Exchange coupling between magnetic layers across non-magnetic superlattices

The oscillation periods of the interlayer exchange coupling are investigated when two magnetic layers are separated by a metallic superlattice of two distinct non-magnetic materials. In spite of the conventional behaviour of the coupling as a function of the spacer thickness, new periods arise when the coupling is looked upon as a function of the number of cells of the superlattice. The new periodicity results from the deformation of the corresponding Fermi surface, which is explicitly related to a few controllable parameters, allowing the oscillation periods to be tuned.Comment: 13 pages; 5 figures; To appear in J. Phys.: Cond. Matte

First-Digit Law in Nonextensive Statistics

Nonextensive statistics, characterized by a nonextensive parameter $q$, is a promising and practically useful generalization of the Boltzmann statistics to describe power-law behaviors from physical and social observations. We here explore the unevenness of the first digit distribution of nonextensive statistics analytically and numerically. We find that the first-digit distribution follows Benford's law and fluctuates slightly in a periodical manner with respect to the logarithm of the temperature. The fluctuation decreases when $q$ increases, and the result converges to Benford's law exactly as $q$ approaches 2. The relevant regularities between nonextensive statistics and Benford's law are also presented and discussed.Comment: 11 pages, 3 figures, published in Phys. Rev.

SimRank*: effective and scalable pairwise similarity search based on graph topology

Given a graph, how can we quantify similarity between two nodes in an effective and scalable way? SimRank is an attractive measure of pairwise similarity based on graph topologies. Its underpinning philosophy that “two nodes are similar if they are pointed to (have incoming edges) from similar nodes” can be regarded as an aggregation of similarities based on incoming paths. Despite its popularity in various applications (e.g., web search and social networks), SimRank has an undesirable trait, i.e., “zero-similarity”: it accommodates only the paths of equal length from a common “center” node, whereas a large portion of other paths are fully ignored. In this paper, we propose an effective and scalable similarity model, SimRank*, to remedy this problem. (1) We first provide a sufficient and necessary condition of the “zero-similarity” problem that exists in Jeh and Widom’s SimRank model, Li et al. ’s SimRank model, Random Walk with Restart (RWR), and ASCOS++. (2) We next present our treatment, SimRank*, which can resolve this issue while inheriting the merit of the simple SimRank philosophy. (3) We reduce the series form of SimRank* to a closed form, which looks simpler than SimRank but which enriches semantics without suffering from increased computational overhead. This leads to an iterative form of SimRank*, which requires O(Knm) time and O(n2) memory for computing all (n2) pairs of similarities on a graph of n nodes and m edges for K iterations. (4) To improve the computational time of SimRank* further, we leverage a novel clustering strategy via edge concentration. Due to its NP-hardness, we devise an efficient heuristic to speed up all-pairs SimRank* computation to O(Knm~) time, where m~ is generally much smaller than m. (5) To scale SimRank* on billion-edge graphs, we propose two memory-efficient single-source algorithms, i.e., ss-gSR* for geometric SimRank*, and ss-eSR* for exponential SimRank*, which can retrieve similarities between all n nodes and a given query on an as-needed basis. This significantly reduces the O(n2) memory of all-pairs search to either O(Kn+m~) for geometric SimRank*, or O(n+m~) for exponential SimRank*, without any loss of accuracy, where m~≪n2 . (6) We also compare SimRank* with another remedy of SimRank that adds self-loops on each node and demonstrate that SimRank* is more effective. (7) Using real and synthetic datasets, we empirically verify the richer semantics of SimRank*, and validate its high computational efficiency and scalability on large graphs with billions of edges

Evolution and networks in ancient and widespread symbioses between Mucoromycotina and liverworts

Like the majority of land plants, liverworts regularly form intimate symbioses with arbuscular mycorrhizal fungi (Glomeromycotina). Recent phylogenetic and physiological studies report that they also form intimate symbioses with Mucoromycotina fungi and that some of these, like those involving Glomeromycotina, represent nutritional mutualisms. To compare these symbioses, we carried out a global analysis of Mucoromycotina fungi in liverworts and other plants using species delimitation, ancestral reconstruction, and network analyses. We found that Mucoromycotina are more common and diverse symbionts of liverworts than previously thought, globally distributed, ancestral, and often co-occur with Glomeromycotina within plants. However, our results also suggest that the associations formed by Mucoromycotina fungi are fundamentally different because, unlike Glomeromycotina, they may have evolved multiple times and their symbiotic networks are un-nested (i.e., not forming nested subsets of species). We infer that the global Mucoromycotina symbiosis is evolutionarily and ecologically distinctive

Analysis of whole genome sequencing for the Escherichia coli O157:H7 typing phages

Background: Shiga toxin producing Escherichia coli O157 can cause severe bloody diarrhea and haemolytic uraemic syndrome. Phage typing of E. coli O157 facilitates public health surveillance and outbreak investigations, certain phage types are more likely to occupy specific niches and are associated with specific age groups and disease severity. The aim of this study was to analyse the genome sequences of 16 (fourteen T4 and two T7) E. coli O157 typing phages and to determine the genes responsible for the subtle differences in phage type profiles. Results: The typing phages were sequenced using paired-end Illumina sequencing at The Genome Analysis Centre and the Animal Health and Veterinary Laboratories Agency and bioinformatics programs including Velvet, Brig and Easyfig were used to analyse them. A two-way Euclidian cluster analysis highlighted the associations between groups of phage types and typing phages. The analysis showed that the T7 typing phages (9 and 10) differed by only three genes and that the T4 typing phages formed three distinct groups of similar genomic sequences: Group 1 (1, 8, 11, 12 and 15, 16), Group 2 (3, 6, 7 and 13) and Group 3 (2, 4, 5 and 14). The E. coli O157 phage typing scheme exhibited a significantly modular network linked to the genetic similarity of each group showing that these groups are specialised to infect a subset of phage types. Conclusion: Sequencing the typing phage has enabled us to identify the variable genes within each group and to determine how this corresponds to changes in phage type.Public Health EnglandNational Institute for Health Research scientific research development fundBiotechnology and Biological Sciences Research Council (BBSRC