Effects of the network structural properties on its controllability

In a recent paper, it has been suggested that the controllability of a diffusively coupled complex network, subject to localized feedback loops at some of its vertices, can be assessed by means of a Master Stability Function approach, where the network controllability is defined in terms of the spectral properties of an appropriate Laplacian matrix. Following that approach, a comparison study is reported here among different network topologies in terms of their controllability. The effects of heterogeneity in the degree distribution, as well as of degree correlation and community structure, are discussed.Comment: Also available online at: http://link.aip.org/link/?CHA/17/03310

Load distribution in small world networks

In this paper we introduce a new model of data packet transport, based on a stochastic approach with the aim of characterizing the load distribution on complex networks. Moreover we analyze the load standard deviation as an index of uniformity of the distribution of packets within the network, to characterize the effects of the network topology. We measure such index on the model proposed by Watts and Strogatz as the redirection probability is increased. We find that the uniformity of the load spread is maximized in the intermediate region, at which the small world effect is observed and both global and local efficiency are high. Moreover we analyze the relationship between load centrality and degree centrality as an approximate measure of the load at the edges. Analogous results are obtained for the load variance computed at the edges as well as at the vertices.Comment: 6 pages, 5 figures. Included in conference proceedings International Conference PhysCon 2005 August 24-26, 2005, Saint Petersburg, RUSSI

Identification of delays and discontinuity points of unknown systems by using synchronization of chaos

In this paper we present an approach in which synchronization of chaos is used to address identification problems. In particular, we are able to identify: (i) the discontinuity points of systems described by piecewise dynamical equations and (ii) the delays of systems described by delay differential equations. Delays and discontinuities are widespread features of the dynamics of both natural and manmade systems. The foremost goal of the paper is to present a general and flexible methodology that can be used in a broad variety of identification problems.Comment: 11 pages, 3 figure

Using synchronism of chaos for adaptive learning of network topology

In this paper we consider networks of dynamical systems that evolve in synchrony and investigate how dynamical information from the synchronization dynamics can be effectively used to learn the network topology, i.e., identify the time evolution of the couplings between the network nodes. To this aim, we present an adaptive strategy that, based on a potential that the network systems seek to minimize in order to maintain synchronization, can be successfully applied to identify the time evolution of the network from limited information. This strategy takes advantage of the properties of synchronism of chaos and of the presence of different communication delays over the network links. As a motivating example we consider a network of sensors surveying an area, in which information regarding the time evolution of the network connections can be used, e.g., to detect changes taking place within the area. We propose two different setups for our strategy. In the first one, synchronization has to be achieved at each node (as well as the identification of the couplings over the network links), based solely on a single scalar signal representing a superposition of signals from the other nodes in the network. In the second one, we incorporate an additional node, termed the maestro, having the function of maintaining network synchronization. We will see that when such an arrangement is realized, it will become possible to effectively identify the time evolution of networks that are much larger than would be possible in the absence of a maestro.Comment: 22 pages, 12 figures, accepted for publication on Physical Review

Semantic and inferencing abilities in children with communication disorders

Background: Semantic and inferencing abilities have not been fully examined in children with communication difficulties. Aims: To investigate the inferential and semantic abilities of children with communication difficulties using newly designed tasks. Methods & Procedures: Children with different types of communication disorder were compared with each other and with three groups of typically developing children: those of the same chronological age and two groups of younger children. In total, 25 children aged 11 years with specific language impairment and 22 children, also 11 years of age, with primary pragmatic difficulties were recruited. Typically developing groups aged 11 (n = 35; age‐match), and those aged 9 (n = 40) and 7 (n = 37; language similar) also participated as comparisons. Outcomes & Results: For Semantic Choices, children with specific language impairment performed significantly more poorly than 9‐ and 11‐year‐olds, whilst the pragmatic difficulties group scored significantly lower than all the typically developing groups. Borderline differences between specific language impairment and pragmatic difficulties groups were found. For inferencing, children with communication impairments performed significantly below the 11‐year‐old peers, but not poorer than 9‐ and 7‐year‐olds, suggesting that this skill is in line with language ability. Six children in the pragmatic difficulties group who met diagnosis for autism performed more poorly than the other two clinical groups on both tasks, but not statistically significantly so. Conclusions: Both tasks were more difficult for those with communication impairments compared with peers. Semantic but not inferencing abilities showed a non‐significant trend for differences between the two clinical groups and children with pragmatic difficulties performed more poorly than all typically developing groups. The tasks may relate to each other in varying ways according to type of communication difficulty

Inhibition of phosphoinositide 3-kinase/protein kinase B signaling hampers the vasopressin-dependent stimulation of myogenic differentiation

Arginine-vasopressin (AVP) promotes muscle differentiation, hypertrophy, and regeneration through the combined activation of the calcineurin and Calcium/Calmodulin-dependent Protein Kinase (CaMK) pathways. The AVP system is impaired in several neuromuscular diseases, suggesting that AVP may act as a physiological factor in skeletal muscle. Since the Phosphoinositide 3-kinases/Protein Kinase B/mammalian Target Of Rapamycin (PI3K/Akt/mTOR) signaling plays a significant role in regulating muscle mass, we evaluated its role in the AVP myogenic effect. In L6 cells AKT1 expression was knocked down, and the AVP-dependent expression of mTOR and Forkhead box O3 (FoxO) was analyzed by Western blotting. The effect of the PI3K inhibitor LY294002 was evaluated by cellular and molecular techniques. Akt knockdown hampered the AVP-dependent mTOR expression while increased the levels of FoxO transcription factor. LY294002 treatment inhibited the AVP-dependent expression of Myocyte Enhancer Factor-2 (MEF2) and myogenin and prevented the nuclear translocation of MEF2. LY294002 also repressed the AVP-dependent nuclear export of histone deacetylase 4 (HDAC4) interfering with the formation of multifactorial complexes on the myogenin promoter. We demonstrate that the PI3K/Akt pathway is essential for the full myogenic effect of AVP and that, by targeting this pathway, one may highlight novel strategies to counteract muscle wasting in aging or neuromuscular disorders

Synchronization of dynamical hypernetworks: dimensionality reduction through simultaneous block-diagonalization of matrices

We present a general framework to study stability of the synchronous solution for a hypernetwork of coupled dynamical systems. We are able to reduce the dimensionality of the problem by using simultaneous block-diagonalization of matrices. We obtain necessary and sufficient conditions for stability of the synchronous solution in terms of a set of lower-dimensional problems and test the predictions of our low-dimensional analysis through numerical simulations. Under certain conditions, this technique may yield a substantial reduction of the dimensionality of the problem. For example, for a class of dynamical hypernetworks analyzed in the paper, we discover that arbitrarily large networks can be reduced to a collection of subsystems of dimensionality no more than 2. We apply our reduction techique to a number of different examples, including a class of undirected unweighted hypermotifs of three nodes.Comment: 9 pages, 6 figures, accepted for publication in Phys. Rev.

Experimental Observations of Group Synchrony in a System of Chaotic Optoelectronic Oscillators

We experimentally demonstrate group synchrony in a network of four nonlinear optoelectronic oscillators with time-delayed coupling. We divide the nodes into two groups of two each, by giving each group different parameters and by enabling only inter-group coupling. When coupled in this fashion, the two groups display different dynamics, with no isochronal synchrony between them, but the nodes in a single group are isochronally synchronized, even though there is no intra-group coupling. We compare experimental behavior with theoretical and numerical results