4,791 research outputs found
Optimizing spatially embedded networks for synchronization
In this paper we consider the problem of organizing networks of spatially embedded oscillators to maximize the propensity for synchronization for limited availability of wire, needed to realize the physical connections between the oscillators. We consider two extensions of previous work (Brede, 2010b): (i) oscillators that can flexibly arrange in space during the optimization process and (ii) a generalization to weighted networks. In the first case, we discuss the emergence of spatially and relationally modular network organizations, while in the second case the emphasis of our analysis is on link heterogeneity and the particular organization of strong and weak links that facilitates synchronization in space
Cascading failures in spatially-embedded random networks
Cascading failures constitute an important vulnerability of interconnected
systems. Here we focus on the study of such failures on networks in which the
connectivity of nodes is constrained by geographical distance. Specifically, we
use random geometric graphs as representative examples of such spatial
networks, and study the properties of cascading failures on them in the
presence of distributed flow. The key finding of this study is that the process
of cascading failures is non-self-averaging on spatial networks, and thus,
aggregate inferences made from analyzing an ensemble of such networks lead to
incorrect conclusions when applied to a single network, no matter how large the
network is. We demonstrate that this lack of self-averaging disappears with the
introduction of a small fraction of long-range links into the network. We
simulate the well studied preemptive node removal strategy for cascade
mitigation and show that it is largely ineffective in the case of spatial
networks. We introduce an altruistic strategy designed to limit the loss of
network nodes in the event of a cascade triggering failure and show that it
performs better than the preemptive strategy. Finally, we consider a real-world
spatial network viz. a European power transmission network and validate that
our findings from the study of random geometric graphs are also borne out by
simulations of cascading failures on the empirical network.Comment: 13 pages, 15 figure
Greedy optimization for growing spatially embedded oscillatory networks
The coupling of some types of oscillators requires the mediation of a
physical link between them, rendering the distance between oscillators a
critical factor to achieve synchronization. In this paper we propose and
explore a greedy algorithm to grow spatially embedded oscillator networks. The
algorithm is constructed in such a way that nodes are sequentially added
seeking to minimize the cost of the added links' length and optimize the linear
stability of the growing network. We show that, for appropriate parameters, the
stability of the resulting network, measured in terms of the dynamics of small
perturbations and the correlation length of the disturbances, can be
significantly improved with a minimal added length cost. In addition, we
analyze numerically the topological properties of the resulting networks and
find that, while being more stable, their degree distribution is approximately
exponential and independent of the algorithm parameters. Moreover, we find that
other topological parameters related with network resilience and efficiency are
also affected by the proposed algorithm. Finally, we extend our findings to
more general classes of networks with different sources of heterogeneity. Our
results are a first step in the development of algorithms for the directed
growth of oscillatory networks with desirable stability, dynamical and
topological properties.Comment: 13 pages, 9 figure
Advanced solutions for quality-oriented multimedia broadcasting
Multimedia content is increasingly being delivered via different types of networks to viewers in a variety of locations and contexts using a variety of devices. The ubiquitous nature of multimedia services comes at a cost, however. The successful delivery of multimedia services will require overcoming numerous technological challenges many of which have a direct effect on the quality of the multimedia experience. For example, due to dynamically changing requirements and networking conditions, the delivery of multimedia content has traditionally adopted a best effort approach. However, this approach has often led to the end-user perceived quality of multimedia-based services being negatively affected. Yet the quality of multimedia content is a vital issue for the continued acceptance and proliferation of these services. Indeed, end-users are becoming increasingly quality-aware in their expectations of multimedia experience and demand an ever-widening spectrum of rich multimedia-based services. As a consequence, there is a continuous and extensive research effort, by both industry and academia, to find solutions for improving the quality of multimedia content delivered to the users; as well, international standards bodies, such as the International Telecommunication Union (ITU), are renewing their effort on the standardization of multimedia technologies. There are very different directions in which research has attempted to find solutions in order to improve the quality of the rich media content delivered over various network types. It is in this context that this special issue on broadcast multimedia quality of the IEEE Transactions on Broadcasting illustrates some of these avenues and presents some of the most significant research results obtained by various teams of researchers from many countries. This special issue provides an example, albeit inevitably limited, of the richness and breath of the current research on multimedia broadcasting services. The research i- - ssues addressed in this special issue include, among others, factors that influence user perceived quality, encoding-related quality assessment and control, transmission and coverage-based solutions and objective quality measurements
Neuronal avalanches of a self-organized neural network with active-neuron-dominant structure
Neuronal avalanche is a spontaneous neuronal activity which obeys a power-law
distribution of population event sizes with an exponent of -3/2. It has been
observed in the superficial layers of cortex both \emph{in vivo} and \emph{in
vitro}. In this paper we analyze the information transmission of a novel
self-organized neural network with active-neuron-dominant structure. Neuronal
avalanches can be observed in this network with appropriate input intensity. We
find that the process of network learning via spike-timing dependent plasticity
dramatically increases the complexity of network structure, which is finally
self-organized to be active-neuron-dominant connectivity. Both the entropy of
activity patterns and the complexity of their resulting post-synaptic inputs
are maximized when the network dynamics are propagated as neuronal avalanches.
This emergent topology is beneficial for information transmission with high
efficiency and also could be responsible for the large information capacity of
this network compared with alternative archetypal networks with different
neural connectivity.Comment: Non-final version submitted to Chao
Brain modularity controls the critical behavior of spontaneous activity
The human brain exhibits a complex structure made of scale-free highly
connected modules loosely interconnected by weaker links to form a small-world
network. These features appear in healthy patients whereas neurological
diseases often modify this structure. An important open question concerns the
role of brain modularity in sustaining the critical behaviour of spontaneous
activity. Here we analyse the neuronal activity of a model, successful in
reproducing on non-modular networks the scaling behaviour observed in
experimental data, on a modular network implementing the main statistical
features measured in human brain. We show that on a modular network, regardless
the strength of the synaptic connections or the modular size and number,
activity is never fully scale-free. Neuronal avalanches can invade different
modules which results in an activity depression, hindering further avalanche
propagation. Critical behaviour is solely recovered if inter-module connections
are added, modifying the modular into a more random structure.Comment: 5 pages, 6 figure
Spatial Evolutionary Generative Adversarial Networks
Generative adversary networks (GANs) suffer from training pathologies such as
instability and mode collapse. These pathologies mainly arise from a lack of
diversity in their adversarial interactions. Evolutionary generative
adversarial networks apply the principles of evolutionary computation to
mitigate these problems. We hybridize two of these approaches that promote
training diversity. One, E-GAN, at each batch, injects mutation diversity by
training the (replicated) generator with three independent objective functions
then selecting the resulting best performing generator for the next batch. The
other, Lipizzaner, injects population diversity by training a two-dimensional
grid of GANs with a distributed evolutionary algorithm that includes neighbor
exchanges of additional training adversaries, performance based selection and
population-based hyper-parameter tuning. We propose to combine mutation and
population approaches to diversity improvement. We contribute a superior
evolutionary GANs training method, Mustangs, that eliminates the single loss
function used across Lipizzaner's grid. Instead, each training round, a loss
function is selected with equal probability, from among the three E-GAN uses.
Experimental analyses on standard benchmarks, MNIST and CelebA, demonstrate
that Mustangs provides a statistically faster training method resulting in more
accurate networks
Multilayer Networks
In most natural and engineered systems, a set of entities interact with each
other in complicated patterns that can encompass multiple types of
relationships, change in time, and include other types of complications. Such
systems include multiple subsystems and layers of connectivity, and it is
important to take such "multilayer" features into account to try to improve our
understanding of complex systems. Consequently, it is necessary to generalize
"traditional" network theory by developing (and validating) a framework and
associated tools to study multilayer systems in a comprehensive fashion. The
origins of such efforts date back several decades and arose in multiple
disciplines, and now the study of multilayer networks has become one of the
most important directions in network science. In this paper, we discuss the
history of multilayer networks (and related concepts) and review the exploding
body of work on such networks. To unify the disparate terminology in the large
body of recent work, we discuss a general framework for multilayer networks,
construct a dictionary of terminology to relate the numerous existing concepts
to each other, and provide a thorough discussion that compares, contrasts, and
translates between related notions such as multilayer networks, multiplex
networks, interdependent networks, networks of networks, and many others. We
also survey and discuss existing data sets that can be represented as
multilayer networks. We review attempts to generalize single-layer-network
diagnostics to multilayer networks. We also discuss the rapidly expanding
research on multilayer-network models and notions like community structure,
connected components, tensor decompositions, and various types of dynamical
processes on multilayer networks. We conclude with a summary and an outlook.Comment: Working paper; 59 pages, 8 figure
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