Theoretical studies of 63Cu Knight shifts of the normal state of YBa2Cu3O7

The 63Cu Knight shifts and g factors for the normal state of YBa2Cu3O7 in tetragonal phase are theoretically studied in a uniform way from the high (fourth-) order perturbation formulas of these parameters for a 3d9 ion under tetragonally elongated octahedra. The calculations are quantitatively correlated with the local structure of the Cu2+(2) site in YBa2Cu3O7. The theoretical results show good agreement with the observed values, and the improvements are achieved by adopting fewer adjustable parameters as compared to the previous works. It is found that the significant anisotropy of the Knight shifts is mainly attributed to the anisotropy of the g factors due to the orbital interactions.Comment: 5 page

Dynamic Magneto-Conductance Fluctuations and Oscillations in Mesoscopic Wires and Rings

Using a finite-frequency recursive Green's function technique, we calculate the dynamic magneto-conductance fluctuations and oscillations in disordered mesoscopic normal metal systems, incorporating inter-particle Coulomb interactions within a self-consistent potential method. In a disordered metal wire, we observe ergodic behavior in the dynamic conductance fluctuations. At low $\omega$, the real part of the conductance fluctuations is essentially given by the dc universal conductance fluctuations while the imaginary part increases linearly from zero, but for $\omega$ greater than the Thouless energy and temperature, the fluctuations decrease as $\omega^{-1/2}$. Similar frequency-dependent behavior is found for the Aharonov-Bohm oscillations in a metal ring. However, the Al'tshuler-Aronov-Spivak oscillations, which predominate at high temperatures or in rings with many channels, are strongly suppressed at high frequencies, leading to interesting crossover effects in the $\omega$-dependence of the magneto-conductance oscillations.Comment: 4 pages, REVTeX 3.0, 5 figures(ps file available upon request), #phd0

Markov Weight Fields for face sketch synthesis

Posters 1C - Vision for Graphics, Sensors, Medical, Vision for Robotics, ApplicationsGreat progress has been made in face sketch synthesis in recent years. State-of-the-art methods commonly apply a Markov Random Fields (MRF) model to select local sketch patches from a set of training data. Such methods, however, have two major drawbacks. Firstly, the MRF model used cannot synthesize new sketch patches. Secondly, the optimization problem in solving the MRF is NP-hard. In this paper, we propose a novel Markov Weight Fields (MWF) model that is capable of synthesizing new sketch patches. We formulate our model into a convex quadratic programming (QP) problem to which the optimal solution is guaranteed. Based on the Markov property of our model, we further propose a cascade decomposition method (CDM) for solving such a large scale QP problem efficiently. Experimental results on the CUHK face sketch database and celebrity photos show that our model outperforms the common MRF model used in other state-of-the-art methods. © 2012 IEEE.published_or_final_versionThe IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI., 16-21 June 2012. In IEEE Conference on Computer Vision and Pattern Recognition Proceedings, 2012, p. 1091-109

Incremental association rule mining based on matrix compression for edge computing

A growing amount of data is being generated, communicated and processed at the edge nodes of cloud systems; this has the potential to improve response times and thus reduce communication bandwidth. We found that traditional static association rule mining cannot solve certain real-world problems with dynamically changing data. Incremental association rule mining algorithms have been studied. This paper combines the fast update pruning (FUP) algorithm with a compressed Boolean matrix and proposes a new incremental association rule mining algorithm, named the FUP algorithm based on a compression matrix (FBCM). This algorithm requires only a single scan of both the database and incremental databases, establishes two compressible Boolean matrices, and applies association rule mining to those matrices. The FBCM algorithm effectively improves the computational efficiency of incremental association rule mining and hence is suitable for knowledge discovery in the edge nodes of cloud systems