Sparse Graph Codes for Quantum Error-Correction

We present sparse graph codes appropriate for use in quantum error-correction. Quantum error-correcting codes based on sparse graphs are of interest for three reasons. First, the best codes currently known for classical channels are based on sparse graphs. Second, sparse graph codes keep the number of quantum interactions associated with the quantum error correction process small: a constant number per quantum bit, independent of the blocklength. Third, sparse graph codes often offer great flexibility with respect to blocklength and rate. We believe some of the codes we present are unsurpassed by previously published quantum error-correcting codes.Comment: Version 7.3e: 42 pages. Extended version, Feb 2004. A shortened version was resubmitted to IEEE Transactions on Information Theory Jan 20, 200

Concentration of Radionuclides in Dardanelle Lake, Arkansas

Concentrations of the nuclides ⁹⁰Sr - ⁹⁰Y, ¹⁴⁴Ce - ¹⁴⁴Pr, ¹³⁷Cs, ³⁸Co, ¹¹⁰ͫAg, ¹⁴¹Ce and ⁸⁹Sr have been measured monthly since November, 1975. The results from the period September, 1976, to August, 1977, depend on the relative intensities of the sources of the radionuclides; emissions from Nuclear I, the Chinese nuclear tests of Fall, 1976, and fallout from older atmospheric tests

Desired Versus Actual Training for Online Instructors in Community Colleges

The growth of distance education and the demand for instructors has developed over the past ten to fifteen years. There is a perception that the type and amount of instructor preparation is highly variable between institutions. Of the faculty members at two year institutions surveyed, nearly half did not attend training over the previous year. With technology changing rapidly, there is a need for training annually to assure faculty members who teach online are prepared. Distance education administrators need to evaluate their distance education programs and develop a consistent and current infrastructure to assure that their faculty members are being properly trained to teach online

Influence of genome-scale RNA structure disruption on the replication of murine norovirus--similar replication kinetics in cell culture but attenuation of viral fitness in vivo

Mechanisms by which certain RNA viruses, such as hepatitis C virus, establish persistent infections and cause chronic disease are of fundamental importance in viral pathogenesis. Mammalian positive-stranded RNA viruses establishing persistence typically possess genome-scale ordered RNA secondary structure (GORS) in their genomes. Murine norovirus (MNV) persists in immunocompetent mice and provides an experimental model to functionally characterize GORS. Substitution mutants were constructed with coding sequences in NS3/4- and NS6/7-coding regions replaced with sequences with identical coding and (di-)nucleotide composition but disrupted RNA secondary structure (F1, F2, F1/F2 mutants). Mutants replicated with similar kinetics to wild-type (WT) MNV3 in RAW264.7 cells and primary macrophages, exhibited similar (highly restricted) induction and susceptibility to interferon-coupled cellular responses and equal replication fitness by serial passaging of co-cultures. In vivo, both WT and F1/F2 mutant viruses persistently infected mice, although F1, F2 and F1/F2 mutant viruses were rapidly eliminated 1–7 days post-inoculation in competition experiments with WT. F1/F2 mutants recovered from tissues at 9 months showed higher synonymous substitution rates than WT and nucleotide substitutions that potentially restored of RNA secondary structure. GORS plays no role in basic replication of MNV but potentially contributes to viral fitness and persistence in vivo

Maximal uniform convergence rates in parametric estimation problems

This paper considers parametric estimation problems with independent, identically nonregularly distributed data. It focuses on rate efficiency, in the sense of maximal possible convergence rates of stochastically bounded estimators, as an optimality criterion, largely unexplored in parametric estimation. Under mild conditions, the Hellinger metric, defined on the space of parametric probability measures, is shown to be an essentially universally applicable tool to determine maximal possible convergence rates. These rates are shown to be attainable in general classes of parametric estimation problems