16,816 research outputs found

    The Spin Stiffness and the Transverse Susceptibility of the Half-filled Hubbard Model

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    The T=0T=0 spin stiffness ρs\rho _{s} and the transverse susceptibility χ\chi _{\perp } of the square lattice half-filled Hubbard model are calculated as a function of the Hubbard parameter ratio U/tU/t by series expansions around the Ising limit. We find that the calculated spin-stiffness, transverse susceptibility, and sublattice magnetization for the Hubbard model smoothly approach the Heisenberg values for large U/tU/t. The results are compared for different U/tU/t with RPA and other numerical studies.Comment: 9 Revtex pages, 3 Postscript figures, Europhys. Lett. in pres

    Constraints on the Inner Cluster Mass Profile and the Power Spectrum Normalization from Strong Lensing Statistics

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    Strong gravitational lensing is a useful probe of both the intrinsic properties of the lenses and the cosmological parameters of the universe. The large number of model parameters and small sample of observed lens systems, however, have made it difficult to obtain useful constraints on more than a few parameters from lensing statistics. Here we examine how the recent WMAP measurements help improve the constraining power of statistics from the radio lensing survey JVAS/CLASS. We find that the absence of theta>3'' lenses in CLASS places an upper bound of beta<1.25 (1.60) at 68% (95%) CL on the inner density profile, rho \propto r^{-beta}, of cluster-sized halos. Furthermore, the favored power spectrum normalization is sigma_8 >= 0.7 (95% CL). We discuss two possibilities for stronger future constraints: a positive detection of at least one large-separation system, and next-generation radio surveys such as LOFAR.Comment: Scatter in concentration included; virial mass used consistently; new Fig 3. Final version published in ApJ

    Enabling controlling complex networks with local topological information

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    Complex networks characterize the nature of internal/external interactions in real-world systems including social, economic, biological, ecological, and technological networks. Two issues keep as obstacles to fulflling control of large-scale networks: structural controllability which describes the ability to guide a dynamical system from any initial state to any desired fnal state in fnite time, with a suitable choice of inputs; and optimal control, which is a typical control approach to minimize the cost for driving the network to a predefned state with a given number of control inputs. For large complex networks without global information of network topology, both problems remain essentially open. Here we combine graph theory and control theory for tackling the two problems in one go, using only local network topology information. For the structural controllability problem, a distributed local-game matching method is proposed, where every node plays a simple Bayesian game with local information and local interactions with adjacent nodes, ensuring a suboptimal solution at a linear complexity. Starring from any structural controllability solution, a minimizing longest control path method can efciently reach a good solution for the optimal control in large networks. Our results provide solutions for distributed complex network control and demonstrate a way to link the structural controllability and optimal control together.The work was partially supported by National Science Foundation of China (61603209), and Beijing Natural Science Foundation (4164086), and the Study of Brain-Inspired Computing System of Tsinghua University program (20151080467), and Ministry of Education, Singapore, under contracts RG28/14, MOE2014-T2-1-028 and MOE2016-T2-1-119. Part of this work is an outcome of the Future Resilient Systems project at the Singapore-ETH Centre (SEC), which is funded by the National Research Foundation of Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. (61603209 - National Science Foundation of China; 4164086 - Beijing Natural Science Foundation; 20151080467 - Study of Brain-Inspired Computing System of Tsinghua University program; RG28/14 - Ministry of Education, Singapore; MOE2014-T2-1-028 - Ministry of Education, Singapore; MOE2016-T2-1-119 - Ministry of Education, Singapore; National Research Foundation of Singapore (NRF) under Campus for Research Excellence and Technological Enterprise (CREATE) programme)Published versio

    Author correction: Enabling controlling complex networks with local topological information

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    Correction to: Scientific Reports https://doi.org/10.1038/s41598-018-22655-5, published online 15 March 2018. The Acknowledgements section in this Article is incomplete.The work was partially supported by National Science Foundation of China (61603209, 61327902), and Beijing Natural Science Foundation (4164086), and the Study of Brain-Inspired Computing System of Tsinghua University program (20151080467), and SuZhou-Tsinghua innovation leading program 2016SZ0102, and Ministry of Education, Singapore, under contracts RG28/14, MOE2014-T2-1-028 and MOE2016-T2-1-119. Part of this work is an outcome of the Future Resilient Systems project at the Singapore-ETH Centre (SEC), which is funded by the National Research Foundation of Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) program. (61603209 - National Science Foundation of China; 61327902 - National Science Foundation of China; 4164086 - Beijing Natural Science Foundation; 20151080467 - Study of Brain-Inspired Computing System of Tsinghua University program; 2016SZ0102 - SuZhou-Tsinghua innovation leading program; RG28/14 - Ministry of Education, Singapore; MOE2014-T2-1-028 - Ministry of Education, Singapore; MOE2016-T2-1-119 - Ministry of Education, Singapore; National Research Foundation of Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) program)Published versio

    RF-thermal-structural-RF coupled analysis on the travelling wave disk-loaded accelerating structure

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    Travelling wave (TW) disk-loaded accelerating structure is one of the key components in normal conducting (NC) linear accelerators, and has been studied for many years. In the design process, usually after the dimensions of each cell and the two couplers are finalized, the structure is fabricated and tuned, and then the whole structure characteristics can be measured by the vector network analyzer. Before the structure fabrication, the whole structure characteristics are less simulated limited by the available computer capability. In this paper, we described the method to do the RF-thermal-structural-RF coupled analysis on the TW disk-loaded structure with one single PC. In order to validate our method, we first analyzed and compared our RF simulation results on the 3m long BEPCII structure with the corresponding experimental results, which shows very good consistency. Finally, the RF-thermal-structure-RF coupled analysis results on the 1.35m long NSC KIPT linac accelerating structure are presented.Comment: 5 pages, 16 figures, Submitted to the Chinese Physics C (Formerly High Energy Physics and Nuclear Physics
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