Relevant bibliographies by topics / Network Correlations

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Network Correlations'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Network Correlations"

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Lary, D. J., M. D. Müller, and H. Y. Mussa. "Using neural networks to describe tracer correlations." Atmospheric Chemistry and Physics 4, no. 1 (January 31, 2004): 143–46. http://dx.doi.org/10.5194/acp-4-143-2004.

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Abstract. 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 coefficient between simulated and training values of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. Such as the dataset from the Halogen Occultation Experiment (HALOE) which has continuously observed CH4 (but not N2O) from 1991 till the present. The neural network Fortran code used is available for download.
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Lary, D. J., M. D. Müller, and H. Y. Mussa. "Using neural networks to describe tracer correlations." Atmospheric Chemistry and Physics Discussions 3, no. 6 (November 13, 2003): 5711–24. http://dx.doi.org/10.5194/acpd-3-5711-2003.

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Abstract. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural 5 network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). In this study a neural network using Quickprop learning and one hidden layer with eight nodes was able to reproduce the CH4-N2O correlation with a correlation co-efficient of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. Such as the 10 dataset from the Halogen Occultation Experiment (HALOE) which has continuously observed CH4 (but not N2O) from 1991 till the present. The neural network Fortran code used is available for download
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Zhao, Li, David C. Alsop, John A. Detre, and Weiying Dai. "Global fluctuations of cerebral blood flow indicate a global brain network independent of systemic factors." Journal of Cerebral Blood Flow & Metabolism 39, no. 2 (August 17, 2017): 302–12. http://dx.doi.org/10.1177/0271678x17726625.

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Global synchronization across specialized brain networks is a common feature of network models and in-vivo electrical measurements. Although the imaging of specialized brain networks with blood oxygenation sensitive resting state functional magnetic resonance imaging (rsfMRI) has enabled detailed study of regional networks, the study of globally correlated fluctuations with rsfMRI is confounded by spurious contributions to the global signal from systemic physiologic factors and other noise sources. Here we use an alternative rsfMRI method, arterial spin labeled perfusion MRI, to characterize global correlations and their relationship to correlations and anti-correlations between regional networks. Global fluctuations that cannot be explained by systemic factors dominate the fluctuations in cerebral blood flow. Power spectra of these fluctuations are band limited to below 0.05 Hz, similar to prior measurements of regional network fluctuations in the brain. Removal of these global fluctuations prior to measurement of regional networks reduces all regional network fluctuation amplitudes to below the global fluctuation amplitude and changes the strength and sign of inter network correlations. Our findings support large amplitude, globally synchronized activity across networks that require a reassessment of regional network amplitude and correlation measures.
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Peron, T. K. D., C. H. Comin, D. R. Amancio, L. da F. Costa, F. A. Rodrigues, and J. Kurths. "Correlations between climate network and relief data." Nonlinear Processes in Geophysics Discussions 1, no. 1 (April 24, 2014): 823–40. http://dx.doi.org/10.5194/npgd-1-823-2014.

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Abstract. In the last few years, the scientific community has witnessed an ongoing trend of using ideas developed in the study of complex networks to analyze climate dynamics. This powerful combination, usually called climate networks, can be used to uncover non-trivial patterns of weather changes along the years. Here we investigate the temperature network of North America region and show that two network characteristics, namely degree and clustering, have markedly differences between the Eastern and Western regions. We show that such differences are a reflection of the presence of a large network community in the western side of the continent. Moreover, we provide evidences that this large community is a consequence of the peculiar characteristics of the western relief of North America.
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Peron, T. K. D., C. H. Comin, D. R. Amancio, L. da F. Costa, F. A. Rodrigues, and J. Kurths. "Correlations between climate network and relief data." Nonlinear Processes in Geophysics 21, no. 6 (November 27, 2014): 1127–32. http://dx.doi.org/10.5194/npg-21-1127-2014.

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Abstract. In the last few years, the scientific community has witnessed an ongoing trend of using ideas developed in the study of complex networks to analyze climate dynamics. This powerful combination, usually called climate networks, can be used to uncover non-trivial patterns of weather changes throughout the years. Here we investigate the temperature network of the North American region and show that two network characteristics, namely degree and clustering, have marked differences between the eastern and western regions. We show that such differences are a reflection of the presence of a large network community on the western side of the continent. Moreover, we provide evidence that this large community is a consequence of the peculiar characteristics of the western relief of North America.
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Daduna, Hans, and Ryszard Szekli. "Correlation formulas for Markovian network processes in a random environment." Advances in Applied Probability 48, no. 1 (March 2016): 176–98. http://dx.doi.org/10.1017/apr.2015.12.

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Abstract We consider Markov processes, which describe, e.g. queueing network processes, in a random environment which influences the network by determining random breakdown of nodes, and the necessity of repair thereafter. Starting from an explicit steady-state distribution of product form available in the literature, we note that this steady-state distribution does not provide information about the correlation structure in time and space (over nodes). We study this correlation structure via one-step correlations for the queueing-environment process. Although formulas for absolute values of these correlations are complicated, the differences of correlations of related networks are simple and have a nice structure. We therefore compare two networks in a random environment having the same invariant distribution, and focus on the time behaviour of the processes when in such a network the environment changes or the rules for travelling are perturbed. Evaluating the comparison formulas we compare spectral gaps and asymptotic variances of related processes.
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Jungsbluth, Magnus, Bernd Burghardt, and Alexander K. Hartmann. "Fingerprinting networks: Correlations of local and global network properties." Physica A: Statistical Mechanics and its Applications 381 (July 2007): 444–56. http://dx.doi.org/10.1016/j.physa.2007.03.029.

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Lary, D. J., and H. Y. Mussa. "Using an extended Kalman filter learning algorithm for feed-forward neural networks to describe tracer correlations." Atmospheric Chemistry and Physics Discussions 4, no. 3 (June 30, 2004): 3653–67. http://dx.doi.org/10.5194/acpd-4-3653-2004.

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Abstract. In this study a new extended Kalman filter (EKF) learning algorithm for feed-forward neural networks (FFN) is used. With the EKF approach, the training of the FFN can be seen as state estimation for a non-linear stationary process. The EKF method gives excellent convergence performances provided that there is enough computer core memory and that the machine precision is high. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). The neural network was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9997. The neural network Fortran code used is available for download.
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Zhang, Zhongzhi, and Shuigeng Zhou. "Correlations in random Apollonian network." Physica A: Statistical Mechanics and its Applications 380 (July 2007): 621–28. http://dx.doi.org/10.1016/j.physa.2007.02.058.

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Liu, Kuan, Haiyuan Liu, Dongyan Sun, and Lei Zhang. "Network Inference from Gene Expression Data with Distance Correlation and Network Topology Centrality." Algorithms 14, no. 2 (February 15, 2021): 61. http://dx.doi.org/10.3390/a14020061.

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The reconstruction of gene regulatory networks based on gene expression data can effectively uncover regulatory relationships between genes and provide a deeper understanding of biological control processes. Non-linear dependence is a common problem in the regulatory mechanisms of gene regulatory networks. Various methods based on information theory have been developed to infer networks. However, the methods have introduced many redundant regulatory relationships in the network inference process. A recent measurement method called distance correlation has, in many cases, shown strong and computationally efficient non-linear correlations. In this paper, we propose a novel regulatory network inference method called the distance-correlation and network topology centrality network (DCNTC) method. The method is based on and extends the Local Density Measurement of Network Node Centrality (LDCNET) algorithm, which has the same choice of network centrality ranking as the LDCNET algorithm, but uses a simpler and more efficient distance correlation measure of association between genes. In this work, we integrate distance correlation and network topological centrality into the reasoning about the structure of gene regulatory networks. We will select optimal thresholds based on the characteristics of the distribution of each gene pair in relation to distance correlation. Experiments were carried out on four network datasets and their performance was compared.
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Dissertations / Theses on the topic "Network Correlations"

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Yaveroglu, Omer Nebil. "Graphlet correlations for network comparison and modelling : World Trade Network example." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/25523.

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We propose methods on two fundamental graph theoretic problems: (1) network comparison, and (2) network modelling. Our methods are applied to five real-world network types, with an emphasis on world trade networks (WTNs), which we choose due to the world's current economic crisis. Finding topological similarities of complex networks is computationally intractable due to NP-Completeness of the subgraph isomorphism problem. Hence, simple heuristics have been used for this purpose. The most sophisticated heuristics are based on graph spectra and small subnetworks including graphlets. Among these, graphlets are preferred since spectra do not provide a direct real-world interpretation of network structure. However, current graphlet-based techniques can be improved. We improve graphlet-based heuristics by defining a new network topology descriptor, Graphlet Correlation Matrix (GCM), which eliminates all redundancies and quantifies the dependencies in graphlet properties. Then, we introduce a new network distance measure, Graphlet Correlation Distance (GCD), that compares GCMs of two networks. We show that GCD has the best network classification performance, is highly noise-tolerant, and is computationally efficient. Using this methodology, we highlight a three-layer organization in the WTNs: core, broker, and periphery. Furthermore, we uncover the link between the dynamic changes in oil price and trade network topology. Network models should shed light on the rules governing the formation of real networks. Using GCD, we identify models that fit five real-world network types. However, none of these standard network models fit WTNs. Hence, we introduce two new network models: one that mimics the Gravity Model of Trade, and the other that mimics brokerage / peripheral positioning of a country in WTN. Also, we show that economic wealth indicators of a country are predictive of its future brokerage position. Finally, we use exponential-family random graph modelling approach to build a generic framework that enables modelling based on any graphlet property.
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El, Boustani Sami. "Learning and coding correlations in stochastic network states." Paris 6, 2010. http://www.theses.fr/2010PA066279.

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L'activité des réseaux de neurones corticaux est caractérisée par des motifs de décharge stochastiques. Cependant, la nature exacte de ces états, et comment un apprentissage stable et un codage d'information peuvent se produire dans de tels états, restent mal compris. Nous avons d'abord étudié des modèles de réseaux de neurones capables de reproduire les régimes d'activité observés in vivo. Ces régimes asynchrones et irréguliers sont modélisés à l'aide d’une description Markovienne en utilisant une équation maîtresse phénoménologique. Afin de disséquer les corrélations présentes dans l'activité corticale, nous avons développé différents outils d'analyse. Nous trouvons que la réponse sous-liminaire de neurones dans V1 peut refléter les corrélations dans le stimulus visuel. Au niveau extracellulaire, nous avons développé un modèle d'Ising qui peut prédire précisément le taux d'occurrence des motifs spatio-temporels de décharge de plusieurs neurones enregistrés simultanément. Nous nous sommes ensuite intéressés à la question du codage de corrélations in vivo. Des enregistrements extracellulaires ont été effectués dans le cortex à tonneaux du rat anesthésié. Nous avons trouvé que le niveau de corrélation affecte les propriétés intégratives des neurones enregistrés. Un modèle fonctionnel suggère que ce résultat peut s'expliquer par des interactions entre cellules de sélectivité opposée. Finalement, nous avons proposé un modèle basé sur la règle de plasticité STDP qui peut stabiliser l'apprentissage dans l'activité spontanée. Ce modèle est équivalent à une règle BCM et peut reproduire des résultats dans l'hippocampe où la méta-plasticité a été observée pour la première fois.
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Jovanovic, Stojan. "Correlations of Higher Order in Networks of Spiking Neurons." Doctoral thesis, KTH, Beräkningsvetenskap och beräkningsteknik (CST), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-193316.

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The topic of this dissertation is the study of the emergence of higher-order correlations in recurrentlyconnected populations of brain cells.Neurons have been experimentally shown to form vast networks in the brain. In these networks, eachbrain cell communicates with tens of thousands of its neighbors by sending out and receiving electricalsignals, known as action potentials or spikes. The effect of a single action potential can propagate throughthe network and cause additional spikes to be generated. Thus, the connectivity of the neuronal networkgreatly influences the network's spiking dynamics. However, while the methods of action potentialgeneration are very well studied, many dynamical features of neuronal networks are still only vaguelyunderstood.The reasons for this mostly have to do with the difficulties of keeping track of the collective, non-linearbehavior of hundreds of millions of brain cells. Even when one focuses on small groups of neurons, all butthe most trivial questions about coordinated activity remain unanswered, due to the combinatorialexplosion that arises in all questions of this sort. In theoretical neuroscience one often needs to resort tomathematical models that try to explain the most important dynamical phenomena while abstractingaway many of the morphological features of real neurons.On the other hand, advances in experimental methods are making simultaneous recording of largeneuronal populations possible. Datasets consisting of collective spike trains of thousands of neurons arebecoming available. With these new developments comes the possibility of finally understanding the wayin which connectivity gives rise to the many interesting dynamical aspects of spiking networks.The main research question, addressed in this thesis, is how connectivity between neurons influences thedegree of synchrony between their respective spike trains. Using a linear model of spiking neurondynamics, we show that there is a mathematical relationship between the network's connectivity and theso-called higher-order cumulants, which quantify beyond-chance-level coordinated activity of groups ofneurons. Our equations describe the specific connectivity patterns that give rise to higher-ordercorrelations. In addition, we explore the special case of correlations of third-order and find that, in large,regular networks, it is the presence of a single subtree that is responsible for third-order synchrony.In summary, the results presented in this dissertation advance our understanding of how higher-ordercorrelations between spike trains of neurons are affected by certain patterns in synaptic connectivity.Our hope is that a better understanding of such complicated neuronal dynamics can lead to a consistenttheory of the network's functional properties.

QC 20161003

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Charles, Adam Shabti. "Dynamics and correlations in sparse signal acquisition." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53592.

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One of the most important parts of engineered and biological systems is the ability to acquire and interpret information from the surrounding world accurately and in time-scales relevant to the tasks critical to system performance. This classical concept of efficient signal acquisition has been a cornerstone of signal processing research, spawning traditional sampling theorems (e.g. Shannon-Nyquist sampling), efficient filter designs (e.g. the Parks-McClellan algorithm), novel VLSI chipsets for embedded systems, and optimal tracking algorithms (e.g. Kalman filtering). Traditional techniques have made minimal assumptions on the actual signals that were being measured and interpreted, essentially only assuming a limited bandwidth. While these assumptions have provided the foundational works in signal processing, recently the ability to collect and analyze large datasets have allowed researchers to see that many important signal classes have much more regularity than having finite bandwidth. One of the major advances of modern signal processing is to greatly improve on classical signal processing results by leveraging more specific signal statistics. By assuming even very broad classes of signals, signal acquisition and recovery can be greatly improved in regimes where classical techniques are extremely pessimistic. One of the most successful signal assumptions that has gained popularity in recet hears is notion of sparsity. Under the sparsity assumption, the signal is assumed to be composed of a small number of atomic signals from a potentially large dictionary. This limit in the underlying degrees of freedom (the number of atoms used) as opposed to the ambient dimension of the signal has allowed for improved signal acquisition, in particular when the number of measurements is severely limited. While techniques for leveraging sparsity have been explored extensively in many contexts, typically works in this regime concentrate on exploring static measurement systems which result in static measurements of static signals. Many systems, however, have non-trivial dynamic components, either in the measurement system's operation or in the nature of the signal being observed. Due to the promising prior work leveraging sparsity for signal acquisition and the large number of dynamical systems and signals in many important applications, it is critical to understand whether sparsity assumptions are compatible with dynamical systems. Therefore, this work seeks to understand how dynamics and sparsity can be used jointly in various aspects of signal measurement and inference. Specifically, this work looks at three different ways that dynamical systems and sparsity assumptions can interact. In terms of measurement systems, we analyze a dynamical neural network that accumulates signal information over time. We prove a series of bounds on the length of the input signal that drives the network that can be recovered from the values at the network nodes~[1--9]. We also analyze sparse signals that are generated via a dynamical system (i.e. a series of correlated, temporally ordered, sparse signals). For this class of signals, we present a series of inference algorithms that leverage both dynamics and sparsity information, improving the potential for signal recovery in a host of applications~[10--19]. As an extension of dynamical filtering, we show how these dynamic filtering ideas can be expanded to the broader class of spatially correlated signals. Specifically, explore how sparsity and spatial correlations can improve inference of material distributions and spectral super-resolution in hyperspectral imagery~[20--25]. Finally, we analyze dynamical systems that perform optimization routines for sparsity-based inference. We analyze a networked system driven by a continuous-time differential equation and show that such a system is capable of recovering a large variety of different sparse signal classes~[26--30].
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Calderini, Matias. "Linear Discriminant Analysis and Noise Correlations in Neuronal Activity." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39962.

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The effects of noise correlations on neuronal stimulus discrimination have been the subject of sustained debate. Both experimental and computational work suggest beneficial and detrimental contributions of noise correlations. The aim of this study is to develop an analytically tractable model of stimulus discrimination that reveals the conditions leading to improved or impaired performance from model parameters and levels of noise correlation. We begin with a mean firing rate integrator model as an approximation of underlying spiking activity in neuronal circuits. We consider two independent units receiving constant input and time fluctuating noise whose correlation across units can be tuned independently of firing rate. We implement a perceptron-like readout with Fisher Linear Discriminant Analysis (LDA). We exploit its closed form solution to find explicit expressions for discrimination error as a function of network parameters (leak, shared inputs, and noise gain) as well as the strength of noise correlation. First, we derive equations for discrimination error as a function of noise correlation. We find that four qualitatively different sets of results exist, based on the ratios of the difference of means and variance of the distributions of neural activity. From network parameters, we find the conditions for which an increase in noise correlation can lead to monotonic decrease or monotonic increase of error, as well as conditions for which error evolves non-monotonically as a function of correlations. These results provide a potential explanation for previously reported contradictory effects of noise correlation. Second, we expand on the dependency of the quantitative behaviour of the error curve on the tuning of specific subsets of network parameters. Particularly, when the noise gain of a pair of units is increased, the error rate as a function of noise correlation increases multiplicatively. However, when the noise gain of a single unit is increased, under certain conditions, the effect of noise can be beneficial to stimulus discrimination. In sum, we present a framework of analysis that explains a series of non-trivial properties of neuronal discrimination via a simple linear classifier. We show explicitly how different configurations of parameters can lead to drastically different conclusions on the impact of noise correlations. These effects shed light on abundant experimental and computational results reporting conflicting effects of noise correlations. The derived analyses rely on few assumptions and may therefore be applicable to a broad class of neural models whose activity can be approximated by a multivariate distribution.
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Kawaguchi, Akio, Hiraku Okada, Takaya Yamazato, and Masaaki Katayama. "Correlations of noise wavements at different outlets in a power-line network." IEEE, 2006. http://hdl.handle.net/2237/7797.

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Thome, Alexander. "Experience-Dependent Network Modification in the Medial Temporal Lobe." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/223358.

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Theoretical models of information storage in the brain have suggested that neurons may undergo an experience-dependent tuning or sharpening of their representations in order to maximize the amount of information that can be stored. Changes in the tuning profiles of neurons have been demonstrated to occur when animals must learn perceptual discriminations, however, whether similar changes occur in the absence of behavioral demands is unclear. To address these questions, the activity of simultaneously recorded medial temporal lobe (MTL) neurons was studied in relation to a passive visual recognition memory task. The structure of this task was such that it allowed for a comparison between novelty related responses as well as tuning properties of individual neurons. A total of 565 well isolated single neurons were recorded. The first contribution of this dissertation is the finding of a dissociation between different medial temporal lobe regions such that neurons in temporal area F (TF), but not perirhinal cortex (PRC) or the hippocampus, show an experience-dependent change in their stimulus selectivity. This finding indicates that tuning of stimulus representations may be an effective mechanism for maximizing information storage in some brain regions. The absence of stimulus tuning in higher level association regions (i.e. TF and PRC) suggests that tuning in these regions may be disadvantageous due to the need to construct unified representations across sensory modalities. A complimentary question to the question of network storage capacity is how networks avoid saturation in the connections between neurons. The second contribution of this dissertation is the finding that there exists a decrease in the magnitude of the short time scale correlations between pairs of neurons; suggesting that networks reduce the number of connections between neurons as a stimulus becomes familiar. Gamma oscillations have been proposed to be the mechanism by which groups of neurons coordinate their activity. However, network coordination has only been indirectly measured. The final contribution of this dissertation is the finding that the magnitude of gamma oscillations is strongly correlated with enhanced magnitude of correlations between neurons.
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Merve, Akis. "Temporal and Spatial Interference Correlations in Cognitive Radio Networks with Vertical Cooperation." Thesis, KTH, Kommunikationsnät, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-91892.

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Cognitive radio technology provides a solution for the spectrum scarcity issue by allowing the unlicensed users which are the cognitive radio devices to share the licensed band with the licensed (primary) users. The abilities of cognitive radio device help the secondary (unlicensed) nodes to observe the licensed band and to adjust their transmission parameters for maintaining the primary communication since in cognitive radio networks, it is essential that the existence of the unlicensed users must not harm the licensed network. Under these circumstances, we consider vertical cooperative transmission where primary transmission is not severely damaged by the secondary interference since each primary pair (transmitter and receiver) selects a neighbor secondary user as a cooperative relay to assist their transmission. Cooperation provides an increase in the signal-tointerference-ratio (SIR) of the primary network which can be harnessed by the secondary network as an additional bandwidth for their communication. We propose three relay selection rules so the influence of the relay’s position over the temporal and spatial correlations can be evaluated for different network conditions. Additionally, we implement primary exclusive region (PER) for each primary pair in the network which covers primary nodes, and all secondary users locate inside the zone become inactive. According to the outage model proposed in paper [2], temporal and spatial correlations are assumed to be 1 and 0 respectively; however it is estimated that regarding the environmental factors and the relay’s location, these correlation values may vary. This thesis work is based on the validation of the assumptions provided in paper [2] and our results demonstrate that the temporal and spatial correlation values changes under different circumstances and with different relay selection rules. The simulation results also show that PER significantly stimulates the cooperation performance thereby increases the transmission quality of the primary network.
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Kraft, Tristan [Verfasser], and Otfried [Gutachter] Gühne. "Aspects of quantum resources: coherence, measurements, and network correlations / Tristan Kraft ; Gutachter: Otfried Gühne." Siegen : Universitätsbibliothek der Universität Siegen, 2020. http://d-nb.info/1225557860/34.

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Wu, Gang Mechanical &amp Manufacturing Engineering Faculty of Engineering UNSW. "The impact of inter-company network technology on correlations between supply chain drivers and performance measures." Publisher:University of New South Wales. Mechanical & Manufacturing Engineering, 2009. http://handle.unsw.edu.au/1959.4/43645.

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This research aims to examine how, and to what extent, the advanced network technology such as custom-built large-scale network, or internet-based technology contribute to the correlations between supply chain drivers and performance measures. The uniqueness of the research is to use network technology as a leverage factor, instead of merely one of the supply chain drivers, to analyse how it would impact on the correlations between supply chain drivers and performance measures. Through literature review, we identified the key drivers in supply chain and the key performance indicators as independent and dependent variables respectively for data analysis in the research. We consider the utilisation of network technology as a selection variable in the analysis. We also proposed a set of research questions and hypotheses resulting from the literature review. The subsequent data analyses attempted to find answers for these questions and test the validity of the hypotheses. This was achieved by a field survey for 1035 major Australian firms through a structured questionnaire. The response rate of the survey was 20.8%. All these data were analysed with statistical models such as reliability test, multi-collinearity test, MANOVA procedures, factor analysis, and multiple regression modelling to validate whether the survey was robust and how the leverage factor (network technology) would impact on the correlations between supply chain drivers and performance measures. Each research question and hypothesis was reviewed, validated, and concluded based on the results from data analysis. The key findings from the data analysis support the perception that the network technologies with their external customers and suppliers dramatically affect the correlations between supply chain drivers and performance measures. Statistically it actually determines whether the supply chain will success or fail when comparing firms using the technologies with firms not using them. In general, the impact on the correlations is directional and positive. A set of validated theoretical models was also proposed to depict the dynamics between supply chain variables under the influence of network technology. Implications of the findings are also provided in the thesis.
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Books on the topic "Network Correlations"

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Armitage, Emily G., Helen L. Kotze, and Kaye J. Williams. Correlation-based network analysis of cancer metabolism. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0615-4.

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Armitage, Emily G. Correlation-based network analysis of cancer metabolism: A new systems biology approach in metabolomics. New York: Springer, 2014.

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Hammami, Manel. Level Doubling Network and Ripple Correlation Control MPPT Algorithm for Grid-Connected Photovoltaic Systems. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10492-4.

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Aizer, Anna. Networks or neighborhoods?: Correlations in the use of publicly-funded maternity care in California. Cambridge, MA: National Bureau of Economic Research, 2002.

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O'Callaghan, Peter. Demonstration of combination of expert system paradigms for telecommunications network alarm correlation and fault diagnosis. (s.l: The Author), 1996.

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Intrusion detection: An introduction to Internet surveillance, correlation, traps, trace back, and response. Sparta, N.J: Intrusion.Net Books, 1998.

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History now: Online explorations in western civilization : correlation guide with access code for western civilization. Sputhbank, Victoria, Australia: Thomson Wadsworth, 2005.

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Die Berechnung der Welt: Von der Weltformel zu Big Data. München: C.H. Beck, 2014.

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Cronley, Thomas J. The use of neural networks as a method of correlating thermal fluid data to provide useful information on thermal systems. Monterey, Calif: Naval Postgraduate School, 2000.

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Ikryannikov, Valentin, and Aleksey Barykin. Problems of standardization in the implementation of the provisions of the Technical regulations of the Russian Federation. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1194152.

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The book provides the results of a study of standardization in the implementation of national technical regulations of the Russian Federation "About fire safety", "On safety of buildings and structures", "On safety of gas distribution networks and gas consumption, On the safety of marine transport", "On safety of inland water transport" and the Technical regulations on tobacco products. The study was the analysis and correlation of the objects and requirements of technical regulations, the approved lists of documents for standardization adopted in 2016-2020 codes of practice, national, intergovernmental, international and regional standards, codes, national standards, the collection of information on enforcement practices and suggestions from industry stakeholders and generalization of the obtained data. According to the results of the study, the key and organizational and methodological problems of standardization in the field of technical regulation are identified, and current directions for improving standardization and national technical regulations are identified. It is of interest to a wide range of specialists in the field of standardization, technical regulation and public administration, and can be used in the preparation of training programs and manuals for bachelor's, master's, additional professional education and MBA programs.
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Book chapters on the topic "Network Correlations"

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Sciortino, Francesco. "Dynamics of the Hydrogen Bond Network in Simulated Liquid Water." In Correlations and Connectivity, 214–24. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-2157-3_17.

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Krishna, P. Murali, Vikram M. Gadre, and Uday B. Desai. "Multifractals: From Modeling to Control of Broadband Network Traffic." In Processes with Long-Range Correlations, 373–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44832-2_20.

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Ishii, Naohiro. "Kernel Correlations of Movements in Neural Network." In ICANN ’94, 110–13. London: Springer London, 1994. http://dx.doi.org/10.1007/978-1-4471-2097-1_26.

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Cheng, Adriel, and Peter Dickinson. "Using Scan-Statistical Correlations for Network Change Analysis." In Lecture Notes in Computer Science, 1–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40319-4_1.

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Hussain, Ajaz S. "Artificial Neural Network Based in Vitro-in Vivo Correlations." In Advances in Experimental Medicine and Biology, 149–58. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4684-6036-0_12.

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Youseph, Ahammed Sherief Kizhakkethil, Madhu Chetty, and Gour Karmakar. "Exploiting Temporal Genetic Correlations for Enhancing Regulatory Network Optimization." In Neural Information Processing, 479–87. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46687-3_53.

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Shao, Weijuan, and Man Li. "Urban Railway Network Traffic Prediction with Spatiotemporal Correlations Matrix." In Proceedings of the 2015 International Conference on Electrical and Information Technologies for Rail Transportation, 335–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49370-0_35.

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Barzaghi, R., A. Borghi, M. Crespi, G. Pietrantonio, and F. Riguzzi. "GPS Permanent Network Solution: the Impact of Temporal Correlations." In International Association of Geodesy Symposia, 179–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-10735-5_24.

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Fukushima, Atsushi, and Kozo Nishida. "Using the DiffCorr Package to Analyze and Visualize Differential Correlations in Biological Networks." In Computational Network Analysis with R, 1–34. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2016. http://dx.doi.org/10.1002/9783527694365.ch1.

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Remondini, Daniel, and Gastone Castellani. "Multiscale Network Reconstruction from Gene Expression Measurements: Correlations, Perturbations, and “A Priori Biological Knowledge”." In Applied Statistics for Network Biology, 105–31. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2011. http://dx.doi.org/10.1002/9783527638079.ch6.

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Conference papers on the topic "Network Correlations"

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Souza, Rose Mary G. P., and Joa˜o M. L. Moreira. "Robustness of a Neural Network Model for Power Peak Factor Estimation in Protection Systems." In 14th International Conference on Nuclear Engineering. ASMEDC, 2006. http://dx.doi.org/10.1115/icone14-89726.

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This work presents results of robustness verification of artificial neural network correlations that improve the real time prediction of the power peak factor for reactor protection systems. The input variables considered in the correlation are those available in the reactor protection systems, namely, the axial power differences obtained from measured ex-core detectors, and the position of control rods. The correlations, based on radial basis function (RBF) and multilayer perceptron (MLP) neural networks, estimate the power peak factor, without faulty signals, with average errors between 0.13%, 0.19% and 0.15%, and maximum relative error of 2.35%. The robustness verification was performed for three different neural network correlations. The results show that they are robust against signal degradation, producing results with faulty signals with a maximum error of 6.90%. The average error associated to faulty signals for the MLP network is about half of that of the RBF network, and the maximum error is about 1% smaller. These results demonstrate that MLP neural network correlation is more robust than the RBF neural network correlation. The results also show that the input variables present redundant information. The axial power difference signals compensate the faulty signal for the position of a given control rod, and improves the results by about 10%. The results show that the errors in the power peak factor estimation by these neural network correlations, even in faulty conditions, are smaller than the current PWR schemes which may have uncertainties as high as 8%. Considering the maximum relative error of 2.35%, these neural network correlations would allow decreasing the power peak factor safety margin by about 5%. Such a reduction could be used for operating the reactor with a higher power level or with more flexibility. The neural network correlation has to meet requirements of high integrity software that performs safety grade actions. It is shown that the correlation is a very simple algorithm that can be easily codified in software. Due to its simplicity, it facilitates the necessary process of validation and verification.
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Fang, Shen, Qi Zhang, Gaofeng Meng, Shiming Xiang, and Chunhong Pan. "GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/317.

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Predicting traffic flow on traffic networks is a very challenging task, due to the complicated and dynamic spatial-temporal dependencies between different nodes on the network. The traffic flow renders two types of temporal dependencies, including short-term neighboring and long-term periodic dependencies. What's more, the spatial correlations over different nodes are both local and non-local. To capture the global dynamic spatial-temporal correlations, we propose a Global Spatial-Temporal Network (GSTNet), which consists of several layers of spatial-temporal blocks. Each block contains a multi-resolution temporal module and a global correlated spatial module in sequence, which can simultaneously extract the dynamic temporal dependencies and the global spatial correlations. Extensive experiments on the real world datasets verify the effectiveness and superiority of the proposed method on both the public transportation network and the road network.
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Brandão, Pedro. "Modelling correlations for Body Sensor Network information." In 7th International Conference on Body Area Networks. ACM, 2012. http://dx.doi.org/10.4108/icst.bodynets.2012.249972.

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Ostanek, Jason K. "Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks." In ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-95903.

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In much of the public literature on pin-fin heat transfer, Nusselt number is presented as a function of Reynolds number using a power-law correlation. Power-law correlations typically have an accuracy of 20% while the experimental uncertainty of such measurements is typically between 5% and 10%. Additionally, the use of power-law correlations may require many sets of empirical constants to fully characterize heat transfer for different geometrical arrangements. In the present work, artificial neural networks were used to predict heat transfer as a function of streamwise spacing, spanwise spacing, pin-fin height, Reynolds number, and row position. When predicting experimental heat transfer data, the neural network was able to predict 73% of array-averaged heat transfer data to within 10% accuracy while published power-law correlations predicted 48% of the data to within 10% accuracy. Similarly, the neural network predicted 81% of row-averaged data to within 10% accuracy while 52% of the data was predicted to within 10% accuracy using power-law correlations. The present work shows that first-order heat transfer predictions may be simplified by using a single neural network model rather than combining or interpolating between power-law correlations. Furthermore, the neural network may be expanded to include additional pin-fin features of interest such as fillets, duct rotation, pin shape, pin inclination angle, and more making neural networks expandable and adaptable models for predicting pin-fin heat transfer.
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Lerner, I. V., I. V. Yurkevich, A. S. Stepanenko, and C. C. Constantinou. "Loss fluctuations and temporal correlations in network queues." In 2008 6th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks and Workshops (WiOPT). IEEE, 2008. http://dx.doi.org/10.1109/wiopt.2008.4586134.

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Shi, Xingang, Chi-Kin Chau, and Dah-Ming Chiu. "Space-efficient tracking of network-wide flow correlations." In IEEE INFOCOM 2011 - IEEE Conference on Computer Communications. IEEE, 2011. http://dx.doi.org/10.1109/infcom.2011.5934899.

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Lerner, I. V., I. V. Yurkevich, A. S. Stepanenko, and C. C. Constantinou. "Loss Fluctuations and Temporal Correlations in Network Queues." In 6th International ICST Symposium on Modeling and Optimization. IEEE, 2008. http://dx.doi.org/10.4108/icst.wiopt2008.3195.

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Sun, Yiwei, Suhang Wang, Tsung-Yu Hsieh, Xianfeng Tang, and Vasant Honavar. "MEGAN: A Generative Adversarial Network for Multi-View Network Embedding." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/489.

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Data from many real-world applications can be naturally represented by multi-view networks where the different views encode different types of relationships (e.g., friendship, shared interests in music, etc.) between real-world individuals or entities. There is an urgent need for methods to obtain low-dimensional, information preserving and typically nonlinear embeddings of such multi-view networks. However, most of the work on multi-view learning focuses on data that lack a network structure, and most of the work on network embeddings has focused primarily on single-view networks. Against this background, we consider the multi-view network representation learning problem, i.e., the problem of constructing low-dimensional information preserving embeddings of multi-view networks. Specifically, we investigate a novel Generative Adversarial Network (GAN) framework for Multi-View Network Embedding, namely MEGAN, aimed at preserving the information from the individual network views, while accounting for connectivity across (and hence complementarity of and correlations between) different views. The results of our experiments on two real-world multi-view data sets show that the embeddings obtained using MEGAN outperform the state-of-the-art methods on node classification, link prediction and visualization tasks.
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Zhang, Zhen, Hongxia Yang, Jiajun Bu, Sheng Zhou, Pinggang Yu, Jianwei Zhang, Martin Ester, and Can Wang. "ANRL: Attributed Network Representation Learning via Deep Neural Networks." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/438.

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Network representation learning (RL) aims to transform the nodes in a network into low-dimensional vector spaces while preserving the inherent properties of the network. Though network RL has been intensively studied, most existing works focus on either network structure or node attribute information. In this paper, we propose a novel framework, named ANRL, to incorporate both the network structure and node attribute information in a principled way. Specifically, we propose a neighbor enhancement autoencoder to model the node attribute information, which reconstructs its target neighbors instead of itself. To capture the network structure, attribute-aware skip-gram model is designed based on the attribute encoder to formulate the correlations between each node and its direct or indirect neighbors. We conduct extensive experiments on six real-world networks, including two social networks, two citation networks and two user behavior networks. The results empirically show that ANRL can achieve relatively significant gains in node classification and link prediction tasks.
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Kiselev, M. V. "Self-organized spiking neural network recognizing phase/frequency correlations." In 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta). IEEE, 2009. http://dx.doi.org/10.1109/ijcnn.2009.5178580.

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Reports on the topic "Network Correlations"

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Baluja, Shumeet, and Scott E. Fahlman. Reducing Network Depth in the Cascade-Correlation Learning Architecture,. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada289352.

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Duvvuri, Sarvani, and Srinivas S. Pulugurtha. Researching Relationships between Truck Travel Time Performance Measures and On-Network and Off-Network Characteristics. Mineta Transportation Institute, July 2021. http://dx.doi.org/10.31979/mti.2021.1946.

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Trucks serve significant amount of freight tonnage and are more susceptible to complex interactions with other vehicles in a traffic stream. While traffic congestion continues to be a significant ‘highway’ problem, delays in truck travel result in loss of revenue to the trucking companies. There is a significant research on the traffic congestion mitigation, but a very few studies focused on data exclusive to trucks. This research is aimed at a regional-level analysis of truck travel time data to identify roads for improving mobility and reducing congestion for truck traffic. The objectives of the research are to compute and evaluate the truck travel time performance measures (by time of the day and day of the week) and use selected truck travel time performance measures to examine their correlation with on-network and off-network characteristics. Truck travel time data for the year 2019 were obtained and processed at the link level for Mecklenburg County, Wake County, and Buncombe County, NC. Various truck travel time performance measures were computed by time of the day and day of the week. Pearson correlation coefficient analysis was performed to select the average travel time (ATT), planning time index (PTI), travel time index (TTI), and buffer time index (BTI) for further analysis. On-network characteristics such as the speed limit, reference speed, annual average daily traffic (AADT), and the number of through lanes were extracted for each link. Similarly, off-network characteristics such as land use and demographic data in the near vicinity of each selected link were captured using 0.25 miles and 0.50 miles as buffer widths. The relationships between the selected truck travel time performance measures and on-network and off-network characteristics were then analyzed using Pearson correlation coefficient analysis. The results indicate that urban areas, high-volume roads, and principal arterial roads are positively correlated with the truck travel time performance measures. Further, the presence of agricultural, light commercial, heavy commercial, light industrial, single-family residential, multi-family residential, office, transportation, and medical land uses increase the truck travel time performance measures (decrease the operational performance). The methodological approach and findings can be used in identifying potential areas to serve as truck priority zones and for planning decentralized delivery locations.
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McDonnell, J. R., and D. E. Waagen. Evolving Cascade-Correlation Networks for Time-Series Forecasting. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada289197.

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Sundermier, Amy, Rigobert Tibi, and Christopher J. Young. Applying Waveform Correlation to Mining Blasts Using a Global Sparse Network. Office of Scientific and Technical Information (OSTI), May 2020. http://dx.doi.org/10.2172/1634280.

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Sundermier, Amy, Rigobert Tibi, and Christopher Young. Applying Waveform Correlation to Mining Blasts Using a Global Sparse Network. Office of Scientific and Technical Information (OSTI), July 2020. http://dx.doi.org/10.2172/1646974.

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Sundermier, Amy, Rigobert Tibi, and Christopher Young. Applying Waveform Correlation to Aftershock Sequences Using a Global Sparse Network. Office of Scientific and Technical Information (OSTI), August 2019. http://dx.doi.org/10.2172/1763210.

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Ivanova, Iryna, and Elena Afanasieva. MODEL OF INTERACTION BETWEEN ADVERTISING, PR AND JOURNALISM. Ivan Franko National University of Lviv, February 2021. http://dx.doi.org/10.30970/vjo.2021.49.11060.

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The article is an overview of the journalism – PR – advertising relationship at the terminological, empirical-analytical and practical levels. It traces the state of the discussion of these correlations in the post-soviet media such as Ukraine. The study describes that domesticating the importance of the appropriate partnership between the three communication technologies. The thesis is that journalism, advertising and PR create a mutual connection that takes place in an atmosphere of PR and advertising permissiveness and deepens with the development of digitalization, Social network development. The present research is based on a comprehensive approach. The inductive and deductive methods are adopted to discuss theoretical materials, and the interdisciplinary research method is used to detect PR-specific features as a philosophy of a new journalism project. The interpretive approach, usually employed to analyze media text as a complex synthetic structure, was also taken into consideration. The analytical method application identified the modern means of substantiating the ideological, esthetical and informative value of brand journalism and spin doctor. The innovative character of modern media as a behavioral strategy in the advertising and PR industry consists in the fact that it is a form of creative production and behavior rather than adapting a specific communication situation. The article examines the main directions of contemporary interactions between PR, advertising and journalism as a media content creation. In this context, it is asserted that advertising, journalism and PR activities can contribute to the creation of media content. At some point, good media content is achieved not only as a result of this competition but also from the correlation between PR, advertising and journalism.
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Templeton, D. C. Evolution of Induced Fracture Networks Using Machine Learning Correlation Image Analysis. Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1572626.

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Aizer, Anna, and Janet Currie. Networks or Neighborhoods? Correlations in the Use of Publicly-Funded Maternity Care in California. Cambridge, MA: National Bureau of Economic Research, September 2002. http://dx.doi.org/10.3386/w9209.

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Young, C. J., J. I. Beiriger, and S. G. Moore. The Waveform Correlation Event Detection System project, Phase II: Testing with the IDC primary network. Office of Scientific and Technical Information (OSTI), April 1998. http://dx.doi.org/10.2172/589201.

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