Study of gossamer superconductivity and antiferromagnetism in the t-J-U model

The d-wave superconductivity (dSC) and antiferromagnetism are analytically studied in a renormalized mean field theory for a two dimensional t-J model plus an on-site repulsive Hubbard interaction $U$. The purpose of introducing the $U$ term is to partially impose the no double occupancy constraint by employing the Gutzwiller approximation. The phase diagrams as functions of doping $\delta$ and $U$ are studied. Using the standard value of $t/J=3.0$ and in the large $U$ limit, we show that the antiferromagnetic (AF) order emerges and coexists with the dSC in the underdoped region below the doping $\delta\sim0.1$. The dSC order parameter increases from zero as the doping increases and reaches a maximum near the optimal doping $\delta\sim0.15$. In the small $U$ limit, only the dSC order survives while the AF order disappears. As $U$ increased to a critical value, the AF order shows up and coexists with the dSC in the underdoped regime. At half filing, the system is in the dSC state for small $U$ and becomes an AF insulator for large $U$. Within the present mean field approach, We show that the ground state energy of the coexistent state is always lower than that of the pure dSC state.Comment: 7 pages, 8 figure

Cascades of Dynamical Transitions in an Adaptive Population

In an adaptive population which models financial markets and distributed control, we consider how the dynamics depends on the diversity of the agents' initial preferences of strategies. When the diversity decreases, more agents tend to adapt their strategies together. This change in the environment results in dynamical transitions from vanishing to non-vanishing step sizes. When the diversity decreases further, we find a cascade of dynamical transitions for the different signal dimensions, supported by good agreement between simulations and theory. Besides, the signal of the largest step size at the steady state is likely to be the initial signal.Comment: 4 pages, 8 figure

Fluctuations in the transmission properties of a quantum dot with interface roughness and impurities

We examine statistical fluctuations in the transmission properties of quantum dots with interface roughness and neutral impurities. For this purpose we employ a supercell model of quantum transport capable of simulating potential variations in three dimensions. We find that sample to sample variations in interface roughness in a quantum dot waveguide can lead to substantial fluctuations in the n=1 transmission resonance position, width and maximum. We also find that a strongly attractive impurity near the centre of a quantum dot can reduce these fluctuations. Nevertheless, the presence of more than a single impurity can give rise to a complex resonance structure that varies with impurity configuration

Fermi surface evolution in the antiferromagnetic state for the electron-doped t-t'-t''-J model

By use of the slave-boson mean-field approach, we have studied the electron-doped t-t'-t''-J model in the antiferromagnetic (AF) state. It is found that at low doping the Fermi surface (FS) pockets appear around $(\pm\pi,0)$ and $(0,\pm\pi)$, and upon increasing doping the other ones will form around $(\pm{\pi\over 2},\pm{\pi\over 2})$. The evolution of the FS with doping as well as the calculated spectral weight are consistent with the experimental results.Comment: Fig. 4 is updated, to appear in Phys. Rev.

Discriminative Tandem Features for HMM-based EEG Classification

Abstract—We investigate the use of discriminative feature extractors in tandem configuration with generative EEG classification system. Existing studies on dynamic EEG classification typically use hidden Markov models (HMMs) which lack discriminative capability. In this paper, a linear and a non-linear classifier are discriminatively trained to produce complementary input features to the conventional HMM system. Two sets of tandem features are derived from linear discriminant analysis (LDA) projection output and multilayer perceptron (MLP) class-posterior probability, before appended to the standard autoregressive (AR) features. Evaluation on a two-class motor-imagery classification task shows that both the proposed tandem features yield consistent gains over the AR baseline, resulting in significant relative improvement of 6.2% and 11.2 % for the LDA and MLP features respectively. We also explore portability of these features across different subjects. Index Terms- Artificial neural network-hidden Markov models, EEG classification, brain-computer-interface (BCI)