Instrumental variables estimation of heteroskedastic linear models using all lags of instruments

estimation;linear models

Coherent transport in Nb/delta-doped-GaAs hybrid microstructures

Coherent transport in Nb/GaAs superconductor-semiconductor microstructures is presented. The structures fabrication procedure is based on delta-doped layers grown by molecular-beam-epitaxy near the GaAs surface, followed by an As cap layer to protect the active semiconductor layers during ex situ transfer. The superconductor is then sputter deposited in situ after thermal desorption of the protective layer. Two types of structures in particular will be discussed, i.e., a reference junction and the engineered one that contains an additional insulating AlGaAs barrier inserted during the growth in the semiconductor. This latter configuration may give rise to controlled interference effects and realizes the model introduced by de Gennes and Saint-James in 1963. While both structures show reflectionless tunneling-dominated transport, only the engineered junction shows additionally a low-temperature single marked resonance peaks superimposed to the characteristic Andreev-dominated subgap conductance. The analysis of coherent magnetotransport in both microstructures is successfully performed within the random matrix theory of Andreev transport and ballistic effects are included by directly solving the Bogoliubov-de Gennes equations. The impact of junction morphology on reflectionless tunneling and the application of the employed fabrication technique to the realization of complex semiconductor-superconductor systems are furthermore discussed.Comment: 9 pages, 8 figures, invited review paper, to be published in Mod. Phys. Lett.

Image reconstruction in fluorescence molecular tomography with sparsity-initialized maximum-likelihood expectation maximization

We present a reconstruction method involving maximum-likelihood expectation maximization (MLEM) to model Poisson noise as applied to fluorescence molecular tomography (FMT). MLEM is initialized with the output from a sparse reconstruction-based approach, which performs truncated singular value decomposition-based preconditioning followed by fast iterative shrinkage-thresholding algorithm (FISTA) to enforce sparsity. The motivation for this approach is that sparsity information could be accounted for within the initialization, while MLEM would accurately model Poisson noise in the FMT system. Simulation experiments show the proposed method significantly improves images qualitatively and quantitatively. The method results in over 20 times faster convergence compared to uniformly initialized MLEM and improves robustness to noise compared to pure sparse reconstruction. We also theoretically justify the ability of the proposed approach to reduce noise in the background region compared to pure sparse reconstruction. Overall, these results provide strong evidence to model Poisson noise in FMT reconstruction and for application of the proposed reconstruction framework to FMT imaging

Bayesian Covariance Matrix Estimation using a Mixture of Decomposable Graphical Models

Estimating a covariance matrix efficiently and discovering its structure are important statistical problems with applications in many fields. This article takes a Bayesian approach to estimate the covariance matrix of Gaussian data. We use ideas from Gaussian graphical models and model selection to construct a prior for the covariance matrix that is a mixture over all decomposable graphs, where a graph means the configuration of nonzero offdiagonal elements in the inverse of the covariance matrix. Our prior for the covariance matrix is such that the probability of each graph size is specified by the user and graphs of equal size are assigned equal probability. Most previous approaches assume that all graphs are equally probable. We give empirical results that show the prior that assigns equal probability over graph sizes outperforms the prior that assigns equal probability over all graphs, both in identifying the correct decomposable graph and in more efficiently estimating the covariance matrix. The advantage is greatest when the number of observations is small relative to the dimension of the covariance matrix. The article also shows empirically that there is minimal change in statistical efficiency in using the mixture over decomposable graphs prior for estimating a general covariance compared to the Bayesian estimator by Wong et al. (2003), even when the graph of the covariance matrix is nondecomposable. However, our approach has some important advantages over that of Wong et al. (2003). Our method requires the number of decomposable graphs for each graph size. We show how to estimate these numbers using simulation and that the simulation results agree with analytic results when such results are known. We also show how to estimate the posterior distribution of the covariance matrix using Markov chain Monte Carlo with the elements of the covariance matrix integrated out and give empirical results that show the sampler is computationally efficient and converges rapidly. Finally, we note that both the prior and the simulation method to evaluate the prior apply generally to any decomposable graphical model.Covariance selection; Graphical models; Reduced conditional sampling; Variable selection

Toxicological approach to setting spacecraft maximum allowable concentrations for carbon monoxide

The Spacecraft Maximum Allowable Concentrations (SMACs) are exposure limits for airborne chemicals used by NASA in spacecraft. The aim of these SMACs is to protect the spacecrew against adverse health effects and performance decrements that would interfere with mission objectives. Because of the 1 and 24 hr SMACs are set for contingencies, minor reversible toxic effects that do not affect mission objectives are acceptable. The 7, 30, or 180 day SMACs are aimed at nominal operations, so they are established at levels that would not cause noncarcinogenic toxic effects and more than one case of tumor per 1000 exposed individuals over the background. The process used to set the SMACs for carbon monoxide (CO) is described to illustrate the approach used by NASA. After the toxicological literature on CO was reviewed, the data were summarized and separated into acute, subchronic, and chronic toxicity data. CO's toxicity depends on the formation of carboxyhemoglobin (COHb) in the blood, reducing the blood's oxygen carrying capacity. The initial task was to estimate the COHb levels that would not produce toxic effects in the brain and heart

Fully nonlinear excitations of non-Abelian plasma

We investigate fully nonlinear, non-Abelian excitations of quark-antiquark plasma, using relativistic fluid theory in cold plasma approximation. There are mainly three important nonlinearities, coming from various sources such as non-Abelian interactions of Yang-Mills (YM) fields, Wong's color dynamics and plasma nonlinearity, in our model. By neglecting nonlinearities due to plasma and color dynamics we get back the earlier results of Blaizot {\it et. al.}, Phys. Rev. Lett. 72, 3317 (1994). Similarly, by neglecting YM fields nonlinearity and plasma nonlinearity, it reduces to the model of Gupta {\it et. al.}, Phys. Lett. B498, 223 (2005). Thus we have the most general non-Abelian mode of quark-gluon plasma (QGP). Further, our model resembles the problem of propagation of laser beam through relativistic plasma, Physica 9D, 96 (1983). in the absence of all non-Abelian interactions.Comment: 8 pages, 2 figures, articl

Fluid Antenna Multiple Access

Fluid antenna system represents an emerging technology that enables an antenna to switch its physical location in a predefined space. This paper explores the potential of using a single fluid antenna at each mobile user for multiple access, which we refer to it as fluid antenna multiple access (FAMA). FAMA exploits spatial moments of deep fade suffered by the interference to achieve a favourable channel condition for the desired signal, without requiring sophisticated signal processing. We analyze the FAMA network by first deriving the outage probability of the signal-to-interference ratio (SIR) in a double integral form. We then obtain an outage probability upper bound in closed form and an average outage rate lower bound for the FAMA system, with an arbitrary number of interferers, from which the multiplexing gain of FAMA is characterized. We also estimate how large the number of locations is required to achieve a given multiplexing gain using fluid antennas with a given size. Results show that it is possible for FAMA to support hundreds of users using only one fluid antenna of a few wavelengths of space at each user, giving rise to significant gain in the average network outage rate