ORCID Profile
0000-0002-0517-9420
Current Organisation
RMIT University
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Complex Physical Systems | Dynamical Systems in Applications | Applied Mathematics | Artificial Intelligence and Image Processing | Neural, Evolutionary and Fuzzy Computation | Pattern Recognition and Data Mining | Expert Systems
Energy Services and Utilities | Energy Transmission and Distribution (excl. Hydrogen) | Energy Conservation and Efficiency in Transport | Energy Systems Analysis | Information and Communication Services not elsewhere classified | Application Tools and System Utilities | Expanding Knowledge in the Physical Sciences | Expanding Knowledge in the Mathematical Sciences |
Publisher: Wiley
Date: 03-2022
DOI: 10.1002/ASJC.2820
Publisher: Wiley
Date: 2010
DOI: 10.1111/J.1469-8986.2009.00971.X
Abstract: The interhemispheric asymmetries that originate from connectivity-related structuring of the cortex are compromised in schizophrenia (SZ). Under the assumption that such abnormalities affect functional connectivity, we analyzed its correlate-EEG synchronization-in SZ patients and matched controls. We applied multivariate synchronization measures based on Laplacian EEG and tuned to various spatial scales. Compared to the controls who had rightward asymmetry at a local level (EEG power), rightward anterior and leftward posterior asymmetries at an intraregional level (1st and 2nd order S-estimator), and rightward global asymmetry (hemispheric S-estimator), SZ patients showed generally attenuated asymmetry, the effect being strongest for intraregional synchronization in the alpha and beta bands. The abnormalities of asymmetry increased with the duration of the disease and correlated with the negative symptoms. We discuss the tentative links between these findings and gross anatomical asymmetries, including the cerebral torque and gyrification pattern, in normal subjects and SZ patients.
Publisher: Springer Science and Business Media LLC
Date: 26-03-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2011
Publisher: Public Library of Science (PLoS)
Date: 21-08-2015
Publisher: American Physical Society (APS)
Date: 29-10-2014
Publisher: IEEE
Date: 11-2017
Publisher: Elsevier BV
Date: 07-2011
Publisher: Springer Science and Business Media LLC
Date: 15-07-2016
DOI: 10.1038/SREP29780
Abstract: The human brain can be modelled as a complex networked structure with brain regions as in idual nodes and their anatomical/functional links as edges. Functional brain networks are constructed by first extracting weighted connectivity matrices and then binarizing them to minimize the noise level. Different methods have been used to estimate the dependency values between the nodes and to obtain a binary network from a weighted connectivity matrix. In this work we study topological properties of EEG-based functional networks in Alzheimer’s Disease (AD). To estimate the connectivity strength between two time series, we use Pearson correlation, coherence, phase order parameter and synchronization likelihood. In order to binarize the weighted connectivity matrices, we use Minimum Spanning Tree (MST), Minimum Connected Component (MCC), uniform threshold and density-preserving methods. We find that the detected AD-related abnormalities highly depend on the methods used for dependency estimation and binarization. Topological properties of networks constructed using coherence method and MCC binarization show more significant differences between AD and healthy subjects than the other methods. These results might explain contradictory results reported in the literature for network properties specific to AD symptoms. The analysis method should be seriously taken into account in the interpretation of network-based analysis of brain signals.
Publisher: Elsevier BV
Date: 02-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2020
Publisher: Elsevier BV
Date: 06-2018
Publisher: Elsevier BV
Date: 11-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Elsevier BV
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 06-2022
Publisher: IEEE
Date: 05-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2013
Publisher: Springer Science and Business Media LLC
Date: 08-2014
Publisher: IEEE
Date: 2009
Publisher: IEEE
Date: 2009
Publisher: Oxford University Press (OUP)
Date: 05-11-2014
Publisher: Elsevier BV
Date: 09-2017
Publisher: IOP Publishing
Date: 05-2012
Publisher: Elsevier BV
Date: 02-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Publisher: Elsevier BV
Date: 12-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2014
DOI: 10.1109/TC.2013.118
Publisher: Elsevier BV
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 20-08-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Publisher: Elsevier BV
Date: 12-2011
DOI: 10.1016/J.COMPBIOMED.2011.05.004
Abstract: Schizophrenia is often considered as a dysconnection syndrome in which, abnormal interactions between large-scale functional brain networks result in cognitive and perceptual deficits. In this article we apply the graph theoretic measures to brain functional networks based on the resting EEGs of fourteen schizophrenic patients in comparison with those of fourteen matched control subjects. The networks were extracted from common-average-referenced EEG time-series through partial and unpartial cross-correlation methods. Unpartial correlation detects functional connectivity based on direct and/or indirect links, while partial correlation allows one to ignore indirect links. We quantified the network properties with the graph metrics, including mall-worldness, vulnerability, modularity, assortativity, and synchronizability. The schizophrenic patients showed method-specific and frequency-specific changes especially pronounced for modularity, assortativity, and synchronizability measures. However, the differences between schizophrenia patients and normal controls in terms of graph theory metrics were stronger for the unpartial correlation method.
Publisher: Elsevier BV
Date: 03-2023
Publisher: IEEE
Date: 10-2017
Publisher: Springer Science and Business Media LLC
Date: 24-04-2013
Abstract: Large-scale optimization tasks have many applications in science and engineering. There are many algorithms to perform such optimization tasks. In this manuscript, we aim at using consensus in multi-agent systems as a tool for solving large-scale optimization tasks. The model is based on consensus of opinions among agents interacting over a complex networked structure. For each optimization task, a number of agents are considered, each with an opinion value. These agents interact over a networked structure and update their opinions based on their best-matching neighbor in the network. A neighbor with the best value of the objective function (of the optimization task) is referred to as the best-matching neighbor for an agent. We use structures such as pure random, small-world and scale-free networks as interaction graph. The optimization algorithm is applied on a number of benchmark problems and its performance is compared with a number of classic methods including genetic algorithms, differential evolution and particle swarm optimization. We show that the agents could solve various large-scale optimization tasks through collaborating with each other and getting into consensus in their opinions. Furthermore, we find pure random topology better than small-world and scale-free topologies in that it leads to faster convergence to the optimal solution. Our experiments show that the proposed consensus-based optimization method outperforms the classic optimization algorithms. Consensus in multi-agents systems can be efficiently used for large-scale optimization problems. Connectivity structure of the consensus network is effective in the convergence to the optimum solution where random structures show better performance as compared to heterogeneous networks. 15A04, 54A20, 60J20, 92D25
Publisher: Elsevier BV
Date: 09-2014
Publisher: IEEE
Date: 2018
Publisher: Oxford University Press (OUP)
Date: 06-07-2017
Publisher: Elsevier BV
Date: 09-2021
Publisher: Springer International Publishing
Date: 2022
Publisher: Public Library of Science (PLoS)
Date: 21-06-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2013
Publisher: IEEE
Date: 11-2016
Publisher: Elsevier BV
Date: 02-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: IEEE
Date: 05-2008
Publisher: IEEE
Date: 11-2015
Publisher: AIP Publishing
Date: 03-2013
DOI: 10.1063/1.4794436
Abstract: In this paper, we investigated phase synchronization in delayed dynamical networks. Non-identical spiking Hindmarsh-Rose neurons were considered as in idual dynamical systems and coupled through a number of network structures such as scale-free, Erdős–Rényi, and modular. The in idual neurons were coupled through excitatory chemical synapses with uniform or distributed time delays. The profile of spike phase synchrony was different when the delay was uniform across the edges as compared to the case when it was distributed, i.e., different delays for the edges. When an identical transmission delay was considered, a quasi-periodic pattern was observed in the spike phase synchrony. There were specific values of delay where the phase synchronization reached to its peaks. The behavior of the phase synchronization in the networks with non-uniform delays was different with the former case, where the phase synchrony decreased as distributed delays introduced to the networks.
Publisher: MDPI AG
Date: 26-02-2023
DOI: 10.3390/EN16052245
Abstract: Electric vehicles (EVs) are advancing the transport sector towards a robust and reliable carbon-neutral future. Given this increasing uptake of EVs, electrical grids and power networks are faced with the challenges of distributed energy resources, specifically the charge and discharge requirements of the electric vehicle infrastructure (EVI). Simultaneously, the rapid digitalisation of electrical grids and EVs has led to the generation of large volumes of data on the supply, distribution and consumption of energy. Artificial intelligence (AI) algorithms can be leveraged to draw insights and decisions from these datasets. Despite several recent work in this space, a comprehensive study of the practical value of AI in charge-demand profiling, data augmentation, demand forecasting, demand explainability and charge optimisation of the EVI has not been formally investigated. The objective of this study was to design, develop and evaluate a comprehensive AI framework that addresses this gap in EVI. Results from the empirical evaluation of this AI framework on a real-world EVI case study confirm its contribution towards addressing the emerging challenges of distributed energy resources in EV adoption.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 07-2014
Publisher: SAGE Publications
Date: 15-10-2013
Abstract: Social networks are inevitable parts of daily life and there has been an increasing interest in analyzing social phenomena on networked structures. Evolution of opinion formation is one of the topics that has attracted many scholars in the field. In this work we consider the influence of leaders and social power in the evolution of opinion formation. A number of central nodes with specific properties (e.g. nodes with highest degree, betweenness or vulnerability values) are taken as leaders whose opinions are kept unchanged, that is, not influenced by other agents. The leaders try to coordinate the opinions of other agents where the connection structure is considered to be preferential attachment scale-free, Watts–Strogatz small-world or Erdős–Rényi random networks. Numerical simulations show that scale-free networks provide faster consensus compared with other networks. We also study the effects of social power on the consensus time. The social power of a node is considered to be a function of its centrality. Having leaders in the network, we show that the consensus time could be significantly decreased by introducing social power. For scale-free networks, there is an optimal degree of social power in which the consensus time is minimal. These results show the appropriateness of scale-free topology in hierarchal organizations where leaders posit influence on peripheral agents.
Publisher: World Scientific Pub Co Pte Lt
Date: 2007
DOI: 10.1142/S0219691307001665
Abstract: This paper deals with the application of neural networks to design intelligent nonlinear predictive controllers. Predictive controllers are now widely used in many industrial applications. They have been used for linear systems in early applications and then some methods based on predictive control theory were proposed to govern the dynamics of nonlinear systems. In this paper, we will make use of multi-layer perceptron neurofuzzy models with Locally Linear Model Tree (LoLiMoT) learning algorithm as a part of intelligent predictive control system, which has shown excellent performance in identifying of nonlinear systems. The nonlinear dynamics of the system is identified using the neural network based method and then the identified model is used as a part of predictive control algorithm. The proposed method is used to solve the control problems in some benchmark systems. As a first study, the viscosity control in a Continuous Stirred Tank Reactor (CSTR) plant is considered. The mathematical model of the plant is used to generate the input output data set and then the dynamic behavior of the system is identified using a proper multi-layer perceptron neural network, which is used in the predictive control loop. Also, the predictive control based on the locally linear neurofuzzy model is applied to temperature control of an electrically heated micro heat exchanger. The dynamic behavior of the heat exchanger is identified based on some experimental data of the real plant. Comparing the identification results obtained by the neurofuzzy model with those of some linear models such as ARX and BJ, confirms the superior performance for the locally linear neurofuzzy model. Then, the predictive control is applied to the identified model to obtain a satisfactory performance in the output temperature that should track a desired reference signal. As another application, the algorithm is applied to temperature control of a solution polymerization methyl methacrylate in a batch reactor. The results show also somehow satisfactory performance for this highly nonlinear system. All the simulation results reveal the effectiveness of the proposed intelligent control strategy.
Publisher: Elsevier BV
Date: 04-2017
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 11-2016
Publisher: IEEE
Date: 12-2018
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Elsevier BV
Date: 08-2010
Publisher: IEEE
Date: 02-2017
Publisher: Wiley
Date: 28-07-2014
DOI: 10.1002/BRB3.252
Publisher: Elsevier BV
Date: 11-2022
DOI: 10.1016/J.NEUNET.2022.08.001
Abstract: Spike sorting - the process of separating spikes from different neurons - is often the first and most critical step in the neural data analysis pipeline. Spike-sorting techniques isolate a single neuron's activity from background electrical noise based on the shapes of the waveforms obtained from extracellular recordings. Despite several advancements in this area, an important remaining challenge in neuroscience is online spike sorting, which has the potential to significantly advance basic neuroscience research and the clinical setting by providing the means to produce real-time perturbations of neurons via closed-loop control. Current approaches to online spike sorting are not fully automated, are computationally expensive and are often outperformed by offline approaches. In this paper, we present a novel algorithm for fast and robust online classification of single neuron activity. This algorithm is based on a deep contractive autoencoder (CAE) architecture. CAEs are neural networks that can learn a latent state representation of their inputs. The main advantage of CAE-based approaches is that they are less sensitive to noise (i.e., small perturbations in their inputs). We therefore reasoned that they can form the basis for robust online spike sorting algorithms. Overall, our deep CAE-based online spike sorting algorithm achieves over 90% accuracy in sorting unseen spike waveforms, outperforming existing models and maintaining a performance close to the offline case. In the offline scenario, our method substantially outperforms the existing models, providing an average improvement of 40% in accuracy over different datasets.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Publisher: Association for Computing Machinery (ACM)
Date: 04-2014
DOI: 10.1145/2501977
Abstract: Social network analysis and mining get ever-increasingly important in recent years, which is mainly due to the availability of large datasets and advances in computing systems. A class of social networks is those with positive and negative links. In such networks, a positive link indicates friendship (or trust), whereas links with a negative sign correspond to enmity (or distrust). Predicting the sign of the links in these networks is an important issue and has many applications, such as friendship recommendation and identifying malicious nodes in the network. In this manuscript, we proposed a new method for sign prediction in networks with positive and negative links. Our algorithm is based first on clustering the network into a number of clusters and then applying a collaborative filtering algorithm. The clusters are such that the number of intra-cluster negative links and inter-cluster positive links are minimal, that is, the clusters are socially balanced as much as possible (a signed graph is socially balanced if it can be ided into clusters with all positive links inside the clusters and all negative links between them). We then used similarity between the clusters (based on the links between them) in a collaborative filtering algorithm. Our experiments on a number of real datasets showed that the proposed method outperformed previous methods, including those based on social balance and status theories and one based on a machine learning framework (logistic regression in this work).
Publisher: IEEE
Date: 05-2018
Publisher: Association for Computing Machinery (ACM)
Date: 15-12-2014
DOI: 10.1145/2668107
Abstract: Recommender systems are in the center of network science, and they are becoming increasingly important in in idual businesses for providing efficient, personalized services and products to users. Previous research in the field of recommendation systems focused on improving the precision of the system through designing more accurate recommendation lists. Recently, the community has been paying attention to ersity and novelty of recommendation lists as key characteristics of modern recommender systems. In many cases, novelty and precision do not go hand in hand, and the accuracy--novelty dilemma is one of the challenging problems in recommender systems, which needs efforts in making a trade-off between them. In this work, we propose an algorithm for providing novel and accurate recommendation to users. We consider the standard definition of accuracy and an effective self-information--based measure to assess novelty of the recommendation list. The proposed algorithm is based on item popularity, which is defined as the number of votes received in a certain time interval. Wavelet transform is used for analyzing popularity time series and forecasting their trend in future timesteps. We introduce two filtering algorithms based on the information extracted from analyzing popularity time series of the items. The popularity-based filtering algorithm gives a higher chance to items that are predicted to be popular in future timesteps. The other algorithm, denoted as a novelty and population-based filtering algorithm, is to move toward items with low popularity in past timesteps that are predicted to become popular in the future. The introduced filters can be applied as adds-on to any recommendation algorithm. In this article, we use the proposed algorithms to improve the performance of classic recommenders, including item-based collaborative filtering and Markov-based recommender systems. The experiments show that the algorithms could significantly improve both the accuracy and effective novelty of the classic recommenders.
Publisher: Elsevier BV
Date: 03-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 09-10-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: AIP Publishing
Date: 17-11-2010
DOI: 10.1063/1.3515170
Abstract: In this paper, we present an algorithm for enhancing synchronizability of dynamical networks with prescribed degree distribution. The algorithm takes an unweighted and undirected network as input and outputs a network with the same node-degree distribution and enhanced synchronization properties. The rewirings are based on the properties of the Laplacian of the connection graph, i.e., the eigenvectors corresponding to the second smallest and the largest eigenvalues of the Laplacian. A term proportional to the eigenvectors is adopted to choose potential edges for rewiring, provided that the node-degree distribution is preserved. The algorithm can be implemented on networks of any sizes as long as their eigenvalues and eigenvectors can be calculated with standard algorithms. The effectiveness of the proposed algorithm in enhancing the network synchronizability is revealed by numerical simulation on a number of s le networks including scale-free, Watts–Strogatz, and Erdős–Rényi graphs. Furthermore, a number of network’s structural parameters such as node betweenness centrality, edge betweenness centrality, average path length, clustering coefficient, and degree assortativity are tracked as a function of optimization steps.
Publisher: Wiley
Date: 04-08-2008
DOI: 10.1111/J.1600-0447.2008.01227.X
Abstract: To reveal the EEG correlates of resting hypofrontality in schizophrenia (SZ). We analyzed the whole-head EEG topography in 14 patients compared to 14 matched controls by applying a new parameterization of the multichannel EEG. We used a combination of power measures tuned for regional surface mapping with power measures that allow evaluation of global effects. The SZ-related EEG abnormalities include i) a global decrease in absolute EEG power robustly manifested in the alpha and beta frequency bands, and ii) a relative increase in the alpha power over the prefrontal brain regions against its reduction over the posterior regions. In the alpha band both effects are linked to the SZ symptoms measured with Positive and Negative Symptom Scales and to chronicity. As alpha activity is related to regional deactivation, our findings support the concept of hypofrontality in SZ and expose the alpha rhythm as a sensitive indicator of it.
Publisher: Elsevier BV
Date: 04-2018
Publisher: Springer Science and Business Media LLC
Date: 22-06-2018
DOI: 10.1038/S41598-018-27385-2
Abstract: Diffusion of information in complex networks largely depends on the network structure. Recent studies have mainly addressed information diffusion in homogeneous networks where there is only a single type of nodes and edges. However, some real-world networks consist of heterogeneous types of nodes and edges. In this manuscript, we model information diffusion in heterogeneous information networks, and use interactions of different meta-paths to predict the diffusion process. A meta-path is a path between nodes across different layers of a heterogeneous network. As its most important feature the proposed method is capable of determining the influence of all meta-paths on the diffusion process. A conditional probability is used assuming interdependent relations between the nodes to calculate the activation probability of each node. As independent cascade models, we consider linear threshold and independent cascade models. Applying the proposed method on two real heterogeneous networks reveals its effectiveness and superior performance over state-of-the-art methods.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Elsevier BV
Date: 2019
Publisher: Elsevier BV
Date: 11-2017
Publisher: IEEE
Date: 2018
Publisher: IEEE
Date: 2018
Publisher: IEEE
Date: 10-2017
Publisher: Elsevier BV
Date: 07-2017
Publisher: Elsevier BV
Date: 08-2019
Publisher: AIP Publishing
Date: 09-2008
DOI: 10.1063/1.2967738
Abstract: In this paper, we present an algorithm for optimizing synchronizability of complex dynamical networks. Starting with an undirected and unweighted network, we end up with an undirected and unweighted network with the same number of nodes and edges having enhanced synchronizability. To this end, based on some network properties, rewirings, i.e., eliminating an edge and creating a new edge elsewhere, are performed iteratively avoiding always self-loops and multiple edges between the same nodes. We show that the method is able to enhance the synchronizability of networks of any size and topological properties in a small number of steps that scales with the network size. For numerical simulations, an optimization algorithm based on simulated annealing is used. Also, the evolution of different topological properties of the network such as distribution of node degree, node and edge betweenness centrality is tracked with the iteration steps. We use networks such as scale-free, Strogatz–Watts and random to start with and we show that regardless of the initial network, the final optimized network becomes homogeneous. In other words, in the network with high synchronizability, parameters, such as, degree, shortest distance, node, and edge betweenness centralities are almost homogeneously distributed. Also, parameters, such as, maximum node and edge betweenness centralities are small for the rewired network. Although we take the eigenratio of the Laplacian as the target function for optimization, we show that it is also possible to choose other appropriate target functions exhibiting almost the same performance. Furthermore, we show that even if the network is optimized taking into account another interpretation of synchronizability, i.e., synchronization cost, the optimal network has the same synchronization properties. Indeed, in networks with optimized synchronizability, different interpretations of synchronizability coincide. The optimized networks are Ramanujan graphs, and thus, this rewiring algorithm could be used to produce Ramanujan graphs of any size and average degree.
Publisher: Wiley
Date: 30-08-2007
DOI: 10.1002/CTA.436
Abstract: Dynamical networks with diffusive couplings are investigated from the point of view of synchronizability. Arbitrary connection graphs are admitted but the coupling is symmetric. Networks with equal interaction coefficients for all edges of the interaction graph are compared with networks where the interaction coefficients vary from edge to edge according to the bounds for global synchronization obtained by the connection graph stability method. Synchronizability is tested numerically by establishing the time to decrease the synchronization error from 1 to 10 −5 in the case of networks of identical Lorenz or Rössler systems. Synchronizability from the point of view of phase synchronization is also tested for networks of non‐identical Lorenz or Rössler systems. In this case the phase‐order parameters are compared, as a function of the mean interaction strength. Throughout, as network structures, scale‐free and Watts–Strogatz small‐world networks are used. Copyright © 2007 John Wiley & Sons, Ltd.
Publisher: Elsevier BV
Date: 03-2021
Publisher: Elsevier BV
Date: 2013
Publisher: BMJ
Date: 14-03-2011
Abstract: Psychogenic non-epileptic seizures (PNES) are paroxysmal events that, in contrast to epileptic seizures, are related to psychological causes without the presence of epileptiform EEG changes. Recent models suggest a multifactorial basis for PNES. A potentially paramount, but currently poorly understood factor is the interplay between psychiatric features and a specific vulnerability of the brain leading to a clinical picture that resembles epilepsy. Hypothesising that functional cerebral network abnormalities may predispose to the clinical phenotype, the authors undertook a characterisation of the functional connectivity in PNES patients. The authors analysed the whole-head surface topography of multivariate phase synchronisation (MPS) in interictal high-density EEG of 13 PNES patients as compared with 13 age- and sex-matched controls. MPS mapping reduces the wealth of dynamic data obtained from high-density EEG to easily readable synchronisation maps, which provide an unbiased overview of any changes in functional connectivity associated with distributed cortical abnormalities. The authors computed MPS maps for both Laplacian and common-average-reference EEGs. In a between-group comparison, only patchy, non-uniform changes in MPS survived conservative statistical testing. However, against the background of these unimpressive group results, the authors found widespread inverse correlations between in idual PNES frequency and MPS within the prefrontal and parietal cortices. PNES appears to be associated with decreased prefrontal and parietal synchronisation, possibly reflecting dysfunction of networks within these regions.
Publisher: Springer International Publishing
Date: 2019
Publisher: Elsevier BV
Date: 10-2013
DOI: 10.1016/J.YGENO.2013.07.012
Abstract: A signaling pathway is a sequence of proteins and passenger molecules that transmits information from the cell surface to target molecules. Understanding signal transduction process requires detailed description of the involved pathways. Several methods and tools resolved this problem by incorporating genomic and proteomic data. However, the difficulty of obtaining prior knowledge of complex signaling networks limited the applicability of these tools. In this study, based on the simulation of signal flow in signaling network, we introduce a method for determining dominant pathways and signal response to stimulations. The model uses topology-weighted transit compartment approach and comprises four main steps which include weighting the edges, simulating signal transduction in the network (weighting the nodes), finding paths between initial and target nodes, and assigning a significance score to each path. We applied the proposed model to eighty-three signaling networks by using biologically derived source and sink molecules. The recovered dominant paths matched many known signaling pathways and suggesting a promising index to analyze the phenotype essentiality of molecule encoding paths. We also modeled the stimulus-response relations in long and short-term synaptic plasticity based on the dominant signaling pathway concept. We showed that the proposed method not only accurately determines dominant signaling pathways, but also identifies effective points of intervention in signal transduction.
Publisher: Springer Science and Business Media LLC
Date: 19-12-2005
Publisher: MDPI AG
Date: 13-02-2023
DOI: 10.3390/S23042118
Abstract: This paper provides a comprehensive review of the applications of smart meters in the control and optimisation of power grids to support a smooth energy transition towards the renewable energy future. The smart grids become more complicated due to the presence of small-scale low inertia generators and the implementation of electric vehicles (EVs), which are mainly based on intermittent and variable renewable energy resources. Optimal and reliable operation of this environment using conventional model-based approaches is very difficult. Advancements in measurement and communication technologies have brought the opportunity of collecting temporal or real-time data from prosumers through Advanced Metering Infrastructure (AMI). Smart metering brings the potential of applying data-driven algorithms for different power system operations and planning services, such as infrastructure sizing and upgrade and generation forecasting. It can also be used for demand-side management, especially in the presence of new technologies such as EVs, 5G/6G networks and cloud computing. These algorithms face privacy-preserving and cybersecurity challenges that need to be well addressed. This article surveys the state-of-the-art of each of these topics, reviewing applications, challenges and opportunities of using smart meters to address them. It also stipulates the challenges that smart grids present to smart meters and the benefits that smart meters can bring to smart grids. Furthermore, the paper is concluded with some expected future directions and potential research questions for smart meters, smart grids and their interplay.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2019
Publisher: Springer Science and Business Media LLC
Date: 03-08-2018
Publisher: IEEE
Date: 09-2017
Publisher: American Physical Society (APS)
Date: 25-10-2011
Publisher: Elsevier BV
Date: 12-2010
Publisher: Springer Science and Business Media LLC
Date: 12-05-2017
DOI: 10.1038/S41598-017-01655-X
Abstract: The shortest path problem is one of the most fundamental networks optimization problems. Nowadays, in iduals interact in extraordinarily numerous ways through their offline and online life (e.g., co-authorship, co-workership, or retweet relation in Twitter). These interactions have two key features. First, they have a heterogeneous nature, and second, they have different strengths that are weighted based on their degree of intimacy, trustworthiness, service exchange or influence among in iduals. These networks are known as multiplex networks. To our knowledge, none of the previous shortest path definitions on social interactions have properly reflected these features. In this work, we introduce a new distance measure in multiplex networks based on the concept of Pareto efficiency taking both heterogeneity and weighted nature of relations into account. We then model the problem of finding the whole set of paths as a form of multiple objective decision making and propose an exact algorithm for that. The method is evaluated on five real-world datasets to test the impact of considering weights and multiplexity in the resulting shortest paths. As an application to find the most influential nodes, we redefine the concept of betweenness centrality based on the proposed shortest paths and evaluate it on a real-world dataset from two-layer trade relation among countries between years 2000 and 2015.
Publisher: Elsevier BV
Date: 09-2022
Publisher: Elsevier BV
Date: 11-2018
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 19-08-2015
Publisher: The Royal Society
Date: 02-2017
DOI: 10.1098/RSOS.160863
Abstract: Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an ex le, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.
Publisher: IEEE
Date: 09-2011
Publisher: Oxford University Press (OUP)
Date: 08-03-2019
Abstract: Many real-world complex systems can be better modelled as multiplex networks, where the same in iduals develop connections in multiple layers. Ex les include social networks between in iduals on multiple social networking platforms, and transportation networks between cities based on air, rail and road networks. Accurately predicting spurious links in multiplex networks is a challenging issue. In this article, we show that one can effectively use interlayer information to build an algorithm for spurious link prediction. We propose a similarity index that combines intralayer similarity with interlayer relevance for the link prediction purpose. The proposed similarity index is used to rank the node pairs, and identify those that are likely to be spurious. Our experimental results show that the proposed metric is much more accurate than intralayer similarity measures in correctly predicting the spurious links. The proposed method is an unsupervised method and has low computation complexity, and thus can be effectively applied for spurious link prediction in large-scale networks.
Publisher: IEEE
Date: 06-2013
Publisher: Elsevier BV
Date: 03-2020
Publisher: Public Library of Science (PLoS)
Date: 26-05-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: Springer Science and Business Media LLC
Date: 23-09-2015
DOI: 10.1038/SREP14339
Abstract: Extracting community structure of complex network systems has many applications from engineering to biology and social sciences. There exist many algorithms to discover community structure of networks. However, it has been significantly under-explored for networks with positive and negative links as compared to unsigned ones. Trying to fill this gap, we measured the quality of partitions by introducing a Map Equation for signed networks. It is based on the assumption that negative relations weaken positive flow from a node towards a community and thus, external (internal) negative ties increase the probability of staying inside (escaping from) a community. We further extended the Constant Potts Model, providing a map spectrum for signed networks. Accordingly, a partition is selected through balancing between abridgment and expatiation of a signed network. Most importantly, multi-scale spectrum of signed networks revealed how informative are negative ties in different scales and quantified the topological placement of negative ties between dense positive ones. Moreover, an inconsistency was found in the signed Modularity: as the number of negative ties increases, the density of positive ties is neglected more. These results shed lights on the community structure of signed networks.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 10-2017
Publisher: Springer Science and Business Media LLC
Date: 12-04-2016
DOI: 10.1038/SREP24252
Abstract: Controlling dynamics of a network from any initial state to a final desired state has many applications in different disciplines from engineering to biology and social sciences. In this work, we optimize the network structure for pinning control. The problem is formulated as four optimization tasks: i ) optimizing the locations of driver nodes, ii ) optimizing the feedback gains, iii ) optimizing simultaneously the locations of driver nodes and feedback gains and iv ) optimizing the connection weights. A newly developed population-based optimization technique (cat swarm optimization) is used as the optimization method. In order to verify the methods, we use both real-world networks and model scale-free and small-world networks. Extensive simulation results show that the optimal placement of driver nodes significantly outperforms heuristic methods including placing drivers based on various centrality measures (degree, betweenness, closeness and clustering coefficient). The pinning controllability is further improved by optimizing the feedback gains. We also show that one can significantly improve the controllability by optimizing the connection weights.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2012
Publisher: Frontiers Media SA
Date: 2012
Publisher: American Physical Society (APS)
Date: 24-09-2018
Publisher: IEEE
Date: 06-2009
Publisher: Elsevier BV
Date: 10-2018
Publisher: AIP Publishing
Date: 11-05-2009
DOI: 10.1063/1.3130929
Abstract: This is a comment on a recent paper by A. Hagberg and D. A. Schult [Chaos 18, 037105 (2008)]. By taking the eigenratio of the Laplacian of an undirected and unweighted network as its synchronizability measure, they proposed a greedy rewiring algorithm for enhancing the synchronizability of the network. The algorithm is not capable of avoiding local minima, and as a consequence, for each initial network, different optimized networks with different synchronizabilities are obtained. Here, we show that by employing a simulated annealing based optimization method, it is possible to further enhance the synchronizability of the network. Moreover, using this approach, the optimized network is not biased by the initial network and regardless of the initial networks, the final optimized networks have similar synchronization properties.
Publisher: Elsevier BV
Date: 2022
Publisher: IEEE
Date: 10-2016
Publisher: Elsevier BV
Date: 10-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Publisher: Elsevier BV
Date: 07-2020
Publisher: IEEE
Date: 08-2018
Publisher: Elsevier BV
Date: 07-2019
Publisher: ACM
Date: 16-09-2015
Publisher: American Institute of Mathematical Sciences (AIMS)
Date: 02-2015
Publisher: Elsevier BV
Date: 07-2010
DOI: 10.1016/J.NEUROBIOLAGING.2008.07.019
Abstract: Alzheimer's disease (AD) is likely to disrupt the synchronization of the bioelectrical processes in the distributed cortical networks underlying cognition. We analyze the surface topography of the multivariate phase synchronization (MPS) of multichannel EEG in 17 patients (Clinical Dementia Rating (CDR) Scale: 0.5-1 Functional Assessment Staging (FAST): 3-4) compared to 17 controls by applying a combination of global and regional MPS measures to the resting EEG. In early AD, whole-head mapping reveals a specific landscape of synchronization characterized by a decrease in MPS over the fronto-temporal region and an increase over the temporo-parieto-occipital region predominantly of the left hemisphere. These features manifest themselves through the EEG delta-beta bands and discriminate patients from controls with an accuracy of up to 94%. Moreover, the abnormal MPS in both anterior and posterior clusters correlates with the Mini Mental State Examination score, binding regional EEG synchronization to cognitive decline in AD patients. The MPS technique reveals that the EEG phenotype of early AD is relevant to the clinical picture and may ultimately become its sensitive and specific biomarker.
Publisher: Springer International Publishing
Date: 2019
Publisher: Elsevier BV
Date: 11-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
Publisher: Springer Berlin Heidelberg
Date: 15-08-2015
Publisher: IEEE
Date: 05-2008
Publisher: IEEE
Date: 10-2016
Publisher: Springer Science and Business Media LLC
Date: 30-08-2019
DOI: 10.1038/S41598-019-49001-7
Abstract: Recently multilayer networks are introduced to model real systems. In these models the in iduals make connection in multiple layers. Transportation networks, biological systems and social networks are some ex les of multilayer networks. There are various link prediction algorithms for single-layer networks and some of them have been recently extended to multilayer networks. In this manuscript, we propose a new link prediction algorithm for multiplex networks using two novel similarity metrics based on the hyperbolic distance of node pairs. We use the proposed methods to predict spurious and missing links in multiplex networks. Missing links are those links that may appear in the future evolution of the network, while spurious links are the existing connections that are unlikely to appear if the network is evolving normally. One may interpret spurious links as abnormal links in the network. We apply the proposed algorithm on real-world multiplex networks and the numerical simulations reveal its superiority than the state-of-the-art algorithms.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 10-2016
Publisher: Public Library of Science (PLoS)
Date: 25-04-2012
Publisher: Elsevier BV
Date: 03-2020
Publisher: Springer Science and Business Media LLC
Date: 16-12-2020
DOI: 10.1038/S41598-020-78909-8
Abstract: Controlling a network structure has many potential applications many fields. In order to have an effective network control, not only finding good driver nodes is important, but also finding the optimal time to apply the external control signals to network nodes has a critical role. If applied in an appropriate time, one might be to control a network with a smaller control signals, and thus less energy. In this manuscript, we show that there is a relationship between the strength of the internal fluxes and the effectiveness of the external control signal. To be more effective, external control signals should be applied when the strength of the internal states is the smallest. We validate this claim on synthetic networks as well as a number of real networks. Our results may have important implications in systems medicine, in order to find the most appropriate time to inject drugs as a signal to control diseases.
Publisher: Elsevier BV
Date: 03-2015
Publisher: Elsevier BV
Date: 08-2021
Publisher: Walter de Gruyter GmbH
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2014
Publisher: Elsevier BV
Date: 06-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2021
Publisher: Elsevier BV
Date: 09-2007
Publisher: World Scientific Pub Co Pte Lt
Date: 06-2011
Publisher: Elsevier BV
Date: 03-2017
Publisher: Oxford University Press (OUP)
Date: 03-09-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2018
Publisher: Elsevier BV
Date: 04-2020
Publisher: Elsevier BV
Date: 07-2011
Publisher: Springer Science and Business Media LLC
Date: 22-02-2016
DOI: 10.1038/NMETH.3773
Publisher: Elsevier BV
Date: 05-2011
Publisher: IOP Publishing
Date: 03-2013
Publisher: IEEE
Date: 2010
Publisher: World Scientific Pub Co Pte Lt
Date: 04-2012
DOI: 10.1142/S0129183112500295
Abstract: Many real-world networks show community structure characterized by dense intra-community connections and sparse inter-community links. In this paper we investigated the synchronization properties of such networks. In this work we constructed such networks in a way that they consist of a number of communities with scale-free or small-world structure. Furthermore, with a probability, the intra-community connections are rewired to inter-community links. Two synchronizability measures were considered as the eigenratio of the Laplacian matrix and the phase order parameter obtained for coupled nonidentical Kuramoto oscillators. We found a power-law relation between the eigenratio and the inter-community rewiring probability in which as the rewiring probability increased, the eigenratio decreased, and hence, the synchronizability enhanced. The phase order parameter also increased by increasing the rewiring probability. Also, small-world networks with community structure showed better synchronization properties as compared to scale-free networks with community structure.
Publisher: Elsevier BV
Date: 05-2020
Publisher: Elsevier BV
Date: 06-2018
Publisher: AIP Publishing
Date: 09-2011
DOI: 10.1063/1.3633079
Abstract: Neuronal synchronization plays an important role in the various functionality of nervous system such as binding, cognition, information processing, and computation. In this paper, we investigated how random and intentional failures in the nodes of a network influence its phase synchronization properties. We considered both artificially constructed networks using models such as preferential attachment, Watts-Strogatz, and Erdős-Rényi as well as a number of real neuronal networks. The failure strategy was either random or intentional based on properties of the nodes such as degree, clustering coefficient, betweenness centrality, and vulnerability. Hindmarsh-Rose model was considered as the mathematical model for the in idual neurons, and the phase synchronization of the spike trains was monitored as a function of the percentage/number of removed nodes. The numerical simulations were supplemented by considering coupled non-identical Kuramoto oscillators. Failures based on the clustering coefficient, i.e., removing the nodes with high values of the clustering coefficient, had the least effect on the spike synchrony in all of the networks. This was followed by errors where the nodes were removed randomly. However, the behavior of the other three attack strategies was not uniform across the networks, and different strategies were the most influential in different network structure.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 07-2022
Publisher: Springer Science and Business Media LLC
Date: 30-01-2014
Publisher: Public Library of Science (PLoS)
Date: 15-02-2018
Publisher: Springer Science and Business Media LLC
Date: 24-10-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2017
Publisher: Elsevier BV
Date: 04-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Publisher: American Physical Society (APS)
Date: 14-07-2008
Publisher: Elsevier BV
Date: 2018
Publisher: Elsevier BV
Date: 04-2019
Publisher: IEEE
Date: 11-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Publisher: Springer Science and Business Media LLC
Date: 31-08-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2023
Publisher: IEEE
Date: 12-09-2022
Publisher: American Physical Society (APS)
Date: 22-03-0022
Publisher: Elsevier BV
Date: 12-2021
Publisher: Elsevier BV
Date: 2022
Publisher: AIP Publishing
Date: 14-07-2009
DOI: 10.1063/1.3157215
Abstract: In this paper, the synchronization behavior of the Hindmarsh–Rose neuron model over Newman–Watts networks is investigated. The uniform synchronizing coupling strength is determined through both numerically solving the network’s differential equations and the master-stability-function method. As the average degree is increased, the gap between the global synchronizing coupling strength, i.e., the one obtained through the numerical analysis, and the strength necessary for the local stability of the synchronization manifold, i.e., the one obtained through the master-stability-function approach, increases. We also find that this gap is independent of network size, at least in a class of networks considered in this work. Limiting the analysis to the master-stability-function formalism for large networks, we find that in those networks with size much larger than the average degree, the synchronizing coupling strength has a power-law relation with the shortcut probability of the Newman–Watts network. The synchronization behavior of the network of nonidentical Hindmarsh–Rose neurons is investigated by numerically solving the equations and tracking the average synchronization error. The synchronization of identical Hindmarsh–Rose neurons coupled over clustered Newman–Watts networks, networks with dense intercluster connections but sparsely in intracluster linkage, is also addressed. It is found that the synchronizing coupling strength is influenced mainly by the probability of intercluster connections with a power-law relation. We also investigate the complementary role of chemical coupling in providing complete synchronization through electrical connections.
Publisher: Springer Science and Business Media LLC
Date: 04-10-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 23-02-2021
DOI: 10.1038/S41467-021-21483-Y
Abstract: Whether it be the passengers’ mobility demand in transportation systems, or the consumers’ energy demand in power grids, the primary purpose of many infrastructure networks is to best serve this flow demand. In reality, the volume of flow demand fluctuates unevenly across complex networks while simultaneously being hindered by some form of congestion or overload. Nevertheless, there is little known about how the heterogeneity of flow demand influences the network flow dynamics under congestion. To explore this, we introduce a percolation-based network analysis framework underpinned by flow heterogeneity. Thereby, we theoretically identify bottleneck links with guaranteed decisive impact on how flows are passed through the network. The effectiveness of the framework is demonstrated on large-scale real transportation networks, where mitigating the congestion on a small fraction of the links identified as bottlenecks results in a significant network improvement.
Publisher: Elsevier BV
Date: 07-2022
Publisher: IEEE
Date: 09-2014
Start Date: 04-2020
End Date: 12-2024
Amount: $500,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 02-2014
End Date: 12-2017
Amount: $395,220.00
Funder: Australian Research Council
View Funded ActivityStart Date: 04-2020
End Date: 12-2022
Amount: $321,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2017
End Date: 12-2020
Amount: $301,500.00
Funder: Australian Research Council
View Funded Activity