Distributed Constrained Recursive Nonlinear Least-Squares Estimation: Algorithms and Asymptotics

This paper focuses on the problem of recursive nonlinear least squares parameter estimation in multi-agent networks, in which the individual agents observe sequentially over time an independent and identically distributed (i.i.d.) time-series consisting of a nonlinear function of the true but unknown parameter corrupted by noise. A distributed recursive estimator of the \emph{consensus} + \emph{innovations} type, namely $\mathcal{CIWNLS}$, is proposed, in which the agents update their parameter estimates at each observation sampling epoch in a collaborative way by simultaneously processing the latest locally sensed information~(\emph{innovations}) and the parameter estimates from other agents~(\emph{consensus}) in the local neighborhood conforming to a pre-specified inter-agent communication topology. Under rather weak conditions on the connectivity of the inter-agent communication and a \emph{global observability} criterion, it is shown that at every network agent, the proposed algorithm leads to consistent parameter estimates. Furthermore, under standard smoothness assumptions on the local observation functions, the distributed estimator is shown to yield order-optimal convergence rates, i.e., as far as the order of pathwise convergence is concerned, the local parameter estimates at each agent are as good as the optimal centralized nonlinear least squares estimator which would require access to all the observations across all the agents at all times. In order to benchmark the performance of the proposed distributed $\mathcal{CIWNLS}$ estimator with that of the centralized nonlinear least squares estimator, the asymptotic normality of the estimate sequence is established and the asymptotic covariance of the distributed estimator is evaluated. Finally, simulation results are presented which illustrate and verify the analytical findings.Comment: 28 pages. Initial Submission: Feb. 2016, Revised: July 2016, Accepted: September 2016, To appear in IEEE Transactions on Signal and Information Processing over Networks: Special Issue on Inference and Learning over Network

Cluster expansion Monte Carlo study of phase stability of vanadium nitrides

Phase stability of stable and metastable vanadium nitrides is studied using density functional theory (DFT) based total-energy calculations combined with cluster expansion Monte Carlo simulation and supercell methods. We have computed the formation enthalpy of the various stable and metastable vanadium nitride phases considering the available structural models and found that the formation enthalpies of the different phases decrease in the same order as they appear in the experimental aging sequence. DFT calculations are known to show stoichiometric V2N to be polymorphic in ϵ-Fe_2N and ζ-Fe2_N structures within a few meV and VN to be more stable in WC(B_h) phase than in the experimentally observed NaCl(B1) structure. As these nitrides are known to be generally nonstoichiometric due to presence of nitrogen vacancies, we used cluster expansion and supercell methods for examining the effect of nitrogen vacancies on the phase stability. It is found that nitrogen vacancies, represented by ◻, stabilize ϵ-Fe_2N phase of V_2N_(1−x◻x) and NaCl(B1) phase of VN_(1−x◻x) compared to ζ-Fe_2N and WC(B_h) phases respectively, rendering the computed phase stability scenario to be in agreement with experiments. Analysis of supercell calculated electronic density of states (DOS) of VN_(1−x◻x) with varying x, shows that the nitrogen vacancies increase the DOS at Fermi level in WC phase, whereas they decrease the DOS in NaCl phase. And this serves as the mechanism of enhancement of the stability of the NaCl phase. Monte Carlo simulations were used for computing the finite temperature formation enthalpies of these phases as a function of nitrogen-vacancy concentration and found close agreement for NaCl(B1) phase of VN_(1−x◻x) for which measured values are available

Ab Initio Theory of Gate Induced Gaps in Graphene Bilayers

We study the gate voltage induced gap that occurs in graphene bilayers using \textit{ab initio} density functional theory. Our calculations confirm the qualitative picture suggested by phenomenological tight-binding and continuum models. We discuss enhanced screening of the external interlayer potential at small gate voltages, which is more pronounced in the \textit{ab initio} calculations, and quantify the role of crystalline inhomogeneity using a tight-binding model self-consistent Hartree calculation.Comment: 7 pages, 7 figures; the effect of r3 coupling included; typo correcte

Warm Asymmetric Nuclear Matter and Proto-Neutron Star

Asymmetric nuclear matter equation of state at finite temperature is studied in SU(2) chiral sigma model using mean field approximation. The effect of temperature on effective mass, entropy, and binding energy is discussed. Treating the system as one with two conserved charges the liquid-gas phase transition is investigated. We have also discussed the effect of proton fraction on critical temperature with and without $\rho$-meson contribution. We have extended our work to study the structure of proto-neutron star with neutron free charge-neutral matter in beta-equilibrium. We found that the mass and radius of the star decreases as it cools from the entropy per baryon S = 2 to S = 0 and the maximum temperature of the core of the star is about 62 MeV for S = 2.Comment: 25 pages, 16 figure

Detection of Minimum-Ionizing Particles and Nuclear Counter Effect with Pure BGO and BSO Crystals with Photodiode Read-out

Long BGO (Bismuth Germanate) and BSO (Bismuth Silicate) crystals coupled with silicon photodiodes have been used to detect minimum-ionizing particles(MIP). With a low noise amplifier customized for this purpose, the crystals can detect MIPs with an excellent signal-to-noise ratio. The NCE(Nuclear Counter Effect} is also clearly observed and measured. Effect of full and partial wrapping of a reflector around the crystal on light collection is also studied.Comment: 18 pages, including 5 figures; LaTeX and EP

Programming DNA Tube Circumferences

Synthesizing molecular tubes with monodisperse, programmable circumferences is an important goal shared by nanotechnology, materials science, and supermolecular chemistry. We program molecular tube circumferences by specifying the complementarity relationships between modular domains in a 42-base single-stranded DNA motif. Single-step annealing results in the self-assembly of long tubes displaying monodisperse circumferences of 4, 5, 6, 7, 8, 10, or 20 DNA helices