953 resultados para poset of Hausdorff topologies
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Basic Network transactions specifies that datagram from source to destination is routed through numerous routers and paths depending on the available free and uncongested paths which results in the transmission route being too long, thus incurring greater delay, jitter, congestion and reduced throughput. One of the major problems of packet switched networks is the cell delay variation or jitter. This cell delay variation is due to the queuing delay depending on the applied loading conditions. The effect of delay, jitter accumulation due to the number of nodes along transmission routes and dropped packets adds further complexity to multimedia traffic because there is no guarantee that each traffic stream will be delivered according to its own jitter constraints therefore there is the need to analyze the effects of jitter. IP routers enable a single path for the transmission of all packets. On the other hand, Multi-Protocol Label Switching (MPLS) allows separation of packet forwarding and routing characteristics to enable packets to use the appropriate routes and also optimize and control the behavior of transmission paths. Thus correcting some of the shortfalls associated with IP routing. Therefore MPLS has been utilized in the analysis for effective transmission through the various networks. This paper analyzes the effect of delay, congestion, interference, jitter and packet loss in the transmission of signals from source to destination. In effect the impact of link failures, repair paths in the various physical topologies namely bus, star, mesh and hybrid topologies are all analyzed based on standard network conditions.
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The orientation of the heliospheric magnetic field (HMF) in near‒Earth space is generally a good indicator of the polarity of HMF foot points at the photosphere. There are times, however, when the HMF folds back on itself (is inverted), as indicated by suprathermal electrons locally moving sunward, even though they must ultimately be carrying the heat flux away from the Sun. Analysis of the near‒Earth solar wind during the period 1998–2011 reveals that inverted HMF is present approximately 5.5% of the time and is generally associated with slow, dense solar wind and relatively weak HMF intensity. Inverted HMF is mapped to the coronal source surface, where a new method is used to estimate coronal structure from the potential‒field source‒surface model. We find a strong association with bipolar streamers containing the heliospheric current sheet, as expected, but also with unipolar or pseudostreamers, which contain no current sheet. Because large‒scale inverted HMF is a widely accepted signature of interchange reconnection at the Sun, this finding provides strong evidence for models of the slow solar wind which involve coronal loop opening by reconnection within pseudostreamer belts as well as the bipolar streamer belt. Occurrence rates of bipolar‒ and pseudostreamers suggest that they are equally likely to result in inverted HMF and, therefore, presumably undergo interchange reconnection at approximately the same rate. Given the different magnetic topologies involved, this suggests the rate of reconnection is set externally, possibly by the differential rotation rate which governs the circulation of open solar flux.
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Epidemic protocols are a bio-inspired communication and computation paradigm for large and extreme-scale networked systems. This work investigates the expansion property of the network overlay topologies induced by epidemic protocols. An expansion quality index for overlay topologies is proposed and adopted for the design of epidemic membership protocols. A novel protocol is proposed, which explicitly aims at improving the expansion quality of the overlay topologies. The proposed protocol is tested with a global aggregation task and compared to other membership protocols. The analysis by means of simulations indicates that the expansion quality directly relates to the speed of dissemination and convergence of epidemic protocols and can be effectively used to design better protocols.
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Tribe Pogonieae (Orchidaceae), as Currently known, comprises live genera distributed from South to North America and Eastern Asia. Phylogenetic inferences within Cleistes and among genera of tribe Pogonieae were made based oil nrDNA (ITS) and cpDNA (trnL-F, rps16, rbcL, and psaB) Sequence data and maximum parsimony. Eighteen species of Cleistes, members of all other genera of Pogonieae, and outgroups were sampled. Analyses based oil individual DNA regions provided similar topologies. All evidence indicates that Cleistes is paraphyletic. The North American C. divaricata and C bifaria are more closely related to the temperate genera Isotria and Pogonia than to their Central and South American congeners, the latter Constituting a monophyletic group characterized by the production of nectar as reward, tuberous roots, and their distribution in Central and South America. The Amazonian Duckeella is sister to the remainder of Pogonieae. Taxonomic and biogeographic implications are discussed, and morphological synapomorphies are given For clades obtained in the inferred molecular phylogeny. (C) 2008 Gesellschaft fur Biologische Systematik. Published by Elsevier GmbH. All rights reserved.
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P>1. Much of the current understanding of ecological systems is based on theory that does not explicitly take into account individual variation within natural populations. However, individuals may show substantial variation in resource use. This variation in turn may be translated into topological properties of networks that depict interactions among individuals and the food resources they consume (individual-resource networks). 2. Different models derived from optimal diet theory (ODT) predict highly distinct patterns of trophic interactions at the individual level that should translate into distinct network topologies. As a consequence, individual-resource networks can be useful tools in revealing the incidence of different patterns of resource use by individuals and suggesting their mechanistic basis. 3. In the present study, using data from several dietary studies, we assembled individual-resource networks of 10 vertebrate species, previously reported to show interindividual diet variation, and used a network-based approach to investigate their structure. 4. We found significant nestedness, but no modularity, in all empirical networks, indicating that (i) these populations are composed of both opportunistic and selective individuals and (ii) the diets of the latter are ordered as predictable subsets of the diets of the more opportunistic individuals. 5. Nested patterns are a common feature of species networks, and our results extend its generality to trophic interactions at the individual level. This pattern is consistent with a recently proposed ODT model, in which individuals show similar rank preferences but differ in their acceptance rate for alternative resources. Our findings therefore suggest a common mechanism underlying interindividual variation in resource use in disparate taxa.
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We characterized four eEF1A genes in the alternative rhabditid nematode model organism Oscheius tipulae. This is twice the copy number of eEF1A genes in C. elegans, C. briggsae, and, probably, many other free-living and parasitic nematodes. The introns show features remarkably different from those of other metazoan eEF1A genes. Most of the introns in the eEF1A genes are specific to O. tipulae and are not shared with any of the other genes described in metazoans. Most of the introns are phase 0 (inserted between two codons), and few are inserted in protosplice sites (introns inserted between the nucleotide sequence A/CAG and G/A). Two of these phase 0 introns are conserved in sequence in two or more of the four eEF1A gene copies, and are inserted in the same position in the genes. Neither of these characteristics has been detected in any of the nematode eEF1A genes characterized to date. The coding sequences were also compared with other eEF1A cDNAs from 11 different nematodes to determine the variability of these genes within the phylum Nematoda. Parsimony and distance trees yielded similar topologies, which were similar to those created using other molecular markers. The presence of more than one copy of the eEF1A gene with nearly identical coding regions makes it difficult to define the orthologous cDNAs. As shown by our data on O. tipulae, careful and extensive examination of intron positions in the eEF1A gene across the phylum is necessary to define their potential for use as valid phylogenetic markers.
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The Main Injector Neutrino Oscillation Search (MINOS) experiment uses an accelerator-produced neutrino beam to perform precision measurements of the neutrino oscillation parameters in the ""atmospheric neutrino"" sector associated with muon neutrino disappearance. This long-baseline experiment measures neutrino interactions in Fermilab`s NuMI neutrino beam with a near detector at Fermilab and again 735 km downstream with a far detector in the Soudan Underground Laboratory in northern Minnesota. The two detectors are magnetized steel-scintillator tracking calorimeters. They are designed to be as similar as possible in order to ensure that differences in detector response have minimal impact on the comparisons of event rates, energy spectra and topologies that are essential to MINOS measurements of oscillation parameters. The design, construction, calibration and performance of the far and near detectors are described in this paper. (C) 2008 Elsevier B.V. All rights reserved.
Resumo:
We show that the Hausdorff dimension of the spectral measure of a class of deterministic, i.e. nonrandom, block-Jacobi matrices may be determined with any degree of precision, improving a result of Zlatos [Andrej Zlatos,. Sparse potentials with fractional Hausdorff dimension, J. Funct. Anal. 207 (2004) 216-252]. (C) 2010 Elsevier Inc. All rights reserved.
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Complex networks obtained from real-world networks are often characterized by incompleteness and noise, consequences of imperfect sampling as well as artifacts in the acquisition process. Because the characterization, analysis and modeling of complex systems underlain by complex networks are critically affected by the quality and completeness of the respective initial structures, it becomes imperative to devise methodologies for identifying and quantifying the effects of the sampling on the network structure. One way to evaluate these effects is through an analysis of the sensitivity of complex network measurements to perturbations in the topology of the network. In this paper, measurement sensibility is quantified in terms of the relative entropy of the respective distributions. Three particularly important kinds of progressive perturbations to the network are considered, namely, edge suppression, addition and rewiring. The measurements allowing the best balance of stability (smaller sensitivity to perturbations) and discriminability (separation between different network topologies) are identified with respect to each type of perturbation. Such an analysis includes eight different measurements applied on six different complex networks models and three real-world networks. This approach allows one to choose the appropriate measurements in order to obtain accurate results for networks where sampling bias cannot be avoided-a very frequent situation in research on complex networks.
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Let F be a singular Riemannian foliation on a compact Riemannian manifold M. By successive blow-ups along the strata of F we construct a regular Riemannian foliation (F) over cap on a compact Riemannian manifold (M) over cap and a desingularization map (rho) over cap : (M) over cap -> M that projects leaves of (F) over cap into leaves of F. This result generalizes a previous result due to Molino for the particular case of a singular Riemannian foliation whose leaves were the closure of leaves of a regular Riemannian foliation. We also prove that, if the leaves of F are compact, then, for each small epsilon > 0, we can find (M) over cap and (F) over cap so that the desingularization map induces an epsilon-isometry between M/F and (M) over cap/(F) over cap. This implies in particular that the space of leaves M/F is a Gromov-Hausdorff limit of a sequence of Riemannian orbifolds {((M) over cap (n)/(F) over cap (n))}.
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The objective of this article is to find out the influence of the parameters of the ARIMA-GARCH models in the prediction of artificial neural networks (ANN) of the feed forward type, trained with the Levenberg-Marquardt algorithm, through Monte Carlo simulations. The paper presents a study of the relationship between ANN performance and ARIMA-GARCH model parameters, i.e. the fact that depending on the stationarity and other parameters of the time series, the ANN structure should be selected differently. Neural networks have been widely used to predict time series and their capacity for dealing with non-linearities is a normally outstanding advantage. However, the values of the parameters of the models of generalized autoregressive conditional heteroscedasticity have an influence on ANN prediction performance. The combination of the values of the GARCH parameters with the ARIMA autoregressive terms also implies in ANN performance variation. Combining the parameters of the ARIMA-GARCH models and changing the ANN`s topologies, we used the Theil inequality coefficient to measure the prediction of the feed forward ANN.
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This work aims to understand the interaction between competition and network formation in the banking market. Combining Matutes and Padilla (1994) and Matutes and Vives (2000), we build a model of imperfect bank competition for deposits in which an interbank relationship network is a key strategic decision: it affects banks’ profit and risk position. The competition level exerts influence in the banking network structure since it affects the network outcomes. As result, we have that different competition levels imply different network topologies. Specifically, greater competition imply denser networks. Finally, when we allow for the possibility of collusion, the denser network can come out in the least competitive environment.
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The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent t~3=2. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.
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Today, the trend within the electronics industry is for the use of rapid and advanced simulation methodologies in association with synthesis toolsets. This paper presents an approach developed to support mixed-signal circuit design and analysis. The methodology proposed shows a novel approach to the problem of developing behvioural model descriptions of mixed-signal circuit topologies, by construction of a set of subsystems, that supports the automated mapping of MATLAB (R)/SINIULINK (R) models to structural VHDL-AMS descriptions. The tool developed, named (MSSV)-S-2, reads a SIMULINK (R) model file and translates it to a structural VHDL-AMS code. It also creates the file structure required to simulate the translated model in the SystemVision (TM). To validate the methodology and the developed program, the DAC08, AD7524 and AD5450 data converters were studied and initially modelled in MATLAB (R)/SIMULINK (R). The VHDL-AMS code generated automatically by (MSSV)-S-2, (MATLAB (R)/SIMULINK (R) to SystemVision (TM)), was then simulated in the SystemVision (TM). The simulation results show that the proposed approach, which is based on VHDL-AMS descriptions of the original model library elements, allows for the behavioural level simulation of complex mixed-signal circuits.
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In this paper, an efficient genetic algorithm (GA) is presented to solve the problem of multistage and coordinated transmission expansion planning. This is a mixed integer nonlinear programming problem, difficult for systems of medium and large size and high complexity. The GA presented has a set of specialized genetic operators and an efficient form of generation of the initial population that finds high quality suboptimal topologies for large size and high complexity systems. In these systems, multistage and coordinated planning present a lower investment than static planning. Tests results are shown in one medium complexity system and one large size high complexity system.