Emergence of a small-world functional network in cultured neurons

The functional networks of cultured neurons exhibit complex network properties similar to those found in vivo. Starting from random seeding, cultures undergo significant reorganization during the initial period in vitro, yet despite providing an ideal platform for observing developmental changes in neuronal connectivity, little is known about how a complex functional network evolves from isolated neurons. In the present study, evolution of functional connectivity was estimated from correlations of spontaneous activity. Network properties were quantified using complex measures from graph theory and used to compare cultures at different stages of development during the first 5 weeks in vitro. Networks obtained from young cultures (14 days in vitro) exhibited a random topology, which evolved to a small-world topology during maturation. The topology change was accompanied by an increased presence of highly connected areas (hubs) and network efficiency increased with age. The small-world topology balances integration of network areas with segregation of specialized processing units. The emergence of such network structure in cultured neurons, despite a lack of external input, points to complex intrinsic biological mechanisms. Moreover, the functional network of cultures at mature ages is efficient and highly suited to complex processing tasks.

Políticas de alocação de espectro em roteamento baseado em balanceamento de cargas e fragmentação para redes ópticas elásticas

A natureza rígida de redes de multiplexação por divisão de comprimentos de onda (WDM) provoca exploração ineficiente de capacidade espectral. Dessa forma, redes flexíveis são um possível avanço para a tecnologia óptica por viabilizarem melhor aproveitamento dos recursos espectrais disponíveis. Com o intuito de aferir a possível aplicabilidade de redes flexíveis, este trabalho propõe uma estratégia de avaliação de desempenho baseada em simulações e comparações entre resultados obtidos. Para tanto, várias simulações a tempo discreto foram implementadas em dois simuladores desenvolvidos em Matlab a fim de analisar diferentes políticas de alocação de espectro (First-Fit, Smallest-Fit, Exact-Fit e Random-Fit) em três algoritmos de roteamento por caminhos ópticos não híbridos: o roteamento por fragmentação externa (FA), por caminhos mais curtos com máxima eficiência de reuso espectral (SPSR) e por balanceamento de cargas (BLSA). Duas topologias de rede foram utilizadas: um pequeno subconjunto de 6 nós da Cost239 e uma topologia aleatória de 7 nós. Admitindo-se que efeitos de camada física não foram configurados como restrições, foram realizadas comparações entre as diversas técnicas estudadas, objetivando-se apontar, baseado nas especificidades dos cenários propostos, qual o método mais adequado de alocação espectral em termos de frequência de bloqueio entre as quatro políticas de alocação de espectro consideradas.

Layering the Internet-of-Things with Multicasting in Flow-sensors for Internet-of-services

Development of Internet-of-Services will be hampered by heterogeneous Internet-of-Things infrastructures, such as inconsistency in communicating with participating objects, connectivity between them, topology definition & data transfer, access via cloud computing for data storage etc. Our proposed solutions are applicable to a random topology scenario that allow establishing of multi-operational sensor networks out of single networks and/or single service networks with the participation of multiple networks; thus allowing virtual links to be created and resources to be shared. The designed layers are context-aware, application-oriented, and capable of representing physical objects to a management system, along with discovery of services. The reliability issue is addressed by deploying IETF supported IEEE 802.15.4 network model for low-rate wireless personal networks. Flow- sensor succeeded better results in comparison to the typical - sensor from reachability, throughput, energy consumption and diversity gain viewpoint and through allowing the multicast groups into maximum number, performances can be improved.

TOPSIG : Topology Preserving Document Signatures

Performance comparisons between File Signatures and Inverted Files for text retrieval have previously shown several significant shortcomings of file signatures relative to inverted files. The inverted file approach underpins most state-of-the-art search engine algorithms, such as Language and Probabilistic models. It has been widely accepted that traditional file signatures are inferior alternatives to inverted files. This paper describes TopSig, a new approach to the construction of file signatures. Many advances in semantic hashing and dimensionality reduction have been made in recent times, but these were not so far linked to general purpose, signature file based, search engines. This paper introduces a different signature file approach that builds upon and extends these recent advances. We are able to demonstrate significant improvements in the performance of signature file based indexing and retrieval, performance that is comparable to that of state of the art inverted file based systems, including Language models and BM25. These findings suggest that file signatures offer a viable alternative to inverted files in suitable settings and positions the file signatures model in the class of Vector Space retrieval models.

Dynamic optimization of migration topology in internet-based distributed genetic algorithms

Distributed Genetic Algorithms (DGAs) designed for the Internet have to take its high communication cost into consideration. For island model GAs, the migration topology has a major impact on DGA performance. This paper describes and evaluates an adaptive migration topology optimizer that keeps the communication load low while maintaining high solution quality. Experiments on benchmark problems show that the optimized topology outperforms static or random topologies of the same degree of connectivity. The applicability of the method on real-world problems is demonstrated on a hard optimization problem in VLSI design.

Impact of mobility and topology on information diffusion in MANETs

In some delay-tolerant communication systems such as vehicular ad-hoc networks, information flow can be represented as an infectious process, where each entity having already received the information will try to share it with its neighbours. The random walk and random waypoint models are popular analysis tools for these epidemic broadcasts, and represent two types of random mobility. In this paper, we introduce a simulation framework investigating the impact of a gradual increase of bias in path selection (i.e. reduction of randomness), when moving from the former to the latter. Randomness in path selection can significantly alter the system performances, in both regular and irregular network structures. The implications of these results for real systems are discussed in details.

Brain network efficiency and topology depend on the fiber tracking method: 11 tractography algorithms compared in 536 subjects

As connectivity analyses become more popular, claims are often made about how the brain's anatomical networks depend on age, sex, or disease. It is unclear how results depend on tractography methods used to compute fiber networks. We applied 11 tractography methods to high angular resolution diffusion images of the brain (4-Tesla 105-gradient HARDI) from 536 healthy young adults. We parcellated 70 cortical regions, yielding 70×70 connectivity matrices, encoding fiber density. We computed popular graph theory metrics, including network efficiency, and characteristic path lengths. Both metrics were robust to the number of spherical harmonics used to model diffusion (4th-8th order). Age effects were detected only for networks computed with the probabilistic Hough transform method, which excludes smaller fibers. Sex and total brain volume affected networks measured with deterministic, tensor-based fiber tracking but not with the Hough method. Each tractography method includes different fibers, which affects inferences made about the reconstructed networks.

Topology-based denoising of chaos

In this article, we propose a denoising algorithm to denoise a time series y(i) = x(i) + e(i), where {x(i)} is a time series obtained from a time- T map of a uniformly hyperbolic or Anosov flow, and {e(i)} a uniformly bounded sequence of independent and identically distributed (i.i.d.) random variables. Making use of observations up to time n, we create an estimate of x(i) for i<n. We show under typical limiting behaviours of the orbit and the recurrence properties of x(i), the estimation error converges to zero as n tends to infinity with probability 1.

Theory and Algorithms for Hop-Count-Based Localization with Random Geometric Graph Models of Dense Sensor Networks

Wireless sensor networks can often be viewed in terms of a uniform deployment of a large number of nodes in a region of Euclidean space. Following deployment, the nodes self-organize into a mesh topology with a key aspect being self-localization. Having obtained a mesh topology in a dense, homogeneous deployment, a frequently used approximation is to take the hop distance between nodes to be proportional to the Euclidean distance between them. In this work, we analyze this approximation through two complementary analyses. We assume that the mesh topology is a random geometric graph on the nodes; and that some nodes are designated as anchors with known locations. First, we obtain high probability bounds on the Euclidean distances of all nodes that are h hops away from a fixed anchor node. In the second analysis, we provide a heuristic argument that leads to a direct approximation for the density function of the Euclidean distance between two nodes that are separated by a hop distance h. This approximation is shown, through simulation, to very closely match the true density function. Localization algorithms that draw upon the preceding analyses are then proposed and shown to perform better than some of the well-known algorithms present in the literature. Belief-propagation-based message-passing is then used to further enhance the performance of the proposed localization algorithms. To our knowledge, this is the first usage of message-passing for hop-count-based self-localization.

Sampling Biases in IP Topology Measurements

Considerable attention has been focused on the properties of graphs derived from Internet measurements. Router-level topologies collected via traceroute studies have led some authors to conclude that the router graph of the Internet is a scale-free graph, or more generally a power-law random graph. In such a graph, the degree distribution of nodes follows a distribution with a power-law tail. In this paper we argue that the evidence to date for this conclusion is at best insufficient. We show that graphs appearing to have power-law degree distributions can arise surprisingly easily, when sampling graphs whose true degree distribution is not at all like a power-law. For example, given a classical Erdös-Rényi sparse, random graph, the subgraph formed by a collection of shortest paths from a small set of random sources to a larger set of random destinations can easily appear to show a degree distribution remarkably like a power-law. We explore the reasons for how this effect arises, and show that in such a setting, edges are sampled in a highly biased manner. This insight allows us to distinguish measurements taken from the Erdös-Rényi graphs from those taken from power-law random graphs. When we apply this distinction to a number of well-known datasets, we find that the evidence for sampling bias in these datasets is strong.

6-T SRAM cell design with nanoscale double-gate SOI MOSFETs: impact of source/drain engineering and circuit topology

The impact of source/drain engineering on the performance of a six-transistor (6-T) static random access memory (SRAM) cell, based on 22 nm double-gate (DG) SOI MOSFETs, has been analyzed using mixed-mode simulation, for three different circuit topologies for low voltage operation. The trade-offs associated with the various conflicting requirements relating to read/write/standby operations have been evaluated comprehensively in terms of eight performance metrics, namely retention noise margin, static noise margin, static voltage/current noise margin, write-ability current, write trip voltage/current and leakage current. Optimal design parameters with gate-underlap architecture have been identified to enhance the overall SRAM performance, and the influence of parasitic source/drain resistance and supply voltage scaling has been investigated. A gate-underlap device designed with a spacer-to-straggle (s/sigma) ratio in the range 2-3 yields improved SRAM performance metrics, regardless of circuit topology. An optimal two word-line double-gate SOI 6-T SRAM cell design exhibits a high SNM similar to 162 mV, I-wr similar to 35 mu A and low I-leak similar to 70 pA at V-DD = 0.6 V, while maintaining SNM similar to 30% V-DD over the supply voltage (V-DD) range of 0.4-0.9 V.

Constructing random matrices to represent real ecosystems

Models of complex systems with n components typically have order n² parameters because each component can potentially interact with every other. When it is impractical to measure these parameters, one may choose random parameter values and study the emergent statistical properties at the system level. Many influential results in theoretical ecology have been derived from two key assumptions: that species interact with random partners at random intensities and that intraspecific competition is comparable between species. Under these assumptions, community dynamics can be described by a community matrix that is often amenable to mathematical analysis. We combine empirical data with mathematical theory to show that both of these assumptions lead to results that must be interpreted with caution. We examine 21 empirically derived community matrices constructed using three established, independent methods. The empirically derived systems are more stable by orders of magnitude than results from random matrices. This consistent disparity is not explained by existing results on predator-prey interactions. We investigate the key properties of empirical community matrices that distinguish them from random matrices. We show that network topology is less important than the relationship between a species’ trophic position within the food web and its interaction strengths. We identify key features of empirical networks that must be preserved if random matrix models are to capture the features of real ecosystems.

Simulating global properties of electroencephalograms with minimal random neural networks

The human electroencephalogram (EEG) is globally characterized by a 1/f power spectrum superimposed with certain peaks, whereby the "alpha peak" in a frequency range of 8-14 Hz is the most prominent one for relaxed states of wakefulness. We present simulations of a minimal dynamical network model of leaky integrator neurons attached to the nodes of an evolving directed and weighted random graph (an Erdos-Renyi graph). We derive a model of the dendritic field potential (DFP) for the neurons leading to a simulated EEG that describes the global activity of the network. Depending on the network size, we find an oscillatory transition of the simulated EEG when the network reaches a critical connectivity. This transition, indicated by a suitably defined order parameter, is reflected by a sudden change of the network's topology when super-cycles are formed from merging isolated loops. After the oscillatory transition, the power spectra of simulated EEG time series exhibit a 1/f continuum superimposed with certain peaks. (c) 2007 Elsevier B.V. All rights reserved.