Approximate Linear Time ML Decoding on Tail-Biting Trellises in Two Rounds

A linear time approximate maximum likelihood decoding algorithm on tail-biting trellises is prsented, that requires exactly two rounds on the trellis. This is an adaptation of an algorithm proposed earlier with the advantage that it reduces the time complexity from O(mlogm) to O(m) where m is the number of nodes in the tail-biting trellis. A necessary condition for the output of the algorithm to differ from the output of the ideal ML decoder is reduced and simulation results on an AWGN channel using tail-biting rrellises for two rate 1/2 convoluational codes with memory 4 and 6 respectively are reporte

Approximate MAP Decoding on Tail-Biting Trellises

We propose two approximate algorithms for MAP decoding on tail-biting trellises. The algorithms work on a subset of nodes of the tail-biting trellis, judiciously selected. We report the results of simulations on an AWGN channel using the approximate algorithms on tail-biting trellises for the $(24,12)$ Extended Golay Code and a rate 1/2 convolutional code with memory 6.Comment: 5 pages, 2 figures, ISIT 200

John Backus- inventor of FORTRAN

This article does not have an abstract

Indian Stock Market during the COVID-19 Pandemic: Vulnerable or Resilient?: Sectoral analysis

This study examines the impact of COVID-19 pandemic on the performance of Indian stock market, measured by daily average returns and trading volume. The analysis is aimed at discovering the vulnerability of the general market as well as nine crucial sectors to the pandemic while also checking the impact on overall volatility in the market. The findings suggest that all the sectors followed a consistent pattern of being significantly impacted by the pandemic. However, the benchmark index remained resilient in the context of average returns. The entire market witnessed decreased returns and increased liquidity, which is explained by reduced volatility in the market

Flock-species richness influences node importance and modularity in mixed-species flock networks

Interdependencies in social groups of animals are a combination of multiple pairwise interactions. Heterospecific groups are often characterized by important species that contribute more to group initiation, maintenance or function than other species. However, in large heterospecific groups, many pairwise interactions are not realised, while others may not be biologically significant, confounding inferences about species importance. Hence, in this study, we examine context dependent changes in species importance and assortment in mixed-species bird flocks from a tropical field site in Southern India using social network analysis. Specifically, we ask how the structural importance of a species and the clustering patterns of species relationships depends on species richness in mixed-species flocks. We constructed both raw and filtered networks; while our results are largely correlated, we believe that filtered networks can provide insights into community-level importance of species in mixed-flocks while raw networks depict flock-level patterns. We find significant differences in flocks of different richness in that different species emerge as structurally important across flocks of varying richness. We also find that assortment is higher in two-species flocks and decreases with an increase in the number of species in the flock (‘flock richness’ hereafter). We argue that the link between structural importance of species in mixed-species flock networks and their functional significance in the community critically depends on the social context: namely, the species richness of the mixed-species flock. We propose that examining species structural importance at different flock-richness values provides insights into biologically meaningful functional roles of species. More generally, we suggest that it is important to consider context when interpreting species centrality and importance in network structure

miRNA Profiling of Naïve, Effector and Memory CD8 T Cells

microRNAs have recently emerged as master regulators of gene expression during development and cell differentiation. Although profound changes in gene expression also occur during antigen-induced T cell differentiation, the role of miRNAs in the process is not known. We compared the miRNA expression profiles between antigen-specific naïve, effector and memory CD8+ T cells using 3 different methods-small RNA cloning, miRNA microarray analysis and real-time PCR. Although many miRNAs were expressed in all the T cell subsets, the frequency of 7 miRNAs (miR-16, miR-21, miR-142-3p, miR-142-5p, miR-150, miR-15b and let-7f) alone accounted for ∼60% of all miRNAs, and their expression was several fold higher than the other expressed miRNAs. Global downregulation of miRNAs (including 6/7 dominantly expressed miRNAs) was observed in effector T cells compared to naïve cells and the miRNA expression levels tended to come back up in memory T cells. However, a few miRNAs, notably miR-21 were higher in effector and memory T cells compared to naïve T cells. These results suggest that concomitant with profound changes in gene expression, miRNA profile also changes dynamically during T cell differentiation. Sequence analysis of the cloned mature miRNAs revealed an extensive degree of end polymorphism. While 3′end polymorphisms dominated, heterogeneity at both ends, resembling drosha/dicer processing shift was also seen in miR-142, suggesting a possible novel mechanism to generate new miRNA and/or to diversify miRNA target selection. Overall, our results suggest that dynamic changes in the expression of miRNAs may be important for the regulation of gene expression during antigen-induced T cell differentiation. Our study also suggests possible novel mechanisms for miRNA biogenesis and function