Quantum Transport of Bosonic Cold Atoms in Double Well Optical Lattices

We numerically investigate, using the time evolving block decimation algorithm, the quantum transport of ultra-cold bosonic atoms in a double well optical lattice through slow and periodic modulation of the lattice parameters (intra- and inter-well tunneling, chemical potential, etc.). The transport of atoms does not depend on the rate of change of the parameters (as along as the change is slow) and can distribute atoms in optical lattices at the quantized level without involving external forces. The transport of atoms depends on the atom filling in each double well and the interaction between atoms. In the strongly interacting region, the bosonic atoms share the same transport properties as non-interacting fermions with quantized transport at the half filling and no atom transport at the integer filling. In the weakly interacting region, the number of the transported atoms is proportional to the atom filling. We show the signature of the quantum transport from the momentum distribution of atoms that can measured in the time of flight image. A semiclassical transport model is developed to explain the numerically observed transport of bosonic atoms in the non-interacting and strongly interacting limits. The scheme may serve as an quantized battery for atomtronics applications.Comment: 8 pages, 9 figures, accepted for publication in Phys. Rev.

The H-index of a network node and its relation to degree and coreness

Identifying influential nodes in dynamical processes is crucial in understanding network structure and function. Degree, H-index and coreness are widely used metrics, but previously treated as unrelated. Here we show their relation by constructing an operator , in terms of which degree, H-index and coreness are the initial, intermediate and steady states of the sequences, respectively. We obtain a family of H-indices that can be used to measure a node’s importance. We also prove that the convergence to coreness can be guaranteed even under an asynchronous updating process, allowing a decentralized local method of calculating a node’s coreness in large-scale evolving networks. Numerical analyses of the susceptible-infected-removed spreading dynamics on disparate real networks suggest that the H-index is a good tradeoff that in many cases can better quantify node influence than either degree or coreness.This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 11205042, 11222543, 11075031, 61433014). L.L. acknowledges the research start-up fund of Hangzhou Normal University under Grant No. PE13002004039 and the EU FP7 Grant 611272 (project GROWTHCOM). The Boston University work was supported by NSF Grants CMMI 1125290, CHE 1213217 and PHY 1505000. (11205042 - National Natural Science Foundation of China; 11222543 - National Natural Science Foundation of China; 11075031 - National Natural Science Foundation of China; 61433014 - National Natural Science Foundation of China; PE13002004039 - research start-up fund of Hangzhou Normal University; 611272 - EU FP7 Grant (project GROWTHCOM); CMMI 1125290 - NSF; CHE 1213217 - NSF; PHY 1505000 - NSF)Published versio

Similarity-Based Classification in Partially Labeled Networks

We propose a similarity-based method, using the similarity between nodes, to address the problem of classification in partially labeled networks. The basic assumption is that two nodes are more likely to be categorized into the same class if they are more similar. In this paper, we introduce ten similarity indices, including five local ones and five global ones. Empirical results on the co-purchase network of political books show that the similarity-based method can give high accurate classification even when the labeled nodes are sparse which is one of the difficulties in classification. Furthermore, we find that when the target network has many labeled nodes, the local indices can perform as good as those global indices do, while when the data is sparce the global indices perform better. Besides, the similarity-based method can to some extent overcome the unconsistency problem which is another difficulty in classification.Comment: 13 pages,3 figures,1 tabl