Near-Optimal Detection in MIMO Systems using Gibbs Sampling

In this paper we study a Markov Chain Monte Carlo (MCMC) Gibbs sampler for solving the integer least-squares problem. In digital communication the problem is equivalent to performing Maximum Likelihood (ML) detection in Multiple-Input Multiple-Output (MIMO) systems. While the use of MCMC methods for such problems has already been proposed, our method is novel in that we optimize the "temperature" parameter so that in steady state, i.e. after the Markov chain has mixed, there is only polynomially (rather than exponentially) small probability of encountering the optimal solution. More precisely, we obtain the largest value of the temperature parameter for this to occur, since the higher the temperature, the faster the mixing. This is in contrast to simulated annealing techniques where, rather than being held fixed, the temperature parameter is tended to zero. Simulations suggest that the resulting Gibbs sampler provides a computationally efficient way of achieving approximative ML detection in MIMO systems having a huge number of transmit and receive dimensions. In fact, they further suggest that the Markov chain is rapidly mixing. Thus, it has been observed that even in cases were ML detection using, e.g. sphere decoding becomes infeasible, the Gibbs sampler can still offer a near-optimal solution using much less computations.Comment: To appear in Globecom 200

\u27Who am I?\u27: Autophotography as a Teaching and Learning Tool

This paper describes a low cost, high student appeal technique for teaching and learning about the self concept via student produced photographs. Autophotography (AP) is a photographic approach to understanding the social world from the perspective of the respondent with reference to one\u27s self concept. The technique\u27s use is described relative to social psychology, the self, and the traditional symbolic interactionist measure -- the Twenty Statement Test (TST). The AP course assignment, evaluation, assessment, and limitations are presented Learnings for both the undergraduate student and sociology instructor are discussed

Dynamic Response of a fast near infra-red Mueller matrix ellipsometer

The dynamic response of a near infrared Ferroelectric Liquid Crystal based Mueller matrix ellipsometer (NIR FLC-MME) is presented. A time dependent simulation model, using the measured time response of the individual FLCs, is used to describe the measured temporal response. Furthermore, the impulse response of the detector and the pre-amplifier is characterized and included in the simulation model. The measured time-dependent intensity response of the MME is reproduced in simulations, and it is concluded that the switching time of the FLCs is the limiting factor for the Mueller matrix measurement time of the FLC-based MME. Based on measurements and simulations our FLC based NIR-MME system is estimated to operate at the maximum speed of approximately 16 ms per Mueller matrix measurement. The FLC-MME may be operated several times faster, since the switching time of the crystals depends on the individual crystal being switched, and to what state it is switched. As a demonstration, the measured temporal response of the Mueller matrix and the retardance of a thick liquid crystal variable retarder upon changing state is demonstrated.Comment: to be published in Journal of Modern Optics 20 pages, 6 figure

Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns

We provide an empirical framework for assessing the distributional properties of daily speculative returns within the context of the continuous-time jump diffusion models traditionally used in asset pricing finance. Our approach builds directly on recently developed realized variation measures and non-parametric jump detection statistics constructed from high-frequency intraday data. A sequence of simple-to-implement moment-based tests involving various transformations of the daily returns speak directly to the importance of different distributional features, and may serve as useful diagnostic tools in the specification of empirically more realistic continuous-time asset pricing models. On applying the tests to the thirty individual stocks in the Dow Jones Industrial Average index, we find that it is important to allow for both time-varying diffusive volatility, jumps, and leverage effects to satisfactorily describe the daily stock price dynamics.return distributions, continuous-time models, mixture-of-distributions hypothesis, financial-time sampling, high-frequency data, volatility signature plots, realized volatilities, jumps, leverage and volatility feedback effects

Stacking-induced fluorescence increase reveals allosteric interactions through DNA

From gene expression to nanotechnology, understanding and controlling DNA requires a detailed knowledge of its higher order structure and dynamics. Here we take advantage of the environment-sensitive photoisomerization of cyanine dyes to probe local and global changes in DNA structure. We report that a covalently attached Cy3 dye undergoes strong enhancement of fluorescence intensity and lifetime when stacked in a nick, gap or overhang region in duplex DNA. This is used to probe hybridization dynamics of a DNA hairpin down to the single-molecule level. We also show that varying the position of a single abasic site up to 20 base pairs away modulates the dye–DNA interaction, indicative of through-backbone allosteric interactions. The phenomenon of stacking-induced fluorescence increase (SIFI) should find widespread use in the study of the structure, dynamics and reactivity of nucleic acids

Markers of early disease and prognosis in COPD

COPD is a complex disease with multiple pathological components, which we unfortunately tend to ignore when spirometry is used as the only method to evaluate the disorder. Additional measures are needed to allow a more complete and clinically relevant assessment of COPD. The earliest potential risk factors of disease in COPD are variations in the genetic background. Genetic variations are present from conception and can determine lifelong changes in enzyme activities and protein concentrations. In contrast, measurements in blood, sputum, exhaled breath, broncho-alveolar lavage, and lung biopsies may vary substantially over time. This review explores potential markers of early disease and prognosis in COPD by examining genetic markers in the α1-antitrypsin, cystic fibrosis transmembrane conductance regulator (CFTR), and MBL-2 genes, and by examining the biochemical markers fibrinogen and C-reactive protein (CRP), which correlate with degree of pulmonary inflammation during stable conditions of COPD. Chronic lung inflammation appears to contribute to the pathogenesis of COPD, and markers of this process have promising predictive value in COPD. To implement markers for COPD in clinical practice, besides those already established for the α1-antitrypsin gene, further research and validation studies are needed