PRSim: Sublinear Time SimRank Computation on Large Power-Law Graphs

{\it SimRank} is a classic measure of the similarities of nodes in a graph. Given a node $u$ in graph $G =(V, E)$, a {\em single-source SimRank query} returns the SimRank similarities $s(u, v)$ between node $u$ and each node $v \in V$. This type of queries has numerous applications in web search and social networks analysis, such as link prediction, web mining, and spam detection. Existing methods for single-source SimRank queries, however, incur query cost at least linear to the number of nodes $n$, which renders them inapplicable for real-time and interactive analysis. { This paper proposes \prsim, an algorithm that exploits the structure of graphs to efficiently answer single-source SimRank queries. \prsim uses an index of size $O(m)$, where $m$ is the number of edges in the graph, and guarantees a query time that depends on the {\em reverse PageRank} distribution of the input graph. In particular, we prove that \prsim runs in sub-linear time if the degree distribution of the input graph follows the power-law distribution, a property possessed by many real-world graphs. Based on the theoretical analysis, we show that the empirical query time of all existing SimRank algorithms also depends on the reverse PageRank distribution of the graph.} Finally, we present the first experimental study that evaluates the absolute errors of various SimRank algorithms on large graphs, and we show that \prsim outperforms the state of the art in terms of query time, accuracy, index size, and scalability.Comment: ACM SIGMOD 201

Ultrafast pump-probe spectroscopic signatures of superconducting and pseudogap phases in YBa2Cu3O7-{\delta} films

Femtosecond pump-probe spectroscopy is applied to identify transient optical signatures of phase transitions in optimally doped YBa2Cu3O7-{\delta} films. To elucidate the dynamics of superconducting and pseudogap phases, the slow thermal component is removed from the time-domain traces of photo-induced reflectivity in a high-flux regime with low frequency pulse rate. The rescaled data exhibit distinct signatures of the phase separation with abrupt changes at the onsets of TSC and TPG in excellent agreement with transport data. Compared to the superconducting phase, the response of the pseudogap phase is characterized by the strongly reduced reflectivity change accompanied by a faster recovery time.Comment: 14 pages, 3 figure

A novel square root adaptive unscented Kalman filter combined with variable forgetting factor recursive least square method for accurate state-of-charge estimation of lithium-ion batteries.

Lithium-ion battery state-of-charge (SOC) serves as an important battery state parameter monitored by the battery management system (BMS), real-time and accurate estimation of the SOC is vital for safe, reasonable, and efficient use of the battery as well as the development of BMS technology. Taking the ternary lithium battery as the research object, based on the second-order RC equivalent circuit model, a variable forgetting factor least square method (VFFRLS) is used for parameter identification and a combination of the square root of covariance and noise statistics estimation techniques to estimate the SOC, to solve the problem of dispersion of the unscented Kalman filter and the error covariance tends to infinity with iterative calculation, thus ensuring the accuracy of SOC estimation. The feasibility and robustness of the algorithm and the battery state estimation strategy are verified under HPPC and BBDST conditions with maximum errors of 1.41% and 1.53%, respectively. The experimental results show that the combined algorithm of VFFRLS and SRAUKF has good robustness and stability, and has high accuracy in the SOC estimation of Li-ion batteries, which provides a reference for the research of lithium-ion batteries