bursting behaviors, and found stochastic synchronization and optimal spatiotemporal patterns. For a given coupling strength, the chaotic burst-firings of the neurons become more and more synchronized as the number of random connections (or randomness) is increased and, rather, the most pronounced spatiotemporal pattern appears for an optimal randomness. As the coupling strength is increased, the optimal randomness shifts towards a smaller strength. This result shows that random long-range connections can tame the chaos in the neural networks and make the neurons more effectively reach synchronization. Since the model studied can be used to account for hypothalamic neurons of dogfish, catfish, etc., this result may reflect the significant role of random connections in transferring biological information.
Modeling Genetic Regulatory Networks Using First-Order Probabilistic Logic
2013-03-01
Researchers can target specific genes or proteins in signal transduction pathways to cure a number of diseases and disorders, with applications in...medication and drug delivery. Genetic Regulatory Networks (GRNs) represent the signal transduction, or the activation and deactivation of genes , as...intelligence, prolog, gene regulation, “Raf” pathway 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF PAGES 28 19a
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.
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. Copyright © 2014 Elsevier Ltd. All rights reserved.
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.
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.
ERIC Educational Resources Information Center
Sen, Anindya; Clemente, Anthony
2010-01-01
We exploit the 1986, 1994, and 2001 waves of the Canadian general social surveys in order to estimate intergenerational correlations in education. The use of these specific data is important because of available information on the final educational attainment of survey respondents and both parents, as well as family size and birth order. OLS…
ERIC Educational Resources Information Center
Sen, Anindya; Clemente, Anthony
2010-01-01
We exploit the 1986, 1994, and 2001 waves of the Canadian general social surveys in order to estimate intergenerational correlations in education. The use of these specific data is important because of available information on the final educational attainment of survey respondents and both parents, as well as family size and birth order. OLS…
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. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.
Correlation of coming limit price with order book in stock markets
NASA Astrophysics Data System (ADS)
Maskawa, Jun-ichi
2007-09-01
We examine the correlation of the limit price with the order book, when a limit order comes. We analyzed the Rebuild Order Book of Stock Exchange Electronic Trading Service, which is the centralized order book market of London Stock Exchange. As a result, the limit price is broadly distributed around the best price according to a power-law, and it is not randomly drawn from the distribution, but has a strong correlation with the size of cumulative unexecuted limit orders on the price. It was also found that the limit price, on the coarse-grained price scale, tends to gather around the price which has a large size of cumulative unexecuted limit orders.
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.
ON THE ORIGIN OF ANISOTROPY IN MAGNETOHYDRODYNAMIC TURBULENCE: THE ROLE OF HIGHER-ORDER CORRELATIONS
Oughton, Sean; Wan Minping; Matthaeus, William H.; Servidio, Sergio
2013-05-01
Evolution of magnetohydrodynamic turbulence is often discussed in terms of second-order statistics like the energy spectra, but consideration of the structure of the von Karman-Howarth hierarchy for MHD indicates that higher-order statistical correlations occupy an influential role. Here we show that both spectral anisotropy and energy decay are strongly associated with higher-order statistics. Dynamical emergence of spectral anisotropy must occur at a higher order in the statistical hierarchy, while numerical evidence suggests that strong variations in energy decay are connected with variations in higher-order statistics.
A general fractional-order dynamical network: synchronization behavior and state tuning.
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.
A unified view on weakly correlated recurrent networks
Grytskyy, Dmytro; Tetzlaff, Tom; Diesmann, Markus; Helias, Moritz
2013-01-01
The diversity of neuron models used in contemporary theoretical neuroscience to investigate specific properties of covariances in the spiking activity raises the question how these models relate to each other. In particular it is hard to distinguish between generic properties of covariances and peculiarities due to the abstracted model. Here we present a unified view on pairwise covariances in recurrent networks in the irregular regime. We consider the binary neuron model, the leaky integrate-and-fire (LIF) model, and the Hawkes process. We show that linear approximation maps each of these models to either of two classes of linear rate models (LRM), including the Ornstein–Uhlenbeck process (OUP) as a special case. The distinction between both classes is the location of additive noise in the rate dynamics, which is located on the output side for spiking models and on the input side for the binary model. Both classes allow closed form solutions for the covariance. For output noise it separates into an echo term and a term due to correlated input. The unified framework enables us to transfer results between models. For example, we generalize the binary model and the Hawkes process to the situation with synaptic conduction delays and simplify derivations for established results. Our approach is applicable to general network structures and suitable for the calculation of population averages. The derived averages are exact for fixed out-degree network architectures and approximate for fixed in-degree. We demonstrate how taking into account fluctuations in the linearization procedure increases the accuracy of the effective theory and we explain the class dependent differences between covariances in the time and the frequency domain. Finally we show that the oscillatory instability emerging in networks of LIF models with delayed inhibitory feedback is a model-invariant feature: the same structure of poles in the complex frequency plane determines the population power
Shimazaki, Hideaki; Amari, Shun-ichi; Brown, Emery N.; Grün, Sonja
2012-01-01
Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods
Ordered bimetallic coordination networks featuring rare earth and silver cations.
Merkens, Carina; Englert, Ulli
2012-04-21
Three lanthanide complexes of the ditopic ligand 3-cyanopentane-2,4-dionate (acacCN) have been synthesized and structurally characterized. Longer intermolecular contacts result in ninefold coordination of the cation in Ce(acacCN)(3)(H(2)O)(2), whereas mononuclear complexes of the same stoichiometry with coordination number eight are obtained for the smaller Eu(III) and Yb(III) cations. Reaction of these labile compounds with AgPF(6) leads to re-organization of the coordination sphere of the rare earth cations: neutral extended structures are formed in which the peripheric -CN moieties of Ln(acacCN)(4) anions coordinate to silver cations. The initially formed heterometallic networks show additional coordination of water or inclusion of solvent molecules; three different structure types, two of them as isomorphous pairs, have been characterized. In the case of Eu(III) and Yb(III), these solids are instable when stored in their mother liquor and undergo a slow aging process, finally resulting in phase pure stable and solvent-free 3D networks Ln(acacCN)(4)Ag.
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.
A unified data representation theory for network visualization, ordering and coarse-graining.
Kovács, István A; Mizsei, Réka; Csermely, Péter
2015-09-08
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
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.
Out-of-time-ordered correlators and purity in rational conformal field theories
NASA Astrophysics Data System (ADS)
Caputa, Paweł; Numasawa, Tokiro; Veliz-Osorio, Alvaro
2016-11-01
In this paper we investigate measures of chaos and entanglement in rational conformal field theories in 1 + 1 dimensions. First, we derive a formula for the late time value of the out-of-time-ordered correlators for this class of theories. Our universal result can be expressed as a particular combination of the modular S-matrix elements known as anyon monodromy scalar. Next, in the explicit setup of an SUN Wess-Zumino-Witten model, we compare the late time behavior of the out-of-time-ordered correlators and the purity. Interestingly, in the large-c limit, the purity grows logarithmically as in holographic theories; in contrast, the out-of-time-ordered correlators remain, in general, nonvanishing.
Büttner, Kathrin; Salau, Jennifer; Krieter, Joachim
2016-01-01
The average topological overlap of two graphs of two consecutive time steps measures the amount of changes in the edge configuration between the two snapshots. This value has to be zero if the edge configuration changes completely and one if the two consecutive graphs are identical. Current methods depend on the number of nodes in the network or on the maximal number of connected nodes in the consecutive time steps. In the first case, this methodology breaks down if there are nodes with no edges. In the second case, it fails if the maximal number of active nodes is larger than the maximal number of connected nodes. In the following, an adaption of the calculation of the temporal correlation coefficient and of the topological overlap of the graph between two consecutive time steps is presented, which shows the expected behaviour mentioned above. The newly proposed adaption uses the maximal number of active nodes, i.e. the number of nodes with at least one edge, for the calculation of the topological overlap. The three methods were compared with the help of vivid example networks to reveal the differences between the proposed notations. Furthermore, these three calculation methods were applied to a real-world network of animal movements in order to detect influences of the network structure on the outcome of the different methods.
Neipert, Christine; Space, Brian
2006-12-14
Sum vibrational frequency spectroscopy, a second order optical process, is interface specific in the dipole approximation. At charged interfaces, there exists a static field, and as a direct consequence, the experimentally detected signal is a combination of enhanced second and static field induced third order contributions. There is significant evidence in the literature of the importance/relative magnitude of this third order contribution, but no previous molecularly detailed approach existed to separately calculate the second and third order contributions. Thus, for the first time, a molecularly detailed time correlation function theory is derived here that allows for the second and third order contributions to sum frequency vibrational spectra to be individually determined. Further, a practical, molecular dynamics based, implementation procedure for the derived correlation functions that describe the third order phenomenon is also presented. This approach includes a novel generalization of point atomic polarizability models to calculate the hyperpolarizability of a molecular system. The full system hyperpolarizability appears in the time correlation functions responsible for third order contributions in the presence of a static field.
Emergence of morphological order in the network formation of Physarum polycephalum.
Shirakawa, T; Gunji, Y-P
2007-07-01
Emergence in a system appears through the interaction of its components, giving rise to higher order or complexity in the system. We tested for the presence of emergent properties in a biological system using the simplest biological entity of a unicellular organism; the plasmodium of Physarum polycephalum, a giant unicellular amoeboid organism that forms a network-like tubular structure connecting its food sources. We let two plasmodium networks within a single cell interact with each other, and observed how the intracellular interaction affected the morphologenesis of the plasmodium networks. We found that the two networks developed homologous morphology. We further discuss the presence of autonomous and emergent properties in homologous network formation.
Magnetic Reversal of an Artificial Square Ice: Dipolar Correlation and Charge Ordering
Stein A.; Morgan J.P.; Langridge S.; Marrows C.H.
2011-10-13
Magnetic reversal of an artificial square ice pattern subject to a sequence of magnetic fields applied slightly off the diagonal axis is investigated via magnetic force microscopy of the remanent states that result. Sublattice independent reversal is observed via correlated incrementally pinned flip cascades along parallel dipolar chains, as evident from analysis of vertex populations and dipolar correlation functions. Weak dipolar interactions between adjacent chains favour antialignment and give rise to weak charge ordering of 'monopole' vertices during the reversal process.
LMI Conditions for Global Stability of Fractional-Order Neural Networks.
Zhang, Shuo; Yu, Yongguang; Yu, Junzhi
2017-10-01
Fractional-order neural networks play a vital role in modeling the information processing of neuronal interactions. It is still an open and necessary topic for fractional-order neural networks to investigate their global stability. This paper proposes some simplified linear matrix inequality (LMI) stability conditions for fractional-order linear and nonlinear systems. Then, the global stability analysis of fractional-order neural networks employs the results from the obtained LMI conditions. In the LMI form, the obtained results include the existence and uniqueness of equilibrium point and its global stability, which simplify and extend some previous work on the stability analysis of the fractional-order neural networks. Moreover, a generalized projective synchronization method between such neural systems is given, along with its corresponding LMI condition. Finally, two numerical examples are provided to illustrate the effectiveness of the established LMI conditions.
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.
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
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 nth 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 that this scaling
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
The costs of ignoring high-order correlations in populations of model neurons.
Michel, Melchi M; Jacobs, Robert A
2006-03-01
Investigators debate the extent to which neural populations use pair-wise and higher-order statistical dependencies among neural responses to represent information about a visual stimulus. To study this issue, three statistical decoders were used to extract the information in the responses of model neurons about the binocular disparities present in simulated pairs of left-eye and right-eye images: (1) the full joint probability decoder considered all possible statistical relations among neural responses as potentially important; (2) the dependence tree decoder also considered all possible relations as potentially important, but it approximated high-order statistical correlations using a computationally tractable procedure; and (3) the independent response decoder, which assumed that neural responses are statistically independent, meaning that all correlations should be zero and thus can be ignored. Simulation results indicate that high-order correlations among model neuron responses contain significant information about binocular disparities and that the amount of this high-order information increases rapidly as a function of neural population size. Furthermore, the results highlight the potential importance of the dependence tree decoder to neuroscientists as a powerful but still practical way of approximating high-order correlations among neural responses.
Electron spin polarization by isospin ordering in correlated two-layer quantum Hall systems.
Tiemann, L; Wegscheider, W; Hauser, M
2015-05-01
Enhancement of the electron spin polarization in a correlated two-layer, two-dimensional electron system at a total Landau level filling factor of 1 is reported. Using resistively detected nuclear magnetic resonance, we demonstrate that the electron spin polarization of two closely spaced two-dimensional electron systems becomes maximized when interlayer Coulomb correlations establish spontaneous isospin ferromagnetic order. This correlation-driven polarization dominates over the spin polarizations of competing single-layer fractional quantum Hall states under electron density imbalances.
Ten-no, Seiichiro
2004-07-01
A rational generator, which fulfills the cusp conditions for singlet and triplet electron pairs, is proposed and applied to explicitly correlated second order Møller-Plesset perturbation theory calculations. It is shown that the generator in conjunction with frozen geminals improves the convergence of correlation energy without introducing any variational parameters in explicitly correlated functions. A new scheme for three-electron integrals based on numerical quadratures is also illustrated. The method is tested for the convergence of reaction enthalpies with various basis sets. (c) 2004 American Institute of Physics.
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
2015-01-12
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.
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.
Igoshin, Oleg A.; Brody, Margaret S.; Price, Chester W.; Savageau, Michael A.
2009-01-01
Summary Regulatory networks controlling bacterial gene expression often evolve from common origins and share homologous proteins and similar network motifs. However, when functioning in different physiological contexts, these motifs may be re-arranged with different topologies that significantly affect network performance. Here we analyze two related signaling networks in the bacterium Bacillus subtilis in order to assess the consequences of their different topologies, with the aim of formulating design principles applicable to other systems. These two networks control the activities of the general stress response factor σB and the first sporulation-specific factor σF. Both networks have at their core a “partner-switching” mechanism, in which an anti-sigma factor forms alternate complexes either with the sigma factor, holding it inactive, or with an anti-anti-sigma factor, thereby freeing sigma. However, clear differences in network structure are apparent: the anti-sigma-factor for σF forms a long-lived, “dead-end” complex with its anti-anti-sigma factor and ADP, whereas the genes encoding σB and its network partners lie in a σB-controlled operon, resulting in positive and negative feedback loops. We constructed mathematical models of both networks and examined which features were critical for the performance of each design. The σF model predicts that the self-enhancing formation of the dead-end complex transforms the network into a largely irreversible hysteretic switch; the simulations reported here also demonstrate that hysteresis and slow turn off kinetics are the only two system properties associated with this complex formation. By contrast, the σB model predicts that the positive and negative feedback loops produce graded, reversible behavior with high regulatory capacity and fast response time. Our models demonstrate how alterations in network design result in different system properties that correlate with regulatory demands. These design
Higher-order correlations in common input shapes the output spiking activity of a neural population
NASA Astrophysics Data System (ADS)
Montangie, Lisandro; Montani, Fernando
2017-04-01
Recent neurophysiological experiments suggest that populations of neurons use a computational scheme in which spike timing is regulated by common non-Gaussian inputs across neurons. The presence of beyond-pairwise correlations in the neuronal inputs and the spiking outputs following a non-Gaussian statistics elicits the need of developing a new theoretical framework taking into account the complexity of synchronous activity patterns. To this end, we quantify the amount of higher-order correlations in the common neuronal inputs and outputs of a population of neurons. We provide a novel formalism, of easy numerical implementation, that can capture the subtle changes of the inputs heterogeneities. Within our approach, correlations across neurons arise from q-Gaussian inputs into threshold neurons and higher-order correlations in the spiking outputs activity are quantified by the parameter q. We present an exhaustive analysis of how input statistics are transformed in this threshold process into output statistics, and we show under which conditions higher-order correlations can lead to either bigger or smaller number of synchronized spikes in the neural population outputs.
Polyakov loop and correlator of Polyakov loops at next-to-next-to-leading order
Brambilla, Nora; Vairo, Antonio; Ghiglieri, Jacopo; Petreczky, Peter
2010-10-01
We study the Polyakov loop and the correlator of two Polyakov loops at finite temperature in the weak-coupling regime. We calculate the Polyakov loop at order g{sup 4}. The calculation of the correlator of two Polyakov loops is performed at distances shorter than the inverse of the temperature and for electric screening masses larger than the Coulomb potential. In this regime, it is accurate up to order g{sup 6}. We also evaluate the Polyakov-loop correlator in an effective field theory framework that takes advantage of the hierarchy of energy scales in the problem and makes explicit the bound-state dynamics. In the effective field theory framework, we show that the Polyakov-loop correlator is at leading order in the multipole expansion the sum of a color-singlet and a color-octet quark-antiquark correlator, which are gauge invariant, and compute the corresponding color-singlet and color-octet free energies.
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…
ERIC Educational Resources Information Center
Facao, M.; Lopes, A.; Silva, A. L.; Silva, P.
2011-01-01
We propose an undergraduate numerical project for simulating the results of the second-order correlation function as obtained by an intensity interference experiment for two kinds of light, namely bunched light with Gaussian or Lorentzian power density spectrum and antibunched light obtained from single-photon sources. While the algorithm for…
Extension of local-type inequality for the higher order correlation functions
Suyama, Teruaki; Yokoyama, Shuichiro E-mail: shu@a.phys.nagoya-u.ac.jp
2011-07-01
For the local-type primordial perturbation, it is known that there is an inequality between the bispectrum and the trispectrum. By using the diagrammatic method, we develop a general formalism to systematically construct the similar inequalities up to any order correlation function. As an application, we explicitly derive all the inequalities up to six and eight-point functions.
Size correlated long and short range order of ternary Co2FeGa Heusler nanoparticles
NASA Astrophysics Data System (ADS)
Wang, C. H.; Guo, Y. Z.; Casper, F.; Balke, B.; Fecher, G. H.; Felser, C.; Hwu, Y.
2010-09-01
The long and short range order of chemically prepared Co2FeGa Heusler nanoparticles with various sizes are determined by x-ray diffraction (XRD) and extended x-ray absorption fine structure (EXAFS) spectroscopy. Specifically, EXAFS fittings reveal the size dependent crystal structure and short range order of the Heusler type Co2FeGa nanoparticles. With decreasing particle size, the degree of L21 order in the nanoparticles decreases and the probability of B2 disorder increases simultaneously. The consequences of antisite disorder on the size correlated structure of Co2FeGa nanoparticles are also discussed.
Order or chaos in Boolean gene networks depends on the mean fraction of canalizing functions
NASA Astrophysics Data System (ADS)
Karlsson, Fredrik; Hörnquist, Michael
2007-10-01
We explore the connection between order/chaos in Boolean networks and the naturally occurring fraction of canalizing functions in such systems. This fraction turns out to give a very clear indication of whether the system possesses ordered or chaotic dynamics, as measured by Derrida plots, and also the degree of order when we compare different networks with the same number of vertices and edges. By studying also a wide distribution of indegrees in a network, we show that the mean probability of canalizing functions is a more reliable indicator of the type of dynamics for a finite network than the classical result on stability relating the bias to the mean indegree. Finally, we compare by direct simulations two biologically derived networks with networks of similar sizes but with power-law and Poisson distributions of indegrees, respectively. The biologically motivated networks are not more ordered than the latter, and in one case the biological network is even chaotic while the others are not.
76 FR 28117 - Order of Suspension of Trading; City Network, Inc.
Federal Register 2010, 2011, 2012, 2013, 2014
2011-05-13
... From the Federal Register Online via the Government Publishing Office SECURITIES AND EXCHANGE COMMISSION Order of Suspension of Trading; City Network, Inc. May 11, 2011. It appears to the Securities and... City Network, Inc. because it has not filed any periodic reports since the period ended September 30...
77 FR 36305 - Stream Communications Network & Media, Inc.; Order of Suspension of Trading
Federal Register 2010, 2011, 2012, 2013, 2014
2012-06-18
... From the Federal Register Online via the Government Publishing Office SECURITIES AND EXCHANGE COMMISSION Stream Communications Network & Media, Inc.; Order of Suspension of Trading June 14, 2012. It... concerning the securities of Stream Communications Network & Media, Inc. because it has not filed any...
Federal Register 2010, 2011, 2012, 2013, 2014
2012-05-07
... COMMISSION Order of Suspension of Trading; Airtrax, Inc., Amedia Networks, Inc., American Business Financial Services, Inc., Appalachian Bancshares, Inc., and Ariel Way, Inc. May 3, 2012. It appears to the Securities... information concerning the securities of Amedia Networks, Inc. because it has not filed any periodic...
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
Analysis of the First-Order Mechanics of Polygonal Fault Networks: Earth and Utopia Planitia, Mars.
NASA Astrophysics Data System (ADS)
Islam, F.; Cooke, M. L.; McGill, G. E.
2008-03-01
We investigate the first-order mechanics of polygonal fault networks in Utopia Planitia, Mars. We use numerical models to study the role buried topography plays in controlling the fault spacing of the giant polygons.
Breakdown of order preservation in symmetric oscillator networks with pulse-coupling.
Kielblock, Hinrich; Kirst, Christoph; Timme, Marc
2011-06-01
Symmetric networks of coupled dynamical units exhibit invariant subspaces with two or more units synchronized. In time-continuously coupled systems, these invariant sets constitute barriers for the dynamics. For networks of units with local dynamics defined on the real line, this implies that the units' ordering is preserved and that their winding number is identical. Here, we show that in permutation-symmetric networks with pulse-coupling, the order is often no longer preserved. We analytically study a class of pulse-coupled oscillators (characterizing for instance the dynamics of spiking neural networks) and derive quantitative conditions for the breakdown of order preservation. We find that in general pulse-coupling yields additional dimensions to the state space such that units may change their order by avoiding the invariant sets. We identify a system of two symmetrically pulse-coupled identical oscillators where, contrary to intuition, the oscillators' average frequencies and thus their winding numbers are different.
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.
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.
Tew, David P; Helmich, Benjamin; Hättig, Christof
2011-08-21
We explore using a pair natural orbital analysis of approximate first-order pair functions as means to truncate the space of both virtual and complementary auxiliary orbitals in the context of explicitly correlated F12 methods using localised occupied orbitals. We demonstrate that this offers an attractive procedure and that only 10-40 virtual orbitals per significant pair are required to obtain second-order valence correlation energies to within 1-2% of the basis set limit. Moreover, for this level of virtual truncation, only 10-40 complementary auxiliary orbitals per pair are required for an accurate resolution of the identity in the computation of the three- and four-electron integrals that arise in explicitly correlated methods.
Characterizing many-body localization by out-of-time-ordered correlation
NASA Astrophysics Data System (ADS)
He, Rong-Qiang; Lu, Zhong-Yi
2017-02-01
The out-of-time-ordered (OTO) correlation is a key quantity for quantifying quantum chaoticity and has been recently used in the investigation of quantum holography. Here we use it to study and characterize many-body localization (MBL). We find that a long-time logarithmic variation of the OTO correlation occurs in the MBL phase but is absent in the Anderson localized and ergodic phases. We extract a localization length in the MBL phase, which depends logarithmically on interaction and diverges at a critical interaction. Furthermore, the infinite-time "thermal" fluctuation of the OTO correlation is zero (finite) in the ergodic (MBL) phase and thus can be considered as an order parameter for the ergodic-MBL transition, through which the transition can be identified and characterized. Specifically, the critical point and the related critical exponents can be calculated.
Spatio-Chromatic Adaptation via Higher-Order Canonical Correlation Analysis of Natural Images
Gutmann, Michael U.; Laparra, Valero; Hyvärinen, Aapo; Malo, Jesús
2014-01-01
Independent component and canonical correlation analysis are two general-purpose statistical methods with wide applicability. In neuroscience, independent component analysis of chromatic natural images explains the spatio-chromatic structure of primary cortical receptive fields in terms of properties of the visual environment. Canonical correlation analysis explains similarly chromatic adaptation to different illuminations. But, as we show in this paper, neither of the two methods generalizes well to explain both spatio-chromatic processing and adaptation at the same time. We propose a statistical method which combines the desirable properties of independent component and canonical correlation analysis: It finds independent components in each data set which, across the two data sets, are related to each other via linear or higher-order correlations. The new method is as widely applicable as canonical correlation analysis, and also to more than two data sets. We call it higher-order canonical correlation analysis. When applied to chromatic natural images, we found that it provides a single (unified) statistical framework which accounts for both spatio-chromatic processing and adaptation. Filters with spatio-chromatic tuning properties as in the primary visual cortex emerged and corresponding-colors psychophysics was reproduced reasonably well. We used the new method to make a theory-driven testable prediction on how the neural response to colored patterns should change when the illumination changes. We predict shifts in the responses which are comparable to the shifts reported for chromatic contrast habituation. PMID:24533049
Spatio-chromatic adaptation via higher-order canonical correlation analysis of natural images.
Gutmann, Michael U; Laparra, Valero; Hyvärinen, Aapo; Malo, Jesús
2014-01-01
Independent component and canonical correlation analysis are two general-purpose statistical methods with wide applicability. In neuroscience, independent component analysis of chromatic natural images explains the spatio-chromatic structure of primary cortical receptive fields in terms of properties of the visual environment. Canonical correlation analysis explains similarly chromatic adaptation to different illuminations. But, as we show in this paper, neither of the two methods generalizes well to explain both spatio-chromatic processing and adaptation at the same time. We propose a statistical method which combines the desirable properties of independent component and canonical correlation analysis: It finds independent components in each data set which, across the two data sets, are related to each other via linear or higher-order correlations. The new method is as widely applicable as canonical correlation analysis, and also to more than two data sets. We call it higher-order canonical correlation analysis. When applied to chromatic natural images, we found that it provides a single (unified) statistical framework which accounts for both spatio-chromatic processing and adaptation. Filters with spatio-chromatic tuning properties as in the primary visual cortex emerged and corresponding-colors psychophysics was reproduced reasonably well. We used the new method to make a theory-driven testable prediction on how the neural response to colored patterns should change when the illumination changes. We predict shifts in the responses which are comparable to the shifts reported for chromatic contrast habituation.
The Ordered Network Structure and Prediction Summary for M≥7 Earthquakes in Xinjiang Region of China
NASA Astrophysics Data System (ADS)
Men, Ke-Pei; Zhao, Kai
2014-12-01
M ≥7 earthquakes have showed an obvious commensurability and orderliness in Xinjiang of China and its adjacent region since 1800. The main orderly values are 30 a × k (k = 1,2,3), 11 ~ 12 a, 41 ~ 43 a, 18 ~ 19 a, and 5 ~ 6 a. In the guidance of the information forecasting theory of Wen-Bo Weng, based on previous research results, combining ordered network structure analysis with complex network technology, we focus on the prediction summary of M ≥ 7 earthquakes by using the ordered network structure, and add new information to further optimize network, hence construct the 2D- and 3D-ordered network structure of M ≥ 7 earthquakes. In this paper, the network structure revealed fully the regularity of seismic activity of M ≥ 7 earthquakes in the study region during the past 210 years. Based on this, the Karakorum M7.1 earthquake in 1996, the M7.9 earthquake on the frontier of Russia, Mongol, and China in 2003, and two Yutian M7.3 earthquakes in 2008 and 2014 were predicted successfully. At the same time, a new prediction opinion is presented that the future two M ≥ 7 earthquakes will probably occur around 2019 - 2020 and 2025 - 2026 in this region. The results show that large earthquake occurred in defined region can be predicted. The method of ordered network structure analysis produces satisfactory results for the mid-and-long term prediction of M ≥ 7 earthquakes.
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.
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
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)
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.
Multilabel image classification via high-order label correlation driven active learning.
Zhang, Bang; Wang, Yang; Chen, Fang
2014-03-01
Supervised machine learning techniques have been applied to multilabel image classification problems with tremendous success. Despite disparate learning mechanisms, their performances heavily rely on the quality of training images. However, the acquisition of training images requires significant efforts from human annotators. This hinders the applications of supervised learning techniques to large scale problems. In this paper, we propose a high-order label correlation driven active learning (HoAL) approach that allows the iterative learning algorithm itself to select the informative example-label pairs from which it learns so as to learn an accurate classifier with less annotation efforts. Four crucial issues are considered by the proposed HoAL: 1) unlike binary cases, the selection granularity for multilabel active learning need to be fined from example to example-label pair; 2) different labels are seldom independent, and label correlations provide critical information for efficient learning; 3) in addition to pair-wise label correlations, high-order label correlations are also informative for multilabel active learning; and 4) since the number of label combinations increases exponentially with respect to the number of labels, an efficient mining method is required to discover informative label correlations. The proposed approach is tested on public data sets, and the empirical results demonstrate its effectiveness.
Tohidi, Vahid; Nadim, Farzan
2009-05-20
Network oscillations typically span a limited range of frequency. In pacemaker-driven networks, including many central pattern generators (CPGs), this frequency range is determined by the properties of bursting pacemaker neurons and their synaptic connections; thus, factors that affect the burst frequency of pacemaker neurons should play a role in determining the network frequency. We examine the role of membrane resonance of pacemaker neurons on the network frequency in the crab pyloric CPG. The pyloric oscillations (frequency of approximately 1 Hz) are generated by a group of pacemaker neurons: the anterior burster (AB) and the pyloric dilator (PD). We examine the impedance profiles of the AB and PD neurons in response to sinusoidal current injections with varying frequency and find that both neuron types exhibit membrane resonance, i.e., demonstrate maximal impedance at a given preferred frequency. The membrane resonance frequencies of the AB and PD neurons fall within the range of the pyloric network oscillation frequency. Experiments with pharmacological blockers and computational modeling show that both calcium currents I(Ca) and the hyperpolarization-activated inward current I(h) are important in producing the membrane resonance in these neurons. We then demonstrate that both the membrane resonance frequency of the PD neuron and its suprathreshold bursting frequency can be shifted in the same direction by either direct current injection or by using the dynamic-clamp technique to inject artificial conductances for I(h) or I(Ca). Together, these results suggest that membrane resonance of pacemaker neurons can be strongly correlated with the CPG oscillation frequency.
Cooperation in an evolutionary prisoner's dilemma on networks with degree-degree correlations.
Devlin, Stephen; Treloar, Thomas
2009-08-01
We study the effects of degree-degree correlations on the success of cooperation in an evolutionary prisoner's dilemma played on a random network. When degree-degree correlations are not present, the standardized variance of the network's degree distribution has been shown to be an accurate analytical measure of network heterogeneity that can be used to predict the success of cooperation. In this paper, we use a local-mechanism interpretation of standardized variance to give a generalization to graphs with degree-degree correlations. Two distinct mechanisms are shown to influence cooperation levels on these types of networks. The first is an intrinsic measurement of base-line heterogeneity coming from the network's degree distribution. The second is the increase in heterogeneity coming from the degree-degree correlations present in the network. A strong linear relationship is found between these two parameters and the average cooperation level in an evolutionary prisoner's dilemma on a network.
Effects of random rewiring on the degree correlation of scale-free networks
Qu, Jing; Wang, Sheng-Jun; Jusup, Marko; Wang, Zhen
2015-01-01
Random rewiring is used to generate null networks for the purpose of analyzing the topological properties of scale-free networks, yet the effects of random rewiring on the degree correlation are subject to contradicting interpretations in the literature. We comprehensively analyze the degree correlation of randomly rewired scale-free networks and show that random rewiring increases disassortativity by reducing the average degree of the nearest neighbors of high-degree nodes. The effect can be captured by the measures of the degree correlation that consider all links in the network, but not by analogous measures that consider only links between degree peers, hence the potential for contradicting interpretations. We furthermore find that random and directional rewiring affect the topology of a scale-free network differently, even if the degree correlation of the rewired networks is the same. Consequently, the network dynamics is changed, which is proven here by means of the biased random walk. PMID:26482005
Effects of random rewiring on the degree correlation of scale-free networks.
Qu, Jing; Wang, Sheng-Jun; Jusup, Marko; Wang, Zhen
2015-10-20
Random rewiring is used to generate null networks for the purpose of analyzing the topological properties of scale-free networks, yet the effects of random rewiring on the degree correlation are subject to contradicting interpretations in the literature. We comprehensively analyze the degree correlation of randomly rewired scale-free networks and show that random rewiring increases disassortativity by reducing the average degree of the nearest neighbors of high-degree nodes. The effect can be captured by the measures of the degree correlation that consider all links in the network, but not by analogous measures that consider only links between degree peers, hence the potential for contradicting interpretations. We furthermore find that random and directional rewiring affect the topology of a scale-free network differently, even if the degree correlation of the rewired networks is the same. Consequently, the network dynamics is changed, which is proven here by means of the biased random walk.
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.
Power-law out of time order correlation functions in the SYK model
NASA Astrophysics Data System (ADS)
Bagrets, Dmitry; Altland, Alexander; Kamenev, Alex
2017-08-01
We evaluate the finite temperature partition sum and correlation functions of the Sachdev-Ye-Kitaev (SYK) model. Starting from a recently proposed mapping of the SYK model onto Liouville quantum mechanics, we obtain our results by exact integration over conformal Goldstone modes reparameterizing physical time. Perhaps, the least expected result of our analysis is that at time scales proportional to the number of particles the out of time order correlation function crosses over from a regime of exponential decay to a universal t-6 power-law behavior.
NASA Astrophysics Data System (ADS)
Garcés, R.; Podgornik, R.; Lorman, V.
2015-06-01
Contrary to the usual "rigid supermolecular assembly" paradigm of chromatin structure, we propose to analyze its eventual ordered state in terms of symmetry properties of individual nucleosomes that give rise to mesophase order parameters, like in many other soft-matter systems. Basing our approach on the Landau-de Gennes phenomenology, we describe the mesoscale order in chromatin by antipolar and anticlinic correlations of chiral individual nucleosomes. This approach leads to a unifying physical picture of a whole series of soft locally ordered states with different apparent structures, including the recently observed heteromorphic chromatin, stemming from the antipolar arrangement of nucleosomes complemented by their chiral twisting. Properties of these states under an external force field can reconcile apparently contradictory results of single-molecule experiments.
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
Structural and psychosocial correlates of birth order anomalies in schizophrenia and homicide.
Schug, Robert A; Yang, Yaling; Raine, Adrian; Han, Chenbo; Liu, Jianghong
2010-12-01
Birth order--a unique index of both neurodevelopmental and/or psychosocial factors in the pathogenesis of psychiatric disorder--remains largely unexplored in violent schizophrenia. We examined whether murderers with schizophrenia would demonstrate birth order anomalies, distinguishing them from both nonviolent schizophrenia patients and murderers without schizophrenia. Self-report birth order, psychosocial history data (i.e., maternal birth age, family size, parental criminality, parental SES), and structural magnetic resonance imaging data were collected from normal controls, nonviolent schizophrenia patients, murderers with schizophrenia, murderers without schizophrenia, and murderers with psychiatric conditions other than schizophrenia at a brain hospital in Nanjing, China. Results indicated that murderers with schizophrenia were characterized by significantly increased (i.e., later) birth order compared with both nonviolent schizophrenia patients and murderers without schizophrenia. Additionally, birth order was negatively correlated with gray matter volume in key frontal subregions for schizophrenic murderers, and was negatively correlated with parental SES. Findings may suggest biological, psychosocial, or interactional trajectories which may lead to a homicidally violent outcome in schizophrenia.
Federal Register 2010, 2011, 2012, 2013, 2014
2013-03-05
... Management, Voice Over Internet Protocol, Small And Medium Business, Tampa, Florida; Verizon Business Networks Services, Inc., Senior Coordinator-Order Management, Voice Over Internet Protocol, Small And... Analysts-Order Management, Voice Over Internet Protocol, Small and Medium Business, Tampa, Florida....
ICE-Based Custom Full-Mesh Network for the CHIME High Bandwidth Radio Astronomy Correlator
NASA Astrophysics Data System (ADS)
Bandura, K.; Cliche, J. F.; Dobbs, M. A.; Gilbert, A. J.; Ittah, D.; Mena Parra, J.; Smecher, G.
2016-03-01
New generation radio interferometers encode signals from thousands of antenna feeds across large bandwidth. Channelizing and correlating this data requires networking capabilities that can handle unprecedented data rates with reasonable cost. The Canadian Hydrogen Intensity Mapping Experiment (CHIME) correlator processes 8-bits from N=2,048 digitizer inputs across 400MHz of bandwidth. Measured in N2× bandwidth, it is the largest radio correlator that is currently commissioning. Its digital back-end must exchange and reorganize the 6.6terabit/s produced by its 128 digitizing and channelizing nodes, and feed it to the 256 graphics processing unit (GPU) node spatial correlator in a way that each node obtains data from all digitizer inputs but across a small fraction of the bandwidth (i.e. ‘corner-turn’). In order to maximize performance and reliability of the corner-turn system while minimizing cost, a custom networking solution has been implemented. The system makes use of Field Programmable Gate Array (FPGA) transceivers to implement direct, passive copper, full-mesh, high speed serial connections between sixteen circuit boards in a crate, to exchange data between crates, and to offload the data to a cluster of 256 GPU nodes using standard 10Gbit/s Ethernet links. The GPU nodes complete the corner-turn by combining data from all crates and then computing visibilities. Eye diagrams and frame error counters confirm error-free operation of the corner-turn network in both the currently operating CHIME Pathfinder telescope (a prototype for the full CHIME telescope) and a representative fraction of the full CHIME hardware providing an end-to-end system validation. An analysis of an equivalent corner-turn system built with Ethernet switches instead of custom passive data links is provided.
NASA Astrophysics Data System (ADS)
Maluck, Julian; Donner, Reik V.
2017-02-01
International trade has grown considerably during the process of globalization. Complex supply chains for the production of goods have resulted in an increasingly connected International Trade Network (ITN). Traditionally, direct trade relations between industries have been regarded as mediators of supply and demand spillovers. With increasing network connectivity the question arises if higher-order relations become more important in explaining a national sector's susceptibility to supply and demand changes of its trading partner. In this study we address this question by investigating empirically to what extent the topological properties of the ITN provide information about positive correlations in the production of two industry sectors. We observe that although direct trade relations between industries serve as important indicators for correlations in the industries' value added growth, opportunities of substitution for required production inputs as well as second-order trade relations cannot be neglected. Our results contribute to a better understanding of the relation between trade and economic productivity and can serve as a basis for the improvement of crisis spreading models that evaluate contagion threats in the case of a node's failure in the ITN.
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. Copyright © 2015 Elsevier Ltd. All rights reserved.
Intensity interferometry and the second-order correlation function g(2) in astrophysics
NASA Astrophysics Data System (ADS)
Foellmi, C.
2009-12-01
Most observational techniques in astronomy can be understood as exploiting the various forms of the first-order correlation function g(1). As demonstrated by the Narrabri stellar intensity interferometer back in the 1960s by Hanbury Brown & Twiss, the first experiment to measure the second-order correlation function g(2), light can carry more information than simply its intensity, spectrum, and polarization. Since this experiment, theoretical and laboratory studies of non-classical properties of light have become a very active field of research, called quantum optics. Despite the variety of results in this field, astrophysics remained focused essentially on first-order coherence. In this paper, we study the possibility that quantum properties of light could be observed in cosmic sources. We provide the basic mathematical ingredients about the first and the second order correlation functions, applied to the modern context of astronomical observations. We aim at replacing the Hanbury Brown & Twiss experiment in this context, and present two fundamental limitations of an intensity interferometer: the requirement of a chaotic light source and the rapid decrease of the amount of correlated fluctuations with the surface temperature. The first of these limitations paradoxically emphasizes that the exploitation of g(2) is richer than what a modern intensity interferometer could bring and is particularly interesting for sources of nonthermal light. We also discuss new photon-counting avalanche photodiodes currently being developed in Grenoble, and their impact on limiting magnitudes of an intensity interferometer. We conclude by briefly presenting why microquasars in our galaxy and their extragalactic parents can represent an excellent first target in the optical/near-infrared where to observe nonthermal light and to test the use of g(2) in astrophysical sources.
Alternative formulation of explicitly correlated third-order Møller-Plesset perturbation theory
NASA Astrophysics Data System (ADS)
Ohnishi, Yu-ya; Ten-no, Seiichiro
2013-09-01
The second-order wave operator in the explicitly correlated wave function theory has been newly defined as an extension of the conventional s- and p-wave (SP) ansatz (also referred to as the FIXED amplitude ansatz) based on the linked-diagram theorem. The newly defined second-order wave operator has been applied to the calculation of the F12 correction to the third-order many-body perturbation (MP3) energy. In addition to this new wave operator, the F12 correction with the conventional first-order wave operator has been derived and calculated. Among three components of the MP3 correlation energy, the particle ladder contribution, which has shown the slowest convergence with respect to the basis set size, is fairly ameliorated by employing these F12 corrections. Both the newly defined and conventional formalisms of the F12 corrections exhibit a similar recovery of over 90% of the complete basis set limit of the particle ladder contribution of the MP3 correlation energy with a triple-zeta quality basis set for the neon atom, while the amount is about 75% without the F12 correction. The corrections to the ring term are small but the corrected energy has shown similar recovery as the particle ladder term. The hole ladder term has shown a rapid convergence even without the F12 corrections. Owing to these balanced recoveries, the deviation of the total MP3 correlation energy from the complete basis set limit has been calculated to be about 1 kcal/mol with the triple-zeta quality basis set, which is more than five times smaller than the error without the F12 correction.
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.
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.
An application of higher order connection to inverse function delayed network
NASA Astrophysics Data System (ADS)
Sota, Takahiro; Hayakawa, Yoshihiro; Sato, Shigeo; Nakajima, Koji
The Inverse function Delayed model (ID model) is a neuron model with negative resistance dynamics. The negative resistance can destabilize local minimum states, which are undesirable network responses. The ID network can remove these states. Actually, we have demonstrated that the ID network can perfectly remove all local minima with N-Queen problems or 4-Color problems, where stationary stable states always give correct answers. However this method cannot apply to Traveling Salesman Problems (TSPs) or Quadratic Assignment Problems (QAPs). Meanwhile, it is proposed that the TSPs are able to be represented in terms of the quartic form energy function. In this representation, the global minimum states that represent correct answers and the local minimum states are separable clearly, thus if it is applied to the ID network, it ensures that only the local minimum states are destabilized by the negative resistance. In this paper, we aim to introduce higher order connections to the ID network to apply the quartic form energy function. We apply the ID network with higher order connections to the TSPs or QAPs, and show that the higher order connection ID network can destabilize only the local minimum states by the negative resistance effect, so that it obtains only correct answers found at stationary stable states. Moreover, we obtain minimum parameter region analytically to destabilize every local minimum state.
LDA+DMFT approach to ordering phenomena and the structural stability of correlated materials
NASA Astrophysics Data System (ADS)
Kuneš, J.; Leonov, I.; Augustinský, P.; Křápek, V.; Kollar, M.; Vollhardt, D.
2017-07-01
Materials with correlated electrons often respond very strongly to external or internal influences, leading to instabilities and states of matter with broken symmetry. This behavior can be studied theoretically either by evaluating the linear response characteristics, or by simulating the ordered phases of the materials under investigation. We developed the necessary tools within the dynamical mean-field theory (DMFT) to search for electronic instabilities in materials close to spin-state crossovers and to analyze the properties of the corresponding ordered states. This investigation, motivated by the physics of LaCoO3, led to a discovery of condensation of spinful excitons in the two-orbital Hubbard model with a surprisingly rich phase diagram. The results are reviewed in the first part of the article. Electronic correlations can also be the driving force behind structural transformations of materials. To be able to investigate correlation-induced phase instabilities we developed and implemented a formalism for the computation of total energies and forces within a fully charge self-consistent combination of density functional theory and DMFT. Applications of this scheme to the study of structural instabilities of selected correlated electron materials such as Fe and FeSe are reviewed in the second part of the paper.
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.
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.
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.
Global Mittag-Leffler Stabilization of Fractional-Order Memristive Neural Networks.
Ailong Wu; Zhigang Zeng
2017-01-01
According to conventional memristive neural network theories, neurodynamic properties are powerful tools for solving many problems in the areas of brain-like associative learning, dynamic information storage or retrieval, etc. However, as have often been noted in most fractional-order systems, system analysis approaches for integral-order systems could not be directly extended and applied to deal with fractional-order systems, and consequently, it raises difficult issues in analyzing and controlling the fractional-order memristive neural networks. By using the set-valued maps and fractional-order differential inclusions, then aided by a newly proposed fractional derivative inequality, this paper investigates the global Mittag-Leffler stabilization for a class of fractional-order memristive neural networks. Two types of control rules (i.e., state feedback stabilizing control and output feedback stabilizing control) are designed for the stabilization of fractional-order memristive neural networks, while a list of stabilization criteria is established. Finally, two numerical examples are given to show the effectiveness and characteristics of the obtained theoretical results.
Shaped Gaussian Dictionaries for Quantized Networked Control Systems With Correlated Dropouts
NASA Astrophysics Data System (ADS)
Peters, Edwin G. W.; Quevedo, Daniel E.; Ostergaard, Jan
2016-01-01
This paper studies fixed rate vector quantisation for noisy networked control systems (NCSs) with correlated packet dropouts. In particular, a discrete-time linear time invariant system is to be controlled over an error-prone digital channel. The controller uses (quantized) packetized predictive control to reduce the impact of packet losses. The proposed vector quantizer is based on sparse regression codes (SPARC), which have recently been shown to be efficient in open-loop systems when coding white Gaussian sources. The dictionaries in existing design of SPARCs consist of independent and identically distributed (i.i.d.) Gaussian entries. However, we show that a significant gain can be achieved by using Gaussian dictionaries that are shaped according to the second-order statistics of the NCS in question. Furthermore, to avoid training of the dictionaries, we provide closed-form expressions for the required second-order statistics in the absence of quantization.
Positive affect, surprise, and fatigue are correlates of network flexibility.
Betzel, Richard F; Satterthwaite, Theodore D; Gold, Joshua I; Bassett, Danielle S
2017-03-31
Advances in neuroimaging have made it possible to reconstruct functional networks from the activity patterns of brain regions distributed across the cerebral cortex. Recent work has shown that flexible reconfiguration of human brain networks over short timescales supports cognitive flexibility and learning. However, modulating network flexibility to enhance learning requires an understanding of an as-yet unknown relationship between flexibility and brain state. Here, we investigate the relationship between network flexibility and affect, leveraging an unprecedented longitudinal data set. We demonstrate that indices associated with positive mood and surprise are both associated with network flexibility - positive mood portends a more flexible brain while increased levels of surprise portend a less flexible brain. In both cases, these relationships are driven predominantly by a subset of brain regions comprising the somatomotor system. Our results simultaneously suggest a network-level mechanism underlying learning deficits in mood disorders as well as a potential target - altering an individual's mood or task novelty - to improve learning.
Order priors for Bayesian network discovery with an application to malware phylogeny
Oyen, Diane; Anderson, Blake; Sentz, Kari; ...
2017-09-15
Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges)more » in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.« less
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.
Ni, Jianhua; Qian, Tianlu; Xi, Changbai; Rui, Yikang; Wang, Jiechen
2016-08-18
The spatial distribution of urban service facilities is largely constrained by the road network. In this study, network point pattern analysis and correlation analysis were used to analyze the relationship between road network and healthcare facility distribution. The weighted network kernel density estimation method proposed in this study identifies significant differences between the outside and inside areas of the Ming city wall. The results of network K-function analysis show that private hospitals are more evenly distributed than public hospitals, and pharmacy stores tend to cluster around hospitals along the road network. After computing the correlation analysis between different categorized hospitals and street centrality, we find that the distribution of these hospitals correlates highly with the street centralities, and that the correlations are higher with private and small hospitals than with public and large hospitals. The comprehensive analysis results could help examine the reasonability of existing urban healthcare facility distribution and optimize the location of new healthcare facilities.
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
Point model equations for neutron correlation counting: Extension of Böhnel's equations to any order
NASA Astrophysics Data System (ADS)
Favalli, Andrea; Croft, Stephen; Santi, Peter
2015-09-01
Various methods of autocorrelation neutron analysis may be used to extract information about a measurement item containing spontaneously fissioning material. The two predominant approaches being the time correlation analysis (that make use of a coincidence gate) methods of multiplicity shift register logic and Feynman sampling. The common feature is that the correlated nature of the pulse train can be described by a vector of reduced factorial multiplet rates. We call these singlets, doublets, triplets etc. Within the point reactor model the multiplet rates may be related to the properties of the item, the parameters of the detector, and basic nuclear data constants by a series of coupled algebraic equations - the so called point model equations. Solving, or inverting, the point model equations using experimental calibration model parameters is how assays of unknown items is performed. Currently only the first three multiplets are routinely used. In this work we develop the point model equations to higher order multiplets using the probability generating functions approach combined with the general derivative chain rule, the so called Faà di Bruno Formula. Explicit expression up to 5th order are provided, as well the general iterative formula to calculate any order. This work represents the first necessary step towards determining if higher order multiplets can add value to nondestructive measurement practice for nuclear materials control and accountancy.
Point model equations for neutron correlation counting: Extension of Böhnel's equations to any order
Favalli, Andrea; Croft, Stephen; Santi, Peter
2015-06-15
Various methods of autocorrelation neutron analysis may be used to extract information about a measurement item containing spontaneously fissioning material. The two predominant approaches being the time correlation analysis (that make use of a coincidence gate) methods of multiplicity shift register logic and Feynman sampling. The common feature is that the correlated nature of the pulse train can be described by a vector of reduced factorial multiplet rates. We call these singlets, doublets, triplets etc. Within the point reactor model the multiplet rates may be related to the properties of the item, the parameters of the detector, and basic 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.
The short and long of it: Neural correlates of temporal-order memory for autobiographical events
Jacques, Peggy St.; Rubin, David C.; LaBar, Kevin S.; Cabeza, Roberto
2009-01-01
Previous functional neuroimaging studies of temporal-order memory have investigated memory for laboratory stimuli that are causally unrelated and poor in sensory detail. In contrast, the present functional MRI (fMRI) study investigated temporal-order memory for autobiographical events that were causally interconnected and rich in sensory detail. Participants took photographs at many campus locations over a period of several hours, and the following day they were scanned while making temporal-order judgments to pairs of photographs from different locations. By manipulating the temporal lag between the two locations in each trial, we compared the neural correlates associated with reconstruction processes, which we hypothesized depended on recollection and contribute mainly to short lags, and distance processes, which we hypothesized to depend on familiarity and contribute mainly to longer lags. Consistent with our hypotheses, parametric fMRI analyses linked shorter lags to activations in regions previously associated with recollection (left prefrontal, parahippocampal, precuneus, and visual cortices) and longer lags with regions previously associated with familiarity (right prefrontal cortex). The hemispheric asymmetry in prefrontal cortex activity fits very well with evidence and theories regarding the contributions of left vs. right prefrontal cortex to memory (recollection vs. familiarity processes) and cognition (systematic vs. heuristic processes). In sum, using a novel photo-paradigm this study provided the first evidence regarding the neural correlates of temporal-order for autobiographical events. PMID:18284345
Wu, Zhenqin; Bi, Huimin; Pan, Sichen; Meng, Lingyi; Zhao, Xin Sheng
2016-11-17
Fluorescence correlation spectroscopy (FCS) is a powerful tool to investigate molecular diffusion and relaxations, which may be utilized to study many problems such as molecular size and aggregation, chemical reaction, molecular transportation and motion, and various kinds of physical and chemical relaxations. This article focuses on a problem related to using the relaxation term to study a reaction. If two species with different fluorescence photon emission efficiencies are connected by a reaction, the kinetic and equilibrium properties will be manifested in the relaxation term of the FCS curve. However, the conventional FCS alone cannot simultaneously determine the equilibrium constant (K) and the relative fluorescence brightness (Q), both of which are indispensable in the extraction of thermodynamic and kinetic information from the experimental data. To circumvent the problem, an assumption of Q = 0 is often made for the weak fluorescent species, which may lead to numerous errors when the actual situation is not the case. We propose to combine the third-order FCS with the conventional second-order FCS to determine K and Q without invoking other resources. The strategy and formalism are verified by computer simulations and demonstrated in a classical example of the hairpin DNA-folding process.
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.
Misra, Avijit; Biswas, Anindya; Pati, Arun K; Sen De, Aditi; Sen, Ujjwal
2015-05-01
Quantum discord is a measure of quantum correlations beyond the entanglement-separability paradigm. It is conceptualized by using the von Neumann entropy as a measure of disorder. We introduce a class of quantum correlation measures as differences between total and classical correlations, in a shared quantum state, in terms of the sandwiched relative Rényi and Tsallis entropies. We compare our results with those obtained by using the traditional relative entropies. We find that the measures satisfy all the plausible axioms for quantum correlations. We evaluate the measures for shared pure as well as paradigmatic classes of mixed states. We show that the measures can faithfully detect the quantum critical point in the transverse quantum Ising model and find that they can be used to remove an unquieting feature of nearest-neighbor quantum discord in this respect. Furthermore, the measures provide better finite-size scaling exponents of the quantum critical point than the ones for other known order parameters, including entanglement and information-theoretic measures of quantum correlations.
NASA Astrophysics Data System (ADS)
Misra, Avijit; Biswas, Anindya; Pati, Arun K.; SenDe, Aditi; Sen, Ujjwal
2015-05-01
Quantum discord is a measure of quantum correlations beyond the entanglement-separability paradigm. It is conceptualized by using the von Neumann entropy as a measure of disorder. We introduce a class of quantum correlation measures as differences between total and classical correlations, in a shared quantum state, in terms of the sandwiched relative Rényi and Tsallis entropies. We compare our results with those obtained by using the traditional relative entropies. We find that the measures satisfy all the plausible axioms for quantum correlations. We evaluate the measures for shared pure as well as paradigmatic classes of mixed states. We show that the measures can faithfully detect the quantum critical point in the transverse quantum Ising model and find that they can be used to remove an unquieting feature of nearest-neighbor quantum discord in this respect. Furthermore, the measures provide better finite-size scaling exponents of the quantum critical point than the ones for other known order parameters, including entanglement and information-theoretic measures of quantum correlations.
Scaling of degree correlations and its influence on diffusion in scale-free networks.
Gallos, Lazaros K; Song, Chaoming; Makse, Hernán A
2008-06-20
Connectivity correlations play an important role in the structure of scale-free networks. While several empirical studies exist, there is no general theoretical analysis that can explain the largely varying behavior of real networks. Here, we use scaling theory to quantify the degree of correlations in the particular case of networks with a power-law degree distribution. These networks are classified in terms of their correlation properties, revealing additional information on their structure. For instance, the studied social networks and the Internet at the router level are clustered around the line of random networks, implying a strongly connected core of hubs. On the contrary, some biological networks and the WWW exhibit strong anticorrelations. The present approach can be used to study robustness or diffusion, where we find that anticorrelations tend to accelerate the diffusion process.
Chang, Catie; Glover, Gary H
2009-10-01
Previous studies have reported that the spontaneous, resting-state time course of the default-mode network is negatively correlated with that of the "task-positive network", a collection of regions commonly recruited in demanding cognitive tasks. However, all studies of negative correlations between the default-mode and task-positive networks have employed some form of normalization or regression of the whole-brain average signal ("global signal"); these processing steps alter the time series of voxels in an uninterpretable manner as well as introduce spurious negative correlations. Thus, the extent of negative correlations with the default mode network without global signal removal has not been well characterized, and it is has recently been hypothesized that the apparent negative correlations in many of the task-positive regions could be artifactually induced by global signal pre-processing. The present study aimed to examine negative and positive correlations with the default-mode network when model-based corrections for respiratory and cardiac noise are applied in lieu of global signal removal. Physiological noise correction consisted of (1) removal of time-locked cardiac and respiratory artifacts using RETROICOR (Glover, G.H., Li, T.Q., Ress, D., 2000. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn. Reson. Med. 44, 162-167), and (2) removal of low-frequency respiratory and heart rate variations by convolving these waveforms with pre-determined transfer functions (Birn et al., 2008; Chang et al., 2009) and projecting the resulting two signals out of the data. It is demonstrated that negative correlations between the default-mode network and regions of the task-positive network are present in the majority of individual subjects both with and without physiological noise correction. Physiological noise correction increased the spatial extent and magnitude of negative correlations, yielding negative
NASA Astrophysics Data System (ADS)
Parshani, Roni; Buldyrev, Sergey V.; Havlin, Shlomo
2010-07-01
We study a system composed from two interdependent networks A and B, where a fraction of the nodes in network A depends on nodes of network B and a fraction of the nodes in network B depends on nodes of network A. Because of the coupling between the networks, when nodes in one network fail they cause dependent nodes in the other network to also fail. This invokes an iterative cascade of failures in both networks. When a critical fraction of nodes fail, the iterative process results in a percolation phase transition that completely fragments both networks. We show both analytically and numerically that reducing the coupling between the networks leads to a change from a first order percolation phase transition to a second order percolation transition at a critical point. The scaling of the percolation order parameter near the critical point is characterized by the critical exponent β=1.
Pinning synchronization of fractional-order complex networks with Lipschitz-type nonlinear dynamics.
Wang, Junwei; Ma, Qinghua; Chen, Aimin; Liang, Zhipeng
2015-07-01
This paper deals with pinning synchronization problem of fractional-order complex networks with Lipschitz-type nonlinear nodes and directed communication topology. We first reformulate the problem as a global asymptotic stability problem by describing network evolution in terms of error dynamics. Then, a novel frequency domain approach is developed by using Laplace transform, algebraic graph theory and generalized Gronwall inequality. We show that pinning synchronization can be ensured if the extended network topology contains a spanning tree and the coupling strength is large enough. Furthermore, we provide an easily testable criterion for global pinning synchronization depending on fractional-order, network topology, oscillator dynamics and state feedback. Numerical simulations are provided to illustrate the effectiveness of the theoretical analysis.
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.
Orientational Order of the Lamellipodial Actin Network as Demonstrated in Living Motile CellsV⃞
Verkhovsky, Alexander B.; Chaga, Oleg Y.; Schaub, Sébastien; Svitkina, Tatyana M.; Meister, Jean-Jacques; Borisy, Gary G.
2003-01-01
Lamellipodia of crawling cells represent both the motor for cell advance and the primary building site for the actin cytoskeleton. The organization of actin in the lamellipodium reflects actin dynamics and is of critical importance for the mechanism of cell motility. In previous structural studies, the lamellipodial actin network was analyzed primarily by electron microscopy (EM). An understanding of lamellipodial organization would benefit significantly if the EM data were complemented and put into a kinetic context by establishing correspondence with structural features observable at the light microscopic level in living cells. Here, we use an enhanced phase contrast microscopy technique to visualize an apparent long-range diagonal actin meshwork in the advancing lamellipodia of living cells. Visualization of this meshwork permitted a correlative light and electron microscopic approach that validated the underlying organization of lamellipodia. The linear features in the light microscopic meshwork corresponded to regions of greater actin filament density. Orientation of features was analyzed quantitatively and compared with the orientation of actin filaments at the EM level. We infer that the light microscopic meshwork reflects the orientational order of actin filaments which, in turn, is related to their branching angle. PMID:13679520
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
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
Rawn, Andrea; Wilson, Katrina
2011-01-01
Unifying, implementing and sustaining a large order set project requires strategic placement of key organizational professionals to provide ongoing user education, communication and support. This article will outline the successful strategies implemented by the Grey Bruce Health Network, Evidence-Based Care Program to reduce length of stay, increase patient satisfaction and increase the use of best practices resulting in quality outcomes, safer practice and better allocation of resources by using standardized Order Sets within a network of 11 hospital sites. Audits conducted in 2007 and again in 2008 revealed a reduced length of stay of 0.96 in-patient days when order sets were used on admission and readmission for the same or a related diagnosis within one month decreased from 5.5% without order sets to 3.5% with order sets.
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.
Correlations in the degeneracy of structurally controllable topologies for networks
NASA Astrophysics Data System (ADS)
Campbell, Colin; Aucott, Steven; Ruths, Justin; Ruths, Derek; Shea, Katriona; Albert, Réka
2017-04-01
Many dynamic systems display complex emergent phenomena. By directly controlling a subset of system components (nodes) via external intervention it is possible to indirectly control every other component in the system. When the system is linear or can be approximated sufficiently well by a linear model, methods exist to identify the number and connectivity of a minimum set of external inputs (constituting a so-called minimal control topology, or MCT). In general, many MCTs exist for a given network; here we characterize a broad ensemble of empirical networks in terms of the fraction of nodes and edges that are always, sometimes, or never a part of an MCT. We study the relationships between the measures, and apply the methodology to the T-LGL leukemia signaling network as a case study. We show that the properties introduced in this report can be used to predict key components of biological networks, with potentially broad applications to network medicine.
Correlations in the degeneracy of structurally controllable topologies for networks.
Campbell, Colin; Aucott, Steven; Ruths, Justin; Ruths, Derek; Shea, Katriona; Albert, Réka
2017-04-12
Many dynamic systems display complex emergent phenomena. By directly controlling a subset of system components (nodes) via external intervention it is possible to indirectly control every other component in the system. When the system is linear or can be approximated sufficiently well by a linear model, methods exist to identify the number and connectivity of a minimum set of external inputs (constituting a so-called minimal control topology, or MCT). In general, many MCTs exist for a given network; here we characterize a broad ensemble of empirical networks in terms of the fraction of nodes and edges that are always, sometimes, or never a part of an MCT. We study the relationships between the measures, and apply the methodology to the T-LGL leukemia signaling network as a case study. We show that the properties introduced in this report can be used to predict key components of biological networks, with potentially broad applications to network medicine.
Fasoli, Diego; Faugeras, Olivier; Panzeri, Stefano
2015-01-01
We introduce a new formalism for evaluating analytically the cross-correlation structure of a finite-size firing-rate network with recurrent connections. The analysis performs a first-order perturbative expansion of neural activity equations that include three different sources of randomness: the background noise of the membrane potentials, their initial conditions, and the distribution of the recurrent synaptic weights. This allows the analytical quantification of the relationship between anatomical and functional connectivity, i.e. of how the synaptic connections determine the statistical dependencies at any order among different neurons. The technique we develop is general, but for simplicity and clarity we demonstrate its efficacy by applying it to the case of synaptic connections described by regular graphs. The analytical equations so obtained reveal previously unknown behaviors of recurrent firing-rate networks, especially on how correlations are modified by the external input, by the finite size of the network, by the density of the anatomical connections and by correlation in sources of randomness. In particular, we show that a strong input can make the neurons almost independent, suggesting that functional connectivity does not depend only on the static anatomical connectivity, but also on the external inputs. Moreover we prove that in general it is not possible to find a mean-field description à la Sznitman of the network, if the anatomical connections are too sparse or our three sources of variability are correlated. To conclude, we show a very counterintuitive phenomenon, which we call stochastic synchronization, through which neurons become almost perfectly correlated even if the sources of randomness are independent. Due to its ability to quantify how activity of individual neurons and the correlation among them depends upon external inputs, the formalism introduced here can serve as a basis for exploring analytically the computational capability of
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…
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.
Hwang, Kyu-Baek; Zhang, Byoung-Tak
2005-12-01
Bayesian model averaging (BMA) can resolve the overfitting problem by explicitly incorporating the model uncertainty into the analysis procedure. Hence, it can be used to improve the generalization performance of Bayesian network classifiers. Until now, BMA of Bayesian network classifiers has only been performed in some restricted forms, e.g., the model is averaged given a single node-order, because of its heavy computational burden. However, it can be hard to obtain a good node-order when the available training dataset is sparse. To alleviate this problem, we propose BMA of Bayesian network classifiers over several distinct node-orders obtained using the Markov chain Monte Carlo sampling technique. The proposed method was examined using two synthetic problems and four real-life datasets. First, we show that the proposed method is especially effective when the given dataset is very sparse. The classification accuracy of averaging over multiple node-orders was higher in most cases than that achieved using a single node-order in our experiments. We also present experimental results for test datasets with unobserved variables, where the quality of the averaged node-order is more important. Through these experiments, we show that the difference in classification performance between the cases of multiple node-orders and single node-order is related to the level of noise, confirming the relative benefit of averaging over multiple node-orders for incomplete data. We conclude that BMA of Bayesian network classifiers over multiple node-orders has an apparent advantage when the given dataset is sparse and noisy, despite the method's heavy computational cost.
Yu, Zhe; Xiang, Guangxin; Pan, Liangbin; Huang, Lihua; Yu, Zhongyao; Xing, Wanli; Cheng, Jing
2004-12-01
Developing new methods and technologies in order to pattern neurons into regular networks is of utmost scientific interest in the field of neurological research. An efficient method here is developed for trapping neurons and constructing ordered neuronal networks on bioelectronic chips by using arrayed negative dielectrophoretic (DEP) forces. A special bioelectronic chip with well defined positioning electrode arrays was designed and fabricated on silicon substrate. When a high frequency AC signal was applied, the cell positioning bioelectronic chip (CPBC) is able to provide a well-defined non-uniform electric field, and thus generate negative DEP forces. The parameters, such as size of positioning electrode, conductivity of working solution, amplitude and frequency of power signal and cell concentration, were investigated to optimize the performance of the CPBC. When the neuron suspension was added onto the energized bioelectronic chip, the neurons were immediately trapped and quickly formed the predetermined pattern. Neurons may adhere and then be cultured directly on the CPBC, and show good neuron viability and neurite development. The formation of the ordered neuronal networks after two-week culture demonstrates that negative dielectrophoretic force assisted construction of ordered neuronal networks is effective, and it could be used to assist in monitoring functional activities of neuronal networks.
Oberer, Richard B.
2002-10-01
The current practice of nondestructive assay (NDA) of fissile materials using neutrons is dominated by the ^{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 ^{3}He detector subsequent to moderation. The process of detection requires from 30 to 60 μs. 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 (α, 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
Stable Second-Order Nonlinear Optical Materials Based on Interpenetrating Polymer Networks
1994-03-17
0IJUN93 to 31MAY94 4. 1I1Lk ANDLSUBI1ILIE D. ?-UNUING NUMBERS •’• Stable Second-Order Nonlinear Optical Materials Based On C:N00014-90-J-1148...release and sale; its distribution is unlimited. I Stable Second-Order Nonlinear Optical Materials Based On Interpenetrating Polymer Networks S... Optical Materials Based On Interpenetrating Polymer Networks by S. Marturunkakul, J. I. Chen, L. Li, X. L. Jiang, R. J. Jeng, S. K. Sengupta, J. Kumar
Dynamic stability analysis of fractional order leaky integrator echo state neural networks
NASA Astrophysics Data System (ADS)
Pahnehkolaei, Seyed Mehdi Abedi; Alfi, Alireza; Tenreiro Machado, J. A.
2017-06-01
The Leaky integrator echo state neural network (Leaky-ESN) is an improved model of the recurrent neural network (RNN) and adopts an interconnected recurrent grid of processing neurons. This paper presents a new proof for the convergence of a Lyapunov candidate function to zero when time tends to infinity by means of the Caputo fractional derivative with order lying in the range (0, 1). The stability of Fractional-Order Leaky-ESN (FO Leaky-ESN) is then analyzed, and the existence, uniqueness and stability of the equilibrium point are provided. A numerical example demonstrates the feasibility of the proposed method.
CMB internal delensing with general optimal estimator for higher-order correlations
Namikawa, Toshiya
2017-05-24
We present here a new method for delensing B modes of the cosmic microwave background (CMB) using a lensing potential reconstructed from the same realization of the CMB polarization (CMB internal delensing). The B -mode delensing is required to improve sensitivity to primary B modes generated by, e.g., the inflationary gravitational waves, axionlike particles, modified gravity, primordial magnetic fields, and topological defects such as cosmic strings. However, the CMB internal delensing suffers from substantial biases due to correlations between observed CMB maps to be delensed and that used for reconstructing a lensing potential. Since the bias depends on realizations, wemore » construct a realization-dependent (RD) estimator for correcting these biases by deriving a general optimal estimator for higher-order correlations. The RD method is less sensitive to simulation uncertainties. Compared to the previous ℓ -splitting method, we find that the RD method corrects the biases without substantial degradation of the delensing efficiency.« less
NASA Astrophysics Data System (ADS)
Aleiner, Igor L.; Faoro, Lara; Ioffe, Lev B.
2016-12-01
We extend the Keldysh technique to enable the computation of out-of-time order correlators such as < O(t) O ˜ (0) O(t) O ˜ (0) > . We show that the behavior of these correlators is described by equations that display initially an exponential instability which is followed by a linear propagation of the decoherence between two initially identically copies of the quantum many body systems with interactions. At large times the decoherence propagation (quantum butterfly effect) is described by a diffusion equation with non-linear dissipation known in the theory of combustion waves. The solution of this equation is a propagating non-linear wave moving with constant velocity despite the diffusive character of the underlying dynamics. Our general conclusions are illustrated by the detailed computations for the specific models describing the electrons interacting with bosonic degrees of freedom (phonons, two-level-systems etc.) or with each other.
Exact out-of-time-ordered correlation functions for an interacting lattice fermion model
NASA Astrophysics Data System (ADS)
Tsuji, Naoto; Werner, Philipp; Ueda, Masahito
2017-01-01
Exact solutions for local equilibrium and nonequilibrium out-of-time-ordered correlation (OTOC) functions are obtained for a lattice fermion model with on-site interactions, namely, the Falicov-Kimball (FK) model, in the large dimensional and thermodynamic limit. Our approach is based on the nonequilibrium dynamical mean-field theory generalized to an extended Kadanoff-Baym contour. We find that the density-density OTOC is most enhanced at intermediate coupling around the metal-insulator phase transition. In the high-temperature limit, the OTOC remains nontrivially finite and interaction dependent, even though dynamical charge correlations probed by an ordinary response function are completely suppressed. We propose an experiment to measure OTOCs of fermionic lattice systems including the FK and Hubbard models in ultracold atomic systems.
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.
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.
Weighill, Deborah A; Jacobson, Daniel A
2017-01-01
We explore the use of a network meta-modeling approach to compare the effects of similarity metrics used to construct biological networks on the topology of the resulting networks. This work reviews various similarity metrics for the construction of networks and various topology measures for the characterization of resulting network topology, demonstrating the use of these metrics in the construction and comparison of phylogenomic and transcriptomic networks.
Weighill, Deborah A; Jacobson, Daniel
2017-01-10
We explore the use of a network meta-modeling approach to compare the effects of similarity metrics used to construct biological networks on the topology of the resulting networks. This work reviews various similarity metrics for the construction of networks and various topology measures for the characterization of resulting network topology, demonstrating the use of these metrics in the construction and comparison of phylogenomic and transcriptomic networks.
Photonic band gaps in three-dimensional network structures with short-range order
Liew, Seng Fatt; Noh, Heeso; Yang, Jin-Kyu; Schreck, Carl F.; Dufresne, Eric R.; O'Hern, Corey S.; Cao, Hui
2011-12-15
We present a systematic study of photonic band gaps (PBGs) in three-dimensional (3D) photonic amorphous structures (PASs) with short-range order. From calculations of the density of optical states (DOS) for PASs with different topologies, we find that tetrahedrally connected dielectric networks produce the largest isotropic PBGs. Local uniformity and tetrahedral order are essential to the formation of PBGs in PASs, in addition to short-range geometric order. This work demonstrates that it is possible to create broad, isotropic PBGs for vector light fields in 3D PASs without long-range order.
NASA Astrophysics Data System (ADS)
Böhmer, R.; Diezemann, G.; Hinze, G.; Sillescu, H.
1998-01-01
Using deuteron NMR techniques two-, effective three-, and various four-time correlation functions were recorded for supercooled ortho-terphenyl at 10-15 K above the calorimetric glass transition in order to characterize the heterogeneous nature of its primary response. The experimental results could successfully be described within various energy landscape models as well as via continuous time random walk simulations. These theoretical considerations provide a suitable basis for a definition of the term dynamic heterogeneity. We discuss the power but also some limitations of the present multidimensional NMR techniques when applied to amorphous materials.
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.
Robust outer synchronization between two complex networks with fractional order dynamics.
Asheghan, Mohammad Mostafa; Míguez, Joaquín; Hamidi-Beheshti, Mohammad T; Tavazoei, Mohammad Saleh
2011-09-01
Synchronization between two coupled complex networks with fractional-order dynamics, hereafter referred to as outer synchronization, is investigated in this work. In particular, we consider two systems consisting of interconnected nodes. The state variables of each node evolve with time according to a set of (possibly nonlinear and chaotic) fractional-order differential equations. One of the networks plays the role of a master system and drives the second network by way of an open-plus-closed-loop (OPCL) scheme. Starting from a simple analysis of the synchronization error and a basic lemma on the eigenvalues of matrices resulting from Kronecker products, we establish various sets of conditions for outer synchronization, i.e., for ensuring that the errors between the state variables of the master and response systems can asymptotically vanish with time. Then, we address the problem of robust outer synchronization, i.e., how to guarantee that the states of the nodes converge to common values when the parameters of the master and response networks are not identical, but present some perturbations. Assuming that these perturbations are bounded, we also find conditions for outer synchronization, this time given in terms of sets of linear matrix inequalities (LMIs). Most of the analytical results in this paper are valid both for fractional-order and integer-order dynamics. The assumptions on the inner (coupling) structure of the networks are mild, involving, at most, symmetry and diffusivity. The analytical results are complemented with numerical examples. In particular, we show examples of generalized and robust outer synchronization for networks whose nodes are governed by fractional-order Lorenz dynamics.
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.
Coarsening kinetics of topologically highly correlated grain boundary networks
NASA Astrophysics Data System (ADS)
Tang, Ming; Reed, Bryan W.; Kumar, Mukul
2012-08-01
We apply phase-field simulations in two dimensions to study the thermal coarsening of grain boundary (GB) networks with high fractions of twin and twin-variant boundaries, which for example are seen in grain-boundary-engineered FCC materials. Two types of grain boundary networks with similar starting special boundary fractions but different topological features were considered as initial conditions for the grain growth simulations. A lattice Monte Carlo method creates polycrystalline microstructures (Reed and Kumar (RK)), which exhibit hierarchical organization of random and special coincidence site lattice boundaries. The other type of microstructures (randomly distributed (RD)) contains random distributions of special boundaries subject only to crystallographic constraints. Under the assumption that random boundaries have larger energy and much higher mobility than special boundaries, simulations show that increasing the initial special boundary fraction in both microstructures slows down grain growth. However, the two starting microstructures exhibit very different behavior in the evolution of GB character and triple junction (TJ) distributions. The RD networks coarsened more slowly than the RK networks with comparable initial fractions of special boundaries. The observed trend in the evolution of the RK microstructures is explained by an extended von Neumann-Mullins analysis. This study demonstrates that the special boundary fraction is not a sufficient indicator of the coarsening behavior of twinned GB networks; the network topology must also be considered to correctly predict the grain growth kinetics.
Correlation between Corneal Topographic Indices and Higher-Order Aberrations in Keratoconus.
Feizi, Sepehr; Einollahi, Bahram; Raminkhoo, Alireza; Salehirad, Shahram
2013-04-01
To compare corneal higher-order aberrations (HOAs) between normal and keratoconic eyes, and to investigate the association between elevation-based corneal topographic indices and corneal wavefront data in the latter group. In this cross-sectional comparative study, 77 normal right eyes of 77 control subjects and 66 eyes of 36 keratoconic patients were included. In each eye, elevation- based corneal topographic indices including mean keratometry readings, best-fit sphere, maximum elevation, and 3-mm and 5-mm zone irregularity indices were measured using Orbscan II. The Galilei Scheimpflug analyzer was used to measure HOAs of the corneal surface. The independent student t-test was used to compare HOAs between the study groups. Spearman correlation was used to investigate possible associations between Orbscan and Galilei data in the keratoconus group. All Zernike coefficients up to the 4th order except for horizontal trefoil, and vertical and horizontal tetrafoil were significantly greater in the keratoconus group than normal eyes (P<0.05). Root mean square (RMS) of HOAs up to the 6th order and total HOAs were significantly higher in the keratoconus group (P<0.05). In the keratoconus group, the strongest association was observed between vertical coma (r=-0.71, P<0.01) and total RMS of HOAs (r=0.94, P<0.01) with irregularity in the 3-mm zone. Spherical and vertical coma aberrations were significantly correlated with mean keratometry (P<0.05 for both comparisons). Centrally located corneal HOAs are significantly greater in keratoconic eyes than normal controls. Anterior and inferior displacement of the cornea causes the majority of higher-order aberrations observed in keratoconus.
Correlation between Corneal Topographic Indices and Higher-Order Aberrations in Keratoconus
Feizi, Sepehr; Einollahi, Bahram; Raminkhoo, Alireza; Salehirad, Shahram
2013-01-01
Purpose To compare corneal higher-order aberrations (HOAs) between normal and keratoconic eyes, and to investigate the association between elevation-based corneal topographic indices and corneal wavefront data in the latter group. Methods In this cross-sectional comparative study, 77 normal right eyes of 77 control subjects and 66 eyes of 36 keratoconic patients were included. In each eye, elevation- based corneal topographic indices including mean keratometry readings, best-fit sphere, maximum elevation, and 3-mm and 5-mm zone irregularity indices were measured using Orbscan II. The Galilei Scheimpflug analyzer was used to measure HOAs of the corneal surface. The independent student t-test was used to compare HOAs between the study groups. Spearman correlation was used to investigate possible associations between Orbscan and Galilei data in the keratoconus group. Results All Zernike coefficients up to the 4th order except for horizontal trefoil, and vertical and horizontal tetrafoil were significantly greater in the keratoconus group than normal eyes (P<0.05). Root mean square (RMS) of HOAs up to the 6th order and total HOAs were significantly higher in the keratoconus group (P<0.05). In the keratoconus group, the strongest association was observed between vertical coma (r=-0.71, P<0.01) and total RMS of HOAs (r=0.94, P<0.01) with irregularity in the 3-mm zone. Spherical and vertical coma aberrations were significantly correlated with mean keratometry (P<0.05 for both comparisons). Conclusion Centrally located corneal HOAs are significantly greater in keratoconic eyes than normal controls. Anterior and inferior displacement of the cornea causes the majority of higher-order aberrations observed in keratoconus. PMID:23943685
Impact of Network Structure and Cellular Response on Spike Time Correlations
Trousdale, James; Hu, Yu; Shea-Brown, Eric; Josić, Krešimir
2012-01-01
Novel experimental techniques reveal the simultaneous activity of larger and larger numbers of neurons. As a result there is increasing interest in the structure of cooperative – or correlated – activity in neural populations, and in the possible impact of such correlations on the neural code. A fundamental theoretical challenge is to understand how the architecture of network connectivity along with the dynamical properties of single cells shape the magnitude and timescale of correlations. We provide a general approach to this problem by extending prior techniques based on linear response theory. We consider networks of general integrate-and-fire cells with arbitrary architecture, and provide explicit expressions for the approximate cross-correlation between constituent cells. These correlations depend strongly on the operating point (input mean and variance) of the neurons, even when connectivity is fixed. Moreover, the approximations admit an expansion in powers of the matrices that describe the network architecture. This expansion can be readily interpreted in terms of paths between different cells. We apply our results to large excitatory-inhibitory networks, and demonstrate first how precise balance – or lack thereof – between the strengths and timescales of excitatory and inhibitory synapses is reflected in the overall correlation structure of the network. We then derive explicit expressions for the average correlation structure in randomly connected networks. These expressions help to identify the important factors that shape coordinated neural activity in such networks. PMID:22457608
Adaptive Synchronization of Fractional Order Complex-Variable Dynamical Networks via Pinning Control
NASA Astrophysics Data System (ADS)
Ding, Da-Wei; Yan, Jie; Wang, Nian; Liang, Dong
2017-09-01
In this paper, the synchronization of fractional order complex-variable dynamical networks is studied using an adaptive pinning control strategy based on close center degree. Some effective criteria for global synchronization of fractional order complex-variable dynamical networks are derived based on the Lyapunov stability theory. From the theoretical analysis, one concludes that under appropriate conditions, the complex-variable dynamical networks can realize the global synchronization by using the proper adaptive pinning control method. Meanwhile, we succeed in solving the problem about how much coupling strength should be applied to ensure the synchronization of the fractional order complex networks. Therefore, compared with the existing results, the synchronization method in this paper is more general and convenient. This result extends the synchronization condition of the real-variable dynamical networks to the complex-valued field, which makes our research more practical. Finally, two simulation examples show that the derived theoretical results are valid and the proposed adaptive pinning method is effective. Supported by National Natural Science Foundation of China under Grant No. 61201227, National Natural Science Foundation of China Guangdong Joint Fund under Grant No. U1201255, the Natural Science Foundation of Anhui Province under Grant No. 1208085MF93, 211 Innovation Team of Anhui University under Grant Nos. KJTD007A and KJTD001B, and also supported by Chinese Scholarship Council
Exploiting multi-lead electrocardiogram correlations using robust third-order tensor decomposition
Dandapat, Samarendra
2015-01-01
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
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.
Topological order of mixed states in correlated quantum many-body systems
NASA Astrophysics Data System (ADS)
Grusdt, F.
2017-02-01
Topological order has become a new paradigm to distinguish ground states of interacting many-body systems without conventional long-range order. Here, we discuss possible extensions of this concept to density matrices describing statistical ensembles. For a large class of quasithermal states, which can be realized as thermal states of some quasilocal Hamiltonian, we generalize earlier definitions of density-matrix topology to generic many-body systems with strong correlations. We point out that the robustness of topological order, defined as a pattern of long-range entanglement, depends crucially on the perturbations under consideration. While it is intrinsically protected against local perturbations of arbitrary strength in an infinite closed quantum system, purely local perturbations can destroy topological order in open systems coupled to baths if the coupling is sufficiently strong. We discuss our classification scheme using the finite-temperature quantum Hall states and point out that the classical Hall effect can be understood as a finite-temperature topological phase.
NASA Astrophysics Data System (ADS)
Zhang, Lingzhong; Yang, Yongqing; Wang, Fei
2017-04-01
This paper investigates the projective synchronization of fractional-order memristor-based neural networks (FMNNs) with switching jumps mismatch and time-varying delays. According to the theory of discontinuous differential system (Filippov) and differential inclusions, a fractional order inequality was introduced. Based on notoriously Barbalat's lemma and Razumikhin-type stability theorem, some projective synchronization criteria of FMNNs are derived. These results extend the previous publications. To illustrate the effectiveness of the proposed results, two numerical examples are presented.
Synchronization-based parameter estimation of fractional-order neural networks
NASA Astrophysics Data System (ADS)
Gu, Yajuan; Yu, Yongguang; Wang, Hu
2017-10-01
This paper focuses on the parameter estimation problem of fractional-order neural network. By combining the adaptive control and parameter update law, we generalize the synchronization-based identification method that has been reported in several literatures on identifying unknown parameters of integer-order systems. With this method, parameter identification and synchronization can be achieved simultaneously. Finally, a numerical example is given to illustrate the effectiveness of the theoretical results.
Activity Changes Induced by Spatio-Temporally Correlated Stimuli in Cultured Cortical Networks
NASA Astrophysics Data System (ADS)
Takayama, Yuzo; Moriguchi, Hiroyuki; Jimbo, Yasuhiko
Activity-dependent plasticity probably plays a key role in learning and memory in biological information processing systems. Though long-term potentiation and depression have been extensively studied in the filed of neuroscience, little is known on the mechanisms for integrating these modifications on network-wide activity changes. In this report, we studied effects of spatio-temporally correlated stimuli on the neuronal network activity. Rat cortical neurons were cultured on substrates with 64 embedded micro-electrodes and the evoked responses were extracellularly recorded and analyzed. We compared spatio-temporal patterns of the responses between before and after repetitive application of correlated stimuli. After the correlated stimuli, the networks showed significantly different responses from those in the initial states. The modified activity reflected structures of the repeatedly applied correlated stimuli. The results suggested that spatiotemporally correlated inputs systematically induced modification of synaptic strengths in neuronal networks, which could serve as an underlying mechanism of associative memory.
Robust projective outer synchronization of coupled uncertain fractional-order complex networks
NASA Astrophysics Data System (ADS)
Wang, Junwei; Zhang, Yun
2013-06-01
In this work, we propose a novel projective outer synchronization (POS) between unidirectionally coupled uncertain fractional-order complex networks through scalar transmitted signals. Based on the state observer theory, a control law is designed and some criteria are given in terms of linear matrix inequalities which guarantee global robust POS between such networks. Interestingly, in the POS regime, we show that different choices of scaling factor give rise to different outer synchrony, with various special cases including complete outer synchrony, anti-outer synchrony and even a state of amplitude death. Furthermore, it is demonstrated that although stability of POS is irrelevant to the inner-coupling strength, it will affect the convergence speed of POS. In particular, stronger inner synchronization can induce faster POS. The effectiveness of our method is revealed by numerical simulations on fractional-order complex networks with small-world communication topology.
Velmurugan, G; Rakkiyappan, R; Vembarasan, V; Cao, Jinde; Alsaedi, Ahmed
2017-02-01
As we know, the notion of dissipativity is an important dynamical property of neural networks. Thus, the analysis of dissipativity of neural networks with time delay is becoming more and more important in the research field. In this paper, the authors establish a class of fractional-order complex-valued neural networks (FCVNNs) with time delay, and intensively study the problem of dissipativity, as well as global asymptotic stability of the considered FCVNNs with time delay. Based on the fractional Halanay inequality and suitable Lyapunov functions, some new sufficient conditions are obtained that guarantee the dissipativity of FCVNNs with time delay. Moreover, some sufficient conditions are derived in order to ensure the global asymptotic stability of the addressed FCVNNs with time delay. Finally, two numerical simulations are posed to ensure that the attention of our main results are valuable. Copyright © 2016 Elsevier Ltd. All rights reserved.
Simulating superradiance from higher-order-intensity-correlation measurements: Single atoms
NASA Astrophysics Data System (ADS)
Wiegner, R.; Oppel, S.; Bhatti, D.; von Zanthier, J.; Agarwal, G. S.
2015-09-01
Superradiance typically requires preparation of atoms in highly entangled multiparticle states, the so-called Dicke states. In this paper we discuss an alternative route where we prepare such states from initially uncorrelated atoms by a measurement process. By measuring higher-order intensity-intensity correlations we demonstrate that we can simulate the emission characteristics of Dicke superradiance by starting with atoms in the fully excited state. We describe the essence of the scheme by first investigating two excited atoms. Here we demonstrate how via Hanbury Brown and Twiss type of measurements we can produce Dicke superradiance and subradiance displayed commonly with two atoms in the single excited symmetric and antisymmetric Dicke states, respectively. We thereafter generalize the scheme to arbitrary numbers of atoms and detectors, and explain in detail the mechanism which leads to this result. The approach shows that the Hanbury Brown and Twiss type of intensity interference and the phenomenon of Dicke superradiance can be regarded as two sides of the same coin. We also present a compact result for the characteristic functional which generates all order intensity-intensity correlations.
78 FR 50480 - In the Matter of Redfin Network, Inc.; Order of Suspension of Trading
Federal Register 2010, 2011, 2012, 2013, 2014
2013-08-19
... From the Federal Register Online via the Government Publishing Office SECURITIES AND EXCHANGE COMMISSION In the Matter of Redfin Network, Inc.; Order of Suspension of Trading August 15, 2013. It appears to the Securities and Exchange Commission that there is a lack of current and accurate...
Proof-of-Concept: Assembling Carbon Nanocrystals for Ordered 3D Network
2011-12-13
for 3D ordering carbon nanotube networks. In this project, a ultra-thin poly( methyl methacrylate ) (PMMA) was coated to ~50nm graphene film. At the...mechanical performance. Subsequently, the filtered graphene film was immersed into acetone to etch the filter membrane, and the resultant freestanding
N(th)-order correlation functions of galaxies from the Sloan Digital Sky Survey
NASA Astrophysics Data System (ADS)
Ross, Ashley Jacob
I present the correlation function measurements and analysis I have conducted with SDSS data. I have focused my measurements on angular N -point area- averaged correlation functions ( o N ([straight theta]) ) and auto-correlation functions (o 2 ([straight theta])) of galaxies. The measured o N ([straight theta]) are used to calculate the projected, s N , and real space, S N , hierarchical amplitudes. I have used SDSS DR3 data to show that the measurements are robust against the systematic effects of reddening and seeing, and to determine that large differences exist in the higher-order clustering of early- and late-type galaxies--quantified in terms of bias parameters. Using photometric redshift catalogs from SDSS DR5 data, I have created two volume limited samples of galaxies, allowing me to measure o N ([straight theta]) as a function of type, redshift, and luminosity. I have found that the higher-order bias of early-type galaxies does not vary significantly with changes in either redshift or luminosity, as c 2, early is consistent with 0.09 for all measurements. I have shown that the higher-order clustering of late-type galaxies shows dramatic differences for galaxies selected with redshifts above and below z = 0.3. Using LRGs photometrically selected from SDSS DR5, I have measured the 2nd-order bias tern, c 2 , using both o 2 ([straight theta]) and s 3 , and I have combined these measurements to determine that the normalization of the matter power spectrum at 8 h -1 Mpc, s 8 , is 0.79 ± 0.05 and c 2, LRG = 0.09 ± 0.04--consistent with the DR5 early-type results. I have calculated o 2 ([straight theta]) of galaxies from SDSS DR5 to constrain the HOD of galaxies as a function of type. I found that a new model that separated early- and late- type galaxies into different dark matter halos as much as possible was required to allow good fits to the measurements. Throughout, my findings are interpreted with respect to both the evolution of structure formation and
Lyapunov Exponent and Out-of-Time-Ordered Correlator's Growth Rate in a Chaotic System
NASA Astrophysics Data System (ADS)
Rozenbaum, Efim B.; Ganeshan, Sriram; Galitski, Victor
2017-02-01
It was proposed recently that the out-of-time-ordered four-point correlator (OTOC) may serve as a useful characteristic of quantum-chaotic behavior, because, in the semiclassical limit ℏ→0 , its rate of exponential growth resembles the classical Lyapunov exponent. Here, we calculate the four-point correlator C (t ) for the classical and quantum kicked rotor—a textbook driven chaotic system—and compare its growth rate at initial times with the standard definition of the classical Lyapunov exponent. Using both quantum and classical arguments, we show that the OTOC's growth rate and the Lyapunov exponent are, in general, distinct quantities, corresponding to the logarithm of the phase-space averaged divergence rate of classical trajectories and to the phase-space average of the logarithm, respectively. The difference appears to be more pronounced in the regime of low kicking strength K , where no classical chaos exists globally. In this case, the Lyapunov exponent quickly decreases as K →0 , while the OTOC's growth rate may decrease much slower, showing a higher sensitivity to small chaotic islands in the phase space. We also show that the quantum correlator as a function of time exhibits a clear singularity at the Ehrenfest time tE: transitioning from a time-independent value of t-1ln C (t ) at t
Lyapunov Exponent and Out-of-Time-Ordered Correlator's Growth Rate in a Chaotic System.
Rozenbaum, Efim B; Ganeshan, Sriram; Galitski, Victor
2017-02-24
It was proposed recently that the out-of-time-ordered four-point correlator (OTOC) may serve as a useful characteristic of quantum-chaotic behavior, because, in the semiclassical limit ℏ→0, its rate of exponential growth resembles the classical Lyapunov exponent. Here, we calculate the four-point correlator C(t) for the classical and quantum kicked rotor-a textbook driven chaotic system-and compare its growth rate at initial times with the standard definition of the classical Lyapunov exponent. Using both quantum and classical arguments, we show that the OTOC's growth rate and the Lyapunov exponent are, in general, distinct quantities, corresponding to the logarithm of the phase-space averaged divergence rate of classical trajectories and to the phase-space average of the logarithm, respectively. The difference appears to be more pronounced in the regime of low kicking strength K, where no classical chaos exists globally. In this case, the Lyapunov exponent quickly decreases as K→0, while the OTOC's growth rate may decrease much slower, showing a higher sensitivity to small chaotic islands in the phase space. We also show that the quantum correlator as a function of time exhibits a clear singularity at the Ehrenfest time t_{E}: transitioning from a time-independent value of t^{-1}lnC(t) at t
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.
Bias and high-order galaxy correlation functions in the APM galaxy survey
NASA Technical Reports Server (NTRS)
Gaztanaga, Enrique; Frieman, Joshua A.
1994-01-01
On large scales, the higher order moments of the mass distribution, S(sub J) = bar-zeta(sub J)/bar-zeta(sup J-1)(sub 2), e.g., the skewness S(sub 3) and kurtosis S(sub 4), can be predicted using nonlinear perturbation theory. Comparison of these predictions with moments of the observed galaxy distribution probes the bias between galaxies and mass. Applying this method to models with initially Gaussian fluctuations and power spectra P(k) similar to that of galaxies in the Automatic Plate Measuring (APM) survey, we find that the predicted higher order moments S(sub J)(R) are in good agreement with those directly inferred from the APM survey in the absence of bias. We use this result to place limits on the linear and nonlinear bias parameters. Models in which the extra power observed on large scales (with respect to the standard cold dark matter (CDM) model) is produced by scale-dependent bias match the APM higher order amplitudes only if nonlinear bias (rather than nonlinear gravity) generates the observed higher order moments. When normalized to Cosmic Background Explorer Differential Microwave Radiometer (COBE DMR), these models are siginificantly ruled out by the S(sub 3) observations. The cold plus hot dark matter model normalized to COBE can reproduce the APM higher order correlations if one introduces nonlinear bias terms, while the low-density CDM model with a cosmological constant does not require any bias to fit the large-scale amplitudes.
Bias and high-order galaxy correlation functions in the APM galaxy survey
NASA Technical Reports Server (NTRS)
Gaztanaga, Enrique; Frieman, Joshua A.
1994-01-01
On large scales, the higher order moments of the mass distribution, S(sub J) = bar-zeta(sub J)/bar-zeta(sup J-1)(sub 2), e.g., the skewness S(sub 3) and kurtosis S(sub 4), can be predicted using nonlinear perturbation theory. Comparison of these predictions with moments of the observed galaxy distribution probes the bias between galaxies and mass. Applying this method to models with initially Gaussian fluctuations and power spectra P(k) similar to that of galaxies in the Automatic Plate Measuring (APM) survey, we find that the predicted higher order moments S(sub J)(R) are in good agreement with those directly inferred from the APM survey in the absence of bias. We use this result to place limits on the linear and nonlinear bias parameters. Models in which the extra power observed on large scales (with respect to the standard cold dark matter (CDM) model) is produced by scale-dependent bias match the APM higher order amplitudes only if nonlinear bias (rather than nonlinear gravity) generates the observed higher order moments. When normalized to Cosmic Background Explorer Differential Microwave Radiometer (COBE DMR), these models are siginificantly ruled out by the S(sub 3) observations. The cold plus hot dark matter model normalized to COBE can reproduce the APM higher order correlations if one introduces nonlinear bias terms, while the low-density CDM model with a cosmological constant does not require any bias to fit the large-scale amplitudes.
Chandrasekaran, Naresh; Gann, Eliot; Jain, Nakul; Kumar, Anshu; Gopinathan, Sreelekha; Sadhanala, Aditya; Friend, Richard H; Kumar, Anil; McNeill, Christopher R; Kabra, Dinesh
2016-08-10
In this paper we correlate the solar cell performance with bimolecular packing of donor:acceptor bulk heterojunction (BHJ) organic solar cells (OSCs), where interchain ordering of the donor molecule and its influence on morphology, optical properties, and charge carrier dynamics of BHJ solar cells are studied in detail. Solar cells that are fabricated using more ordered defect free 100% regioregular poly(3-hexylthiophene) (DF-P3HT) as the donor polymer show ca. 10% increase in the average power conversion efficiency (PCE) when compared to that of the solar cell fabricated using 92% regioregularity P3HT, referred to as rr-P3HT. EQE and UV-vis absorption spectrum show a clear increase in the 607 nm vibronic shoulder of the DF-P3HT blend suggesting better interchain ordering which was also reflected in the less Urbach energy (Eu) value for this system. The increase in ordering inside the blend has enhanced the hole-mobility which is calculated from the single carrier device J-V characteristics. Electroluminance (EL) studies on the DF-P3HT system showed a red-shifted peak when compared to rr-P3HT-based devices suggesting low CT energy states in DF-P3HT. The morphologies of the blend films are studied using AFM and grazing-incidence wide-angle X-ray scattering (GIWAXS) suggesting increase in the roughness and phase segregation which could enhance the internal scattering of the light inside the device and improvement in the crystallinity along alkyl and π-stacking direction. Hence, higher PCE, lower Eu, red-shifted EL emission, high hole-mobility, and better crystallinity suggest improved interchain ordering has facilitated a more delocalized HOMO state in DF-P3HT-based BHJ solar cells.
Dynamic Brain Network Correlates of Spontaneous Fluctuations in Attention.
Kucyi, Aaron; Hove, Michael J; Esterman, Michael; Hutchison, R Matthew; Valera, Eve M
2017-03-01
Human attention is intrinsically dynamic, with focus continuously shifting between elements of the external world and internal, self-generated thoughts. Communication within and between large-scale brain networks also fluctuates spontaneously from moment to moment. However, the behavioral relevance of dynamic functional connectivity and possible link with attentional state shifts is unknown. We used a unique approach to examine whether brain network dynamics reflect spontaneous fluctuations in moment-to-moment behavioral variability, a sensitive marker of attentional state. Nineteen healthy adults were instructed to tap their finger every 600 ms while undergoing fMRI. This novel, but simple, approach allowed us to isolate moment-to-moment fluctuations in behavioral variability related to attention, independent of common confounds in cognitive tasks (e.g., stimulus changes, response inhibition). Spontaneously increasing tap variance ("out-of-the-zone" attention) was associated with increasing activation in dorsal-attention and salience network regions, whereas decreasing tap variance ("in-the-zone" attention) was marked by increasing activation of default mode network (DMN) regions. Independent of activation, tap variance representing out-of-the-zone attention was also time-locked to connectivity both within DMN and between DMN and salience network regions. These results provide novel mechanistic data on the understudied neural dynamics of everyday, moment-to-moment attentional fluctuations, elucidating the behavioral importance of spontaneous, transient coupling within and between attention-relevant networks. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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.
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
Pre-Processing Noise Cross-Correlations with Equalizing the Network Covariance Matrix Eigen-Spectrum
NASA Astrophysics Data System (ADS)
Seydoux, L.; de Rosny, J.; Shapiro, N.
2016-12-01
Theoretically, the extraction of Green functions from noise cross-correlation requires the ambient seismic wavefield to be generated by uncorrelated sources evenly distributed in the medium. Yet, this condition is often not verified. Strong events such as earthquakes often produce highly coherent transient signals. Also, the microseismic noise is generated at specific places on the Earth's surface with source regions often very localized in space. Different localized and persistent seismic sources may contaminate the cross-correlations of continuous records resulting in spurious arrivals or asymmetry and, finally, in biased travel-time measurements. Pre-processing techniques therefore must be applied to the seismic data in order to reduce the effect of noise anisotropy and the influence of strong localized events. Here we describe a pre-processing approach that uses the covariance matrix computed from signals recorded by a network of seismographs. We extend the widely used time and spectral equalization pre-processing to the equalization of the covariance matrix spectrum (i.e., its ordered eigenvalues). This approach can be considered as a spatial equalization. This method allows us to correct for the wavefield anisotropy in two ways: (1) the influence of strong directive sources is substantially attenuated, and (2) the weakly excited modes are reinforced, allowing to partially recover the conditions required for the Green's function retrieval. We also present an eigenvector-based spatial filter used to distinguish between surface and body waves. This last filter is used together with the equalization of the eigenvalue spectrum. We simulate two-dimensional wavefield in a heterogeneous medium with strongly dominating source. We show that our method greatly improves the travel-time measurements obtained from the inter-station cross-correlation functions. Also, we apply the developed method to the USArray data and pre-process the continuous records strongly influenced
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
NASA Astrophysics Data System (ADS)
Müller, Clemens; Stace, Thomas M.
2017-01-01
Motivated by correlated decay processes producing gain, loss, and lasing in driven semiconductor quantum dots [Phys. Rev. Lett. 113, 036801 (2014), 10.1103/PhysRevLett.113.036801; Science 347, 285 (2015), 10.1126/science.aaa2501; Phys. Rev. Lett. 114, 196802 (2015), 10.1103/PhysRevLett.114.196802], we develop a theoretical technique by using Keldysh diagrammatic perturbation theory to derive a Lindblad master equation that goes beyond the usual second-order perturbation theory. We demonstrate the method on the driven dissipative Rabi model, including terms up to fourth order in the interaction between the qubit and both the resonator and environment. This results in a large class of Lindblad dissipators and associated rates which go beyond the terms that have previously been proposed to describe similar systems. All of the additional terms contribute to the system behavior at the same order of perturbation theory. We then apply these results to analyze the phonon-assisted steady-state gain of a microwave field driving a double quantum dot in a resonator. We show that resonator gain and loss are substantially affected by dephasing-assisted dissipative processes in the quantum-dot system. These additional processes, which go beyond recently proposed polaronic theories, are in good quantitative agreement with experimental observations.
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.
Functional network connectivity analysis based on partial correlation in Alzheimer's disease
NASA Astrophysics Data System (ADS)
Zhang, Nan; Guan, Xiaoting; Zhang, Yumei; Li, Jingjing; Chen, Hongyan; Chen, Kewei; Fleisher, Adam; Yao, Li; Wu, Xia
2009-02-01
Functional network connectivity (FNC) measures the temporal dependency among the time courses of functional networks. However, the marginal correlation between two networks used in the classic FNC analysis approach doesn't separate the FNC from the direct/indirect effects of other networks. In this study, we proposed an alternative approach based on partial correlation to evaluate the FNC, since partial correlation based FNC can reveal the direct interaction between a pair of networks, removing dependencies or influences from others. Previous studies have demonstrated less task-specific activation and less rest-state activity in Alzheimer's disease (AD). We applied present approach to contrast FNC differences of resting state network (RSN) between AD and normal controls (NC). The fMRI data under resting condition were collected from 15 AD and 16 NC. FNC was calculated for each pair of six RSNs identified using Group ICA, thus resulting in 15 (2 out of 6) pairs for each subject. Partial correlation based FNC analysis indicated 6 pairs significant differences between groups, while marginal correlation only revealed 2 pairs (involved in the partial correlation results). Additionally, patients showed lower correlation than controls among most of the FNC differences. Our results provide new evidences for the disconnection hypothesis in AD.
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.
Matković, Aleksandar; Genser, Jakob; Lüftner, Daniel; Kratzer, Markus; Gajić, Radoš; Puschnig, Peter; Teichert, Christian
2016-01-01
This study focuses on hexagonal boron nitride as an ultra-thin van der Waals dielectric substrate for the epitaxial growth of highly ordered crystalline networks of the organic semiconductor parahexaphenyl. Atomic force microscopy based morphology analysis combined with density functional theory simulations reveal their epitaxial relation. As a consequence, needle-like crystallites of parahexaphenyl grow with their long axes oriented five degrees off the hexagonal boron nitride zigzag directions. In addition, by tuning the deposition temperature and the thickness of hexagonal boron nitride, ordered networks of needle-like crystallites as long as several tens of micrometers can be obtained. A deeper understanding of the organic crystallites growth and ordering at ultra-thin van der Waals dielectric substrates will lead to grain boundary-free organic field effect devices, limited only by the intrinsic properties of the organic semiconductors. PMID:27929042
New Stability Criteria for High-Order Neural Networks with Proportional Delays
NASA Astrophysics Data System (ADS)
Xu, Chang-Jin; Li, Pei-Luan
2017-03-01
This paper is concerned with high-order neural networks with proportional delays. The proportional delay is a time-varying unbounded delay which is different from the constant delay, bounded time-varying delay and distributed delay. By the nonlinear transformation {y}i(t)={u}i({{{e}}}t){{ }}(i=1,2,\\ldots ,n), we transform a class of high-order neural networks with proportional delays into a class of high-order neural networks with constant delays and time-varying coefficients. With the aid of Brouwer fixed point theorem and constructing the delay differential inequality, we obtain some delay-independent and delay-dependent sufficient conditions to ensure the existence, uniqueness and global exponential stability of equilibrium of the network. Two examples with their simulations are given to illustrate the theoretical findings. Our results are new and complement previously known results. Supported by National Natural Science Foundation of China under Grant Nos. 61673008 and 11261010, and Project of High-level Innovative Talents of Guizhou Province ([2016]5651)
A novel nonlinear adaptive filter using a pipelined second-order Volterra recurrent neural network.
Zhao, Haiquan; Zhang, Jiashu
2009-12-01
To enhance the performance and overcome the heavy computational complexity of recurrent neural networks (RNN), a novel nonlinear adaptive filter based on a pipelined second-order Volterra recurrent neural network (PSOVRNN) is proposed in this paper. A modified real-time recurrent learning (RTRL) algorithm of the proposed filter is derived in much more detail. The PSOVRNN comprises of a number of simple small-scale second-order Volterra recurrent neural network (SOVRNN) modules. In contrast to the standard RNN, these modules of a PSOVRNN can be performed simultaneously in a pipelined parallelism fashion, which can lead to a significant improvement in its total computational efficiency. Moreover, since each module of the PSOVRNN is a SOVRNN in which nonlinearity is introduced by the recursive second-order Volterra (RSOV) expansion, its performance can be further improved. Computer simulations have demonstrated that the PSOVRNN performs better than the pipelined recurrent neural network (PRNN) and RNN for nonlinear colored signals prediction and nonlinear channel equalization. However, the superiority of the PSOVRNN over the PRNN is at the cost of increasing computational complexity due to the introduced nonlinear expansion of each module.
Multidimensional mutual ordering of patterns using a set of pre-trained artificial neural networks
NASA Astrophysics Data System (ADS)
Kulagin, V. P.; Ivanov, A. I.; Kuznetsov, Yu M.; Chulkova, G. M.
2017-01-01
The article shows that large artificial neural networks can be used for mutual ordering of a set of multi-dimensional patterns of the same nature (handwritten text, voice, smells, taste). Each neural network must be pre-trained to recognize one of the patterns. As a measure of ordering one can use the entropy of patterns “Strangers” that are input to a neural network trained to recognize only examples of the pattern “familiar”. The neural network after training reduces the entropy of the examples of the pattern “Familiar” and increases the entropy of examples of pattern “Stranger.” It is shown that the entropy measure of the ordering always has two global minima. The first minimum corresponds to the pattern “Familiar”, the second to the inversion of the pattern “Familiar”. It is also shown that the Hamming distance between the patterns belonging to two different groups (groups of the two global minima) is always as large as possible.
High-order neural networks and kernel methods for peptide-MHC binding prediction.
Kuksa, Pavel P; Min, Martin Renqiang; Dugar, Rishabh; Gerstein, Mark
2015-11-15
Effective computational methods for peptide-protein binding prediction can greatly help clinical peptide vaccine search and design. However, previous computational methods fail to capture key nonlinear high-order dependencies between different amino acid positions. As a result, they often produce low-quality rankings of strong binding peptides. To solve this problem, we propose nonlinear high-order machine learning methods including high-order neural networks (HONNs) with possible deep extensions and high-order kernel support vector machines to predict major histocompatibility complex-peptide binding. The proposed high-order methods improve quality of binding predictions over other prediction methods. With the proposed methods, a significant gain of up to 25-40% is observed on the benchmark and reference peptide datasets and tasks. In addition, for the first time, our experiments show that pre-training with high-order semi-restricted Boltzmann machines significantly improves the performance of feed-forward HONNs. Moreover, our experiments show that the proposed shallow HONN outperform the popular pre-trained deep neural network on most tasks, which demonstrates the effectiveness of modelling high-order feature interactions for predicting major histocompatibility complex-peptide binding. There is no associated distributable software. renqiang@nec-labs.com or mark.gerstein@yale.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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)
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.; ...
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 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.
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.
Jarzynski-like equality for the out-of-time-ordered correlator
NASA Astrophysics Data System (ADS)
Yunger Halpern, Nicole
2017-01-01
The out-of-time-ordered correlator (OTOC) diagnoses quantum chaos and the scrambling of quantum information via the spread of entanglement. The OTOC encodes forward and reverse evolutions and has deep connections with the flow of time. So do fluctuation relations such as Jarzynski's equality, derived in nonequilibrium statistical mechanics. I unite these two powerful, seemingly disparate tools by deriving a Jarzynski-like equality for the OTOC. The equality's left-hand side equals the OTOC. The right-hand side suggests a protocol for measuring the OTOC indirectly. The protocol is platform-nonspecific and can be performed with weak measurement or with interference. Time evolution need not be reversed in any interference trial. The equality enables fluctuation relations to provide insights into holography, condensed matter, and quantum information and vice versa.
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 H2O, CH4, and C6H6 within a few mEh 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.
Automatic angle measurement of a 2D object using optical correlator-neural networks hybrid system
NASA Astrophysics Data System (ADS)
Manivannan, N.; Neil, M. A. A.
2011-04-01
In this paper a novel method is proposed and demonstrated for automatic rotation angle measurement of a 2D object using a hybrid architecture, consisting of a 4f optical correlator with a binary phase only multiplexed matched filter and a single layer neural network. The hybrid set-up can be considered as a two-layer perceptron-like neural network; an optical correlator is the first layer and the standard single layer neural network is the second layer. The training scheme used to train the hybrid architecture is a combination of a Direct Binary Search algorithm, to train the optical correlator, and an Error Back Propagation algorithm, to train the neural network. The aim is to perform the major information processing by the optical correlator with a small additional processing by the neural network stage. This allows the system to be used for real-time applications as optics has the inherent ability to process information in a parallel manner at high speed. The neural network stage gives an extra dimension of freedom so that complicated tasks like automatic rotation angle measurement can be achieved. Results of both computer simulation and experimental set-up are presented for rotation angle measurement of an English alphabetic character as a 2D object. The experimental set-up consists of a real optical correlator using two spatial light modulators for both input and frequency plane representations and a PC based model of a single layer network.
Long-time coherence in fourth-order spin correlation functions
NASA Astrophysics Data System (ADS)
Fröhling, Nina; Anders, Frithjof B.
2017-07-01
We study the long-time decay of fourth-order electron spin correlation functions for an isolated singly charged semiconductor quantum dot. The electron spin dynamics is governed by the applied external magnetic field as well as the hyperfine interaction. While the long-time coherent oscillations in the correlation functions can be understood within a semiclassical approach treating the Overhauser field as frozen, the field dependent decay of its amplitude reported in different experiments cannot be explained by the central-spin model indicating the insufficiency of such a description. By incorporating the nuclear Zeeman splitting and the strain induced nuclear-electric quadrupolar interaction, we find the correct crossover from a fast decay in small magnetic fields to a slow exponential asymptotic in large magnetic fields. It originates from a competition between the quadrupolar interaction inducing an enhanced spin decay and the nuclear Zeeman term that suppressed the spin-flip processes. We are able to explain the magnetic field dependency of the characteristic long-time decay time T2 depending on the experimental setups. The calculated asymptotic values of T2=3 -4 μ s agree qualitatively well with the experimental data.
Measuring Out-of-Time-Order Correlators on a Nuclear Magnetic Resonance Quantum Simulator
NASA Astrophysics Data System (ADS)
Li, Jun; Fan, Ruihua; Wang, Hengyan; Ye, Bingtian; Zeng, Bei; Zhai, Hui; Peng, Xinhua; Du, Jiangfeng
2017-07-01
The idea of the out-of-time-order correlator (OTOC) has recently emerged in the study of both condensed matter systems and gravitational systems. It not only plays a key role in investigating the holographic duality between a strongly interacting quantum system and a gravitational system, it also diagnoses the chaotic behavior of many-body quantum systems and characterizes information scrambling. Based on OTOCs, three different concepts—quantum chaos, holographic duality, and information scrambling—are found to be intimately related to each other. Despite its theoretical importance, the experimental measurement of the OTOC is quite challenging, and thus far there is no experimental measurement of the OTOC for local operators. Here, we report the measurement of OTOCs of local operators for an Ising spin chain on a nuclear magnetic resonance quantum simulator. We observe that the OTOC behaves differently in the integrable and nonintegrable cases. Based on the recent discovered relationship between OTOCs and the growth of entanglement entropy in the many-body system, we extract the entanglement entropy from the measured OTOCs, which clearly shows that the information entropy oscillates in time for integrable models and scrambles for nonintgrable models. With the measured OTOCs, we also obtain the experimental result of the butterfly velocity, which measures the speed of correlation propagation. Our experiment paves a way for experimentally studying quantum chaos, holographic duality, and information scrambling in many-body quantum systems with quantum simulators.
Davis, J. C. Séamus; Lee, Dung-Hai
2013-01-01
Unconventional superconductivity (SC) is said to occur when Cooper pair formation is dominated by repulsive electron–electron interactions, so that the symmetry of the pair wave function is other than an isotropic s-wave. The strong, on-site, repulsive electron–electron interactions that are the proximate cause of such SC are more typically drivers of commensurate magnetism. Indeed, it is the suppression of commensurate antiferromagnetism (AF) that usually allows this type of unconventional superconductivity to emerge. Importantly, however, intervening between these AF and SC phases, intertwined electronic ordered phases (IP) of an unexpected nature are frequently discovered. For this reason, it has been extremely difficult to distinguish the microscopic essence of the correlated superconductivity from the often spectacular phenomenology of the IPs. Here we introduce a model conceptual framework within which to understand the relationship between AF electron–electron interactions, IPs, and correlated SC. We demonstrate its effectiveness in simultaneously explaining the consequences of AF interactions for the copper-based, iron-based, and heavy-fermion superconductors, as well as for their quite distinct IPs. PMID:24114268
Davis, J C Séamus; Lee, Dung-Hai
2013-10-29
Unconventional superconductivity (SC) is said to occur when Cooper pair formation is dominated by repulsive electron-electron interactions, so that the symmetry of the pair wave function is other than an isotropic s-wave. The strong, on-site, repulsive electron-electron interactions that are the proximate cause of such SC are more typically drivers of commensurate magnetism. Indeed, it is the suppression of commensurate antiferromagnetism (AF) that usually allows this type of unconventional superconductivity to emerge. Importantly, however, intervening between these AF and SC phases, intertwined electronic ordered phases (IP) of an unexpected nature are frequently discovered. For this reason, it has been extremely difficult to distinguish the microscopic essence of the correlated superconductivity from the often spectacular phenomenology of the IPs. Here we introduce a model conceptual framework within which to understand the relationship between AF electron-electron interactions, IPs, and correlated SC. We demonstrate its effectiveness in simultaneously explaining the consequences of AF interactions for the copper-based, iron-based, and heavy-fermion superconductors, as well as for their quite distinct IPs.
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.
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.
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
Hu, Yang; Wang, Jijun; Li, Chunbo; Wang, Yin-Shan; Yang, Zhi; Zuo, Xi-Nian
2016-01-01
A brain network consisting of two key parietal nodes, the precuneus and the posterior cingulate cortex, has emerged from recent fMRI studies. Though it is anatomically adjacent to and spatially overlaps with the default mode network (DMN), its function has been associated with memory processing, and it has been referred to as the parietal memory network (PMN). Independent component analysis (ICA) is the most common data-driven method used to extract PMN and DMN simultaneously. However, the effects of data preprocessing and parameter determination in ICA on PMN-DMN segregation are completely unknown. Here, we employ three typical algorithms of group ICA to assess how spatial smoothing and model order influence the degree of PMN-DMN segregation. Our findings indicate that PMN and DMN can only be stably separated using a combination of low-level spatial smoothing and high model order across the three ICA algorithms. We thus argue for more considerations on parametric settings for interpreting DMN data.
NASA Astrophysics Data System (ADS)
Chen, Yong-Zhou; Fu, Chun-Hua; Chang, Hui; Li, Nan; He, Da-Ren
2008-10-01
In this paper, an empirical investigation is presented, which focuses on unveiling the universality of connectivity correlations in three spaces (the route space, the stop geographical space and bus-transferring space) of urban bus-transport networks (BTNs) in four major cities of China. The underlying features of the connectivity correlations are shown in two statistical ways. One is the correlation between the (weighted) average degree of all the nearest neighbouring vertices with degree k, (Knnw (k)) Knn(k), and k, and the other is the correlations between the assortativity coefficient r and, respectively, the network size N, the network diameter D, the averaged clustering coefficient C, and the averaged distance
Order recall in verbal short-term memory: The role of semantic networks.
Poirier, Marie; Saint-Aubin, Jean; Mair, Ali; Tehan, Gerry; Tolan, Anne
2015-04-01
In their recent article, Acheson, MacDonald, and Postle (Journal of Experimental Psychology: Learning, Memory, and Cognition 37:44-59, 2011) made an important but controversial suggestion: They hypothesized that (a) semantic information has an effect on order information in short-term memory (STM) and (b) order recall in STM is based on the level of activation of items within the relevant lexico-semantic long-term memory (LTM) network. However, verbal STM research has typically led to the conclusion that factors such as semantic category have a large effect on the number of correctly recalled items, but little or no impact on order recall (Poirier & Saint-Aubin, Quarterly Journal of Experimental Psychology 48A:384-404, 1995; Saint-Aubin, Ouellette, & Poirier, Psychonomic Bulletin & Review 12:171-177, 2005; Tse, Memory 17:874-891, 2009). Moreover, most formal models of short-term order memory currently suggest a separate mechanism for order coding-that is, one that is separate from item representation and not associated with LTM lexico-semantic networks. Both of the experiments reported here tested the predictions that we derived from Acheson et al. The findings show that, as predicted, manipulations aiming to affect the activation of item representations significantly impacted order memory.
Chu, Tianjiang; Sheng, Qiang; Wang, Sikai; Wu, Jihua
2014-01-01
Dendritic tidal creek networks are important habitats for sustaining biodiversity and ecosystem functioning in salt marsh wetlands. To evaluate the importance of creek heterogeneity in supporting benthic secondary production, we assess the spatial distribution and secondary production of a representative polychaete species (Dentinephtys glabra) in creek networks along a stream-order gradient in a Yangtze River estuarine marsh. Density, biomass, and secondary production of polychaetes were found to be highest in intermediate order creeks. In high order (3rd and 4th) creeks, the density and biomass of D. glabra were higher in creek edge sites than in creek bottom sites, whereas the reverse was true for low order (1st and 2nd) creeks. Secondary production was highest in 2nd order creeks (559.7 mg AFDM m−2 year−1) and was ca. 2 folds higher than in 1st and 4th order creeks. Top fitting AIC models indicated that the secondary production of D. glabra was mainly associated with geomorphological characters including cross-sectional area and bank slope. This suggests that hydrodynamic forces are essential factors influencing secondary production of macrobenthos in salt marshes. This study emphasizes the importance of microhabitat variability when evaluating secondary production and ecosystem functions. PMID:24817092
Stability Switches of Arbitrary High-Order Consensus in Multiagent Networks with Time Delays
2013-01-01
High-order consensus seeking, in which individual high-order dynamic agents share a consistent view of the objectives and the world in a distributed manner, finds its potential broad applications in the field of cooperative control. This paper presents stability switches analysis of arbitrary high-order consensus in multiagent networks with time delays. By employing a frequency domain method, we explicitly derive analytical equations that clarify a rigorous connection between the stability of general high-order consensus and the system parameters such as the network topology, communication time-delays, and feedback gains. Particularly, our results provide a general and a fairly precise notion of how increasing communication time-delay causes the stability switches of consensus. Furthermore, under communication constraints, the stability and robustness problems of consensus algorithms up to third order are discussed in details to illustrate our central results. Numerical examples and simulation results for fourth-order consensus are provided to demonstrate the effectiveness of our theoretical results. PMID:24109207
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.
Liu, Wei; Huang, Jie
2017-01-11
This paper studies the cooperative global robust output regulation problem for a class of heterogeneous second-order nonlinear uncertain multiagent systems with jointly connected switching networks. The main contributions consist of the following three aspects. First, we generalize the result of the adaptive distributed observer from undirected jointly connected switching networks to directed jointly connected switching networks. Second, by performing a new coordinate and input transformation, we convert our problem into the cooperative global robust stabilization problem of a more complex augmented system via the distributed internal model principle. Third, we solve the stabilization problem by a distributed state feedback control law. Our result is illustrated by the leader-following consensus problem for a group of Van der Pol oscillators.
The Correlation Structure of Local Neuronal Networks Intrinsically Results from Recurrent Dynamics
Helias, Moritz; Tetzlaff, Tom; Diesmann, Markus
2014-01-01
Correlated neuronal activity is a natural consequence of network connectivity and shared inputs to pairs of neurons, but the task-dependent modulation of correlations in relation to behavior also hints at a functional role. Correlations influence the gain of postsynaptic neurons, the amount of information encoded in the population activity and decoded by readout neurons, and synaptic plasticity. Further, it affects the power and spatial reach of extracellular signals like the local-field potential. A theory of correlated neuronal activity accounting for recurrent connectivity as well as fluctuating external sources is currently lacking. In particular, it is unclear how the recently found mechanism of active decorrelation by negative feedback on the population level affects the network response to externally applied correlated stimuli. Here, we present such an extension of the theory of correlations in stochastic binary networks. We show that (1) for homogeneous external input, the structure of correlations is mainly determined by the local recurrent connectivity, (2) homogeneous external inputs provide an additive, unspecific contribution to the correlations, (3) inhibitory feedback effectively decorrelates neuronal activity, even if neurons receive identical external inputs, and (4) identical synaptic input statistics to excitatory and to inhibitory cells increases intrinsically generated fluctuations and pairwise correlations. We further demonstrate how the accuracy of mean-field predictions can be improved by self-consistently including correlations. As a byproduct, we show that the cancellation of correlations between the summed inputs to pairs of neurons does not originate from the fast tracking of external input, but from the suppression of fluctuations on the population level by the local network. This suppression is a necessary constraint, but not sufficient to determine the structure of correlations; specifically, the structure observed at finite network
Stock market networks: The dynamic conditional correlation approach
NASA Astrophysics Data System (ADS)
Lyócsa, Štefan; Výrost, Tomáš; Baumöhl, Eduard
2012-08-01
We demonstrate the economic relevance of minimum spanning trees (MSTs) constructed from dynamic conditional correlations (DCC) for a sample of S&P 100 constituents. An empirical comparison of MST properties shows that using the standard approach of rolling (or sliding-window) correlations yields trees that are more robust, have higher densities and exhibit higher industry clustering than MSTs based on DCC. Our results suggest that these properties are achieved at the expense of the smoothing of market dynamics, which is better preserved by DCC. The DCC approach offers a new perspective for the analysis of complex systems such as stock markets.
Lin, Naibo; Liu, Xiang Yang
2015-11-07
This review examines how the concepts and ideas of crystallization can be extended further and applied to the field of mesoscopic soft materials. It concerns the structural characteristics vs. the macroscopic performance, and the formation mechanism of crystal networks. Although this subject can be discussed in a broad sense across the area of mesoscopic soft materials, our main focus is on supramolecular materials, spider and silkworm silks, and biominerals. First, the occurrence of a hierarchical structure, i.e. crystal network and domain network structures, will facilitate the formation kinetics of mesoscopic phases and boost up the macroscopic performance of materials in some cases (i.e. spider silk fibres). Second, the structure and performance of materials can be correlated in some way by the four factors: topology, correlation length, symmetry/ordering, and strength of association of crystal networks. Moreover, four different kinetic paths of crystal network formation are identified, namely, one-step process of assembly, two-step process of assembly, mixed mode of assembly and foreign molecule mediated assembly. Based on the basic mechanisms of crystal nucleation and growth, the formation of crystal networks, such as crystallographic mismatch (or noncrystallographic) branching (tip branching and fibre side branching) and fibre/polymeric side merging, are reviewed. This facilitates the rational design and construction of crystal networks in supramolecular materials. In this context, the (re-)construction of a hierarchical crystal network structure can be implemented by thermal, precipitate, chemical, and sonication stimuli. As another important class of soft materials, the unusual mechanical performance of spider and silkworm silk fibres are reviewed in comparison with the regenerated silk protein derivatives. It follows that the considerably larger breaking stress and unusual breaking strain of spider silk fibres vs. silkworm silk fibres can be interpreted
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
NASA Astrophysics Data System (ADS)
Diercksen, G. H. F.; Sadlej, A. J.
1981-08-01
The many-body perturbation theory is applied for the calculation of the second- and third-order correlation corrections to the SCF HF dipole moments and polarizabilities of FH, H2O, NH3, and CH4. All calculations are performed by using the finite-field perturbation approach. The pertinent correlation corrections follow from the numerical differentiation of the second- and third-order field-dependent correlation energies. This computational scheme corresponds to a completely self-consistent treatment of the perturbation effects. The third-order corrected dipole moments are in excellent agreement with the experimental data and the best results of other authors. A comparison of the present perturbation corrections for polarizabilities with the PNO-CI and CEPA results of Werner and Meyer reveals that some cancellation of the third- and fourth-order correlation contributions can be expected. The second-order corrected polarizabilities are as a rule better than the results of the third-order perturbation approach. It is concluded that also for polarizabilities the low-order many-body perturbation scheme is able to account for the major portion of the relevant correlation effects.
A novel joint sparse partial correlation method for estimating group functional networks.
Liang, Xiaoyun; Connelly, Alan; Calamante, Fernando
2016-03-01
Advances in graph theory have provided a powerful tool to characterize brain networks. In particular, functional networks at group-level have great appeal to gain further insight into complex brain function, and to assess changes across disease conditions. These group networks, however, often have two main limitations. First, they are popularly estimated by directly averaging individual networks that are compromised by confounding variations. Secondly, functional networks have been estimated mainly through Pearson cross-correlation, without taking into account the influence of other regions. In this study, we propose a sparse group partial correlation method for robust estimation of functional networks based on a joint graphical models approach. To circumvent the issue of choosing the optimal regularization parameters, a stability selection method is employed to extract networks. The proposed method is, therefore, denoted as JGMSS. By applying JGMSS across simulated datasets, the resulting networks show consistently higher accuracy and sensitivity than those estimated using an alternative approach (the elastic-net regularization with stability selection, ENSS). The robustness of the JGMSS is evidenced by the independence of the estimated networks to choices of the initial set of regularization parameters. The performance of JGMSS in estimating group networks is further demonstrated with in vivo fMRI data (ASL and BOLD), which show that JGMSS can more robustly estimate brain hub regions at group-level and can better control intersubject variability than it is achieved using ENSS.
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.
Sikkink, Kristin L.; Reynolds, Rose M.; Cresko, William A.; Phillips, Patrick C.
2017-01-01
Selection in novel environments can lead to a coordinated evolutionary response across a suite of characters. Environmental conditions can also potentially induce changes in the genetic architecture of complex traits, which in turn could alter the pattern of the multivariate response to selection. We describe a factorial selection experiment using the nematode Caenorhabditis remanei in which two different stress-related phenotypes (heat and oxidative stress resistance) were selected under three different environmental conditions. The pattern of covariation in the evolutionary response between phenotypes or across environments differed depending on the environment in which selection occurred, including asymmetrical responses to selection in some cases. These results indicate that variation in pleiotropy across the stress response network is highly sensitive to the external environment. Our findings highlight the complexity of the interaction between genes and environment that influences the ability of organisms to acclimate to novel environments. They also make clear the need to identify the underlying genetic basis of genetic correlations in order understand how patterns of pleiotropy are distributed across complex genetic networks. PMID:25809411
NASA Astrophysics Data System (ADS)
Xu, Chang-Jin; Li, Pei-Luan; Pang, Yi-Cheng
2017-02-01
This paper is concerned with fractional-order bidirectional associative memory (BAM) neural networks with time delays. Applying Laplace transform, the generalized Gronwall inequality and estimates of Mittag-Leffler functions, some sufficient conditions which ensure the finite-time stability of fractional-order bidirectional associative memory neural networks with time delays are obtained. Two examples with their simulations are given to illustrate the theoretical findings. Our results are new and complement previously known results. Supported by National Natural Science Foundation of China under Grant Nos.~61673008, 11261010, 11101126, Project of High-Level Innovative Talents of Guizhou Province ([2016]5651), Natural Science and Technology Foundation of Guizhou Province (J[2015]2025 and J[2015]2026), 125 Special Major Science and Technology of Department of Education of Guizhou Province ([2012]011) and Natural Science Foundation of the Education Department of Guizhou Province (KY[2015]482)
Basal ganglia and cortical networks for sequential ordering and rhythm of complex movements
Bednark, Jeffery G.; Campbell, Megan E. J.; Cunnington, Ross
2015-01-01
Voluntary actions require the concurrent engagement and coordinated control of complex temporal (e.g., rhythm) and ordinal motor processes. Using high-resolution functional magnetic resonance imaging (fMRI) and multi-voxel pattern analysis (MVPA), we sought to determine the degree to which these complex motor processes are dissociable in basal ganglia and cortical networks. We employed three different finger-tapping tasks that differed in the demand on the sequential temporal rhythm or sequential ordering of submovements. Our results demonstrate that sequential rhythm and sequential order tasks were partially dissociable based on activation differences. The sequential rhythm task activated a widespread network centered around the supplementary motor area (SMA) and basal-ganglia regions including the dorsomedial putamen and caudate nucleus, while the sequential order task preferentially activated a fronto-parietal network. There was also extensive overlap between sequential rhythm and sequential order tasks, with both tasks commonly activating bilateral premotor, supplementary motor, and superior/inferior parietal cortical regions, as well as regions of the caudate/putamen of the basal ganglia and the ventro-lateral thalamus. Importantly, within the cortical regions that were active for both complex movements, MVPA could accurately classify different patterns of activation for the sequential rhythm and sequential order tasks. In the basal ganglia, however, overlapping activation for the sequential rhythm and sequential order tasks, which was found in classic motor circuits of the putamen and ventro-lateral thalamus, could not be accurately differentiated by MVPA. Overall, our results highlight the convergent architecture of the motor system, where complex motor information that is spatially distributed in the cortex converges into a more compact representation in the basal ganglia. PMID:26283945
Generalization transitions in hidden-layer neural networks for third-order feature discrimination
NASA Astrophysics Data System (ADS)
Romeo, August
1993-03-01
Stochastic learning processes for a specific feature detector are studied. This technique is applied to nonsmooth multilayer neural networks requested to perform a discrimination task of order 3 based on the ssT-block-ssC-block problem. Our system proves to be capable of achieving perfect generalization, after presenting finite numbers of examples, by undergoing a phase transition. The corresponding annealed theory, which involves the Ising model under external field, shows good agreement with Monte Carlo simulations.
Robust synchronization of fractional-order complex dynamical networks with parametric uncertainties
NASA Astrophysics Data System (ADS)
Wong, W. K.; Li, Hongjie; Leung, S. Y. S.
2012-12-01
The study investigates robust synchronization of fractional-order complex dynamical networks with parametric uncertainties. Based on the properties of the kronecker product and the stability of the fractional-order system, the robust synchronization criteria are derived by applying the nonlinear control. These criteria are in the form of linear matrix inequalities which can be readily solved by applying the LMI toolbox. The coupling configuration matrix is not necessary to be symmetric or irreducible, and the inner coupling matrix needs not to be symmetric, diagonal or positive definite. Two numerical examples are provided to demonstrate the validity of the presented synchronization scheme.
Effects of frequency-degree correlation on synchronization transition in scale-free networks
NASA Astrophysics Data System (ADS)
Liu, Weiqing; Wu, Ye; Xiao, Jinghua; Zhan, Meng
2013-02-01
Explosive synchronization in the scale-free network with a positive frequency-degree correlation has been reported recently (Gomez G. J. et al., Phys. Rev. Lett., 106 (2011) 128701). In this article, we generalize this study and find that the explosive synchronization is replaced by a kind of hierarchical synchronization if the microscopic correlation between the frequency and the interacting topology of the network becomes negative. A star network model is set to prove this novel behavior. We also find that the degree assortativity has significant influence on the explosive synchronization but slight impact on the hierarchical synchronization. These findings are meaningful for revealing unusual effects of correlations between dynamics and structure of complex networks.
Monte Carlo explicitly correlated second-order many-body perturbation theory
NASA Astrophysics Data System (ADS)
Johnson, Cole M.; Doran, Alexander E.; Zhang, Jinmei; Valeev, Edward F.; Hirata, So
2016-10-01
A stochastic algorithm is proposed and implemented that computes a basis-set-incompleteness (F12) correction to an ab initio second-order many-body perturbation energy as a short sum of 6- to 15-dimensional integrals of Gaussian-type orbitals, an explicit function of the electron-electron distance (geminal), and its associated excitation amplitudes held fixed at the values suggested by Ten-no. The integrals are directly evaluated (without a resolution-of-the-identity approximation or an auxiliary basis set) by the Metropolis Monte Carlo method. Applications of this method to 17 molecular correlation energies and 12 gas-phase reaction energies reveal that both the nonvariational and variational formulas for the correction give reliable correlation energies (98% or higher) and reaction energies (within 2 kJ mol-1 with a smaller statistical uncertainty) near the complete-basis-set limits by using just the aug-cc-pVDZ basis set. The nonvariational formula is found to be 2-10 times less expensive to evaluate than the variational one, though the latter yields energies that are bounded from below and is, therefore, slightly but systematically more accurate for energy differences. Being capable of using virtually any geminal form, the method confirms the best overall performance of the Slater-type geminal among 6 forms satisfying the same cusp conditions. Not having to precompute lower-dimensional integrals analytically, to store them on disk, or to transform them in a nonscalable dense-matrix-multiplication algorithm, the method scales favorably with both system size and computer size; the cost increases only as O(n4) with the number of orbitals (n), and its parallel efficiency reaches 99.9% of the ideal case on going from 16 to 4096 computer processors.
Martens, Marijn B; Houweling, Arthur R; E Tiesinga, Paul H
2017-02-01
Neuronal circuits in the rodent barrel cortex are characterized by stable low firing rates. However, recent experiments show that short spike trains elicited by electrical stimulation in single neurons can induce behavioral responses. Hence, the underlying neural networks provide stability against internal fluctuations in the firing rate, while simultaneously making the circuits sensitive to small external perturbations. Here we studied whether stability and sensitivity are affected by the connectivity structure in recurrently connected spiking networks. We found that anti-correlation between the number of afferent (in-degree) and efferent (out-degree) synaptic connections of neurons increases stability against pathological bursting, relative to networks where the degrees were either positively correlated or uncorrelated. In the stable network state, stimulation of a few cells could lead to a detectable change in the firing rate. To quantify the ability of networks to detect the stimulation, we used a receiver operating characteristic (ROC) analysis. For a given level of background noise, networks with anti-correlated degrees displayed the lowest false positive rates, and consequently had the highest stimulus detection performance. We propose that anti-correlation in the degree distribution may be a computational strategy employed by sensory cortices to increase the detectability of external stimuli. We show that networks with anti-correlated degrees can in principle be formed by applying learning rules comprised of a combination of spike-timing dependent plasticity, homeostatic plasticity and pruning to networks with uncorrelated degrees. To test our prediction we suggest a novel experimental method to estimate correlations in the degree distribution.
Fast network oscillations in vitro exhibit a slow decay of temporal auto-correlations.
Poil, Simon-Shlomo; Jansen, Rick; van Aerde, Karlijn; Timmerman, Jaap; Brussaard, Arjen B; Mansvelder, Huibert D; Linkenkaer-Hansen, Klaus
2011-08-01
Ongoing neuronal oscillations in vivo exhibit non-random amplitude fluctuations as reflected in a slow decay of temporal auto-correlations that persist for tens of seconds. Interestingly, the decay of auto-correlations is altered in several brain-related disorders, including epilepsy, depression and Alzheimer's disease, suggesting that the temporal structure of oscillations depends on intact neuronal networks in the brain. Whether structured amplitude modulation occurs only in the intact brain or whether isolated neuronal networks can also give rise to amplitude modulation with a slow decay is not known. Here, we examined the temporal structure of cholinergic fast network oscillations in acute hippocampal slices. For the first time, we show that a slow decay of temporal correlations can emerge from synchronized activity in isolated hippocampal networks from mice, and is maximal at intermediate concentrations of the cholinergic agonist carbachol. Using zolpidem, a positive allosteric modulator of GABA(A) receptor function, we found that increased inhibition leads to longer oscillation bursts and more persistent temporal correlations. In addition, we asked if these findings were unique for mouse hippocampus, and we therefore analysed cholinergic fast network oscillations in rat prefrontal cortex slices. We observed significant temporal correlations, which were similar in strength to those found in mouse hippocampus and human cortex. Taken together, our data indicate that fast network oscillations with temporal correlations can be induced in isolated networks in vitro in different species and brain areas, and therefore may serve as model systems to investigate how altered temporal correlations in disease may be rescued with pharmacology. © 2011 The Authors. European Journal of Neuroscience © 2011 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.
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
1989-12-01
Ohio ’aPw iorlipuab muo i 0I2, AFIT/GE/ENG/89D-10 CLASSIFICATION OF ACOUSTO - OPTIC CORRELATION SIGNATURES OF SPREAD SPECTRUM SIGNALS USING ARTIFICIAL...ENG/89D- 10 CLASSIFICATION OF ACOUSTO - OPTIC CORRELATION SIGNATURES OF SPREAD SPECTRUM SIGNALS USING ARTIFICIAL NEURAL NETWORKS THESIS John W. DeBerry...Captain, USAF AFIT/GE/ENG/89D- 10 Approved for public release; distribution unlimited. AFIT/GE/ENG/89D-10 CLASSIFICATION OF ACOUSTO - OPTIC CORRELATION
Rakkiyappan, R; Cao, Jinde; Velmurugan, G
2015-01-01
This paper deals with the problem of existence and uniform stability analysis of fractional-order complex-valued neural networks with constant time delays. Complex-valued recurrent neural networks is an extension of real-valued recurrent neural networks that includes complex-valued states, connection weights, or activation functions. This paper explains sufficient condition for the existence and uniform stability analysis of such networks. Three numerical simulations are delineated to substantiate the effectiveness of the theoretical results.
The Open Host Network Packet Process Correlator for Windows
2014-06-17
The Hone sensors are packet-process correlation engines that log the relationships between applications and the communications they are responsible for. Hone sensors are available for a variety of platforms including Linux, Windows, and MacOSX. Hone sensors are designed to help analysts understand the meaning of communications on a deeper level by associating the origin or destination process to the communication. They do this by tracing communications on a per-packet basis, through the kernel of the operating system to determine their ultimate source/destination on the monitored machine.
The Open Host Network Packet Process Correlator for Windows
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.
Enhanced Nonclassical Third-Order Spatial Intensity Correlations in Three-Beam Interferences<
NASA Astrophysics Data System (ADS)
Richter, Th.
The joint probability of detecting simultaneously three photons at two and three points in the superposition field of three independent plane waves with the same frequency but random phases is calculated and compared with the results of a classical wave theory. Significant differences occur for sufficiently weak fields. We find that the differences between the quantum mechanical and classical predictions concerning the third order intensity correlation functions are more pronounced in a three-beam than in a two-beam interference experiment.Translated AbstractDreistrahlinterferenzen und nichtklassische räumliche Intensitätskorrelationen dritter OrdnungDie Verbundwahrscheinlichkeit des gleichzeitigen Nachweises von drei Photonen an zwei bzw. drei verschiedenen Orten im Interferenzfeld dreier unabhängiger Lichtstrahlen mit statistisch fluktuierenden Phasen wird berechnet und mit den Aussagen der klassischen Theorie verglichen. Bedeutende Unterschiede treten bei sehr geringen Lichtintensitäten auf. Dabei sind diese Unterschiede bei einem Dreistrahlinterferenz-Experiment ausgeprägter als im Zweistrahl-Fall.
Wang, Xiao; Broch, Katharina; Scholz, Reinhard; Schreiber, Frank; Meixner, Alfred J; Zhang, Dai
2014-04-03
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.
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.
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.
Godwin, Christine A; Hunter, Michael A; Bezdek, Matthew A; Lieberman, Gregory; Elkin-Frankston, Seth; Romero, Victoria L; Witkiewitz, Katie; Clark, Vincent P; Schumacher, Eric H
2017-08-01
Individual differences across a variety of cognitive processes are functionally associated with individual differences in intrinsic networks such as the default mode network (DMN). The extent to which these networks correlate or anticorrelate has been associated with performance in a variety of circumstances. Despite the established role of the DMN in mind wandering processes, little research has investigated how large-scale brain networks at rest relate to mind wandering tendencies outside the laboratory. Here we examine the extent to which the DMN, along with the dorsal attention network (DAN) and frontoparietal control network (FPCN) correlate with the tendency to mind wander in daily life. Participants completed the Mind Wandering Questionnaire and a 5-min resting state fMRI scan. In addition, participants completed measures of executive function, fluid intelligence, and creativity. We observed significant positive correlations between trait mind wandering and 1) increased DMN connectivity at rest and 2) increased connectivity between the DMN and FPCN at rest. Lastly, we found significant positive correlations between trait mind wandering and fluid intelligence (Ravens) and creativity (Remote Associates Task). We interpret these findings within the context of current theories of mind wandering and executive function and discuss the possibility that certain instances of mind wandering may not be inherently harmful. Due to the controversial nature of global signal regression (GSReg) in functional connectivity analyses, we performed our analyses with and without GSReg and contrast the results from each set of analyses. Copyright © 2017 Elsevier Ltd. All rights reserved.
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.
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.
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.
Wrinkles of graphene on Ir(111): Macroscopic network ordering and internal multi-lobed structure
Petrovic, Marin; Sadowski, Jerzy T.; Siber, Antonio; ...
2015-07-17
The large-scale production of graphene monolayer greatly relies on epitaxial samples which often display stress-relaxation features in the form of wrinkles. Wrinkles of graphene on Ir(111) are found to exhibit a fairly well ordered interconnecting network which is characterized by low-energy electron microscopy (LEEM). The high degree of quasi-hexagonal network arrangement for the graphene aligned to the underlying substrate can be well described as a (non-Poissonian) Voronoi partition of a plane. The results obtained strongly suggest that the wrinkle network is frustrated at low temperatures, retaining the order inherited from elevated temperatures when the wrinkles interconnect in junctions which mostmore » often join three wrinkles. Such frustration favors the formation of multi-lobed wrinkles which are found in scanning tunneling microscopy (STM) measurements. The existence of multiple lobes is explained within a model accounting for the interplay of the van der Waals attraction between graphene and iridium and bending energy of the wrinkle. The presented study provides new insights into wrinkling of epitaxial graphene and can be exploited to further expedite its application.« less
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.
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.
Characterizing highly correlated video traffic in high-speed asynchronous transfer mode networks
NASA Astrophysics Data System (ADS)
Shroff, Ness; Schwartz, Mischa
1996-04-01
The enormous bandwidth potential of optical fiber has resulted in a worldwide effort to develop high-speed ATM networks, also called broadband integrated services digital networks (B-ISDN). Many of the applications that ATM networks will support will have a strong video component to them. Hence, it is important to understand the behavior of video traffic as it travels through these networks. To that end, we develop the generalized histogram model (GHM) to characterize 'highly correlated' traffic, such as motion JPEG or 'smoothed' MPEG traffic over ATM networks end-to- end. Using our GHM model we show how to determine the loss rate at any node in an ATM network. We find that, for highly correlated video sources, increasing the buffer size beyond a certain region called the 'cell region' only marginally decreases the probability of loss. This implies that large buffers cannot be used to control the loss for such sources. The analytical model provided in this paper can be used for admission control, and network dimensioning and design in ATM networks. We have validated our results using simulations of real traces of video sources.
Taheri, Mehdi; Sheikholeslam, Farid; Najafi, Majddedin; Zekri, Maryam
2017-07-01
In this paper, consensus problem is considered for second order multi-agent systems with unknown nonlinear dynamics under undirected graphs. A novel distributed control strategy is suggested for leaderless systems based on adaptive fuzzy wavelet networks. Adaptive fuzzy wavelet networks are employed to compensate for the effect of unknown nonlinear dynamics. Moreover, the proposed method is developed for leader following systems and leader following systems with state time delays. Lyapunov functions are applied to prove uniformly ultimately bounded stability of closed loop systems and to obtain adaptive laws. Three simulation examples are presented to illustrate the effectiveness of the proposed control algorithms. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
NASA Technical Reports Server (NTRS)
Kerr, R. M.
1985-01-01
The classical approach to the investigation of small-scale intermittency in turbulence is based on the higher-order derivative correlations such as skewness and flatness factors. In the study of the small scales, numerial simulations can provide more detail than experiments. In the present paper, a variety of velocity- and scalar-derivative correlations are calculated over a range of Reynolds numbers. Particular attention is given to third- and fourth-order correlations, taking into account also some fifth- and sixth-order correlations to allow comparisons with the phenomenological models. The governing equations are the incompresssible Navier-Stokes equation for the velocity and transport equation for a passive scalar. Two numerical codes are used for the simulations presented. Attention is given to details regarding the numerical method used, forcing, simulation parameters, spectra and skewnesses, and graphics.
Brusco, Michael; Stolze, Hannah J; Hoffman, Michaela; Steinley, Douglas
2017-01-01
A popular objective criterion for partitioning a set of actors into core and periphery subsets is the maximization of the correlation between an ideal and observed structure associated with intra-core and intra-periphery ties. The resulting optimization problem has commonly been tackled using heuristic procedures such as relocation algorithms, genetic algorithms, and simulated annealing. In this paper, we present a computationally efficient simulated annealing algorithm for maximum correlation core/periphery partitioning of binary networks. The algorithm is evaluated using simulated networks consisting of up to 2000 actors and spanning a variety of densities for the intra-core, intra-periphery, and inter-core-periphery components of the network. Core/periphery analyses of problem solving, trust, and information sharing networks for the frontline employees and managers of a consumer packaged goods manufacturer are provided to illustrate the use of the model.
A Higher-Order Neural Network Design for Improving Segmentation Performance in Medical Image Series
NASA Astrophysics Data System (ADS)
Selvi, Eşref; Selver, M. Alper; Güzeliş, Cüneyt; Dicle, Oǧuz
2014-03-01
Segmentation of anatomical structures from medical image series is an ongoing field of research. Although, organs of interest are three-dimensional in nature, slice-by-slice approaches are widely used in clinical applications because of their ease of integration with the current manual segmentation scheme. To be able to use slice-by-slice techniques effectively, adjacent slice information, which represents likelihood of a region to be the structure of interest, plays critical role. Recent studies focus on using distance transform directly as a feature or to increase the feature values at the vicinity of the search area. This study presents a novel approach by constructing a higher order neural network, the input layer of which receives features together with their multiplications with the distance transform. This allows higher-order interactions between features through the non-linearity introduced by the multiplication. The application of the proposed method to 9 CT datasets for segmentation of the liver shows higher performance than well-known higher order classification neural networks.
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.
NASA Astrophysics Data System (ADS)
Mendez, Raul; Bastolla, Ugo
2010-06-01
We introduce the torsional network model (TNM), an elastic network model whose degrees of freedom are the torsion angles of the protein backbone. Normal modes of the TNM displace backbone atoms including Cβ maintaining their covalent geometry. For many proteins, low frequency TNM modes are localized in torsion space yet collective in Cartesian space, reminiscent of hinge motions. A smaller number of TNM modes than anisotropic network model modes are enough to represent experimentally observed conformation changes. We observed significant correlation between the contribution of each normal mode to equilibrium fluctuations and to conformation changes, and defined the excess correlation with respect to a simple neutral model. The stronger this excess correlation, the lower the predicted free energy barrier of the conformation change and the fewer modes contribute to the change.
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
Discrete-time adaptive backstepping nonlinear control via high-order neural networks.
Alanis, Alma Y; Sanchez, Edgar N; Loukianov, Alexander G
2007-07-01
This paper deals with adaptive tracking for discrete-time multiple-input-multiple-output (MIMO) nonlinear systems in presence of bounded disturbances. In this paper, a high-order neural network (HONN) structure is used to approximate a control law designed by the backstepping technique, applied to a block strict feedback form (BSFF). This paper also includes the respective stability analysis, on the basis of the Lyapunov approach, for the whole controlled system, including the extended Kalman filter (EKF)-based NN learning algorithm. Applicability of the scheme is illustrated via simulation for a discrete-time nonlinear model of an electric induction motor.
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.
First-order transition to perfect generalization in a neural network with binary synapses
NASA Astrophysics Data System (ADS)
Györgyi, Géza
1990-06-01
Learning from examples by a perceptron with binary synaptic parameters is studied. The examples are given by a reference (teacher) perceptron. It is shown that as the number of examples increases, the network undergoes a first-order transition, where it freezes into the state of the reference perceptron. When the transition point is approached from below, the generalization error reaches a minimal positive value, while above that point the error is constantly zero. The transition is found to occur at αGD=1.245 examples per coupling [E. Gardner and B. Derrida, J. Phys. A 22, 1983 (1989)].
NASA Astrophysics Data System (ADS)
Nazaripour, Hamid; Mansouri Daneshvar, Mohammad Reza
2017-08-01
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.
Spatial and time correlation of thermometers and pluviometers in a weather network database
NASA Astrophysics Data System (ADS)
Tardivo, Gianmarco
2015-04-01
A basic issue that arises when analysing data bases from weather networks is the correlation system that characterizes the set of weather stations. Some statistical models being used for simulating temperature and precipitation or estimating missing data often exploit the Pearson's correlation coefficient, whereby a selection of predictors is carried out. In this paper, a specific analysis was made to understand the relationship between the distances (between the stations) and the correlation structure (of the network) and to assess the evolution of the stations ranking over the time from the network establishment, given that they were ranked on the basis of their correlation coefficient values with a target station. This study was first carried out over the whole of the Veneto region in Northeast Italy, and subsequently, it was repeated, subdividing the area into three main climatic zones: mountain, plain and coast. The variables that are involved in this study are the following: daily precipitation and daily maximum, mean and minimum temperature. Generally, the correlation coefficients of the database of precipitation are, on average, inversely proportional to the mean distances from the target station. Considering that the same behaviour was not detected on analysing the temperature database, the main results of this work can be summarized as follows: (1) the most correlated stations of precipitation are generally closer to a target station than the most correlated stations of temperature (entire area); (2) starting from 5.5 years after the network was established, the temperature variable is characterized by a high stability (over time) of the correlation rankings, up to a wide radius from the target station; (3) this trend is not so clear in precipitation data. However, when taking into account the first result, (4) generally, the most correlated stations are placed within the radius of stability, more frequently so for precipitation than for temperature.
NASA Astrophysics Data System (ADS)
Kaliuzhnyi, Mykola; Bushuev, Felix; Shulga, Oleksandr; Sybiryakova, Yevgeniya; Shakun, Leonid; Bezrukovs, Vladislavs; Moskalenko, Sergiy; Kulishenko, Vladislav; Malynovskyi, Yevgen
2016-12-01
An international network of passive correlation ranging of a geostationary telecommunication satellite is considered in the article. The network is developed by the RI "MAO". The network consists of five spatially separated stations of synchronized reception of DVB-S signals of digital satellite TV. The stations are located in Ukraine and Latvia. The time difference of arrival (TDOA) on the network stations of the DVB-S signals, radiated by the satellite, is a measured parameter. The results of TDOA estimation obtained by the network in May-August 2016 are presented in the article. Orbital parameters of the tracked satellite are determined using measured values of the TDOA and two models of satellite motion: the analytical model SGP4/SDP4 and the model of numerical integration of the equations of satellite motion. Both models are realized using the free low-level space dynamics library OREKIT (ORbit Extrapolation KIT).
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.
Parallel access alignment network with barrel switch implementation for d-ordered vector elements
NASA Technical Reports Server (NTRS)
Barnes, George H. (Inventor)
1980-01-01
An alignment network between N parallel data input ports and N parallel data outputs includes a first and a second barrel switch. The first barrel switch fed by the N parallel input ports shifts the N outputs thereof and in turn feeds the N-1 input data paths of the second barrel switch according to the relationship X=k.sup.y modulo N wherein x represents the output data path ordering of the first barrel switch, y represents the input data path ordering of the second barrel switch, and k equals a primitive root of the number N. The zero (0) ordered output data path of the first barrel switch is fed directly to the zero ordered output port. The N-1 output data paths of the second barrel switch are connected to the N output ports in the reverse ordering of the connections between the output data paths of the first barrel switch and the input data paths of the second barrel switch. The second switch is controlled by a value m, which in the preferred embodiment is produced at the output of a ROM addressed by the value d wherein d represents the incremental spacing or distance between data elements to be accessed from the N input ports, and m is generated therefrom according to the relationship d=k.sup.m modulo N.
Degree Correlations Optimize Neuronal Network Sensitivity to Sub-Threshold Stimuli
Schmeltzer, Christian; Kihara, Alexandre Hiroaki; Sokolov, Igor Michailovitsch; Rüdiger, Sten
2015-01-01
Information processing in the brain crucially depends on the topology of the neuronal connections. We investigate how the topology influences the response of a population of leaky integrate-and-fire neurons to a stimulus. We devise a method to calculate firing rates from a self-consistent system of equations taking into account the degree distribution and degree correlations in the network. We show that assortative degree correlations strongly improve the sensitivity for weak stimuli and propose that such networks possess an advantage in signal processing. We moreover find that there exists an optimum in assortativity at an intermediate level leading to a maximum in input/output mutual information. PMID:26115374
Understanding structure of urban traffic network based on spatial-temporal correlation analysis
NASA Astrophysics Data System (ADS)
Yang, Yanfang; Jia, Limin; Qin, Yong; Han, Shixiu; Dong, Honghui
2017-08-01
Understanding the structural characteristics of urban traffic network comprehensively can provide references for improving road utilization rate and alleviating traffic congestion. This paper focuses on the spatial-temporal correlations between different pairs of traffic series and proposes a complex network-based method of constructing the urban traffic network. In the network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding spatial-temporal correlation. Further, a modified PageRank algorithm, named the geographical weight-based PageRank algorithm (GWPA), is proposed to analyze the spatial distribution of important segments in the road network. Finally, experiments are conducted by using three kinds of traffic series collected from the urban road network in Beijing. Experimental results show that the urban traffic networks constructed by three traffic variables all indicate both small-world and scale-free characteristics. Compared with the results of PageRank algorithm, GWPA is proved to be valid in evaluating the importance of segments and identifying the important segments with small degree.
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.
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.
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.
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.
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.
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
NASA Astrophysics Data System (ADS)
Bennett, C. Brad; Kruczek, James; Rabson, D. A.; Matthews, W. Garrett; Pandit, Sagar A.
2013-07-01
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.
Degree-ordered percolation on a hierarchical scale-free network.
Lee, Hyun Keun; Shim, Pyoung-Seop; Noh, Jae Dong
2014-06-01
We investigate the critical phenomena of the degree-ordered percolation (DOP) model on the hierarchical (u,v) flower network with u ≤ v. Highest degree nodes are linked directly without intermediate nodes for u=1, while this is not the case for u ≠ 1. Using the renormalization-group-like procedure, we derive the recursion relations for the percolating probability and the percolation order parameter, from which the percolation threshold and the critical exponents are obtained. When u ≠ 1, the DOP critical behavior turns out to be identical to that of the bond percolation with a shifted nonzero percolation threshold. When u=1, the DOP and the bond percolation have the same vanishing percolation threshold but the critical behaviors are different. Implication to an epidemic spreading phenomenon is discussed.
2010-01-01
Background For large-scale biological networks represented as signed graphs, the index of frustration measures how far a network is from a monotone system, i.e., how incoherently the system responds to perturbations. Results In this paper we find that the frustration is systematically lower in transcriptional networks (modeled at functional level) than in signaling and metabolic networks (modeled at stoichiometric level). A possible interpretation of this result is in terms of energetic cost of an interaction: an erroneous or contradictory transcriptional action costs much more than a signaling/metabolic error, and therefore must be avoided as much as possible. Averaging over all possible perturbations, however, we also find that unlike for transcriptional networks, in the signaling/metabolic networks the probability of finding the system in its least frustrated configuration tends to be high also in correspondence of a moderate energetic regime, meaning that, in spite of the higher frustration, these networks can achieve a globally ordered response to perturbations even for moderate values of the strength of the interactions. Furthermore, an analysis of the energy landscape shows that signaling and metabolic networks lack energetic barriers around their global optima, a property also favouring global order. Conclusion In conclusion, transcriptional and signaling/metabolic networks appear to have systematic differences in both the index of frustration and the transition to global order. These differences are interpretable in terms of the different functions of the various classes of networks. PMID:20537143
Total-order multi-agent task-network planning for contract bridge
Smith, S.J.J.; Nau, D.S.; Throop, T.A.
1996-12-31
This paper describes the results of applying a modified version of hierarchical task-network (HTN) planning to the problem of declarer play in contract bridge. We represent information about bridge in a task network that is extended to represent multi-agency and uncertainty. Our game-playing procedure uses this task network to generate game trees in which the set of alternative choices is determined not by the set of possible actions, but by the set of available tactical and strategic schemes. This approach avoids the difficulties that traditional game-tree search techniques have with imperfect-information games such as bridge--but it also differs in several significant ways from the planning techniques used in typical HTN planners. We describe why these modifications were needed in order to build a successful planner for bridge. This same modified HTN planning strategy appears to be useful in a variety of application domains, for example, we have used the same planning techniques in a process-planning system for the manufacture of complex electromechanical devices. We discuss why the same technique has been successful in two such diverse domains.
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.
Wu, Ailong; Zeng, Zhigang
2016-02-01
We show that the ω-periodic fractional-order fuzzy neural networks cannot generate non-constant ω-periodic signals. In addition, several sufficient conditions are obtained to ascertain the boundedness and global Mittag-Leffler stability of fractional-order fuzzy neural networks. Furthermore, S-asymptotical ω-periodicity and global asymptotical ω-periodicity of fractional-order fuzzy neural networks is also characterized. The obtained criteria improve and extend the existing related results. To illustrate and compare the theoretical criteria, some numerical examples with simulation results are discussed in detail. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.
Cellular neural network implementation using a phase-only joint transform correlator
NASA Astrophysics Data System (ADS)
Zhang, Shuqun; Karim, Mohammad A.
1999-04-01
A phase-only joint transform correlator (JTC) is used to realize cellular neural networks (CNNs). The operation of summing cross-correlations of bipolar data in CNNs can be realized in parallel by phase-encoding bipolar data. Compared to other optical systems for implementing CNNs, the proposed method offers the advantages of easier implementation and robustness in terms of system alignment, and requires neither electronic precalculation nor data rearrangement. Simulation results of the proposed optical CNNs for edge detection are provided.
Co-occurrence correlations of heavy metals in sediments revealed using network analysis.
Liu, Lili; Wang, Zhiping; Ju, Feng; Zhang, Tong
2015-01-01
In this study, the correlation-based study was used to identify the co-occurrence correlations among metals in marine sediment of Hong Kong, based on the long-term (from 1991 to 2011) temporal and spatial monitoring data. 14 stations out of the total 45 marine sediment monitoring stations were selected from three representative areas, including Deep Bay, Victoria Harbour and Mirs Bay. Firstly, Spearman's rank correlation-based network analysis was conducted as the first step to identify the co-occurrence correlations of metals from raw metadata, and then for further analysis using the normalized metadata. The correlations patterns obtained by network were consistent with those obtained by the other statistic normalization methods, including annual ratios, R-squared coefficient and Pearson correlation coefficient. Both Deep Bay and Victoria Harbour have been polluted by heavy metals, especially for Pb and Cu, which showed strong co-occurrence with other heavy metals (e.g. Cr, Ni, Zn and etc.) and little correlations with the reference parameters (Fe or Al). For Mirs Bay, which has better marine sediment quality compared with Deep Bay and Victoria Harbour, the co-occurrence patterns revealed by network analysis indicated that the metals in sediment dominantly followed the natural geography process. Besides the wide applications in biology, sociology and informatics, it is the first time to apply network analysis in the researches of environment pollutions. This study demonstrated its powerful application for revealing the co-occurrence correlations among heavy metals in marine sediments, which could be further applied for other pollutants in various environment systems.
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.
Connectivity strategies for higher-order neural networks applied to pattern recognition
NASA Technical Reports Server (NTRS)
Spirkovska, Lilly; Reid, Max B.
1990-01-01
Different strategies for non-fully connected HONNs (higher-order neural networks) are discussed, showing that by using such strategies an input field of 128 x 128 pixels can be attained while still achieving in-plane rotation and translation-invariant recognition. These techniques allow HONNs to be used with the larger input scenes required for practical pattern-recognition applications. The number of interconnections that must be stored has been reduced by a factor of approximately 200,000 in a T/C case and about 2000 in a Space Shuttle/F-18 case by using regional connectivity. Third-order networks have been simulated using several connection strategies. The method found to work best is regional connectivity. The main advantages of this strategy are the following: (1) it considers features of various scales within the image and thus gets a better sample of what the image looks like; (2) it is invariant to shape-preserving geometric transformations, such as translation and rotation; (3) the connections are predetermined so that no extra computations are necessary during run time; and (4) it does not require any extra storage for recording which connections were formed.
Word sense disambiguation via high order of learning in complex networks
NASA Astrophysics Data System (ADS)
Silva, Thiago C.; Amancio, Diego R.
2012-06-01
Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model.
Connectivity strategies for higher-order neural networks applied to pattern recognition
NASA Technical Reports Server (NTRS)
Spirkovska, Lilly; Reid, Max B.
1990-01-01
Different strategies for non-fully connected HONNs (higher-order neural networks) are discussed, showing that by using such strategies an input field of 128 x 128 pixels can be attained while still achieving in-plane rotation and translation-invariant recognition. These techniques allow HONNs to be used with the larger input scenes required for practical pattern-recognition applications. The number of interconnections that must be stored has been reduced by a factor of approximately 200,000 in a T/C case and about 2000 in a Space Shuttle/F-18 case by using regional connectivity. Third-order networks have been simulated using several connection strategies. The method found to work best is regional connectivity. The main advantages of this strategy are the following: (1) it considers features of various scales within the image and thus gets a better sample of what the image looks like; (2) it is invariant to shape-preserving geometric transformations, such as translation and rotation; (3) the connections are predetermined so that no extra computations are necessary during run time; and (4) it does not require any extra storage for recording which connections were formed.
NASA Technical Reports Server (NTRS)
Mcclelland, J.; Silk, J.
1978-01-01
Higher-order correlation functions for the large-scale distribution of galaxies in space are investigated. It is demonstrated that the three-point correlation function observed by Peebles and Groth (1975) is not consistent with a distribution of perturbations that at present are randomly distributed in space. The two-point correlation function is shown to be independent of how the perturbations are distributed spatially, and a model of clustered perturbations is developed which incorporates a nonuniform perturbation distribution and which explains the three-point correlation function. A model with hierarchical perturbations incorporating the same nonuniform distribution is also constructed; it is found that this model also explains the three-point correlation function, but predicts different results for the four-point and higher-order correlation functions than does the model with clustered perturbations. It is suggested that the model of hierarchical perturbations might be explained by the single assumption of having density fluctuations or discrete objects all of the same mass randomly placed at some initial epoch.