The Hamiltonian index of a graph and its branch-bonds

Let $G$ be an undirected and loopless finite graph that is not a path. The minimum $m$ such that the iterated line graph $L^m(G)$ is hamiltonian is called the hamiltonian index of $G,$ denoted by $h(G).$ A reduction method to determine the hamiltonian index of a graph $G$ with $h(G)\geq 2$ is given here. With it we will establish a sharp lower bound and a sharp upper bound for $h(G)$, respectively, which improves some known results of P.A. Catlin et al. [J. Graph Theory 14 (1990)] and H.-J. Lai [Discrete Mathematics 69 (1988)]. Examples show that $h(G)$ may reach all integers between the lower bound and the upper bound. \u

Macroscopical Entangled Coherent State Generator in V configuration atom system

In this paper, we propose a scheme to produce pure and macroscopical entangled coherent state. When a three-level ''V'' configuration atom interacts with a doubly reasonant cavity, under the strong classical driven condition, entangled coherent state can be generated from vacuum fields. An analytical solution for this system under the presence of cavity losses is also given

Aqua MODIS Electronic Crosstalk on SMWIR Bands 20 to 26

Aqua MODIS Moon images obtained with bands 20 to 26 (3.66 - 4.55 and 1.36 - 1.39 $\mu$m) during scheduled lunar events show evidence of electronic crosstalk contamination of the response of detector 1. In this work, we determined the sending bands for each receiving band. We found that the contaminating signal originates, in all cases, from the detector 10 of the corresponding sending band and that the signals registered by the receiving and sending detectors are always read out in immediate sequence. We used the lunar images to derive the crosstalk coefficients, which were then applied in the correction of electronic crosstalk striping artifacts present in L1B images, successfully restoring product quality.Comment: Accepted to be published in the IEEE 2017 International Geoscience & Remote Sensing Symposium (IGARSS 2017), scheduled for July 23-28, 2017 in Fort Worth, Texas, US

Topological Influence-Aware Recommendation on Social Networks

Users in online networks exert different influence during the process of information propagation, and the heterogeneous influence may contribute to personalized recommendations. In this paper, we analyse the topology of social networks to investigate users’ influence strength on their neighbours. We also exploit the user-item rating matrix to find the importance of users’ ratings and determine their influence on entire social networks. Based on the local influence between users and global influence over the whole network, we propose a recommendation method with indirect interactions that makes adequate use of users’ relationships on social networks and users’ rating data. The two kinds of influence are incorporated into a matrix factorization framework. We also consider indirect interactions between users who do not have direct links with each other. Experimental results on two real-world datasets demonstrate that our proposed framework performs better than other state-of-the-art methods for all users and cold-start users. Compared with node degrees, betweenness, and clustering coefficients, coreness constitutes the best topological descriptor to identify users’ local influence, and recommendations with the measure of coreness outperform other descriptors of user influence.</jats:p