On the Noisy Feedback Capacity of Gaussian Broadcast Channels

It is well known that, in general, feedback may enlarge the capacity region of Gaussian broadcast channels. This has been demonstrated even when the feedback is noisy (or partial-but-perfect) and only from one of the receivers. The only case known where feedback has been shown not to enlarge the capacity region is when the channel is physically degraded (El Gamal 1978, 1981). In this paper, we show that for a class of two-user Gaussian broadcast channels (not necessarily physically degraded), passively feeding back the stronger user's signal over a link corrupted by Gaussian noise does not enlarge the capacity region if the variance of feedback noise is above a certain threshold.Comment: 5 pages, 3 figures, to appear in IEEE Information Theory Workshop 2015, Jerusale

Impact of dietary changes on hepatic homocysteine metabolism in young broilers

Information regarding the impact of sulfur amino acids (SAA) on hepatic homocysteine (Hcy) flux through the various metabolic pathways competing for Hcy in young broilers is lacking. An experiment was conducted to evaluate the impact of varying levels of dietary methionine (Met), choline, and betaine on hepatic Hcy flux in young broiler chickens. A standard starter basal diet was fed to chicks until 8 d of age; 12 experimental diets were given from 8-22 d. The experimental basal diet contained deficient levels of Met and cysteine (Cys); supplemental Met (0, 0.08, 0.16, and 0.24%) was added to the basal diet in combination with isomethyl levels of choline (0 or 0.25%) or betaine (0 or 0.28%). The 12 dietary treatments were replicated with three pens containing five chicks each (15 birds per treatment). Weight gain and feed efficiency increased (P \u3c 0.05) with Met addition and were maximized with the addition of 0.16% digestible Met. No significant interactions (P \u3e 0.05) with choline or betaine addition were noted for weight gain, feed intake, or feed efficiency, but numerical improvements for these variables were observed with the addition of choline and betaine to the Met-deficient basal diet. Analysis of liver tissue indicated that folate-dependent remethylation of Hcy predominated over betaine-dependent remethylation. Further, folate-dependent remethylation of Hcy appeared to be impacted by dietary choline and betaine levels, whereas betaine-dependent remethylation appeared to be more impacted by dietary SAA levels

Ecology of the cladocerans of the plankton community in the Cochin backwater

Occurrence and seasonal changes in the distribution of two cladocerans in the eochin Backwater, Evadne tergestina Claus and Penilia avirostris Dana have been considered. Fluctuations in temperature of tbe estuary are of the order of SoC., while tbe cbanges in salinity are considerable. Seasonal variation in distribution of tbe two species bas been correlated with temperature and salin ity in T-S-P diagram for a period of fifteen months. Their numerical abundance month-wise bas also been investigated. A comparative study of the distribution of cJadocerans of the insbore waters of India wilh that of the Cochin Backwater has also been carried out

Caridina pseudogracilirostris sp.nov. (Atyidae: Caridina) from the Cochin Backwater

A few specimens of Cardina collected from the Cochin Backwater during try net operations, resembled Caridina gracilirostris de Man in many respects. But close examination of the material revealed that they differed from it in the shape and armature of the telson and the absence of the appendix interna on the first pleopod of male specimens. Therefore, a new species Cardina pseudogracilirostris is proposed to describe the animal. Detailed discription of the new species and its affinities to allied species are given in the present note

Diffusion limits of the random walk Metropolis algorithm in high dimensions

Diffusion limits of MCMC methods in high dimensions provide a useful theoretical tool for studying computational complexity. In particular, they lead directly to precise estimates of the number of steps required to explore the target measure, in stationarity, as a function of the dimension of the state space. However, to date such results have mainly been proved for target measures with a product structure, severely limiting their applicability. The purpose of this paper is to study diffusion limits for a class of naturally occurring high-dimensional measures found from the approximation of measures on a Hilbert space which are absolutely continuous with respect to a Gaussian reference measure. The diffusion limit of a random walk Metropolis algorithm to an infinite-dimensional Hilbert space valued SDE (or SPDE) is proved, facilitating understanding of the computational complexity of the algorithm.Comment: Published in at http://dx.doi.org/10.1214/10-AAP754 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

Demersal fish assemblages of the Southwest Coast of India

Demersal surveys from the southwest coast of India were analyzed to determine the general pattern of distribution of demersal species assemblages in the area. Seasonality is pronounced, indicating three major periods, pre-monsoon, monsoon and post-monsoon. Each of the periods is characterized by different oceanographic circulation patterns that mainly determine the pattern of distribution of species assemblages. Spatial analysis confirmed that the Wadge Bank has the highest potential for producing good quality fish. Region-wise analysis of data indicated that maximum effort and highest landings are from the known grounds along the southwest coast, although certain northern areas were also found to be fairly productive.Fishery resources, Demersal fisheries, Fishery surveys, Biomass, Population density, Shrimp fisheries, Catch/effort, Trawling, Population characteristics, ISW, India, Southwest,

A Function Space HMC Algorithm With Second Order Langevin Diffusion Limit

We describe a new MCMC method optimized for the sampling of probability measures on Hilbert space which have a density with respect to a Gaussian; such measures arise in the Bayesian approach to inverse problems, and in conditioned diffusions. Our algorithm is based on two key design principles: (i) algorithms which are well-defined in infinite dimensions result in methods which do not suffer from the curse of dimensionality when they are applied to approximations of the infinite dimensional target measure on \bbR^N; (ii) non-reversible algorithms can have better mixing properties compared to their reversible counterparts. The method we introduce is based on the hybrid Monte Carlo algorithm, tailored to incorporate these two design principles. The main result of this paper states that the new algorithm, appropriately rescaled, converges weakly to a second order Langevin diffusion on Hilbert space; as a consequence the algorithm explores the approximate target measures on \bbR^N in a number of steps which is independent of $N$. We also present the underlying theory for the limiting non-reversible diffusion on Hilbert space, including characterization of the invariant measure, and we describe numerical simulations demonstrating that the proposed method has favourable mixing properties as an MCMC algorithm.Comment: 41 pages, 2 figures. This is the final version, with more comments and an extra appendix adde

Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximations

We construct a new framework for accelerating Markov chain Monte Carlo in posterior sampling problems where standard methods are limited by the computational cost of the likelihood, or of numerical models embedded therein. Our approach introduces local approximations of these models into the Metropolis-Hastings kernel, borrowing ideas from deterministic approximation theory, optimization, and experimental design. Previous efforts at integrating approximate models into inference typically sacrifice either the sampler's exactness or efficiency; our work seeks to address these limitations by exploiting useful convergence characteristics of local approximations. We prove the ergodicity of our approximate Markov chain, showing that it samples asymptotically from the \emph{exact} posterior distribution of interest. We describe variations of the algorithm that employ either local polynomial approximations or local Gaussian process regressors. Our theoretical results reinforce the key observation underlying this paper: when the likelihood has some \emph{local} regularity, the number of model evaluations per MCMC step can be greatly reduced without biasing the Monte Carlo average. Numerical experiments demonstrate multiple order-of-magnitude reductions in the number of forward model evaluations used in representative ODE and PDE inference problems, with both synthetic and real data.Comment: A major update of the theory and example