Directory of Open Access Journals (Sweden)
David F. Putrino
2011-01-01
Full Text Available Neurons in the Primary Motor Cortex (MI are known to form functional ensembles with one another in order to produce voluntary movement. Neural network changes during skill learning are thought to be involved in improved fluency and accuracy of motor tasks. Unforced errors during skilled tasks provide an avenue to study network connections related to motor learning. In order to investigate network activity in MI, microwires were implanted in the MI of cats trained to perform a reaching task. Spike trains from eight groups of simultaneously recorded cells (95 neurons in total were acquired. A point process generalized linear model (GLM was developed to assess simultaneously recorded cells for functional connectivity during reaching attempts where unforced errors or no errors were made. Whilst the same groups of neurons were often functionally connected regardless of trial success, functional connectivity between neurons was significantly different at fine time scales when the outcome of task performance changed. Furthermore, connections were shown to be significantly more robust across multiple latencies during successful trials of task performance. The results of this study indicate that reach-related neurons in MI form dynamic spiking dependencies whose temporal features are highly sensitive to unforced movement errors.
Sang, Dong; Lv, Bin; He, Huiguang; He, Jiping; Wang, Feiyue
2010-01-01
In this work, we took the analysis of neural interaction based on the data recorded from the motor cortex of a monkey, when it was trained to complete multi-targets reach-to-grasp tasks. As a recently proved effective tool, Dynamic Bayesian Network (DBN) was applied to model and infer interactions of dependence between neurons. In the results, the gained networks of neural interactions, which correspond to different tasks with different directions and orientations, indicated that the target information was not encoded in simple ways by neuronal networks. We also explored the difference of neural interactions between delayed period and peri-movement period during reach-to-grasp task. We found that the motor control process always led to relatively more complex neural interaction networks than the plan thinking process.
SPATIAL REACH OF MIGRANTS NETWORKS
Directory of Open Access Journals (Sweden)
Marden Barbosa de Campos
2015-11-01
Full Text Available Migration scholars have highlighted the crucial role that social networks play in migration flows. The fact that individuals live in specific locations, both before and after the migration, makes their movements set up a network of places, called migration spaces. The objective of this paper is to discuss the spatialization process of migration networks. By relying on personal contacts, networks are configured for a kind of proximity. The flow of information and the recruitment of individuals belonging to the migratory network shape the borders of these networks, due to the internal nature of the diffusion processes. Thus, the multidimensional nature of migration networks includes socio-cultural and spatial processes. All these aspects are articulated in different levels, acting as agents of a continuous spatial creation.
Optical technologies in extended-reach access networks
DEFF Research Database (Denmark)
Wong, Elaine; Amaya Fernández, Ferney Orlando; Tafur Monroy, Idelfonso
2009-01-01
The merging of access and metro networks has been proposed as a solution to lower the unit cost of customer bandwidth. This paper reviews some of the recent advances and challenges in extended-reach optical access networks....
Maslennikov, O. V.; Nekorkin, V. I.
2017-10-01
Dynamical networks are systems of active elements (nodes) interacting with each other through links. Examples are power grids, neural structures, coupled chemical oscillators, and communications networks, all of which are characterized by a networked structure and intrinsic dynamics of their interacting components. If the coupling structure of a dynamical network can change over time due to nodal dynamics, then such a system is called an adaptive dynamical network. The term ‘adaptive’ implies that the coupling topology can be rewired; the term ‘dynamical’ implies the presence of internal node and link dynamics. The main results of research on adaptive dynamical networks are reviewed. Key notions and definitions of the theory of complex networks are given, and major collective effects that emerge in adaptive dynamical networks are described.
WDM extended reach passive optical networks using OFDM-QAM.
Chow, Chi-Wai; Yeh, Chien-Hung; Wang, Chia-Hsuan; Shih, Fu-Yuan; Pan, Ci-Ling; Chi, Sien
2008-08-04
In order to reduce the cost for delivering future broadband services, network operators are inclined to simplify the network architectures by integrating the metro and access networks into a single system. Hence, extended reach passive optical networks (ER-PONs) have been proposed. ER-PON usually has four new features: high data rate in both upstream and downstream signals (>1 Gb/s); reach extension to >100 km; a high split ratio (>100); and using wavelength division multiplexing (WDM). In this work, we propose and demonstrate a highly spectral efficient ER-PON using 4 Gb/s OFDM-QAM for both upstream and downstream signals, while achieving a high split-ratio of 256. The ER-PON employs optical components optimized for GPON (bandwidth of approximately 1 GHz) and reaches 100 km without dispersion compensation. Numerical analysis using 16, 64 and 256-QAM OFDM are also performed to study the back-to-back receiver sensitivities and power penalties at different electrical driving ratios.
Universality in network dynamics.
Barzel, Baruch; Barabási, Albert-László
2013-01-01
Despite significant advances in characterizing the structural properties of complex networks, a mathematical framework that uncovers the universal properties of the interplay between the topology and the dynamics of complex systems continues to elude us. Here we develop a self-consistent theory of dynamical perturbations in complex systems, allowing us to systematically separate the contribution of the network topology and dynamics. The formalism covers a broad range of steady-state dynamical processes and offers testable predictions regarding the system's response to perturbations and the development of correlations. It predicts several distinct universality classes whose characteristics can be derived directly from the continuum equation governing the system's dynamics and which are validated on several canonical network-based dynamical systems, from biochemical dynamics to epidemic spreading. Finally, we collect experimental data pertaining to social and biological systems, demonstrating that we can accurately uncover their universality class even in the absence of an appropriate continuum theory that governs the system's dynamics.
Ebel, Holger; Davidsen, Joern; Bornholdt, Stefan
2003-01-01
Complex networks as the World Wide Web, the web of human sexual contacts or criminal networks often do not have an engineered architecture but instead are self-organized by the actions of a large number of individuals. From these local interactions non-trivial global phenomena can emerge as small-world properties or scale-free degree distributions. A simple model for the evolution of acquaintance networks highlights the essential dynamical ingredients necessary to obtain such complex network ...
Assimilation Dynamic Network (ADN) Project
National Aeronautics and Space Administration — The Assimilation Dynamic Network (ADN) is a dynamic inter-processor communication network that spans heterogeneous processor architectures, unifying components,...
Directory of Open Access Journals (Sweden)
Simone Gabriele
2007-07-01
Full Text Available The present paper discusses the possibility of using dynamic sensor networks for measurements over large areas. The main idea is to replace static networks, where the allocation of the sensors is pre defined, no matter how, and then the configuration is assumed and maintained, with a dynamic network whose configuration continuously changes in order to cover the whole measure area getting the different measures of the same point within a prefixed time interval. The main advantages are the reduced number of sensors required, a faster recover from damages, the possibility of changing dynamically the area or the subareas of interest, etc. The feasibility of such solution is shown by finding, in an optimal control problem formulation context, the trajectories of each sensor and the controls for such a motion. Simulation results are reported to highlight such results.
Activating and inhibiting connections in biological network dynamics
Directory of Open Access Journals (Sweden)
Knight Rob
2008-12-01
Full Text Available Abstract Background Many studies of biochemical networks have analyzed network topology. Such work has suggested that specific types of network wiring may increase network robustness and therefore confer a selective advantage. However, knowledge of network topology does not allow one to predict network dynamical behavior – for example, whether deleting a protein from a signaling network would maintain the network's dynamical behavior, or induce oscillations or chaos. Results Here we report that the balance between activating and inhibiting connections is important in determining whether network dynamics reach steady state or oscillate. We use a simple dynamical model of a network of interacting genes or proteins. Using the model, we study random networks, networks selected for robust dynamics, and examples of biological network topologies. The fraction of activating connections influences whether the network dynamics reach steady state or oscillate. Conclusion The activating fraction may predispose a network to oscillate or reach steady state, and neutral evolution or selection of this parameter may affect the behavior of biological networks. This principle may unify the dynamics of a wide range of cellular networks. Reviewers Reviewed by Sergei Maslov, Eugene Koonin, and Yu (Brandon Xia (nominated by Mark Gerstein. For the full reviews, please go to the Reviewers' comments section.
Complex Dynamics in Communication Networks
Kocarev, Ljupco
2005-01-01
Computer and communication networks are among society's most important infrastructures. The internet, in particular, is a giant global network of networks without central control or administration. It is a paradigm of a complex system, where complexity may arise from different sources: topological structure, network evolution, connection and node diversity, or dynamical evolution. The present volume is the first book entirely devoted to the new and emerging field of nonlinear dynamics of TCP/IP networks. It addresses both scientists and engineers working in the general field of communication networks.
Studying Dynamics in Business Networks
DEFF Research Database (Denmark)
Andersen, Poul Houman; Anderson, Helen; Havila, Virpi
1998-01-01
This paper develops a theory on network dynamics using the concepts of role and position from sociological theory. Moreover, the theory is further tested using case studies from Denmark and Finland......This paper develops a theory on network dynamics using the concepts of role and position from sociological theory. Moreover, the theory is further tested using case studies from Denmark and Finland...
Adaptability in dynamic wireless networks
Iyer, V.G.
2012-01-01
Software for networked embedded systems faces several challenges when the deployed network is subject to changing circumstances during operation. Typically, inter-node communication and network connectivity are two crucial aspects that are directly affected by dynamics such as failing wireless links
Dynamic Connectivity in the Middle Ganga Reaches Impacted by Human Disturbance
Mohanta, H.; Carbonneau, P.; Sinha, R.
2014-12-01
River systems are often perturbed by human impacts that can be difficult to quantify. In the middle Ganga, Lav Kush barrage at Kanpur was constructed in 2004 for augmenting drinking water supply for Kanpur city. Following this perturbation, the main flow of the river was diverted to the right bank downstream of this barrage. The left bank of the river is now almost dry in the pre-monsoon period. This complex change of flow pattern is difficult to quantify and characterize. However, the resulting changes in flow pathways do broadly fall under the concepts of connectivity. Here we start with a quantitative framework of connectivity based on the Graph Theory. In order to develop a new index of dynamic connectivity in a river network, we examine the channel structure in the vicinity of Lav Kush barrage over a period of 24 years (1990-2014). Based on the graph theory nodes and links, the idea was to extract the channel network from Landsat imagery, and then to construct the nodal structure with connecting links. The results were then used to compute the Hararry index, characteristic path length and the effective path length. Finally, the connectivity indices were analyzed in a multi-scalar framework by using hyperscale graphs which display connectivity indices as a function of both scale and position in the reach (Figure 1). Fig-1a shows undisturbed connectivity with a more heterogeneous nodal structure in the pre-barrage era. Fig-1b indicates a significant loss in connectivity after Lav Kush barrage construction. In the upstream reaches, the channel is almost uniform showing very less nodal density. This lesser nodal density signifies a uniform channel belt thatleads to a homogeneous geomorphic condition. This type of analysis was repeated at seasonal intervals in order to track changes in graph theoretic properties in an attempt to infer changes to the dynamic connectivity structure and their potential relevance to the geomorphic evolution of this river reach.
Fractional dynamics of complex networks
Turalska, Malgorzata; West, Bruce J.
2014-03-01
The relation between the behavior of a single element and the global dynamics of its host network is an open problem in the science of complex networks. Typically one attempts to infer the global dynamics by combining the behavior of single elements within the system, following a bottom-up approach. Here we address an inverse problem. We show that for a generic model within the Ising universality class it is possible to construct a description of the dynamics of an individual element, given the information about the network's global behavior. We demonstrate that the individual trajectory response to the collective motion of the network is described by a linear fractional differential equation, whose analytic solution is the Mittag-Leffler function. This solution is obtained through a subordination procedure without the necessity of linearizing the underlying dynamics, that is, the solution retains the influence of the nonlinear network dynamics on the individual. Moreover the solutions to the fractional equation of motion suggest a new direction for designing control mechanisms for complex networks. The implications of this new perspective are explored by introducing a control signal into a small number of network elements and analyzing the subsequent change in the network dynamics.
Rashvand, Habib
2013-01-01
Motivated by the exciting new application paradigm of using amalgamated technologies of the Internet and wireless, the next generation communication networks (also called 'ubiquitous', 'complex' and 'unstructured' networking) are changing the way we develop and apply our future systems and services at home and on local, national and global scales. Whatever the interconnection - a WiMAX enabled networked mobile vehicle, MEMS or nanotechnology enabled distributed sensor systems, Vehicular Ad hoc Networking (VANET) or Mobile Ad hoc Networking (MANET) - all can be classified under new networking s
Dynamics of interacting information waves in networks
Mirshahvalad, Atieh; Lizana, Ludvig; Rosvall, Martin
2013-01-01
To better understand the inner workings of information spreading, network researchers often use simple models to capture the spreading dynamics. But most models only highlight the effect of local interactions on the global spreading of a single information wave, and ignore the effects of interactions between multiple waves. Here we take into account the effect of multiple interacting waves by using an agent-based model in which the interaction between information waves is based on their novelty. We analyzed the global effects of such interactions and found that information that actually reaches nodes reaches them faster. This effect is caused by selection between information waves: slow waves die out and only fast waves survive. As a result, and in contrast to models with non-interacting information dynamics, the access to information decays with the distance from the source. Moreover, when we analyzed the model on various synthetic and real spatial road networks, we found that the decay rate also depends on ...
Cognitive Dynamic Optical Networks
DEFF Research Database (Denmark)
de Miguel, Ignacio; Duran, Ramon J.; Lorenzo, Ruben M.
2013-01-01
Cognitive networks are a promising solution for the control of heterogeneous optical networks. We review their fundamentals as well as a number of applications developed in the framework of the EU FP7 CHRON project.......Cognitive networks are a promising solution for the control of heterogeneous optical networks. We review their fundamentals as well as a number of applications developed in the framework of the EU FP7 CHRON project....
Cognitive Dynamic Optical Networks
DEFF Research Database (Denmark)
de Miguel, Ignacio; Duran, Ramon J.; Jimenez, Tamara
2013-01-01
The use of cognition is a promising element for the control of heterogeneous optical networks. Not only are cognitive networks able to sense current network conditions and act according to them, but they also take into account the knowledge acquired through past experiences; that is, they include...... learning with the aim of improving performance. In this paper, we review the fundamentals of cognitive networks and focus on their application to the optical networking area. In particular, a number of cognitive network architectures proposed so far, as well as their associated supporting technologies......, are reviewed. Moreover, several applications, mainly developed in the framework of the EU FP7 Cognitive Heterogeneous Reconfigurable Optical Network (CHRON) project, are also described....
Structurally Dynamic Spin Market Networks
Horváth, Denis; Kuscsik, Zoltán
The agent-based model of stock price dynamics on a directed evolving complex network is suggested and studied by direct simulation. The stationary regime is maintained as a result of the balance between the extremal dynamics, adaptivity of strategic variables and reconnection rules. The inherent structure of node agent "brain" is modeled by a recursive neural network with local and global inputs and feedback connections. For specific parametric combination the complex network displays small-world phenomenon combined with scale-free behavior. The identification of a local leader (network hub, agent whose strategies are frequently adapted by its neighbors) is carried out by repeated random walk process through network. The simulations show empirically relevant dynamics of price returns and volatility clustering. The additional emerging aspects of stylized market statistics are Zipfian distributions of fitness.
Nonlinear Dynamics on Interconnected Networks
Arenas, Alex; De Domenico, Manlio
2016-06-01
Networks of dynamical interacting units can represent many complex systems, from the human brain to transportation systems and societies. The study of these complex networks, when accounting for different types of interactions has become a subject of interest in the last few years, especially because its representational power in the description of users' interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.) [1], or in representing different transportation modes in urban networks [2,3]. The general name coined for these networks is multilayer networks, where each layer accounts for a type of interaction (see Fig. 1).
Parallel Void Thread in Long-Reach Ethernet Passive Optical Networks
Elrasad, Amr
2015-06-25
This work investigates void filling (idle periods) in long-reach Ethernet passive optical networks. We focus on reducing grant delays and hence reducing the average packet delay. We introduce a novel approach called parallel void thread (PVT), which allocates bandwidth grants during voids baseless of bandwidth requests. We introduce three different grant sizing schemes for PVT, namely void extension, count controlled batch void filling, and size controlled batch void filling. The proposed approaches canbe integrated with almost all of the previously reported dynamic bandwidth allocation schemes. Unlike other void filling schemes, PVT is less sensitive to the differential distance between optical network units and can work very well in the case of limited differential distances. We have analytically investigated the packet delay and derived a bound condition for PVT to outperform the other competitors. We support our work with extensive simulation study considering bursty traffic with long range dependence for both the single-class and differentiated services (DiffServ) scenarios. Numerical results show delay reduction up to 35% compared with the non-void filling scheme for the single-class scenario. For DiffServ traffic, PVT achieves delay reduction up to 80% for expedited forward traffic, 52% for assured forward traffic, and 56% for best effort traffic. © 2015 OSA.
Decoding network dynamics in cancer
DEFF Research Database (Denmark)
Linding, Rune
2014-01-01
Biological systems are composed of highly dynamic and interconnected molecular networks that drive biological decision processes. The goal of network biology is to describe, quantify and predict the information flow and functional behaviour of living systems in a formal language and with an accur...... of disease mutations on cellular signaling networks [Creixell et al. Nature Biotechnology 2012, Erler & Linding Cell 2012, Horn et al. Nature Methods 2014]....
Beliefs dynamics in communication networks
Azomahou, T.T.; Opolot, D.
2014-01-01
We study the dynamics of individual beliefs and information aggregation when agents communicate via a social network. We provide a general framework of social learning that captures the interactive effects of three main factors on the structure of individual beliefs resulting from such a dynamic
Dynamical detection of network communities
Quiles, Marcos G.; Macau, Elbert E. N.; Rubido, Nicolás
2016-05-01
A prominent feature of complex networks is the appearance of communities, also known as modular structures. Specifically, communities are groups of nodes that are densely connected among each other but connect sparsely with others. However, detecting communities in networks is so far a major challenge, in particular, when networks evolve in time. Here, we propose a change in the community detection approach. It underlies in defining an intrinsic dynamic for the nodes of the network as interacting particles (based on diffusive equations of motion and on the topological properties of the network) that results in a fast convergence of the particle system into clustered patterns. The resulting patterns correspond to the communities of the network. Since our detection of communities is constructed from a dynamical process, it is able to analyse time-varying networks straightforwardly. Moreover, for static networks, our numerical experiments show that our approach achieves similar results as the methodologies currently recognized as the most efficient ones. Also, since our approach defines an N-body problem, it allows for efficient numerical implementations using parallel computations that increase its speed performance.
Sliding Mode Control of Dynamic Voltage Restorer by Using a New Adaptive Reaching Law
Pandey, Achala; Agrawal, Rekha; Mandloi, Ravindra S.; Sarkar, Biswaroop
2017-08-01
This paper presents a new kind of adaptive reaching law for sliding mode control of Dynamic Voltage Restorer (DVR). Such an adaptive reaching law follows under-damped sinusoidal nature that causes the initial state to reach the sliding regime in extremely less time with negligible chattering. Moreover, it is robust in the sense the trajectory does not deviate from the sliding surface. This new approach is developed and successfully applied to DVR. The simulation results are presented that show its robustness.
2013-06-13
possible network configurations. It is important to address the impacts this modification has to the cryptanalysis of AES. In [19], the creators address...differential and linear cryptanalysis through the propagation of bit patterns over the algorithm steps. Daemen and Rijmen state the ShiftRow has the...linear cryptanalysis . The ShiftInterfaces step provides further diffusion of the bit pattern, since the step moves bits of one layer into another. The
Predictive structural dynamic network analysis.
Chen, Rong; Herskovits, Edward H
2015-04-30
Classifying individuals based on magnetic resonance data is an important task in neuroscience. Existing brain network-based methods to classify subjects analyze data from a cross-sectional study and these methods cannot classify subjects based on longitudinal data. We propose a network-based predictive modeling method to classify subjects based on longitudinal magnetic resonance data. Our method generates a dynamic Bayesian network model for each group which represents complex spatiotemporal interactions among brain regions, and then calculates a score representing that subject's deviation from expected network patterns. This network-derived score, along with other candidate predictors, are used to construct predictive models. We validated the proposed method based on simulated data and the Alzheimer's Disease Neuroimaging Initiative study. For the Alzheimer's Disease Neuroimaging Initiative study, we built a predictive model based on the baseline biomarker characterizing the baseline state and the network-based score which was constructed based on the state transition probability matrix. We found that this combined model achieved 0.86 accuracy, 0.85 sensitivity, and 0.87 specificity. For the Alzheimer's Disease Neuroimaging Initiative study, the model based on the baseline biomarkers achieved 0.77 accuracy. The accuracy of our model is significantly better than the model based on the baseline biomarkers (p-value=0.002). We have presented a method to classify subjects based on structural dynamic network model based scores. This method is of great importance to distinguish subjects based on structural network dynamics and the understanding of the network architecture of brain processes and disorders. Copyright © 2015 Elsevier B.V. All rights reserved.
Network Dynamics of Innovation Processes
Iacopini, Iacopo; Milojević, Staša; Latora, Vito
2018-01-01
We introduce a model for the emergence of innovations, in which cognitive processes are described as random walks on the network of links among ideas or concepts, and an innovation corresponds to the first visit of a node. The transition matrix of the random walk depends on the network weights, while in turn the weight of an edge is reinforced by the passage of a walker. The presence of the network naturally accounts for the mechanism of the "adjacent possible," and the model reproduces both the rate at which novelties emerge and the correlations among them observed empirically. We show this by using synthetic networks and by studying real data sets on the growth of knowledge in different scientific disciplines. Edge-reinforced random walks on complex topologies offer a new modeling framework for the dynamics of correlated novelties and are another example of coevolution of processes and networks.
Competitive Dynamics on Complex Networks
Zhao, Jiuhua; Liu, Qipeng; Wang, Xiaofan
2014-07-01
We consider a dynamical network model in which two competitors have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. The state of each normal agent converges to a steady value which is a convex combination of the competitors' states, and is independent of the initial states of agents. This implies that the competition result is fully determined by the network structure and positions of competitors in the network. We compute an Influence Matrix (IM) in which each element characterizing the influence of an agent on another agent in the network. We use the IM to predict the bias of each normal agent and thus predict which competitor will win. Furthermore, we compare the IM criterion with seven node centrality measures to predict the winner. We find that the competitor with higher Katz Centrality in an undirected network or higher PageRank in a directed network is most likely to be the winner. These findings may shed new light on the role of network structure in competition and to what extent could competitors adjust network structure so as to win the competition.
Data modeling of network dynamics
Jaenisch, Holger M.; Handley, James W.; Faucheux, Jeffery P.; Harris, Brad
2004-01-01
This paper highlights Data Modeling theory and its use for text data mining as a graphical network search engine. Data Modeling is then used to create a real-time filter capable of monitoring network traffic down to the port level for unusual dynamics and changes in business as usual. This is accomplished in an unsupervised fashion without a priori knowledge of abnormal characteristics. Two novel methods for converting streaming binary data into a form amenable to graphics based search and change detection are introduced. These techniques are then successfully applied to 1999 KDD Cup network attack data log-on sessions to demonstrate that Data Modeling can detect attacks without prior training on any form of attack behavior. Finally, two new methods for data encryption using these ideas are proposed.
Coordination Games on Dynamical Networks
Directory of Open Access Journals (Sweden)
Enea Pestelacci
2010-07-01
Full Text Available We propose a model in which agents of a population interacting according to a network of contacts play games of coordination with each other and can also dynamically break and redirect links to neighbors if they are unsatisfied. As a result, there is co-evolution of strategies in the population and of the graph that represents the network of contacts. We apply the model to the class of pure and general coordination games. For pure coordination games, the networks co-evolve towards the polarization of different strategies. In the case of general coordination games our results show that the possibility of refusing neighbors and choosing different partners increases the success rate of the Pareto-dominant equilibrium.
Chow, C W; Lin, Y H
2012-04-09
To provide broadband services in a single and low cost perform, the convergent optical wired and wireless access network is promising. Here, we propose and demonstrate a convergent optical wired and wireless long-reach access networks based on orthogonal wavelength division multiplexing (WDM). Both the baseband signal and the radio-over-fiber (ROF) signal are multiplexed and de-multiplexed in optical domain, hence it is simple and the operation speed is not limited by the electronic bottleneck caused by the digital signal processing (DSP). Error-free de-multiplexing and down-conversion can be achieved for all the signals after 60 km (long-reach) fiber transmission. The scalability of the system for higher bit-rate (60 GHz) is also simulated and discussed.
Hydrogen application dynamics and networks
Energy Technology Data Exchange (ETDEWEB)
Schmidt, E. [Air Liquide Large Industries, Champigny-sur-Marne (France)
2010-12-30
The Chemical Industry consumes large volumes of hydrogen as raw material for the manufacture of numerous products (e.g. polyamides and polyurethanes account for 60% of hydrogen demand). The hydrogen demand was in the recent past and will continue to be driven by the polyurethane family. China will host about 60% of new hydrogen needs over the period 2010-2015 becoming the first hydrogen market next year and reaching 25% of market share by 2015 (vs. only 4% in 2001). Air Liquide supplies large volumes of Hydrogen (and other Industrial Gases) to customers by on-site plants and through pipeline networks which offer significant benefits such as higher safety, reliability and flexibility of supply. Thanks to its long term strategy and heavy investment in large units and pipeline networks, Air Liquide is the Industrial Gas leader in most of the world class Petrochemical basins (Rotterdam, Antwerp, US Gulf Coast, Yosu, Caojing,..) (orig.)
Did public health travel advice reach EURO 2012 football fans? A social network survey.
Janiec, J; Zielicka-Hardy, A; Polkowska, A; Rogalska, J; Sadkowska-Todys, M
2012-08-02
We posted a survey on the Union of European Football Associations (UEFA)’s EURO 2012 Facebook profile to evaluate whether public health travel advice, specifically on the importance of measles vaccination,reached fans attending EURO 2012. Responses suggested that these messages were missed by 77% of fans. Social networks could serve as innovative platforms to conduct surveys, enabling rapid access to target populations at low cost and could be of use during upcoming mass gatherings such as the Olympics.
Dynamic Network Security Control Using Software Defined Networking
2016-03-24
not subject to copyright protection in the United States. AFIT-ENG-MS-16-M-049 DYNAMIC NETWORK SECURITY CONTROL USING SOFTWARE DEFINED NETWORKING... software and tools vetted by industry leaders in networking and security. After considering the technologies previously discussed, the four components...DYNAMIC NETWORK SECURITY CONTROL USING SOFTWARE DEFINED NETWORKING THESIS Michael C. Todd, Captain, USAF AFIT-ENG-MS-16-M-049 DEPARTMENT OF THE AIR
Infection Dynamics on Growing Networks
Lai, Ying-Cheng; Liu, Zonghua; Ye, Nong
We consider the entire spectrum of architectures for large, growing, and complex networks, ranging from being heterogeneous (scale-free) to homogeneous (random or small-world), and investigate the infection dynamics by using a realistic three-state epidemiological model. In this framework, a node can be in one of the three states: susceptible (S), infected (I), or refractory (R), and the populations in the three groups are approximately described by a set of nonlinear differential equations. Our heuristic analysis predicts that, (1) regardless of the network architecture, there exists a substantial fraction of nodes that can never be infected, and (2) heterogeneous networks are relatively more robust against spread of infection as compared with homogeneous networks. These are confirmed numerically. We have also considered the problem of deliberate immunization for preventing wide spread of infection, with the result that targeted immunization can be quite effective for heterogeneous networks. We believe these results are important for a host of problems in many areas of natural science and engineering, and in social sciences as well.
Anomaly Detection in Dynamic Networks
Energy Technology Data Exchange (ETDEWEB)
Turcotte, Melissa [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2014-10-14
Anomaly detection in dynamic communication networks has many important security applications. These networks can be extremely large and so detecting any changes in their structure can be computationally challenging; hence, computationally fast, parallelisable methods for monitoring the network are paramount. For this reason the methods presented here use independent node and edge based models to detect locally anomalous substructures within communication networks. As a first stage, the aim is to detect changes in the data streams arising from node or edge communications. Throughout the thesis simple, conjugate Bayesian models for counting processes are used to model these data streams. A second stage of analysis can then be performed on a much reduced subset of the network comprising nodes and edges which have been identified as potentially anomalous in the first stage. The first method assumes communications in a network arise from an inhomogeneous Poisson process with piecewise constant intensity. Anomaly detection is then treated as a changepoint problem on the intensities. The changepoint model is extended to incorporate seasonal behavior inherent in communication networks. This seasonal behavior is also viewed as a changepoint problem acting on a piecewise constant Poisson process. In a static time frame, inference is made on this extended model via a Gibbs sampling strategy. In a sequential time frame, where the data arrive as a stream, a novel, fast Sequential Monte Carlo (SMC) algorithm is introduced to sample from the sequence of posterior distributions of the change points over time. A second method is considered for monitoring communications in a large scale computer network. The usage patterns in these types of networks are very bursty in nature and don’t fit a Poisson process model. For tractable inference, discrete time models are considered, where the data are aggregated into discrete time periods and probability models are fitted to the
Cheng, Sen; Sabes, Philip N
2007-04-01
The sensorimotor calibration of visually guided reaching changes on a trial-to-trial basis in response to random shifts in the visual feedback of the hand. We show that a simple linear dynamical system is sufficient to model the dynamics of this adaptive process. In this model, an internal variable represents the current state of sensorimotor calibration. Changes in this state are driven by error feedback signals, which consist of the visually perceived reach error, the artificial shift in visual feedback, or both. Subjects correct for > or =20% of the error observed on each movement, despite being unaware of the visual shift. The state of adaptation is also driven by internal dynamics, consisting of a decay back to a baseline state and a "state noise" process. State noise includes any source of variability that directly affects the state of adaptation, such as variability in sensory feedback processing, the computations that drive learning, or the maintenance of the state. This noise is accumulated in the state across trials, creating temporal correlations in the sequence of reach errors. These correlations allow us to distinguish state noise from sensorimotor performance noise, which arises independently on each trial from random fluctuations in the sensorimotor pathway. We show that these two noise sources contribute comparably to the overall magnitude of movement variability. Finally, the dynamics of adaptation measured with random feedback shifts generalizes to the case of constant feedback shifts, allowing for a direct comparison of our results with more traditional blocked-exposure experiments.
Airborne Network Optimization with Dynamic Network Update
2015-03-26
Faculty Department of Electrical and Computer Engineering Graduate School of Engineering and Management Air Force Institute of Technology Air University...require small amounts of network bandwidth to perform routing. This thesis advocates the use of Kalman filters to predict network congestion in...airborne networks. Intelligent agents can make use of Kalman filter predictions to make informed decisions to manage communication in airborne networks. The
Tourism-planning network knowledge dynamics
DEFF Research Database (Denmark)
Dredge, Dianne
2014-01-01
This chapter explores the characteristics and functions of tourism networks as a first step in understanding how networks facilitate and reproduce knowledge. A framework to progress understandings of knowledge dynamics in tourism networks is presented that includes four key dimensions: context......, network agents, network boundaries and network resources. A case study of the development of the Next Generation Tourism Handbook (Queensland, Australia), a policy initiative that sought to bring tourism and land use planning knowledge closer together is presented. The case study illustrates...... that the tourism policy and land use planning networks operate in very different spheres and that context, network agents, network boundaries and network resources have a significant influence not only on knowledge dynamics but also on the capacity of network agents to overcome barriers to learning and to innovate....
The Challenge of Reaching Transparency: 'T-readiness' of Enterprises and Sector Networks
Directory of Open Access Journals (Sweden)
Melanie Fritz
2010-10-01
Full Text Available Discussions on the safety and quality of food as well as growing interest in the sustainability of the production, distribution and consumption of food have contributed to the emergence of ‘transparency’ as a critical success factor for the food sector. However, reaching transparency for different stakeholders from different backgrounds and cultural identities is a dynamic process which depends on certain capabilities of enterprises and organizations along the food value chain but also on the realization of a fitting communication scheme within the sector. This discussion asks for the identification and utilization of an indicator that could identify deficiencies and support enterprises and the sector in reaching a level of transparency that could serve specified transparency needs.
Directory of Open Access Journals (Sweden)
Chie eHabagishi
2014-02-01
Full Text Available In daily life, we encounter situations where we must quickly decide which hand to use for a motor action. Here, we investigated whether the hand chosen for a motor action varied over a short timescale (i.e., hours with changes in arm dynamics. Participants performed a reaching task in which they moved a specified hand to reach a target on a virtual reality display. During the task, a resistive viscous force field was abruptly applied to only the dominant hand. To evaluate changes in hand choice caused by this perturbation, participants performed an interleaved choice test in which they could freely choose either hand for reaching. Furthermore, to investigate the effect of temporal changes on arm dynamics and hand choice, we exposed the same participants to another condition in which the force field was introduced gradually. When the abrupt force was applied, use of the perturbed hand significantly decreased and not changed during the training. In contrast, when the incremental force was applied, use of the perturbed hand gradually decreased as force increased. Surprisingly, even though the final amount of force was identical between the two conditions, hand choice was significantly biased toward the unperturbed hand in the gradual condition. These results suggest that time-varying changes in arm dynamics may have a greater influence on hand choice than the amplitude of the resistant force itself.
Network Dynamic Connectivity for Identifying Hotspots of Fluvial Geomorphic Change
Czuba, J. A.; Foufoula-Georgiou, E.
2014-12-01
The hierarchical branching structure of a river network serves as a template upon which environmental fluxes of water, sediment, nutrients, etc. are conveyed and organized both spatially and temporally within a basin. Dynamical processes occurring on a river network tend to heterogeneously distribute fluxes on the network, often concentrating them into "clusters," i.e., places of excess flux accumulation. Here, we put forward the hypothesis that places in the network predisposed (due to process dynamics and network topology) to accumulate excess bed-material sediment over a considerable river reach and over a considerable period of time reflect locations where a local imbalance in sediment flux may occur thereby highlighting a susceptibility to potential fluvial geomorphic change. We have developed a framework where we are able to track fluxes on a "static" river network using a simplified Lagrangian transport model and use the spatial-temporal distribution of that flux to form a new "dynamic" network of the flux that evolves over time. From this dynamic network we can quantify the dynamic connectivity of the flux and integrate emergent "clusters" over time through a cluster persistence index (CPI) to assess the persistence of mass throughout the network. The framework was applied to sand transport on the Greater Blue Earth River Network in Minnesota where three hotspots of fluvial geomorphic change have been defined based on high rates of channel migration observed from aerial photographic analysis. Locations within the network with high CPI coincided with two of these hotspots, possibly suggesting that channel migration here is driven by sediment deposition "pushing" the stream into and thus eroding the opposite bank. The third hotspot was not identified by high CPI, but instead is believed to be a hotspot of streamflow-driven change based on additional information and the fact that high bed shear stress coincided with this hotspot. The proposed network
Network biomarkers, interaction networks and dynamical network biomarkers in respiratory diseases
Wu, Xiaodan; Chen, Luonan; Wang, Xiangdong
2014-01-01
Identification and validation of interaction networks and network biomarkers have become more critical and important in the development of disease-specific biomarkers, which are functionally changed during disease development, progression or treatment. The present review headlined the definition, significance, research and potential application for network biomarkers, interaction networks and dynamical network biomarkers (DNB). Disease-specific interaction networks, network biomarkers, or DNB...
Dynamic information routing in complex networks
Kirst, Christoph; Timme, Marc; Battaglia, Demian
2016-04-01
Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how such systems may specifically communicate and dynamically route information is not well understood. Here we identify a generic mechanism to route information on top of collective dynamical reference states in complex networks. Switching between collective dynamics induces flexible reorganization of information sharing and routing patterns, as quantified by delayed mutual information and transfer entropy measures between activities of a network's units. We demonstrate the power of this mechanism specifically for oscillatory dynamics and analyse how individual unit properties, the network topology and external inputs co-act to systematically organize information routing. For multi-scale, modular architectures, we resolve routing patterns at all levels. Interestingly, local interventions within one sub-network may remotely determine nonlocal network-wide communication. These results help understanding and designing information routing patterns across systems where collective dynamics co-occurs with a communication function.
Calibration of visually-guided reaching is driven by error corrective learning and internal dynamics
Cheng, Sen; Sabes, Philip N.
2008-01-01
The sensorimotor calibration of visually-guided reaching changes on a trial-to-trial basis in response to random shifts in the visual feedback of the hand. We show that a simple linear dynamical system is sufficient to model the dynamics of this adaptive process. In this model, an internal variable represents the current state of sensorimotor calibration. Changes in this state are driven by error feedback signals, which consist of the visually perceived reach error, the artificial shift in visual feedback, or both. Subjects correct for at least 20% of the error observed on each movement, despite being unaware of the visual shift. The state of adaptation is also driven by internal dynamics, consisting of a decay back to a baseline state and a “state noise” process. State noise includes any source of variability that directly affects the state of adaptation, such as variability in sensory feedback processing, the computations that drive learning, or the maintenance of the state. This noise is accumulated in the state across trials, creating temporal correlations in the sequence of reach errors. These correlations allow us to distinguish state noise from sensorimotor performance noise, which arises independently on each trial from random fluctuations in the sensorimotor pathway. We show that these two noise sources contribute comparably to the overall magnitude of movement variability. Finally, the dynamics of adaptation measured with random feedback shifts generalizes to the case of constant feedback shifts, allowing for a direct comparison of our results with more traditional blocked-exposure experiments. PMID:17202230
Learning dynamic Bayesian networks with mixed variables
DEFF Research Database (Denmark)
Bøttcher, Susanne Gammelgaard
This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learned...
Out of control: Fluctuation of cascading dynamics in networks
Wang, Jianwei; Cai, Lin; Xu, Bo; Li, Peng; Sun, Enhui; Zhu, Zhiguo
2016-11-01
Applying two preferential selection mechanisms of flow destination, we develop two new methods to quantify the initial load of a node, where the flow is transported along the shortest path between two nodes. We propose a simple cascading model and study cascading dynamics induced by attacking the node with the highest load in some synthetic and actual networks. Surprisingly, we observe the abnormal fluctuation of cascading dynamics, i.e., more damage can be triggered if we spend significantly higher cost to protect a network. In particular, this phenomenon is independent of the initial flow distribution and the preferential selection mechanisms of flow destination. However, it remains unclear which specific structural patterns may affect the fluctuation of cascading dynamics. In this paper, we examine the local evolution of the cascading propagation by constructing some special networks. We show that revivals of some nodes in the double ring structure facilitate the transportation of the flow between two unconnected sub-networks, cause more damage and subsequently lead to the abnormal fluctuation of cascading dynamics. Compared with the traditional definition of the betweenness, we adopt two new proposed methods to further evaluate the resilience of several actual networks. We find that some real world networks reach the strongest resilience level against cascading failures in our preferential selection mechanisms of flow destination. Moreover, we explore how to use the minimum cost to maximize the resilience of the studied networks.
Forced synchronization of autonomous dynamical Boolean networks
Energy Technology Data Exchange (ETDEWEB)
Rivera-Durón, R. R., E-mail: roberto.rivera@ipicyt.edu.mx; Campos-Cantón, E., E-mail: eric.campos@ipicyt.edu.mx [División de Matemáticas Aplicadas, Instituto Potosino de Investigación Científica y Tecnológica A. C., Camino a la Presa San José 2055, Col. Lomas 4 Sección, C.P. 78216, San Luis Potosí, S.L.P. (Mexico); Campos-Cantón, I. [Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, Álvaro Obregón 64, C.P. 78000, San Luis Potosí, S.L.P. (Mexico); Gauthier, Daniel J. [Department of Physics and Center for Nonlinear and Complex Systems, Duke University, Box 90305, Durham, North Carolina 27708 (United States)
2015-08-15
We present the design of an autonomous time-delay Boolean network realized with readily available electronic components. Through simulations and experiments that account for the detailed nonlinear response of each circuit element, we demonstrate that a network with five Boolean nodes displays complex behavior. Furthermore, we show that the dynamics of two identical networks display near-instantaneous synchronization to a periodic state when forced by a common periodic Boolean signal. A theoretical analysis of the network reveals the conditions under which complex behavior is expected in an individual network and the occurrence of synchronization in the forced networks. This research will enable future experiments on autonomous time-delay networks using readily available electronic components with dynamics on a slow enough time-scale so that inexpensive data collection systems can faithfully record the dynamics.
Expanding palliative care's reach in the community via the elder service agency network.
Reid, M Carrington; Ghesquiere, Angela; Kenien, Cara; Capezuti, Elizabeth; Gardner, Daniel
2017-08-01
Over the past two decades, palliative care has established itself as a promising approach to address the complex needs of individuals with advanced illness. Palliative care is well-established in US hospitals and has recently begun to expand outside of the hospital setting to meet the needs of non-hospitalized individuals. Experts have called for the development of innovative community-based models that facilitate delivery of palliative care to this target population. Elder service agencies are important partners that researchers should collaborate with to develop new and promising models. Millions of older adults receive aging network services in the U.S., highlighting the potential reach of these models. Recent health care reform efforts provide support for community-based initiatives, where coordination of care and services, delivered via health and social service agencies, is highly prioritized. This article describes the rationale for developing such approaches, including efforts to educate elder service agency clients about palliative care; training agency staff in palliative care principles; building capacity for elder services providers to screen individuals for palliative care needs; embedding palliative care "champions" in agencies to educate staff and clients and coordinate access to services among those with palliative care needs; and leveraging telehealth resources to conduct comprehensive assessments by hospital palliative care teams for elder service clients who have palliative care needs. We maintain that leveraging the resources of elder service agencies could measurably expand the reach of palliative care in the community.
Sensitive Dependence of Optimal Network Dynamics on Network Structure
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Takashi Nishikawa
2017-11-01
Full Text Available The relation between network structure and dynamics is determinant for the behavior of complex systems in numerous domains. An important long-standing problem concerns the properties of the networks that optimize the dynamics with respect to a given performance measure. Here, we show that such optimization can lead to sensitive dependence of the dynamics on the structure of the network. Specifically, using diffusively coupled systems as examples, we demonstrate that the stability of a dynamical state can exhibit sensitivity to unweighted structural perturbations (i.e., link removals and node additions for undirected optimal networks and to weighted perturbations (i.e., small changes in link weights for directed optimal networks. As mechanisms underlying this sensitivity, we identify discontinuous transitions occurring in the complement of undirected optimal networks and the prevalence of eigenvector degeneracy in directed optimal networks. These findings establish a unified characterization of networks optimized for dynamical stability, which we illustrate using Turing instability in activator-inhibitor systems, synchronization in power-grid networks, network diffusion, and several other network processes. Our results suggest that the network structure of a complex system operating near an optimum can potentially be fine-tuned for a significantly enhanced stability compared to what one might expect from simple extrapolation. On the other hand, they also suggest constraints on how close to the optimum the system can be in practice. Finally, the results have potential implications for biophysical networks, which have evolved under the competing pressures of optimizing fitness while remaining robust against perturbations.
The stochastic network dynamics underlying perceptual discrimination
Directory of Open Access Journals (Sweden)
Genis Prat-Ortega
2015-04-01
Full Text Available The brain is able to interpret streams of high-dimensional ambiguous information and yield coherent percepts. The mechanisms governing sensory integration have been extensively characterized using time-varying visual stimuli (Britten et al. 1996; Roitman and Shadlen 2002, but some of the basic principles regarding the network dynamics underlying this process remain largely unknown. We captured the basic features of a neural integrator using three canonical one-dimensional models: (1 the Drift Diffusion Model (DDM, (2 the Perfect Integrator (PI which is a particular case of the DDM where the bounds are set to infinity and (3 the double-well potential (DW which captures the dynamics of the attractor networks (Wang 2002; Roxin and Ledberg 2008. Although these models has been widely studied (Bogacz et al. 2006; Roxin and Ledberg 2008; Gold and Shadlen 2002, it has been difficult to experimentally discriminate among them because most of the observables measured are only quantitatively different among these models (e.g. psychometric curves. Here we aim to find experimentally measurable quantities that can yield qualitatively different behaviors depending on the nature of the underlying network dynamics. We examined the categorization dynamics of these models in response to fluctuating stimuli of different duration (T. On each time step, stimuli are drawn from a Gaussian distribution N(μ, σ and the two stimulus categories are defined by μ > 0 and μ < 0. Psychometric curves can therefore be obtained by quantifying the probability of the integrator to yield one category versus μ . We find however that varying σ can reveal more clearly the differences among the different integrators. In the small σ regime, both the DW and the DDM perform transient integration and exhibit a decaying stimulus reverse correlation kernel revealing a primacy effect (Nienborg and Cumming 2009; Wimmer et al. 2015 . In the large σ regime, the integration in the DDM
Temporal fidelity in dynamic social networks
DEFF Research Database (Denmark)
Stopczynski, Arkadiusz; Sapiezynski, Piotr; Pentland, Alex ‘Sandy’
2015-01-01
of the network dynamics can be used to inform the process of measuring social networks. The details of measurement are of particular importance when considering dynamic processes where minute-to-minute details are important, because collection of physical proximity interactions with high temporal resolution......It has recently become possible to record detailed social interactions in large social systems with high resolution. As we study these datasets, human social interactions display patterns that emerge at multiple time scales, from minutes to months. On a fundamental level, understanding...... is difficult and expensive. Here, we consider the dynamic network of proximity-interactions between approximately 500 individuals participating in the Copenhagen Networks Study. We show that in order to accurately model spreading processes in the network, the dynamic processes that occur on the order...
Monegaglia, Federico; Henshaw, Alex; Zolezzi, Guido; Tubino, Marco
2016-04-01
Planform development of evolving meander bends is a beautiful and complex dynamic phenomenon, controlled by the interplay among hydrodynamics, sediments and floodplain characteristics. In the past decades, morphodynamic models of river meandering have provided a thorough understanding of the unit physical processes interacting at the reach scale during meander planform evolution. On the other hand, recent years have seen advances in satellite geosciences able to provide data with increasing resolution and earth coverage, which are becoming an important tool for studying and managing river systems. Analysis of the planform development of meandering rivers through Landsat satellite imagery have been provided in very recent works. Methodologies for the objective and automatic extraction of key river development metrics from multi-temporal satellite images have been proposed though often limited to the extraction of channel centerlines, and not always able to yield quantitative data on channel width, migration rates and bed morphology. Overcoming such gap would make a major step forward to integrate morphodynamic theories, models and real-world data for an increased understanding of meandering river dynamics. In order to fulfill such gaps, a novel automatic procedure for extracting and analyzing the topography and planform dynamics of meandering rivers through time from satellite images is implemented. A robust algorithm able to compute channel centerline in complex contexts such as the presence of channel bifurcations and anabranching structures is used. As a case study, the procedure is applied to the Landsat database for a reach of the well-known case of Rio Beni, a large, suspended load dominated, tropical meandering river flowing through the Bolivian Amazon Basin. The reach-averaged evolution of single bends along Rio Beni over a 30 years period is analyzed, in terms of bend amplification rates computed according to the local centerline migration rate. A
Dynamics of Bottlebrush Networks: A Computational Study
Dobrynin, Andrey; Cao, Zhen; Sheiko, Sergei
We study dynamics of deformation of bottlebrush networks using molecular dynamics simulations and theoretical calculations. Analysis of our simulation results show that the dynamics of bottlebrush network deformation can be described by a Rouse model for polydisperse networks with effective Rouse time of the bottlebrush network strand, τR =τ0Ns2 (Nsc + 1) where, Ns is the number-average degree of polymerization of the bottlebrush backbone strands between crosslinks, Nsc is the degree of polymerization of the side chains and τ0is a characteristic monomeric relaxation time. At time scales t smaller than the Rouse time, t crosslinks, the network response is pure elastic with shear modulus G (t) =G0 , where G0 is the equilibrium shear modulus at small deformation. The stress evolution in the bottlebrush networks can be described by a universal function of t /τR . NSF DMR-1409710.
Pinning Synchronization of Switched Complex Dynamical Networks
Directory of Open Access Journals (Sweden)
Liming Du
2015-01-01
Full Text Available Network topology and node dynamics play a key role in forming synchronization of complex networks. Unfortunately there is no effective synchronization criterion for pinning synchronization of complex dynamical networks with switching topology. In this paper, pinning synchronization of complex dynamical networks with switching topology is studied. Two basic problems are considered: one is pinning synchronization of switched complex networks under arbitrary switching; the other is pinning synchronization of switched complex networks by design of switching when synchronization cannot achieved by using any individual connection topology alone. For the two problems, common Lyapunov function method and single Lyapunov function method are used respectively, some global synchronization criteria are proposed and the designed switching law is given. Finally, simulation results verify the validity of the results.
Psychology and social networks: a dynamic network theory perspective.
Westaby, James D; Pfaff, Danielle L; Redding, Nicholas
2014-04-01
Research on social networks has grown exponentially in recent years. However, despite its relevance, the field of psychology has been relatively slow to explain the underlying goal pursuit and resistance processes influencing social networks in the first place. In this vein, this article aims to demonstrate how a dynamic network theory perspective explains the way in which social networks influence these processes and related outcomes, such as goal achievement, performance, learning, and emotional contagion at the interpersonal level of analysis. The theory integrates goal pursuit, motivation, and conflict conceptualizations from psychology with social network concepts from sociology and organizational science to provide a taxonomy of social network role behaviors, such as goal striving, system supporting, goal preventing, system negating, and observing. This theoretical perspective provides psychologists with new tools to map social networks (e.g., dynamic network charts), which can help inform the development of change interventions. Implications for social, industrial-organizational, and counseling psychology as well as conflict resolution are discussed, and new opportunities for research are highlighted, such as those related to dynamic network intelligence (also known as cognitive accuracy), levels of analysis, methodological/ethical issues, and the need to theoretically broaden the study of social networking and social media behavior. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
From topology to dynamics in biochemical networks.
Fox, Jeffrey J.; Hill, Colin C.
2001-12-01
Abstract formulations of the regulation of gene expression as random Boolean switching networks have been studied extensively over the past three decades. These models have been developed to make statistical predictions of the types of dynamics observed in biological networks based on network topology and interaction bias, p. For values of mean connectivity chosen to correspond to real biological networks, these models predict disordered dynamics. However, chaotic dynamics seems to be absent from the functioning of a normal cell. While these models use a fixed number of inputs for each element in the network, recent experimental evidence suggests that several biological networks have distributions in connectivity. We therefore study randomly constructed Boolean networks with distributions in the number of inputs, K, to each element. We study three distributions: delta function, Poisson, and power law (scale free). We analytically show that the critical value of the interaction bias parameter, p, above which steady state behavior is observed, is independent of the distribution in the limit of the number of elements N--> infinity. We also study these networks numerically. Using three different measures (types of attractors, fraction of elements that are active, and length of period), we show that finite, scale-free networks are more ordered than either the Poisson or delta function networks below the critical point. Thus the topology of scale-free biochemical networks, characterized by a wide distribution in the number of inputs per element, may provide a source of order in living cells. (c) 2001 American Institute of Physics.
The Dynamics of Network Topology
Voicu, Ramiro; Legrand, Iosif; Newman, Harvey; Barczyk, Artur; Grigoras, Costin; Dobre, Ciprian
2011-12-01
Network monitoring is vital to ensure proper network operation over time, and is tightly integrated with all the data intensive processing tasks used by the LHC experiments. In order to build a coherent set of network management services it is very important to collect in near real-time information about the network topology, the main data flows, traffic volume and the quality of connectivity. A set of dedicated modules were developed in the MonALISA framework to periodically perform network measurements tests between all sites. We developed global services to present in near real-time the entire network topology used by a community. For any LHC experiment such a network topology includes several hundred of routers and tens of Autonomous Systems. Any changes in the global topology are recorded and this information is can be easily correlated with traffic patterns. The evolution in time of global network topology is shown a dedicated GUI. Changes in the global topology at this level occur quite frequently and even small modifications in the connectivity map may significantly affect the network performance. The global topology graphs are correlated with active end to end network performance measurements, done with the Fast Data Transfer application, between all sites. Access to both real-time and historical data, as provided by MonALISA, is also important for developing services able to predict the usage pattern, to aid in efficiently allocating resources globally.
Complex networks repair strategies: Dynamic models
Fu, Chaoqi; Wang, Ying; Gao, Yangjun; Wang, Xiaoyang
2017-09-01
Network repair strategies are tactical methods that restore the efficiency of damaged networks; however, unreasonable repair strategies not only waste resources, they are also ineffective for network recovery. Most extant research on network repair focuses on static networks, but results and findings on static networks cannot be applied to evolutionary dynamic networks because, in dynamic models, complex network repair has completely different characteristics. For instance, repaired nodes face more severe challenges, and require strategic repair methods in order to have a significant effect. In this study, we propose the Shell Repair Strategy (SRS) to minimize the risk of secondary node failures due to the cascading effect. Our proposed method includes the identification of a set of vital nodes that have a significant impact on network repair and defense. Our identification of these vital nodes reduces the number of switching nodes that face the risk of secondary failures during the dynamic repair process. This is positively correlated with the size of the average degree 〈 k 〉 and enhances network invulnerability.
Dynamic properties of cellular neural networks
Directory of Open Access Journals (Sweden)
Angela Slavova
1993-01-01
Full Text Available Dynamic behavior of a new class of information-processing systems called Cellular Neural Networks is investigated. In this paper we introduce a small parameter in the state equation of a cellular neural network and we seek for periodic phenomena. New approach is used for proving stability of a cellular neural network by constructing Lyapunov's majorizing equations. This algorithm is helpful for finding a map from initial continuous state space of a cellular neural network into discrete output. A comparison between cellular neural networks and cellular automata is made.
Modeling the Dynamics of Compromised Networks
Energy Technology Data Exchange (ETDEWEB)
Soper, B; Merl, D M
2011-09-12
Accurate predictive models of compromised networks would contribute greatly to improving the effectiveness and efficiency of the detection and control of network attacks. Compartmental epidemiological models have been applied to modeling attack vectors such as viruses and worms. We extend the application of these models to capture a wider class of dynamics applicable to cyber security. By making basic assumptions regarding network topology we use multi-group epidemiological models and reaction rate kinetics to model the stochastic evolution of a compromised network. The Gillespie Algorithm is used to run simulations under a worst case scenario in which the intruder follows the basic connection rates of network traffic as a method of obfuscation.
Evolutionary dynamics of complex communications networks
Karyotis, Vasileios; Papavassiliou, Symeon
2013-01-01
Until recently, most network design techniques employed a bottom-up approach with lower protocol layer mechanisms affecting the development of higher ones. This approach, however, has not yielded fascinating results in the case of wireless distributed networks. Addressing the emerging aspects of modern network analysis and design, Evolutionary Dynamics of Complex Communications Networks introduces and develops a top-bottom approach where elements of the higher layer can be exploited in modifying the lowest physical topology-closing the network design loop in an evolutionary fashion similar to
Dynamical Adaptation in Terrorist Cells/Networks
DEFF Research Database (Denmark)
Hussain, Dil Muhammad Akbar; Ahmed, Zaki
2010-01-01
Typical terrorist cells/networks have dynamical structure as they evolve or adapt to changes which may occur due to capturing or killing of a member of the cell/network. Analytical measures in graph theory like degree centrality, betweenness and closeness centralities are very common and have long...... history of their successful use in revealing the importance of various members of the network. However, modeling of covert, terrorist or criminal networks through social graph dose not really provide the hierarchical structure which exist in these networks as these networks are composed of leaders...... and followers etc. In this research we analyze and predict the most likely role a particular node can adapt once a member of the network is either killed or caught. The adaptation is based on computing Bayes posteriori probability of each node and the level of the said node in the network structure....
Geometric evolutionary dynamics of protein interaction networks.
Przulj, Natasa; Kuchaiev, Oleksii; Stevanović, Aleksandar; Hayes, Wayne
2010-01-01
Understanding the evolution and structure of protein-protein interaction (PPI) networks is a central problem of systems biology. Since most processes in the cell are carried out by groups of proteins acting together, a theoretical model of how PPI networks develop based on duplications and mutations is an essential ingredient for understanding the complex wiring of the cell. Many different network models have been proposed, from those that follow power-law degree distributions and those that model complementarity of protein binding domains, to those that have geometric properties. Here, we introduce a new model for PPI network (and thus gene) evolution that produces well-fitting network models for currently available PPI networks. The model integrates geometric network properties with evolutionary dynamics of PPI network evolution.
Algorithms for radio networks with dynamic topology
Shacham, Nachum; Ogier, Richard; Rutenburg, Vladislav V.; Garcia-Luna-Aceves, Jose
1991-08-01
The objective of this project was the development of advanced algorithms and protocols that efficiently use network resources to provide optimal or nearly optimal performance in future communication networks with highly dynamic topologies and subject to frequent link failures. As reflected by this report, we have achieved our objective and have significantly advanced the state-of-the-art in this area. The research topics of the papers summarized include the following: efficient distributed algorithms for computing shortest pairs of disjoint paths; minimum-expected-delay alternate routing algorithms for highly dynamic unreliable networks; algorithms for loop-free routing; multipoint communication by hierarchically encoded data; efficient algorithms for extracting the maximum information from event-driven topology updates; methods for the neural network solution of link scheduling and other difficult problems arising in communication networks; and methods for robust routing in networks subject to sophisticated attacks.
Collective dynamics of active cytoskeletal networks
Köhler, Simone; Bausch, Andreas R
2011-01-01
Self organization mechanisms are essential for the cytoskeleton to adapt to the requirements of living cells. They rely on the intricate interplay of cytoskeletal filaments, crosslinking proteins and molecular motors. Here we present an in vitro minimal model system consisting of actin filaments, fascin and myosin-II filaments exhibiting pulsative collective long range dynamics. The reorganizations in the highly dynamic steady state of the active gel are characterized by alternating periods of runs and stalls resulting in a superdiffusive dynamics of the network's constituents. They are dominated by the complex competition of crosslinking molecules and motor filaments in the network: Collective dynamics are only observed if the relative strength of the binding of myosin-II filaments to the actin network allows exerting high enough forces to unbind actin/fascin crosslinks. The feedback between structure formation and dynamics can be resolved by combining these experiments with phenomenological simulations base...
Dynamic Dilution Effects in Polymeric Networks
DEFF Research Database (Denmark)
Skov, Anne Ladegaard; Sommer-Larsen, Peter; Hassager, Ole
2006-01-01
processes, namely the reptation of linear species within the network and the arm withdrawal process of star arms in the sol fraction and of dangling single-chain ends attached to the network. The relaxation spectra are influenced by the stoichiometry to a large extent due to dynamic dilution effects caused...
The Social Dynamics of Innovation Networks
Rutten, Roel; Benneworth, Paul Stephen; Irawati, Dessy; Boekema, Frans
2014-01-01
The social dynamics of innovation networks captures the important role of trust, social capital, institutions and norms and values in the creation of knowledge in innovation networks. In doing so, this book connects to a long-standing debate on the socio-spatial context of innovation in economic
Dynamical Properties of Discrete Reaction Networks
Paulevé, Loïc; Craciun, Gheorghe; Koeppl, Heinz
2013-01-01
International audience; Reaction networks are commonly used to model the evolution of populations of species subject to transformations following an imposed stoichiometry. This paper focuses on the efficient characterisation of dynamical properties of Discrete Reaction Networks (DRNs). DRNs can be seen as modelling the underlying discrete nondeterministic transitions of stochastic models of reactions networks. In that sense, any proof of non-reachability in DRNs directly applies to any concre...
On the dynamics of random neuronal networks
Robert, Philippe; Touboul, Jonathan D.
2014-01-01
We study the mean-field limit and stationary distributions of a pulse-coupled network modeling the dynamics of a large neuronal assemblies. Our model takes into account explicitly the intrinsic randomness of firing times, contrasting with the classical integrate-and-fire model. The ergodicity properties of the Markov process associated to finite networks are investigated. We derive the limit in distribution of the sample path of the state of a neuron of the network when its size gets large. T...
Opinion dynamics in activity-driven networks
Li, Dandan; Han, Dun; Ma, Jing; Sun, Mei; Tian, Lixin; Khouw, Timothy; Stanley, H. Eugene
2017-10-01
Social interaction between individuals constantly affects the development of their personal opinions. Previous models such as the Deffuant model and the Hegselmann-Krause (HK) model have assumed that individuals only update their opinions after interacting with neighbors whose opinions are similar to their own. However, people are capable of communicating widely with all of their neighbors to gather their ideas and opinions, even if they encounter a number of opposing attitudes. We propose a model in which agents listen to the opinions of all their neighbors. Continuous opinion dynamics are investigated in activity-driven networks with a tolerance threshold. We study how the initial opinion distribution, tolerance threshold, opinion-updating speed, and activity rate affect the evolution of opinion. We find that when the initial fraction of positive opinion is small, all opinions become negative by the end of the simulation. As the initial fraction of positive opinions rises above a certain value —about 0.45— the final fraction of positive opinions sharply increases and eventually equals 1. Increased tolerance threshold δ is found to lead to a more varied final opinion distribution. We also find that if the negative opinion has an initial advantage, the final fraction of negative opinion increases and reaches its peak as the updating speed λ approaches 0.5. Finally we show that the lower the activity rate of individuals, the greater the fluctuation range of their opinions.
Complex networks: Dynamics and security
Indian Academy of Sciences (India)
We study a mechanism for cascades in complex networks by constructing a model incorporating the flows of information and physical quantities in the network. Using this model we can also show that the cascading phenomenon can be understood as a phase transition in terms of the key parameter characterizing the node ...
Complex networks: Dynamics and security
Indian Academy of Sciences (India)
a node, has an exponential tail, in contrast to the algebraic one that characterizes scale-free networks recently discovered in a variety of real-world situations [3,4]. Scale-free networks are heterogeneous as their connectivity can vary significantly from node to node and a considerable number of links can be associated with ...
Network Physiology: How Organ Systems Dynamically Interact.
Directory of Open Access Journals (Sweden)
Ronny P Bartsch
Full Text Available We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS, we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems.
Network Physiology: How Organ Systems Dynamically Interact.
Bartsch, Ronny P; Liu, Kang K L; Bashan, Amir; Ivanov, Plamen Ch
2015-01-01
We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems.
Dynamic bandwidth allocation in GPON networks
DEFF Research Database (Denmark)
Ozimkiewiez, J.; Ruepp, Sarah Renée; Dittmann, Lars
2009-01-01
Two Dynamic Bandwidth Allocation algorithms used for coordination of the available bandwidth between end users in a GPON network have been simulated using OPNET to determine and compare the performance, scalability and efficiency of status reporting and non status reporting dynamic bandwidth...
Dynamic Image Networks for Action Recognition
Bilen, H.; Fernando, B.; Gavves, E.; Vedaldi, A.; Gould, S.
2016-01-01
We introduce the concept of dynamic image, a novel compact representation of videos useful for video analysis especially when convolutional neural networks (CNNs) are used. The dynamic image is based on the rank pooling concept and is obtained through the parameters of a ranking machine that encodes
Developing a Global Network of River Reaches in Preparation of SWOT
Lion, C.; Pavelsky, T.; Allen, G. H.; Beighley, E.; Schumann, G.; Durand, M. T.
2016-12-01
In 2020, the Surface Water and Ocean Topography satellite (SWOT), a joint mission of NASA/CNES/CSA/UK will be launched. One of its major products will be the measurements of continental water surfaces, including the width, height, and slope of rivers and the surface area and elevations of lakes. The mission will improve the monitoring of continental water and also our understanding of the interactions between different hydrologic reservoirs. For rivers, SWOT measurements of slope will be carried out over predefined river reaches. As such, an a priori dataset for rivers is needed in order to facilitate analysis of the raw SWOT data. The information required to produce this dataset includes measurements of river width, elevation, slope, planform, river network topology, and flow accumulation. To produce this product, we have linked two existing global datasets: the Global River Widths from Landsat (GRWL) database, which contains river centerline locations, widths, and a braiding index derived from Landsat imagery, and a modified version of the HydroSHEDS hydrologically corrected digital elevation product, which contains heights and flow accumulation measurements for streams at 3 arcseconds spatial resolution. Merging these two datasets requires considerable care. The difficulties, among others, lie in the difference of resolution: 30m versus 3 arseconds, and the age of the datasets: 2000 versus 2010 (some rivers have moved, the braided sections are different). As such, we have developed custom software to merge the two datasets, taking into account the spatial proximity of river channels in the two datasets and ensuring that flow accumulation in the final dataset always increases downstream. Here, we present our results for the globe.
Collective dynamics of `small-world' networks
Watts, Duncan J.; Strogatz, Steven H.
1998-06-01
Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays,, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks `rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them `small-world' networks, by analogy with the small-world phenomenon, (popularly known as six degrees of separation). The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
Cognitive radio networks dynamic resource allocation schemes
Wang, Shaowei
2014-01-01
This SpringerBrief presents a survey of dynamic resource allocation schemes in Cognitive Radio (CR) Systems, focusing on the spectral-efficiency and energy-efficiency in wireless networks. It also introduces a variety of dynamic resource allocation schemes for CR networks and provides a concise introduction of the landscape of CR technology. The author covers in detail the dynamic resource allocation problem for the motivations and challenges in CR systems. The Spectral- and Energy-Efficient resource allocation schemes are comprehensively investigated, including new insights into the trade-off
Evolution of cooperation on stochastic dynamical networks.
Wu, Bin; Zhou, Da; Fu, Feng; Luo, Qingjun; Wang, Long; Traulsen, Arne
2010-06-30
Cooperative behavior that increases the fitness of others at a cost to oneself can be promoted by natural selection only in the presence of an additional mechanism. One such mechanism is based on population structure, which can lead to clustering of cooperating agents. Recently, the focus has turned to complex dynamical population structures such as social networks, where the nodes represent individuals and links represent social relationships. We investigate how the dynamics of a social network can change the level of cooperation in the network. Individuals either update their strategies by imitating their partners or adjust their social ties. For the dynamics of the network structure, a random link is selected and breaks with a probability determined by the adjacent individuals. Once it is broken, a new one is established. This linking dynamics can be conveniently characterized by a Markov chain in the configuration space of an ever-changing network of interacting agents. Our model can be analytically solved provided the dynamics of links proceeds much faster than the dynamics of strategies. This leads to a simple rule for the evolution of cooperation: The more fragile links between cooperating players and non-cooperating players are (or the more robust links between cooperators are), the more likely cooperation prevails. Our approach may pave the way for analytically investigating coevolution of strategy and structure.
Evolution of cooperation on stochastic dynamical networks.
Directory of Open Access Journals (Sweden)
Bin Wu
Full Text Available Cooperative behavior that increases the fitness of others at a cost to oneself can be promoted by natural selection only in the presence of an additional mechanism. One such mechanism is based on population structure, which can lead to clustering of cooperating agents. Recently, the focus has turned to complex dynamical population structures such as social networks, where the nodes represent individuals and links represent social relationships. We investigate how the dynamics of a social network can change the level of cooperation in the network. Individuals either update their strategies by imitating their partners or adjust their social ties. For the dynamics of the network structure, a random link is selected and breaks with a probability determined by the adjacent individuals. Once it is broken, a new one is established. This linking dynamics can be conveniently characterized by a Markov chain in the configuration space of an ever-changing network of interacting agents. Our model can be analytically solved provided the dynamics of links proceeds much faster than the dynamics of strategies. This leads to a simple rule for the evolution of cooperation: The more fragile links between cooperating players and non-cooperating players are (or the more robust links between cooperators are, the more likely cooperation prevails. Our approach may pave the way for analytically investigating coevolution of strategy and structure.
Cascade dynamics on complex networks.
Hackett, Adam W.
2011-01-01
peer-reviewed The network topologies on which many natural and synthetic systems are built provide ideal settings for the emergence of complex phenomena. One well-studied manifestation of this, called a cascade or avalanche, is observed when interactions between the components of a system allow an initially localized effect to propagate globally. For example, the malfunction of technological systems like email networks or electrical power grids is often attributable to a cascade o...
Control theory of digitally networked dynamic systems
Lunze, Jan
2013-01-01
The book gives an introduction to networked control systems and describes new modeling paradigms, analysis methods for event-driven, digitally networked systems, and design methods for distributed estimation and control. Networked model predictive control is developed as a means to tolerate time delays and packet loss brought about by the communication network. In event-based control the traditional periodic sampling is replaced by state-dependent triggering schemes. Novel methods for multi-agent systems ensure complete or clustered synchrony of agents with identical or with individual dynamic
Näslund, Annika; Sundelin, Gunnevi; Hirschfeld, Helga
2007-11-01
To investigate the co-ordination between reaching, ground reaction forces and muscle activity in standing children with severe spastic diplegia wearing dynamic ankle-foot orthoses compared with typically developing children. Clinical experimental study. Six children with spastic diplegia (Gross Motor Function Classification System level III-IV) and 6 controls. Ground reaction forces (AMTI force plates), ankle muscle activity (electromyography and displacement of the hand (ELITE systems) were investigated while reaching for an object. For the children with severe spastic diplegia who were wearing dynamic ankle-foot orthoses, co-ordination between upward and forward reach velocity differed regarding the temporal sequencing and amplitude of velocity peaks. During reaching, these children lacked interplay of pushing force beneath the reach leg and braking force beneath the non-reach leg and co-ordinated ankle muscle activity, compared with controls. The results suggest differences in reach performance and postural adjustments for balance control during a reaching movement in standing between children with spastic diplegia Gross Motor Function Classification System level III-IV, wearing dynamic ankle-foot orthoses compared with typically developing children.
Dynamic simulation of regulatory networks using SQUAD
Directory of Open Access Journals (Sweden)
Xenarios Ioannis
2007-11-01
Full Text Available Abstract Background The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology. Results We developed SQUAD, a software for the dynamic simulation of signaling networks using the standardized qualitative dynamical systems approach. SQUAD converts the network into a discrete dynamical system, and it uses a binary decision diagram algorithm to identify all the steady states of the system. Then, the software creates a continuous dynamical system and localizes its steady states which are located near the steady states of the discrete system. The software permits to make simulations on the continuous system, allowing for the modification of several parameters. Importantly, SQUAD includes a framework for perturbing networks in a manner similar to what is performed in experimental laboratory protocols, for example by activating receptors or knocking out molecular components. Using this software we have been able to successfully reproduce the behavior of the regulatory network implicated in T-helper cell differentiation. Conclusion The simulation of regulatory networks aims at predicting the behavior of a whole system when subject
Dynamic Protection of Optical Networks
DEFF Research Database (Denmark)
Ruepp, Sarah Renée
2008-01-01
This thesis deals with making optical networks resilient to failures. The recovery performance of path, segment and span restoration is evaluated in a network with limited wavelength conversion capability using both standard and enhanced wavelength assignment schemes. The enhanced wavelength...... to recovery initiation, has shown to increase the recovery percentage. If the lack of span resources limits successful connection recovery, both span and node resources (i.e., Wavelength Converters (WCs)) should be released, while it is suffcient to release the WCs if they are the limiting factor. The effect...... of the modularity of capacity units is investigated for resilient network design. Different span upgrading strategies and algorithms for finding restoration paths are evaluated. Furthermore, the capacity effciency of constraining restoration requests for the same destination node to the same restoration path...
Innovation networking between stability and political dynamics
DEFF Research Database (Denmark)
Koch, Christian
2004-01-01
is a segment-collaboration between a few manufacturing companies and a software house, the other a complex and extensive innovation network. These studies show how negotiations, shifting positions of players, mobilising stable elements of the network, when developing new ones, and interplays between internal......This contribution views innovation as a social activity of building networks, using software product development in multicompany alliances and networks as example. Innovation networks are frequently understood as quite stable arrangements characterised by high trust among the participants. The aim...... of the contribution is to challenge and transcend these notions and develop an understanding of innovation networks as an interplay between stable and dynamic elements, where political processes in innovation are much more than a disruptive and even a counterproductive feature. It reviews the growing number...
Fundamental structures of dynamic social networks
DEFF Research Database (Denmark)
Sekara, Vedran; Stopczynski, Arkadiusz; Jørgensen, Sune Lehmann
2016-01-01
, and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals...... and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection...... a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework...
Dynamics-based centrality for directed networks.
Masuda, Naoki; Kori, Hiroshi
2010-11-01
Determining the relative importance of nodes in directed networks is important in, for example, ranking websites, publications, and sports teams, and for understanding signal flows in systems biology. A prevailing centrality measure in this respect is the PageRank. In this work, we focus on another class of centrality derived from the Laplacian of the network. We extend the Laplacian-based centrality, which has mainly been applied to strongly connected networks, to the case of general directed networks such that we can quantitatively compare arbitrary nodes. Toward this end, we adopt the idea used in the PageRank to introduce global connectivity between all the pairs of nodes with a certain strength. Numerical simulations are carried out on some networks. We also offer interpretations of the Laplacian-based centrality for general directed networks in terms of various dynamical and structural properties of networks. Importantly, the Laplacian-based centrality defined as the stationary density of the continuous-time random walk with random jumps is shown to be equivalent to the absorption probability of the random walk with sinks at each node but without random jumps. Similarly, the proposed centrality represents the importance of nodes in dynamics on the original network supplied with sinks but not with random jumps.
Random graph models for dynamic networks
Zhang, Xiao; Moore, Cristopher; Newman, Mark E. J.
2017-10-01
Recent theoretical work on the modeling of network structure has focused primarily on networks that are static and unchanging, but many real-world networks change their structure over time. There exist natural generalizations to the dynamic case of many static network models, including the classic random graph, the configuration model, and the stochastic block model, where one assumes that the appearance and disappearance of edges are governed by continuous-time Markov processes with rate parameters that can depend on properties of the nodes. Here we give an introduction to this class of models, showing for instance how one can compute their equilibrium properties. We also demonstrate their use in data analysis and statistical inference, giving efficient algorithms for fitting them to observed network data using the method of maximum likelihood. This allows us, for example, to estimate the time constants of network evolution or infer community structure from temporal network data using cues embedded both in the probabilities over time that node pairs are connected by edges and in the characteristic dynamics of edge appearance and disappearance. We illustrate these methods with a selection of applications, both to computer-generated test networks and real-world examples.
Symbolic dynamics and computation in model gene networks.
Edwards, R.; Siegelmann, H. T.; Aziza, K.; Glass, L.
2001-03-01
We analyze a class of ordinary differential equations representing a simplified model of a genetic network. In this network, the model genes control the production rates of other genes by a logical function. The dynamics in these equations are represented by a directed graph on an n-dimensional hypercube (n-cube) in which each edge is directed in a unique orientation. The vertices of the n-cube correspond to orthants of state space, and the edges correspond to boundaries between adjacent orthants. The dynamics in these equations can be represented symbolically. Starting from a point on the boundary between neighboring orthants, the equation is integrated until the boundary is crossed for a second time. Each different cycle, corresponding to a different sequence of orthants that are traversed during the integration of the equation always starting on a boundary and ending the first time that same boundary is reached, generates a different letter of the alphabet. A word consists of a sequence of letters corresponding to a possible sequence of orthants that arise from integration of the equation starting and ending on the same boundary. The union of the words defines the language. Letters and words correspond to analytically computable Poincare maps of the equation. This formalism allows us to define bifurcations of chaotic dynamics of the differential equation that correspond to changes in the associated language. Qualitative knowledge about the dynamics found by integrating the equation can be used to help solve the inverse problem of determining the underlying network generating the dynamics. This work places the study of dynamics in genetic networks in a context comprising both nonlinear dynamics and the theory of computation. (c) 2001 American Institute of Physics.
Dynamical and bursty interactions in social networks
Stehlé, Juliette; Barrat, Alain; Bianconi, Ginestra
2010-03-01
We present a modeling framework for dynamical and bursty contact networks made of agents in social interaction. We consider agents’ behavior at short time scales in which the contact network is formed by disconnected cliques of different sizes. At each time a random agent can make a transition from being isolated to being part of a group or vice versa. Different distributions of contact times and intercontact times between individuals are obtained by considering transition probabilities with memory effects, i.e., the transition probabilities for each agent depend both on its state (isolated or interacting) and on the time elapsed since the last change in state. The model lends itself to analytical and numerical investigations. The modeling framework can be easily extended and paves the way for systematic investigations of dynamical processes occurring on rapidly evolving dynamical networks, such as the propagation of an information or spreading of diseases.
Agent-based modeling and network dynamics
Namatame, Akira
2016-01-01
The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the gi...
Cascading Edge Failures: A Dynamic Network Process
Zhang, June
2016-01-01
This paper considers the dynamics of edges in a network. The Dynamic Bond Percolation (DBP) process models, through stochastic local rules, the dependence of an edge $(a,b)$ in a network on the states of its neighboring edges. Unlike previous models, DBP does not assume statistical independence between different edges. In applications, this means for example that failures of transmission lines in a power grid are not statistically independent, or alternatively, relationships between individuals (dyads) can lead to changes in other dyads in a social network. We consider the time evolution of the probability distribution of the network state, the collective states of all the edges (bonds), and show that it converges to a stationary distribution. We use this distribution to study the emergence of global behaviors like consensus (i.e., catastrophic failure or full recovery of the entire grid) or coexistence (i.e., some failed and some operating substructures in the grid). In particular, we show that, depending on...
Relaxation dynamics of maximally clustered networks
Klaise, Janis; Johnson, Samuel
2018-01-01
We study the relaxation dynamics of fully clustered networks (maximal number of triangles) to an unclustered state under two different edge dynamics—the double-edge swap, corresponding to degree-preserving randomization of the configuration model, and single edge replacement, corresponding to full randomization of the Erdős-Rényi random graph. We derive expressions for the time evolution of the degree distribution, edge multiplicity distribution and clustering coefficient. We show that under both dynamics networks undergo a continuous phase transition in which a giant connected component is formed. We calculate the position of the phase transition analytically using the Erdős-Rényi phenomenology.
Complex networks under dynamic repair model
Chaoqi, Fu; Ying, Wang; Kun, Zhao; Yangjun, Gao
2018-01-01
Invulnerability is not the only factor of importance when considering complex networks' security. It is also critical to have an effective and reasonable repair strategy. Existing research on network repair is confined to the static model. The dynamic model makes better use of the redundant capacity of repaired nodes and repairs the damaged network more efficiently than the static model; however, the dynamic repair model is complex and polytropic. In this paper, we construct a dynamic repair model and systematically describe the energy-transfer relationships between nodes in the repair process of the failure network. Nodes are divided into three types, corresponding to three structures. We find that the strong coupling structure is responsible for secondary failure of the repaired nodes and propose an algorithm that can select the most suitable targets (nodes or links) to repair the failure network with minimal cost. Two types of repair strategies are identified, with different effects under the two energy-transfer rules. The research results enable a more flexible approach to network repair.
Dynamics of High-Resolution Networks
DEFF Research Database (Denmark)
Sekara, Vedran
NETWORKS are everywhere. From the smallest confines of the cells within our bodies to the webs of social relations across the globe. Networks are not static, they constantly change, adapt, and evolve to suit new conditions. In order to understand the fundamental laws that govern networks we need...... the unprecedented amounts of information collected by mobile phones to gain detailed insight into the dynamics of social systems. This dissertation presents an unparalleled data collection campaign, collecting highly detailed traces for approximately 1000 people over the course of multiple years. The availability...
Stocco, Gabriel; Savell, Robert; Cybenko, George
2010-04-01
In many security environments, the textual content of communications may be unavailable. In these instances, it is often desirable to infer the status of the network and its component entities from patterns of communication flow. Conversational dynamics among entities in the network may provide insight into important aspects of the underlying social network such as the formational dynamics of group structures, the active state of these groups, individuals' roles within groups, and the likelihood of individual participation in conversations. To gain insight into the use of conversational dynamics to facilitate Dynamic Social Network Analysis, we explore the use of interevent timings to associate entities in the Twitter social networking and micro-blogging environment. Specifically, we use message timings to establish inter-nodal relationships among participants. In addition, we demonstrate a new visualization technique for tracking levels of coordination or synchronization within the community via measures of socio-temporal coherence of the participants.
Spreading dynamics in complex networks
Pei, Sen; Makse, Hernán A.
2013-12-01
Searching for influential spreaders in complex networks is an issue of great significance for applications across various domains, ranging from epidemic control, innovation diffusion, viral marketing, and social movement to idea propagation. In this paper, we first display some of the most important theoretical models that describe spreading processes, and then discuss the problem of locating both the individual and multiple influential spreaders respectively. Recent approaches in these two topics are presented. For the identification of privileged single spreaders, we summarize several widely used centralities, such as degree, betweenness centrality, PageRank, k-shell, etc. We investigate the empirical diffusion data in a large scale online social community—LiveJournal. With this extensive dataset, we find that various measures can convey very distinct information of nodes. Of all the users in the LiveJournal social network, only a small fraction of them are involved in spreading. For the spreading processes in LiveJournal, while degree can locate nodes participating in information diffusion with higher probability, k-shell is more effective in finding nodes with a large influence. Our results should provide useful information for designing efficient spreading strategies in reality.
Stochastic dynamics of macromolecular-assembly networks.
Saiz, Leonor; Vilar, Jose
2006-03-01
The formation and regulation of macromolecular complexes provides the backbone of most cellular processes, including gene regulation and signal transduction. The inherent complexity of assembling macromolecular structures makes current computational methods strongly limited for understanding how the physical interactions between cellular components give rise to systemic properties of cells. Here we present a stochastic approach to study the dynamics of networks formed by macromolecular complexes in terms of the molecular interactions of their components [1]. Exploiting key thermodynamic concepts, this approach makes it possible to both estimate reaction rates and incorporate the resulting assembly dynamics into the stochastic kinetics of cellular networks. As prototype systems, we consider the lac operon and phage λ induction switches, which rely on the formation of DNA loops by proteins [2] and on the integration of these protein-DNA complexes into intracellular networks. This cross-scale approach offers an effective starting point to move forward from network diagrams, such as those of protein-protein and DNA-protein interaction networks, to the actual dynamics of cellular processes. [1] L. Saiz and J.M.G. Vilar, submitted (2005). [2] J.M.G. Vilar and L. Saiz, Current Opinion in Genetics & Development, 15, 136-144 (2005).
Dynamical networks with topological self-organization
Zak, M.
2001-01-01
Coupled evolution of state and topology of dynamical networks is introduced. Due to the well organized tensor structure, the governing equations are presented in a canonical form, and required attractors as well as their basins can be easily implanted and controlled.
Network ecology: topological constraints on ecosystem dynamics
Jordán, Ferenc; Scheuring, István
2004-12-01
Ecological systems are complex assemblages of various species with interactions between them. The interactions can be even more important than the species themselves for understanding how the whole system is functioning and organized. For the representation of the topological space of interspecific relationships, graph theory is a suitable mathematical tool: the network perspective and the various techniques of network analysis are more and more elaborated and invading ecology. Beyond a static view on networks, fundamental questions can only be answered if dynamical analyses are also made, and now it is clear that structural and dynamical studies must not “compete” but strongly complement each other. Our aim is to give a menu of classical and more recently suggested network indices and to discuss what do we know about their relations to ecosystem dynamics. Since ecologists have very diverse problems, they need diverse techniques and a good insight in matching the adequate method to a particular problem. The main question is how to link certain graph properties to understanding and predicting the behaviour of an ecosystem. We wish to contribute to bridging the gap between extreme structural and extreme dynamical views.
Synchronization of coupled chaotic dynamics on networks
Indian Academy of Sciences (India)
Abstract. We review some recent work on the synchronization of coupled dynamical systems on a variety of networks. When nodes show synchronized behaviour, two inter- esting phenomena can be observed. First, there are some nodes of the floating type that show intermittent behaviour between getting attached to some ...
The fundamental structures of dynamic social networks
Sekara, Vedran; Lehmann, Sune
2015-01-01
Networks provide a powerful mathematical framework for analyzing the structure and dynamics of complex systems (1-3). The study of group behavior has deep roots in the social science literature (4,5) and community detection is a central part of modern network science. Network communities have been found to be highly overlapping and organized in a hierarchical structure (6-9). Recent technological advances have provided a toolset for measuring the detailed social dynamics at scale (10,11). In spite of great progress, a quantitative description of the complex temporal behavior of social groups-with dynamics spanning from minute-by-minute changes to patterns expressed on the timescale of years-is still absent. Here we uncover a class of fundamental structures embedded within highly dynamic social networks. On the shortest time-scale, we find that social gatherings are fluid, with members coming and going, but organized via a stable core of individuals. We show that cores represent social contexts (9), with recur...
Dynamic regimes of random fuzzy logic networks
Energy Technology Data Exchange (ETDEWEB)
Wittmann, Dominik M; Theis, Fabian J, E-mail: dominik.wittmann@helmholtz-muenchen.de [Computational Modeling in Biology, Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen-German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, 85764 Munich-Neuherberg (Germany); Centre for Mathematical Sciences, Technische Universitaet Muenchen, Boltzmannstrasse 3, 85748 Garching (Germany)
2011-01-15
Random multistate networks, generalizations of the Boolean Kauffman networks, are generic models for complex systems of interacting agents. Depending on their mean connectivity, these networks exhibit ordered as well as chaotic behavior with a critical boundary separating both regimes. Typically, the nodes of these networks are assigned single discrete states. Here, we describe nodes by fuzzy numbers, i.e. vectors of degree-of-membership (DOM) functions specifying the degree to which the nodes are in each of their discrete states. This allows our models to deal with imprecision and uncertainties. Compatible update rules are constructed by expressing the update rules of the multistate network in terms of Boolean operators and generalizing them to fuzzy logic (FL) operators. The standard choice for these generalizations is the Goedel FL, where AND and OR are replaced by the minimum and maximum of two DOMs, respectively. In mean-field approximations we are able to analytically describe the percolation and asymptotic distribution of DOMs in random Goedel FL networks. This allows us to characterize the different dynamic regimes of random multistate networks in terms of FL. In a low-dimensional example, we provide explicit computations and validate our mean-field results by showing that they agree well with network simulations.
Power Aware Dynamic Provisioning of HPC Networks
Energy Technology Data Exchange (ETDEWEB)
Groves, Taylor [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Grant, Ryan [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-10-01
Future exascale systems are under increased pressure to find power savings. The network, while it consumes a considerable amount of power is often left out of the picture when discussing total system power. Even when network power is being considered, the references are frequently a decade or older and rely on models that lack validation on modern inter- connects. In this work we explore how dynamic mechanisms of an Infiniband network save power and at what granularity we can engage these features. We explore this within the context of the host controller adapter (HCA) on the node and for the fabric, i.e. switches, using three different mechanisms of dynamic link width, frequency and disabling of links for QLogic and Mellanox systems. Our results show that while there is some potential for modest power savings, real world systems need to improved responsiveness to adjustments in order to fully leverage these savings. This page intentionally left blank.
Dynamical systems on networks a tutorial
Porter, Mason A
2016-01-01
This volume is a tutorial for the study of dynamical systems on networks. It discusses both methodology and models, including spreading models for social and biological contagions. The authors focus especially on “simple” situations that are analytically tractable, because they are insightful and provide useful springboards for the study of more complicated scenarios. This tutorial, which also includes key pointers to the literature, should be helpful for junior and senior undergraduate students, graduate students, and researchers from mathematics, physics, and engineering who seek to study dynamical systems on networks but who may not have prior experience with graph theory or networks. Mason A. Porter is Professor of Nonlinear and Complex Systems at the Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, UK. He is also a member of the CABDyN Complexity Centre and a Tutorial Fellow of Somerville College. James P. Gleeson is Professor of Industrial and Appli...
Shape, connectedness and dynamics in neuronal networks.
Comin, Cesar Henrique; da Fontoura Costa, Luciano
2013-11-15
The morphology of neurons is directly related to several aspects of the nervous system, including its connectedness, health, development, evolution, dynamics and, ultimately, behavior. Such interplays of the neuronal morphology can be understood within the more general shape-function paradigm. The current article reviews, in an introductory way, some key issues regarding the role of neuronal morphology in the nervous system, with emphasis on works developed in the authors' group. The following topics are addressed: (a) characterization of neuronal shape; (b) stochastic synthesis of neurons and neuronal systems; (c) characterization of the connectivity of neuronal networks by using complex networks concepts; and (d) investigations of influences of neuronal shape on network dynamics. The presented concepts and methods are useful also for several other multiple object systems, such as protein-protein interaction, tissues, aggregates and polymers. Copyright © 2013 Elsevier B.V. All rights reserved.
Dynamic analysis of biochemical network using complex network method
Directory of Open Access Journals (Sweden)
Wang Shuqiang
2015-01-01
Full Text Available In this study, the stochastic biochemical reaction model is proposed based on the law of mass action and complex network theory. The dynamics of biochemical reaction system is presented as a set of non-linear differential equations and analyzed at the molecular-scale. Given the initial state and the evolution rules of the biochemical reaction system, the system can achieve homeostasis. Compared with random graph, the biochemical reaction network has larger information capacity and is more efficient in information transmission. This is consistent with theory of evolution.
REACH MUSC: A telemedicine facilitated network for urgent stroke: initial experience
Directory of Open Access Journals (Sweden)
Robert J. Adams
2012-03-01
Full Text Available REACH MUSC provides stroke consults via the internet in South Carolina. From May 2008 to April 2011 231 patients were treated with intravenous (IV thrombolysis and 369 were transferred to Medical University of South Carolina (MUSC including 42 for intra-arterial revascularization (with or without IV tPA. Medical outcomes and hemorrhage rates, reported elsewhere, were good (Lazaridis et al. 2011, in press. Here we report operational features of REACH MUSC which covers 15 sites with 2,482 beds and 471,875 Emergency Department (ED visits per year. Eight Academic Faculty from MUSC worked with 165 different physicians and 325 different nurses in the conduct of 1085 consults. For the 231 who received tissue plasminogen activator (tPA, time milestones were: Onset to Door: 62 min (mean, 50 (median; Door to REACH Consult: 43 and 33, Consult Request to Consult Start: was 9 and 7 minutes, Consult Start to tPA Decision: 31 and 25 minutes; Decision to Infusion: 20 and 14 minutes, and total Door to Needle: 98 and 87 minutes. The comparable times for the 854 not receiving tPA were: Onset to Door: 140 and 75 minutes; Door to REACH Consult: 61 and 41 minutes; Consult Request to Consult Start 9 and 7 minutes, Consult Start to tPA Decision 27 and 23 minutes. While the consultants respond to consult requests in < 10 minutes, there is a long delay between arrival and Consult request. Tracking of operations indicates if we target shortening Door to Call time and time from tPA decision to start of drug infusion we may be able to improve Door to Needle times to target of < 60 minutes. The large number of individuals involved in the care of these patients, most of whom had no training in REACH usage,will require novel approaches to staff education in ED based operations where turnover is high. Despite these challenges this robust system delivered tPA safely and in a high fraction of patients evaluated using the REACH MUSC system.
Nonparametric inference of network structure and dynamics
Peixoto, Tiago P.
The network structure of complex systems determine their function and serve as evidence for the evolutionary mechanisms that lie behind them. Despite considerable effort in recent years, it remains an open challenge to formulate general descriptions of the large-scale structure of network systems, and how to reliably extract such information from data. Although many approaches have been proposed, few methods attempt to gauge the statistical significance of the uncovered structures, and hence the majority cannot reliably separate actual structure from stochastic fluctuations. Due to the sheer size and high-dimensionality of many networks, this represents a major limitation that prevents meaningful interpretations of the results obtained with such nonstatistical methods. In this talk, I will show how these issues can be tackled in a principled and efficient fashion by formulating appropriate generative models of network structure that can have their parameters inferred from data. By employing a Bayesian description of such models, the inference can be performed in a nonparametric fashion, that does not require any a priori knowledge or ad hoc assumptions about the data. I will show how this approach can be used to perform model comparison, and how hierarchical models yield the most appropriate trade-off between model complexity and quality of fit based on the statistical evidence present in the data. I will also show how this general approach can be elegantly extended to networks with edge attributes, that are embedded in latent spaces, and that change in time. The latter is obtained via a fully dynamic generative network model, based on arbitrary-order Markov chains, that can also be inferred in a nonparametric fashion. Throughout the talk I will illustrate the application of the methods with many empirical networks such as the internet at the autonomous systems level, the global airport network, the network of actors and films, social networks, citations among
Networks of conforming or nonconforming individuals tend to reach satisfactory decisions
Ramazi, Pouria; Riehl, James Robert; Cao, Ming
2016-01-01
Binary decisions of agents coupled in networks can often be classified into two types: “coordination,” where an agent takes an action if enough neighbors are using that action, as in the spread of social norms, innovations, and viral epidemics, and “anticoordination,” where too many neighbors taking
Volunteerism: Social Network Dynamics and Education
Ajrouch, Kristine J.; Antonucci, Toni C.; Webster, Noah J.
2016-01-01
Objectives . We examine how changes in social networks influence volunteerism through bridging (diversity) and bonding (spending time) mechanisms. We further investigate whether social network change substitutes or amplifies the effects of education on volunteerism. Methods . Data (n = 543) are drawn from a two-wave survey of Social Relations and Health over the Life Course (SRHLC). Zero-inflated negative binomial regressions were conducted to test competing hypotheses about how changes in social network characteristics alone and in conjunction with education level predict likelihood and frequency of volunteering. Results . Changes in social networks were associated with volunteerism: as the proportion of family members decreased and the average number of network members living within a one-hour drive increased over time, participants reported higher odds of volunteering. The substitution hypothesis was supported: social networks that exhibited more geographic proximity and greater contact frequency over-time compensated for lower levels of education to predict volunteering more hours. Discussion . The dynamic role of social networks and the ways in which they may work through bridging and bonding to influence both likelihood and frequency of volunteering are discussed. The potential benefits of volunteerism in light of longer life expectancies and smaller families are also considered. PMID:25512570
Dynamic networks from hierarchical bayesian graph clustering.
Directory of Open Access Journals (Sweden)
Yongjin Park
Full Text Available Biological networks change dynamically as protein components are synthesized and degraded. Understanding the time-dependence and, in a multicellular organism, tissue-dependence of a network leads to insight beyond a view that collapses time-varying interactions into a single static map. Conventional algorithms are limited to analyzing evolving networks by reducing them to a series of unrelated snapshots.Here we introduce an approach that groups proteins according to shared interaction patterns through a dynamical hierarchical stochastic block model. Protein membership in a block is permitted to evolve as interaction patterns shift over time and space, representing the spatial organization of cell types in a multicellular organism. The spatiotemporal evolution of the protein components are inferred from transcript profiles, using Arabidopsis root development (5 tissues, 3 temporal stages as an example.The new model requires essentially no parameter tuning, out-performs existing snapshot-based methods, identifies protein modules recruited to specific cell types and developmental stages, and could have broad application to social networks and other similar dynamic systems.
Dynamic functional network connectivity using distance correlation
Rudas, Jorge; Guaje, Javier; Demertzi, Athena; Heine, Lizette; Tshibanda, Luaba; Soddu, Andrea; Laureys, Steven; Gómez, Francisco
2015-01-01
Investigations about the intrinsic brain organization in resting-state are critical for the understanding of healthy, pathological and pharmacological cerebral states. Recent studies on fMRI suggest that resting state activity is organized on large scale networks of coordinated activity, in the so called, Resting State Networks (RSNs). The assessment of the interactions among these functional networks plays an important role for the understanding of different brain pathologies. Current methods to quantify these interactions commonly assume that the underlying coordination mechanisms are stationary and linear through the whole recording of the resting state phenomena. Nevertheless, recent evidence suggests that rather than stationary, these mechanisms may exhibit a rich set of time-varying repertoires. In addition, these approaches do not consider possible non-linear relationships maybe linked to feed-back communication mechanisms between RSNs. In this work, we introduce a novel approach for dynamical functional network connectivity for functional magnetic resonance imaging (fMRI) resting activity, which accounts for non-linear dynamic relationships between RSNs. The proposed method is based on a windowed distance correlations computed on resting state time-courses extracted at single subject level. We showed that this strategy is complementary to the current approaches for dynamic functional connectivity and will help to enhance the discrimination capacity of patients with disorder of consciousness.
Advances in dynamic network modeling in complex transportation systems
Ukkusuri, Satish V
2013-01-01
This book focuses on the latest in dynamic network modeling, including route guidance and traffic control in transportation systems and other complex infrastructure networks. Covers dynamic traffic assignment, flow modeling, mobile sensor deployment and more.
Dynamic Networks in the Emotional Brain.
Pessoa, Luiz; McMenamin, Brenton
2016-10-25
Research on the emotional brain has often focused on a few structures thought to be central to this type of processing-hypothalamus, amygdala, insula, and so on. Conceptual thinking about emotion has viewed this mental faculty as linked to broader brain circuits, too, including early ideas by Papez and others. In this article, we discuss research that embraces a distributed view of emotion circuits and efforts to unravel the impact on emotional manipulations on the processing of several large-scale brain networks that are chiefly important for mental operations traditionally labeled with terms such as "perception," "action," and "cognition." Furthermore, we describe networks as dynamic processes and how emotion-laden stimuli strongly affect network structure. As networks are not static entities, their organization unfolds temporally, such that specific brain regions affiliate with them in a time-varying fashion. Thus, at a specific moment, brain regions participate more strongly in some networks than others. In this dynamic view of brain function, emotion has broad, distributed effects on processing in a manner that transcends traditional boundaries and inflexible labels, such as "emotion" and "cognition." What matters is the coordinated action that supports behaviors. © The Author(s) 2016.
Dynamic social networks based on movement
Scharf, Henry; Hooten, Mevin B.; Fosdick, Bailey K.; Johnson, Devin S.; London, Joshua M.; Durban, John W.
2016-01-01
Network modeling techniques provide a means for quantifying social structure in populations of individuals. Data used to define social connectivity are often expensive to collect and based on case-specific, ad hoc criteria. Moreover, in applications involving animal social networks, collection of these data is often opportunistic and can be invasive. Frequently, the social network of interest for a given population is closely related to the way individuals move. Thus, telemetry data, which are minimally invasive and relatively inexpensive to collect, present an alternative source of information. We develop a framework for using telemetry data to infer social relationships among animals. To achieve this, we propose a Bayesian hierarchical model with an underlying dynamic social network controlling movement of individuals via two mechanisms: an attractive effect and an aligning effect. We demonstrate the model and its ability to accurately identify complex social behavior in simulation, and apply our model to telemetry data arising from killer whales. Using auxiliary information about the study population, we investigate model validity and find the inferred dynamic social network is consistent with killer whale ecology and expert knowledge.
MONOMIALS AND BASIN CYLINDERS FOR NETWORK DYNAMICS.
Austin, Daniel; Dinwoodie, Ian H
We describe methods to identify cylinder sets inside a basin of attraction for Boolean dynamics of biological networks. Such sets are used for designing regulatory interventions that make the system evolve towards a chosen attractor, for example initiating apoptosis in a cancer cell. We describe two algebraic methods for identifying cylinders inside a basin of attraction, one based on the Groebner fan that finds monomials that define cylinders and the other on primary decomposition. Both methods are applied to current examples of gene networks.
Mean field methods for cortical network dynamics
DEFF Research Database (Denmark)
Hertz, J.; Lerchner, Alexander; Ahmadi, M.
2004-01-01
We review the use of mean field theory for describing the dynamics of dense, randomly connected cortical circuits. For a simple network of excitatory and inhibitory leaky integrate- and-fire neurons, we can show how the firing irregularity, as measured by the Fano factor, increases...... with the strength of the synapses in the network and with the value to which the membrane potential is reset after a spike. Generalizing the model to include conductance-based synapses gives insight into the connection between the firing statistics and the high- conductance state observed experimentally in visual...
Filtering in hybrid dynamic Bayesian networks (left)
DEFF Research Database (Denmark)
Andersen, Morten Nonboe; Andersen, Rasmus Ørum; Wheeler, Kevin
We demonstrate experimentally that inference in a complex hybrid Dynamic Bayesian Network (DBN) is possible using the 2-Time Slice DBN (2T-DBN) from (Koller & Lerner, 2000) to model fault detection in a watertank system. In (Koller & Lerner, 2000) a generic Particle Filter (PF) is used...... that the choice of network structure is very important for the performance of the generic PF and the EKF algorithms, but not for the UKF algorithms. Furthermore, we investigate the influence of data noise in the watertank simulation. Theory and implementation is based on the theory presented in (v.d. Merwe et al...
Filtering in hybrid dynamic Bayesian networks
DEFF Research Database (Denmark)
Andersen, Morten Nonboe; Andersen, Rasmus Ørum; Wheeler, Kevin
2004-01-01
We demonstrate experimentally that inference in a complex hybrid Dynamic Bayesian Network (DBN) is possible using the 2-Time Slice DBN (2T-DBN) from (Koller & Lerner, 2000) to model fault detection in a watertank system. In (Koller & Lerner, 2000) a generic Particle Filter (PF) is used...... that the choice of network structure is very important for the performance of the generic PF and the EKF algorithms, but not for the UKF algorithms. Furthermore, we investigate the influence of data noise in the watertank simulation. Theory and implementation is based on the theory presented in (v.d. Merwe et al...
Filtering in hybrid dynamic Bayesian networks (center)
DEFF Research Database (Denmark)
Andersen, Morten Nonboe; Andersen, Rasmus Ørum; Wheeler, Kevin
We demonstrate experimentally that inference in a complex hybrid Dynamic Bayesian Network (DBN) is possible using the 2-Time Slice DBN (2T-DBN) from (Koller & Lerner, 2000) to model fault detection in a watertank system. In (Koller & Lerner, 2000) a generic Particle Filter (PF) is used...... that the choice of network structure is very important for the performance of the generic PF and the EKF algorithms, but not for the UKF algorithms. Furthermore, we investigate the influence of data noise in the watertank simulation. Theory and implementation is based on the theory presented in (v.d. Merwe et al...
Dynamic queuing transmission model for dynamic network loading
DEFF Research Database (Denmark)
Raovic, Nevena; Nielsen, Otto Anker; Prato, Carlo Giacomo
2017-01-01
This paper presents a new macroscopic multi-class dynamic network loading model called Dynamic Queuing Transmission Model (DQTM). The model utilizes ‘good’ properties of the Dynamic Queuing Model (DQM) and the Link Transmission Model (LTM) by offering a DQM consistent with the kinematic wave theory...... and allowing for the representation of multiple vehicle classes, queue spillbacks and shock waves. The model assumes that a link is split into a moving part plus a queuing part, and p that traffic dynamics are given by a triangular fundamental diagram. A case-study is investigated and the DQTM is compared...... for two vehicle classes. Moreover, the results show that the travel time will be underestimated without considering the shock wave property...
Personality traits and ego-network dynamics.
Centellegher, Simone; López, Eduardo; Saramäki, Jari; Lepri, Bruno
2017-01-01
Strong and supportive social relationships are fundamental to our well-being. However, there are costs to their maintenance, resulting in a trade-off between quality and quantity, a typical strategy being to put a lot of effort on a few high-intensity relationships while maintaining larger numbers of less close relationships. It has also been shown that there are persistent individual differences in this pattern; some individuals allocate their efforts more uniformly across their networks, while others strongly focus on their closest relationships. Furthermore, some individuals maintain more stable networks than others. Here, we focus on how personality traits of individuals affect this picture, using mobile phone calls records and survey data from the Mobile Territorial Lab (MTL) study. In particular, we look at the relationship between personality traits and the (i) persistence of social signatures, namely the similarity of the social signature shape of an individual measured in different time intervals; (ii) the turnover in egocentric networks, that is, differences in the set of alters present at two consecutive temporal intervals; and (iii) the rank dynamics defined as the variation of alter rankings in egocentric networks in consecutive intervals. We observe that some traits have effects on the stability of the social signatures as well as network turnover and rank dynamics. As an example, individuals who score highly in the Openness to Experience trait tend to have higher levels of network turnover and larger alter rank variations. On broader terms, our study shows that personality traits clearly affect the ways in which individuals maintain their personal networks.
Dynamic Trust Management for Mobile Networks and Its Applications
Bao, Fenye
2013-01-01
Trust management in mobile networks is challenging due to dynamically changing network environments and the lack of a centralized trusted authority. In this dissertation research, we "design" and "validate" a class of dynamic trust management protocols for mobile networks, and demonstrate the utility of dynamic trust management…
Programming the dynamics of biochemical reaction networks.
Simmel, Friedrich C
2013-01-22
The development of complex self-organizing molecular systems for future nanotechnology requires not only robust formation of molecular structures by self-assembly but also precise control over their temporal dynamics. As an exquisite example of such control, in this issue of ACS Nano, Fujii and Rondelez demonstrate a particularly compact realization of a molecular "predator-prey" ecosystem consisting of only three DNA species and three enzymes. The system displays pronounced oscillatory dynamics, in good agreement with the predictions of a simple theoretical model. Moreover, its considerable modularity also allows for ecological studies of competition and cooperation within molecular networks.
A network-based dynamical ranking system
Motegi, Shun
2012-01-01
Ranking players or teams in sports is of practical interests. From the viewpoint of networks, a ranking system is equivalent a centrality measure for sports networks, whereby a directed link represents the result of a single game. Previously proposed network-based ranking systems are derived from static networks, i.e., aggregation of the results of games over time. However, the score (i.e., strength) of a player, for example, depends on time. Defeating a renowned player in the peak performance is intuitively more rewarding than defeating the same player in other periods. To account for this factor, we propose a dynamic variant of such a network-based ranking system and apply it to professional men's tennis data. Our ranking system, also interpreted as a centrality measure for directed temporal networks, has two parameters. One parameter represents the exponential decay rate of the past score, and the other parameter controls the effect of indirect wins on the score. We derive a set of linear online update equ...
Evolutionary epistemology and dynamical virtual learning networks.
Giani, Umberto
2004-01-01
This paper is an attempt to define the main features of a new educational model aimed at satisfying the needs of a rapidly changing society. The evolutionary epistemology paradigm of culture diffusion in human groups could be the conceptual ground for the development of this model. Multidimensionality, multi-disciplinarity, complexity, connectivity, critical thinking, creative thinking, constructivism, flexible learning, contextual learning, are the dimensions that should characterize distance learning models aimed at increasing the epistemological variability of learning communities. Two multimedia educational software, Dynamic Knowledge Networks (DKN) and Dynamic Virtual Learning Networks (DVLN) are described. These two complementary tools instantiate these dimensions, and were tested in almost 150 online courses. Even if the examples are framed in the medical context, the analysis of the shortcomings of the traditional educational systems and the proposed solutions can be applied to the vast majority of the educational contexts.
The maximum flow in dynamic networks
Directory of Open Access Journals (Sweden)
Maria A. Fonoberova
2005-01-01
Full Text Available The dynamic maximum flow problem that generalizes the static maximum flow problem is formulated and studied. We consider the problem on a network with capacities depending on time, fixed transit times on the arcs, and a given time horizon. The corresponding algorithm to solve this problem is proposed and some details concerning its complexity are discussed. Mathematics Subject Classification 2000: 90B10, 90C35, 90C27.
Hierarchical organization for large, dynamic radio networks
Shacham, Nachum
1988-01-01
As new technology is incorporated into weapons and command and control systems, the need for data communications and data processing at the tactical level will increase dramatically. Tactical ground forces are mobile and they operate in a highly stressed environment complete with noise, false messages and attrition of communication resources. Packet radio network (PRNET) is a promising technology that can serve tactical data communication applications if its architecture and the functionality of its protocols is enhanced to allow it to adapt to frequently changing environment and incorporation of a large number of users. Developing such architectures and protocols for large, survivable PRNETs and evaluating their performance were the principal thrusts of this research project. The main advances made in this effort include development of architectures and algorithms that fall in the following categories: (1) Hierarchical architectures and routing for dynamic networks; (2) Multichannel networks--architectures and protocols; (3) Dynamic selection of radio FEC code rate to adapt to channel variations; (4) Broadcast algorithms that provide for transport of messages to multiple destinations amidst topological changes; (5) Self-organizing networks in which the nodes control their local connectivity. The correctness of some algorithms was validated analytically; others were tested by simulations.
Failure dynamics of the global risk network
Szymanski, Boleslaw K.; Lin, Xin; Asztalos, Andrea; Sreenivasan, Sameet
2015-06-01
Risks threatening modern societies form an intricately interconnected network that often underlies crisis situations. Yet, little is known about how risk materializations in distinct domains influence each other. Here we present an approach in which expert assessments of likelihoods and influence of risks underlie a quantitative model of the global risk network dynamics. The modeled risks range from environmental to economic and technological, and include difficult to quantify risks, such as geo-political and social. Using the maximum likelihood estimation, we find the optimal model parameters and demonstrate that the model including network effects significantly outperforms the others, uncovering full value of the expert collected data. We analyze the model dynamics and study its resilience and stability. Our findings include such risk properties as contagion potential, persistence, roles in cascades of failures and the identity of risks most detrimental to system stability. The model provides quantitative means for measuring the adverse effects of risk interdependencies and the materialization of risks in the network.
Features of Social Networking in Reaching Professional Goals of University Educators
Directory of Open Access Journals (Sweden)
Bordovskaia N.V.,
2017-02-01
Full Text Available The paper focuses on features of Internet support of professional activities in modern university educators and of learning activities in students through the organization of a new web-based space for communication between instructors and learners. It describes the authors’ experience in creating such space basing on social networking within the context of modern educational environment of university. Four major groups of university educators’ professional goals which can affectively be attained using social-networking educational resources are identified. Based on an observational study, we offer techniques that can be used for personality traits identification in students, for providing more individualized instruction and making information concerning education and achievements in an academic course more accessible, as well as for control and optimization of interactions initiated either by educator or by students (including interactions among students and those between students and educators. The effectiveness of the new instructor/learner communication channel among the participants of the educational process is proved empirically. This work was supported by grants: Russian Foundation for Basic Research 14-07-00694-а and Russian Foundation for Humanities 16-06-00486
Asymmetric disease dynamics in multihost interconnected networks.
Pilosof, Shai; Greenbaum, Gili; Krasnov, Boris R; Zelnik, Yuval R
2017-10-07
Epidemic spread in single-host systems strongly depends on the population's transmission network. However, little is known regarding the spread of epidemics across networks representing populations of multiple hosts. We explored cross-species transmission in a multilayer network where layers represent populations of two distinct hosts, and disease can spread across intralayer (within-host) and interlayer (between-host) edges. We developed an analytic framework for the SIR epidemic model to examine the effect of (i) source of infection and (ii) between-host asymmetry in infection probabilities, on disease risk. We measured risk as outbreak probability and outbreak size in a focal host, represented by one network layer. Numeric simulations were used to validate the analytic formulations. We found that outbreak probability is determined by a complex interaction between source of infection and between-host infection probabilities, whereas outbreak size is mainly affected by the non-focal host to focal host infection probability. Hence, inter-specific asymmetry in infection probabilities shapes disease dynamics in multihost networks. These results highlight the importance of considering multiple measures of disease risk and advance our understanding of disease spread in multihost systems. The study provides a flexible way to model disease dynamics in multiple hosts while considering contact heterogeneity within and between species. We strongly encourage empirical studies that include information on both cross-species infection rates and network structure of multiple hosts. Such studies are necessary to corroborate our theoretical results and to improve our understanding of multihost epidemiology. Copyright © 2017 Elsevier Ltd. All rights reserved.
Dynamics of the ethanolamine glycerophospholipid remodeling network.
Directory of Open Access Journals (Sweden)
Lu Zhang
Full Text Available Acyl chain remodeling in lipids is a critical biochemical process that plays a central role in disease. However, remodeling remains poorly understood, despite massive increases in lipidomic data. In this work, we determine the dynamic network of ethanolamine glycerophospholipid (PE remodeling, using data from pulse-chase experiments and a novel bioinformatic network inference approach. The model uses a set of ordinary differential equations based on the assumptions that (1 sn1 and sn2 acyl positions are independently remodeled; (2 remodeling reaction rates are constant over time; and (3 acyl donor concentrations are constant. We use a novel fast and accurate two-step algorithm to automatically infer model parameters and their values. This is the first such method applicable to dynamic phospholipid lipidomic data. Our inference procedure closely fits experimental measurements and shows strong cross-validation across six independent experiments with distinct deuterium-labeled PE precursors, demonstrating the validity of our assumptions. In contrast, fits of randomized data or fits using random model parameters are worse. A key outcome is that we are able to robustly distinguish deacylation and reacylation kinetics of individual acyl chain types at the sn1 and sn2 positions, explaining the established prevalence of saturated and unsaturated chains in the respective positions. The present study thus demonstrates that dynamic acyl chain remodeling processes can be reliably determined from dynamic lipidomic data.
Interdisciplinary applications of network dynamics: From microscopic to Macroscopic
Jeong, Hawoong
``Everything touches everything.'' We are living in a connected world, which has been modeled successfully by complex networks. Ever since, network science becomes new paradigm for understanding our connected yet complex world. After investigating network structure itself, our focus naturally moved to dynamics of/on the network because our connected world is not static but dynamic. In this presentation, we will briefly review the historical development of network science and show some applications of network dynamics ranging from microscopic (metabolic engineering, PNAS, 104 13638) to macroscopic scale (price of anarchy in transportation network, Phys.Rev.Lett. 101 128701). Supported by National Research Foundation of Korea through Grant No. 2011-0028908.
Fundamental structures of dynamic social networks
DEFF Research Database (Denmark)
Sekara, Vedran; Stopczynski, Arkadiusz; Jørgensen, Sune Lehmann
2016-01-01
unnecessary. Starting from 5-min time slices, we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit...... a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework......Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships...
Propagation dynamics on networks featuring complex topologies
Hébert-Dufresne, Laurent; Noël, Pierre-André; Marceau, Vincent; Allard, Antoine; Dubé, Louis J.
2010-09-01
Analytical description of propagation phenomena on random networks has flourished in recent years, yet more complex systems have mainly been studied through numerical means. In this paper, a mean-field description is used to coherently couple the dynamics of the network elements (such as nodes, vertices, individuals, etc.) on the one hand and their recurrent topological patterns (such as subgraphs, groups, etc.) on the other hand. In a susceptible-infectious-susceptible (SIS) model of epidemic spread on social networks with community structure, this approach yields a set of ordinary differential equations for the time evolution of the system, as well as analytical solutions for the epidemic threshold and equilibria. The results obtained are in good agreement with numerical simulations and reproduce the behavior of random networks in the appropriate limits which highlights the influence of topology on the processes. Finally, it is demonstrated that our model predicts higher epidemic thresholds for clustered structures than for equivalent random topologies in the case of networks with zero degree correlation.
Laptops in Classroom Interaction: The Dynamic Reach of the Laptoped Situation
Lindroth, Tomas; Lundin, Johan; Svensson, Lars
2013-01-01
Laptops and other networked technologies are commonplace at university campuses. While a range of studies researches the negative effects of multitasking, screenpeeking and other laptop related side effects this article emphasize the situational impact of student-laptop interaction. Departing from Goffman's framework on unfocused interaction and…
2014-10-01
missiles , providing precision navigation and timing, or protecting networks. It is also a way of influencing world situations in ways which support...integrated air and missile defense and maritime domain awareness critical to operating in highly con- tested environments. More capable partner...Exchange Program allows Airmen fluent in foreign languages to swap jobs with a member of an allied nation’s air force to improve interoper- ability
Discrete Opinion Dynamics on Online Social Networks
Hu, Yan-Li; Bai, Liang; Zhang, Wei-Ming
2013-01-01
This paper focuses on the dynamics of binary opinions {+1, -1} on online social networks consisting of heterogeneous actors. In our model, actors update their opinions under the interplay of social influence and self- affirmation, which leads to rich dynamical behaviors on online social networks. We find that the opinion leading to the consensus features an advantage of the initially weighted fraction based on actors' strength over the other, instead of the population. For the role of specific actors, the consensus converges towards the opinion that a small fraction of high-strength actors hold, and individual diversity of self-affirmation slows down the ordering process of consensus. These indicate that high-strength actors play an essential role in opinion formation with strong social influence as well as high persistence. Further investigations show that the initial fraction of high-strength actors to dominate the evolution depends on the heterogeneity of the strength distribution, and less high-strength actors are needed in the case of a smaller exponent of power-law distribution of actors' strength. Our study provides deep insights into the role of social influence and self-affirmation on opinion formation on online social networks.
Baranya, Sándor; Goda, László; Józsa, János; Rákóczi, László
2008-11-01
Detailed hydrodynamic survey of two critical river reaches has been performed from hydro- and sediment dynamics points of view, in order to explore the main features, moreover, provide calibration and verification data to related 3D flow and sediment transport modelling. Special attention has been paid to compare moving and fix boat measurement modes for estimating various flow and large-scale bed form features, resulting in recommendations e.g. on the time period needed in stationary mode operation to obtain sufficiently stabilized average velocity profiles and related parameter estimations. As to the study reaches, the first comprises a 5 km long sandy-gravel bed reach of river Danube located in Central-Hungary, presenting problems for navigation. As a conventional remedy, groyne fields have been implemented to make and maintain the reach sufficiently deep, navigable even in low flow periods. As is usually the case, these works resulted in rather complex flow characteristics and related bed topography at places. The second site is another 5 km long reach of river Danube, close to the southern border to Serbia. There the river presents navigational problems similar to the previously mentioned reach, however, having entirely sand bed conditions, abundant in a variety of dunes, especially in the shallower parts. In both study reaches ADCP measurements were done with around 2.5 Hz sampling frequency both in moving boat operation mode providing overall, though locally moderately representative picture, and in fixed boat mode at a considerable number of selected verticals with 10 minutes long measuring time.
Art reaches within: aesthetic experience, the self and the default mode network
Directory of Open Access Journals (Sweden)
Edward A Vessel
2013-12-01
Full Text Available In a task of rating images of artworks in an fMRI scanner, regions in the medial prefrontal cortex that are known to be part of the default mode network (DMN were positively activated on the highest-rated trials. This is surprising given the DMN's original characterization as the set of brain regions that show greater fMRI activity during rest periods than during performance of tasks requiring focus on external stimuli. But further research showed that DMN regions could be positively activated also in structured tasks, if those tasks involved self-referential thought or self-relevant information. How may our findings be understood in this context? Although our task had no explicit self-referential aspect and the stimuli had no a priori self-relevance to the observers, the experimental design we employed emphasized the personal aspects of aesthetic experience. Observers were told that we were interested in their individual tastes, and asked to base their ratings on how much each artwork "moved" them. Moreover, we used little-known artworks that covered a wide range of styles, which led to high individual variability: each artwork was rated highly by some observers and poorly by others. This means that rating-specific neural responses cannot be attributed to the features of any particular artworks, but rather to the aesthetic experience itself. The DMN activity therefore suggests that certain artworks, albeit unfamiliar, may be so well-matched to an individual’s unique makeup that they obtain access to the neural substrates concerned with the self – access which other external stimuli normally do not get. This mediates a sense of being moved, or touched from within. This account is consistent with the modern notion that individuals’ taste in art is linked with their sense of identity, and suggests that DMN activity may serve to signal self-relevance in a broader sense than has been thought so far.
Art reaches within: aesthetic experience, the self and the default mode network.
Vessel, Edward A; Starr, G Gabrielle; Rubin, Nava
2013-12-30
In a task of rating images of artworks in an fMRI scanner, regions in the medial prefrontal cortex that are known to be part of the default mode network (DMN) were positively activated on the highest-rated trials. This is surprising given the DMN's original characterization as the set of brain regions that show greater fMRI activity during rest periods than during performance of tasks requiring focus on external stimuli. But further research showed that DMN regions could be positively activated also in structured tasks, if those tasks involved self-referential thought or self-relevant information. How may our findings be understood in this context? Although our task had no explicit self-referential aspect and the stimuli had no a priori self-relevance to the observers, the experimental design we employed emphasized the personal aspects of aesthetic experience. Observers were told that we were interested in their individual tastes, and asked to base their ratings on how much each artwork "moved" them. Moreover, we used little-known artworks that covered a wide range of styles, which led to high individual variability: each artwork was rated highly by some observers and poorly by others. This means that rating-specific neural responses cannot be attributed to the features of any particular artworks, but rather to the aesthetic experience itself. The DMN activity therefore suggests that certain artworks, albeit unfamiliar, may be so well-matched to an individual's unique makeup that they obtain access to the neural substrates concerned with the self-access which other external stimuli normally do not get. This mediates a sense of being "moved," or "touched from within." This account is consistent with the modern notion that individuals' taste in art is linked with their sense of identity, and suggests that DMN activity may serve to signal "self-relevance" in a broader sense than has been thought so far.
Competing dynamical processes on two interacting networks
Alvarez-Zuzek, L G; Braunstein, L A; Vazquez, F
2016-01-01
We propose and study a model for the competition between two different dynamical processes, one for opinion formation and the other for decision making, on two interconnected networks. The networks represent two interacting social groups, the society and the Congress. An opinion formation process takes place on the society, where the opinion S of each individual can take one of four possible values (S=-2,-1,1,2), describing its level of agreement on a given issue, from totally against (S=-2) to totally in favor (S=2). The dynamics is controlled by a reinforcement parameter r, which measures the ratio between the likelihood to become an extremist or a moderate. The dynamics of the Congress is akin to that of the Abrams-Strogatz model, where congressmen can adopt one of two possible positions, to be either in favor (+) or against (-) the issue. The probability that a congressman changes his decision is proportional to the fraction of interacting neighbors that hold the opposite opinion raised to a power $\\beta$...
Collective Dynamics in Physical and Social Networks
Isakov, Alexander
We study four systems where individual units come together to display a range of collective behavior. First, we consider a physical system of phase oscillators on a network that expands the Kuramoto model to include oscillator-network interactions and the presence of noise: using a Hebbian-like learning rule, oscillators that synchronize in turn strengthen their connections to each other. We find that the average degree of connectivity strongly affects rates of flipping between aligned and anti-aligned states, and that this result persists to the case of complex networks. Turning to a fully multi-player, multi-strategy evolutionary dynamics model of cooperating bacteria that change who they give resources to and take resources from, we find several regimes that give rise to high levels of collective structure in the resulting networks. In this setting, we also explore the conditions in which an intervention that affects cooperation itself (e.g. "seeding the network with defectors") can lead to wiping out an infection. We find a non-monotonic connection between the percent of disabled cooperation and cure rate, suggesting that in some regimes a limited perturbation can lead to total population collapse. At a larger scale, we study how the locomotor system recovers after amputation in fruit flies. Through experiment and a theoretical model of multi-legged motion controlled by neural oscillators, we find that proprioception plays a role in the ability of flies to control leg forces appropriately to recover from a large initial turning bias induced by the injury. Finally, at the human scale, we consider a social network in a traditional society in Africa to understand how social ties lead to group formation for collective action (stealth raids). We identify critical and distinct roles for both leadership (important for catalyzing a group) and friendship (important for final composition). We conclude with prospects for future work.
A dynamic evidential network for fall detection.
Aguilar, Paulo Armando Cavalcante; Boudy, Jerome; Istrate, Dan; Dorizzi, Bernadette; Mota, Joao Cesar Moura
2014-07-01
This study is part of the development of a remote home healthcare monitoring application designed to detect distress situations through several types of sensors. The multisensor fusion can provide more accurate and reliable information compared to information provided by each sensor separately. Furthermore, data from multiple heterogeneous sensors present in the remote home healthcare monitoring systems have different degrees of imperfection and trust. Among the multisensor fusion methods, Dempster-Shafer theory (DST) is currently considered the most appropriate for representing and processing the imperfect information. Based on a graphical representation of the DST called evidential networks, a structure of heterogeneous data fusion from multiple sensors for fall detection has been proposed. The evidential networks, implemented on our remote medical monitoring platform, are also proposed in this paper to maximize the performance of automatic fall detection and thus make the system more reliable. However, the presence of noise, the variability of recorded signals by the sensors, and the failing or unreliable sensors may thwart the evidential networks performance. In addition, the sensors signals nonstationary nature may degrade the experimental conditions. To compensate the nonstationary effect, the time evolution is considered by introducing the dynamic evidential network which was evaluated by the simulated fall scenarios corresponding to various use cases.
Dynamic Privacy Management in Pervasive Sensor Networks
Gong, Nan-Wei; Laibowitz, Mathew; Paradiso, Joseph A.
This paper describes the design and implementation of a dynamic privacy management system aimed at enabling tangible privacy control and feedback in a pervasive sensor network. Our work began with the development of a potentially invasive sensor network (with high resolution video, audio, and motion tracking capabilities) featuring different interactive applications that created incentive for accepting this network as an extension of people's daily social space. A user study was then conducted to evaluate several privacy management approaches - an active badge system for both online and on-site control, on/off power switches for physically disabling the hardware, and touch screen input control. Results from a user study indicated that an active badge for on-site privacy control is the most preferable method among all provided options. We present a set of results that yield insight into the privacy/benefit tradeoff from various sensing capabilities in pervasive sensor networks and how privacy settings and user behavior relate in these environments.
Research on dynamic routing mechanisms in wireless sensor networks.
Zhao, A Q; Weng, Y N; Lu, Y; Liu, C Y
2014-01-01
WirelessHART is the most widely applied standard in wireless sensor networks nowadays. However, it does not provide any dynamic routing mechanism, which is important for the reliability and robustness of the wireless network applications. In this paper, a collection tree protocol based, dynamic routing mechanism was proposed for WirelessHART network. The dynamic routing mechanism was evaluated through several simulation experiments in three aspects: time for generating the topology, link quality, and stability of network. Besides, the data transmission efficiency of this routing mechanism was analyzed. The simulation and evaluation results show that this mechanism can act as a dynamic routing mechanism for the TDMA-based wireless sensor network.
Directory of Open Access Journals (Sweden)
K. Geina
2015-03-01
Full Text Available Purpose. To analyze the dynamics of pike (Esox luceus Linnaeus, 1758 age structure of the Dnieper lower reaches in conditions of the modification of fishing pressure. Methodology. An analysis of fishing situation has been performed based on data of official fishery statistics. Fish sampling was done at control-observation posts of the Institute of Fisheries of the NAAS of Ukraine and directly in the fishery. Juvenile fish yield was determined using a complex of fry fishing gears using a stationary net-station. Field and cameral processing of the material was performed using generally accepted methods. Findings. A retrospective analysis of the situation in the Dnieper-Bug lower reach system clearly indicates on the presence of continuous tendency of catch decline of representative of native fish fauna – pike. With relatively uniform indices of the “yield” of its juveniles before Dnieper flow impoundment and in conditions of present time, its commercial catches significantly dropped. The dynamics of pike current age structure indicates on an increase of relative density of age groups, which form the recruitment of the commercial portion of the population (1-1+ and a decrease of importance of the component of the right side of age series. A discrepancy between the observed changes of the age group and commercial harvest quantities indicates on increased human pressure on this species. Originality. For the first, we analyzed the dynamics of fish juvenile “yield” and age structure of pike commercial stock of the Dnieper lower reaches in the river flow transformation process. Practical value. A decrease of the ichthyomass of piscivorous fishes in the Dnieper lower reaches results in changes of fish populations of littoral biotopes towards the prevalence of the dominance of coarse species that lead to a deterioration of forage availability for a number of valuable commercial species. An increase of the number of pike can regulate the strain
Directory of Open Access Journals (Sweden)
Xiao-Hong Li
2014-01-01
Full Text Available Cooperative communication (CC is used in topology control as it can reduce the transmission power and expand the transmission range. However, all previous research on topology control under the CC model focused on maintaining network connectivity and minimizing the total energy consumption, which would lead to low network capacity, transmission interruption, or even network paralysis. Meanwhile, without considering the balance of energy consumption in the network, it would reduce the network lifetime and greatly affect the network performance. This paper tries to solve the above problems existing in the research on topology control under the CC model by proposing a power assignment (DCCPA algorithm based on dynamic cooperative clustering in cooperative ad hoc networks. The new algorithm clusters the network to maximize network capacity and makes the clusters communicate with each other by CC. To reduce the number of redundant links between clusters, we design a static clustering method by using Kruskal algorithm. To maximize the network lifetime, we also propose a cluster head rotating method which can reach a good tradeoff between residual energy and distance for the cluster head reselection. Experimental results show that DCCPA can improve 80% network capacity with Cooperative Bridges algorithm; meanwhile, it can improve 20% network lifetime.
Content Dynamics Over the Network Cloud
2015-11-04
AFRL-AFOSR-CL-TR-2015-0003 Content dynamics over the network cloud Fernando Paganini UNIVERSIDAD ORT URUGUAY CUAREIM 1451 MONTEVIDEO, 11100 UY 11/04...approved for public release. FINAL PERFORMANCE REPORT: 7-15-2012 to 7-14-2015 AFOSR GRANT NUMBER: FA9550-12-1-0398 PI: Fernando Paganini Universidad ORT...349-362, Apr 2014. 7. M. Zubeldía, “From resource allocation to neighbor selection in peer-to-peer networks”, MS Thesis, Universidad ORT Uruguay
Impact of informal networks on opinion dynamics in hierarchically formal organization
Song, Xiao; Shi, Wen; Ma, Yaofei; Yang, Chen
2015-10-01
Traditional opinion dynamics model focused mainly on the conditions under which a group of agents would reach a consensus. Conclusion has been gained that continuous opinion dynamics are subject to the constraint that convergent opinion adjustment only proceeds when opinion difference is below a given tolerance. This conclusion is useful but neglected the fact that an organization often consists of overlapped networks including formally hierarchical network and small-world/scale-free informal networks. To study the impact of different types of informal networks on converging speed or the number of opinion clusters, four typical types of informal networks (small-world, scale-free, tree and fully connected) are modeled and proposed as complements to formal communications. Experiments to compare formal network and hybrid networks are then carried out. It is observed that opinion dynamics with supplemented communications of informal networks can benefit convergence speed and reduce opinion clusters. More importantly, it is revealed that three key factors of informal networks affect their impact on formal network. These factors of informal network in descending orders are: agents' tolerances, scale and number of links.
The Intracellular Dynamics of Circadian Clocks Reach for the Light of Ecology and Evolution.
Millar, Andrew J
2016-04-29
A major challenge for biology is to extend our understanding of molecular regulation from the simplified conditions of the laboratory to ecologically relevant environments. Tractable examples are essential to make these connections for complex, pleiotropic regulators and, to go further, to link relevant genome sequences to field traits. Here, I review the case for the biological clock in higher plants. The gene network of the circadian clock drives pervasive, 24-hour rhythms in metabolism, behavior, and physiology across the eukaryotes and in some prokaryotes. In plants, the scope of chronobiology is now extending from the most tractable, intracellular readouts to the clock's many effects at the whole-organism level and across the life cycle, including biomass and flowering. I discuss five research areas where recent progress might be integrated in the future, to understand not only circadian functions in natural conditions but also the evolution of the clock's molecular mechanisms.
Ocean Networks Canada: Live Sensing of a Dynamic Ocean System
Heesemann, Martin; Juniper, Kim; Hoeberechts, Maia; Matabos, Marjolaine; Mihaly, Steven; Scherwath, Martin; Dewey, Richard
2013-04-01
Ocean Networks Canada operates two advanced cabled networks on the west coast of British Columbia. VENUS, the coastal network consisting of two cabled arrays with four Nodes reaching an isolated fjord (Saanich Inlet) and a busy shipping corridor near Vancouver (the Strait of Georgia) went into operation in February 2006. NEPTUNE Canada is the first operational deep-sea regional cabled ocean observatory worldwide. Since the first data began streaming to the public in 2009, instruments on the five active nodes along the 800 km cable loop have gathered a time-series documenting three years in the northeastern Pacific. Observations cover the northern Juan de Fuca tectonic plate from ridge to trench and the continental shelf and slope off Vancouver Island. The cabled systems provide power and high bandwidth communications to a wide range of oceanographic instrument systems which measure the physical, chemical, geological, and biological conditions of the dynamic earth-ocean system. Over the years significant challenges have been overcome and currently we have more than 100 instruments with hundreds of sensors reporting data in real-time. Salient successes are the first open-ocean seafloor to sea-surface vertical profiling system, three years of operation of Wally—a seafloor crawler that explores a hydrate mound, and a proven resilient cable design that can recover from trawler hits and major equipment meltdown with minimal loss of data. A network wide array of bottom mounted pressure recorders and seismometers recorded the passage of three major tsunamis, numerous earthquakes and frequent whale calls. At the Endeavour segment of the Juan de Fuca ridge high temperature and diffuse vent fluids were monitored and sampled using novel equipment, including high resolution active acoustics instrumentation to study plume dynamics at a massive sulfide hydrothermal vent. Also, four deep sea cabled moorings (300 m high) were placed in the precipitous bathymetry of the 2200 m
Generalized synchronization in complex dynamical networks via adaptive couplings
Liu, Hui; Chen, Juan; Lu, Jun-an; Cao, Ming
2010-01-01
This paper investigates generalized synchronization of three typical classes of complex dynamical networks: scale-free networks, small-world networks. and interpolating networks. The proposed synchronization strategy is to adjust adaptively a node's coupling strength based oil the node's local
Stochastic dynamics of genetic broadcasting networks
Potoyan, Davit A.; Wolynes, Peter G.
2017-11-01
The complex genetic programs of eukaryotic cells are often regulated by key transcription factors occupying or clearing out of a large number of genomic locations. Orchestrating the residence times of these factors is therefore important for the well organized functioning of a large network. The classic models of genetic switches sidestep this timing issue by assuming the binding of transcription factors to be governed entirely by thermodynamic protein-DNA affinities. Here we show that relying on passive thermodynamics and random release times can lead to a "time-scale crisis" for master genes that broadcast their signals to a large number of binding sites. We demonstrate that this time-scale crisis for clearance in a large broadcasting network can be resolved by actively regulating residence times through molecular stripping. We illustrate these ideas by studying a model of the stochastic dynamics of the genetic network of the central eukaryotic master regulator NFκ B which broadcasts its signals to many downstream genes that regulate immune response, apoptosis, etc.
Information diversity in structure and dynamics of simulated neuronal networks.
Mäki-Marttunen, Tuomo; Aćimović, Jugoslava; Nykter, Matti; Kesseli, Juha; Ruohonen, Keijo; Yli-Harja, Olli; Linne, Marja-Leena
2011-01-01
Neuronal networks exhibit a wide diversity of structures, which contributes to the diversity of the dynamics therein. The presented work applies an information theoretic framework to simultaneously analyze structure and dynamics in neuronal networks. Information diversity within the structure and dynamics of a neuronal network is studied using the normalized compression distance. To describe the structure, a scheme for generating distance-dependent networks with identical in-degree distribution but variable strength of dependence on distance is presented. The resulting network structure classes possess differing path length and clustering coefficient distributions. In parallel, comparable realistic neuronal networks are generated with NETMORPH simulator and similar analysis is done on them. To describe the dynamics, network spike trains are simulated using different network structures and their bursting behaviors are analyzed. For the simulation of the network activity the Izhikevich model of spiking neurons is used together with the Tsodyks model of dynamical synapses. We show that the structure of the simulated neuronal networks affects the spontaneous bursting activity when measured with bursting frequency and a set of intraburst measures: the more locally connected networks produce more and longer bursts than the more random networks. The information diversity of the structure of a network is greatest in the most locally connected networks, smallest in random networks, and somewhere in between in the networks between order and disorder. As for the dynamics, the most locally connected networks and some of the in-between networks produce the most complex intraburst spike trains. The same result also holds for sparser of the two considered network densities in the case of full spike trains.
Information Diversity in Structure and Dynamics of Simulated Neuronal Networks
Directory of Open Access Journals (Sweden)
Tuomo eMäki-Marttunen
2011-06-01
Full Text Available Neuronal networks exhibit a wide diversity of structures, which contributes to the diversity of the dynamics therein. The presented work applies an information theoretic framework to simultaneously analyze structure and dynamics in neuronal networks. Information diversity within the structure and dynamics of a neuronal network is studied using the normalized compression distance (NCD. To describe the structure, a scheme for generating distance-dependent networks with identical in-degree distribution but variable strength of dependence on distance is presented. The resulting network structure classes possess diﬀering path length and clustering coeﬃcient distributions. In parallel, comparable realistic neuronal networks are generated with NETMORPH simulator and similar analysis is done on them. To describe the dynamics, network spike trains are simulated using diﬀerent network structures and their bursting behaviours are analyzed. For the simulation of the network activity the Izhikevich model of spiking neurons is used together with the Tsodyks model of dynamical synapses.We show that the structure of the simulated neuronal networks aﬀects the spontaneous bursting activity when measured with bursting frequency and a set of intraburst measures: the more locally connected networks produce more and longer bursts than the more random networks. The information diversity of the structure of a network is greatest in the most locally connected networks, smallest in random networks, and somewhere in between in the networks between order and disorder. As for the dynamics, the most locally connected networks and some of the in-between networks produce the most complex intraburst spike trains. The same result also holds for sparser of the two considered network densities in the case of full spike trains.
Stochastic Online Learning in Dynamic Networks under Unknown Models
2016-08-02
Stochastic Online Learning in Dynamic Networks under Unknown Models This research aims to develop fundamental theories and practical algorithms for...12211 Research Triangle Park, NC 27709-2211 Online learning , multi-armed bandit, dynamic networks REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S... Online Learning in Dynamic Networks under Unknown Models Report Title This research aims to develop fundamental theories and practical algorithms for
Agent Based Modeling on Organizational Dynamics of Terrorist Network
Bo Li; Duoyong Sun; Renqi Zhu; Ze Li
2015-01-01
Modeling organizational dynamics of terrorist network is a critical issue in computational analysis of terrorism research. The first step for effective counterterrorism and strategic intervention is to investigate how the terrorists operate with the relational network and what affects the performance. In this paper, we investigate the organizational dynamics by employing a computational experimentation methodology. The hierarchical cellular network model and the organizational dynamics model ...
Adaptive Dynamics, Control, and Extinction in Networked Populations
2015-07-09
Adaptive Dynamics, Control, and Extinction in Networked Populations Ira B. Schwartz US Naval Research Laboratory Code 6792 Nonlinear System Dynamics...theory of large deviations to stochastic network extinction to predict extinction times. In particular, we use the theory to find the most probable...paths leading to extinction . We then apply the methodology to network models and discover how mean extinction times scale with network parameters in Erdos
Miritello, Giovanna; Lara, Rubén; Moro, Esteban
Recent research has shown the deep impact of the dynamics of human interactions (or temporal social networks) on the spreading of information, opinion formation, etc. In general, the bursty nature of human interactions lowers the interaction between people to the extent that both the speed and reach of information diffusion are diminished. Using a large database of 20 million users of mobile phone calls we show evidence this effect is not homogeneous in the social network but in fact, there is a large correlation between this effect and the social topological structure around a given individual. In particular, we show that social relations of hubs in a network are relatively weaker from the dynamical point than those that are poorer connected in the information diffusion process. Our results show the importance of the temporal patterns of communication when analyzing and modeling dynamical process on social networks.
Miritello, Giovanna; Moro, Esteban
2013-01-01
Recent research has shown the deep impact of the dynamics of human interactions (or temporal social networks) on the spreading of information, opinion formation, etc. In general, the bursty nature of human interactions lowers the interaction between people to the extent that both the speed and reach of information diffusion are diminished. Using a large database of 20 million users of mobile phone calls we show evidence this effect is not homogeneous in the social network but in fact, there is a large correlation between this effect and the social topological structure around a given individual. In particular, we show that social relations of hubs in a network are relatively weaker from the dynamical point than those that are poorer connected in the information diffusion process. Our results show the importance of the temporal patterns of communication when analyzing and modeling dynamical process on social networks.
Complex Dynamical Network Control for Trajectory Tracking Using Delayed Recurrent Neural Networks
Directory of Open Access Journals (Sweden)
Jose P. Perez
2014-01-01
Full Text Available In this paper, the problem of trajectory tracking is studied. Based on the V-stability and Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a complex dynamical network is obtained. To illustrate the analytic results, we present a tracking simulation of a dynamical network with each node being just one Lorenz’s dynamical system and three identical Chen’s dynamical systems.
Foetal ECG recovery using dynamic neural networks.
Camps-Valls, Gustavo; Martínez-Sober, Marcelino; Soria-Olivas, Emilio; Magdalena-Benedito, Rafael; Calpe-Maravilla, Javier; Guerrero-Martínez, Juan
2004-07-01
Non-invasive electrocardiography has proven to be a very interesting method for obtaining information about the foetus state and thus to assure its well-being during pregnancy. One of the main applications in this field is foetal electrocardiogram (ECG) recovery by means of automatic methods. Evident problems found in the literature are the limited number of available registers, the lack of performance indicators, and the limited use of non-linear adaptive methods. In order to circumvent these problems, we first introduce the generation of synthetic registers and discuss the influence of different kinds of noise to the modelling. Second, a method which is based on numerical (correlation coefficient) and statistical (analysis of variance, ANOVA) measures allows us to select the best recovery model. Finally, finite impulse response (FIR) and gamma neural networks are included in the adaptive noise cancellation (ANC) scheme in order to provide highly non-linear, dynamic capabilities to the recovery model. Neural networks are benchmarked with classical adaptive methods such as the least mean squares (LMS) and the normalized LMS (NLMS) algorithms in simulated and real registers and some conclusions are drawn. For synthetic registers, the most determinant factor in the identification of the models is the foetal-maternal signal-to-noise ratio (SNR). In addition, as the electromyogram contribution becomes more relevant, neural networks clearly outperform the LMS-based algorithm. From the ANOVA test, we found statistical differences between LMS-based models and neural models when complex situations (high foetal-maternal and foetal-noise SNRs) were present. These conclusions were confirmed after doing robustness tests on synthetic registers, visual inspection of the recovered signals and calculation of the recognition rates of foetal R-peaks for real situations. Finally, the best compromise between model complexity and outcomes was provided by the FIR neural network. Both
Discrete opinion dynamics on networks based on social influence
Energy Technology Data Exchange (ETDEWEB)
Hu Haibo; Wang Xiaofan [Complex Networks and Control Lab, Shanghai Jiao Tong University, Shanghai 200240 (China)
2009-06-05
A model of opinion dynamics based on social influence on networks was studied. The opinion of each agent can have integer values i = 1, 2, ..., I and opinion exchanges are restricted to connected agents. It was found that for any I {>=} 2 and self-confidence parameter 0 {<=} u < 1, when u is a degree-independent constant, the weighted proportion (q{sub i}) of the population that hold a given opinion i is a martingale, and the fraction q{sub i} of opinion i will gradually converge to (q{sub i}). The tendency can slow down with the increase of degree assortativity of networks. When u is degree dependent, (q{sub i}) does not possess the martingale property, however q{sub i} still converges to it. In both cases for a finite network the states of all agents will finally reach consensus. Further if there exist stubborn persons in the population whose opinions do not change over time, it was found that for degree-independent constant u, both q{sub i} and (q{sub i}) will converge to fixed proportions which only depend on the distribution of initial obstinate persons, and naturally the final equilibrium state will be the coexistence of diverse opinions held by the stubborn people. The analytical results were verified by numerical simulations on Barabasi-Albert (BA) networks. The model highlights the influence of high-degree agents on the final consensus or coexistence state and captures some realistic features of the diffusion of opinions in social networks.
Google matrix, dynamical attractors, and Ulam networks.
Shepelyansky, D L; Zhirov, O V
2010-03-01
We study the properties of the Google matrix generated by a coarse-grained Perron-Frobenius operator of the Chirikov typical map with dissipation. The finite-size matrix approximant of this operator is constructed by the Ulam method. This method applied to the simple dynamical model generates directed Ulam networks with approximate scale-free scaling and characteristics being in certain features similar to those of the world wide web with approximate scale-free degree distributions as well as two characteristics similar to the web: a power-law decay in PageRank that mirrors the decay of PageRank on the world wide web and a sensitivity to the value alpha in PageRank. The simple dynamical attractors play here the role of popular websites with a strong concentration of PageRank. A variation in the Google parameter alpha or other parameters of the dynamical map can drive the PageRank of the Google matrix to a delocalized phase with a strange attractor where the Google search becomes inefficient.
Kim, Chang-Hun; Jung, Sang-Min; Kang, Su-Min; Han, Sang-Kook
2015-01-01
We propose an all-optical virtual private network (VPN) system in an orthogonal frequency division multiplexing (OFDM) based long reach PON (LR-PON). In the optical access network field, technologies based on fundamental upstream (U/S) and downstream (D/S) have been actively researched to accommodate explosion of data capacity. However, data transmission among the end users which is arisen from cloud computing, file-sharing and interactive game takes a large weight inside of internet traffic. Moreover, this traffic is predicted to increase more if Internet of Things (IoT) services are activated. In a conventional PON, VPN data is transmitted through ONU-OLT-ONU via U/S and D/S carriers. It leads to waste of bandwidth and energy due to O-E-O conversion in the OLT and round-trip propagation between OLT and remote node (RN). Also, it causes inevitable load to the OLT for electrical buffer, scheduling and routing. The network inefficiency becomes more critical in a LR-PON which has been researched as an effort to reduce CAPEX and OPEX through metro-access consolidation. In the proposed system, the VPN data is separated from conventional U/S and re-modulated on the D/S carrier by using RSOA in the ONUs to avoid bandwidth consumption of U/S and D/S unlike in previously reported system. Moreover, the transmitted VPN data is re-directed to the ONUs by wavelength selective reflector device in the RN without passing through the OLT. Experimental demonstration for the VPN communication system in an OFDM based LR-PON has been verified.
Structure and dynamics of core-periphery networks
Csermely, Peter; Wu, Ling-Yun; Uzzi, Brian
2013-01-01
Recent studies uncovered important core/periphery network structures characterizing complex sets of cooperative and competitive interactions between network nodes, be they proteins, cells, species or humans. Better characterization of the structure, dynamics and function of core/periphery networks is a key step of our understanding cellular functions, species adaptation, social and market changes. Here we summarize the current knowledge of the structure and dynamics of "traditional" core/periphery networks, rich-clubs, nested, bow-tie and onion networks. Comparing core/periphery structures with network modules, we discriminate between global and local cores. The core/periphery network organization lies in the middle of several extreme properties, such as random/condensed structures, clique/star configurations, network symmetry/asymmetry, network assortativity/disassortativity, as well as network hierarchy/anti-hierarchy. These properties of high complexity together with the large degeneracy of core pathways e...
Interestingness-Driven Diffusion Process Summarization in Dynamic Networks
DEFF Research Database (Denmark)
Qu, Qiang; Liu, Siyuan; Jensen, Christian S.
2014-01-01
The widespread use of social networks enables the rapid diffusion of information, e.g., news, among users in very large communities. It is a substantial challenge to be able to observe and understand such diffusion processes, which may be modeled as networks that are both large and dynamic. A key...... tool in this regard is data summarization. However, few existing studies aim to summarize graphs/networks for dynamics. Dynamic networks raise new challenges not found in static settings, including time sensitivity and the needs for online interestingness evaluation and summary traceability, which...... render existing techniques inapplicable. We study the topic of dynamic network summarization: how to summarize dynamic networks with millions of nodes by only capturing the few most interesting nodes or edges over time, and we address the problem by finding interestingness-driven diffusion processes...
Spatial Dynamics of Multilayer Cellular Neural Networks
Wu, Shi-Liang; Hsu, Cheng-Hsiung
2018-02-01
The purpose of this work is to study the spatial dynamics of one-dimensional multilayer cellular neural networks. We first establish the existence of rightward and leftward spreading speeds of the model. Then we show that the spreading speeds coincide with the minimum wave speeds of the traveling wave fronts in the right and left directions. Moreover, we obtain the asymptotic behavior of the traveling wave fronts when the wave speeds are positive and greater than the spreading speeds. According to the asymptotic behavior and using various kinds of comparison theorems, some front-like entire solutions are constructed by combining the rightward and leftward traveling wave fronts with different speeds and a spatially homogeneous solution of the model. Finally, various qualitative features of such entire solutions are investigated.
Magnetoencephalography from signals to dynamic cortical networks
Aine, Cheryl
2014-01-01
"Magnetoencephalography (MEG) provides a time-accurate view into human brain function. The concerted action of neurons generates minute magnetic fields that can be detected---totally noninvasively---by sensitive multichannel magnetometers. The obtained millisecond accuracycomplements information obtained by other modern brain-imaging tools. Accurate timing is quintessential in normal brain function, often distorted in brain disorders. The noninvasiveness and time-sensitivityof MEG are great assets to developmental studies, as well. This multiauthored book covers an ambitiously wide range of MEG research from introductory to advanced level, from sensors to signals, and from focal sources to the dynamics of cortical networks. Written by active practioners of this multidisciplinary field, the book contains tutorials for newcomers and chapters of new challenging methods and emerging technologies to advanced MEG users. The reader will obtain a firm grasp of the possibilities of MEG in the study of audition, vision...
Attractor dynamics in local neuronal networks
Directory of Open Access Journals (Sweden)
Jean-Philippe eThivierge
2014-03-01
Full Text Available Patterns of synaptic connectivity in various regions of the brain are characterized by the presence of synaptic motifs, defined as unidirectional and bidirectional synaptic contacts that follow a particular configuration and link together small groups of neurons. Recent computational work proposes that a relay network (two populations communicating via a third, relay population of neurons can generate precise patterns of neural synchronization. Here, we employ two distinct models of neuronal dynamics and show that simulated neural circuits designed in this way are caught in a global attractor of activity that prevents neurons from modulating their response on the basis of incoming stimuli. To circumvent the emergence of a fixed global attractor, we propose a mechanism of selective gain inhibition that promotes flexible responses to external stimuli. We suggest that local neuronal circuits may employ this mechanism to generate precise patterns of neural synchronization whose transient nature delimits the occurrence of a brief stimulus.
Dynamics of subway networks based on vehicles operation timetable
Xiao, Xue-mei; Jia, Li-min; Wang, Yan-hui
2017-05-01
In this paper, a subway network is represented as a dynamic, directed and weighted graph, in which vertices represent subway stations and weights of edges represent the number of vehicles passing through the edges by considering vehicles operation timetable. Meanwhile the definitions of static and dynamic metrics which can represent vertices' and edges' local and global attributes are proposed. Based on the model and metrics, standard deviation is further introduced to study the dynamic properties (heterogeneity and vulnerability) of subway networks. Through a detailed analysis of the Beijing subway network, we conclude that with the existing network structure, the heterogeneity and vulnerability of the Beijing subway network varies over time when the vehicle operation timetable is taken into consideration, and the distribution of edge weights affects the performance of the network. In other words, although the vehicles operation timetable is restrained by the physical structure of the network, it determines the performances and properties of the Beijing subway network.
Spreading dynamics on spatially constrained complex brain networks.
O'Dea, Reuben; Crofts, Jonathan J; Kaiser, Marcus
2013-04-06
The study of dynamical systems defined on complex networks provides a natural framework with which to investigate myriad features of neural dynamics and has been widely undertaken. Typically, however, networks employed in theoretical studies bear little relation to the spatial embedding or connectivity of the neural networks that they attempt to replicate. Here, we employ detailed neuroimaging data to define a network whose spatial embedding represents accurately the folded structure of the cortical surface of a rat brain and investigate the propagation of activity over this network under simple spreading and connectivity rules. By comparison with standard network models with the same coarse statistics, we show that the cortical geometry influences profoundly the speed of propagation of activation through the network. Our conclusions are of high relevance to the theoretical modelling of epileptic seizure events and indicate that such studies which omit physiological network structure risk simplifying the dynamics in a potentially significant way.
Choice Shift in Opinion Network Dynamics
Gabbay, Michael
Choice shift is a phenomenon associated with small group dynamics whereby group discussion causes group members to shift their opinions in a more extreme direction so that the mean post-discussion opinion exceeds the mean pre-discussion opinion. Also known as group polarization, choice shift is a robust experimental phenomenon and has been well-studied within social psychology. In opinion network models, shifts toward extremism are typically produced by the presence of stubborn agents at the extremes of the opinion axis, whose opinions are much more resistant to change than moderate agents. However, we present a model in which choice shift can arise without the assumption of stubborn agents; the model evolves member opinions and uncertainties using coupled nonlinear differential equations. In addition, we briefly describe the results of a recent experiment conducted involving online group discussion concerning the outcome of National Football League games are described. The model predictions concerning the effects of network structure, disagreement level, and team choice (favorite or underdog) are in accord with the experimental results. This research was funded by the Office of Naval Research and the Defense Threat Reduction Agency.
Polarization dynamics in optical ground wire network.
Leeson, Jesse; Bao, Xiaoyi; Côté, Alain
2009-04-20
We report the polarization dynamics in an optical ground wire (OPGW) network for a summer period and a fall period for what is believed to be the first time. To better observe the surrounding magnetic fields contribution to modulating the state of polarization (SOP) we installed a Faraday rotating mirror to correct reciprocal birefringence from quasi-static changes. We also monitored the OPGW while no electrical current was present in the towers' electrical conductors. The spectral analysis, the arc length mapped out over a given time interval on a Poincaré sphere, histograms of the arc length, and the SOP autocorrelation function are calculated to analyze the SOP changes. Ambient temperature changes, wind, Sun-induced temperature gradients, and electrical current all have a significant impact on the SOP drift in an OPGW network. Wind-generated cable oscillations and Sun-induced temperature gradients are shown to be the dominant slow SOP modulations, while Aeolian vibrations and electrical current are shown to be the dominant fast SOP modulations. The spectral analysis revealed that the electrical current gives the fastest SOP modulation to be 300 Hz for the sampling frequency of 1 KHz. This has set the upper speed limit for real-time polarization mode dispersion compensation devices.
Higher-order structure and epidemic dynamics in clustered networks.
Ritchie, Martin; Berthouze, Luc; House, Thomas; Kiss, Istvan Z
2014-05-07
Clustering is typically measured by the ratio of triangles to all triples regardless of whether open or closed. Generating clustered networks, and how clustering affects dynamics on networks, is reasonably well understood for certain classes of networks (Volz et al., 2011; Karrer and Newman, 2010), e.g. networks composed of lines and non-overlapping triangles. In this paper we show that it is possible to generate networks which, despite having the same degree distribution and equal clustering, exhibit different higher-order structure, specifically, overlapping triangles and other order-four (a closed network motif composed of four nodes) structures. To distinguish and quantify these additional structural features, we develop a new network metric capable of measuring order-four structure which, when used alongside traditional network metrics, allows us to more accurately describe a network׳s topology. Three network generation algorithms are considered: a modified configuration model and two rewiring algorithms. By generating homogeneous networks with equal clustering we study and quantify their structural differences, and using SIS (Susceptible-Infected-Susceptible) and SIR (Susceptible-Infected-Recovered) dynamics we investigate computationally how differences in higher-order structure impact on epidemic threshold, final epidemic or prevalence levels and time evolution of epidemics. Our results suggest that characterising and measuring higher-order network structure is needed to advance our understanding of the impact of network topology on dynamics unfolding on the networks. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.
Topological effects of network structure on long-term social network dynamics in a wild mammal.
Ilany, Amiyaal; Booms, Andrew S; Holekamp, Kay E
2015-07-01
Social structure influences ecological processes such as dispersal and invasion, and affects survival and reproductive success. Recent studies have used static snapshots of social networks, thus neglecting their temporal dynamics, and focused primarily on a limited number of variables that might be affecting social structure. Here, instead we modelled effects of multiple predictors of social network dynamics in the spotted hyena, using observational data collected during 20 years of continuous field research in Kenya. We tested the hypothesis that the current state of the social network affects its long-term dynamics. We employed stochastic agent-based models that allowed us to estimate the contribution of multiple factors to network changes. After controlling for environmental and individual effects, we found that network density and individual centrality affected network dynamics, but that social bond transitivity consistently had the strongest effects. Our results emphasise the significance of structural properties of networks in shaping social dynamics. © 2015 John Wiley & Sons Ltd/CNRS.
Towards blueprints for network architecture, biophysical dynamics and signal transduction.
Coombes, Stephen; Doiron, Brent; Josić, Kresimir; Shea-Brown, Eric
2006-12-15
We review mathematical aspects of biophysical dynamics, signal transduction and network architecture that have been used to uncover functionally significant relations between the dynamics of single neurons and the networks they compose. We focus on examples that combine insights from these three areas to expand our understanding of systems neuroscience. These range from single neuron coding to models of decision making and electrosensory discrimination by networks and populations and also coincidence detection in pairs of dendrites and dynamics of large networks of excitable dendritic spines. We conclude by describing some of the challenges that lie ahead as the applied mathematics community seeks to provide the tools which will ultimately underpin systems neuroscience.
Dynamic Mobile IP routers in ad hoc networks
Kock, B.A.; Schmidt, J.R.
2005-01-01
This paper describes a concept combining mobile IP and ad hoc routing to create a robust mobile network. In this network all nodes are mobile and globally and locally reachable under the same IP address. Essential for implementing this network are the dynamic mobile IP routers. They act as gateways
Stochastic simulation of HIV population dynamics through complex network modelling
Sloot, P. M. A.; Ivanov, S. V.; Boukhanovsky, A. V.; van de Vijver, D. A. M. C.; Boucher, C. A. B.
We propose a new way to model HIV infection spreading through the use of dynamic complex networks. The heterogeneous population of HIV exposure groups is described through a unique network degree probability distribution. The time evolution of the network nodes is modelled by a Markov process and
Optical-router-based dynamically reconfigurable photonic access network
Roy, R.
2014-01-01
The Broadband photonics (BBP) project under the Freeband consortium of projects investigated the design of a dynamically reconfigurable photonic access network. Access networks form a key link in ensuring optimal bandwidth to the end user without which any improvements deeper in the network in the
Stochastic simulation of HIV population dynamics through complex network modelling
Sloot, P.M.A.; Ivanov, S.V.; Boukhanovsky, A.V.; van de Vijver, D.A.M.C.; Boucher, C.A.B.
2008-01-01
We propose a new way to model HIV infection spreading through the use of dynamic complex networks. The heterogeneous population of HIV exposure groups is described through a unique network degree probability distribution. The time evolution of the network nodes is modelled by a Markov process and
Energy Efficiency Analysis for Dynamic Routing in Optical Transport Networks
DEFF Research Database (Denmark)
Vizcaíno, Jorge López; Ye, Yabin; Tafur Monroy, Idelfonso
2012-01-01
The energy efficiency in telecommunication networks is gaining more relevance as the Internet traffic is growing. The introduction of OFDM and dynamic operation opens new horizons in the operation of optical networks, improving the network flexibility and its efficiency. In this paper, we compare...
Gender, Friendship Networks, and Delinquency: A Dynamic Network Approach**
Haynie, Dana L.; Doogan, Nathan J.; Soller, Brian
2014-01-01
Researchers have examined selection and influence processes in shaping delinquency similarity among friends, but little is known about the role of gender in moderating these relationships. Our objective is to examine differences between adolescent boys and girls regarding delinquency-based selection and influence processes. Using longitudinal network data from adolescents attending two large schools in AddHealth (N = 1,857) and stochastic actor-oriented models, we evaluate whether girls are influenced to a greater degree by friends' violence or delinquency than boys (influence hypothesis) and whether girls are more likely to select friends based on violent or delinquent behavior than boys (selection hypothesis). The results indicate that girls are more likely than boys to be influenced by their friends' involvement in violence. Although a similar pattern emerges for nonviolent delinquency, the gender differences are not significant. Some evidence shows that boys are influenced toward increasing their violence or delinquency when exposed to more delinquent or violent friends but are immune to reducing their violence or delinquency when associating with less violent or delinquent friends. In terms of selection dynamics, although both boys and girls have a tendency to select friends based on friends' behavior, girls have a stronger tendency to do so, suggesting that among girls, friends' involvement in violence or delinquency is an especially decisive factor for determining friendship ties. PMID:26097241
Gender, Friendship Networks, and Delinquency: A Dynamic Network Approach.
Haynie, Dana L; Doogan, Nathan J; Soller, Brian
2014-11-01
Researchers have examined selection and influence processes in shaping delinquency similarity among friends, but little is known about the role of gender in moderating these relationships. Our objective is to examine differences between adolescent boys and girls regarding delinquency-based selection and influence processes. Using longitudinal network data from adolescents attending two large schools in AddHealth (N = 1,857) and stochastic actor-oriented models, we evaluate whether girls are influenced to a greater degree by friends' violence or delinquency than boys (influence hypothesis) and whether girls are more likely to select friends based on violent or delinquent behavior than boys (selection hypothesis). The results indicate that girls are more likely than boys to be influenced by their friends' involvement in violence. Although a similar pattern emerges for nonviolent delinquency, the gender differences are not significant. Some evidence shows that boys are influenced toward increasing their violence or delinquency when exposed to more delinquent or violent friends but are immune to reducing their violence or delinquency when associating with less violent or delinquent friends. In terms of selection dynamics, although both boys and girls have a tendency to select friends based on friends' behavior, girls have a stronger tendency to do so, suggesting that among girls, friends' involvement in violence or delinquency is an especially decisive factor for determining friendship ties.
Balance between noise and information flow maximizes set complexity of network dynamics.
Directory of Open Access Journals (Sweden)
Tuomo Mäki-Marttunen
Full Text Available Boolean networks have been used as a discrete model for several biological systems, including metabolic and genetic regulatory networks. Due to their simplicity they offer a firm foundation for generic studies of physical systems. In this work we show, using a measure of context-dependent information, set complexity, that prior to reaching an attractor, random Boolean networks pass through a transient state characterized by high complexity. We justify this finding with a use of another measure of complexity, namely, the statistical complexity. We show that the networks can be tuned to the regime of maximal complexity by adding a suitable amount of noise to the deterministic Boolean dynamics. In fact, we show that for networks with Poisson degree distributions, all networks ranging from subcritical to slightly supercritical can be tuned with noise to reach maximal set complexity in their dynamics. For networks with a fixed number of inputs this is true for near-to-critical networks. This increase in complexity is obtained at the expense of disruption in information flow. For a large ensemble of networks showing maximal complexity, there exists a balance between noise and contracting dynamics in the state space. In networks that are close to critical the intrinsic noise required for the tuning is smaller and thus also has the smallest effect in terms of the information processing in the system. Our results suggest that the maximization of complexity near to the state transition might be a more general phenomenon in physical systems, and that noise present in a system may in fact be useful in retaining the system in a state with high information content.
Arresting Strategy Based on Dynamic Criminal Networks Changing over Time
Directory of Open Access Journals (Sweden)
Junqing Yuan
2013-01-01
Full Text Available We investigate a sequence of dynamic criminal networks on a time series based on the dynamic network analysis (DNA. According to the change of networks’ structure, networks’ variation trend is analyzed to forecast its future structure. Finally, an optimal arresting time and priority list are designed based on our analysis. Better results can be expected than that based on social network analysis (SNA.
Fractional diffusion on circulant networks: emergence of a dynamical small world
Riascos, A. P.; Mateos, José L.
2015-07-01
In this paper, we study fractional random walks on networks defined from the equivalent of the fractional diffusion equation in graphs. We explore this process analytically in circulant networks; in particular, interacting cycles and limit cases such as a ring and a complete graph. From the spectra and the eigenvectors of the Laplacian matrix, we deduce explicit results for different quantities that characterize this dynamical process. We obtain analytical expressions for the fractional transition matrix, the fractional degrees and the average probability of return of the random walker. Also, we discuss the Kemeny constant, which gives the average number of steps necessary to reach any site of the network. Throughout this work, we analyze the mechanisms behind fractional transport on circulant networks and how this long-range process dynamically induces the small-world property in different structures.
Dynamical Systems Theory for Transparent Symbolic Computation in Neuronal Networks
Carmantini, Giovanni Sirio
2017-01-01
In this thesis, we explore the interface between symbolic and dynamical system computation, with particular regard to dynamical system models of neuronal networks. In doing so, we adhere to a definition of computation as the physical realization of a formal system, where we say that a dynamical system performs a computation if a correspondence can be found between its dynamics on a vectorial space and the formal system’s dynamics on a symbolic space. Guided by this definition, we characterize...
DiBiase, R.; Liu, X.; Chen, Y.
2016-12-01
Understanding flow hydraulics in mountain streams is important for assessing flooding hazard, and for quantifying sediment transport and bedrock incision in upland landscapes. In such settings, reach-scale flow resistance is sensitive to grain-scale roughness in channel bed sediment cover, which has traditionally been characterized by particle size distributions derived from time-consuming point counts performed in the field. However, developing a general framework for quantifying frictional relationships in mountain channels has proven a significant challenge. Here we combine millimeter-scale Structure-from-Motion (SfM) surveys of bed topography, traditional point counts of surface clast size and shape, and computational fluid dynamics (CFD) simulations in order to better evaluate the important scales of roughness for turbulent flow in mountain rivers. We focused our field surveys on gravel, cobble, and boulder bedded channels in Southern California and Central Pennsylvania spanning a wide range of grain size, sorting, and shape, with the goal of deriving empirical relationships between metrics of bed microtopography roughness and particle size and shape distributions. The resulting reach-scale topographic models were then used in large eddy simulation CFD experiments to quantify flow behavior and bed resistance. By analyzing bed microtopography using structure function analysis, we identified three scaling regimes that correspond to roughness length scales important for constraining the geometric complexity required for CFD simulations. Our preliminary results highlight the potential for rapid estimation of reach-scale microtopography using SfM methods that in addition provides a more direct measure of flow resistance than particle size statistics. Additionally, SfM methods enable a similarly straightforward assessment of vegetation and bedrock channel roughness, with broad applications in fluvial geomorphology.
Cui, Lijuan; Gao, Changjun; Zhao, Xinsheng; Ma, Qiongfang; Zhang, Manyin; Li, Wei; Song, Hongtao; Wang, Yifei; Li, Shengnan; Zhang, Yan
2013-05-01
The middle and lower reaches of the Yangtze River basin have the most representative and largest concentration of freshwater lakes in China. However, the size and number of these lakes have changed considerably over the past century due to the natural and anthropogenic impact. The lakes, larger than 10 km(2) in size, were chosen from relief maps and remotely sensed images in 1875, 1950, 1970, 1990, 2000, and 2008 to study the dynamics of lakes in the middle and lower reaches of the Yangtze River basin and to examine the causes and consequences of these changes. Results indicated that there was a dramatic reduction in lake areas, which decreased by 7,841.2 km(2) (42.64 %) during the study period (1875-2008), and the number of lakes in this region changed moderately. Meanwhile, a large number of lakes in the middle and lower reaches of the Yangtze River basin were directly converted into paddy fields, ponds, building lands, or other land-use types over the study period. Therefore, all kinds of lake reclamation should be identified as the major driving factors for the loss of lake in this region. Furthermore, flooding, soil erosion, and sedimentation were also the main factors which triggered lake changes in different periods. Some wetland conservation and restoration projects have been implemented since the 1970s, but they have not reversed the lake degradation. These findings were of great importance to managers involved in making policy for the conservation of lake ecosystems and the utilization of lake resources.
Directory of Open Access Journals (Sweden)
Junyu Zou
Full Text Available The carbon isotopic composition (δ13C of dissolved and particulate inorganic carbon (DIC; PIC was used to compare and analyze the origin, dynamics and evolution of inorganic carbon in two headwater tributaries of the Xi River, Southwest China. Carbonate dissolution and soil CO2 were regarded as the primary sources of DIC on the basis of δ13CDIC values which varied along the Nanpan and Beipan Rivers, from -13.9‰ to 8.1‰. Spatial trends in DIC differed between the two rivers (i.e., the tributaries, in part because factors controlling pCO2, which strongly affected carbonate dissolution, differed between the two river basins. Transport of soil CO2 and organic carbon through hydrologic conduits predominately controlled the levels of pCO2 in the Nanpan River. However, pCO2 along the upper reaches of the Nanpan River also was controlled by the extent of urbanization and industrialization relative to agriculture. DIC concentrations in the highly urbanized upper reaches of the Nanpan River were typical higher than in other carbonate-dominated areas of the upper Xi River. Within the Beipan River, the oxidation of organic carbon is the primary process that maintains pCO2 levels. The pCO2 within the Beipan River was more affected by sulfuric acid from coal industries, inputs from a scenic spot, and groundwater than along the Nanpan River. With regards to PIC, the contents and δ13C values in the Nanpan River were generally lower than those in the Beipan River, indicating that chemical and physical weathering contributes more marine carbonate detritus to the PIC along the Beipan River. The CO2 evasion flux from the Nanpan River was higher than that in the Beipan River, and generally higher than along the middle and lower reaches of the Xi River, demonstrating that the Nanpan River is an important net source of atmospheric CO2 in Southwest China.
Identify Dynamic Network Modules with Temporal and Spatial Constraints
Energy Technology Data Exchange (ETDEWEB)
Jin, R; McCallen, S; Liu, C; Almaas, E; Zhou, X J
2007-09-24
Despite the rapid accumulation of systems-level biological data, understanding the dynamic nature of cellular activity remains a difficult task. The reason is that most biological data are static, or only correspond to snapshots of cellular activity. In this study, we explicitly attempt to detangle the temporal complexity of biological networks by using compilations of time-series gene expression profiling data.We define a dynamic network module to be a set of proteins satisfying two conditions: (1) they form a connected component in the protein-protein interaction (PPI) network; and (2) their expression profiles form certain structures in the temporal domain. We develop the first efficient mining algorithm to discover dynamic modules in a temporal network, as well as frequently occurring dynamic modules across many temporal networks. Using yeast as a model system, we demonstrate that the majority of the identified dynamic modules are functionally homogeneous. Additionally, many of them provide insight into the sequential ordering of molecular events in cellular systems. We further demonstrate that identifying frequent dynamic network modules can significantly increase the signal to noise separation, despite the fact that most dynamic network modules are highly condition-specific. Finally, we note that the applicability of our algorithm is not limited to the study of PPI systems, instead it is generally applicable to the combination of any type of network and time-series data.
Directory of Open Access Journals (Sweden)
Levent Kurt
Full Text Available We studied the behavioral and emotional dynamics displayed by two people trying to resolve a conflict. 59 groups of two people were asked to talk for 20 minutes to try to reach a consensus about a topic on which they disagreed. The topics were abortion, affirmative action, death penalty, and euthanasia. Behavior data were determined from audio recordings where each second of the conversation was assessed as proself, neutral, or prosocial. We determined the probability density function of the durations of time spent in each behavioral state. These durations were well fit by a stretched exponential distribution, [Formula: see text] with an exponent, [Formula: see text], of approximately 0.3. This indicates that the switching between behavioral states is not a random Markov process, but one where the probability to switch behavioral states decreases with the time already spent in that behavioral state. The degree of this "memory" was stronger in those groups who did not reach a consensus and where the conflict grew more destructive than in those that did. Emotion data were measured by having each person listen to the audio recording and moving a computer mouse to recall their negative or positive emotional valence at each moment in the conversation. We used the Hurst rescaled range analysis and power spectrum to determine the correlations in the fluctuations of the emotional valence. The emotional valence was well described by a random walk whose increments were uncorrelated. Thus, the behavior data demonstrated a "memory" of the duration already spent in a behavioral state while the emotion data fluctuated as a random walk whose steps did not have a "memory" of previous steps. This work demonstrates that statistical analysis, more commonly used to analyze physical phenomena, can also shed interesting light on the dynamics of processes in social psychology and conflict management.
Modeling the Network Dynamics of Pulse-Coupled Neurons
Chandra, Sarthak; Hathcock, David; Crain, Kimberly; Antonsen, Thomas M.; Girvan, Michelle; Ott, Edward
2017-01-01
We derive a mean-field approximation for the macroscopic dynamics of large networks of pulse-coupled theta neurons in order to study the effects of different network degree distributions, as well as degree correlations (assortativity). Using the ansatz of Ott and Antonsen (Chaos, 19 (2008) 037113), we obtain a reduced system of ordinary differential equations describing the mean-field dynamics, with significantly lower dimensionality compared with the complete set of dynamical equations for t...
Common and Unique Network Dynamics in Football Games
Yamamoto, Yuji; Yokoyama, Keiko
2011-01-01
The sport of football is played between two teams of eleven players each using a spherical ball. Each team strives to score by driving the ball into the opposing goal as the result of skillful interactions among players. Football can be regarded from the network perspective as a competitive relationship between two cooperative networks with a dynamic network topology and dynamic network node. Many complex large-scale networks have been shown to have topological properties in common, based on a small-world network and scale-free network models. However, the human dynamic movement pattern of this network has never been investigated in a real-world setting. Here, we show that the power law in degree distribution emerged in the passing behavior in the 2006 FIFA World Cup Final and an international “A” match in Japan, by describing players as vertices connected by links representing passes. The exponent values are similar to the typical values that occur in many real-world networks, which are in the range of , and are larger than that of a gene transcription network, . Furthermore, we reveal the stochastically switched dynamics of the hub player throughout the game as a unique feature in football games. It suggests that this feature could result not only in securing vulnerability against intentional attack, but also in a power law for self-organization. Our results suggest common and unique network dynamics of two competitive networks, compared with the large-scale networks that have previously been investigated in numerous works. Our findings may lead to improved resilience and survivability not only in biological networks, but also in communication networks. PMID:22216336
Adaptive Dynamics of Regulatory Networks: Size Matters
Directory of Open Access Journals (Sweden)
Martinetz Thomas
2009-01-01
Full Text Available To accomplish adaptability, all living organisms are constructed of regulatory networks on different levels which are capable to differentially respond to a variety of environmental inputs. Structure of regulatory networks determines their phenotypical plasticity, that is, the degree of detail and appropriateness of regulatory replies to environmental or developmental challenges. This regulatory network structure is encoded within the genotype. Our conceptual simulation study investigates how network structure constrains the evolution of networks and their adaptive abilities. The focus is on the structural parameter network size. We show that small regulatory networks adapt fast, but not as good as larger networks in the longer perspective. Selection leads to an optimal network size dependent on heterogeneity of the environment and time pressure of adaptation. Optimal mutation rates are higher for smaller networks. We put special emphasis on discussing our simulation results on the background of functional observations from experimental and evolutionary biology.
Mathematical model for spreading dynamics of social network worms
Sun, Xin; Liu, Yan-Heng; Li, Bin; Li, Jin; Han, Jia-Wei; Liu, Xue-Jie
2012-04-01
In this paper, a mathematical model for social network worm spreading is presented from the viewpoint of social engineering. This model consists of two submodels. Firstly, a human behavior model based on game theory is suggested for modeling and predicting the expected behaviors of a network user encountering malicious messages. The game situation models the actions of a user under the condition that the system may be infected at the time of opening a malicious message. Secondly, a social network accessing model is proposed to characterize the dynamics of network users, by which the number of online susceptible users can be determined at each time step. Several simulation experiments are carried out on artificial social networks. The results show that (1) the proposed mathematical model can well describe the spreading dynamics of social network worms; (2) weighted network topology greatly affects the spread of worms; (3) worms spread even faster on hybrid social networks.
Dynamic Object Identification with SOM-based neural networks
Directory of Open Access Journals (Sweden)
Aleksey Averkin
2014-03-01
Full Text Available In this article a number of neural networks based on self-organizing maps, that can be successfully used for dynamic object identification, is described. Unique SOM-based modular neural networks with vector quantized associative memory and recurrent self-organizing maps as modules are presented. The structured algorithms of learning and operation of such SOM-based neural networks are described in details, also some experimental results and comparison with some other neural networks are given.
Dynamical Networks for Smog Pattern Analysis
Zong, Linqi; Zhu, Jia
2015-01-01
Smog, as a form of air pollution, poses as a serious problem to the environment, health, and economy of the world[1-4] . Previous studies on smog mostly focused on the components and the effects of smog [5-10]. However, as the smog happens with increased frequency and duration, the smog pattern which is critical for smog forecast and control, is rarely investigated, mainly due to the complexity of the components, the causes, and the spreading processes of smog. Here we report the first analysis on smog pattern applying the model of dynamical networks with spontaneous recovery. We show that many phenomena such as the sudden outbreak and dissipation of smog and the long duration smog can be revealed with the mathematical mechanism under a random walk simulation. We present real-world air quality index data in accord with the predictions of the model. Also we found that compared to external causes such as pollution spreading from nearby, internal causes such as industrial pollution and vehicle emission generated...
Opinion competition dynamics on multiplex networks
Amato, R.; Kouvaris, N. E.; San Miguel, M.; Díaz-Guilera, A.
2017-12-01
Multilayer and multiplex networks represent a good proxy for the description of social phenomena where social structure is important and can have different origins. Here, we propose a model of opinion competition where individuals are organized according to two different structures in two layers. Agents exchange opinions according to the Abrams–Strogatz model in each layer separately and opinions can be copied across layers by the same individual. In each layer a different opinion is dominant, so each layer has a different absorbing state. Consensus in one opinion is not the only possible stable solution because of the interaction between the two layers. A new mean field solution has been found where both opinions coexist. In a finite system there is a long transient time for the dynamical coexistence of both opinions. However, the system ends in a consensus state due to finite size effects. We analyze sparse topologies in the two layers and the existence of positive correlations between them, which enables the coexistence of inter-layer groups of agents sharing the same opinion.
sizzled function and secreted factor network dynamics
Directory of Open Access Journals (Sweden)
Jianli Shi
2012-02-01
Studies on the role of the E-box binding transcription factor Snail2 (Slug in the induction of neural crest by mesoderm (Shi et al., 2011 revealed an unexpected increase in the level of sizzled RNA in the dorsolateral mesodermal zone (DMLZ of morphant Xenopus embryos. sizzled encodes a secreted protein with both Wnt and BMP inhibitor activities. Morpholino-mediated down-regulation of sizzled expression in one cell of two cell embryos or the C2/C3 blastomeres of 32-cell embryos, which give rise to the DLMZ, revealed decreased expression of the mesodermal marker brachyury and subsequent defects in neural crest induction, pronephros formation, and muscle patterning. Loss of sizzled expression led to decreases in RNAs encoding the secreted Wnt inhibitor SFRP2 and the secreted BMP inhibitor Noggin; the sizzled morphant phenotype could be rescued by co-injection of RNAs encoding Noggin and either SFRP2 or Dickkopf (a mechanistically distinct Wnt inhibitor. Together, these observations reveal that sizzled, in addition to its established role in dorsal-ventral patterning, is also part of a dynamic BMP and Wnt signaling network involved in both mesodermal patterning and neural crest induction.
Metapopulation perspective to institutional fit: maintenance of dynamic habitat networks
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Henna Fabritius
2017-06-01
Full Text Available Species living in metapopulations depend on connected habitat networks for their survival. If habitat networks experience fast temporal dynamics, species conservation requires preventing habitat discontinuities that could lead to metapopulation extinctions. However, few institutional solutions exist for the maintenance of spatiotemporally dynamic habitat networks outside of protected areas. To explore this often neglected problem, we studied the institutional fit of false heath fritillary (Melitaea diamina conservation in Finland from the perspective of conservation institutions' ability to manage early successional habitat availability for this endangered species. We identified four institutional arrangements that enable effective conservation management of dynamic habitat networks: (1 acknowledgment of habitat dynamics, (2 monitoring of and responding to changes in the habitat network, (3 management of resources for fluctuating resource needs, and (4 scaling of activities through flexible collaborations. These arrangements provide the institutional flexibility needed for responding to temporal changes in habitat availability.
Synthesis of recurrent neural networks for dynamical system simulation.
Trischler, Adam P; D'Eleuterio, Gabriele M T
2016-08-01
We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time. Copyright © 2016 Elsevier Ltd. All rights reserved.
On investigating social dynamics in tactical opportunistic mobile networks
Gao, Wei; Li, Yong
2014-06-01
The efficiency of military mobile network operations at the tactical edge is challenging due to the practical Disconnected, Intermittent, and Limited (DIL) environments at the tactical edge which make it hard to maintain persistent end-to-end wireless network connectivity. Opportunistic mobile networks are hence devised to depict such tactical networking scenarios. Social relations among warfighters in tactical opportunistic mobile networks are implicitly represented by their opportunistic contacts via short-range radios, but were inappropriately considered as stationary over time by the conventional wisdom. In this paper, we develop analytical models to probabilistically investigate the temporal dynamics of this social relationship, which is critical to efficient mobile communication in the battlespace. We propose to formulate such dynamics by developing various sociological metrics, including centrality and community, with respect to the opportunistic mobile network contexts. These metrics investigate social dynamics based on the experimentally validated skewness of users' transient contact distributions over time.
Complex systems and networks dynamics, controls and applications
Yu, Xinghuo; Chen, Guanrong; Yu, Wenwu
2016-01-01
This elementary book provides some state-of-the-art research results on broad disciplinary sciences on complex networks. It presents an in-depth study with detailed description of dynamics, controls and applications of complex networks. The contents of this book can be summarized as follows. First, the dynamics of complex networks, for example, the cluster dynamic analysis by using kernel spectral methods, community detection algorithms in bipartite networks, epidemiological modeling with demographics and epidemic spreading on multi-layer networks, are studied. Second, the controls of complex networks are investigated including topics like distributed finite-time cooperative control of multi-agent systems by applying homogenous-degree and Lyapunov methods, composite finite-time containment control for disturbed second-order multi-agent systems, fractional-order observer design of multi-agent systems, chaos control and anticontrol of complex systems via Parrondos game and many more. Third, the applications of ...
The dynamic and geometric phase transition in the cellular network of pancreatic islet
Wang, Xujing
2013-03-01
The pancreatic islet is a micro-organ that contains several thousands of endocrine cells, majority of which being the insulin releasing β - cells . - cellsareexcitablecells , andarecoupledtoeachother through gap junctional channels. Here, using percolation theory, we investigate the role of network structure in determining the dynamics of the β-cell network. We show that the β-cell synchronization depends on network connectivity. More specifically, as the site occupancy is reducing, initially the β-cell synchronization is barely affected, until it reaches around a critical value, where the synchronization exhibit a sudden rapid decline, followed by an slow exponential tail. This critical value coincides with the critical site open probability for percolation transition. The dependence over bond strength is similar, exhibiting critical-behavior like dependence around a certain value of bond strength. These results suggest that the β-cell network undergoes a dynamic phase transition when the network is percolated. We further apply the findings to study diabetes. During the development of diabetes, the β - cellnetworkconnectivitydecreases . Siteoccupancyreducesfromthe reducing β-cell mass, and the bond strength is increasingly impaired from β-cell stress and chronic hyperglycemia. We demonstrate that the network dynamics around the percolation transition explain the disease dynamics around onset, including a long time mystery in diabetes, the honeymoon phenomenon.
Understanding migraine using dynamic network biomarkers
Dahlem, M.A.; Kurths, J.; Ferrari, M.D.; Aihara, K.; Scheffer, M.; May, A.
2015-01-01
Background: Mathematical modeling approaches are becoming ever more established in clinical neuroscience. They provide insight that is key to understanding complex interactions of network phenomena, in general, and interactions within the migraine-generator network, in particular. Purpose: In this
Ledley, T. S.; Carley, S.; Niepold, F.; Duggan-Haas, D. A.; Hollweg, K.; McCaffrey, M. S.
2012-12-01
There are a wide range of programs, activities, and projects focused on improving the understanding of climate science by citizens in a multitude of contexts. While most of these are necessarily customized for the particular audiences, communities, or regions they address, they can learn a lot from each other by sharing their experiences, expertise, and materials. The Climate Literacy Network (CLN, http://cleanet.org/cln), established in 2008 to facilitate the implementation of the Climate Literacy Essential Principles of Climate Science, is a diverse group of over 370 stakeholders with a wide range of expertise in, for example, science, policy, media, arts, economics, psychology, education, and social sciences. The CLN meets virtually weekly to share information about ongoing activities and new resources, discuss controversial public issues and ways to address them, get input from this diverse community on directions individual efforts might take, organize climate literacy sessions at professional meetings, provide input on documents relevant to climate literacy, and address common needs of the individual members. The weekly CLN teleconferences are also a venue for presentations from climate change education efforts to extend their reach and potential impact. The teleconferences are supported by an active listserv that is archived on the CLN website along with recordings of past teleconference and the schedule of upcoming teleconferences (http://cleanet.org/clean/community/cln/telecon_schedule.html). In this presentation we will describe the details of these various activities, give examples of how discussions within the CLN has led to funded efforts and expanded partnerships, and identify ways you can participate in and leverage this very active community.
A User Driven Dynamic Circuit Network Implementation
Energy Technology Data Exchange (ETDEWEB)
Guok, Chin; Robertson, David; Chaniotakis, Evangelos; Thompson, Mary; Johnston, William; Tierney, Brian
2008-10-01
The requirements for network predictability are becoming increasingly critical to the DoE science community where resources are widely distributed and collaborations are world-wide. To accommodate these emerging requirements, the Energy Sciences Network has established a Science Data Network to provide user driven guaranteed bandwidth allocations. In this paper we outline the design, implementation, and secure coordinated use of such a network, as well as some lessons learned.
Network rewiring dynamics with convergence towards a star network.
Whigham, P A; Dick, G; Parry, M
2016-10-01
Network rewiring as a method for producing a range of structures was first introduced in 1998 by Watts & Strogatz (Nature393, 440-442. (doi:10.1038/30918)). This approach allowed a transition from regular through small-world to a random network. The subsequent interest in scale-free networks motivated a number of methods for developing rewiring approaches that converged to scale-free networks. This paper presents a rewiring algorithm (RtoS) for undirected, non-degenerate, fixed size networks that transitions from regular, through small-world and scale-free to star-like networks. Applications of the approach to models for the spread of infectious disease and fixation time for a simple genetics model are used to demonstrate the efficacy and application of the approach.
Quantifying complex network evolution using mutual entropy and dynamical clustering
Montealegre, Vladimir
The study of the structure and evolution of complex networks plays an important role for a diversity of areas which include computer science, biology, sociology, pattern recognition and cryptography. Here we use methods based on the principles of classical mechanics and statistical physics to study the topology and dynamics of complex networks. Dynamical clustering is used to detect the main structural blocks of networks (communities) and generalized mutual information of the Renyi type is used to rank the detected communities according to how crucial they are for the network's structure. Two approaches to measure the information content of networks are presented. One approach is based on the elements of the connectivity matrix, and the other on the concept of distance between nodes. These methods are applied for simulated networks of the scale-free, random and mixed types and for real world networks. The concept of triangles distribution as an extension to the degree distribution is also considered.
Design and implementation of dynamic hybrid Honeypot network
Qiao, Peili; Hu, Shan-Shan; Zhai, Ji-Qiang
2013-05-01
The method of constructing a dynamic and self-adaptive virtual network is suggested to puzzle adversaries, delay and divert attacks, exhaust attacker resources and collect attacking information. The concepts of Honeypot and Honeyd, which is the frame of virtual Honeypot are introduced. The techniques of network scanning including active fingerprint recognition are analyzed. Dynamic virtual network system is designed and implemented. A virtual network similar to real network topology is built according to the collected messages from real environments in this system. By doing this, the system can perplex the attackers when Hackers attack and can further analyze and research the attacks. The tests to this system prove that this design can successfully simulate real network environment and can be used in network security analysis.
Dynamics and control of diseases in networks with community structure.
Directory of Open Access Journals (Sweden)
Marcel Salathé
2010-04-01
Full Text Available The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc. depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies.
Shuggi, Isabelle M; Oh, Hyuk; Shewokis, Patricia A; Gentili, Rodolphe J
2017-09-30
The assessment of mental workload can inform attentional resource allocation during task performance that is essential for understanding the underlying principles of human cognitive-motor behavior. While many studies have focused on mental workload in relation to human performance, a modest body of work has examined it in a motor practice/learning context without considering individual variability. Thus, this work aimed to examine mental workload by employing the NASA TLX as well as the changes in motor performance resulting from the practice of a novel reaching task. Two groups of participants practiced a reaching task at a high and low nominal difficulty during which a group-level analysis assessed the mental workload, motor performance and motor improvement dynamics. A secondary cluster analysis was also conducted to identify specific individual patterns of cognitive-motor responses. Overall, both group- and cluster-level analyses revealed that: (i) all participants improved their performance throughout motor practice, and (ii) an increase in mental workload was associated with a reduction of the quality of motor performance along with a slower rate of motor improvement. The results are discussed in the context of the optimal challenge point framework and in particular it is proposed that under the experimental conditions employed here, functional task difficulty: (i) would possibly depend on an individuals' information processing capabilities, and (ii) could be indexed by the level of mental workload which, when excessively heightened can decrease the quality of performance and more generally result in delayed motor improvements. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.
Propagation, cascades, and agreement dynamics in complex communication and social networks
Lu, Qiming
-range communication links are added on top of the random geometric graph, resulting in a "small-world"-like network and yielding a significantly reduced time to reach global agreement. We construct a finite-size scaling analysis for the agreement times in this case. When applying the model of Naming Game on empirical social networks, this stylized agent-based model captures essential features of agreement dynamics in a network of autonomous agents, corresponding to the development of shared classification schemes in a network of artificial agents or opinion spreading and social dynamics in social networks. Our study focuses on the impact that communities in the underlying social graphs have on the outcome of the agreement process. We find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the Naming Game in these networks maintains clusters of coexisting opinions indefinitely. Further, we investigate agent-based network strategies to facilitate convergence to global consensus.
Perception of Communication Network Fraud Dynamics by Network ...
African Journals Online (AJOL)
The massive growth of electronic commerce represent a new set of vulnerabilities aimed at the distortion, disruption, and destruction of the global and national information infrastructures, and are indeed significant threats to the integrity of networked systems. This paper investigates the perception of communication network ...
Dynamics of multi-stage infections on networks
Sherborne, N; Kiss, I Z
2015-01-01
This paper investigates the dynamics of infectious diseases with a non-exponentially distributed infectious period. This is achieved by considering a multi-stage infection model on networks. Using pairwise approximation with a standard closure, a number of important characteristics of disease dynamics are derived analytically, including the final size of an epidemic and a threshold for epidemic outbreaks. Stochastic simulations of dynamics on networks are performed and compared to the results of pairwise models for several realistic examples of infectious diseases to illustrate the role played by the number of stages in the disease dynamics. The agreement between the pairwise and simulation methods is excellent in the cases we consider.
Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks
Onaga, Tomokatsu; Gleeson, James P.; Masuda, Naoki
2017-09-01
Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node's concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations.
An efficient neural network approach to dynamic robot motion planning.
Yang, S X; Meng, M
2000-03-01
In this paper, a biologically inspired neural network approach to real-time collision-free motion planning of mobile robots or robot manipulators in a nonstationary environment is proposed. Each neuron in the topologically organized neural network has only local connections, whose neural dynamics is characterized by a shunting equation. Thus the computational complexity linearly depends on the neural network size. The real-time robot motion is planned through the dynamic activity landscape of the neural network without any prior knowledge of the dynamic environment, without explicitly searching over the free workspace or the collision paths, and without any learning procedures. Therefore it is computationally efficient. The global stability of the neural network is guaranteed by qualitative analysis and the Lyapunov stability theory. The effectiveness and efficiency of the proposed approach are demonstrated through simulation studies.
Waiting time dynamics of priority-queue networks.
Min, Byungjoon; Goh, K-I; Kim, I-M
2009-05-01
We study the dynamics of priority-queue networks, generalizations of the binary interacting priority-queue model introduced by Oliveira and Vazquez [Physica A 388, 187 (2009)]. We found that the original AND-type protocol for interacting tasks is not scalable for the queue networks with loops because the dynamics becomes frozen due to the priority conflicts. We then consider a scalable interaction protocol, an OR-type one, and examine the effects of the network topology and the number of queues on the waiting time distributions of the priority-queue networks, finding that they exhibit power-law tails in all cases considered, yet with model-dependent power-law exponents. We also show that the synchronicity in task executions, giving rise to priority conflicts in the priority-queue networks, is a relevant factor in the queue dynamics that can change the power-law exponent of the waiting time distribution.
Spontaneous formation of dynamical groups in an adaptive networked system
Li, Menghui; Guan, Shuguang; Lai, C.-H.
2010-10-01
In this work, we investigate a model of an adaptive networked dynamical system, where the coupling strengths among phase oscillators coevolve with the phase states. It is shown that in this model the oscillators can spontaneously differentiate into two dynamical groups after a long time evolution. Within each group, the oscillators have similar phases, while oscillators in different groups have approximately opposite phases. The network gradually converts from the initial random structure with a uniform distribution of connection strengths into a modular structure that is characterized by strong intra-connections and weak inter-connections. Furthermore, the connection strengths follow a power-law distribution, which is a natural consequence of the coevolution of the network and the dynamics. Interestingly, it is found that if the inter-connections are weaker than a certain threshold, the two dynamical groups will almost decouple and evolve independently. These results are helpful in further understanding the empirical observations in many social and biological networks.
A compositional model for confluent dynamic data-flow networks
F.S. de Boer (Frank); M.M. Bonsangue (Marcello)
2000-01-01
textabstractWe introduce a state-based language for programming dynamically changing networks which consist of processes that communicate asynchronously. For this language we introduce an operational semantics and a notion of observable which includes both partial
Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism
DEFF Research Database (Denmark)
Buescher, Joerg Martin; Liebermeister, Wolfram; Jules, Matthieu
2012-01-01
Adaptation of cells to environmental changes requires dynamic interactions between metabolic and regulatory networks, but studies typically address only one or a few layers of regulation. For nutritional shifts between two preferred carbon sources of Bacillus subtilis, we combined statistical...
Recovery time after localized perturbations in complex dynamical networks
Mitra, Chiranjit; Kittel, Tim; Choudhary, Anshul; Kurths, Jürgen; Donner, Reik V.
2017-10-01
Maintaining the synchronous motion of dynamical systems interacting on complex networks is often critical to their functionality. However, real-world networked dynamical systems operating synchronously are prone to random perturbations driving the system to arbitrary states within the corresponding basin of attraction, thereby leading to epochs of desynchronized dynamics with a priori unknown durations. Thus, it is highly relevant to have an estimate of the duration of such transient phases before the system returns to synchrony, following a random perturbation to the dynamical state of any particular node of the network. We address this issue here by proposing the framework of single-node recovery time (SNRT) which provides an estimate of the relative time scales underlying the transient dynamics of the nodes of a network during its restoration to synchrony. We utilize this in differentiating the particularly slow nodes of the network from the relatively fast nodes, thus identifying the critical nodes which when perturbed lead to significantly enlarged recovery time of the system before resuming synchronized operation. Further, we reveal explicit relationships between the SNRT values of a network, and its global relaxation time when starting all the nodes from random initial conditions. Earlier work on relaxation time generally focused on investigating its dependence on macroscopic topological properties of the respective network. However, we employ the proposed concept for deducing microscopic relationships between topological features of nodes and their respective SNRT values. The framework of SNRT is further extended to a measure of resilience of the different nodes of a networked dynamical system. We demonstrate the potential of SNRT in networks of Rössler oscillators on paradigmatic topologies and a model of the power grid of the United Kingdom with second-order Kuramoto-type nodal dynamics illustrating the conceivable practical applicability of the proposed
A dynamical approach to identify vertices' centrality in complex networks
Guo, Long; Zhang, Wen-Yao; Luo, Zhong-Jie; Gao, Fu-Juan; Zhang, Yi-Cheng
2017-12-01
In this paper, we proposed a dynamical approach to assess vertices' centrality according to the synchronization process of the Kuramoto model. In our approach, the vertices' dynamical centrality is calculated based on the Difference of vertices' Synchronization Abilities (DSA), which are different from traditional centrality measurements that are related to the topological properties. Through applying our approach to complex networks with a clear community structure, we have calculated all vertices' dynamical centrality and found that vertices at the end of weak links have higher dynamical centrality. Meanwhile, we analyzed the robustness and efficiency of our dynamical approach through testing the probabilities that some known vital vertices were recognized. Finally, we applied our dynamical approach to identify community due to its satisfactory performance in assessing overlapping vertices. Our present work provides a new perspective and tools to understand the crucial role of heterogeneity in revealing the interplay between the dynamics and structure of complex networks.
Dynamic Data-Driven UAV Network for Plume Characterization
2016-05-23
AFRL-AFOSR-VA-TR-2016-0203 Dynamic Data-Driven UAV Network for Plume Characterization Kamran Mohseni UNIVERSITY OF FLORIDA Final Report 05/23/2016...AND SUBTITLE Dynamic Data-Driven UAV Network for Plume Characterization 5a. CONTRACT NUMBER 5b. GRANT NUMBER FA9550-13-1-0090 5c. PROGRAM ELEMENT...toxic plume characterization, Intelligence, Surveillance and Reconnaissance (ISR), environmental monitoring, weather forecasting, and disaster
Reconstruction of a random phase dynamics network from observations
Pikovsky, A.
2018-01-01
We consider networks of coupled phase oscillators of different complexity: Kuramoto-Daido-type networks, generalized Winfree networks, and hypernetworks with triple interactions. For these setups an inverse problem of reconstruction of the network connections and of the coupling function from the observations of the phase dynamics is addressed. We show how a reconstruction based on the minimization of the squared error can be implemented in all these cases. Examples include random networks with full disorder both in the connections and in the coupling functions, as well as networks where the coupling functions are taken from experimental data of electrochemical oscillators. The method can be directly applied to asynchronous dynamics of units, while in the case of synchrony, additional phase resettings are necessary for reconstruction.
Structure-based control of complex networks with nonlinear dynamics
Zañudo, Jorge G T; Albert, Réka
2016-01-01
Given the network of interactions underlying a complex system, what can we learn about controlling such a system solely from its structure? Over a century of research in control theory has given us tools to answer this question, which were widely applied in science and engineering. Yet the current tools do not always consider the inherently nonlinear dynamics of real systems and the naturally occurring system states in their definition of "control", a term whose interpretation varies across disciplines. Here we use a new mathematical framework for structure-based control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This framework provides realizable node overrides that steer a system towards any of its natural long term dynamic behaviors and which are guaranteed to be effective regardless of the dynamic details and parameters of the underlying system. We use this framework on several real networks, compar...
Cell transmission model of dynamic assignment for urban rail transit networks.
Directory of Open Access Journals (Sweden)
Guangming Xu
Full Text Available For urban rail transit network, the space-time flow distribution can play an important role in evaluating and optimizing the space-time resource allocation. For obtaining the space-time flow distribution without the restriction of schedules, a dynamic assignment problem is proposed based on the concept of continuous transmission. To solve the dynamic assignment problem, the cell transmission model is built for urban rail transit networks. The priority principle, queuing process, capacity constraints and congestion effects are considered in the cell transmission mechanism. Then an efficient method is designed to solve the shortest path for an urban rail network, which decreases the computing cost for solving the cell transmission model. The instantaneous dynamic user optimal state can be reached with the method of successive average. Many evaluation indexes of passenger flow can be generated, to provide effective support for the optimization of train schedules and the capacity evaluation for urban rail transit network. Finally, the model and its potential application are demonstrated via two numerical experiments using a small-scale network and the Beijing Metro network.
Hierarchy measurement for modeling network dynamics under directed attacks
Rubinson, M.; Levit-Binnun, N.; Peled, A.; Naim-Feil, J.; Freche, D.; Moses, E.
2017-11-01
A fundamental issue in the dynamics of complex systems is the resilience of the network in response to targeted attacks. This paper explores the local dynamics of the network attack process by investigating the order of removal of the nodes that have maximal degree, and shows that this dynamic network response can be predicted from the graph's initial connectivity. We demonstrate numerically that the maximal degree M(τ) of the network at time step τ decays exponentially with τ via a topology-dependent exponent. Moreover, the order in which sites are removed can be approximated by considering the network's "hierarchy" function h , which measures for each node Vi how many of its initial nearest neighbors have lower degree versus those that have a higher one. Finally, we show that the exponents we identified for the attack dynamics are related to the exponential behavior of spreading activation dynamics. The results suggest that the function h , which has both local and global properties, is a novel nodal measurement for network dynamics and structure.
Tourist activated networks: Implications for dynamic packaging systems in tourism
DEFF Research Database (Denmark)
Zach, Florian; Gretzel, Ulrike; Fesenmaier, Daniel R.
2008-01-01
This paper discusses tourist activated networks as a concept to inform technological applications supporting dynamic bundling and en-route recommendations. Empirical data was collected from travellers who visited a regional destination in the US and then analyzed with respect to its network struc...... marketing....
Popularity and Adolescent Friendship Networks: Selection and Influence Dynamics
Dijkstra, Jan Kornelis; Cillessen, Antonius H. N.; Borch, Casey
2013-01-01
This study examined the dynamics of popularity in adolescent friendship networks across 3 years in middle school. Longitudinal social network modeling was used to identify selection and influence in the similarity of popularity among friends. It was argued that lower status adolescents strive to enhance their status through befriending higher…
Dynamic Relaying in 3GPP LTE-Advanced Networks
DEFF Research Database (Denmark)
Teyeb, Oumer Mohammed; Van Phan, Vinh; Redana, Simone
2009-01-01
Relaying is one of the proposed technologies for LTE-Advanced networks. In order to enable a flexible and reliable relaying support, the currently adopted architectural structure of LTE networks has to be modified. In this paper, we extend the LTE architecture to enable dynamic relaying, while ma...
Popularity and Adolescent Friendship Networks: Selection and Influence Dynamics
Dijkstra, J.K.; Cillessen, A.H.N.; Borch, C.
2013-01-01
This study examined the dynamics of popularity in adolescent friendship networks across 3 years in middle school. Longitudinal social network modeling was used to identify selection and influence in the similarity of popularity among friends. It was argued that lower status adolescents strive to
Popularity and Adolescent Friendship Networks : Selection and Influence Dynamics
Dijkstra, Jan Kornelis; Cillessen, Antonius H. N.; Borch, Casey
This study examined the dynamics of popularity in adolescent friendship networks across 3 years in middle school. Longitudinal social network modeling was used to identify selection and influence in the similarity of popularity among friends. It was argued that lower status adolescents strive to
Energy Technology Data Exchange (ETDEWEB)
Montani, S. [Dipartimento di Informatica, Universita del Piemonte Orientale, Via Bellini 25g, 15100 Alessandria (Italy)], E-mail: stefania@mfn.unipmn.it; Portinale, L. [Dipartimento di Informatica, Universita del Piemonte Orientale, Via Bellini 25g, 15100 Alessandria (Italy)], E-mail: portinal@mfn.unipmn.it; Bobbio, A. [Dipartimento di Informatica, Universita del Piemonte Orientale, Via Bellini 25g, 15100 Alessandria (Italy)], E-mail: bobbio@mfn.unipmn.it; Codetta-Raiteri, D. [Dipartimento di Informatica, Universita del Piemonte Orientale, Via Bellini 25g, 15100 Alessandria (Italy)], E-mail: raiteri@mfn.unipmn.it
2008-07-15
In this paper, we present RADYBAN (Reliability Analysis with DYnamic BAyesian Networks), a software tool which allows to analyze a dynamic fault tree relying on its conversion into a dynamic Bayesian network. The tool implements a modular algorithm for automatically translating a dynamic fault tree into the corresponding dynamic Bayesian network and exploits classical algorithms for the inference on dynamic Bayesian networks, in order to compute reliability measures. After having described the basic features of the tool, we show how it operates on a real world example and we compare the unreliability results it generates with those returned by other methodologies, in order to verify the correctness and the consistency of the results obtained.
DAWN: Dynamic Ad-hoc Wireless Network
2016-06-19
Wireless Network 1 Introduction The network-centric battlefield includes sensors, troop carriers, unmanned air vehicle (UAV), aircraft, smart ...Bellman Control Heritage Award. • Honorary Doctorate at Technical University of Crete. • Best paper award at 2008 IEEE International Conference on Mobile Ad-hoc and Sensor Systems.
Discriminating lysosomal membrane protein types using dynamic neural network.
Tripathi, Vijay; Gupta, Dwijendra Kumar
2014-01-01
This work presents a dynamic artificial neural network methodology, which classifies the proteins into their classes from their sequences alone: the lysosomal membrane protein classes and the various other membranes protein classes. In this paper, neural networks-based lysosomal-associated membrane protein type prediction system is proposed. Different protein sequence representations are fused to extract the features of a protein sequence, which includes seven feature sets; amino acid (AA) composition, sequence length, hydrophobic group, electronic group, sum of hydrophobicity, R-group, and dipeptide composition. To reduce the dimensionality of the large feature vector, we applied the principal component analysis. The probabilistic neural network, generalized regression neural network, and Elman regression neural network (RNN) are used as classifiers and compared with layer recurrent network (LRN), a dynamic network. The dynamic networks have memory, i.e. its output depends not only on the input but the previous outputs also. Thus, the accuracy of LRN classifier among all other artificial neural networks comes out to be the highest. The overall accuracy of jackknife cross-validation is 93.2% for the data-set. These predicted results suggest that the method can be effectively applied to discriminate lysosomal associated membrane proteins from other membrane proteins (Type-I, Outer membrane proteins, GPI-Anchored) and Globular proteins, and it also indicates that the protein sequence representation can better reflect the core feature of membrane proteins than the classical AA composition.
The role of dimensionality in neuronal network dynamics
Ulloa Severino, Francesco Paolo; Ban, Jelena; Song, Qin; Tang, Mingliang; Bianconi, Ginestra; Cheng, Guosheng; Torre, Vincent
2016-01-01
Recent results from network theory show that complexity affects several dynamical properties of networks that favor synchronization. Here we show that synchronization in 2D and 3D neuronal networks is significantly different. Using dissociated hippocampal neurons we compared properties of cultures grown on a flat 2D substrates with those formed on 3D graphene foam scaffolds. Both 2D and 3D cultures had comparable glia to neuron ratio and the percentage of GABAergic inhibitory neurons. 3D cultures because of their dimension have many connections among distant neurons leading to small-world networks and their characteristic dynamics. After one week, calcium imaging revealed moderately synchronous activity in 2D networks, but the degree of synchrony of 3D networks was higher and had two regimes: a highly synchronized (HS) and a moderately synchronized (MS) regime. The HS regime was never observed in 2D networks. During the MS regime, neuronal assemblies in synchrony changed with time as observed in mammalian brains. After two weeks, the degree of synchrony in 3D networks decreased, as observed in vivo. These results show that dimensionality determines properties of neuronal networks and that several features of brain dynamics are a consequence of its 3D topology. PMID:27404281
Data Driven Broiler Weight Forecasting using Dynamic Neural Network Models
DEFF Research Database (Denmark)
Johansen, Simon Vestergaard; Bendtsen, Jan Dimon; Riisgaard-Jensen, Martin
2017-01-01
In this article, the dynamic influence of environmental broiler house conditions and broiler growth is investigated. Dynamic neural network forecasting models have been trained on farm-scale broiler batch production data from 12 batches from the same house. The model forecasts future broiler weight...
Wavelength converter placement in optical networks with dynamic traffic
DEFF Research Database (Denmark)
Buron, Jakob Due; Ruepp, Sarah Renée; Wessing, Henrik
2008-01-01
We evaluate the connection provisioning performance of GMPLS-controlled wavelength routed networks under dynamic traffic load and using three different wavelength converter placement heuristics. Results show that a simple uniform placement heuristic matches the performance of complex heuristics...... under dynamic traffic assumptions....
Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks.
Pu, Yi-Fei; Yi, Zhang; Zhou, Ji-Liu
2017-10-01
This paper mainly discusses a novel conceptual framework: fractional Hopfield neural networks (FHNN). As is commonly known, fractional calculus has been incorporated into artificial neural networks, mainly because of its long-term memory and nonlocality. Some researchers have made interesting attempts at fractional neural networks and gained competitive advantages over integer-order neural networks. Therefore, it is naturally makes one ponder how to generalize the first-order Hopfield neural networks to the fractional-order ones, and how to implement FHNN by means of fractional calculus. We propose to introduce a novel mathematical method: fractional calculus to implement FHNN. First, we implement fractor in the form of an analog circuit. Second, we implement FHNN by utilizing fractor and the fractional steepest descent approach, construct its Lyapunov function, and further analyze its attractors. Third, we perform experiments to analyze the stability and convergence of FHNN, and further discuss its applications to the defense against chip cloning attacks for anticounterfeiting. The main contribution of our work is to propose FHNN in the form of an analog circuit by utilizing a fractor and the fractional steepest descent approach, construct its Lyapunov function, prove its Lyapunov stability, analyze its attractors, and apply FHNN to the defense against chip cloning attacks for anticounterfeiting. A significant advantage of FHNN is that its attractors essentially relate to the neuron's fractional order. FHNN possesses the fractional-order-stability and fractional-order-sensitivity characteristics.
Dynamic baseline detection method for power data network service
Chen, Wei
2017-08-01
This paper proposes a dynamic baseline Traffic detection Method which is based on the historical traffic data for the Power data network. The method uses Cisco's NetFlow acquisition tool to collect the original historical traffic data from network element at fixed intervals. This method uses three dimensions information including the communication port, time, traffic (number of bytes or number of packets) t. By filtering, removing the deviation value, calculating the dynamic baseline value, comparing the actual value with the baseline value, the method can detect whether the current network traffic is abnormal.
Spatial price dynamics: From complex network perspective
Li, Y. L.; Bi, J. T.; Sun, H. J.
2008-10-01
The spatial price problem means that if the supply price plus the transportation cost is less than the demand price, there exists a trade. Thus, after an amount of exchange, the demand price will decrease. This process is continuous until an equilibrium state is obtained. However, how the trade network structure affects this process has received little attention. In this paper, we give a evolving model to describe the levels of spatial price on different complex network structures. The simulation results show that the network with shorter path length is sensitive to the variation of prices.
Actin dynamics and the elasticity of cytoskeletal networks
Directory of Open Access Journals (Sweden)
2009-09-01
Full Text Available The structural integrity of a cell depends on its cytoskeleton, which includes an actin network. This network is transient and depends upon the continual polymerization and depolymerization of actin. The degradation of an actin network, and a corresponding reduction in cell stiffness, can indicate the presence of disease. Numerical simulations will be invaluable for understanding the physics of these systems and the correlation between actin dynamics and elasticity. Here we develop a model that is capable of generating actin network structures. In particular, we develop a model of actin dynamics which considers the polymerization, depolymerization, nucleation, severing, and capping of actin filaments. The structures obtained are then fed directly into a mechanical model. This allows us to qualitatively assess the effects of changing various parameters associated with actin dynamics on the elasticity of the material.
Congested Link Inference Algorithms in Dynamic Routing IP Network
Directory of Open Access Journals (Sweden)
Yu Chen
2017-01-01
Full Text Available The performance descending of current congested link inference algorithms is obviously in dynamic routing IP network, such as the most classical algorithm CLINK. To overcome this problem, based on the assumptions of Markov property and time homogeneity, we build a kind of Variable Structure Discrete Dynamic Bayesian (VSDDB network simplified model of dynamic routing IP network. Under the simplified VSDDB model, based on the Bayesian Maximum A Posteriori (BMAP and Rest Bayesian Network Model (RBNM, we proposed an Improved CLINK (ICLINK algorithm. Considering the concurrent phenomenon of multiple link congestion usually happens, we also proposed algorithm CLILRS (Congested Link Inference algorithm based on Lagrangian Relaxation Subgradient to infer the set of congested links. We validated our results by the experiments of analogy, simulation, and actual Internet.
Agent Based Modeling on Organizational Dynamics of Terrorist Network
Directory of Open Access Journals (Sweden)
Bo Li
2015-01-01
Full Text Available Modeling organizational dynamics of terrorist network is a critical issue in computational analysis of terrorism research. The first step for effective counterterrorism and strategic intervention is to investigate how the terrorists operate with the relational network and what affects the performance. In this paper, we investigate the organizational dynamics by employing a computational experimentation methodology. The hierarchical cellular network model and the organizational dynamics model are developed for modeling the hybrid relational structure and complex operational processes, respectively. To intuitively elucidate this method, the agent based modeling is used to simulate the terrorist network and test the performance in diverse scenarios. Based on the experimental results, we show how the changes of operational environments affect the development of terrorist organization in terms of its recovery and capacity to perform future tasks. The potential strategies are also discussed, which can be used to restrain the activities of terrorists.
Dynamic Virtual LANs for Adaptive Network Security
National Research Council Canada - National Science Library
Merani, Diego; Berni, Alessandro; Leonard, Michel
2004-01-01
The development of Network-Enabled capabilities in support of undersea research requires architectures for the interconnection and data sharing that are flexible, scalable, and built on open standards...
Modelling sequences and temporal networks with dynamic community structures.
Peixoto, Tiago P; Rosvall, Martin
2017-09-19
In evolving complex systems such as air traffic and social organisations, collective effects emerge from their many components' dynamic interactions. While the dynamic interactions can be represented by temporal networks with nodes and links that change over time, they remain highly complex. It is therefore often necessary to use methods that extract the temporal networks' large-scale dynamic community structure. However, such methods are subject to overfitting or suffer from effects of arbitrary, a priori-imposed timescales, which should instead be extracted from data. Here we simultaneously address both problems and develop a principled data-driven method that determines relevant timescales and identifies patterns of dynamics that take place on networks, as well as shape the networks themselves. We base our method on an arbitrary-order Markov chain model with community structure, and develop a nonparametric Bayesian inference framework that identifies the simplest such model that can explain temporal interaction data.The description of temporal networks is usually simplified in terms of their dynamic community structures, whose identification however relies on a priori assumptions. Here the authors present a data-driven method that determines relevant timescales for the dynamics and uses it to identify communities.
Dynamical complexity in the perception-based network formation model
Jo, Hang-Hyun; Moon, Eunyoung
2016-12-01
Many link formation mechanisms for the evolution of social networks have been successful to reproduce various empirical findings in social networks. However, they have largely ignored the fact that individuals make decisions on whether to create links to other individuals based on cost and benefit of linking, and the fact that individuals may use perception of the network in their decision making. In this paper, we study the evolution of social networks in terms of perception-based strategic link formation. Here each individual has her own perception of the actual network, and uses it to decide whether to create a link to another individual. An individual with the least perception accuracy can benefit from updating her perception using that of the most accurate individual via a new link. This benefit is compared to the cost of linking in decision making. Once a new link is created, it affects the accuracies of other individuals' perceptions, leading to a further evolution of the actual network. As for initial actual networks, we consider both homogeneous and heterogeneous cases. The homogeneous initial actual network is modeled by Erdős-Rényi (ER) random networks, while we take a star network for the heterogeneous case. In any cases, individual perceptions of the actual network are modeled by ER random networks with controllable linking probability. Then the stable link density of the actual network is found to show discontinuous transitions or jumps according to the cost of linking. As the number of jumps is the consequence of the dynamical complexity, we discuss the effect of initial conditions on the number of jumps to find that the dynamical complexity strongly depends on how much individuals initially overestimate or underestimate the link density of the actual network. For the heterogeneous case, the role of the highly connected individual as an information spreader is also discussed.
Information Dynamics in Networks: Models and Algorithms
2016-09-13
ICDCS). 29-JUN-15, Columbus, OH, USA. : , . Value-Based Network Externalities and Optimal Auction Design, Conference on Web and Internet Economics...NAME Total Number: NAME Total Number: PERCENT_SUPPORTEDNAME FTE Equivalent: Total Number: ...... ...... Inventions (DD882) Scientific Progress In...Value-based network externalities and optimal auction design. In Web and Internet Economics - 10th International Conference, WINE 2014, Beijing, China, December 14-17, pages 147–160, 2014. 6
Adaptive Influence Maximization in Dynamic Social Networks
Tong, Guangmo; Wu, Weili; Tang, Shaojie; Du, Ding-Zhu
2015-01-01
For the purpose of propagating information and ideas through a social network, a seeding strategy aims to find a small set of seed users that are able to maximize the spread of the influence, which is termed as influence maximization problem. Despite a large number of works have studied this problem, the existing seeding strategies are limited to the static social networks. In fact, due to the high speed data transmission and the large population of participants, the diffusion processes in re...
Task-Based Cohesive Evolution of Dynamic Brain Networks
Davison, Elizabeth
2014-03-01
Applications of graph theory to neuroscience have resulted in significant progress towards a mechanistic understanding of the brain. Functional network representation of the brain has linked efficient network structure to psychometric intelligence and altered configurations with disease. Dynamic graphs provide us with tools to further study integral properties of the brain; specifically, the mathematical convention of hyperedges has allowed us to study the brain's cross-linked structure. Hyperedges capture the changes in network structure by identifying groups of brain regions with correlation patterns that change cohesively through time. We performed a hyperedge analysis on functional MRI data from 86 subjects and explored the cohesive evolution properties of their functional brain networks as they performed a series of tasks. Our results establish the hypergraph as a useful measure in understanding functional brain dynamics over tasks and reveal characteristic differences in the co-evolution structure of task-specific networks.
Analysing Stagecoach Network Problem Using Dynamic ...
African Journals Online (AJOL)
In this paper we present a recursive dynamic programming algorithm for solving the stagecoach problem. The algorithm is computationally more efficient than the first method as it obtains its minimum total cost using the suboptimal policies of the different stages without computing the cost of all the routes. By the dynamic ...
Dynamics of Innovation Networks in the German Automotive Industry
DEFF Research Database (Denmark)
Buchmann, Tobias; Hain, Daniel; Kudic, Muhamed
2017-01-01
Innovation is, above all, a social process depending on mutual interactions of individuals aiming at accessing and exchanging external inputs in order to generate novel good and services. Accordingly, the interest in – and research on – interfirm innovation networks has sharply increased over...... the last decade. The structural dynamics of networks are driven by endogenous and exogenous forces. In this paper, we particularly stress the role of endogenous determinants of network evolution of interfirm networks – a category of often underestimated forces. We employ a longitudinal dataset...
Efficient rewirings for enhancing synchronizability of dynamical networks
Rad, Ali Ajdari; Jalili, Mahdi; Hasler, Martin
2008-09-01
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
Dynamics of regulatory networks in gastrin-treated adenocarcinoma cells.
Directory of Open Access Journals (Sweden)
Naresh Doni Jayavelu
Full Text Available Understanding gene transcription regulatory networks is critical to deciphering the molecular mechanisms of different cellular states. Most studies focus on static transcriptional networks. In the current study, we used the gastrin-regulated system as a model to understand the dynamics of transcriptional networks composed of transcription factors (TFs and target genes (TGs. The hormone gastrin activates and stimulates signaling pathways leading to various cellular states through transcriptional programs. Dysregulation of gastrin can result in cancerous tumors, for example. However, the regulatory networks involving gastrin are highly complex, and the roles of most of the components of these networks are unknown. We used time series microarray data of AR42J adenocarcinoma cells treated with gastrin combined with static TF-TG relationships integrated from different sources, and we reconstructed the dynamic activities of TFs using network component analysis (NCA. Based on the peak expression of TGs and activity of TFs, we created active sub-networks at four time ranges after gastrin treatment, namely immediate-early (IE, mid-early (ME, mid-late (ML and very late (VL. Network analysis revealed that the active sub-networks were topologically different at the early and late time ranges. Gene ontology analysis unveiled that each active sub-network was highly enriched in a particular biological process. Interestingly, network motif patterns were also distinct between the sub-networks. This analysis can be applied to other time series microarray datasets, focusing on smaller sub-networks that are activated in a cascade, allowing better overview of the mechanisms involved at each time range.
Dynamics of regulatory networks in gastrin-treated adenocarcinoma cells.
Doni Jayavelu, Naresh; Bar, Nadav
2014-01-01
Understanding gene transcription regulatory networks is critical to deciphering the molecular mechanisms of different cellular states. Most studies focus on static transcriptional networks. In the current study, we used the gastrin-regulated system as a model to understand the dynamics of transcriptional networks composed of transcription factors (TFs) and target genes (TGs). The hormone gastrin activates and stimulates signaling pathways leading to various cellular states through transcriptional programs. Dysregulation of gastrin can result in cancerous tumors, for example. However, the regulatory networks involving gastrin are highly complex, and the roles of most of the components of these networks are unknown. We used time series microarray data of AR42J adenocarcinoma cells treated with gastrin combined with static TF-TG relationships integrated from different sources, and we reconstructed the dynamic activities of TFs using network component analysis (NCA). Based on the peak expression of TGs and activity of TFs, we created active sub-networks at four time ranges after gastrin treatment, namely immediate-early (IE), mid-early (ME), mid-late (ML) and very late (VL). Network analysis revealed that the active sub-networks were topologically different at the early and late time ranges. Gene ontology analysis unveiled that each active sub-network was highly enriched in a particular biological process. Interestingly, network motif patterns were also distinct between the sub-networks. This analysis can be applied to other time series microarray datasets, focusing on smaller sub-networks that are activated in a cascade, allowing better overview of the mechanisms involved at each time range.
Social Insects: A Model System for Network Dynamics
Charbonneau, Daniel; Blonder, Benjamin; Dornhaus, Anna
Social insect colonies (ants, bees, wasps, and termites) show sophisticated collective problem-solving in the face of variable constraints. Individuals exchange information and materials such as food. The resulting network structure and dynamics can inform us about the mechanisms by which the insects achieve particular collective behaviors and these can be transposed to man-made and social networks. We discuss how network analysis can answer important questions about social insects, such as how effective task allocation or information flow is realized. We put forward the idea that network analysis methods are under-utilized in social insect research, and that they can provide novel ways to view the complexity of collective behavior, particularly if network dynamics are taken into account. To illustrate this, we present an example of network tasks performed by ant workers, linked by instances of workers switching from one task to another. We show how temporal network analysis can propose and test new hypotheses on mechanisms of task allocation, and how adding temporal elements to static networks can drastically change results. We discuss the benefits of using social insects as models for complex systems in general. There are multiple opportunities emergent technologies and analysis methods in facilitating research on social insect network. The potential for interdisciplinary work could significantly advance diverse fields such as behavioral ecology, computer sciences, and engineering.
Rumor Diffusion in an Interests-Based Dynamic Social Network
Directory of Open Access Journals (Sweden)
Mingsheng Tang
2013-01-01
Full Text Available To research rumor diffusion in social friend network, based on interests, a dynamic friend network is proposed, which has the characteristics of clustering and community, and a diffusion model is also proposed. With this friend network and rumor diffusion model, based on the zombie-city model, some simulation experiments to analyze the characteristics of rumor diffusion in social friend networks have been conducted. The results show some interesting observations: (1 positive information may evolve to become a rumor through the diffusion process that people may modify the information by word of mouth; (2 with the same average degree, a random social network has a smaller clustering coefficient and is more beneficial for rumor diffusion than the dynamic friend network; (3 a rumor is spread more widely in a social network with a smaller global clustering coefficient than in a social network with a larger global clustering coefficient; and (4 a network with a smaller clustering coefficient has a larger efficiency.
Irrelevant stimulus processing in ADHD: catecholamine dynamics and attentional networks
Directory of Open Access Journals (Sweden)
Francisco eAboitiz
2014-03-01
Full Text Available A cardinal symptom of Attenion Deficit and Hyperactivity Disorder (ADHD is a general distractibility where children and adults shift their attentional focus to stimuli that are irrelevant to the ongoing behavior. This has been attributed to a deficit in dopaminergic signaling in cortico-striatal networks that regulate goal-directed behavior. Furthermore, recent imaging evidence points to an impairment of large scale, antagonistic brain networks that normally contribute to attentional engagement and disengagement, such as the task-positive networks and the Default Mode Network (DMN. Related networks are the ventral attentional network (VAN involved in attentional shifting, and the salience network (SN related to task expectancy. Here we discuss the tonic-phasic dynamics of catecholaminergic signaling in the brain, and attempt to provide a link between this and the activities of the large-scale cortical networks that regulate behavior. More specifically, we propose that a disbalance of tonic catecholamine levels during task performance produce an emphasis of phasic signaling and increased excitability of the VAN, yielding distractibility symptoms. Likewise, immaturity of the SN may relate to abnormal tonic signaling and an incapacity to build up a proper executive system during task performance. We discuss different lines of evidence including pharmacology, brain imaging and electrophysiology, that are consistent with our proposal. Finally, restoring the pharmacodynamics of catecholaminergic signaling seems crucial to alleviate ADHD symptoms; however, the possibility is open to explore cognitive rehabilitation strategies to top-down modulate network dynamics compensating the pharmacological deficits.
Recruitment dynamics in adaptive social networks
Shkarayev, Maxim S.; Schwartz, Ira B.; Shaw, Leah B.
2013-06-01
We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean-field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime).
Recurrence networks to study dynamical transitions in a turbulent combustor
Godavarthi, V.; Unni, V. R.; Gopalakrishnan, E. A.; Sujith, R. I.
2017-06-01
Thermoacoustic instability and lean blowout are the major challenges faced when a gas turbine combustor is operated under fuel lean conditions. The dynamics of thermoacoustic system is the result of complex nonlinear interactions between the subsystems—turbulent reactive flow and the acoustic field of the combustor. In order to study the transitions between the dynamical regimes in such a complex system, the time series corresponding to one of the dynamic variables is transformed to an ɛ-recurrence network. The topology of the recurrence network resembles the structure of the attractor representing the dynamics of the system. The transitions in the thermoacoustic system are then captured as the variation in the topological characteristics of the network. We show the presence of power law degree distribution in the recurrence networks constructed from time series acquired during the occurrence of combustion noise and during the low amplitude aperiodic oscillations prior to lean blowout. We also show the absence of power law degree distribution in the recurrence networks constructed from time series acquired during the occurrence of thermoacoustic instability and during the occurrence of intermittency. We demonstrate that the measures derived from recurrence network can be used as tools to capture the transitions in the turbulent combustor and also as early warning measures for predicting impending thermoacoustic instability and blowout.
Slow diffusive dynamics in a chaotic balanced neural network.
Shaham, Nimrod; Burak, Yoram
2017-05-01
It has been proposed that neural noise in the cortex arises from chaotic dynamics in the balanced state: in this model of cortical dynamics, the excitatory and inhibitory inputs to each neuron approximately cancel, and activity is driven by fluctuations of the synaptic inputs around their mean. It remains unclear whether neural networks in the balanced state can perform tasks that are highly sensitive to noise, such as storage of continuous parameters in working memory, while also accounting for the irregular behavior of single neurons. Here we show that continuous parameter working memory can be maintained in the balanced state, in a neural circuit with a simple network architecture. We show analytically that in the limit of an infinite network, the dynamics generated by this architecture are characterized by a continuous set of steady balanced states, allowing for the indefinite storage of a continuous parameter. In finite networks, we show that the chaotic noise drives diffusive motion along the approximate attractor, which gradually degrades the stored memory. We analyze the dynamics and show that the slow diffusive motion induces slowly decaying temporal cross correlations in the activity, which differ substantially from those previously described in the balanced state. We calculate the diffusivity, and show that it is inversely proportional to the system size. For large enough (but realistic) neural population sizes, and with suitable tuning of the network connections, the proposed balanced network can sustain continuous parameter values in memory over time scales larger by several orders of magnitude than the single neuron time scale.
Reliability-based Dynamic Network Design with Stochastic Networks
Li, H.
2009-01-01
Transportation systems are stochastic and dynamic systems. The road capacities and the travel demand are fluctuating from time to time within a day and at the same time from day to day. For road users, the travel time and travel costs experienced over time and space are stochastic, thus desire
Flexible Interconnection Network for Dynamically and Partially Reconfigurable Architectures
Directory of Open Access Journals (Sweden)
Ludovic Devaux
2010-01-01
Full Text Available The dynamic and partial reconfiguration of FPGAs enables the dynamic placement in reconfigurable zones of the tasks that describe an application. However, the dynamic management of the tasks impacts the communications since tasks are not present in the FPGA during all computation time. So, the task manager should ensure the allocation of each new task and their interconnection which is performed by a flexible interconnection network. In this article, various communication architectures, in particular interconnection networks, are studied. Each architecture is evaluated with respect to its suitability for the paradigm of the dynamic and partial reconfiguration in FPGA implementations. This study leads us to propose the DRAFT network that supports the communication constraints into the context of dynamic reconfiguration. We also present DRAGOON, the automatic generator of networks, which allows to implement and to simulate the DRAFT topology. Finally, DRAFT and the two most popular Networks-on-Chip are implemented in several configurations using DRAGOON, and compared considering real implementation results.
The relevance of network micro-structure for neural dynamics
Directory of Open Access Journals (Sweden)
Volker ePernice
2013-06-01
Full Text Available The activity of cortical neurons is determined by the input they receive from presynaptic neurons. Many previousstudies have investigated how specific aspects of the statistics of the input affect the spike trains of single neurons and neuronsin recurrent networks. However, typically very simple random network models are considered in such studies. Here weuse a recently developed algorithm to construct networks based on a quasi-fractal probability measure which are much morevariable than commonly used network models, and which therefore promise to sample the space of recurrent networks ina more exhaustive fashion than previously possible. We use the generated graphs as the underlying network topology insimulations of networks of integrate-and-fire neurons in an asynchronous and irregular state. Based on an extensive datasetof networks and neuronal simulations we assess statistical relations between features of the network structure and the spikingactivity. Our results highlight the strong influence that some details of the network structure have on the activity dynamics ofboth single neurons and populations, even if some global network parameters are kept fixed. We observe specific and consistentrelations between activity characteristics like spike-train irregularity or correlations and network properties, for example thedistributions of the numbers of in- and outgoing connections or clustering. Exploiting these relations, we demonstrate that itis possible to estimate structural characteristics of the network from activity data. We also assess higher order correlationsof spiking activity in the various networks considered here, and find that their occurrence strongly depends on the networkstructure. These results provide directions for further theoretical studies on recurrent networks, as well as new ways to interpretspike train recordings from neural circuits.
Exploring dynamic mechanisms of learning networks for resource conservation
Directory of Open Access Journals (Sweden)
Petr Matous
2015-06-01
Full Text Available The importance of networks for social-ecological processes has been recognized in the literature; however, existing studies have not sufficiently addressed the dynamic nature of networks. Using data on the social learning networks of 265 farmers in Ethiopia for 2011 and 2012 and stochastic actor-oriented modeling, we explain the mechanisms of network evolution and soil conservation. The farmers' preferences for information exchange within the same social groups support the creation of interactive, clustered, nonhierarchical structures within the evolving learning networks, which contributed to the diffusion of the practice of composting. The introduced methods can be applied to determine whether and how social networks can be used to facilitate environmental interventions in various contexts.
Functional roles of slow enzyme conformational changes in network dynamics.
Wu, Zhanghan; Xing, Jianhua
2012-09-05
Extensive studies from different fields reveal that many macromolecules, especially enzymes, show slow transitions among different conformations. This phenomenon is named such things as dynamic disorder, heterogeneity, hysteretic or mnemonic enzymes across these different fields, and has been directly demonstrated by single molecule enzymology and NMR studies recently. We analyzed enzyme slow conformational changes in the context of regulatory networks. A single enzymatic reaction with slow conformational changes can filter upstream network noises, and can either resonantly respond to the system stimulus at certain frequencies or respond adaptively for sustained input signals of the network fluctuations. It thus can serve as a basic functional motif with properties that are normally for larger intermolecular networks in the field of systems biology. We further analyzed examples including enzymes functioning against pH fluctuations, metabolic state change of Artemia embryos, and kinetic insulation of fluctuations in metabolic networks. The study also suggests that hysteretic enzymes may be building blocks of synthetic networks with various properties such as narrow-banded filtering. The work fills the missing gap between studies on enzyme biophysics and network level dynamics, and reveals that the coupling between the two is functionally important; it also suggests that the conformational dynamics of some enzymes may be evolutionally selected. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.
Impact of constrained rewiring on network structure and node dynamics.
Rattana, P; Berthouze, L; Kiss, I Z
2014-11-01
In this paper, we study an adaptive spatial network. We consider a susceptible-infected-susceptible (SIS) epidemic on the network, with a link or contact rewiring process constrained by spatial proximity. In particular, we assume that susceptible nodes break links with infected nodes independently of distance and reconnect at random to susceptible nodes available within a given radius. By systematically manipulating this radius we investigate the impact of rewiring on the structure of the network and characteristics of the epidemic. We adopt a step-by-step approach whereby we first study the impact of rewiring on the network structure in the absence of an epidemic, then with nodes assigned a disease status but without disease dynamics, and finally running network and epidemic dynamics simultaneously. In the case of no labeling and no epidemic dynamics, we provide both analytic and semianalytic formulas for the value of clustering achieved in the network. Our results also show that the rewiring radius and the network's initial structure have a pronounced effect on the endemic equilibrium, with increasingly large rewiring radiuses yielding smaller disease prevalence.
DEFF Research Database (Denmark)
Hundebøll, Martin; Pedersen, Morten Videbæk; Roetter, Daniel Enrique Lucani
2014-01-01
This work studies the potential and impact of the FRANC network coding protocol for delivering high quality Dynamic Adaptive Streaming over HTTP (DASH) in wireless networks. Although DASH aims to tailor the video quality rate based on the available throughput to the destination, it relies...
Tahmassebi, Amirhessam; Pinker-Domenig, Katja; Wengert, Georg; Lobbes, Marc; Stadlbauer, Andreas; Romero, Francisco J.; Morales, Diego P.; Castillo, Encarnacion; Garcia, Antonio; Botella, Guillermo; Meyer-Bäse, Anke
2017-05-01
Graph network models in dementia have become an important computational technique in neuroscience to study fundamental organizational principles of brain structure and function of neurodegenerative diseases such as dementia. The graph connectivity is reflected in the connectome, the complete set of structural and functional connections of the graph network, which is mostly based on simple Pearson correlation links. In contrast to simple Pearson correlation networks, the partial correlations (PC) only identify direct correlations while indirect associations are eliminated. In addition to this, the state-of-the-art techniques in brain research are based on static graph theory, which is unable to capture the dynamic behavior of the brain connectivity, as it alters with disease evolution. We propose a new research avenue in neuroimaging connectomics based on combining dynamic graph network theory and modeling strategies at different time scales. We present the theoretical framework for area aggregation and time-scale modeling in brain networks as they pertain to disease evolution in dementia. This novel paradigm is extremely powerful, since we can derive both static parameters pertaining to node and area parameters, as well as dynamic parameters, such as system's eigenvalues. By implementing and analyzing dynamically both disease driven PC-networks and regular concentration networks, we reveal differences in the structure of these network that play an important role in the temporal evolution of this disease. The described research is key to advance biomedical research on novel disease prediction trajectories and dementia therapies.
Network Dynamics: Modeling And Generation Of Very Large Heterogeneous Social Networks
2015-11-23
OF RESPONSIBLE PERSON Sidney Redner 19b. TELEPHONE NUMBER (Include area code) 617-353-2618 Standard Form 298 (Rev. 8/98) Prescribed by ANSI Std...2 We generated heterogeneous social networks from homogeneous interaction rules. Het- erogeneity emerges dynamically as the network grows, rather than
Robust adaptive synchronization of general dynamical networks ...
Indian Academy of Sciences (India)
1School of Information Science & Engineering, Northeastern University, Shenyang,. Liaoning, 110819, People's ... Introduction. Complex networks exist extensively in ecosystems, power grids, food webs and in many other spheres in our daily lives. Over the course of the past 30 years, technological revolu- tions of complex ...
Modular networks with hierarchical organization: The dynamical ...
Indian Academy of Sciences (India)
another example, this time a hierarchical structure, viz., the Cayley tree with b branches at each vertex. Again, it is easy to see that the clustering vs. degree curve will not show the characteristic scaling seen for the RB model. In fact, in the next section, we show that even for networks where both hierarchy and modularity are.
Dynamics of hate based Internet user networks
Sobkowicz, P.; Sobkowicz, A.
2010-02-01
We present a study of the properties of network of political discussions on one of the most popular Polish Internet forums. This provides the opportunity to study the computer mediated human interactions in strongly bipolar environment. The comments of the participants are found to be mostly disagreements, with strong percentage of invective and provocative ones. Binary exchanges (quarrels) play significant role in the network growth and topology. Statistical analysis shows that the growth of the discussions depends on the degree of controversy of the subject and the intensity of personal conflict between the participants. This is in contrast to most previously studied social networks, for example networks of scientific citations, where the nature of the links is much more positive and based on similarity and collaboration rather than opposition and abuse. The work discusses also the implications of the findings for more general studies of consensus formation, where our observations of increased conflict contradict the usual assumptions that interactions between people lead to averaging of opinions and agreement.
Dynamic Optical Networks for Future Internet Environments
Matera, Francesco
2014-05-01
This article reports an overview on the evolution of the optical network scenario taking into account the exponential growth of connected devices, big data, and cloud computing that is driving a concrete transformation impacting the information and communication technology world. This hyper-connected scenario is deeply affecting relationships between individuals, enterprises, citizens, and public administrations, fostering innovative use cases in practically any environment and market, and introducing new opportunities and new challenges. The successful realization of this hyper-connected scenario depends on different elements of the ecosystem. In particular, it builds on connectivity and functionalities allowed by converged next-generation networks and their capacity to support and integrate with the Internet of Things, machine-to-machine, and cloud computing. This article aims at providing some hints of this scenario to contribute to analyze impacts on optical system and network issues and requirements. In particular, the role of the software-defined network is investigated by taking into account all scenarios regarding data centers, cloud computing, and machine-to-machine and trying to illustrate all the advantages that could be introduced by advanced optical communications.
Statistical dynamics of early river networks
Wang, Xu-Ming; Wang, Peng; Zhang, Ping; Hao, Rui; Huo, Jie
2012-10-01
Based on local erosion rule and fluctuations in rainfall, geology and parameters of a river channel, a generalized Langevin equation is proposed to describe the random prolongation of a river channel. This equation is transformed into the Fokker-Plank equation to follow the early evolution of a river network and the variation of probability distribution of channel lengths. The general solution of the equation is in the product form of two terms. One term is in power form and the other is in exponent form. This distribution shows a complete history of a river network evolving from its infancy to “adulthood”). The infancy is characterized by the Gaussian distribution of the channel lengths, while the adulthood is marked by a power law distribution of the channel lengths. The variation of the distribution from the Gaussian to the power law displays a gradual developing progress of the river network. The distribution of basin areas is obtained by means of Hack's law. These provide us with new understandings towards river networks.
Li, Jie; Li, Qiyue; Qu, Yugui; Zhao, Baohua
2011-01-01
Conventional MAC protocols for wireless sensor network perform poorly when faced with a delay-tolerant mobile network environment. Characterized by a highly dynamic and sparse topology, poor network connectivity as well as data delay-tolerance, delay-tolerant mobile sensor networks exacerbate the severe power constraints and memory limitations of nodes. This paper proposes an energy-efficient MAC protocol using dynamic queue management (EQ-MAC) for power saving and data queue management. Via data transfers initiated by the target sink and the use of a dynamic queue management strategy based on priority, EQ-MAC effectively avoids untargeted transfers, increases the chance of successful data transmission, and makes useful data reach the target terminal in a timely manner. Experimental results show that EQ-MAC has high energy efficiency in comparison with a conventional MAC protocol. It also achieves a 46% decrease in packet drop probability, 79% increase in system throughput, and 25% decrease in mean packet delay.
Employing Deceptive Dynamic Network Topology Through Software-Defined Networking
2014-03-01
onePK Beacon , Floodlight , IRIS, Jaxon, Maestro, OpenDaylight (Java) HP VAN SDN NodeFlow (JavaScript) Table 3.1: Some common vendor and opensource SDN...supports, which we will further discuss in §3.4. As well, some open-source controller projects are sponsored by major switch vendors, (e.g., Floodlight ) [63...2008, pp. 105–110. [63] Project floodlight . [Online]. Available: http://www.projectfloodlight.org/ [64] OpenFlow Switch Specification, Open Networking
Modeling and interpreting mesoscale network dynamics.
Khambhati, Ankit N; Sizemore, Ann E; Betzel, Richard F; Bassett, Danielle S
2017-06-20
Recent advances in brain imaging techniques, measurement approaches, and storage capacities have provided an unprecedented supply of high temporal resolution neural data. These data present a remarkable opportunity to gain a mechanistic understanding not just of circuit structure, but also of circuit dynamics, and its role in cognition and disease. Such understanding necessitates a description of the raw observations, and a delineation of computational models and mathematical theories that accurately capture fundamental principles behind the observations. Here we review recent advances in a range of modeling approaches that embrace the temporally-evolving interconnected structure of the brain and summarize that structure in a dynamic graph. We describe recent efforts to model dynamic patterns of connectivity, dynamic patterns of activity, and patterns of activity atop connectivity. In the context of these models, we review important considerations in statistical testing, including parametric and non-parametric approaches. Finally, we offer thoughts on careful and accurate interpretation of dynamic graph architecture, and outline important future directions for method development. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
Brain connectivity dynamics during social interaction reflect social network structure.
Schmälzle, Ralf; Brook O'Donnell, Matthew; Garcia, Javier O; Cascio, Christopher N; Bayer, Joseph; Bassett, Danielle S; Vettel, Jean M; Falk, Emily B
2017-05-16
Social ties are crucial for humans. Disruption of ties through social exclusion has a marked effect on our thoughts and feelings; however, such effects can be tempered by broader social network resources. Here, we use fMRI data acquired from 80 male adolescents to investigate how social exclusion modulates functional connectivity within and across brain networks involved in social pain and understanding the mental states of others (i.e., mentalizing). Furthermore, using objectively logged friendship network data, we examine how individual variability in brain reactivity to social exclusion relates to the density of participants' friendship networks, an important aspect of social network structure. We find increased connectivity within a set of regions previously identified as a mentalizing system during exclusion relative to inclusion. These results are consistent across the regions of interest as well as a whole-brain analysis. Next, examining how social network characteristics are associated with task-based connectivity dynamics, we find that participants who showed greater changes in connectivity within the mentalizing system when socially excluded by peers had less dense friendship networks. This work provides insight to understand how distributed brain systems respond to social and emotional challenges and how such brain dynamics might vary based on broader social network characteristics.
Exploring the evolution of node neighborhoods in Dynamic Networks
Orman, Günce Keziban; Labatut, Vincent; Naskali, Ahmet Teoman
2017-09-01
Dynamic Networks are a popular way of modeling and studying the behavior of evolving systems. However, their analysis constitutes a relatively recent subfield of Network Science, and the number of available tools is consequently much smaller than for static networks. In this work, we propose a method specifically designed to take advantage of the longitudinal nature of dynamic networks. It characterizes each individual node by studying the evolution of its direct neighborhood, based on the assumption that the way this neighborhood changes reflects the role and position of the node in the whole network. For this purpose, we define the concept of neighborhood event, which corresponds to the various transformations such groups of nodes can undergo, and describe an algorithm for detecting such events. We demonstrate the interest of our method on three real-world networks: DBLP, LastFM and Enron. We apply frequent pattern mining to extract meaningful information from temporal sequences of neighborhood events. This results in the identification of behavioral trends emerging in the whole network, as well as the individual characterization of specific nodes. We also perform a cluster analysis, which reveals that, in all three networks, one can distinguish two types of nodes exhibiting different behaviors: a very small group of active nodes, whose neighborhood undergo diverse and frequent events, and a very large group of stable nodes.
Dual adaptive dynamic control of mobile robots using neural networks.
Bugeja, Marvin K; Fabri, Simon G; Camilleri, Liberato
2009-02-01
This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.
Modeling human dynamics of face-to-face interaction networks
Starnini, Michele; Pastor-Satorras, Romualdo
2013-01-01
Face-to-face interaction networks describe social interactions in human gatherings, and are the substrate for processes such as epidemic spreading and gossip propagation. The bursty nature of human behavior characterizes many aspects of empirical data, such as the distribution of conversation lengths, of conversations per person, or of inter-conversation times. Despite several recent attempts, a general theoretical understanding of the global picture emerging from data is still lacking. Here we present a simple model that reproduces quantitatively most of the relevant features of empirical face-to-face interaction networks. The model describes agents which perform a random walk in a two dimensional space and are characterized by an attractiveness whose effect is to slow down the motion of people around them. The proposed framework sheds light on the dynamics of human interactions and can improve the modeling of dynamical processes taking place on the ensuing dynamical social networks.
The Dynamics of network and dyad level supply management
DEFF Research Database (Denmark)
Ellegaard, Chris
Various academic disciplines have treated the task of managing supply in the industrial company. These disciplines have focussed on various (inter-)organisational levels, e.g. the dyadic relation, the supply chain or the network. The present article argues that supply managers must manage both...... the dyadic supplier relations and the overall supply network of the company. By applying data from a longitudinal case study, this two-sided management task is investigated from a dynamic perspective. Three episodes from the case study, which describes the 14-year development of an industrial buyer......-supplier relation and its immediate network context, are presented. In analysing the data, the dynamic interdependency between management of one level and management of the other, will be demonstrated. The analysis reveals a need for an alternating approach to supply management, which takes the dynamic complexity...
Node-Dependence-Based Dynamic Incentive Algorithm in Opportunistic Networks
Directory of Open Access Journals (Sweden)
Ruiyun Yu
2014-01-01
Full Text Available Opportunistic networks lack end-to-end paths between source nodes and destination nodes, so the communications are mainly carried out by the “store-carry-forward” strategy. Selfish behaviors of rejecting packet relay requests will severely worsen the network performance. Incentive is an efficient way to reduce selfish behaviors and hence improves the reliability and robustness of the networks. In this paper, we propose the node-dependence-based dynamic gaming incentive (NDI algorithm, which exploits the dynamic repeated gaming to motivate nodes relaying packets for other nodes. The NDI algorithm presents a mechanism of tolerating selfish behaviors of nodes. Reward and punishment methods are also designed based on the node dependence degree. Simulation results show that the NDI algorithm is effective in increasing the delivery ratio and decreasing average latency when there are a lot of selfish nodes in the opportunistic networks.
Dynamic Relaying in 3GPP LTE-Advanced Networks
Directory of Open Access Journals (Sweden)
Van Phan Vinh
2009-01-01
Full Text Available Relaying is one of the proposed technologies for LTE-Advanced networks. In order to enable a flexible and reliable relaying support, the currently adopted architectural structure of LTE networks has to be modified. In this paper, we extend the LTE architecture to enable dynamic relaying, while maintaining backward compatibility with LTE Release 8 user equipments, and without limiting the flexibility and reliability expected from relaying. With dynamic relaying, relays can be associated with base stations on a need basis rather than in a fixed manner which is based only on initial radio planning. Proposals are also given on how to further improve a relay enhanced LTE network by enabling multiple interfaces between the relay nodes and their controlling base stations, which can possibly be based on technologies different from LTE, so that load balancing can be realized. This load balancing can be either between different base stations or even between different networks.
Triadic closure dynamics drives scaling laws in social multiplex networks
Klimek, Peter; Thurner, Stefan
2013-06-01
Social networks exhibit scaling laws for several structural characteristics, such as degree distribution, scaling of the attachment kernel and clustering coefficients as a function of node degree. A detailed understanding if and how these scaling laws are inter-related is missing so far, let alone whether they can be understood through a common, dynamical principle. We propose a simple model for stationary network formation and show that the three mentioned scaling relations follow as natural consequences of triadic closure. The validity of the model is tested on multiplex data from a well-studied massive multiplayer online game. We find that the three scaling exponents observed in the multiplex data for the friendship, communication and trading networks can simultaneously be explained by the model. These results suggest that triadic closure could be identified as one of the fundamental dynamical principles in social multiplex network formation.
An Efficient Dynamic Trust Evaluation Model for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Zhengwang Ye
2017-01-01
Full Text Available Trust evaluation is an effective method to detect malicious nodes and ensure security in wireless sensor networks (WSNs. In this paper, an efficient dynamic trust evaluation model (DTEM for WSNs is proposed, which implements accurate, efficient, and dynamic trust evaluation by dynamically adjusting the weights of direct trust and indirect trust and the parameters of the update mechanism. To achieve accurate trust evaluation, the direct trust is calculated considering multitrust including communication trust, data trust, and energy trust with the punishment factor and regulating function. The indirect trust is evaluated conditionally by the trusted recommendations from a third party. Moreover, the integrated trust is measured by assigning dynamic weights for direct trust and indirect trust and combining them. Finally, we propose an update mechanism by a sliding window based on induced ordered weighted averaging operator to enhance flexibility. We can dynamically adapt the parameters and the interactive history windows number according to the actual needs of the network to realize dynamic update of direct trust value. Simulation results indicate that the proposed dynamic trust model is an efficient dynamic and attack-resistant trust evaluation model. Compared with existing approaches, the proposed dynamic trust model performs better in defending multiple malicious attacks.
2003-12-01
combat reach while efficiently managing his/her resources. As an analogy, consider the human body and its immune system that uses antigens to...of the PF decomposition process and the recomposition of “packs” necessary to support the Strike and TAMD MCPs. • Generated system inter...GEMINII supported SSG XXII’s first order assessment of the PF decomposition process and the recomposition of “packs” based on the five CRCs. As was
DEFF Research Database (Denmark)
Osadchiy, Alexey Vladimirovich; Prince, Kamau; Tafur Monroy, Idelfonso
2010-01-01
on a 2.4-GHz RF carrier over an 80-km long-reach access link at 100 Mb/s (down) and 64 Mb/s (up). Transmission of 64-QAM and 256-QAM-modulated signals on a 5.8-GHz RF carrier over a 118.8-km access link converged with four baseband differential quadrature phase shift keying (DQPSK) modulated wireline...
Dynamic Network Centrality Summarizes Learning in the Human Brain
Mantzaris, Alexander V.; Bassett, Danielle S.; Wymbs, Nicholas F.; Estrada, Ernesto; Porter, Mason A.; Mucha, Peter J; Grafton, Scott T.; Higham, Desmond J.
2012-01-01
We study functional activity in the human brain using functional Magnetic Resonance Imaging and recently developed tools from network science. The data arise from the performance of a simple behavioural motor learning task. Unsupervised clustering of subjects with respect to similarity of network activity measured over three days of practice produces significant evidence of `learning', in the sense that subjects typically move between clusters (of subjects whose dynamics are similar) as time ...
SEWER NETWORK DISCHARGE OPTIMIZATION USING THE DYNAMIC PROGRAMMING
Directory of Open Access Journals (Sweden)
Viorel MINZU
2015-12-01
Full Text Available It is necessary to adopt an optimal control that allows an efficient usage of the existing sewer networks, in order to avoid the building of new retention facilities. The main objective of the control action is to minimize the overflow volume of a sewer network. This paper proposes a method to apply a solution obtained by discrete dynamic programming through a realistic closed loop system.
Measuring the regulation of keratin filament network dynamics
Moch, Marcin; Herberich, Gerlind; Aach, Til; Leube, Rudolf E.; Windoffer, Reinhard
2013-01-01
The organization of the keratin intermediate filament cytoskeleton is closely linked to epithelial function. To study keratin network plasticity and its regulation at different levels, tools are needed to localize and measure local network dynamics. In this paper, we present image analysis methods designed to determine the speed and direction of keratin filament motion and to identify locations of keratin filament polymerization and depolymerization at subcellular resolution. Using these meth...
Dynamics of macro- and microscopic neural networks
DEFF Research Database (Denmark)
Mikkelsen, Kaare
2014-01-01
GN), which is a class of signals with a non-trivial low-frequency component. It is assumed that certain characteristica about the low-frequency component can yield information about the neural processes behind the signal. The method has been used in a range of different studies over the course of the past 10...... that the method continues to find use, of which examples are presented. In the second part of the thesis, numerical simulations of networks of neurons are described. To simplify the analysis, a relatively simpled neuron model - Leaky Integrate and Fire - is chosen. The strengths of the connections between...... shown that the syncronizing effect of the plasticity disappears when the strengths of the connections are frozen in time. Subsequently, the so-called ``Sisyphus'' mechanism is discussed, which is shown to cause slow fluctuations in the both the network synchronization and the strengths...
Community Vitality in Dynamic Temporal Networks
Fu Cai; Li Min; Zou Deqing; Qu Shuyan; Han Lansheng; James J. Park
2013-01-01
Current researches on temporal networks mainly tend to detect community structure. A number of community detection algorithms can obtain community structure on each time slice or each period of time but rarely present the evolution of community structure. Some papers discussed the process of community structure evolution but lacked quantifying the evolution. In this paper, we put forward the concept of Community Vitality (CV), which shows a community's life intensity on a time slice. In the p...
A Dynamic Neural Network Approach to CBM
2011-03-15
Therefore post-processing is needed to extract the time difference between corresponding events from which to calculate the crankshaft rotational speed...potentially already available from existing sensors (such as a crankshaft timing device) and a Neural Network processor to carry out the calculation . As...files are designated with the “_genmod” suffix. These files were the sources for the training and testing sets and made the extraction process easy
Transport efficiency and dynamics of hydraulic fracture networks
Directory of Open Access Journals (Sweden)
Till eSachau
2015-08-01
Full Text Available Intermittent fluid pulses in the Earth's crust can explain a variety of geological phenomena, for instance the occurrence of hydraulic breccia. Fluid transport in the crust is usually modeled as continuous darcian flow, ignoring that sufficient fluid overpressure can cause hydraulic fractures as fluid pathways with very dynamic behavior. Resulting hydraulic fracture networks are largely self-organized: opening and healing of hydraulic fractures depends on local fluid pressure, which is, in turn, largely controlled by the fracture network. We develop a crustal-scale 2D computer model designed to simulate this process. To focus on the dynamics of the process we chose a setup as simple as possible. Control factors are constant overpressure at a basal fluid source and a constant 'viscous' parameter controlling fracture-healing. Our results indicate that at large healing rates hydraulic fractures are mobile, transporting fluid in intermittent pulses to the surface and displaying a 1/fα behavior. Low healing rates result in stable networks and constant flow. The efficiency of the fluid transport is independent from the closure dynamics of veins or fractures. More important than preexisting fracture networks is the distribution of fluid pressure. A key requirement for dynamic fracture networks is the presence of a fluid pressure gradient.
Neural network approaches to dynamic collision-free trajectory generation.
Yang, S X; Meng, M
2001-01-01
In this paper, dynamic collision-free trajectory generation in a nonstationary environment is studied using biologically inspired neural network approaches. The proposed neural network is topologically organized, where the dynamics of each neuron is characterized by a shunting equation or an additive equation. The state space of the neural network can be either the Cartesian workspace or the joint space of multi-joint robot manipulators. There are only local lateral connections among neurons. The real-time optimal trajectory is generated through the dynamic activity landscape of the neural network without explicitly searching over the free space nor the collision paths, without explicitly optimizing any global cost functions, without any prior knowledge of the dynamic environment, and without any learning procedures. Therefore the model algorithm is computationally efficient. The stability of the neural network system is guaranteed by the existence of a Lyapunov function candidate. In addition, this model is not very sensitive to the model parameters. Several model variations are presented and the differences are discussed. As examples, the proposed models are applied to generate collision-free trajectories for a mobile robot to solve a maze-type of problem, to avoid concave U-shaped obstacles, to track a moving target and at the same to avoid varying obstacles, and to generate a trajectory for a two-link planar robot with two targets. The effectiveness and efficiency of the proposed approaches are demonstrated through simulation and comparison studies.
Transport efficiency and dynamics of hydraulic fracture networks
Sachau, Till; Bons, Paul; Gomez-Rivas, Enrique
2015-08-01
Intermittent fluid pulses in the Earth's crust can explain a variety of geological phenomena, for instance the occurrence of hydraulic breccia. Fluid transport in the crust is usually modeled as continuous darcian flow, ignoring that sufficient fluid overpressure can cause hydraulic fractures as fluid pathways with very dynamic behavior. Resulting hydraulic fracture networks are largely self-organized: opening and healing of hydraulic fractures depends on local fluid pressure, which is, in turn, largely controlled by the fracture network. We develop a crustal-scale 2D computer model designed to simulate this process. To focus on the dynamics of the process we chose a setup as simple as possible. Control factors are constant overpressure at a basal fluid source and a constant 'viscous' parameter controlling fracture-healing. Our results indicate that at large healing rates hydraulic fractures are mobile, transporting fluid in intermittent pulses to the surface and displaying a 1/fα behavior. Low healing rates result in stable networks and constant flow. The efficiency of the fluid transport is independent from the closure dynamics of veins or fractures. More important than preexisting fracture networks is the distribution of fluid pressure. A key requirement for dynamic fracture networks is the presence of a fluid pressure gradient.
Revealing the hidden structure of dynamic ecological networks.
Miele, Vincent; Matias, Catherine
2017-06-01
In ecology, recent technological advances and long-term data studies now provide longitudinal interaction data (e.g. between individuals or species). Most often, time is the parameter along which interactions evolve but any other one-dimensional gradient (temperature, altitude, depth, humidity, etc.) can be considered. These data can be modelled through a sequence of different snapshots of an evolving ecological network, i.e. a dynamic network. Here, we present how the dynamic stochastic block model approach developed by Matias & Miele (Matias & Miele In press J. R. Stat. Soc. B (doi:10.1111/rssb.12200)) can capture the complexity and dynamics of these networks. First, we analyse a dynamic contact network of ants and we observe a clear high-level assembly with some variations in time at the individual level. Second, we explore the structure of a food web evolving during a year and we detect a stable predator-prey organization but also seasonal differences in the prey assemblage. Our approach, based on a rigorous statistical method implemented in the R package dynsbm, can pave the way for exploration of evolving ecological networks.
Signaling network dynamics investigated by quantitative phosphoproteomics
Giansanti, Piero
2014-01-01
This thesis describes the application of proteomics technologies to get insight into several aspects of phosphorylation signaling dynamics. The core tool in all performed experiments is mass spectrometry (MS)-based phosphoproteomics. In Chapter 1, a general introduction is given into proteomics and
Stochastic population dynamic models as probability networks
M.E. and D.C. Lee. Borsuk
2009-01-01
The dynamics of a population and its response to environmental change depend on the balance of birth, death and age-at-maturity, and there have been many attempts to mathematically model populations based on these characteristics. Historically, most of these models were deterministic, meaning that the results were strictly determined by the equations of the model and...
Neural network dynamics in Parkinson's disease
Lourens, Marcel Antonius Johannes
2013-01-01
Parkinson's disease (PD) is characterized by the cell death of neuronal brain cells producing the signaling molecule dopamine. Due to resulting shortage of dopamine, the dynamics of neuronal cells changes, most notably abnormal synchronization of neuronal activity. Such changes complicate the
Integrative avenues for exploring the dynamics and evolution of protein interaction networks.
Diss, Guillaume; Filteau, Marie; Freschi, Luca; Leducq, Jean-Baptiste; Rochette, Samuel; Torres-Quiroz, Francisco; Landry, Christian R
2013-08-01
Over the past decade, the study of protein interaction networks (PINs) has shed light on the organizing principles of living cells. However, PINs have been mostly mapped in one single condition. We outline three of the most promising avenues of investigation in this field, namely the study of first, how PINs are rewired by mutations and environmental perturbations; secondly, how inter-species interactions affect PIN achitectures; thirdly, what mechanisms and forces drive PIN evolution. These investigations will unravel the dynamics and condition dependence of PINs and will thus lead to a better functional annotation of network architecture. One major challenge to reach these goals is the integration of PINs with other cellular regulatory networks in the context of complex cellular phenotypes. Copyright © 2013 Elsevier Ltd. All rights reserved.
Fractional Dynamics of Network Growth Constrained by Aging Node Interactions.
Safdari, Hadiseh; Zare Kamali, Milad; Shirazi, Amirhossein; Khalighi, Moein; Jafari, Gholamreza; Ausloos, Marcel
2016-01-01
In many social complex systems, in which agents are linked by non-linear interactions, the history of events strongly influences the whole network dynamics. However, a class of "commonly accepted beliefs" seems rarely studied. In this paper, we examine how the growth process of a (social) network is influenced by past circumstances. In order to tackle this cause, we simply modify the well known preferential attachment mechanism by imposing a time dependent kernel function in the network evolution equation. This approach leads to a fractional order Barabási-Albert (BA) differential equation, generalizing the BA model. Our results show that, with passing time, an aging process is observed for the network dynamics. The aging process leads to a decay for the node degree values, thereby creating an opposing process to the preferential attachment mechanism. On one hand, based on the preferential attachment mechanism, nodes with a high degree are more likely to absorb links; but, on the other hand, a node's age has a reduced chance for new connections. This competitive scenario allows an increased chance for younger members to become a hub. Simulations of such a network growth with aging constraint confirm the results found from solving the fractional BA equation. We also report, as an exemplary application, an investigation of the collaboration network between Hollywood movie actors. It is undubiously shown that a decay in the dynamics of their collaboration rate is found, even including a sex difference. Such findings suggest a widely universal application of the so generalized BA model.
Applications of flow-networks to opinion-dynamics
Tupikina, Liubov; Kurths, Jürgen
2015-04-01
Networks were successfully applied to describe complex systems, such as brain, climate, processes in society. Recently a socio-physical problem of opinion-dynamics was studied using network techniques. We present the toy-model of opinion-formation based on the physical model of advection-diffusion. We consider spreading of the opinion on the fixed subject, assuming that opinion on society is binary: if person has opinion then the state of the node in the society-network equals 1, if the person doesn't have opinion state of the node equals 0. Opinion can be spread from one person to another if they know each other, or in the network-terminology, if the nodes are connected. We include into the system governed by advection-diffusion equation the external field to model such effects as for instance influence from media. The assumptions for our model can be formulated as the following: 1.the node-states are influenced by the network structure in such a way, that opinion can be spread only between adjacent nodes (the advective term of the opinion-dynamics), 2.the network evolution can have two scenarios: -network topology is not changing with time; -additional links can appear or disappear each time-step with fixed probability which requires adaptive networks properties. Considering these assumptions for our system we obtain the system of equations describing our model-dynamics which corresponds well to other socio-physics models, for instance, the model of the social cohesion and the famous voter-model. We investigate the behavior of the suggested model studying "waiting time" of the system, time to get to the stable state, stability of the model regimes for different values of model parameters and network topology.
Coupled disease-behavior dynamics on complex networks: A review.
Wang, Zhen; Andrews, Michael A; Wu, Zhi-Xi; Wang, Lin; Bauch, Chris T
2015-12-01
It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years. Copyright © 2015 Elsevier B.V. All rights reserved.
Coupled disease-behavior dynamics on complex networks: A review
Wang, Zhen; Andrews, Michael A.; Wu, Zhi-Xi; Wang, Lin; Bauch, Chris T.
2015-12-01
It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.
Pipe-cleaner Model of Neuronal Network Dynamics
Armstrong, Eve
2016-01-01
We present a functional model of neuronal network connectivity in which the single architectural element is the object commonly known in handicraft circles as a pipe cleaner. We argue that the dual nature of a neuronal circuit - that it be at times highly robust to external manipulation and yet sufficiently flexible to allow for learning and adaptation - is embodied in the pipe cleaner, and thus that a pipe cleaner framework serves as an instructive scaffold in which to examine network dynamics. Regarding the dynamics themselves: as pipe cleaners possess no intrinsic dynamics, in our model we attribute the emergent circuit dynamics to magic. Magic is a strategy that has been largely neglected in the neuroscience community, and may serve as an illuminating comparison to the common physics-based approaches. This model makes predictions that it would be really awesome to test experimentally. Moreover, the relative simplicity of the pipe cleaner - setting aside the fact that it comes in an overwhelming variety of...
Rewiring Chemical Networks Based on Dynamic Dithioacetal and Disulfide Bonds.
Orrillo, A Gastón; La-Venia, Agustina; Escalante, Andrea M; Furlan, Ricardo L E
2018-01-18
The control of the connectivity between nodes of synthetic networks is still largely unexplored. To address this point we take advantage of a simple dynamic chemical system with two exchange levels that are mutually connected and can be activated simultaneously or sequentially. Dithioacetals and disulfides can be exchanged simultaneously under UV light in the presence of a sensitizer. Crossover reactions between both exchange processes produce a fully connected chemical network. On the other hand, the use of acid, base or UV light connects different nodes allowing network rewiring. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Danube Delta Biosphere Reserve hydrographic network morphological dynamics
Directory of Open Access Journals (Sweden)
CIOACĂ Eugenia
2009-09-01
Full Text Available The paper presents the Danube Delta Biosphere Reserve (DDBR hydrographic network morphological changes investigated and presented as geospatial data as they resulted from fieldmeasurements. These data are part of a complex project started in 2007 (with the acronym MORFDD. As a preliminary stage of this project, they contribute to the DDBR hydrographic network mathematical / hydraulic model construction related to hydro-morphology and water quality dynamics. Geospatial data, related tomorphological parameters, aim to create a scientific knowledge on hydro-morphologic changes by emphasizing the DDBR hydrographic network zones where fluvial processes, erosion and alluvial sedimentation, are active.
Synchronization in Complex Networks of Nonlinear Dynamical Systems
Wu, Chai Wah
2007-01-01
This book brings together two emerging research areas: synchronization in coupled nonlinear systems and complex networks, and study conditions under which a complex network of dynamical systems synchronizes. While there are many texts that study synchronization in chaotic systems or properties of complex networks, there are few texts that consider the intersection of these two very active and interdisciplinary research areas. The main theme of this book is that synchronization conditions can be related to graph theoretical properties of the underlying coupling topology. The book introduces ide
Suppressing epidemic spreading by risk-averse migration in dynamical networks
Yang, Han-Xin; Tang, Ming; Wang, Zhen
2018-01-01
In this paper, we study the interplay between individual behaviors and epidemic spreading in a dynamical network. We distribute agents on a square-shaped region with periodic boundary conditions. Every agent is regarded as a node of the network and a wireless link is established between two agents if their geographical distance is less than a certain radius. At each time, every agent assesses the epidemic situation and make decisions on whether it should stay in or leave its current place. An agent will leave its current place with a speed if the number of infected neighbors reaches or exceeds a critical value E. Owing to the movement of agents, the network's structure is dynamical. Interestingly, we find that there exists an optimal value of E leading to the maximum epidemic threshold. This means that epidemic spreading can be effectively controlled by risk-averse migration. Besides, we find that the epidemic threshold increases as the recovering rate increases, decreases as the contact radius increases, and is maximized by an optimal moving speed. Our findings offer a deeper understanding of epidemic spreading in dynamical networks.
Dynamic Wireless Network Based on Open Physical Layer
2011-02-18
based on the notion of processing soft information is the basic building block for a large number of multi-terminal communication problems , such as...formulations of communication problems are only suitable for point-to-point communications, and in order to address dynamic network problems, new...formulations are needed. We believe that a geometric view is the key of solving the new dynamic communication problems . However, it is important that such new
Glucans monomer-exchange dynamics as an open chemical network
Rao, Riccardo; Esposito, Massimiliano
2015-01-01
We describe the oligosaccharides-exchange dynamics performed by so-called D-enzymes on polysaccharides. To mimic physiological conditions, we treat this process as an open chemical network by assuming some of the polymer concentrations fixed (chemostatting). We show that three different long-time behaviors may ensue: equilibrium states, nonequilibrium steady states, and continuous growth states. We dynamically and thermodynamically characterize these states and emphasize the crucial role of conservation laws in identifying the chemostatting conditions inducing them.
2003-01-01
Network Physics, provider of business-level, traffic flow-based network management solutions, today announced the introduction of the Network Physics NP/BizFlow-1000. With the NP/BizFlow-1000, Fortune 1000 companies with complex and dynamic networks can analyze the flows that link business groups, critical applications, and network software and hardware (1 page).
A network-based dynamical ranking system for competitive sports
Motegi, Shun; Masuda, Naoki
2012-12-01
From the viewpoint of networks, a ranking system for players or teams in sports is equivalent to a centrality measure for sports networks, whereby a directed link represents the result of a single game. Previously proposed network-based ranking systems are derived from static networks, i.e., aggregation of the results of games over time. However, the score of a player (or team) fluctuates over time. Defeating a renowned player in the peak performance is intuitively more rewarding than defeating the same player in other periods. To account for this factor, we propose a dynamic variant of such a network-based ranking system and apply it to professional men's tennis data. We derive a set of linear online update equations for the score of each player. The proposed ranking system predicts the outcome of the future games with a higher accuracy than the static counterparts.
A network-based dynamical ranking system for competitive sports.
Motegi, Shun; Masuda, Naoki
2012-01-01
From the viewpoint of networks, a ranking system for players or teams in sports is equivalent to a centrality measure for sports networks, whereby a directed link represents the result of a single game. Previously proposed network-based ranking systems are derived from static networks, i.e., aggregation of the results of games over time. However, the score of a player (or team) fluctuates over time. Defeating a renowned player in the peak performance is intuitively more rewarding than defeating the same player in other periods. To account for this factor, we propose a dynamic variant of such a network-based ranking system and apply it to professional men's tennis data. We derive a set of linear online update equations for the score of each player. The proposed ranking system predicts the outcome of the future games with a higher accuracy than the static counterparts.
Dynamics of the mitochondrial network during mitosis.
Kanfer, Gil; Kornmann, Benoît
2016-04-15
During mitosis, cells undergo massive deformation and reorganization, impacting on all cellular structures. Mitochondria, in particular, are highly dynamic organelles, which constantly undergo events of fission, fusion and cytoskeleton-based transport. This plasticity ensures the proper distribution of the metabolism, and the proper inheritance of functional organelles. During cell cycle, mitochondria undergo dramatic changes in distribution. In this review, we focus on the dynamic events that target mitochondria during mitosis. We describe how the cell-cycle-dependent microtubule-associated protein centromeric protein F (Cenp-F) is recruited to mitochondria by the mitochondrial Rho GTPase (Miro) to promote mitochondrial transport and re-distribution following cell division. © 2016 Authors; published by Portland Press Limited.
Dynamic Defensive Posture for Computer Network Defence
2006-12-01
des algorithmes pour le classement de la sévérité des attaques sur le réseau et des mécanismes permettant d’attribuer une valeur aux éléments...power outages and social engineering attacks. Because it has such a large knowledge base on which to draw, it can reason very thoroughly about network...service attacks, eavesdropping and sniffing attacks on data in transit, or data tampering; more complex still would be models of social engineering
The fidelity of dynamic signaling by noisy biomolecular networks.
Directory of Open Access Journals (Sweden)
Clive G Bowsher
Full Text Available Cells live in changing, dynamic environments. To understand cellular decision-making, we must therefore understand how fluctuating inputs are processed by noisy biomolecular networks. Here we present a general methodology for analyzing the fidelity with which different statistics of a fluctuating input are represented, or encoded, in the output of a signaling system over time. We identify two orthogonal sources of error that corrupt perfect representation of the signal: dynamical error, which occurs when the network responds on average to other features of the input trajectory as well as to the signal of interest, and mechanistic error, which occurs because biochemical reactions comprising the signaling mechanism are stochastic. Trade-offs between these two errors can determine the system's fidelity. By developing mathematical approaches to derive dynamics conditional on input trajectories we can show, for example, that increased biochemical noise (mechanistic error can improve fidelity and that both negative and positive feedback degrade fidelity, for standard models of genetic autoregulation. For a group of cells, the fidelity of the collective output exceeds that of an individual cell and negative feedback then typically becomes beneficial. We can also predict the dynamic signal for which a given system has highest fidelity and, conversely, how to modify the network design to maximize fidelity for a given dynamic signal. Our approach is general, has applications to both systems and synthetic biology, and will help underpin studies of cellular behavior in natural, dynamic environments.
Transient dynamics for sequence processing neural networks
Energy Technology Data Exchange (ETDEWEB)
Kawamura, Masaki [Faculty of Science, Yamaguchi University, Yamaguchi (Japan)]. E-mail: kawamura@sci.yamaguchi-u.ac.jp; Okada, Masato [RIKEN BSI, Hirosawa, Wako-shi (Japan)
2002-01-18
An exact solution of the transient dynamics for a sequential associative memory model is discussed through both the path-integral method and the statistical neurodynamics. Although the path-integral method has the ability to give an exact solution of the transient dynamics, only stationary properties have been discussed for the sequential associative memory. We have succeeded in deriving an exact macroscopic description of the transient dynamics by analysing the correlation of crosstalk noise. Surprisingly, the order parameter equations of this exact solution are completely equivalent to those of the statistical neurodynamics, which is an approximation theory that assumes crosstalk noise to obey the Gaussian distribution. In order to examine our theoretical findings, we numerically obtain cumulants of the crosstalk noise. We verify that the third- and fourth-order cumulants are equal to zero, and that the crosstalk noise is normally distributed even in the non-retrieval case. We show that the results obtained by our theory agree with those obtained by computer simulations. We have also found that the macroscopic unstable state completely coincides with the separatrix. (author)
Critical dynamics in genetic regulatory networks: examples from four kingdoms.
Directory of Open Access Journals (Sweden)
Enrique Balleza
2008-06-01
Full Text Available The coordinated expression of the different genes in an organism is essential to sustain functionality under the random external perturbations to which the organism might be subjected. To cope with such external variability, the global dynamics of the genetic network must possess two central properties. (a It must be robust enough as to guarantee stability under a broad range of external conditions, and (b it must be flexible enough to recognize and integrate specific external signals that may help the organism to change and adapt to different environments. This compromise between robustness and adaptability has been observed in dynamical systems operating at the brink of a phase transition between order and chaos. Such systems are termed critical. Thus, criticality, a precise, measurable, and well characterized property of dynamical systems, makes it possible for robustness and adaptability to coexist in living organisms. In this work we investigate the dynamical properties of the gene transcription networks reported for S. cerevisiae, E. coli, and B. subtilis, as well as the network of segment polarity genes of D. melanogaster, and the network of flower development of A. thaliana. We use hundreds of microarray experiments to infer the nature of the regulatory interactions among genes, and implement these data into the Boolean models of the genetic networks. Our results show that, to the best of the current experimental data available, the five networks under study indeed operate close to criticality. The generality of this result suggests that criticality at the genetic level might constitute a fundamental evolutionary mechanism that generates the great diversity of dynamically robust living forms that we observe around us.
Characterizing dynamic changes in the human blood transcriptional network.
Directory of Open Access Journals (Sweden)
Jun Zhu
2010-02-01
Full Text Available Gene expression data generated systematically in a given system over multiple time points provides a source of perturbation that can be leveraged to infer causal relationships among genes explaining network changes. Previously, we showed that food intake has a large impact on blood gene expression patterns and that these responses, either in terms of gene expression level or gene-gene connectivity, are strongly associated with metabolic diseases. In this study, we explored which genes drive the changes of gene expression patterns in response to time and food intake. We applied the Granger causality test and the dynamic Bayesian network to gene expression data generated from blood samples collected at multiple time points during the course of a day. The simulation result shows that combining many short time series together is as powerful to infer Granger causality as using a single long time series. Using the Granger causality test, we identified genes that were supported as the most likely causal candidates for the coordinated temporal changes in the network. These results show that PER1 is a key regulator of the blood transcriptional network, in which multiple biological processes are under circadian rhythm regulation. The fasted and fed dynamic Bayesian networks showed that over 72% of dynamic connections are self links. Finally, we show that different processes such as inflammation and lipid metabolism, which are disconnected in the static network, become dynamically linked in response to food intake, which would suggest that increasing nutritional load leads to coordinate regulation of these biological processes. In conclusion, our results suggest that food intake has a profound impact on the dynamic co-regulation of multiple biological processes, such as metabolism, immune response, apoptosis and circadian rhythm. The results could have broader implications for the design of studies of disease association and drug response in clinical
Coevolutionary network approach to cultural dynamics controlled by intolerance
Gracia-Lázaro, Carlos; Quijandría, Fernando; Hernández, Laura; Floría, Luis Mario; Moreno, Yamir
2011-12-01
Starting from Axelrod's model of cultural dissemination, we introduce a rewiring probability, enabling agents to cut the links with their unfriendly neighbors if their cultural similarity is below a tolerance parameter. For low values of tolerance, rewiring promotes the convergence to a frozen monocultural state. However, intermediate tolerance values prevent rewiring once the network is fragmented, resulting in a multicultural society even for values of initial cultural diversity in which the original Axelrod model reaches globalization.
Firing rate dynamics in recurrent spiking neural networks with intrinsic and network heterogeneity.
Ly, Cheng
2015-12-01
Heterogeneity of neural attributes has recently gained a lot of attention and is increasing recognized as a crucial feature in neural processing. Despite its importance, this physiological feature has traditionally been neglected in theoretical studies of cortical neural networks. Thus, there is still a lot unknown about the consequences of cellular and circuit heterogeneity in spiking neural networks. In particular, combining network or synaptic heterogeneity and intrinsic heterogeneity has yet to be considered systematically despite the fact that both are known to exist and likely have significant roles in neural network dynamics. In a canonical recurrent spiking neural network model, we study how these two forms of heterogeneity lead to different distributions of excitatory firing rates. To analytically characterize how these types of heterogeneities affect the network, we employ a dimension reduction method that relies on a combination of Monte Carlo simulations and probability density function equations. We find that the relationship between intrinsic and network heterogeneity has a strong effect on the overall level of heterogeneity of the firing rates. Specifically, this relationship can lead to amplification or attenuation of firing rate heterogeneity, and these effects depend on whether the recurrent network is firing asynchronously or rhythmically firing. These observations are captured with the aforementioned reduction method, and furthermore simpler analytic descriptions based on this dimension reduction method are developed. The final analytic descriptions provide compact and descriptive formulas for how the relationship between intrinsic and network heterogeneity determines the firing rate heterogeneity dynamics in various settings.
Klemm, Konstantin; Eguiluz, Victor M; Miguel, Maxi San
2010-01-01
Identifying key players in complex networks remains a challenge affecting a great variety of research fields, from the efficient dissemination of ideas to drug target discovery in biomedical problems. The difficulty lies at several levels: how to single out the role of individual elements in such intermingled systems, or which is the best way to quantify their importance. Centrality measures aim at capturing the influence of a node from its position in a network. The key issue obviated is that the contribution of a node to the collective behaviour is not uniquely determined by the structure of the system but a result of both dynamics and network structure. Here we define dynamical influence as an explicit measure of how strongly a node's dynamical state affects collective behavior. Influence is derived analytically for dissipative processes in complex networks, directed or undirected. We show that it quantifies precisely how efficiently real systems may be driven by manipulating the state of single nodes. It ...
A Dynamic Evolutionary Game Model of Modular Production Network
Directory of Open Access Journals (Sweden)
Wei He
2016-01-01
Full Text Available As a new organization mode of production in the 21st century, modular production network is deemed extensively to be a source of competitiveness for lead firms in manufacturing industries. However, despite the abundant studies on the modular production network, there are very few studies from a dynamic perspective to discuss the conditions on which a modular production network develops. Based on the dynamic evolutionary game theory, this paper constructs a model, which incorporates several main factors influencing the development of modular production network. By calculating the replicator dynamics equations and analyzing the evolutionary stable strategies, this paper discusses the evolution process of cooperation strategies of member enterprises in a modular production network. Furthermore, by using NetLogo software to simulate the model, this paper verifies the effectiveness of the model. From the model, we can find that the final stable equilibrium strategy is related to such factors as the initial cost, the extra payoff, the cooperation willingness of both parties, the cooperation efforts, and the proportion each party can get from the extra payoff. To encourage the cooperation of production integrator and modular supplier, some suggestions are also provided.
Age structure and cooperation in coevolutionary games on dynamic network
Qin, Zilong; Hu, Zhenhua; Zhou, Xiaoping; Yi, Jingzhang
2015-04-01
Our proposed model imitates the growth of a population and describes the age structure and the level of cooperation in games on dynamic network with continuous changes of structure and topology. The removal of nodes and links caused by age-dependent attack, together with the nodes addition standing for the newborns of population, badly ruins Matthew effect in this coevolutionary process. Though the network is generated by growth and preferential attachment, it degenerates into random network and it is no longer heterogeneous. When the removal of nodes and links is equal to the addition of nodes and links, the size of dynamic network is maintained in steady-state, so is the low level of cooperation. Severe structure variation, homogeneous topology and continuous invasion of new defection jointly make dynamic network unsuitable for the survival of cooperator even when the probability with which the newborn players initially adopt the strategy cooperation is high, while things change slightly when the connections of newborn players are restricted. Fortunately, moderate interactions in a generation trigger an optimal recovering process to encourage cooperation. The model developed in this paper outlines an explanation of the cohesion changes in the development process of an organization. Some suggestions for cooperative behavior improvement are given in the end.
Evaluation of dynamic bandwidth allocation algorithms in GPON networks
DEFF Research Database (Denmark)
Ozimkiewicz, J.; Ruepp, Sarah Renée; Dittmann, Lars
2010-01-01
In this paper, two approaches for Dynamic Bandwidth Allocation in GPON networks are proposed, and validated through simulations in the OPNET modeler. One approach address a Status Reporting scheme, where the bandwidth allocation originates from the client request. The second use a centralized Non...
A Neural Network Model for Dynamics Simulation | Bholoa ...
African Journals Online (AJOL)
University of Mauritius Research Journal. Journal Home · ABOUT · Advanced Search · Current Issue · Archives · Journal Home > Vol 15, No 1 (2009) >. Log in or Register to get access to full text downloads. Username, Password, Remember me, or Register. A Neural Network Model for Dynamics Simulation. Ajeevsing ...
A generalized clustering algorithm for dynamic wireless sensor networks
Marin Perianu, Raluca; Hurink, Johann L.; Hartel, Pieter H.
We propose a general clustering algorithm for dynamic sensor networks, that makes localized decisions (1-hop neighbourhood) and produces disjoint clusters. The purpose is to extract and emphasise the essential clustering mechanisms common for a set of state-of-the-art algorithms, which allows for a
A Generalized Clustering Algorithm for Dynamic Wireless Sensor Networks
Marin Perianu, Raluca; Hurink, Johann L.; Hartel, Pieter H.
2008-01-01
We propose a general clustering algorithm for dynamic sensor networks, that makes localized decisions (1-hop neighbourhood) and produces disjoint clusters. The purpose is to extract and emphasise the essential clustering mechanisms common for a set of state-of-the-art algorithms, which allows for a
Adaptive dynamic capacity borrowing in road-covering mobile networks
Ule, A.; Boucherie, Richardus J.; Li, W.; Pan, Y.
2006-01-01
This paper introduces adaptive dynamic capacity borrowing strategies for wireless networks covering a road. In a F/TDMA-based model, road traffic prediction models are used to characterise the movement of hot spots, such as traffic jams, and subsequently to predict the teletraffic load offered to
RD2: Resilient Dynamic Desynchronization for TDMA over Lossy Networks
DEFF Research Database (Denmark)
Hinterhofer, Thomas; Schwefel, Hans-Peter; Tomic, Slobodanka
2012-01-01
We present a distributed TDMA negotiation approach for single-hop ad-hoc network communication. It is distributed, resilient to arbitrary transient packet loss and defines a non-overlapping TDMA schedule without the need of global time synchronization. A participating node can dynamically request...
An Equilibrium-Correction Model for Dynamic Network Data
D.J. Dekker (David); Ph.H.B.F. Franses (Philip Hans); D. Krackhardt (David)
2001-01-01
textabstractWe propose a two-stage MRQAP to analyze dynamic network data, within the framework of an equilibrium-correction (EC) model. Extensive simulation results indicate practical relevance of our method and its improvement over standard OLS. An empirical illustration additionally shows that the
Dynamic Adaptive Neural Network Arrays: A Neuromorphic Architecture
Energy Technology Data Exchange (ETDEWEB)
Disney, Adam [University of Tennessee (UT); Reynolds, John [University of Tennessee (UT)
2015-01-01
Dynamic Adaptive Neural Network Array (DANNA) is a neuromorphic hardware implementation. It differs from most other neuromorphic projects in that it allows for programmability of structure, and it is trained or designed using evolutionary optimization. This paper describes the DANNA structure, how DANNA is trained using evolutionary optimization, and an application of DANNA to a very simple classification task.
Epilepsy as a dynamic disease of neuronal networks
Lopes da Silva, F.H.; Gorter, J.A.; Wadman, W.J.; Stefan, H.; Theodore, W.H.
2012-01-01
Epilepsy is a dynamic disease of neuronal networks. To understand how epileptic seizures occur, it is necessary to take into account that the brain of epileptic subjects is able to function in two very distinct modes: a normal state and a state characterized by abnormal oscillations, i.e., epileptic
Adaptive spectrum decision framework for heterogeneous dynamic spectrum access networks
CSIR Research Space (South Africa)
Masonta, M
2015-09-01
Full Text Available Spectrum decision is the ability of a cognitive radio (CR) system to select the best available spectrum band to satisfy dynamic spectrum access network (DSAN) users¿ quality of service (QoS) requirements without causing harmful interference...
A proposed concept for a crustal dynamics information management network
Lohman, G. M.; Renfrow, J. T.
1980-01-01
The findings of a requirements and feasibility analysis of the present and potential producers, users, and repositories of space-derived geodetic information are summarized. A proposed concept is presented for a crustal dynamics information management network that would apply state of the art concepts of information management technology to meet the expanding needs of the producers, users, and archivists of this geodetic information.
Hasegawa, Mikio; Tran, Ha Nguyen; Miyamoto, Goh; Murata, Yoshitoshi; Harada, Hiroshi; Kato, Shuzo
We propose a neurodynamical approach to a large-scale optimization problem in Cognitive Wireless Clouds, in which a huge number of mobile terminals with multiple different air interfaces autonomously utilize the most appropriate infrastructure wireless networks, by sensing available wireless networks, selecting the most appropriate one, and reconfiguring themselves with seamless handover to the target networks. To deal with such a cognitive radio network, game theory has been applied in order to analyze the stability of the dynamical systems consisting of the mobile terminals' distributed behaviors, but it is not a tool for globally optimizing the state of the network. As a natural optimization dynamical system model suitable for large-scale complex systems, we introduce the neural network dynamics which converges to an optimal state since its property is to continually decrease its energy function. In this paper, we apply such neurodynamics to the optimization problem of radio access technology selection. We compose a neural network that solves the problem, and we show that it is possible to improve total average throughput simply by using distributed and autonomous neuron updates on the terminal side.
Dynamic Enhanced Inter-Cell Interference Coordination for Realistic Networks
DEFF Research Database (Denmark)
Pedersen, Klaus I.; Alvarez, Beatriz Soret; Barcos, Sonia
2016-01-01
ICIC configuration leads to modest gains, whereas the set of proposed fast dynamic eICIC algorithms result in capacity gains on the order of 35-120% depending on the local environment characteristics. These attractive gains together with the simplicity of the proposed solutions underline the practical relevance...... area. Rather than the classical semi-static and network-wise configuration, the importance of having highly dynamic and distributed mechanisms that are able to adapt to local environment conditions is revealed. We propose two promising cell association algorithms: one aiming at pure load balancing...... and an opportunistic approach exploiting the varying cell conditions. Moreover, an autonomous fast distributed muting algorithm is presented, which is simple, robust, and well suited for irregular network deployments. Performance results for realistic network deployments show that the traditional semi-static e...
A Cayley Tree Immune Network Model with Antibody Dynamics
Anderson, R W; Perelson, A S; Anderson, Russell W.; Neumann, Avidan U.; Perelson, Alan S.
1993-01-01
Abstract: A Cayley tree model of idiotypic networks that includes both B cell and antibody dynamics is formulated and analyzed. As in models with B cells only, localized states exist in the network with limited numbers of activated clones surrounded by virgin or near-virgin clones. The existence and stability of these localized network states are explored as a function of model parameters. As in previous models that have included antibody, the stability of immune and tolerant localized states are shown to depend on the ratio of antibody to B cell lifetimes as well as the rate of antibody complex removal. As model parameters are varied, localized steady-states can break down via two routes: dynamically, into chaotic attractors, or structurally into percolation attractors. For a given set of parameters, percolation and chaotic attractors can coexist with localized attractors, and thus there do not exist clear cut boundaries in parameter space that separate regions of localized attractors from regions of percola...
Clustering determines the dynamics of complex contagions in multiplex networks
Zhuang, Yong; Yağan, Osman
2016-01-01
We present the mathematical analysis of generalized complex contagions in clustered multiplex networks for susceptible-infected-recovered (SIR)-like dynamics. The model is intended to understand diffusion of influence, or any other spreading process implying a threshold dynamics, in setups of interconnected networks with significant clustering. The contagion is assumed to be general enough to account for a content-dependent linear threshold model, where each link type has a different weight (for spreading influence) that may depend on the content (e.g., product, rumor, political view) that is being spread. Using the generating functions formalism, we determine the conditions, probability, and expected size of the emergent global cascades. This analysis provides a generalization of previous approaches and is specially useful in problems related to spreading and percolation. The results present non trivial dependencies between the clustering coefficient of the networks and its average degree. In particular, sev...
Dynamic Business Networks: A Headache for Sustainable Systems Interoperability
Agostinho, Carlos; Jardim-Goncalves, Ricardo
Collaborative networked environments emerged with the spread of the internet, contributing to overcome past communication barriers, and identifying interoperability as an essential property. When achieved seamlessly, efficiency is increased in the entire product life cycle. Nowadays, most organizations try to attain interoperability by establishing peer-to-peer mappings with the different partners, or in optimized networks, by using international standard models as the core for information exchange. In current industrial practice, mappings are only defined once, and the morphisms that represent them, are hardcoded in the enterprise systems. This solution has been effective for static environments, where enterprise and product models are valid for decades. However, with an increasingly complex and dynamic global market, models change frequently to answer new customer requirements. This paper draws concepts from the complex systems science and proposes a framework for sustainable systems interoperability in dynamic networks, enabling different organizations to evolve at their own rate.
Persistent activity in neural networks with dynamic synapses.
Directory of Open Access Journals (Sweden)
Omri Barak
2007-02-01
Full Text Available Persistent activity states (attractors, observed in several neocortical areas after the removal of a sensory stimulus, are believed to be the neuronal basis of working memory. One of the possible mechanisms that can underlie persistent activity is recurrent excitation mediated by intracortical synaptic connections. A recent experimental study revealed that connections between pyramidal cells in prefrontal cortex exhibit various degrees of synaptic depression and facilitation. Here we analyze the effect of synaptic dynamics on the emergence and persistence of attractor states in interconnected neural networks. We show that different combinations of synaptic depression and facilitation result in qualitatively different network dynamics with respect to the emergence of the attractor states. This analysis raises the possibility that the framework of attractor neural networks can be extended to represent time-dependent stimuli.
Dynamic reorganization of functional brain networks during picture naming.
Hassan, Mahmoud; Benquet, Pascal; Biraben, Arnaud; Berrou, Claude; Dufor, Olivier; Wendling, Fabrice
2015-12-01
For efficient information processing during cognitive activity, functional brain networks have to rapidly and dynamically reorganize on a sub-second time scale. Tracking the spatiotemporal dynamics of large scale networks over this short time duration is a very challenging issue. Here, we tackle this problem by using dense electroencephalography (EEG) recorded during a picture naming task. We found that (i) the picture naming task can be divided into six brain network states (BNSs) characterized by significantly high synchronization of gamma (30-45 Hz) oscillations, (ii) fast transitions occur between these BNSs that last from 30 msec to 160 msec, (iii) based on the state of the art of the picture naming task, we consider that the spatial location of their nodes and edges, as well as the timing of transitions, indicate that each network can be associated with one or several specific function (from visual processing to articulation) and (iv) the comparison with previously-used approach aimed at localizing the sources showed that the network-based approach reveals networks that are more specific to the performed task. We speculate that the persistence of several brain regions in successive BNSs participates to fast and efficient information processing in the brain. Copyright © 2015 Elsevier Ltd. All rights reserved.
Dynamic network-based epistasis analysis: Boolean examples
Directory of Open Access Journals (Sweden)
Eugenio eAzpeitia
2011-12-01
Full Text Available In this review we focus on how the hierarchical and single-path assumptions of epistasis analysis can bias the topologies of gene interactions infered. This has been acknowledged in several previous papers and reviews, but here we emphasize the critical importance of dynamic analyses, and specifically illustrate the use of Boolean network models. Epistasis in a broad sense refers to gene interactions, however, as originally proposed by Bateson (herein, classical epistasis, defined as the blocking of a particular allelic effect due to the effect of another allele at a different locus. Classical epistasis analysis has proven powerful and useful, allowing researchers to infer and assign directionality to gene interactions. As larger data sets are becoming available, the analysis of classical epistasis is being complemented with computer science tools and system biology approaches. We show that when the hierarchical and single-path assumptions are not met in classical epistasis analysis, the access to relevant information and the correct gene interaction topologies are hindered, and it becomes necessary to consider the temporal dynamics of gene interactions. The use of dynamical networks can overcome these limitations. We particularly focus on the use of Boolean networks that, like classical epistasis analysis, relies on logical formalisms, and hence can complement classical epistasis analysis and relax its assumptions. We develop a couple of theoretical examples and analyze them from a dynamic Boolean network model perspective. Boolean networks could help to guide additional experiments and discern among alternative regulatory schemes that would be impossible or difficult to infer without the elimination of these assumption from the classical epistasis analysis. We also use examples from the literature to show how a Boolean network-based approach has resolved ambiguities and guided epistasis analysis. Our review complements previous accounts, not
Slow diffusive dynamics in a chaotic balanced neural network.
Directory of Open Access Journals (Sweden)
Nimrod Shaham
2017-05-01
Full Text Available It has been proposed that neural noise in the cortex arises from chaotic dynamics in the balanced state: in this model of cortical dynamics, the excitatory and inhibitory inputs to each neuron approximately cancel, and activity is driven by fluctuations of the synaptic inputs around their mean. It remains unclear whether neural networks in the balanced state can perform tasks that are highly sensitive to noise, such as storage of continuous parameters in working memory, while also accounting for the irregular behavior of single neurons. Here we show that continuous parameter working memory can be maintained in the balanced state, in a neural circuit with a simple network architecture. We show analytically that in the limit of an infinite network, the dynamics generated by this architecture are characterized by a continuous set of steady balanced states, allowing for the indefinite storage of a continuous parameter. In finite networks, we show that the chaotic noise drives diffusive motion along the approximate attractor, which gradually degrades the stored memory. We analyze the dynamics and show that the slow diffusive motion induces slowly decaying temporal cross correlations in the activity, which differ substantially from those previously described in the balanced state. We calculate the diffusivity, and show that it is inversely proportional to the system size. For large enough (but realistic neural population sizes, and with suitable tuning of the network connections, the proposed balanced network can sustain continuous parameter values in memory over time scales larger by several orders of magnitude than the single neuron time scale.
Multiple-node basin stability in complex dynamical networks
Mitra, Chiranjit; Choudhary, Anshul; Sinha, Sudeshna; Kurths, Jürgen; Donner, Reik V.
2017-03-01
Dynamical entities interacting with each other on complex networks often exhibit multistability. The stability of a desired steady regime (e.g., a synchronized state) to large perturbations is critical in the operation of many real-world networked dynamical systems such as ecosystems, power grids, the human brain, etc. This necessitates the development of appropriate quantifiers of stability of multiple stable states of such systems. Motivated by the concept of basin stability (BS) [P. J. Menck et al., Nat. Phys. 9, 89 (2013), 10.1038/nphys2516], we propose here the general framework of multiple-node basin stability for gauging the global stability and robustness of networked dynamical systems in response to nonlocal perturbations simultaneously affecting multiple nodes of a system. The framework of multiple-node BS provides an estimate of the critical number of nodes that, when simultaneously perturbed, significantly reduce the capacity of the system to return to the desired stable state. Further, this methodology can be applied to estimate the minimum number of nodes of the network to be controlled or safeguarded from external perturbations to ensure proper operation of the system. Multiple-node BS can also be utilized for probing the influence of spatially localized perturbations or targeted attacks to specific parts of a network. We demonstrate the potential of multiple-node BS in assessing the stability of the synchronized state in a deterministic scale-free network of Rössler oscillators and a conceptual model of the power grid of the United Kingdom with second-order Kuramoto-type nodal dynamics.
On-the-field performance of quintuple-play long-reach OFDM-based WDM-PON optical access networks.
Llorente, Roberto; Morant, Maria; Pellicer, Eloy; Herman, Milan; Nagy, Zsolt; Alves, Tiago; Cartaxo, Adolfo; Herrera, Javier; Correcher, Jose; Quinlan, Terence; Walker, Stuart; Rodrigues, Cláudio; Cluzeaud, Pierre; Schmidt, Axel; Piesiewicz, Radoslaw; Sambaraju, Rakesh
2014-03-24
In this paper the on-the-field performance of a WDM-PON optical access providing quintuple-play services using orthogonal frequency division multiplexing (OFDM) modulation is evaluated in a real fiber-to-the-home (FTTH) network deployed by Towercom operator in Bratislava (Slovakia). A bundle of quintuple-play services comprising full-standard OFDM-based signals (LTE, WiMAX, UWB and DVB-T) and an ad-hoc OFDM-GbE signal is transmitted in coexistence per single user. Both downstream and upstream transmission performances are evaluated in different on-the-field long-reach optical link distance configurations. Four wavelength multi-user transmission of quintuple-play OFDM services is demonstrated exceeding 60.8 km reach in standard single mode fiber.
Design of multi-phase dynamic chemical networks
Chen, Chenrui; Tan, Junjun; Hsieh, Ming-Chien; Pan, Ting; Goodwin, Jay T.; Mehta, Anil K.; Grover, Martha A.; Lynn, David G.
2017-08-01
Template-directed polymerization reactions enable the accurate storage and processing of nature's biopolymer information. This mutualistic relationship of nucleic acids and proteins, a network known as life's central dogma, is now marvellously complex, and the progressive steps necessary for creating the initial sequence and chain-length-specific polymer templates are lost to time. Here we design and construct dynamic polymerization networks that exploit metastable prion cross-β phases. Mixed-phase environments have been used for constructing synthetic polymers, but these dynamic phases emerge naturally from the growing peptide oligomers and create environments suitable both to nucleate assembly and select for ordered templates. The resulting templates direct the amplification of a phase containing only chain-length-specific peptide-like oligomers. Such multi-phase biopolymer dynamics reveal pathways for the emergence, self-selection and amplification of chain-length- and possibly sequence-specific biopolymers.
Optimal Dynamical Range of Excitable Networks at Criticality
Kinouchi, Osame
2006-01-01
A recurrent idea in the study of complex systems is that optimal information processing is to be found near bifurcation points or phase transitions. However, this heuristic hypothesis has few (if any) concrete realizations where a standard and biologically relevant quantity is optimized at criticality. Here we give a clear example of such a phenomenon: a network of excitable elements has its sensitivity and dynamic range maximized at the critical point of a non-equilibrium phase transition. Our results are compatible with the essential role of gap junctions in olfactory glomeruli and retinal ganglionar cell output. Synchronization and global oscillations also appear in the network dynamics. We propose that the main functional role of electrical coupling is to provide an enhancement of dynamic range, therefore allowing the coding of information spanning several orders of magnitude. The mechanism could provide a microscopic neural basis for psychophysical laws.
Collective dynamics in heterogeneous networks of neuronal cellular automata
Manchanda, Kaustubh; Bose, Amitabha; Ramaswamy, Ramakrishna
2017-12-01
We examine the collective dynamics of heterogeneous random networks of model neuronal cellular automata. Each automaton has b active states, a single silent state and r - b - 1 refractory states, and can show 'spiking' or 'bursting' behavior, depending on the values of b. We show that phase transitions that occur in the dynamical activity can be related to phase transitions in the structure of Erdõs-Rényi graphs as a function of edge probability. Different forms of heterogeneity allow distinct structural phase transitions to become relevant. We also show that the dynamics on the network can be described by a semi-annealed process and, as a result, can be related to the Boolean Lyapunov exponent.
Network Signaling Channel for Improving ZigBee Performance in Dynamic Cluster-Tree Networks
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Kohvakka Mikko
2008-01-01
Full Text Available Abstract ZigBee is one of the most potential standardized technologies for wireless sensor networks (WSNs. Yet, sufficient energy-efficiency for the lowest power WSNs is achieved only in rather static networks. This severely limits the applicability of ZigBee in outdoor and mobile applications, where operation environment is harsh and link failures are common. This paper proposes a network channel beaconing (NCB algorithm for improving ZigBee performance in dynamic cluster-tree networks. NCB reduces the energy consumption of passive scans by dedicating one frequency channel for network beacon transmissions and by energy optimizing their transmission rate. According to an energy analysis, the power consumption of network maintenance operations reduces by 70%–76% in dynamic networks. In static networks, energy overhead is negligible. Moreover, the service time for data routing increases up to 37%. The performance of NCB is validated by ns-2 simulations. NCB can be implemented as an extension on MAC and NWK layers and it is fully compatible with ZigBee.
Network Signaling Channel for Improving ZigBee Performance in Dynamic Cluster-Tree Networks
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D. Hämäläinen
2008-03-01
Full Text Available ZigBee is one of the most potential standardized technologies for wireless sensor networks (WSNs. Yet, sufficient energy-efficiency for the lowest power WSNs is achieved only in rather static networks. This severely limits the applicability of ZigBee in outdoor and mobile applications, where operation environment is harsh and link failures are common. This paper proposes a network channel beaconing (NCB algorithm for improving ZigBee performance in dynamic cluster-tree networks. NCB reduces the energy consumption of passive scans by dedicating one frequency channel for network beacon transmissions and by energy optimizing their transmission rate. According to an energy analysis, the power consumption of network maintenance operations reduces by 70%Ã¢Â€Â“76% in dynamic networks. In static networks, energy overhead is negligible. Moreover, the service time for data routing increases up to 37%. The performance of NCB is validated by ns-2 simulations. NCB can be implemented as an extension on MAC and NWK layers and it is fully compatible with ZigBee.
Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks
Jorgensen, Charles C.
1997-01-01
A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.
Interpersonal interactions and human dynamics in a large social network
Grabowski, Andrzej
2007-11-01
We study a large social network consisting of over 106 individuals, who form an Internet community and organize themselves in groups of different sizes. On the basis of the users’ list of friends and other data registered in the database we investigate the structure and time development of the network. The structure of this friendship network is very similar to the structure of different social networks. However, here a degree distribution exhibiting two scaling regimes, power-law for low connectivity and exponential for large connectivity, was found. The groups size distribution and distribution of number of groups of an individual have power-law form. We found very interesting scaling laws concerning human dynamics. Our research has shown how long people are interested in a single task.
The Dynamics of Protest Recruitment through an Online Network
González-Bailón, Sandra; Borge-Holthoefer, Javier; Rivero, Alejandro; Moreno, Yamir
2011-12-01
The recent wave of mobilizations in the Arab world and across Western countries has generated much discussion on how digital media is connected to the diffusion of protests. We examine that connection using data from the surge of mobilizations that took place in Spain in May 2011. We study recruitment patterns in the Twitter network and find evidence of social influence and complex contagion. We identify the network position of early participants (i.e. the leaders of the recruitment process) and of the users who acted as seeds of message cascades (i.e. the spreaders of information). We find that early participants cannot be characterized by a typical topological position but spreaders tend to be more central in the network. These findings shed light on the connection between online networks, social contagion, and collective dynamics, and offer an empirical test to the recruitment mechanisms theorized in formal models of collective action.
The Dynamics of Protest Recruitment through an Online Network
Gonzalez-Bailon, Sandra; Rivero, Alejandro; Moreno, Yamir
2011-01-01
The recent wave of mobilizations in the Arab world and across Western countries has generated much discussion on how digital media is connected to the diffusion of protests. We examine that connection using data from the surge of mobilizations that took place in Spain in May 2011. We study recruitment patterns in the Twitter network and find evidence of social influence and complex contagion. We identify the network position of early participants (i.e. the leaders of the recruitment process) and of the users who acted as seeds of message cascades (i.e. the spreaders of information). We find that early participants cannot be characterized by a typical topological position but spreaders tend to me more central to the network. These findings shed light on the connection between online networks, social contagion, and collective dynamics, and offer an empirical test to the recruitment mechanisms theorized in formal models of collective action.
Submodularity in dynamics and control of networked systems
Clark, Andrew; Bushnell, Linda; Poovendran, Radha
2016-01-01
This book presents a framework for the control of networked systems utilizing submodular optimization techniques. The main focus is on selecting input nodes for the control of networked systems, an inherently discrete optimization problem with applications in power system stability, social influence dynamics, and the control of vehicle formations. The first part of the book is devoted to background information on submodular functions, matroids, and submodular optimization, and presents algorithms for distributed submodular optimization that are scalable to large networked systems. In turn, the second part develops a unifying submodular optimization approach to controlling networked systems based on multiple performance and controllability criteria. Techniques are introduced for selecting input nodes to ensure smooth convergence, synchronization, and robustness to environmental and adversarial noise. Submodular optimization is the first unifying approach towards guaranteeing both performance and controllabilit...
de Luis Balaguer, Maria Angels; Sozzani, Rosangela
2017-01-01
Gene regulatory network (GRN) models have been shown to predict and represent interactions among sets of genes. Here, we first show the basic steps to implement a simple but computationally efficient algorithm to infer GRNs based on dynamic Bayesian networks (DBNs), and we then explain how to approximate DBN-based GRN models with continuous models. In addition, we show a MATLAB implementation of the key steps of this method, which we use to infer an Arabidopsis root GRN.
Competitive dynamics of lexical innovations in multi-layer networks
Javarone, Marco Alberto
2014-04-01
We study the introduction of lexical innovations into a community of language users. Lexical innovations, i.e. new term added to people's vocabulary, plays an important role in the process of language evolution. Nowadays, information is spread through a variety of networks, including, among others, online and offline social networks and the World Wide Web. The entire system, comprising networks of different nature, can be represented as a multi-layer network. In this context, lexical innovations diffusion occurs in a peculiar fashion. In particular, a lexical innovation can undergo three different processes: its original meaning is accepted; its meaning can be changed or misunderstood (e.g. when not properly explained), hence more than one meaning can emerge in the population. Lastly, in the case of a loan word, it can be translated into the population language (i.e. defining a new lexical innovation or using a synonym) or into a dialect spoken by part of the population. Therefore, lexical innovations cannot be considered simply as information. We develop a model for analyzing this scenario using a multi-layer network comprising a social network and a media network. The latter represents the set of all information systems of a society, e.g. television, the World Wide Web and radio. Furthermore, we identify temporal directed edges between the nodes of these two networks. In particular, at each time-step, nodes of the media network can be connected to randomly chosen nodes of the social network and vice versa. In doing so, information spreads through the whole system and people can share a lexical innovation with their neighbors or, in the event they work as reporters, by using media nodes. Lastly, we use the concept of "linguistic sign" to model lexical innovations, showing its fundamental role in the study of these dynamics. Many numerical simulations have been performed to analyze the proposed model and its outcomes.
Dynamic reorganization of intrinsic functional networks in the mouse brain.
Grandjean, Joanes; Preti, Maria Giulia; Bolton, Thomas A W; Buerge, Michaela; Seifritz, Erich; Pryce, Christopher R; Van De Ville, Dimitri; Rudin, Markus
2017-05-15
Functional connectivity (FC) derived from resting-state functional magnetic resonance imaging (rs-fMRI) allows for the integrative study of neuronal processes at a macroscopic level. The majority of studies to date have assumed stationary interactions between brain regions, without considering the dynamic aspects of network organization. Only recently has the latter received increased attention, predominantly in human studies. Applying dynamic FC (dFC) analysis to mice is attractive given the relative simplicity of the mouse brain and the possibility to explore mechanisms underlying network dynamics using pharmacological, environmental or genetic interventions. Therefore, we have evaluated the feasibility and research potential of mouse dFC using the interventions of social stress or anesthesia duration as two case-study examples. By combining a sliding-window correlation approach with dictionary learning, several dynamic functional states (dFS) with a complex organization were identified, exhibiting highly dynamic inter- and intra-modular interactions. Each dFS displayed a high degree of reproducibility upon changes in analytical parameters and across datasets. They fluctuated at different degrees as a function of anesthetic depth, and were sensitive indicators of pathology as shown for the chronic psychosocial stress mouse model of depression. Dynamic functional states are proposed to make a major contribution to information integration and processing in the healthy and diseased brain. Copyright © 2017 Elsevier Inc. All rights reserved.
Unveiling protein functions through the dynamics of the interaction network.
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Irene Sendiña-Nadal
Full Text Available Protein interaction networks have become a tool to study biological processes, either for predicting molecular functions or for designing proper new drugs to regulate the main biological interactions. Furthermore, such networks are known to be organized in sub-networks of proteins contributing to the same cellular function. However, the protein function prediction is not accurate and each protein has traditionally been assigned to only one function by the network formalism. By considering the network of the physical interactions between proteins of the yeast together with a manual and single functional classification scheme, we introduce a method able to reveal important information on protein function, at both micro- and macro-scale. In particular, the inspection of the properties of oscillatory dynamics on top of the protein interaction network leads to the identification of misclassification problems in protein function assignments, as well as to unveil correct identification of protein functions. We also demonstrate that our approach can give a network representation of the meta-organization of biological processes by unraveling the interactions between different functional classes.
Computational analysis of complex systems: Applications to population dynamics and networks
Molnar, Ferenc
In most complex evolving systems, we can often find a critical subset of the constituents that can initiate a global change in the entire system. For example, in complex networks, a critical subset of nodes can efficiently spread information, influence, or control dynamical processes over the entire network. Similarly, in nonlinear dynamics, we can locate key variables, or find the necessary parameters, to reach the attraction basin of a desired global state. In both cases, a fundamental goal is finding the ability to efficiently control these systems. We study two distinct complex systems in this dissertation, exploring these topics. First, we analyze a population dynamics model describing interactions of sex-structured population groups. Specifically, we analyze how a sex-linked genetic trait's ecological consequence (population survival or extinction) can be influenced by the presence of sex-specific cultural mortality traits, motivated by the desire to expand the theoretical understanding of the role of biased sex ratios in organisms. We analyze dynamics within a single population group, as well as between competing groups. We find that there is a finite range of sex ratio bias that can be maintained in stable equilibrium by sex-specific mortalities. We also find that the outcome of an invasion and the ensuing between-group competition depends not on larger equilibrium group densities, but on the higher allocation of sex-ratio genes. When we extend the model with diffusive dispersal, we find that a critical patch size for achieving positive growth only exists if the population expands into an empty environment. If a resident population is already present that can be exploited by the invading group, then any small seed of invader can advance from rarity, in the mean-field approximation, as long as the local competition dynamics favors the invader's survival. Most spatial models assume initial populations with a uniform distribution inside a finite patch; a
Analyzing, Modeling, and Simulation for Human Dynamics in Social Network
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Yunpeng Xiao
2012-01-01
Full Text Available This paper studies the human behavior in the top-one social network system in China (Sina Microblog system. By analyzing real-life data at a large scale, we find that the message releasing interval (intermessage time obeys power law distribution both at individual level and at group level. Statistical analysis also reveals that human behavior in social network is mainly driven by four basic elements: social pressure, social identity, social participation, and social relation between individuals. Empirical results present the four elements' impact on the human behavior and the relation between these elements. To further understand the mechanism of such dynamic phenomena, a hybrid human dynamic model which combines “interest” of individual and “interaction” among people is introduced, incorporating the four elements simultaneously. To provide a solid evaluation, we simulate both two-agent and multiagent interactions with real-life social network topology. We achieve the consistent results between empirical studies and the simulations. The model can provide a good understanding of human dynamics in social network.
Multi-Topic Tracking Model for dynamic social network
Li, Yuhua; Liu, Changzheng; Zhao, Ming; Li, Ruixuan; Xiao, Hailing; Wang, Kai; Zhang, Jun
2016-07-01
The topic tracking problem has attracted much attention in the last decades. However, existing approaches rarely consider network structures and textual topics together. In this paper, we propose a novel statistical model based on dynamic bayesian network, namely Multi-Topic Tracking Model for Dynamic Social Network (MTTD). It takes influence phenomenon, selection phenomenon, document generative process and the evolution of textual topics into account. Specifically, in our MTTD model, Gibbs Random Field is defined to model the influence of historical status of users in the network and the interdependency between them in order to consider the influence phenomenon. To address the selection phenomenon, a stochastic block model is used to model the link generation process based on the users' interests to topics. Probabilistic Latent Semantic Analysis (PLSA) is used to describe the document generative process according to the users' interests. Finally, the dependence on the historical topic status is also considered to ensure the continuity of the topic itself in topic evolution model. Expectation Maximization (EM) algorithm is utilized to estimate parameters in the proposed MTTD model. Empirical experiments on real datasets show that the MTTD model performs better than Popular Event Tracking (PET) and Dynamic Topic Model (DTM) in generalization performance, topic interpretability performance, topic content evolution and topic popularity evolution performance.
Direct effect of a dynamic wrist and hand orthosis on reach and grasp kinematics in chronic stroke
Nijenhuis, Sharon Maria; Prange, Grada Berendina; Stienen, Arno; Buurke, Jaap; Rietman, Johan Swanik; Yu, Haoyong; Braun, David; Campolo, Domenico
2015-01-01
Many stroke patients have impaired arm and hand function. Distal arm and hand devices may support functional use of the upper extremity in activities in daily life. The present study assessed the direct effects of a passive dynamic wrist and hand orthosis on hand and arm movements during the
Latino social network dynamics and the Hurricane Katrina disaster.
Hilfinger Messias, DeAnne K; Barrington, Clare; Lacy, Elaine
2012-01-01
The aim of this qualitative research was to examine the dynamics of existing and emerging social networks among Latino survivors of Hurricane Katrina. Data were generated through individual, in-depth interviews conducted with 65 Latinos within six months of the storm striking the Gulf Coast of the United States in August 2005. The findings illustrated both the role of social networks in gathering information, making decisions and accessing resources, and how these existing social networks were disrupted and strained by overwhelming needs. Broader structural issues, including poverty and a lack of transportation, combined with marginalised status as immigrants, further constrained access to essential information and resources. In response, new, if temporary, social networks emerged, based primarily on shared nationality, language, and a sense of collective commitment. Practice implications include the need to consider the social network dynamics of marginalised groups in developing innovative strategies to overcome structural barriers to accessing resources essential for disaster preparedness and survival. © 2012 The Author(s). Disasters © Overseas Development Institute, 2012.
Stochastic Simulation of Biomolecular Networks in Dynamic Environments.
Voliotis, Margaritis; Thomas, Philipp; Grima, Ramon; Bowsher, Clive G
2016-06-01
Simulation of biomolecular networks is now indispensable for studying biological systems, from small reaction networks to large ensembles of cells. Here we present a novel approach for stochastic simulation of networks embedded in the dynamic environment of the cell and its surroundings. We thus sample trajectories of the stochastic process described by the chemical master equation with time-varying propensities. A comparative analysis shows that existing approaches can either fail dramatically, or else can impose impractical computational burdens due to numerical integration of reaction propensities, especially when cell ensembles are studied. Here we introduce the Extrande method which, given a simulated time course of dynamic network inputs, provides a conditionally exact and several orders-of-magnitude faster simulation solution. The new approach makes it feasible to demonstrate-using decision-making by a large population of quorum sensing bacteria-that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate. Our approach has the potential to significantly advance both understanding of molecular systems biology and design of synthetic circuits.
Stochastic Simulation of Biomolecular Networks in Dynamic Environments.
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Margaritis Voliotis
2016-06-01
Full Text Available Simulation of biomolecular networks is now indispensable for studying biological systems, from small reaction networks to large ensembles of cells. Here we present a novel approach for stochastic simulation of networks embedded in the dynamic environment of the cell and its surroundings. We thus sample trajectories of the stochastic process described by the chemical master equation with time-varying propensities. A comparative analysis shows that existing approaches can either fail dramatically, or else can impose impractical computational burdens due to numerical integration of reaction propensities, especially when cell ensembles are studied. Here we introduce the Extrande method which, given a simulated time course of dynamic network inputs, provides a conditionally exact and several orders-of-magnitude faster simulation solution. The new approach makes it feasible to demonstrate-using decision-making by a large population of quorum sensing bacteria-that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate. Our approach has the potential to significantly advance both understanding of molecular systems biology and design of synthetic circuits.
The Dynamics of Coalition Formation on Complex Networks.
Auer, S; Heitzig, J; Kornek, U; Schöll, E; Kurths, J
2015-08-25
Complex networks describe the structure of many socio-economic systems. However, in studies of decision-making processes the evolution of the underlying social relations are disregarded. In this report, we aim to understand the formation of self-organizing domains of cooperation ("coalitions") on an acquaintance network. We include both the network's influence on the formation of coalitions and vice versa how the network adapts to the current coalition structure, thus forming a social feedback loop. We increase complexity from simple opinion adaptation processes studied in earlier research to more complex decision-making determined by costs and benefits, and from bilateral to multilateral cooperation. We show how phase transitions emerge from such coevolutionary dynamics, which can be interpreted as processes of great transformations. If the network adaptation rate is high, the social dynamics prevent the formation of a grand coalition and therefore full cooperation. We find some empirical support for our main results: Our model develops a bimodal coalition size distribution over time similar to those found in social structures. Our detection and distinguishing of phase transitions may be exemplary for other models of socio-economic systems with low agent numbers and therefore strong finite-size effects.
An Efficient Route Maintenance Protocol for Dynamic Bluetooth Networks
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Sabeen Tahir
2017-10-01
Full Text Available Bluetooth is a widespread technology for small wireless networks that permits Bluetooth devices to construct a multi-hop network called scatternet. Routing in multi-hop dynamic Bluetooth network, where a number of masters and bridges exist creates technical hitches. It is observed that frequent link disconnections and a new route construction consume extra system resources that degrade the whole network performance. Therefore, in this paper an Efficient Route Maintenance Protocol for Dynamic Bluetooth Networks (ERMP is proposed that repairs the weak routing paths based on the prediction of weak links and weak devices. The ERMP predicts the weak links through the signal strength and weak devices through low energy levels. During the main route construction, routing masters and bridges keep the information of the Fall Back Devices (FBDs for route maintenance. On the prediction of a weak link, the ERMP activates an alternate link, on the other hand, for a weak device it activates the FBD. The proposed ERMP is compared with some existing closely related protocols, and the simulation results show that the proposed ERMP successfully recovers the weak paths and improves the system performance.
Fractional Dynamics of Network Growth Constrained by Aging Node Interactions.
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Hadiseh Safdari
Full Text Available In many social complex systems, in which agents are linked by non-linear interactions, the history of events strongly influences the whole network dynamics. However, a class of "commonly accepted beliefs" seems rarely studied. In this paper, we examine how the growth process of a (social network is influenced by past circumstances. In order to tackle this cause, we simply modify the well known preferential attachment mechanism by imposing a time dependent kernel function in the network evolution equation. This approach leads to a fractional order Barabási-Albert (BA differential equation, generalizing the BA model. Our results show that, with passing time, an aging process is observed for the network dynamics. The aging process leads to a decay for the node degree values, thereby creating an opposing process to the preferential attachment mechanism. On one hand, based on the preferential attachment mechanism, nodes with a high degree are more likely to absorb links; but, on the other hand, a node's age has a reduced chance for new connections. This competitive scenario allows an increased chance for younger members to become a hub. Simulations of such a network growth with aging constraint confirm the results found from solving the fractional BA equation. We also report, as an exemplary application, an investigation of the collaboration network between Hollywood movie actors. It is undubiously shown that a decay in the dynamics of their collaboration rate is found, even including a sex difference. Such findings suggest a widely universal application of the so generalized BA model.
Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui
2017-10-06
Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli, and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.
Shrirang Ambaji KULKARNI; Raghavendra G . RAO
2017-01-01
Routing data packets in a dynamic network is a difficult and important problem in computer networks. As the network is dynamic, it is subject to frequent topology changes and is subject to variable link costs due to congestion and bandwidth. Existing shortest path algorithms fail to converge to better solutions under dynamic network conditions. Reinforcement learning algorithms posses better adaptation techniques in dynamic environments. In this paper we apply model based Q-Routing technique ...
Dynamic Pricing in Electronic Commerce Using Neural Network
Ghose, Tapu Kumar; Tran, Thomas T.
In this paper, we propose an approach where feed-forward neural network is used for dynamically calculating a competitive price of a product in order to maximize sellers’ revenue. In the approach we considered that along with product price other attributes such as product quality, delivery time, after sales service and seller’s reputation contribute in consumers purchase decision. We showed that once the sellers, by using their limited prior knowledge, set an initial price of a product our model adjusts the price automatically with the help of neural network so that sellers’ revenue is maximized.
Fast Distributed Dynamics of Semantic Networks via Social Media
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Facundo Carrillo
2015-01-01
Full Text Available We investigate the dynamics of semantic organization using social media, a collective expression of human thought. We propose a novel, time-dependent semantic similarity measure (TSS, based on the social network Twitter. We show that TSS is consistent with static measures of similarity but provides high temporal resolution for the identification of real-world events and induced changes in the distributed structure of semantic relationships across the entire lexicon. Using TSS, we measured the evolution of a concept and its movement along the semantic neighborhood, driven by specific news/events. Finally, we showed that particular events may trigger a temporary reorganization of elements in the semantic network.
New Heuristic Algorithm for Dynamic Traffic in WDM Optical Networks
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Arturo Benito Rodríguez Garcia
2015-12-01
Full Text Available The results and comparison of the simulation of a new heuristic algorithm called Snake One are presented. The comparison is made with three heuristic algorithms, Genetic Algorithms, Simulated Annealing, and Tabu Search, using blocking probability and network utilization as standard indicators. The simulation was made on the WDM NSFNET under dynamic traffic conditions. The results show a substantial decrease of blocking, but this causes a relative growth of network utilization. There are also load intervals at which its performance improves, decreasing the number of blocked requests.
Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks
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Khandakar Ahmed
2016-02-01
Full Text Available Dynamic skyline query is one of the most popular and significant variants of skyline query in the field of multi-criteria decision-making. However, designing a distributed dynamic skyline query possesses greater challenge, especially for the distributed data centric storage within wireless sensor networks (WSNs. In this paper, a novel Enhanced Distributed Dynamic Skyline (EDDS approach is proposed and implemented in Disk Based Data Centric Storage (DBDCS architecture. DBDCS is an adaptation of magnetic disk storage platter consisting tracks and sectors. In DBDCS, the disc track and sector analogy is used to map data locations. A distance based indexing method is used for storing and querying multi-dimensional similar data. EDDS applies a threshold based hierarchical approach, which uses temporal correlation among sectors and sector segments to calculate a dynamic skyline. The efficiency and effectiveness of EDDS has been evaluated in terms of latency, energy consumption and accuracy through a simulation model developed in Castalia.
Arayasirikul, Sean; Chen, Yea-Hung; Jin, Harry; Wilson, Erin
2016-06-01
Respondent-driven sampling (RDS) peer referral has been proven to be an effective recruitment method for hard-to-reach populations; however, its application in diverse populations is limited. Recruitment occurred in two phases: RDS-only followed by development and implementation of an online social network strategy in combination with RDS peer referral (RDS + SNS). Compared to RDS-only, RDS + SNS reached a sample that was younger (χ(2) = 9.19, P = .03), more likely to identify with a non-binary gender identity (χ(2) = 10.4247, P = .03), with less housing instability (50.5 vs. 68.6 %, χ(2) = 9.0038, P = .002) and less sex work (19.7 vs. 31.4 %, χ(2) = 5.0798, P = .02). Additionally, we describe lessons learned as a result of implementing our online social network strategy. Our findings underscore the importance of integrating Internet-driven strategies to meet challenges in sample diversity and recruitment of young transwomen.
Memory dynamics in attractor networks with saliency weights.
Tang, Huajin; Li, Haizhou; Yan, Rui
2010-07-01
Memory is a fundamental part of computational systems like the human brain. Theoretical models identify memories as attractors of neural network activity patterns based on the theory that attractor (recurrent) neural networks are able to capture some crucial characteristics of memory, such as encoding, storage, retrieval, and long-term and working memory. In such networks, long-term storage of the memory patterns is enabled by synaptic strengths that are adjusted according to some activity-dependent plasticity mechanisms (of which the most widely recognized is the Hebbian rule) such that the attractors of the network dynamics represent the stored memories. Most of previous studies on associative memory are focused on Hopfield-like binary networks, and the learned patterns are often assumed to be uncorrelated in a way that minimal interactions between memories are facilitated. In this letter, we restrict our attention to a more biological plausible attractor network model and study the neuronal representations of correlated patterns. We have examined the role of saliency weights in memory dynamics. Our results demonstrate that the retrieval process of the memorized patterns is characterized by the saliency distribution, which affects the landscape of the attractors. We have established the conditions that the network state converges to unique memory and multiple memories. The analytical result also holds for other cases for variable coding levels and nonbinary levels, indicating a general property emerging from correlated memories. Our results confirmed the advantage of computing with graded-response neurons over binary neurons (i.e., reducing of spurious states). It was also found that the nonuniform saliency distribution can contribute to disappearance of spurious states when they exit.
Networks dynamics in the case of emerging technologies
Energy Technology Data Exchange (ETDEWEB)
Rotolo, D
2016-07-01
This research in progress aims at increasing our understanding of how collaborative networks form, evolve and are configured in the case of emerging technologies. The architecture of the relationships among the variety of organisational actors involved in the emergence process exerts a significant influence in shaping technological change in certain directions rather than others, especially in the early stage of emergence. As a result, socially optimal or desirable technological trajectories may be ‘opportunistically’ rejected. Our empirical analysis is based on a case-study of an emerging medical technology, namely ‘microneedles’. On the basis of co-authorship data reported in 1,943 publications on the topic from 1990 to 2014, longitudinal collaboration (co-authorship) networks were built at two levels: affiliation and author. We examined the dynamics of co-authorship networks by building on recent methodological advancements in network analysis, i.e. Exponential Random Graph Models (ERGMs). These models enable us to make statistical inferences about on the extent to which a network configuration occurs more than could be expected by chance and to identify which social mechanisms may be shaping the network in certain configurations. The findings of the statistical analyses (currently in progress) combined with the qualitative understanding of the case will increase our understanding of which mechanisms are more likely to drive the network dynamics in the case of emerging technologies. These include evidence of the extent to which the likelihood of forming, maintaining, or terminating ties among actors (authors or affiliations) is affected by actors’ covariates such as types of organisations, diversity/specialisation of the research undertaken, and status. These findings have potential to provide important inputs for policymaking process in the case of emerging technologies. (Author)
Liu, Zugang
Network systems, including transportation and logistic systems, electric power generation and distribution networks as well as financial networks, provide the critical infrastructure for the functioning of our societies and economies. The understanding of the dynamic behavior of such systems is also crucial to national security and prosperity. The identification of new connections between distinct network systems is the inspiration for the research in this dissertation. In particular, I answer two questions raised by Beckmann, McGuire, and Winsten (1956) and Copeland (1952) over half a century ago, which are, respectively, how are electric power flows related to transportation flows and does money flow like water or electricity? In addition, in this dissertation, I achieve the following: (1) I establish the relationships between transportation networks and three other classes of complex network systems: supply chain networks, electric power generation and transmission networks, and financial networks with intermediation. The establishment of such connections provides novel theoretical insights as well as new pricing mechanisms, and efficient computational methods. (2) I develop new modeling frameworks based on evolutionary variational inequality theory that capture the dynamics of such network systems in terms of the time-varying flows and incurred costs, prices, and, where applicable, profits. This dissertation studies the dynamics of such network systems by addressing both internal competition and/or cooperation, and external changes, such as varying costs and demands. (3) I focus, in depth, on electric power supply chains. By exploiting the relationships between transportation networks and electric power supply chains, I develop a large-scale network model that integrates electric power supply chains and fuel supply markets. The model captures both the economic transactions as well as the physical transmission constraints. The model is then applied to the New
Dynamic model of a PEM electrolyser based on artificial neural networks
Energy Technology Data Exchange (ETDEWEB)
Chavez-Ramirez, A.U.; Munoz-Guerrero, R.; Sanchez-Huerta, V.; Ramirez-Arredondo, Juan M.; Ornelas, R.; Arriaga, L.G.; Siracusano, S.; Brunaccini, G.; Napoli, G.; Antonucci, V.; Arico, A.S.
2011-01-15
Hydrogen production by electrolysis is emerging as a promising way to meet future fuel demand, and developing models capable of simulating the operation of electrolysis devices is indispensable to efficiently design power generation systems, reduce manufacturing costs and save resources. The nonlinear nature of the Artificial Neural Network (ANN) plays a key role in developing models predicting the performance of complex systems. The behaviour of a Polymer Electrolyte Membrane (PEM) Electrolyser of three cell stack was modelled successfully using a Multilayer Perceptron Network (MLP). This dynamic model was trained to learn the internal relationships of this electrolysis device and predict its behaviour without physical equations. Electric current supply and operation temperature were used as input vector able to predict each cell voltage behaviour. An accuracy (< 2%) was reached after comparing the single cell performance of the real electrolyser versus the ANN based model. This predictive model can be used as a virtual device into a more complex energy system.
The scaling properties of dynamical fluctuations in temporal networks
Chi, Liping
2015-01-01
The factorial moments analyses are performed to study the scaling properties of the dynamical fluctuations of contacts and nodes in temporal networks based on empirical data sets. The intermittent behaviors are observed in the fluctuations for all orders of the moments. It indicates that the interaction has self-similarity structure in time interval and the fluctuations are not purely random but dynamical and correlated. The scaling exponents for contacts in Prostitution data and nodes in Conference data are very close to that for 2D Ising model undergoing a second-order phase transition.
Global value chains: Building blocks and network dynamics
Tsekeris, Theodore
2017-12-01
The paper employs measures and tools from complex network analysis to enhance the understanding and interpretation of structural characteristics pertaining to the Global Value Chains (GVCs) during the period 1995-2011. The analysis involves the country, sector and country-sector value chain networks to identify main drivers of structural change. The results indicate significant intertemporal changes, mirroring the increased globalization in terms of network size, strength and connectivity. They also demonstrate higher clustering and increased concentration of the most influential countries and country-sectors relative to all others in the GVC network, with the geographical dimension to prevail over the sectoral dimension in the formation of value chains. The regionalization and less hierarchical organization drive country-sector production sharing, while the sectoral value chain network has become more integrated and more competitive over time. The findings suggest that the impact of country-sector policies and/or shocks may vary with the own-group and network-wide influence of each country, take place in multiple geographical scales, as GVCs have a block structure, and involve time dynamics.
Architecture and dynamics of overlapped RNA regulatory networks.
Lapointe, Christopher P; Preston, Melanie A; Wilinski, Daniel; Saunders, Harriet A J; Campbell, Zachary T; Wickens, Marvin
2017-11-01
A single protein can bind and regulate many mRNAs. Multiple proteins with similar specificities often bind and control overlapping sets of mRNAs. Yet little is known about the architecture or dynamics of overlapped networks. We focused on three proteins with similar structures and related RNA-binding specificities-Puf3p, Puf4p, and Puf5p of S. cerevisiae Using RNA Tagging, we identified a "super-network" comprised of four subnetworks: Puf3p, Puf4p, and Puf5p subnetworks, and one controlled by both Puf4p and Puf5p. The architecture of individual subnetworks, and thus the super-network, is determined by competition among particular PUF proteins to bind mRNAs, their affinities for binding elements, and the abundances of the proteins. The super-network responds dramatically: The remaining network can either expand or contract. These strikingly opposite outcomes are determined by an interplay between the relative abundance of the RNAs and proteins, and their affinities for one another. The diverse interplay between overlapping RNA-protein networks provides versatile opportunities for regulation and evolution. © 2017 Lapointe et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.
Sampling dynamic networks with application to investigation of HIV epidemic drivers.
Goyal, Ravi; De Gruttola, Victor
2015-09-01
We propose a method for randomly sampling dynamic networks that permits isolation of the impact of different network features on processes that propagate on networks. The new methods permit uniform sampling of dynamic networks in ways that ensure that they are consistent with both a given cumulative network and with specified values for constraints on the dynamic network properties. Development of such methods is challenging because modifying one network property will generally tend to modify others as well. Methods to sample constrained dynamic networks are particularly useful in the investigation of network-based interventions that target and modify specific dynamic network properties, especially in settings where the whole network is unobservable and therefore many network properties are unmeasurable. We illustrate this method by investigating the incremental impact of changes in networks properties that are relevant for the spread of infectious diseases, such as concurrency in sexual relationships. Development of the method is motivated by the challenges that arise in investigating the role of HIV epidemic drivers due to the often limited information available about contact networks. The proposed methods for randomly sampling dynamic networks facilitate investigation of the type of network data that can best contribute to an understanding of the HIV epidemic dynamics as well as of the limitations of conclusions drawn in the absence of such information. Hence, the methods are intended to aid in the design and interpretation of studies of network-based interventions. Copyright © 2015 Elsevier Inc. All rights reserved.
Successive lag synchronization on dynamical networks with communication delay
Xin-Jian, Zhang; Ai-Ju, Wei; Ke-Zan, Li
2016-03-01
In this paper, successive lag synchronization (SLS) on a dynamical network with communication delay is investigated. In order to achieve SLS on the dynamical network with communication delay, we design linear feedback control and adaptive control, respectively. By using the Lyapunov function method, we obtain some sufficient conditions for global stability of SLS. To verify these results, some numerical examples are further presented. This work may find potential applications in consensus of multi-agent systems. Project supported by the National Natural Science Foundation of China (Grant No. 61004101), the Natural Science Foundation Program of Guangxi Province, China (Grant No. 2015GXNSFBB139002), the Graduate Innovation Project of Guilin University of Electronic Technology, China (Grant No. GDYCSZ201472), and the Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, China.
Sign Inference for Dynamic Signed Networks via Dictionary Learning
Directory of Open Access Journals (Sweden)
Yi Cen
2013-01-01
Full Text Available Mobile online social network (mOSN is a burgeoning research area. However, most existing works referring to mOSNs deal with static network structures and simply encode whether relationships among entities exist or not. In contrast, relationships in signed mOSNs can be positive or negative and may be changed with time and locations. Applying certain global characteristics of social balance, in this paper, we aim to infer the unknown relationships in dynamic signed mOSNs and formulate this sign inference problem as a low-rank matrix estimation problem. Specifically, motivated by the Singular Value Thresholding (SVT algorithm, a compact dictionary is selected from the observed dataset. Based on this compact dictionary, the relationships in the dynamic signed mOSNs are estimated via solving the formulated problem. Furthermore, the estimation accuracy is improved by employing a dictionary self-updating mechanism.
Simulating dynamic plastic continuous neural networks by finite elements.
Joghataie, Abdolreza; Torghabehi, Omid Oliyan
2014-08-01
We introduce dynamic plastic continuous neural network (DPCNN), which is comprised of neurons distributed in a nonlinear plastic medium where wire-like connections of neural networks are replaced with the continuous medium. We use finite element method to model the dynamic phenomenon of information processing within the DPCNNs. During the training, instead of weights, the properties of the continuous material at its different locations and some properties of neurons are modified. Input and output can be vectors and/or continuous functions over lines and/or areas. Delay and feedback from neurons to themselves and from outputs occur in the DPCNNs. We model a simple form of the DPCNN where the medium is a rectangular plate of bilinear material, and the neurons continuously fire a signal, which is a function of the horizontal displacement.
Fiber-Optic Transmission Networks Efficient Design and Dynamic Operation
Pachnicke, Stephan
2012-01-01
Next generation optical communication systems will have to transport a significantly increased data volume at a reduced cost per transmitted bit. To achieve these ambitious goals optimum design is crucial in combination with dynamic adaptation to actual traffic demands and improved energy efficiency. In the first part of the book the author elaborates on the design of optical transmission systems. Several methods for efficient numerical simulation are presented ranging from meta-model based optimization to parallelization techniques for solving the nonlinear Schrödinger equation. Furthermore, fast analytical and semi-analytical models are described to estimate the various degradation effects occurring on the transmission line. In the second part of the book operational aspects of optical networks are investigated. Physical layer impairment-aware routing and regenerator placement are studied. Finally, it is analyzed how the energy efficiency of a multi-layer optical core network can be increased by dynamic ad...
Robust dynamics in minimal hybrid models of genetic networks.
Perkins, Theodore J; Wilds, Roy; Glass, Leon
2010-11-13
Many gene-regulatory networks necessarily display robust dynamics that are insensitive to noise and stable under evolution. We propose that a class of hybrid systems can be used to relate the structure of these networks to their dynamics and provide insight into the origin of robustness. In these systems, the genes are represented by logical functions, and the controlling transcription factor protein molecules are real variables, which are produced and destroyed. As the transcription factor concentrations cross thresholds, they control the production of other transcription factors. We discuss mathematical analysis of these systems and show how the concepts of robustness and minimality can be used to generate putative logical organizations based on observed symbolic sequences. We apply the methods to control of the cell cycle in yeast.
Kirst, Christoph
It is astonishing how the sub-parts of a brain co-act to produce coherent behavior. What are mechanism that coordinate information processing and communication and how can those be changed flexibly in order to cope with variable contexts? Here we show that when information is encoded in the deviations around a collective dynamical reference state of a recurrent network the propagation of these fluctuations is strongly dependent on precisely this underlying reference. Information here 'surfs' on top of the collective dynamics and switching between states enables fast and flexible rerouting of information. This in turn affects local processing and consequently changes in the global reference dynamics that re-regulate the distribution of information. This provides a generic mechanism for self-organized information processing as we demonstrate with an oscillatory Hopfield network that performs contextual pattern recognition. Deep neural networks have proven to be very successful recently. Here we show that generating information channels via collective reference dynamics can effectively compress a deep multi-layer architecture into a single layer making this mechanism a promising candidate for the organization of information processing in biological neuronal networks.
Dynamical modeling and analysis of large cellular regulatory networks
Bérenguier, D.; Chaouiya, C.; Monteiro, P. T.; Naldi, A.; Remy, E.; Thieffry, D.; Tichit, L.
2013-06-01
The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.
Open innovation, networking, and business model dynamics: The two sides
Gay, Brigitte
2014-01-01
A business model describes the design of the value creation and capture mechanisms needed to yield profit. We contend that for a business model to be viable in turbulent and hypercompetitive environments, its dynamics are important and must leverage, out of all key business model modules proposed in different studies, on a combined value and network perspective. These different elements present, however, distinctive challenges for small innovative companies and larger firms. Moreover, the bus...
Dynamic Subsidy Method for Congestion Management in Distribution Networks
Huang, Shaojun; Wu, Qiuwei
2016-01-01
Dynamic subsidy (DS) is a locational price paid by the distribution system operator (DSO) to its customers in order to shift energy consumption to designated hours and nodes. It is promising for demand side management and congestion management. This paper proposes a new DS method for congestion management in distribution networks, including the market mechanism, the mathematical formulation through a two-level optimization, and the method solving the optimization by tightening the constraints...
Dynamical modeling and analysis of large cellular regulatory networks.
Bérenguier, D; Chaouiya, C; Monteiro, P T; Naldi, A; Remy, E; Thieffry, D; Tichit, L
2013-06-01
The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.
Empirical Modeling of the Plasmasphere Dynamics Using Neural Networks
Zhelavskaya, Irina S.; Shprits, Yuri Y.; Spasojević, Maria
2017-11-01
We present the PINE (Plasma density in the Inner magnetosphere Neural network-based Empirical) model - a new empirical model for reconstructing the global dynamics of the cold plasma density distribution based only on solar wind data and geomagnetic indices. Utilizing the density database obtained using the NURD (Neural-network-based Upper hybrid Resonance Determination) algorithm for the period of 1 October 2012 to 1 July 2016, in conjunction with solar wind data and geomagnetic indices, we develop a neural network model that is capable of globally reconstructing the dynamics of the cold plasma density distribution for 2≤L≤6 and all local times. We validate and test the model by measuring its performance on independent data sets withheld from the training set and by comparing the model-predicted global evolution with global images of He+ distribution in the Earth's plasmasphere from the IMAGE Extreme UltraViolet (EUV) instrument. We identify the parameters that best quantify the plasmasphere dynamics by training and comparing multiple neural networks with different combinations of input parameters (geomagnetic indices, solar wind data, and different durations of their time history). The optimal model is based on the 96 h time history of Kp, AE, SYM-H, and F10.7 indices. The model successfully reproduces erosion of the plasmasphere on the nightside and plume formation and evolution. We demonstrate results of both local and global plasma density reconstruction. This study illustrates how global dynamics can be reconstructed from local in situ observations by using machine learning techniques.
A High-Resolution Sensor Network for Monitoring Glacier Dynamics
Edwards, S.; Murray, T.; O'Farrell, T.; Rutt, I. C.; Loskot, P.; Martin, I.; Selmes, N.; Aspey, R.; James, T.; Bevan, S. L.; Baugé, T.
2013-12-01
Changes in Greenland and Antarctic ice sheets due to ice flow/ice-berg calving are a major uncertainty affecting sea-level rise forecasts. Latterly GNSS (Global Navigation Satellite Systems) have been employed extensively to monitor such glacier dynamics. Until recently however, the favoured methodology has been to deploy sensors onto the glacier surface, collect data for a period of time, then retrieve and download the sensors. This approach works well in less dynamic environments where the risk of sensor loss is low. In more extreme environments e.g. approaching the glacial calving front, the risk of sensor loss and hence data loss increases dramatically. In order to provide glaciologists with new insights into flow dynamics and calving processes we have developed a novel sensor network to increase the robustness of data capture. We present details of the technological requirements for an in-situ Zigbee wireless streaming network infrastructure supporting instantaneous data acquisition from high resolution GNSS sensors thereby increasing data capture robustness. The data obtained offers new opportunities to investigate the interdependence of mass flow, uplift, velocity and geometry and the network architecture has been specifically designed for deployment by helicopter close to the calving front to yield unprecedented detailed information. Following successful field trials of a pilot three node network during 2012, a larger 20 node network was deployed on the fast-flowing Helheim glacier, south-east Greenland over the summer months of 2013. The utilisation of dual wireless transceivers in each glacier node, multiple frequencies and four ';collector' stations located on the valley sides creates overlapping networks providing enhanced capacity, diversity and redundancy of data 'back-haul', even close to ';floor' RSSI (Received Signal Strength Indication) levels around -100 dBm. Data loss through radio packet collisions within sub-networks are avoided through the
Global dynamics of a network of stochastic neurons maximizes local mutual information.
Rodriguez, F B; Huerta, R; López, V
2001-02-01
We define a stochastic neuron as an element that increases its internal state with probability p until a threshold value is reached; after that its internal state is set back to the initial value. We study the local information of a stochastic neuron between the message arriving from the input neurons and the response of the neuron. We study the dependence of the local information on the firing probability alpha of the synaptic inputs in a network of such stochastic neurons. The values of alpha obtained in the simulations are the same as those obtained theoretically by maximization of local mutual information. We conclude that the global dynamics maximizes the local mutual information of single units, which means that the self-selected parameter value of the population dynamics is such that each neuron behaves as an optimal encoder.
Spatiotemporal neural network dynamics for the processing of dynamic facial expressions
Sato, Wataru; Kochiyama, Takanori; Uono, Shota
2015-01-01
The dynamic facial expressions of emotion automatically elicit multifaceted psychological activities; however, the temporal profiles and dynamic interaction patterns of brain activities remain unknown. We investigated these issues using magnetoencephalography. Participants passively observed dynamic facial expressions of fear and happiness, or dynamic mosaics. Source-reconstruction analyses utilizing functional magnetic-resonance imaging data revealed higher activation in broad regions of the bilateral occipital and temporal cortices in response to dynamic facial expressions than in response to dynamic mosaics at 150–200 ms and some later time points. The right inferior frontal gyrus exhibited higher activity for dynamic faces versus mosaics at 300–350 ms. Dynamic causal-modeling analyses revealed that dynamic faces activated the dual visual routes and visual–motor route. Superior influences of feedforward and feedback connections were identified before and after 200 ms, respectively. These results indicate that hierarchical, bidirectional neural network dynamics within a few hundred milliseconds implement the processing of dynamic facial expressions. PMID:26206708
Evolution properties of the community members for dynamic networks
Yang, Kai; Guo, Qiang; Li, Sheng-Nan; Han, Jing-Ti; Liu, Jian-Guo
2017-03-01
The collective behaviors of community members for dynamic social networks are significant for understanding evolution features of communities. In this Letter, we empirically investigate the evolution properties of the new community members for dynamic networks. Firstly, we separate data sets into different slices, and analyze the statistical properties of new members as well as communities they joined in for these data sets. Then we introduce a parameter φ to describe community evolution between different slices and investigate the dynamic community properties of the new community members. The empirical analyses for the Facebook, APS, Enron and Wiki data sets indicate that both the number of new members and joint communities increase, the ratio declines rapidly and then becomes stable over time, and most of the new members will join in the small size communities that is s ≤ 10. Furthermore, the proportion of new members in existed communities decreases firstly and then becomes stable and relatively small for these data sets. Our work may be helpful for deeply understanding the evolution properties of community members for social networks.
Nonequilibrium dynamics of probe filaments in actin-myosin networks.
Gladrow, J; Broedersz, C P; Schmidt, C F
2017-08-01
Active dynamic processes of cells are largely driven by the cytoskeleton, a complex and adaptable semiflexible polymer network, motorized by mechanoenzymes. Small dimensions, confined geometries, and hierarchical structures make it challenging to probe dynamics and mechanical response of such networks. Embedded semiflexible probe polymers can serve as nonperturbing multiscale probes to detect force distributions in active polymer networks. We show here that motor-induced forces transmitted to the probe polymers are reflected in nonequilibrium bending dynamics, which we analyze in terms of spatial eigenmodes of an elastic beam under steady-state conditions. We demonstrate how these active forces induce correlations among the mode amplitudes, which furthermore break time-reversal symmetry. This leads to a breaking of detailed balance in this mode space. We derive analytical predictions for the magnitude of resulting probability currents in mode space in the white-noise limit of motor activity. We relate the structure of these currents to the spatial profile of motor-induced forces along the probe polymers and provide a general relation for observable currents on two-dimensional hyperplanes.
Information dynamics of brain-heart physiological networks during sleep
Faes, L.; Nollo, G.; Jurysta, F.; Marinazzo, D.
2014-10-01
This study proposes an integrated approach, framed in the emerging fields of network physiology and information dynamics, for the quantitative analysis of brain-heart interaction networks during sleep. With this approach, the time series of cardiac vagal autonomic activity and brain wave activities measured respectively as the normalized high frequency component of heart rate variability and the EEG power in the δ, θ, α, σ, and β bands, are considered as realizations of the stochastic processes describing the dynamics of the heart system and of different brain sub-systems. Entropy-based measures are exploited to quantify the predictive information carried by each (sub)system, and to dissect this information into a part actively stored in the system and a part transferred to it from the other connected systems. The application of this approach to polysomnographic recordings of ten healthy subjects led us to identify a structured network of sleep brain-brain and brain-heart interactions, with the node described by the β EEG power acting as a hub which conveys the largest amount of information flowing between the heart and brain nodes. This network was found to be sustained mostly by the transitions across different sleep stages, as the information transfer was weaker during specific stages than during the whole night, and vanished progressively when moving from light sleep to deep sleep and to REM sleep.
Dynamical networks of influence in small group discussions.
Moussaïd, Mehdi; Noriega Campero, Alejandro; Almaatouq, Abdullah
2018-01-01
In many domains of life, business and management, numerous problems are addressed by small groups of individuals engaged in face-to-face discussions. While research in social psychology has a long history of studying the determinants of small group performances, the internal dynamics that govern a group discussion are not yet well understood. Here, we rely on computational methods based on network analyses and opinion dynamics to describe how individuals influence each other during a group discussion. We consider the situation in which a small group of three individuals engages in a discussion to solve an estimation task. We propose a model describing how group members gradually influence each other and revise their judgments over the course of the discussion. The main component of the model is an influence network-a weighted, directed graph that determines the extent to which individuals influence each other during the discussion. In simulations, we first study the optimal structure of the influence network that yields the best group performances. Then, we implement a social learning process by which individuals adapt to the past performance of their peers, thereby affecting the structure of the influence network in the long run. We explore the mechanisms underlying the emergence of efficient or maladaptive networks and show that the influence network can converge towards the optimal one, but only when individuals exhibit a social discounting bias by downgrading the relative performances of their peers. Finally, we find a late-speaker effect, whereby individuals who speak later in the discussion are perceived more positively in the long run and are thus more influential. The numerous predictions of the model can serve as a basis for future experiments, and this work opens research on small group discussion to computational social sciences.
Multiplex visibility graphs to investigate recurrent neural network dynamics
Bianchi, Filippo Maria; Livi, Lorenzo; Alippi, Cesare; Jenssen, Robert
2017-03-01
A recurrent neural network (RNN) is a universal approximator of dynamical systems, whose performance often depends on sensitive hyperparameters. Tuning them properly may be difficult and, typically, based on a trial-and-error approach. In this work, we adopt a graph-based framework to interpret and characterize internal dynamics of a class of RNNs called echo state networks (ESNs). We design principled unsupervised methods to derive hyperparameters configurations yielding maximal ESN performance, expressed in terms of prediction error and memory capacity. In particular, we propose to model time series generated by each neuron activations with a horizontal visibility graph, whose topological properties have been shown to be related to the underlying system dynamics. Successively, horizontal visibility graphs associated with all neurons become layers of a larger structure called a multiplex. We show that topological properties of such a multiplex reflect important features of ESN dynamics that can be used to guide the tuning of its hyperparamers. Results obtained on several benchmarks and a real-world dataset of telephone call data records show the effectiveness of the proposed methods.
Spectral analysis and slow spreading dynamics on complex networks
Ódor, Géza
2013-09-01
The susceptible-infected-susceptible (SIS) model is one of the simplest memoryless systems for describing information or epidemic spreading phenomena with competing creation and spontaneous annihilation reactions. The effect of quenched disorder on the dynamical behavior has recently been compared to quenched mean-field (QMF) approximations in scale-free networks. QMF can take into account topological heterogeneity and clustering effects of the activity in the steady state by spectral decomposition analysis of the adjacency matrix. Therefore, it can provide predictions on possible rare-region effects, thus on the occurrence of slow dynamics. I compare QMF results of SIS with simulations on various large dimensional graphs. In particular, I show that for Erdős-Rényi graphs this method predicts correctly the occurrence of rare-region effects. It also provides a good estimate for the epidemic threshold in case of percolating graphs. Griffiths Phases emerge if the graph is fragmented or if we apply a strong, exponentially suppressing weighting scheme on the edges. The latter model describes the connection time distributions in the face-to-face experiments. In case of a generalized Barabási-Albert type of network with aging connections, strong rare-region effects and numerical evidence for Griffiths Phase dynamics are shown. The dynamical simulation results agree well with the predictions of the spectral analysis applied for the weighted adjacency matrices.
Active and dynamic information fusion for multisensor systems with dynamic Bayesian networks.
Zhang, Yongmian; Ji, Qiang
2006-04-01
Many information fusion applications are often characterized by a high degree of complexity because: (1) data are often acquired from sensors of different modalities and with different degrees of uncertainty; (2) decisions must be made efficiently; and (3) the world situation evolves over time. To address these issues, we propose an information fusion framework based on dynamic Bayesian networks to provide active, dynamic, purposive and sufficing information fusion in order to arrive at a reliable conclusion with reasonable time and limited resources. The proposed framework is suited to applications where the decision must be made efficiently from dynamically available information of diverse and disparate sources.
Spatio-Temporal Dynamics in Cellular Neural Networks
Directory of Open Access Journals (Sweden)
Liviu GORAS
2009-07-01
Full Text Available Analog Parallel Architectures like Cellular Neural Networks (CNN’s have been thoroughly studied not only for their potential in high-speed image processing applications but also for their rich and exciting spatio-temporal dynamics. An interesting behavior such architectures can exhibit is spatio-temporal filtering and pattern formation, aspects that will be discussed in this work for a general structure consisting of linear cells locally and homogeneously connected within a specified neighborhood. The results are generalizations of those regarding Turing pattern formation in CNN’s. Using linear cells (or piecewise linear cells working in the central linear part of their characteristic allows the use of the decoupling technique – a powerful technique that gives significant insight into the dynamics of the CNN. The roles of the cell structure as well as that of the connection template are discussed and models for the spatial modes dynamics are made as well.
Introduction to modern dynamics chaos, networks, space and time
Nolte, David D
2015-01-01
The best parts of physics are the last topics that our students ever see. These are the exciting new frontiers of nonlinear and complex systems that are at the forefront of university research and are the basis of many high-tech businesses. Topics such as traffic on the World Wide Web, the spread of epidemics through globally-mobile populations, or the synchronization of global economies are governed by universal principles just as profound as Newton's laws. Nonetheless, the conventional university physics curriculum reserves most of these topics for advanced graduate study. Two justifications are given for this situation: first, that the mathematical tools needed to understand these topics are beyond the skill set of undergraduate students, and second, that these are speciality topics with no common theme and little overlap. Introduction to Modern Dynamics dispels these myths. The structure of this book combines the three main topics of modern dynamics - chaos theory, dynamics on complex networks, and gener...
A neural network approach to dynamic task assignment of multirobots.
Zhu, Anmin; Yang, Simon X
2006-09-01
In this paper, a neural network approach to task assignment, based on a self-organizing map (SOM), is proposed for a multirobot system in dynamic environments subject to uncertainties. It is capable of dynamically controlling a group of mobile robots to achieve multiple tasks at different locations, so that the desired number of robots will arrive at every target location from arbitrary initial locations. In the proposed approach, the robot motion planning is integrated with the task assignment, thus the robots start to move once the overall task is given. The robot navigation can be dynamically adjusted to guarantee that each target location has the desired number of robots, even under uncertainties such as when some robots break down. The proposed approach is capable of dealing with changing environments. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.
Supporting Dynamic Spectrum Access in Heterogeneous LTE+ Networks
Energy Technology Data Exchange (ETDEWEB)
Luiz A. DaSilva; Ryan E. Irwin; Mike Benonis
2012-08-01
As early as 2014, mobile network operators’ spectral capac- ity is expected to be overwhelmed by the demand brought on by new devices and applications. With Long Term Evo- lution Advanced (LTE+) networks likely as the future one world 4G standard, network operators may need to deploy a Dynamic Spectrum Access (DSA) overlay in Heterogeneous Networks (HetNets) to extend coverage, increase spectrum efficiency, and increase the capacity of these networks. In this paper, we propose three new management frameworks for DSA in an LTE+ HetNet: Spectrum Accountability Client, Cell Spectrum Management, and Domain Spectrum Man- agement. For these spectrum management frameworks, we define protocol interfaces and operational signaling scenar- ios to support cooperative sensing, spectrum lease manage- ment, and alarm scenarios for rule adjustment. We also quan- tify, through integer programs, the benefits of using DSA in an LTE+ HetNet, that can opportunistically reuse vacant TV and GSM spectrum. Using integer programs, we consider a topology using Geographic Information System data from the Blacksburg, VA metro area to assess the realistic benefits of DSA in an LTE+ HetNet.
Intelligent On/Off Dynamic Link Management for On-Chip Networks
Directory of Open Access Journals (Sweden)
Andreas G. Savva
2012-01-01
Full Text Available Networks-on-chips (NoCs provide scalable on-chip communication and are expected to be the dominant interconnection architectures in multicore and manycore systems. Power consumption, however, is a major limitation in NoCs today, and researchers have been constantly working on reducing both dynamic and static power. Among the NoC components, links that connect the NoC routers are the most power-hungry components. Several attempts have been made to reduce the link power consumption at both the circuit level and the system level. Most past research efforts have proposed selective on/off link state switching based on system-level information based on link utilization levels. Most of these proposed algorithms focus on a pessimistic and simple static threshold mechanism which determines whether or not a link should be turned on/off. This paper presents an intelligent dynamic power management policy for NoCs with improved predictive abilities based on supervised online learning of the system status (i.e., expected future utilization link levels, where links are turned off and on via the use of a small and scalable neural network. Simulation results with various synthetic traffic models over various network topologies show that the proposed work can reach up to 13% power savings when compared to a trivial threshold computation, at very low (<4% hardware overheads.
Dynamics to equilibrium in network games: individual behavior and global response.
Cimini, Giulio; Castellano, Claudio; Sánchez, Angel
2015-01-01
Various social contexts can be depicted as games of strategic interactions on networks, where an individual's welfare depends on both her and her partners' actions. Whereas much attention has been devoted to Bayes-Nash equilibria in such games, here we look at strategic interactions from an evolutionary perspective. To this end, we present the results of a numerical simulations program for these games, which allows us to find out whether Nash equilibria are accessible by adaptation of player strategies, and in general to identify the attractors of the evolution. Simulations allow us to go beyond a global characterization of the cooperativeness at equilibrium and probe into individual behavior. We find that when players imitate each other, evolution does not reach Nash equilibria and, worse, leads to very unfavorable states in terms of welfare. On the contrary, when players update their behavior rationally, they self-organize into a rich variety of Nash equilibria, where individual behavior and payoffs are shaped by the nature of the game, the social network's structure and the players' position within the network. Our results allow to assess the validity of mean-field approaches we use to describe the dynamics of these games. Interestingly, our dynamically-found equilibria generally do not coincide with (but show qualitatively the same features of) those resulting from theoretical predictions in the context of one-shot games under incomplete information.
Formal derivation of qualitative dynamical models from biochemical networks.
Abou-Jaoudé, Wassim; Thieffry, Denis; Feret, Jérôme
2016-11-01
As technological advances allow a better identification of cellular networks, large-scale molecular data are swiftly produced, allowing the construction of large and detailed molecular interaction maps. One approach to unravel the dynamical properties of such complex systems consists in deriving coarse-grained dynamical models from these maps, which would make the salient properties emerge. We present here a method to automatically derive such models, relying on the abstract interpretation framework to formally relate model behaviour at different levels of description. We illustrate our approach on two relevant case studies: the formation of a complex involving a protein adaptor, and a race between two competing biochemical reactions. States and traces of reaction networks are first abstracted by sampling the number of instances of chemical species within a finite set of intervals. We show that the qualitative models induced by this abstraction are too coarse to reproduce properties of interest. We then refine our approach by taking into account additional constraints, the mass invariants and the limiting resources for interval crossing, and by introducing information on the reaction kinetics. The resulting qualitative models are able to capture sophisticated properties of interest, such as a sequestration effect, which arise in the case studies and, more generally, participate in shaping the dynamics of cell signaling and regulatory networks. Our methodology offers new trade-offs between complexity and accuracy, and clarifies the implicit assumptions made in the process of qualitative modelling of biological networks. Copyright © 2016 The Author(s). Published by Elsevier Ireland Ltd.. All rights reserved.
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Barbara Martini
2016-06-01
Full Text Available Emerging technologies such as Software-Defined Networks (SDN and Network Function Virtualization (NFV promise to address cost reduction and flexibility in network operation while enabling innovative network service delivery models. However, operational network service delivery solutions still need to be developed that actually exploit these technologies, especially at the multi-provider level. Indeed, the implementation of network functions as software running over a virtualized infrastructure and provisioned on a service basis let one envisage an ecosystem of network services that are dynamically and flexibly assembled by orchestrating Virtual Network Functions even across different provider domains, thereby coping with changeable user and service requirements and context conditions. In this paper we propose an approach that adopts Service-Oriented Architecture (SOA technology-agnostic architectural guidelines in the design of a solution for orchestrating and dynamically chaining Virtual Network Functions. We discuss how SOA, NFV, and SDN may complement each other in realizing dynamic network function chaining through service composition specification, service selection, service delivery, and placement tasks. Then, we describe the architecture of a SOA-inspired NFV orchestrator, which leverages SDN-based network control capabilities to address an effective delivery of elastic chains of Virtual Network Functions. Preliminary results of prototype implementation and testing activities are also presented. The benefits for Network Service Providers are also described that derive from the adaptive network service provisioning in a multi-provider environment through the orchestration of computing and networking services to provide end users with an enhanced service experience.
Efficient mapping of ligand migration channel networks in dynamic proteins.
Lin, Tu-Liang; Song, Guang
2011-08-01
For many proteins such as myoglobin, the binding site lies in the interior, and there is no obvious route from the exterior to the binding site in the average structure. Although computer simulations for a limited number of proteins have found some transiently open channels, it is not clear if there exist more channels elsewhere or how the channels are regulated. A systematic approach that can map out the whole ligand migration channel network is lacking. Ligand migration in a dynamic protein resembles closely a well-studied problem in robotics, namely, the navigation of a mobile robot in a dynamic environment. In this work, we present a novel robotic motion planning inspired approach that can map the ligand migration channel network in a dynamic protein. The method combines an efficient spatial mapping of protein inner space with a temporal exploration of protein structural heterogeneity, which is represented by a structure ensemble. The spatial mapping of each conformation in the ensemble produces a partial map of protein inner cavities and their inter-connectivity. These maps are then merged to form a super map that contains all the channels that open dynamically. Results on the pathways in myoglobin for gaseous ligands demonstrate the efficiency of our approach in mapping the ligand migration channel networks. The results, obtained in a significantly less amount of time than trajectory-based approaches, are in agreement with previous simulation results. Additionally, the method clearly illustrates how and what conformational changes open or close a channel. Copyright © 2011 Wiley-Liss, Inc.
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Rock Melanie J
2010-01-01
Full Text Available Abstract Background Sampling in the absence of accurate or comprehensive information routinely poses logistical, ethical, and resource allocation challenges in social science, clinical, epidemiological, health service and population health research. These challenges are compounded if few members of a target population know each other or regularly interact. This paper reports on the sampling methods adopted in ethnographic case study research with a 'hard-to-reach' population. Methods To identify and engage a small yet diverse sample of people who met an unusual set of criteria (i.e., pet owners who had been treating cats or dogs for diabetes, four sampling strategies were used. First, copies of a recruitment letter were posted in pet-friendly places. Second, information about the study was diffused throughout the study period via word of mouth. Third, the lead investigator personally sent the recruitment letter via email to a pet owner, who then circulated the information to others, and so on. Fourth, veterinarians were enlisted to refer people who had diabetic pets. The second, third and fourth strategies rely on social networks and represent forms of chain referral sampling. Results Chain referral sampling via email proved to be the most efficient and effective, yielding a small yet diverse group of respondents within one month, and at negligible cost. Conclusions The widespread popularity of electronic communication technologies offers new methodological opportunities for researchers seeking to recruit from hard-to-reach populations.
Rock, Melanie J
2010-01-20
Sampling in the absence of accurate or comprehensive information routinely poses logistical, ethical, and resource allocation challenges in social science, clinical, epidemiological, health service and population health research. These challenges are compounded if few members of a target population know each other or regularly interact. This paper reports on the sampling methods adopted in ethnographic case study research with a 'hard-to-reach' population. To identify and engage a small yet diverse sample of people who met an unusual set of criteria (i.e., pet owners who had been treating cats or dogs for diabetes), four sampling strategies were used. First, copies of a recruitment letter were posted in pet-friendly places. Second, information about the study was diffused throughout the study period via word of mouth. Third, the lead investigator personally sent the recruitment letter via email to a pet owner, who then circulated the information to others, and so on. Fourth, veterinarians were enlisted to refer people who had diabetic pets. The second, third and fourth strategies rely on social networks and represent forms of chain referral sampling. Chain referral sampling via email proved to be the most efficient and effective, yielding a small yet diverse group of respondents within one month, and at negligible cost. The widespread popularity of electronic communication technologies offers new methodological opportunities for researchers seeking to recruit from hard-to-reach populations.
Climate dynamics: a network-based approach for the analysis of global precipitation.
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Stefania Scarsoglio
Full Text Available Precipitation is one of the most important meteorological variables for defining the climate dynamics, but the spatial patterns of precipitation have not been fully investigated yet. The complex network theory, which provides a robust tool to investigate the statistical interdependence of many interacting elements, is used here to analyze the spatial dynamics of annual precipitation over seventy years (1941-2010. The precipitation network is built associating a node to a geographical region, which has a temporal distribution of precipitation, and identifying possible links among nodes through the correlation function. The precipitation network reveals significant spatial variability with barely connected regions, as Eastern China and Japan, and highly connected regions, such as the African Sahel, Eastern Australia and, to a lesser extent, Northern Europe. Sahel and Eastern Australia are remarkably dry regions, where low amounts of rainfall are uniformly distributed on continental scales and small-scale extreme events are rare. As a consequence, the precipitation gradient is low, making these regions well connected on a large spatial scale. On the contrary, the Asiatic South-East is often reached by extreme events such as monsoons, tropical cyclones and heat waves, which can all contribute to reduce the correlation to the short-range scale only. Some patterns emerging between mid-latitude and tropical regions suggest a possible impact of the propagation of planetary waves on precipitation at a global scale. Other links can be qualitatively associated to the atmospheric and oceanic circulation. To analyze the sensitivity of the network to the physical closeness of the nodes, short-term connections are broken. The African Sahel, Eastern Australia and Northern Europe regions again appear as the supernodes of the network, confirming furthermore their long-range connection structure. Almost all North-American and Asian nodes vanish, revealing that
Studies on the population dynamics of a rumor-spreading model in online social networks
Dong, Suyalatu; Fan, Feng-Hua; Huang, Yong-Chang
2018-02-01
This paper sets up a rumor spreading model in online social networks based on the European fox rabies SIR model. The model considers the impact of changing number of online social network users, combines the transmission dynamics to set up a population dynamics of rumor spreading model in online social networks. Simulation is carried out on online social network, and results show that the new rumor spreading model is in accordance with the real propagation characteristics in online social networks.
Energy Efficient Routing Algorithms in Dynamic Optical Core Networks with Dual Energy Sources
DEFF Research Database (Denmark)
Wang, Jiayuan; Fagertun, Anna Manolova; Ruepp, Sarah Renée
2013-01-01
This paper proposes new energy efficient routing algorithms in optical core networks, with the application of solar energy sources and bundled links. A comprehensive solar energy model is described in the proposed network scenarios. Network performance in energy savings, connection blocking...... probability, resource utilization and bundled link usage are evaluated with dynamic network simulations. Results show that algorithms proposed aiming for reducing the dynamic part of the energy consumption of the network may raise the fixed part of the energy consumption meanwhile....
Modelling animal group fission using social network dynamics.
Sueur, Cédric; Maire, Anaïs
2014-01-01
Group life involves both advantages and disadvantages, meaning that individuals have to compromise between their nutritional needs and their social links. When a compromise is impossible, the group splits in order to reduce conflict of interests and favour positive social interactions between its members. In this study we built a dynamic model of social networks to represent a succession of temporary fissions involving a change in social relations that could potentially lead to irreversible group fission (i.e. no more group fusion). This is the first study that assesses how a social network changes according to group fission-fusion dynamics. We built a model that was based on different parameters: the group size, the influence of nutritional needs compared to social needs, and the changes in the social network after a temporary fission. The results obtained from this theoretical data indicate how the percentage of social relation transfer, the number of individuals and the relative importance of nutritional requirements and social links influence the average number of days before irreversible fission occurs. The greater the nutritional needs and the higher the transfer of social relations during temporary fission, the fewer days will be observed before an irreversible fission. It is crucial to bridge the gap between the individual and the population level if we hope to understand how simple, local interactions may drive ecological systems.
Modelling animal group fission using social network dynamics.
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Cédric Sueur
Full Text Available Group life involves both advantages and disadvantages, meaning that individuals have to compromise between their nutritional needs and their social links. When a compromise is impossible, the group splits in order to reduce conflict of interests and favour positive social interactions between its members. In this study we built a dynamic model of social networks to represent a succession of temporary fissions involving a change in social relations that could potentially lead to irreversible group fission (i.e. no more group fusion. This is the first study that assesses how a social network changes according to group fission-fusion dynamics. We built a model that was based on different parameters: the group size, the influence of nutritional needs compared to social needs, and the changes in the social network after a temporary fission. The results obtained from this theoretical data indicate how the percentage of social relation transfer, the number of individuals and the relative importance of nutritional requirements and social links influence the average number of days before irreversible fission occurs. The greater the nutritional needs and the higher the transfer of social relations during temporary fission, the fewer days will be observed before an irreversible fission. It is crucial to bridge the gap between the individual and the population level if we hope to understand how simple, local interactions may drive ecological systems.
A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses
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Karim El-Laithy
2011-01-01
Full Text Available An integration of both the Hebbian-based and reinforcement learning (RL rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.
Dynamical Structure of a Traditional Amazonian Social Network
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Paul L. Hooper
2013-11-01
Full Text Available Reciprocity is a vital feature of social networks, but relatively little is known about its temporal structure or the mechanisms underlying its persistence in real world behavior. In pursuit of these two questions, we study the stationary and dynamical signals of reciprocity in a network of manioc beer (Spanish: chicha; Tsimane’: shocdye’ drinking events in a Tsimane’ village in lowland Bolivia. At the stationary level, our analysis reveals that social exchange within the community is heterogeneously patterned according to kinship and spatial proximity. A positive relationship between the frequencies at which two families host each other, controlling for kinship and proximity, provides evidence for stationary reciprocity. Our analysis of the dynamical structure of this network presents a novel method for the study of conditional, or non-stationary, reciprocity effects. We find evidence that short-timescale reciprocity (within three days is present among non- and distant-kin pairs; conversely, we find that levels of cooperation among close kin can be accounted for on the stationary hypothesis alone.
Modelling Animal Group Fission Using Social Network Dynamics
Sueur, Cédric; Maire, Anaïs
2014-01-01
Group life involves both advantages and disadvantages, meaning that individuals have to compromise between their nutritional needs and their social links. When a compromise is impossible, the group splits in order to reduce conflict of interests and favour positive social interactions between its members. In this study we built a dynamic model of social networks to represent a succession of temporary fissions involving a change in social relations that could potentially lead to irreversible group fission (i.e. no more group fusion). This is the first study that assesses how a social network changes according to group fission-fusion dynamics. We built a model that was based on different parameters: the group size, the influence of nutritional needs compared to social needs, and the changes in the social network after a temporary fission. The results obtained from this theoretical data indicate how the percentage of social relation transfer, the number of individuals and the relative importance of nutritional requirements and social links influence the average number of days before irreversible fission occurs. The greater the nutritional needs and the higher the transfer of social relations during temporary fission, the fewer days will be observed before an irreversible fission. It is crucial to bridge the gap between the individual and the population level if we hope to understand how simple, local interactions may drive ecological systems. PMID:24831471
Dynamic Retransmission Limit Scheme in MAC Layer for Routing in Multihop Ad hoc Networks
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Rachid El-Azouzi
2008-05-01
Full Text Available We consider a wireless ad hoc network with random access channel. We present a model that takes into account topology, routing, random access in MAC layer, and forwarding probability. In this paper, we focus on drawing benefit from the interaction of the MAC (governed by IEEE 802.11 or slotted Aloha and routing by defining a new cross-layer scheme for routing based on the limit number of retransmission. By adjusting dynamically and judiciously this parameter in a saturated network, we have realized that both stability of forwarding queues and average throughput are significantly improved in linear networks with symmetric traffic: a gain of 100% can be reached. While in asymmetric topology network with asymmetric traffic, we achieve a better average delay (resp., throughput for each connection without changing the average throughput (resp., delay. In addition, we show the efficiency of our new scheme in case of multimedia applications with delay constraint. A detailed performance study is presented using analytical and simulation evaluation.
Forecasting financial asset processes: stochastic dynamics via learning neural networks.
Giebel, S; Rainer, M
2010-01-01
Models for financial asset dynamics usually take into account their inherent unpredictable nature by including a suitable stochastic component into their process. Unknown (forward) values of financial assets (at a given time in the future) are usually estimated as expectations of the stochastic asset under a suitable risk-neutral measure. This estimation requires the stochastic model to be calibrated to some history of sufficient length in the past. Apart from inherent limitations, due to the stochastic nature of the process, the predictive power is also limited by the simplifying assumptions of the common calibration methods, such as maximum likelihood estimation and regression methods, performed often without weights on the historic time series, or with static weights only. Here we propose a novel method of "intelligent" calibration, using learning neural networks in order to dynamically adapt the parameters of the stochastic model. Hence we have a stochastic process with time dependent parameters, the dynamics of the parameters being themselves learned continuously by a neural network. The back propagation in training the previous weights is limited to a certain memory length (in the examples we consider 10 previous business days), which is similar to the maximal time lag of autoregressive processes. We demonstrate the learning efficiency of the new algorithm by tracking the next-day forecasts for the EURTRY and EUR-HUF exchange rates each.
Electrokinetic confinement of axonal growth for dynamically configurable neural networks
Honegger, Thibault; Scott, Mark A.; Yanik, Mehmet F.; Voldman, Joel
2013-01-01
Axons in the developing nervous system are directed via guidance cues, whose expression varies both spatially and temporally, to create functional neural circuits. Existing methods to create patterns of neural connectivity in vitro use only static geometries, and are unable to dynamically alter the guidance cues imparted on the cells. We introduce the use of AC electrokinetics to dynamically control axonal growth in cultured rat hippocampal neurons. We find that the application of modest voltages at frequencies on the order of 105 Hz can cause developing axons to be stopped adjacent to the electrodes while axons away from the electric fields exhibit uninhibited growth. By switching electrodes on or off, we can reversibly inhibit or permit axon passage across the electrodes. Our models suggest that dielectrophoresis is the causative AC electrokinetic effect. We make use of our dynamic control over axon elongation to create an axon-diode via an axon-lock system that consists of a pair of electrode `gates' that either permit or prevent axons from passing through. Finally, we developed a neural circuit consisting of three populations of neurons, separated by three axon-locks to demonstrate the assembly of a functional, engineered neural network. Action potential recordings demonstrate that the AC electrokinetic effect does not harm axons, and Ca2+ imaging demonstrated the unidirectional nature of the synaptic connections. AC electrokinetic confinement of axonal growth has potential for creating configurable, directional neural networks. PMID:23314575
Dynamic Control of Synchronous Activity in Networks of Spiking Neurons.
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Axel Hutt
Full Text Available Oscillatory brain activity is believed to play a central role in neural coding. Accumulating evidence shows that features of these oscillations are highly dynamic: power, frequency and phase fluctuate alongside changes in behavior and task demands. The role and mechanism supporting this variability is however poorly understood. We here analyze a network of recurrently connected spiking neurons with time delay displaying stable synchronous dynamics. Using mean-field and stability analyses, we investigate the influence of dynamic inputs on the frequency of firing rate oscillations. We show that afferent noise, mimicking inputs to the neurons, causes smoothing of the system's response function, displacing equilibria and altering the stability of oscillatory states. Our analysis further shows that these noise-induced changes cause a shift of the peak frequency of synchronous oscillations that scales with input intensity, leading the network towards critical states. We lastly discuss the extension of these principles to periodic stimulation, in which externally applied driving signals can trigger analogous phenomena. Our results reveal one possible mechanism involved in shaping oscillatory activity in the brain and associated control principles.
Dynamics of learning near singularities in radial basis function networks.
Wei, Haikun; Amari, Shun-Ichi
2008-09-01
The radial basis function (RBF) networks are one of the most widely used models for function approximation in the regression problem. In the learning paradigm, the best approximation is recursively or iteratively searched for based on observed data (teacher signals). One encounters difficulties in such a process when two component basis functions become identical, or when the magnitude of one component becomes null. In this case, the number of the components reduces by one, and then the reduced component recovers as the learning process proceeds further, provided such a component is necessary for the best approximation. Strange behaviors, especially the plateau phenomena, have been observed in dynamics of learning when such reduction occurs. There exist singularities in the space of parameters, and the above reduction takes place at the singular regions. This paper focuses on a detailed analysis of the dynamical behaviors of learning near the overlap and elimination singularities in RBF networks, based on the averaged learning equation that is applicable to both on-line and batch mode learning. We analyze the stability on the overlap singularity by solving the eigenvalues of the Hessian explicitly. Based on the stability analysis, we plot the analytical dynamic vector fields near the singularity, which are then compared to those real trajectories obtained by a numeric method. We also confirm the existence of the plateaus in both batch and on-line learning by simulation.
Slow dynamics in a primitive tetrahedral network model.
De Michele, Cristiano; Tartaglia, Piero; Sciortino, Francesco
2006-11-28
We report extensive Monte Carlo and event-driven molecular dynamics simulations of the fluid and liquid phase of a primitive model for silica recently introduced by Ford et al. [J. Chem. Phys. 121, 8415 (2004)]. We evaluate the isodiffusivity lines in the temperature-density plane to provide an indication of the shape of the glass transition line. Except for large densities, arrest is driven by the onset of the tetrahedral bonding pattern and the resulting dynamics is strong in Angell's classification scheme [J. Non-Cryst. Solids 131-133, 13 (1991)]. We compare structural and dynamic properties with corresponding results of two recently studied primitive models of network forming liquids-a primitive model for water and an angular-constraint-free model of four-coordinated particles-to pin down the role of the geometric constraints associated with bonding. Eventually we discuss the similarities between "glass" formation in network forming liquids and "gel" formation in colloidal dispersions of patchy particles.
Zhao, Yongli; Chen, Zhendong; Zhang, Jie; Wang, Xinbo
2016-07-25
Driven by the forthcoming of 5G mobile communications, the all-IP architecture of mobile core networks, i.e. evolved packet core (EPC) proposed by 3GPP, has been greatly challenged by the users' demands for higher data rate and more reliable end-to-end connection, as well as operators' demands for low operational cost. These challenges can be potentially met by software defined optical networking (SDON), which enables dynamic resource allocation according to the users' requirement. In this article, a novel network architecture for mobile core network is proposed based on SDON. A software defined network (SDN) controller is designed to realize the coordinated control over different entities in EPC networks. We analyze the requirement of EPC-lightpath (EPCL) in data plane and propose an optical switch load balancing (OSLB) algorithm for resource allocation in optical layer. The procedure of establishment and adjustment of EPCLs is demonstrated on a SDON-based EPC testbed with extended OpenFlow protocol. We also evaluate the OSLB algorithm through simulation in terms of bandwidth blocking ratio, traffic load distribution, and resource utilization ratio compared with link-based load balancing (LLB) and MinHops algorithms.
Network dynamics of social influence in the wisdom of crowds.
Becker, Joshua; Brackbill, Devon; Centola, Damon
2017-06-27
A longstanding problem in the social, biological, and computational sciences is to determine how groups of distributed individuals can form intelligent collective judgments. Since Galton's discovery of the "wisdom of crowds" [Galton F (1907) Nature 75:450-451], theories of collective intelligence have suggested that the accuracy of group judgments requires individuals to be either independent, with uncorrelated beliefs, or diverse, with negatively correlated beliefs [Page S (2008) The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies]. Previous experimental studies have supported this view by arguing that social influence undermines the wisdom of crowds. These results showed that individuals' estimates became more similar when subjects observed each other's beliefs, thereby reducing diversity without a corresponding increase in group accuracy [Lorenz J, Rauhut H, Schweitzer F, Helbing D (2011) Proc Natl Acad Sci USA 108:9020-9025]. By contrast, we show general network conditions under which social influence improves the accuracy of group estimates, even as individual beliefs become more similar. We present theoretical predictions and experimental results showing that, in decentralized communication networks, group estimates become reliably more accurate as a result of information exchange. We further show that the dynamics of group accuracy change with network structure. In centralized networks, where the influence of central individuals dominates the collective estimation process, group estimates become more likely to increase in error.
Predicting Physical Time Series Using Dynamic Ridge Polynomial Neural Networks
Al-Jumeily, Dhiya; Ghazali, Rozaida; Hussain, Abir
2014-01-01
Forecasting naturally occurring phenomena is a common problem in many domains of science, and this has been addressed and investigated by many scientists. The importance of time series prediction stems from the fact that it has wide range of applications, including control systems, engineering processes, environmental systems and economics. From the knowledge of some aspects of the previous behaviour of the system, the aim of the prediction process is to determine or predict its future behaviour. In this paper, we consider a novel application of a higher order polynomial neural network architecture called Dynamic Ridge Polynomial Neural Network that combines the properties of higher order and recurrent neural networks for the prediction of physical time series. In this study, four types of signals have been used, which are; The Lorenz attractor, mean value of the AE index, sunspot number, and heat wave temperature. The simulation results showed good improvements in terms of the signal to noise ratio in comparison to a number of higher order and feedforward neural networks in comparison to the benchmarked techniques. PMID:25157950
Logical Modeling and Dynamical Analysis of Cellular Networks.
Abou-Jaoudé, Wassim; Traynard, Pauline; Monteiro, Pedro T; Saez-Rodriguez, Julio; Helikar, Tomáš; Thieffry, Denis; Chaouiya, Claudine
2016-01-01
The logical (or logic) formalism is increasingly used to model regulatory and signaling networks. Complementing these applications, several groups contributed various methods and tools to support the definition and analysis of logical models. After an introduction to the logical modeling framework and to several of its variants, we review here a number of recent methodological advances to ease the analysis of large and intricate networks. In particular, we survey approaches to determine model attractors and their reachability properties, to assess the dynamical impact of variations of external signals, and to consistently reduce large models. To illustrate these developments, we further consider several published logical models for two important biological processes, namely the differentiation of T helper cells and the control of mammalian cell cycle.
Strategy selection in evolutionary game dynamics on group interaction networks.
Tan, Shaolin; Feng, Shasha; Wang, Pei; Chen, Yao
2014-11-01
Evolutionary game theory provides an appropriate tool for investigating the competition and diffusion of behavioral traits in biological or social populations. A core challenge in evolutionary game theory is the strategy selection problem: Given two strategies, which one is favored by the population? Recent studies suggest that the answer depends not only on the payoff functions of strategies but also on the interaction structure of the population. Group interactions are one of the fundamental interactive modes within populations. This work aims to investigate the strategy selection problem in evolutionary game dynamics on group interaction networks. In detail, the strategy selection conditions are obtained for some typical networks with group interactions. Furthermore, the obtained conditions are applied to investigate selection between cooperation and defection in populations. The conditions for evolution of cooperation are derived for both the public goods game and volunteer's dilemma game. Numerical experiments validate the above analytical results.
Pseudo paths towards minimum energy states in network dynamics
Hedayatifar, L.; Hassanibesheli, F.; Shirazi, A. H.; Vasheghani Farahani, S.; Jafari, G. R.
2017-10-01
The dynamics of networks forming on Heider balance theory moves towards lower tension states. The condition derived from this theory enforces agents to reevaluate and modify their interactions to achieve equilibrium. These possible changes in network's topology can be considered as various paths that guide systems to minimum energy states. Based on this theory the final destination of a system could reside on a local minimum energy, ;jammed state;, or the global minimum energy, balanced states. The question we would like to address is whether jammed states just appear by chance? Or there exist some pseudo paths that bound a system towards a jammed state. We introduce an indicator to suspect the location of a jammed state based on the Inverse Participation Ratio method (IPR). We provide a margin before a local minimum where the number of possible paths dramatically drastically decreases. This is a condition that proves adequate for ending up on a jammed states.
Mapping Language Networks Using the Structural and Dynamic Brain Connectomes.
Del Gaizo, John; Fridriksson, Julius; Yourganov, Grigori; Hillis, Argye E; Hickok, Gregory; Misic, Bratislav; Rorden, Chris; Bonilha, Leonardo
2017-01-01
Lesion-symptom mapping is often employed to define brain structures that are crucial for human behavior. Even though poststroke deficits result from gray matter damage as well as secondary white matter loss, the impact of structural disconnection is overlooked by conventional lesion-symptom mapping because it does not measure loss of connectivity beyond the stroke lesion. This study describes how traditional lesion mapping can be combined with structural connectome lesion symptom mapping (CLSM) and connectome dynamics lesion symptom mapping (CDLSM) to relate residual white matter networks to behavior. Using data from a large cohort of stroke survivors with aphasia, we observed improved prediction of aphasia severity when traditional lesion symptom mapping was combined with CLSM and CDLSM. Moreover, only CLSM and CDLSM disclosed the importance of temporal-parietal junction connections in aphasia severity. In summary, connectome measures can uniquely reveal brain networks that are necessary for function, improving the traditional lesion symptom mapping approach.
Samarasinghe, S; Ling, H
In this paper, we show how to extend our previously proposed novel continuous time Recurrent Neural Networks (RNN) approach that retains the advantage of continuous dynamics offered by Ordinary Differential Equations (ODE) while enabling parameter estimation through adaptation, to larger signalling networks using a modular approach. Specifically, the signalling network is decomposed into several sub-models based on important temporal events in the network. Each sub-model is represented by the proposed RNN and trained using data generated from the corresponding ODE model. Trained sub-models are assembled into a whole system RNN which is then subjected to systems dynamics and sensitivity analyses. The concept is illustrated by application to G1/S transition in cell cycle using Iwamoto et al. (2008) ODE model. We decomposed the G1/S network into 3 sub-models: (i) E2F transcription factor release; (ii) E2F and CycE positive feedback loop for elevating cyclin levels; and (iii) E2F and CycA negative feedback to degrade E2F. The trained sub-models accurately represented system dynamics and parameters were in good agreement with the ODE model. The whole system RNN however revealed couple of parameters contributing to compounding errors due to feedback and required refinement to sub-model 2. These related to the reversible reaction between CycE/CDK2 and p27, its inhibitor. The revised whole system RNN model very accurately matched dynamics of the ODE system. Local sensitivity analysis of the whole system model further revealed the most dominant influence of the above two parameters in perturbing G1/S transition, giving support to a recent hypothesis that the release of inhibitor p27 from Cyc/CDK complex triggers cell cycle stage transition. To make the model useful in a practical setting, we modified each RNN sub-model with a time relay switch to facilitate larger interval input data (≈20min) (original model used data for 30s or less) and retrained them that produced
Two-photon imaging and analysis of neural network dynamics
Lütcke, Henry; Helmchen, Fritjof
2011-08-01
The glow of a starry night sky, the smell of a freshly brewed cup of coffee or the sound of ocean waves breaking on the beach are representations of the physical world that have been created by the dynamic interactions of thousands of neurons in our brains. How the brain mediates perceptions, creates thoughts, stores memories and initiates actions remains one of the most profound puzzles in biology, if not all of science. A key to a mechanistic understanding of how the nervous system works is the ability to measure and analyze the dynamics of neuronal networks in the living organism in the context of sensory stimulation and behavior. Dynamic brain properties have been fairly well characterized on the microscopic level of individual neurons and on the macroscopic level of whole brain areas largely with the help of various electrophysiological techniques. However, our understanding of the mesoscopic level comprising local populations of hundreds to thousands of neurons (so-called 'microcircuits') remains comparably poor. Predominantly, this has been due to the technical difficulties involved in recording from large networks of neurons with single-cell spatial resolution and near-millisecond temporal resolution in the brain of living animals. In recent years, two-photon microscopy has emerged as a technique which meets many of these requirements and thus has become the method of choice for the interrogation of local neural circuits. Here, we review the state-of-research in the field of two-photon imaging of neuronal populations, covering the topics of microscope technology, suitable fluorescent indicator dyes, staining techniques, and in particular analysis techniques for extracting relevant information from the fluorescence data. We expect that functional analysis of neural networks using two-photon imaging will help to decipher fundamental operational principles of neural microcircuits.
Two-photon imaging and analysis of neural network dynamics
Energy Technology Data Exchange (ETDEWEB)
Luetcke, Henry; Helmchen, Fritjof [Brain Research Institute, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich (Switzerland)
2011-08-15
The glow of a starry night sky, the smell of a freshly brewed cup of coffee or the sound of ocean waves breaking on the beach are representations of the physical world that have been created by the dynamic interactions of thousands of neurons in our brains. How the brain mediates perceptions, creates thoughts, stores memories and initiates actions remains one of the most profound puzzles in biology, if not all of science. A key to a mechanistic understanding of how the nervous system works is the ability to measure and analyze the dynamics of neuronal networks in the living organism in the context of sensory stimulation and behavior. Dynamic brain properties have been fairly well characterized on the microscopic level of individual neurons and on the macroscopic level of whole brain areas largely with the help of various electrophysiological techniques. However, our understanding of the mesoscopic level comprising local populations of hundreds to thousands of neurons (so-called 'microcircuits') remains comparably poor. Predominantly, this has been due to the technical difficulties involved in recording from large networks of neurons with single-cell spatial resolution and near-millisecond temporal resolution in the brain of living animals. In recent years, two-photon microscopy has emerged as a technique which meets many of these requirements and thus has become the method of choice for the interrogation of local neural circuits. Here, we review the state-of-research in the field of two-photon imaging of neuronal populations, covering the topics of microscope technology, suitable fluorescent indicator dyes, staining techniques, and in particular analysis techniques for extracting relevant information from the fluorescence data. We expect that functional analysis of neural networks using two-photon imaging will help to decipher fundamental operational principles of neural microcircuits.
Dynamical networks of person to person interactions from RFID sensor networks
Isella, Lorenzo; Cattuto, Ciro; Barrat, Alain
2010-03-01
We present a scalable experimental framework for gathering real-time data on face-to-face social interactions with tunable spatial and temporal resolution. We use active Radio Frequency Identification (RFID) devices that assess mutual proximity in a distributed fashion by exchanging low-power radio packets. We show results on the analysis of the dynamical networks of person-to-person interaction obtained in four high- resolution experiments carried out at different orders of magnitude in community size.
Mathematical Approaches to WMD Defense and Vulnerability Assessments of Dynamic Networks
2016-07-01
The objective function in (25) aims to minimize the number of people waiting to be evacuated in the transportation network, either waiting in one of...Assessments of Dynamic Networks Distribution Statement A. Approved for public release; distribution is unlimited. July...telecommunications, transportation , and electricity grids. More importantly, we analyze network dynamics that include cascading failures due to
Quantum Processes and Dynamic Networks in Physical and Biological Systems.
Dudziak, Martin Joseph
Quantum theory since its earliest formulations in the Copenhagen Interpretation has been difficult to integrate with general relativity and with classical Newtonian physics. There has been traditionally a regard for quantum phenomena as being a limiting case for a natural order that is fundamentally classical except for microscopic extrema where quantum mechanics must be applied, more as a mathematical reconciliation rather than as a description and explanation. Macroscopic sciences including the study of biological neural networks, cellular energy transports and the broad field of non-linear and chaotic systems point to a quantum dimension extending across all scales of measurement and encompassing all of Nature as a fundamentally quantum universe. Theory and observation lead to a number of hypotheses all of which point to dynamic, evolving networks of fundamental or elementary processes as the underlying logico-physical structure (manifestation) in Nature and a strongly quantized dimension to macroscalar processes such as are found in biological, ecological and social systems. The fundamental thesis advanced and presented herein is that quantum phenomena may be the direct consequence of a universe built not from objects and substance but from interacting, interdependent processes collectively operating as sets and networks, giving rise to systems that on microcosmic or macroscopic scales function wholistically and organically, exhibiting non-locality and other non -classical phenomena. The argument is made that such effects as non-locality are not aberrations or departures from the norm but ordinary consequences of the process-network dynamics of Nature. Quantum processes are taken to be the fundamental action-events within Nature; rather than being the exception quantum theory is the rule. The argument is also presented that the study of quantum physics could benefit from the study of selective higher-scale complex systems, such as neural processes in the brain
Display techniques for dynamic network data in transportation GIS
Energy Technology Data Exchange (ETDEWEB)
Ganter, J.H.; Cashwell, J.W.
1994-05-01
Interest in the characteristics of urban street networks is increasing at the same time new monitoring technologies are delivering detailed traffic data. These emerging streams of data may lead to the dilemma that airborne remote sensing has faced: how to select and access the data, and what meaning is hidden in them? computer-assisted visualization techniques are needed to portray these dynamic data. Of equal importance are controls that let the user filter, symbolize, and replay the data to reveal patterns and trends over varying time spans. We discuss a prototype software system that addresses these requirements.
Dynamic Subsidy Method for Congestion Management in Distribution Networks
DEFF Research Database (Denmark)
Huang, Shaojun; Wu, Qiuwei
2016-01-01
Dynamic subsidy (DS) is a locational price paid by the distribution system operator (DSO) to its customers in order to shift energy consumption to designated hours and nodes. It is promising for demand side management and congestion management. This paper proposes a new DS method for congestion...... of the Roy Billinton Test System (RBTS) with high penetration of electric vehicles (EVs) and heat pumps (HPs). The case studies demonstrate the efficacy of the DS method for congestion management in distribution networks. Studies in this paper show that the DS method offers the customers a fair opportunity...
Imura, Jun-ichi; Ueta, Tetsushi
2015-01-01
This book is the first to report on theoretical breakthroughs on control of complex dynamical systems developed by collaborative researchers in the two fields of dynamical systems theory and control theory. As well, its basic point of view is of three kinds of complexity: bifurcation phenomena subject to model uncertainty, complex behavior including periodic/quasi-periodic orbits as well as chaotic orbits, and network complexity emerging from dynamical interactions between subsystems. Analysis and Control of Complex Dynamical Systems offers a valuable resource for mathematicians, physicists, and biophysicists, as well as for researchers in nonlinear science and control engineering, allowing them to develop a better fundamental understanding of the analysis and control synthesis of such complex systems.
Conceptualizing the Dynamics between Bicultural Identification and Personal Social Networks.
Repke, Lydia; Benet-Martínez, Verónica
2017-01-01
An adequate understanding of the acculturation processes affecting immigrants and their descendants involves ascertaining the dynamic interplay between the way these individuals manage their multiple (and sometimes conflictual) cultural value systems and identifications and possible changes in their social networks. To fill this gap, the present research examines how key acculturation variables (e.g., strength of ethnic/host cultural identifications, bicultural identity integration or BII) relate to the composition and structure of bicultural individuals' personal social networks. In Study 1, we relied on a generationally and culturally diverse community sample of 123 Latinos residing in the US. Participants nominated eight individuals (i.e., alters) from their habitual social networks and across two relational domains: friendships and colleagues. Results indicated that the interconnection of same ethnicity alters across different relationship domains is linked to cultural identifications, while the amount of coethnic and host individuals in the network is not. In particular, higher interconnection between Latino friends and colleagues was linked to lower levels of U.S. Conversely, the interconnection of non-Latino friends and colleagues was associated with lower levels of Latino identification. This pattern of results suggests that the relational context for each type of cultural identification works in a subtractive and inverse manner. Further, time spent in the US was linked to both Latino and U.S. cultural identifications, but this relationship was moderated by the level of BII. Specifically, the association between time in the US and strength of both cultural identities was stronger for individuals reporting low levels of BII. Taking the findings from Study 1 as departure point, Study 2 used an agent-based model data simulation approach to explore the dynamic ways in which the content and the structure of an immigrant's social network might matter over time in
Advanced models of neural networks nonlinear dynamics and stochasticity in biological neurons
Rigatos, Gerasimos G
2015-01-01
This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical systems theory. It is suitable for researchers and postgraduate students engaged with neural networks and dynamical systems theory.
Comment on "Network analysis of the state space of discrete dynamical systems"
Li, Chengqing; Shu, Shi
2016-01-01
This paper comments the letter entitled "Network analysis of the state space of discrete dynamical systems" by A. Shreim et al. [Physical Review Letters, 98, 198701 (2007)]. We found that some theoretical analyses are wrong and the proposed indicators based on parameters of phase network can not discriminate dynamical complexity of the discrete dynamical systems composed by 1-D Cellular Automata.
DEFF Research Database (Denmark)
Papaleo, Elena
2015-01-01
that we observe and the functional properties of these important cellular machines. To make progresses in this direction, we need to improve the physical models used to describe proteins and solvent in molecular dynamics, as well as to strengthen the integration of experiments and simulations to overcome...... simulations with attention to the effects that can be propagated over long distances and are often associated to important biological functions. In this context, approaches inspired by network analysis can make an important contribution to the analysis of molecular dynamics simulations....
From cognitive networks to seizures: Stimulus evoked dynamics in a coupled cortical network
Lee, Jaejin; Ermentrout, Bard; Bodner, Mark
2013-12-01
Epilepsy is one of the most common neuropathologies worldwide. Seizures arising in epilepsy or in seizure disorders are characterized generally by uncontrolled spread of excitation and electrical activity to a limited region or even over the entire cortex. While it is generally accepted that abnormal excessive firing and synchronization of neuron populations lead to seizures, little is known about the precise mechanisms underlying human epileptic seizures, the mechanisms of transitions from normal to paroxysmal activity, or about how seizures spread. Further complication arises in that seizures do not occur with a single type of dynamics but as many different phenotypes and genotypes with a range of patterns, synchronous oscillations, and time courses. The concept of preventing, terminating, or modulating seizures and/or paroxysmal activity through stimulation of brain has also received considerable attention. The ability of such stimulation to prevent or modulate such pathological activity may depend on identifiable parameters. In this work, firing rate networks with inhibitory and excitatory populations were modeled. Network parameters were chosen to model normal working memory behaviors. Two different models of cognitive activity were developed. The first model consists of a single network corresponding to a local area of the brain. The second incorporates two networks connected through sparser recurrent excitatory connectivity with transmission delays ranging from approximately 3 ms within local populations to 15 ms between populations residing in different cortical areas. The effect of excitatory stimulation to activate working memory behavior through selective persistent activation of populations is examined in the models, and the conditions and transition mechanisms through which that selective activation breaks down producing spreading paroxysmal activity and seizure states are characterized. Specifically, we determine critical parameters and architectural
Dynamics of the global virtual water trade network
Dalin, C.; Konar, M.; Hanasaki, N.; Rodriguez-Iturbe, I.
2011-12-01
Water resources are under increasing pressure from population growth, socio-economic development and climate change. Since agriculture is by far the most freshwater-consuming process, the international food trade may be a way of transferring water resources to water-scarce countries, and of saving water globally by encouraging trade from water-efficient countries to less water-efficient countries. We applied complex network theory to analyze the dynamics of the global virtual water trade network. Our goal was to assess how the properties of the virtual water trade network changed in time, and how these changes are related to national policies, economic and weather conditions or events. We found that, on average, the number of trade partners of each country in the network doubled from 1986 to 2007, while the volume of water associated with food trade tripled. Despite this growth of the network, we found that the shape of the network properties distributions remained similar: for all years studied, the degree distribution is well fitted by an exponential distribution and the strength distribution compares well with a stretched exponential distribution, indicating high heterogeneity of flows between nations. Other global network structure characteristics, such as the power law relationship between node strength and node degree, dissasortative behavior and weighted rich club phenomenon were also stable through the 22 year-period. However, there are significant changes at the country and link scale of analysis. The USA has remained the world's top exporter of virtual water, while, since 2001, China has been the world's largest virtual water importer, a position formerly occupied by Russia and Japan. The sharp increase in China's virtual water imports is mostly due to its increased soybean imports, following a domestic policy shift regarding the soy trade in 2000 and 2001. Importantly, the food trade has led to enhanced savings in global water resources over the last few
Lowering the Healing Temperature of Photoswitchable Dynamic Covalent Polymer Networks.
Fuhrmann, Anne; Broi, Kevin; Hecht, Stefan
2018-01-01
To reduce the environmental footprint of the modern society, it is desirable to elongate the lifetime of consumer products, for example by implementing healable coatings and protective layers. However, since most healing processes carried out by heat or light suffer from material degradation, improving the robustness and integrity of healable materials is of tremendous importance to prolong their lifetime. In recent work, a prototype is created of a dynamic covalent polymer network, whose thermal healing ability can be switched "on" and "off" by light to provide a means to locally control repair of a damaged coating. Based on the initial concept, herein a new set of difunctional crosslinkers and linear polymers of various compositions is presented to form dynamic covalent polymer networks, in which the barrier for the retro Diels-Alder decrosslinking reaction is decreased. The approach results in lower healing temperatures and thus a longer lifetime of the material. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Dynamic range in the C. elegans brain network
Antonopoulos, Chris G.
2016-01-01
We study external electrical perturbations and their responses in the brain dynamic network of the Caenorhabditis elegans soil worm, given by the connectome of its large somatic nervous system. Our analysis is inspired by a realistic experiment where one stimulates externally specific parts of the brain and studies the persistent neural activity triggered in other cortical regions. In this work, we perturb groups of neurons that form communities, identified by the walktrap community detection method, by trains of stereotypical electrical Poissonian impulses and study the propagation of neural activity to other communities by measuring the corresponding dynamic ranges and Steven law exponents. We show that when one perturbs specific communities, keeping the rest unperturbed, the external stimulations are able to propagate to some of them but not to all. There are also perturbations that do not trigger any response. We found that this depends on the initially perturbed community. Finally, we relate our findings for the former cases with low neural synchronization, self-criticality, and large information flow capacity, and interpret them as the ability of the brain network to respond to external perturbations when it works at criticality and its information flow capacity becomes maximal.
Complex Dynamic Behavior in Simple Gene Regulatory Networks
Santillán Zerón, Moisés
2007-02-01
Knowing the complete genome of a given species is just a piece of the puzzle. To fully unveil the systems behavior of an organism, an organ, or even a single cell, we need to understand the underlying gene regulatory dynamics. Given the complexity of the whole system, the ultimate goal is unattainable for the moment. But perhaps, by analyzing the most simple genetic systems, we may be able to develop the mathematical techniques and procedures required to tackle more complex genetic networks in the near future. In the present work, the techniques for developing mathematical models of simple bacterial gene networks, like the tryptophan and lactose operons are introduced. Despite all of the underlying assumptions, such models can provide valuable information regarding gene regulation dynamics. Here, we pay special attention to robustness as an emergent property. These notes are organized as follows. In the first section, the long historical relation between mathematics, physics, and biology is briefly reviewed. Recently, the multidisciplinary work in biology has received great attention in the form of systems biology. The main concepts of this novel science are discussed in the second section. A very slim introduction to the essential concepts of molecular biology is given in the third section. In the fourth section, a brief introduction to chemical kinetics is presented. Finally, in the fifth section, a mathematical model for the lactose operon is developed and analyzed..
Spontaneous neuronal network dynamics reveal circuit's functional adaptations for behavior.
Romano, Sebastián A; Pietri, Thomas; Pérez-Schuster, Verónica; Jouary, Adrien; Haudrechy, Mathieu; Sumbre, Germán
2015-03-04
Spontaneous neuronal activity is spatiotemporally structured, influencing brain computations. Nevertheless, the neuronal interactions underlying these spontaneous activity patterns, and their biological relevance, remain elusive. Here, we addressed these questions using two-photon calcium imaging of intact zebrafish larvae to monitor the neuron-to-neuron spontaneous activity fine structure in the tectum, a region involved in visual spatial detection. Spontaneous activity was organized in topographically compact assemblies, grouping functionally similar neurons rather than merely neighboring ones, reflecting the tectal retinotopic map despite being independent of retinal drive. Assemblies represent all-or-none-like sub-networks shaped by competitive dynamics, mechanisms advantageous for visual detection in noisy natural environments. Notably, assemblies were tuned to the same angular sizes and spatial positions as prey-detection performance in behavioral assays, and their spontaneous activation predicted directional tail movements. Therefore, structured spontaneous activity represents "preferred" network states, tuned to behaviorally relevant features, emerging from the circuit's intrinsic non-linear dynamics, adapted for its functional role. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
Shooting movies of signaling network dynamics with multiparametric cytometry.
Claassen, Manfred
2014-01-01
Single-cell technologies like mass cytometry enable researchers to comprehensively monitor signaling network responses in the context of heterogeneous cell populations. Cell-to-cell variability, the possibly nonlinear topology of signaling processes, and the destructive nature of mass cytometry necessitate nontrivial computational approaches to reconstruct and sensibly describe signaling dynamics. Modeling of signaling states depends on a set of coherent examples, that is, a set of cell events representing the same cell state. This requirement is frequently compromized by process asynchrony phenomena or nonlinear process topologies. We discuss various computational deconvolution approaches to define molecular process coordinates and enable compilation of coherent data sets for cell state inference. In addition to the conceptual presentation of these approaches, we discuss the application of these methods to modeling of TRAIL-induced apoptosis. Due to their generic applicability these computational approaches will contribute to the elucidation of dynamic intracellular signaling networks in various settings. The resulting signaling maps constitute a promising source for novel interventions and are expected to be particularly valuable in clinical settings.
Dynamic Constrained Economic/Emission Dispatch Scheduling Using Neural Network
Directory of Open Access Journals (Sweden)
Farid Benhamida
2013-01-01
Full Text Available In this paper, a Dynamic Economic/Emission Dispatch (DEED problem is obtained by considering both the economy and emission objectives with required constraints dynamically. This paper presents an optimization algorithm for solving constrained combined economic emission dispatch (EED problem and DEED, through the application of neural network, which is a flexible Hopfield neural network (FHNN. The constrained DEED must not only satisfy the system load demand and the spinning reserve capacity, but some practical operation constraints of generators, such as ramp rate limits and prohibited operating zone, are also considered in practical generator operation. The feasibility of the proposed FHNN using to solve DEED is demonstrated using three power systems, and it is compared with the other methods in terms of solution quality and computation efficiency. The simulation results showed that the proposed FHNN method was indeed capable of obtaining higher quality solutions efficiently in constrained DEED and EED problems with a much shorter computation time compared to other methods.
Structure and dynamics in network-forming materials.
Wilson, Mark
2016-12-21
The study of the structure and dynamics of network-forming materials is reviewed. Experimental techniques used to extract key structural information are briefly considered. Strategies for building simulation models, based on both targeting key (experimentally-accessible) materials and on systematically controlling key model parameters, are discussed. As an example of the first class of materials, a key target system, SiO2, is used to highlight how the changing structure with applied pressure can be effectively modelled (in three dimensions) and used to link to both experimental results and simple structural models. As an example of the second class the topology of networks of tetrahedra in the MX2 stoichiometry are controlled using a single model parameter linked to the M-X-M bond angles. The evolution of ordering on multiple length-scales is observed as are the links between the static structure and key dynamical properties. The isomorphous relationship between the structures of amorphous Si and SiO2 is discussed as are the similarities and differences in the phase diagrams, the latter linked to potential polyamorphic and 'anomalous' (e.g. density maxima) behaviour. Links to both two-dimensional structures for C, Si and Ge and near-two-dimensional bilayers of SiO2 are discussed. Emerging low-dimensional structures in low temperature molten carbonates are also uncovered.
Neural network simulation of the industrial producer price index dynamical series
Soshnikov, L. E.
2013-01-01
This paper is devoted the simulation and forecast of dynamical series of the economical indicators. Multilayer perceptron and Radial basis function neural networks have been used. The neural networks model results are compared with the econometrical modeling.
Dynamic pricing of network goods with boundedly rational consumers.
Radner, Roy; Radunskaya, Ami; Sundararajan, Arun
2014-01-07
We present a model of dynamic monopoly pricing for a good that displays network effects. In contrast with the standard notion of a rational-expectations equilibrium, we model consumers as boundedly rational and unable either to pay immediate attention to each price change or to make accurate forecasts of the adoption of the network good. Our analysis shows that the seller's optimal price trajectory has the following structure: The price is low when the user base is below a target level, is high when the user base is above the target, and is set to keep the user base stationary once the target level has been attained. We show that this pricing policy is robust to a number of extensions, which include the product's user base evolving over time and consumers basing their choices on a mixture of a myopic and a "stubborn" expectation of adoption. Our results differ significantly from those that would be predicted by a model based on rational-expectations equilibrium and are more consistent with the pricing of network goods observed in practice.
Dynamics of HIV infection in lymphoid tissue network.
Nakaoka, Shinji; Iwami, Shingo; Sato, Kei
2016-03-01
Human immunodeficiency virus (HIV) is a fast replicating ribonucleic acid virus, which can easily mutate in order to escape the effects of drug administration. Hence, understanding the basic mechanisms underlying HIV persistence in the body is essential in the development of new therapies that could eradicate HIV infection. Lymphoid tissues are the primary sites of HIV infection. Despite the recent progress in real-time monitoring technology, HIV infection dynamics in a whole body is unknown. Mathematical modeling and simulations provide speculations on global behavior of HIV infection in the lymphatic system. We propose a new mathematical model that describes the spread of HIV infection throughout the lymphoid tissue network. In order to represent the volume difference between lymphoid tissues, we propose the proportionality of several kinetic parameters to the lymphoid tissues' volume distribution. Under this assumption, we perform extensive numerical computations in order to simulate the spread of HIV infection in the lymphoid tissue network. Numerical computations simulate single drug treatments of an HIV infection. One of the important biological speculations derived from this study is a drug saturation effect generated by lymphoid network connection. This implies that a portion of reservoir lymphoid tissues to which drug is not sufficiently delivered would inhibit HIV eradication despite of extensive drug injection.
Gap Gene Regulatory Dynamics Evolve along a Genotype Network
Crombach, Anton; Wotton, Karl R.; Jiménez-Guri, Eva; Jaeger, Johannes
2016-01-01
Developmental gene networks implement the dynamic regulatory mechanisms that pattern and shape the organism. Over evolutionary time, the wiring of these networks changes, yet the patterning outcome is often preserved, a phenomenon known as “system drift.” System drift is illustrated by the gap gene network—involved in segmental patterning—in dipteran insects. In the classic model organism Drosophila melanogaster and the nonmodel scuttle fly Megaselia abdita, early activation and placement of gap gene expression domains show significant quantitative differences, yet the final patterning output of the system is essentially identical in both species. In this detailed modeling analysis of system drift, we use gene circuits which are fit to quantitative gap gene expression data in M. abdita and compare them with an equivalent set of models from D. melanogaster. The results of this comparative analysis show precisely how compensatory regulatory mechanisms achieve equivalent final patterns in both species. We discuss the larger implications of the work in terms of “genotype networks” and the ways in which the structure of regulatory networks can influence patterns of evolutionary change (evolvability). PMID:26796549
Neural network based adaptive control for nonlinear dynamic regimes
Shin, Yoonghyun
Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named 'composite model reference adaptive control' is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of 'pseudo-control hedging' techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties.
Ikin, Karen; Tulloch, Ayesha; Gibbons, Philip; Ansell, Dean; Seddon, Julian; Lindenmayer, David
2016-10-01
Multibillion dollar investments in land restoration make it critical that conservation goals are achieved cost-effectively. Approaches developed for systematic conservation planning offer opportunities to evaluate landscape-scale, temporally dynamic biodiversity outcomes from restoration and improve on traditional approaches that focus on the most species-rich plantings. We investigated whether it is possible to apply a complementarity-based approach to evaluate the extent to which an existing network of restoration plantings meets representation targets. Using a case study of woodland birds of conservation concern in southeastern Australia, we compared complementarity-based selections of plantings based on temporally dynamic species occurrences with selections based on static species occurrences and selections based on ranking plantings by species richness. The dynamic complementarity approach, which incorporated species occurrences over 5 years, resulted in higher species occurrences and proportion of targets met compared with the static complementarity approach, in which species occurrences were taken at a single point in time. For equivalent cost, the dynamic complementarity approach also always resulted in higher average minimum percent occurrence of species maintained through time and a higher proportion of the bird community meeting representation targets compared with the species-richness approach. Plantings selected under the complementarity approaches represented the full range of planting attributes, whereas those selected under the species-richness approach were larger in size. Our results suggest that future restoration policy should not attempt to achieve all conservation goals within individual plantings, but should instead capitalize on restoration opportunities as they arise to achieve collective value of multiple plantings across the landscape. Networks of restoration plantings with complementary attributes of age, size, vegetation structure, and
Optimal dynamics for quality control in spatially distributed mitochondrial networks.
Directory of Open Access Journals (Sweden)
Pinkesh K Patel
Full Text Available Recent imaging studies of mitochondrial dynamics have implicated a cycle of fusion, fission, and autophagy in the quality control of mitochondrial function by selectively increasing the membrane potential of some mitochondria at the expense of the turnover of others. This complex, dynamical system creates spatially distributed networks that are dependent on active transport along cytoskeletal networks and on protein import leading to biogenesis. To study the relative impacts of local interactions between neighboring mitochondria and their reorganization via transport, we have developed a spatiotemporal mathematical model encompassing all of these processes in which we focus on the dynamics of a health parameter meant to mimic the functional state of mitochondria. In agreement with previous models, we show that both autophagy and the generation of membrane potential asymmetry following a fusion/fission cycle are required for maintaining a healthy mitochondrial population. This health maintenance is affected by mitochondrial density and motility primarily through changes in the frequency of fusion events. Health is optimized when the selectivity thresholds for fusion and fission are matched, providing a mechanistic basis for the observed coupling of the two processes through the protein OPA1. We also demonstrate that the discreteness of the components exchanged during fusion is critical for quality control, and that the effects of limiting total amounts of autophagy and biogenesis have distinct consequences on health and population size, respectively. Taken together, our results show that several general principles emerge from the complexity of the quality control cycle that can be used to focus and interpret future experimental studies, and our modeling framework provides a road-map for deconstructing the functional importance of local interactions in communities of cells as well as organelles.
Dynamic safety assessment of natural gas stations using Bayesian network
Energy Technology Data Exchange (ETDEWEB)
Zarei, Esmaeil, E-mail: smlzarei65@gmail.com [Center of Excellence for Occupational Health Engineering, Research Center for Health Sciences, Faculty of Health, Hamadan University of Medical Sciences, Hamadan (Iran, Islamic Republic of); Azadeh, Ali [School of Industrial and Systems Engineering, Center of Excellence for Intelligent-Based Experimental Mechanic, College of Engineering, University of Tehran (Iran, Islamic Republic of); Khakzad, Nima [Safety and Security Science Section, Delft University of Technology, Delft (Netherlands); Aliabadi, Mostafa Mirzaei [Center of Excellence for Occupational Health Engineering, Research Center for Health Sciences, Faculty of Health, Hamadan University of Medical Sciences, Hamadan (Iran, Islamic Republic of); Mohammadfam, Iraj, E-mail: mohammadfam@umsha.ac.ir [Center of Excellence for Occupational Health Engineering, Research Center for Health Sciences, Faculty of Health, Hamadan University of Medical Sciences, Hamadan (Iran, Islamic Republic of)
2017-01-05
Graphical abstract: Dynamic cause-consequence analysis of the regulator system failure using BN. - Highlights: • A dynamic and comprehensive QRA (DCQRA) framework is proposed for safety assessment of CGSs. • Bow-tie diagram and Bayesian network are employed for accident scenario modeling. • Critical basic events and minimal cut sets are identified using probability updating. - Abstract: Pipelines are one of the most popular and effective ways of transporting hazardous materials, especially natural gas. However, the rapid development of gas pipelines and stations in urban areas has introduced a serious threat to public safety and assets. Although different methods have been developed for risk analysis of gas transportation systems, a comprehensive methodology for risk analysis is still lacking, especially in natural gas stations. The present work is aimed at developing a dynamic and comprehensive quantitative risk analysis (DCQRA) approach for accident scenario and risk modeling of natural gas stations. In this approach, a FMEA is used for hazard analysis while a Bow-tie diagram and Bayesian network are employed to model the worst-case accident scenario and to assess the risks. The results have indicated that the failure of the regulator system was the worst-case accident scenario with the human error as the most contributing factor. Thus, in risk management plan of natural gas stations, priority should be given to the most probable root events and main contribution factors, which have identified in the present study, in order to reduce the occurrence probability of the accident scenarios and thus alleviate the risks.
Cooperation Dynamics on Mobile Crowd Networks of Device-to-Device Communications
Directory of Open Access Journals (Sweden)
Yong Deng
2016-01-01
Full Text Available The explosive use of smart devices enabled the emergence of collective resource sharing among mobile individuals. Mobile users need to cooperate with each other to improve the whole network’s quality of service. By modeling the cooperative behaviors in a mobile crowd into an evolutionary Prisoner’s dilemma game, we investigate the relationships between cooperation rate and some main influence factors, including crowd density, communication range, temptation to defect, and mobility attributes. Using evolutionary game theory, our analysis on the cooperative behaviors of mobile takes a deep insight into the cooperation promotion in a dynamical network with selfish autonomous users. The experiment results show that mobile user’s features, including speed, moving probability, and reaction radius, have an obvious influence on the formation of a cooperative mobile social network. We also found some optimal status when the crowd’s cooperation rate reaches the best. These findings are important if we want to establish a mobile social network with a good performance.
Hock, Karlo; Ng, Kah Loon; Fefferman, Nina H.
2010-01-01
Social networks can be used to represent group structure as a network of interacting components, and also to quantify both the position of each individual and the global properties of a group. In a series of simulation experiments based on dynamic social networks, we test the prediction that social behaviors that help individuals reach prominence within their social group may conflict with their potential to benefit from their social environment. In addition to cases where individuals were able to benefit from improving both their personal relative importance and group organization, using only simple rules of social affiliation we were able to obtain results in which individuals would face a trade-off between these factors. While selection would favor (or work against) social behaviors that concordantly increase (or decrease, respectively) fitness at both individual and group level, when these factors conflict with each other the eventual selective pressure would depend on the relative returns individuals get from their social environment and their position within it. The presented results highlight the importance of a systems approach to studying animal sociality, in which the effects of social behaviors should be viewed not only through the benefits that those provide to individuals, but also in terms of how they affect broader social environment and how in turn this is reflected back on an individual's fitness. PMID:21203425
Ghrab, Nadya; Kallel, Hichem
2013-01-01
A comparative study between static and dynamic neural networks for robotic systems control is considered. So, two approaches of neural robot control were selected, exposed, and compared. One uses a static neural network; the other uses a dynamic neural network. Both compensate the nonlinear modeling and uncertainties of robotic systems. The first approach is direct; it approximates the nonlinearities and uncertainties by a static neural network. The second approach is indirect; it uses a dyna...
Short-term memory in olfactory network dynamics
Stopfer, Mark; Laurent, Gilles
1999-12-01
Neural assemblies in a number of animal species display self-organized, synchronized oscillations in response to sensory stimuli in a variety of brain areas.. In the olfactory system of insects, odour-evoked oscillatory synchronization of antennal lobe projection neurons (PNs) is superimposed on slower and stimulus-specific temporal activity patterns. Hence, each odour activates a specific and dynamic projection neuron assembly whose evolution during a stimulus is locked to the oscillation clock. Here we examine, using locusts, the changes in population dynamics of projection-neuron assemblies over repeated odour stimulations, as would occur when an animal first encounters and then repeatedly samples an odour for identification or localization. We find that the responses of these assemblies rapidly decrease in intensity, while they show a marked increase in spike time precision and inter-neuronal oscillatory coherence. Once established, this enhanced precision in the representation endures for several minutes. This change is stimulus-specific, and depends on events within the antennal lobe circuits, independent of olfactory receptor adaptation: it may thus constitute a form of sensory memory. Our results suggest that this progressive change in olfactory network dynamics serves to converge, over repeated odour samplings, on a more precise and readily classifiable odour representation, using relational information contained across neural assemblies.
Forecasting abrupt changes in foreign exchange markets: method using dynamical network marker
Oya, Shunsuke; Aihara, Kazuyuki; Hirata, Yoshito
2014-11-01
We apply the idea of dynamical network markers (Chen et al 2012 Sci. Rep. 2 342) to foreign exchange markets so that early warning signals can be provided for any abrupt changes. The dynamical network marker constructed achieves a high odds ratio for forecasting these sudden changes. In addition, we also extend the notion of the dynamical network marker by using recurrence plots so that the notion can be applied to delay coordinates and point processes. Thus, the dynamical network marker is useful in a variety of contexts in science, technology, and society.
Network dynamics of eukaryotic LTR retroelements beyond phylogenetic trees
Llorens, Carlos; Muñoz-Pomer, Alfonso; Bernad, Lucia; Botella, Hector; Moya, Andrés
2009-01-01
Background Sequencing projects have allowed diverse retroviruses and LTR retrotransposons from different eukaryotic organisms to be characterized. It is known that retroviruses and other retro-transcribing viruses evolve from LTR retrotransposons and that this whole system clusters into five families: Ty3/Gypsy, Retroviridae, Ty1/Copia, Bel/Pao and Caulimoviridae. Phylogenetic analyses usually show that these split into multiple distinct lineages but what is yet to be understood is how deep evolution occurred in this system. Results We combined phylogenetic and graph analyses to investigate the history of LTR retroelements both as a tree and as a network. We used 268 non-redundant LTR retroelements, many of them introduced for the first time in this work, to elucidate all possible LTR retroelement phylogenetic patterns. These were superimposed over the tree of eukaryotes to investigate the dynamics of the system, at distinct evolutionary times. Next, we investigated phenotypic features such as duplication and variability of amino acid motifs, and several differences in genomic ORF organization. Using this information we characterized eight reticulate evolution markers to construct phenotypic network models. Conclusion The evolutionary history of LTR retroelements can be traced as a time-evolving network that depends on phylogenetic patterns, epigenetic host-factors and phenotypic plasticity. The Ty1/Copia and the Ty3/Gypsy families represent the oldest patterns in this network that we found mimics eukaryotic macroevolution. The emergence of the Bel/Pao, Retroviridae and Caulimoviridae families in this network can be related with distinct inflations of the Ty3/Gypsy family, at distinct evolutionary times. This suggests that Ty3/Gypsy ancestors diversified much more than their Ty1/Copia counterparts, at distinct geological eras. Consistent with the principle of preferential attachment, the connectivities among phenotypic markers, taken as network
Network dynamics of eukaryotic LTR retroelements beyond phylogenetic trees.
Llorens, Carlos; Muñoz-Pomer, Alfonso; Bernad, Lucia; Botella, Hector; Moya, Andrés
2009-11-02
Sequencing projects have allowed diverse retroviruses and LTR retrotransposons from different eukaryotic organisms to be characterized. It is known that retroviruses and other retro-transcribing viruses evolve from LTR retrotransposons and that this whole system clusters into five families: Ty3/Gypsy, Retroviridae, Ty1/Copia, Bel/Pao and Caulimoviridae. Phylogenetic analyses usually show that these split into multiple distinct lineages but what is yet to be understood is how deep evolution occurred in this system. We combined phylogenetic and graph analyses to investigate the history of LTR retroelements both as a tree and as a network. We used 268 non-redundant LTR retroelements, many of them introduced for the first time in this work, to elucidate all possible LTR retroelement phylogenetic patterns. These were superimposed over the tree of eukaryotes to investigate the dynamics of the system, at distinct evolutionary times. Next, we investigated phenotypic features such as duplication and variability of amino acid motifs, and several differences in genomic ORF organization. Using this information we characterized eight reticulate evolution markers to construct phenotypic network models. The evolutionary history of LTR retroelements can be traced as a time-evolving network that depends on phylogenetic patterns, epigenetic host-factors and phenotypic plasticity. The Ty1/Copia and the Ty3/Gypsy families represent the oldest patterns in this network that we found mimics eukaryotic macroevolution. The emergence of the Bel/Pao, Retroviridae and Caulimoviridae families in this network can be related with distinct inflations of the Ty3/Gypsy family, at distinct evolutionary times. This suggests that Ty3/Gypsy ancestors diversified much more than their Ty1/Copia counterparts, at distinct geological eras. Consistent with the principle of preferential attachment, the connectivities among phenotypic markers, taken as network-represented combinations, are power
Network dynamics of eukaryotic LTR retroelements beyond phylogenetic trees
Directory of Open Access Journals (Sweden)
Bernad Lucia
2009-11-01
Full Text Available Abstract Background Sequencing projects have allowed diverse retroviruses and LTR retrotransposons from different eukaryotic organisms to be characterized. It is known that retroviruses and other retro-transcribing viruses evolve from LTR retrotransposons and that this whole system clusters into five families: Ty3/Gypsy, Retroviridae, Ty1/Copia, Bel/Pao and Caulimoviridae. Phylogenetic analyses usually show that these split into multiple distinct lineages but what is yet to be understood is how deep evolution occurred in this system. Results We combined phylogenetic and graph analyses to investigate the history of LTR retroelements both as a tree and as a network. We used 268 non-redundant LTR retroelements, many of them introduced for the first time in this work, to elucidate all possible LTR retroelement phylogenetic patterns. These were superimposed over the tree of eukaryotes to investigate the dynamics of the system, at distinct evolutionary times. Next, we investigated phenotypic features such as duplication and variability of amino acid motifs, and several differences in genomic ORF organization. Using this information we characterized eight reticulate evolution markers to construct phenotypic network models. Conclusion The evolutionary history of LTR retroelements can be traced as a time-evolving network that depends on phylogenetic patterns, epigenetic host-factors and phenotypic plasticity. The Ty1/Copia and the Ty3/Gypsy families represent the oldest patterns in this network that we found mimics eukaryotic macroevolution. The emergence of the Bel/Pao, Retroviridae and Caulimoviridae families in this network can be related with distinct inflations of the Ty3/Gypsy family, at distinct evolutionary times. This suggests that Ty3/Gypsy ancestors diversified much more than their Ty1/Copia counterparts, at distinct geological eras. Consistent with the principle of preferential attachment, the connectivities among phenotypic markers, taken as
Prezel, Elea; Elie, Auréliane; Delaroche, Julie; Stoppin-Mellet, Virginie; Bosc, Christophe; Serre, Laurence; Fourest-Lieuvin, Anne; Andrieux, Annie; Vantard, Marylin; Arnal, Isabelle
2017-11-22
In neurons, microtubule networks alternate between single filaments and bundled arrays under the influence of effectors controlling their dynamics and organization. Tau is a microtubule bundler which stabilizes microtubules by stimulating growth and inhibiting shrinkage. The mechanisms by which tau organizes microtubule networks remain poorly understood. Here, we studied the self-organization of microtubules growing in the presence of tau isoforms and mutants. The results show that tau's ability to induce stable microtubule bundles requires two hexapeptides located in its microtubule-binding domain, and is modulated by its projection domain. Site-specific pseudo-phosphorylation of tau promotes distinct microtubule organizations: stable single microtubules, stable bundles or dynamic bundles. Disease-related tau mutations increase the formation of highly dynamic bundles. Finally, cryo-electron microscopy experiments indicate that tau and its variants similarly change the microtubule lattice structure by increasing both the protofilament number and lattice defects. Overall, our results uncover novel phospho-dependent mechanisms governing tau's ability to trigger microtubule organization and reveal that disease-related modifications of tau promote specific microtubule organizations which may have a deleterious impact during neurodegeneration. © 2017 by The American Society for Cell Biology.
Analyzing the Dynamics of Communication in Online Social Networks
de Choudhury, Munmun; Sundaram, Hari; John, Ajita; Seligmann, Doree Duncan
This chapter deals with the analysis of interpersonal communication dynamics in online social networks and social media. Communication is central to the evolution of social systems. Today, the different online social sites feature variegated interactional affordances, ranging from blogging, micro-blogging, sharing media elements (i.e., image, video) as well as a rich set of social actions such as tagging, voting, commenting and so on. Consequently, these communication tools have begun to redefine the ways in which we exchange information or concepts, and how the media channels impact our online interactional behavior. Our central hypothesis is that such communication dynamics between individuals manifest themselves via two key aspects: the information or concept that is the content of communication, and the channel i.e., the media via which communication takes place. We present computational models and discuss large-scale quantitative observational studies for both these organizing ideas. First, we develop a computational framework to determine the "interestingness" property of conversations cented around rich media. Second, we present user models of diffusion of social actions and study the impact of homophily on the diffusion process. The outcome of this research is twofold. First, extensive empirical studies on datasets from YouTube have indicated that on rich media sites, the conversations that are deemed "interesting" appear to have consequential impact on the properties of the social network they are associated with: in terms of degree of participation of the individuals in future conversations, thematic diffusion as well as emergent cohesiveness in activity among the concerned participants in the network. Second, observational and computational studies on large social media datasets such as Twitter have indicated that diffusion of social actions in a network can be indicative of future information cascades. Besides, given a topic, these cascades are often a
Miconi, Thomas
2017-02-23
Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.
Asymptotic theory for the dynamic of networks with heterogenous social capital allocation
Ubaldi, Enrico; Karsai, Márton; Vezzani, Alessandro; Burioni, Raffaella; Vespignani, Alessandro
2015-01-01
The structure and dynamic of social network are largely determined by the heterogeneous interaction activity and social capital allocation of individuals. These features interplay in a non-trivial way in the formation of network and challenge a rigorous dynamical system theory of network evolution. Here we study seven real networks describing temporal human interactions in three different settings: scientific collaborations, Twitter mentions, and mobile phone calls. We find that the node's activity and social capital allocation can be described by two general functional forms that can be used to define a simple stochastic model for social network dynamic. This model allows the explicit asymptotic solution of the Master Equation describing the system dynamic, and provides the scaling laws characterizing the time evolution of the social network degree distribution and individual node's ego network. The analytical predictions reproduce with accuracy the empirical observations validating the theoretical approach....
Directory of Open Access Journals (Sweden)
Stefan Wuchty
Full Text Available Digital communication data has created opportunities to advance the knowledge of human dynamics in many areas, including national security, behavioral health, and consumerism. While digital data uniquely captures the totality of a person's communication, past research consistently shows that a subset of contacts makes up a person's "social network" of unique resource providers. To address this gap, we analyzed the correspondence between self-reported social network data and email communication data with the objective of identifying the dynamics in e-communication that correlate with a person's perception of a significant network tie. First, we examined the predictive utility of three popular methods to derive social network data from email data based on volume and reciprocity of bilateral email exchanges. Second, we observed differences in the response dynamics along self-reported ties, allowing us to introduce and test a new method that incorporates time-resolved exchange data. Using a range of robustness checks for measurement and misreporting errors in self-report and email data, we find that the methods have similar predictive utility. Although e-communication has lowered communication costs with large numbers of persons, and potentially extended our number of, and reach to contacts, our case results suggest that underlying behavioral patterns indicative of friendship or professional contacts continue to operate in a classical fashion in email interactions.
Wuchty, Stefan; Uzzi, Brian
2011-01-01
Digital communication data has created opportunities to advance the knowledge of human dynamics in many areas, including national security, behavioral health, and consumerism. While digital data uniquely captures the totality of a person's communication, past research consistently shows that a subset of contacts makes up a person's "social network" of unique resource providers. To address this gap, we analyzed the correspondence between self-reported social network data and email communication data with the objective of identifying the dynamics in e-communication that correlate with a person's perception of a significant network tie. First, we examined the predictive utility of three popular methods to derive social network data from email data based on volume and reciprocity of bilateral email exchanges. Second, we observed differences in the response dynamics along self-reported ties, allowing us to introduce and test a new method that incorporates time-resolved exchange data. Using a range of robustness checks for measurement and misreporting errors in self-report and email data, we find that the methods have similar predictive utility. Although e-communication has lowered communication costs with large numbers of persons, and potentially extended our number of, and reach to contacts, our case results suggest that underlying behavioral patterns indicative of friendship or professional contacts continue to operate in a classical fashion in email interactions.
Dynamic traffic grooming for port number optimization in WDM optical mesh networks
Huang, Jun; Zeng, Qingji; Liu, Jimin; Xiao, Pengcheng; Liu, Hua; Xiao, Shilin
2004-04-01
In this paper, the objective was optimizing the port number with dynamic traffic grooming of SDH/SONET WDM mesh networks to give useful referenced data to networks design and the cost control of networks. The performances of different path select routing algorithms were evaluated in WDM grooming networks by considering traffic of different bandwidth requests. Finally, the results were presented and compared with in distributed-controlled WDM mesh networks.
Associative nature of event participation dynamics: a network theory approach
Smiljanić, Jelena
2016-01-01
Affiliation with various social groups can be a critical factor when it comes to quality of life of every individual, making these groups an essential element of every society. The group dynamics, longevity and effectiveness strongly depend on group's ability to attract new members and keep them engaged in group activities. It was shown that high heterogeneity of scientist's engagement in conference activities of the specific scientific community depends on the balance between the number of previous attendance and non-attendance and is directly related to scientist's association with that community. Here we show that the same holds for leisure groups of Meetup website and further quantify member's association with the group. We examine how structure of personal social networks is evolving with event attendance. Our results show that member's increasing engagement in group activities is primarily associated with the strengthening of already existing ties and increase of bonding social capital. We also show tha...
Dynamic Bayesian Networks for Context-Aware Fall Risk Assessment
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Gregory Koshmak
2014-05-01
Full Text Available Fall incidents among the elderly often occur in the home and can cause serious injuries affecting their independent living. This paper presents an approach where data from wearable sensors integrated in a smart home environment is combined using a dynamic Bayesian network. The smart home environment provides contextual data, obtained from environmental sensors, and contributes to assessing a fall risk probability. The evaluation of the developed system is performed through simulation. Each time step is represented by a single user activity and interacts with a fall sensors located on a mobile device. A posterior probability is calculated for each recognized activity or contextual information. The output of the system provides a total risk assessment of falling given a response from the fall sensor.