Upper Pseudogap Phase: Magnetic Characterizations

It is proposed that the upper pseudogap phase (UPP) observed in the high-Tc cuprates correspond to the formation of spin singlet pairing under the bosonic resonating-valence-bond (RVB) description. We present a series of evidence in support of such a scenario based on the calculated magnetic properties including uniform spin susceptibility, spin-lattice and spin-echo relaxation rates, which consistently show that strong spin correlations start to develop upon entering the UPP, being enhanced around the momentum (\pi, \pi) while suppressed around (0, 0). The phase diagram in the parameter space of doping concentration, temperature, and external magnetic field, is obtained based on the the bosonic RVB theory. In particular, the competition between the Zeeman splitting and singlet pairing determines a simple relation between the "critical" magnetic field, H_{PG}, and characteristic temperature scale, T0, of the UPP. We also discuss the magnetic behavior in the lower pseudogap phase at a temperature Tv lower than T0, which is characterized by the formation of Cooper pair amplitude where the low-lying spin fluctuations get suppressed at both (0, 0) and (\pi, \pi). Properties of the UPP involving charge channels will be also briefly discussed.Comment: 11 pages, 5 figures, final version to appear in PR

Scattering on two Aharonov-Bohm vortices with opposite fluxes

The scattering of an incident plane wave on two Aharonov-Bohm vortices with opposite fluxes is considered in detail. The presence of the vortices imposes non-trivial boundary conditions for the partial waves on a cut joining the two vortices. These conditions result in an infinite system of equations for scattering amplitudes between incoming and outgoing partial waves, which can be solved numerically. The main focus of the paper is the analytic determination of the scattering amplitude in two limits, the small flux limit and the limit of small vortex separation. In the latter limit the dominant contribution comes from the S-wave amplitude. Calculating it, however, still requires solving an infinite system of equations, which is achieved by the Riemann-Hilbert method. The results agree well with the numerical calculations

Self-adaptive step fruit fly algorithm optimized support vector regression model for dynamic response prediction of magnetorheological elastomer base isolator

© 2016 Elsevier B.V. Parameter optimization of support vector regression (SVR) plays a challenging role in improving the generalization ability of machine learning. Fruit fly optimization algorithm (FFOA) is a recently developed swarm optimization algorithm for complicated multi-objective optimization problems and is also suitable for optimizing SVR parameters. In this work, parameter optimization in SVR using FFOA is investigated. In view of problems of premature and local optimum in FFOA, an improved FFOA algorithm based on self-adaptive step update strategy (SSFFOA) is presented to obtain the optimal SVR model. Moreover, the proposed method is utilized to characterize magnetorheological elastomer (MRE) base isolator, a typical hysteresis device. In this application, the obtained displacement, velocity and current level are used as SVR inputs while the output is the shear force response of the device. Experimental testing of the isolator with two types of excitations is applied for model performance evaluation. The results demonstrate that the proposed SSFFOA-optimized SVR (SSFFOA_SVR) has perfect generalization ability and more accurate prediction accuracy than other machine learning models, and it is a suitable and effective method to predict the dynamic behaviour of MRE isolator

Fluctuations and scaling of inverse participation ratios in random binary resonant composites

We study the statistics of local field distribution solved by the Green's-function formalism (GFF) [Y. Gu et al., Phys. Rev. B {\bf 59} 12847 (1999)] in the disordered binary resonant composites. For a percolating network, the inverse participation ratios (IPR) with $q=2$ are illustrated, as well as the typical local field distributions of localized and extended states. Numerical calculations indicate that for a definite fraction $p$ the distribution function of IPR $P_q$ has a scale invariant form. It is also shown the scaling behavior of the ensemble averaged $$ described by the fractal dimension $D_q$. To relate the eigenvectors correlations to resonance level statistics, the axial symmetry between $D_2$ and the spectral compressibility $\chi$ is obtained.Comment: 7 pages, 6 figures, accepted by Physical Review

Beyond Wigner's isobaric multiplet mass equation: Effect of charge-symmetry-breaking interaction and Coulomb polarization

The quadratic form of the isobaric multiplet mass equation (IMME), which was originally suggested by Wigner and has been generally regarded as valid, is seriously questioned by recent high-precision nuclear mass measurements. The usual resolution to this problem is to add empirically the cubic and quartic $T_z$-terms to characterize the deviations from the IMME, but finding the origin of these terms remains an unsolved difficulty. Based on a strategy beyond the Wigner's first-order perturbation, we derive explicitly the cubic and quartic $T_z$-terms. These terms are shown to be generated by the effective charge-symmetry breaking and charge-independent breaking interactions in nuclear medium combined with the Coulomb polarization effect. Calculations for the $sd$- and lower $fp$-shells explore a systematical emergence of the cubic $T_z$-term, suggesting a general deviation from the original IMME. Intriguingly, the magnitude of the deviation exhibits an oscillation-like behavior with mass number, modulated by the shell effect.Comment: 13 pages, 4 figure

Nonlinear and hysteretic modelling of magnetorheological elastomer base isolator using adaptive neuro-fuzzy inference system

Magnetorheological elastomer (MRE) base isolator is a semi-active control device which has currently obtained increasing attention in the field of vibration control of civil structures. However, the inherent nonlinear and hysteretic response of the device is regarded as a challenge aspect for using the smart device to realize the high performance. Therefore, an accurate and robust model is essential to make full use of these unique features for its engineering applications. In this paper, to solve this issue, adaptive neuro-fuzzy inference system (ANFIS) is utilized to characterize the dynamic behavior of the device. In this proposed model, the inputs are historical displacements and applied current of the device while the output is the shear force generated. To validate its forecast performance, the ANFIS model is also compared with some conventional models. Finally, the result verifies that ANFIS has the best perfection ability among existing MRE-based device models

Maximum Likelihood Estimation for Semiparametric Regression Models with Interval-Censored Multi-State Data

Interval-censored multi-state data arise in many studies of chronic diseases, where the health status of a subject can be characterized by a finite number of disease states and the transition between any two states is only known to occur over a broad time interval. We formulate the effects of potentially time-dependent covariates on multi-state processes through semiparametric proportional intensity models with random effects. We adopt nonparametric maximum likelihood estimation (NPMLE) under general interval censoring and develop a stable expectation-maximization (EM) algorithm. We show that the resulting parameter estimators are consistent and that the finite-dimensional components are asymptotically normal with a covariance matrix that attains the semiparametric efficiency bound and can be consistently estimated through profile likelihood. In addition, we demonstrate through extensive simulation studies that the proposed numerical and inferential procedures perform well in realistic settings. Finally, we provide an application to a major epidemiologic cohort study.Comment: 49 page

Motor Imagery Classification Based on Bilinear Sub-Manifold Learning of Symmetric Positive-Definite Matrices

In motor imagery brain-computer interfaces (BCIs), the symmetric positive-definite (SPD) covariance matrices of electroencephalogram (EEG) signals carry important discriminative information. In this paper, we intend to classify motor imagery EEG signals by exploiting the fact that the space of SPD matrices endowed with Riemannian distance is a high-dimensional Riemannian manifold. To alleviate the overfitting and heavy computation problems associated with conventional classification methods on high-dimensional manifold, we propose a framework for intrinsic sub-manifold learning from a high-dimensional Riemannian manifold. Considering a special case of SPD space, a simple yet efficient bilinear sub-manifold learning (BSML) algorithm is derived to learn the intrinsic sub-manifold by identifying a bilinear mapping that maximizes the preservation of the local geometry and global structure of the original manifold. Two BSML-based classification algorithms are further proposed to classify the data on a learned intrinsic sub-manifold. Experimental evaluation of the classification of EEG revealed that the BSML method extracts the intrinsic sub-manifold approximately 5× faster and with higher classification accuracy compared with competing algorithms. The BSML also exhibited strong robustness against a small training dataset, which often occurs in BCI studies