Crossover from a pseudogap state to a superconducting state

On the basis of our calculation we deduce that the particular electronic structure of cuprate superconductors confines Cooper pairs to be firstly formed in the antinodal region which is far from the Fermi surface, and these pairs are incoherent and result in the pseudogap state. With the change of doping or temperature, some pairs are formed in the nodal region which locates the Fermi surface, and these pairs are coherent and lead to superconductivity. Thus the coexistence of the pseudogap and the superconducting gap is explained when the two kinds of gaps are not all on the Fermi surface. It is also shown that the symmetry of the pseudogap and the superconducting gap are determined by the electronic structure, and non-s wave symmetry gap favors the high-temperature superconductivity. Why the high-temperature superconductivity occurs in the metal region near the Mott metal-insulator transition is also explained.Comment: 7 pages, 2 figure

Fluence dependent femtosecond quasi-particle and Eu^{2+} -spin relaxation dynamics in EuFe_{2}(As,P)_{2}

We investigated temperature and fluence dependent dynamics of the time resolved optical reflectivity in undoped spin-density-wave (SDW) and doped superconducting (SC) EuFe$_{2}$(As,P)$_{2}$ with emphasis on the ordered Eu$^{2+}$-spin temperature region. The data indicate that the SDW order coexists at low temperature with the SC and Eu$^{2+}$-ferromagnetic order. Increasing the excitation fluence leads to a thermal suppression of the Eu$^{2+}$-spin order due to the crystal-lattice heating while the SDW order is suppressed nonthermally at a higher fluence

A multimodel fusion engine for filtering webpages

© 2013 IEEE. Fusing multiple existing models for filtering webpages can mitigate the shortcomings of individual filtering models. To provide an engine for such fusion, we propose a multimodel fusion engine for filtering webpages for the extraction of target webpages. This engine can handle large datasets of webpages crawled from websites and supports five individual filtering models and the fusion of any two of them. There are two possible fusion methods: one is to simultaneously satisfy the conditions of both individual models, and the other is to satisfy the conditions of one of the two individual models. We present the functions, architecture, and software design of the proposed engine. We use recall ratio (RR) and precision ratio (PR) as the evaluation indices of the filtering models and propose rules describing how PR and RR change when individual models are fused. We use 200 000 webpages collected by crawling the popular online shopping website 'http://www.jd.com' as the experimental dataset to verify these rules. The experimental results show that two-model fusion can improve either PR or RR. Thus, the proposed engine has good practical value for engineering applications

Erraticity Analysis of Soft Production by ECOMB

Event-to-event fluctuations of the spatial patterns of the final states of high-enery collisions, referred to as erraticity, are studied for the data generated by a soft-interaction model called ECOMB. The moments $C_{p,q}$ do not show simple power-law dependences on the bin size. New measures of erraticity are proposed that generalizes the bin-size dependence. The method should be applied not only to the soft production data of NA22 and NA27 to check the dynamical content of ECOMB, but also to other collision processes, such as $e^+e^-$ annihilation and heavy-ion collisions.Comment: 8 pages (Latex) + 7 figures (ps file), submitted to Phys. Rev.

Parallel Community Detection Based on Distance Dynamics for Large-Scale Network

© 2013 IEEE. Data mining task is a challenge on finding a high-quality community structure from large-scale networks. The distance dynamics model was proved to be active on regular-size network community, but it is difficult to discover the community structure effectively from the large-scale network (0.1-1 billion edges), due to the limit of machine hardware and high time complexity. In this paper, we proposed a parallel community detection algorithm based on the distance dynamics model called P-Attractor, which is capable of handling the detection problem of large networks community. Our algorithm first developed a graph partitioning method to divide large network into lots of sub-networks, yet maintaining the complete neighbor structure of the original network. Then, the traditional distance dynamics model was improved by the dynamic interaction process to simulate the distance evolution of each sub-network. Finally, we discovered the real community structure by removing all external edges after evolution process. In our extensive experiments on multiple synthetic networks and real-world networks, the results showed the effectiveness and efficiency of P-Attractor, and the execution time on 4 threads and 32 threads are around 10 and 2 h, respectively. Our proposed algorithm is potential to discover community from a billion-scale network, such as Uk-2007