Magneto--optical matter wave Bragg diffraction

We have performed a principle-proof-experiment of a magneto-optical diffraction (MOD) technique that requires no energy level splitting by homogeneous magnetic field and a circularly polarized optical lattice, avoiding system errors in an interferometer based on the MOD. The principle for this new MOD is that asynchronized switching of quadrupole trap and Ioffe trap in a quadrupole-Ioffe-configuration trap can generate a residual magnetic force to drive a Bose-Einstein condensate (BEC) to move. We have observed asymmetric atomic diffraction resulting from the asymmetric distribution of the Bloch eigenstates involved in the diffraction process when the condensate is driven by such a force, and matter-wave self-imaging due to coherent population oscillation of the dominantly occupied Bloch eigenstates. We have classified the mechanisms that lead to symmetric or asymmetric diffraction, and found that our experiment presents a magnetic alternative to a moving optical lattice, with a great potential to achieve a very large momentum transfer ($>110 hbar k$) to a BEC using well-developed magnetic trapping techniques.

Characterizing and Modeling the Dynamics of Activity and Popularity

Social media, regarded as two-layer networks consisting of users and items, turn out to be the most important channels for access to massive information in the era of Web 2.0. The dynamics of human activity and item popularity is a crucial issue in social media networks. In this paper, by analyzing the growth of user activity and item popularity in four empirical social media networks, i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links between users and items are more likely to be created by active users and to be acquired by popular items, where user activity and item popularity are measured by the number of cross links associated with users and items. This indicates that users generally trace popular items, overall. However, it is found that the inactive users more severely trace popular items than the active users. Inspired by empirical analysis, we propose an evolving model for such networks, in which the evolution is driven only by two-step random walk. Numerical experiments verified that the model can qualitatively reproduce the distributions of user activity and item popularity observed in empirical networks. These results might shed light on the understandings of micro dynamics of activity and popularity in social media networks.

The resilience of interdependent transportation networks under targeted attack

Modern world builds on the resilience of interdependent infrastructures characterized as complex networks. Recently, a framework for analysis of interdependent networks has been developed to explain the mechanism of resilience in interdependent networks. Here we extend this interdependent network model by considering flows in the networks and study the systems resilience under different attack strategies. In our model, nodes may fail due to either overload or loss of interdependency. Under the interaction between these two failure mechanisms, it is shown that interdependent scale-free networks show extreme vulnerability. The resilience of interdependent SF networks is found in our simulation much smaller than single SF network or interdependent SF networks without flows.

Steady-state traffic flow on a ring road with up- and down- slopes

This paper studies steady-state traffic flow on a ring road with up- and down- slopes using a semi-discrete model. By exploiting the relations between the semi-discrete and the continuum models, a steady-state solution is uniquely determined for a given total number of vehicles on the ring road. The solution is exact and always stable with respect to the first-order continuum model, whereas it is a good approximation with respect to the semi-discrete model provided that the involved equilibrium constant states are linearly stable. In an otherwise case, the instability of one or more equilibria could trigger stop-and-go waves propagating in certain road sections or throughout the ring road. The indicated results are reasonable and thus physically significant for a better understanding of real traffic flow on an inhomogeneous road.

The Different Nature in Seyfert 2 Galaxies With and Without Hidden Broad-Line Regions

We compile a large sample of 120 Seyfert 2 galaxies (Sy2s) which contains 49 hidden broad-line region (HBLR) Sy2s and 71 non-HBLR Sy2s. From the difference in the power sources between two groups, we test if HBLR Sy2s are dominated by active galactic nuclei (AGNs), and if non-HBLR Sy2s are dominated by starbursts. We show that: (1) HBLR Sy2s have larger accretion rates than non-HBLR Sy2s; (2) HBLR Sy2s have larger Nev $lambda 14.32$/Neii $lambda 12.81$ and oiv $lambda 25.89$/Neii $lambda 12.81$ line ratios than non-HBLR Sy2s; (3) HBLR Sy2s have smaller $IRAS$ $f_{60}/f_{25}$ flux ratio which shows the relative strength of the host galaxy and nuclear emission than non-HBLR Sy2s. So we suggest that HBLR Sy2s and non-HBLR Sy2s are AGN-dominated and starburst-dominated, respectively. In addition, non-HBLR Sy2s can be classified into the luminous ($L_{rm [O III]}>10^{41} rm ergs s^{-1}$) and less luminous ($L_{rm [O III]}<10^{41} rm ergs s^{-1}$) samples, when considering only their obscuration. We suggest that: (1) the invisibility of polarized broad lines (PBLs) in the luminous non-HBLR Sy2s depends on the obscuration; (2) the invisibility of PBLs in the less luminous non-HBLR Sy2s depends on the very low Eddington ratio rather than the obscuration.

A New Comparative Definition of Community and Corresponding Identifying Algorithm

In this paper, a new comparative definition for community in networks is proposed and the corresponding detecting algorithm is given. A community is defined as a set of nodes, which satisfy that each nodes degree inside the community should not be smaller than the nodes degree toward any other community. In the algorithm, the attractive force of a community to a node is defined as the connections between them. Then employing attractive force based self-organizing process, without any extra parameter, the best communities can be detected. Several artificial and real-world networks, including Zachary Karate club network and College football network are analyzed. The algorithm works well in detecting communities and it also gives a nice description for network division and group formation.

Community Detecting By Signaling on Complex Networks

Based on signaling process on complex networks, a method for identification community structure is proposed. For a network with $n$ nodes, every node is assumed to be a system which can send, receive, and record signals. Each node is taken as the initial signal source once to inspire the whole network by exciting its neighbors and then the source node is endowed a $n$d vector which recording the effects of signaling process. So by this process, the topological relationship of nodes on networks could be transferred into the geometrical structure of vectors in $n$d Euclidian space. Then the best partition of groups is determined by $F$-statistic and the final community structure is given by Fuzzy $C$-means clustering method (FCM). This method can detect community structure both in unweighted and weighted networks without any extra parameters. It has been applied to ad hoc networks and some real networks including Zachary Karate Club network and football team network. The results are compared with that of other approaches and the evidence indicates that the algorithm based on signaling process is effective.

The clustering coefficient and community structure of bipartite networks

Many real-world networks display a natural bipartite structure. It is necessary and important to study the bipartite networks by using the bipartite structure of the data. Here we propose a modification of the clustering coefficient given by the fraction of cycles with size four in bipartite networks. Then we compare the two definitions in a special graph, and the results show that the modification one is better to character the network. Next we define a edge-clustering coefficient of bipartite networks to detect the community structure in original bipartite networks.