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

Diffusion Adaptation over Networks under Imperfect Information Exchange and Non-stationary Data

Adaptive networks rely on in-network and collaborative processing among distributed agents to deliver enhanced performance in estimation and inference tasks. Information is exchanged among the nodes, usually over noisy links. The combination weights that are used by the nodes to fuse information from their neighbors play a critical role in influencing the adaptation and tracking abilities of the network. This paper first investigates the mean-square performance of general adaptive diffusion algorithms in the presence of various sources of imperfect information exchanges, quantization errors, and model non-stationarities. Among other results, the analysis reveals that link noise over the regression data modifies the dynamics of the network evolution in a distinct way, and leads to biased estimates in steady-state. The analysis also reveals how the network mean-square performance is dependent on the combination weights. We use these observations to show how the combination weights can be optimized and adapted. Simulation results illustrate the theoretical findings and match well with theory.Comment: 36 pages, 7 figures, to appear in IEEE Transactions on Signal Processing, June 201

Plasmoid ejection and secondary current sheet generation from magnetic reconnection in laser-plasma interaction

Reconnection of the self-generated magnetic fields in laser-plasma interaction was first investigated experimentally by Nilson {\it et al.} [Phys. Rev. Lett. 97, 255001 (2006)] by shining two laser pulses a distance apart on a solid target layer. An elongated current sheet (CS) was observed in the plasma between the two laser spots. In order to more closely model magnetotail reconnection, here two side-by-side thin target layers, instead of a single one, are used. It is found that at one end of the elongated CS a fan-like electron outflow region including three well-collimated electron jets appears. The ($>1$ MeV) tail of the jet energy distribution exhibits a power-law scaling. The enhanced electron acceleration is attributed to the intense inductive electric field in the narrow electron dominated reconnection region, as well as additional acceleration as they are trapped inside the rapidly moving plasmoid formed in and ejected from the CS. The ejection also induces a secondary CS

The LAMOST Survey of Background Quasars in the Vicinity of the Andromeda and Triangulum Galaxies -- II. Results from the Commissioning Observations and the Pilot Surveys

We present new quasars discovered in the vicinity of the Andromeda and Triangulum galaxies with the LAMOST during the 2010 and 2011 observational seasons. Quasar candidates are selected based on the available SDSS, KPNO 4 m telescope, XSTPS optical, and WISE near infrared photometric data. We present 509 new quasars discovered in a stripe of ~135 sq. deg from M31 to M33 along the Giant Stellar Stream in the 2011 pilot survey datasets, and also 17 new quasars discovered in an area of ~100 sq. deg that covers the central region and the southeastern halo of M31 in the 2010 commissioning datasets. These 526 new quasars have i magnitudes ranging from 15.5 to 20.0, redshifts from 0.1 to 3.2. They represent a significant increase of the number of identified quasars in the vicinity of M31 and M33. There are now 26, 62 and 139 known quasars in this region of the sky with i magnitudes brighter than 17.0, 17.5 and 18.0 respectively, of which 5, 20 and 75 are newly-discovered. These bright quasars provide an invaluable collection with which to probe the kinematics and chemistry of the ISM/IGM in the Local Group of galaxies. A total of 93 quasars are now known with locations within 2.5 deg of M31, of which 73 are newly discovered. Tens of quasars are now known to be located behind the Giant Stellar Stream, and hundreds behind the extended halo and its associated substructures of M31. The much enlarged sample of known quasars in the vicinity of M31 and M33 can potentially be utilized to construct a perfect astrometric reference frame to measure the minute PMs of M31 and M33, along with the PMs of substructures associated with the Local Group of galaxies. Those PMs are some of the most fundamental properties of the Local Group.Comment: 26 pages, 6 figures, AJ accepte

Reversal of the CD8+ T-Cell Exhaustion Induced by Chronic HIV-1 Infection Through Combined Blockade of the Adenosine and PD-1 Pathways

BackgroundTargeting immune checkpoints for HIV treatment potentially provides a double benefit resulting from the ability to restore viral-specific CD8+ T-cell functions and enhance HIV production from reservoir cells. Despite promising pre-clinical data, PD-1 blockade alone in HIV-1-infected patients with advanced cancer has shown limited benefits in controlling HIV, suggesting the need for additional targets beyond PD-1. CD39 and PD-1 are highly co-expressed on CD8+ T cells in HIV-1 infection. However, the characteristics of CD39 and PD-1 dual-positive CD8+ T-cell subsets in chronic HIV-1 infection remain poorly understood.MethodsThis study enrolled 72 HIV-1-infected patients, including 40 treatment naïve and 32 ART patients. A total of 11 healthy individuals were included as controls. Different subsets of CD8+ T cells defined by CD39 and/or PD-1 expression were studied by flow cytometry. The relationships between the frequencies of the different subsets and parameters indicating HIV-1 disease progression were analyzed. Functional (i.e., cytokine secretion, viral inhibition) assays were performed to evaluate the impact of the blockade of adenosine and/or PD-1 signaling on CD8+ T cells.ResultsThe proportions of PD-1+, CD39+, and PD-1+CD39+ CD8+ T cells were significantly increased in treatment naïve patients but were partially lowered in patients on antiretroviral therapy. In treatment naïve patients, the proportions of PD-1+CD39+ CD8+ T cells were negatively correlated with CD4+ T-cell counts and the CD4/CD8 ratio, and were positively correlated with viral load. CD39+CD8+ T cells expressed high levels of the A2A adenosine receptor and were more sensitive to 2-chloroadenosine-mediated functional inhibition than their CD39- counterparts. In vitro, a combination of blocking CD39/adenosine and PD-1 signaling showed a synergic effect in restoring CD8+ T-cell function, as evidenced by enhanced abilities to secrete functional cytokines and to kill autologous reservoir cells.ConclusionIn patients with chronic HIV-1 infection there are increased frequencies of PD-1+, CD39+, and PD-1+CD39+ CD8+ T cells. In treatment naïve patients, the frequencies of PD-1+CD39+ CD8+ T cells are negatively correlated with CD4+ T-cell counts and the CD4/CD8 ratio and positively correlated with viral load. Combined blockade of CD39/adenosine and PD-1 signaling in vitro may exert a synergistic effect in restoring CD8+ T-cell function in HIV-1-infected patients

High temperature (HT) polymer electrolyte membrande fuel cells (PEMFC) - A review

One possible solution of combating issues posed by climate change is the use of the High Temperature (HT) Polymer Electrolyte Membrane (PEM) Fuel Cell (FC) in some applications. The typical HT-PEMFC operating temperatures are in the range of 100e200 o C which allows for co-generation of heat and power, high tolerance to fuel impurities and simpler system design. This paper reviews the current literature concerning the HT-PEMFC, ranging from cell materials to stack and stack testing. Only acid doped PBI membranes meet the US DOE (Department of Energy) targets for high temperature membranes operating under no humidification on both anode and cathode sides (barring the durability). This eliminates the stringent requirement for humidity however, they have many potential drawbacks including increased degradation, leaching of acid and incompatibility with current state-of-the-art fuel cell materials. In this type of fuel cell, the choice of membrane material determines the other fuel cell component material composition, for example when using an acid doped system, the flow field plate material must be carefully selected to take into account the advanced degradation. Novel research is required in all aspects of the fuel cell components in order to ensure that they meet stringent durability requirements for mobile applications.Web of Scienc