New upper bounds on the rate of a code via the Delsarte-MacWilliams inequalities

With the Delsarte-MacWilliams inequalities as a starting point, an upper bound is obtained on the rate of a binary code as a function of its minimum distance. This upper bound is asymptotically less than Levenshtein's bound, and so also Elias's

Distribution of Spectral Characteristics and the Cosmological Evolution of GRBs

We investigate the cosmological evolution of GRBs, using the total gamma ray fluence as a measure of the burst strength. This involves an understanding of the distributions of the spectral parameters of GRBs as well as the total fluence distribution - both of which are subject to detector selection effects. We present new non-parametric statistical techniques to account for these effects, and use these methods to estimate the true distribution of the peak of the nu F_nu spectrum, E_p, from the raw distribution. The distributions are obtained from four channel data and therefore are rough estimates. Here, we emphasize the methods and present qualitative results. Given its spectral parameters, we then calculate the total fluence for each burst, and compute its cumulative and differential distributions. We use these distributions to estimate the cosmological rate evolution of GRBs, for three cosmological models. Our two main conclusions are the following: 1) Given our estimates of the spectral parameters, we find that there may exist a significant population of high E_p bursts that are not detected by BATSE, 2) We find a GRB co-moving rate density quite different from that of other extragalactic objects; in particular, it is different from the recently determined star formation rate.Comment: 20 pages, including 10 postscript figures. Submitted to Ap

A Block Minorization--Maximization Algorithm for Heteroscedastic Regression

The computation of the maximum likelihood (ML) estimator for heteroscedastic regression models is considered. The traditional Newton algorithms for the problem require matrix multiplications and inversions, which are bottlenecks in modern Big Data contexts. A new Big Data-appropriate minorization--maximization (MM) algorithm is considered for the computation of the ML estimator. The MM algorithm is proved to generate monotonically increasing sequences of likelihood values and to be convergent to a stationary point of the log-likelihood function. A distributed and parallel implementation of the MM algorithm is presented and the MM algorithm is shown to have differing time complexity to the Newton algorithm. Simulation studies demonstrate that the MM algorithm improves upon the computation time of the Newton algorithm in some practical scenarios where the number of observations is large

Semantics of Input-Consuming Logic Programs

Input-consuming programs are logic programs with an additional restriction on the selectability (actually, on the resolvability) of atoms. this class of programs arguably allows to model logic programs employing a dynamic selection rule and constructs such as delay declarations: as shown also in [5], a large number of them are actually input-consuming. \ud in this paper we show that - under some syntactic restrictions - the tex2html_wrap_inline117-semantics of a program is correct and fully abstract also for input-consuming programs. this allows us to conclude that for a large class of programs employing delay declarations there exists a model-theoretic semantics which is equivalent to the operational one