Diffusion Strategies Outperform Consensus Strategies for Distributed Estimation over Adaptive Networks

Adaptive networks consist of a collection of nodes with adaptation and learning abilities. The nodes interact with each other on a local level and diffuse information across the network to solve estimation and inference tasks in a distributed manner. In this work, we compare the mean-square performance of two main strategies for distributed estimation over networks: consensus strategies and diffusion strategies. The analysis in the paper confirms that under constant step-sizes, diffusion strategies allow information to diffuse more thoroughly through the network and this property has a favorable effect on the evolution of the network: diffusion networks are shown to converge faster and reach lower mean-square deviation than consensus networks, and their mean-square stability is insensitive to the choice of the combination weights. In contrast, and surprisingly, it is shown that consensus networks can become unstable even if all the individual nodes are stable and able to solve the estimation task on their own. When this occurs, cooperation over the network leads to a catastrophic failure of the estimation task. This phenomenon does not occur for diffusion networks: we show that stability of the individual nodes always ensures stability of the diffusion network irrespective of the combination topology. Simulation results support the theoretical findings.Comment: 37 pages, 7 figures, To appear in IEEE Transactions on Signal Processing, 201

On the Influence of Informed Agents on Learning and Adaptation over Networks

Adaptive networks consist of a collection of agents with adaptation and learning abilities. The agents interact with each other on a local level and diffuse information across the network through their collaborations. In this work, we consider two types of agents: informed agents and uninformed agents. The former receive new data regularly and perform consultation and in-network tasks, while the latter do not collect data and only participate in the consultation tasks. We examine the performance of adaptive networks as a function of the proportion of informed agents and their distribution in space. The results reveal some interesting and surprising trade-offs between convergence rate and mean-square performance. In particular, among other results, it is shown that the performance of adaptive networks does not necessarily improve with a larger proportion of informed agents. Instead, it is established that the larger the proportion of informed agents is, the faster the convergence rate of the network becomes albeit at the expense of some deterioration in mean-square performance. The results further establish that uninformed agents play an important role in determining the steady-state performance of the network, and that it is preferable to keep some of the highly connected agents uninformed. The arguments reveal an important interplay among three factors: the number and distribution of informed agents in the network, the convergence rate of the learning process, and the estimation accuracy in steady-state. Expressions that quantify these relations are derived, and simulations are included to support the theoretical findings. We further apply the results to two models that are widely used to represent behavior over complex networks, namely, the Erdos-Renyi and scale-free models.Comment: 35 pages, 8 figure

Use of a lambda gt11 expression library to localize a neutralizing antibody-binding site in glycoprotein E2 of Sindbis virus

The Sindbis virus envelope contains two species of integral membrane glycoproteins, E1 and E2. These proteins form heterodimers, and three dimeric units assemble to form spikes incorporated into the viral surface which play an important role in the specific attachment of Sindbis virus to host cells. To map the neutralization epitopes on the surface of the virus, we constructed a lambda gt11 expression library with cDNA inserts 100 to 300 nucleotides long obtained from randomly primed synthesis on Sindbis virus genomic RNA. This library was screened with five different neutralizing monoclonal antibodies (MAbs) specific for E2 (MAbs 50, 51, 49, 18, and 23) and with one neutralizing MAb specific for E1 (MAb 33). When 10(6) lambda gt11 plaques were screened with each antibody, four positive clones that reacted with E2-specific MAb 23 were found. These four clones contained overlapping inserts from glycoprotein E2; the domain from residues 173 to 220 of glycoprotein E2 was present in all inserts, and we concluded that this region contains the neutralization epitope recognized by the antibody. No clones that reacted with the other antibodies examined were found, and we concluded that these antibodies probably recognize conformational epitopes not present in the lambda gt11 library. We suggest that the E2 domain from residues 173 to 220 is a major antigenic determinant of Sindbis virus and that this domain is important for virus attachment to cells

The Strong Multifield Slowroll Condition and Spiral Inflation

We point out the existing confusions about the slowroll parameters and conditions for multifield inflation. If one requires the fields to roll down the gradient flow, we find that only articles adopting the Hubble slowroll expansion are on the right track, and a correct condition can be found in a recent book by Liddle and Lyth. We further analyze this condition and show that the gradient flow requirement is stronger than just asking for a slowly changing, quasi-de Sitter solution. Therefore it is possible to have a multifield slowroll model that does not follow the gradient flow. Consequently, it no longer requires the gradient to be small. It even bypasses the first slowroll condition and some related no-go theorems from string theory. We provide the "spiral inflation" as a generic blueprint of such inflation model and show that it relies on a monodromy locus---a common structure in string theory effective potentials.Comment: 12 pages, version 4, cosmetic changes recommended by referee, resubmitting to PR

Probability of Slowroll Inflation in the Multiverse

Slowroll after tunneling is a crucial step in one popular framework of the multiverse---false vacuum eternal inflation (FVEI). In a landscape with a large number of fields, we provide a heuristic estimation for its probability. We find that the chance to slowroll is exponentially suppressed, where the exponent comes from the number of fields. However, the relative probability to have more e-foldings is only mildly suppressed as $N_e^{-\alpha}$ with $\alpha\sim3$. Base on these two properties, we show that the FVEI picture is still self-consistent and may have a strong preference between different slowroll models.Comment: version 3, 21 pages, resubmit to PRD recommanded by refere

Replica Monte Carlo Simulation (Revisited)

In 1986, Swendsen and Wang proposed a replica Monte Carlo algorithm for spin glasses [Phys. Rev. Lett. 57 (1986) 2607]. Two important ingredients are present, (1) the use of a collection of systems (replicas) at different of temperatures, but with the same random couplings, (2) defining and flipping clusters. Exchange of information between the systems is facilitated by fixing the tau spin (tau=sigma^1\sigma^2) and flipping the two neighboring systems simultaneously. In this talk, we discuss this algorithm and its relationship to replica exchange (also known as parallel tempering) and Houdayer's cluster algorithm for spin glasses. We review some of the early results obtained using this algorithm. We also present new results for the correlation times of replica Monte Carlo dynamics in two and three dimensions and compare them with replica exchange.Comment: For "Statistical Physics of Disordered Systems and Its Applications", 12-15 July 2004, Shonan Village Center, Hayama, Japan, 7 page

Coupled rotor-body vibrations with inplane degrees of freedom

In an effort to understand the vibration mechanisms of helicopters, the following basic studies are considered. A coupled rotor-fuselage vibration analysis including inplane degrees of freedom of both rotor and airframe is performed by matching of rotor and fuselage impedances at the hub. A rigid blade model including hub motion is used to set up the rotor flaplag equations. For the airframe, 9 degrees of freedom and hub offsets are used. The equations are solved by harmonic balance. For a 4-bladed rotor, the coupled responses and hub loads are calculated for various parameters in forward flight. The results show that the addition of inplane degrees of freedom does not significantly affect the vertical vibrations for the cases considered, and that inplane vibrations have similar resonance trends as do flapping vibrations