13,919 research outputs found
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 . The purpose of introducing
the term is to partially impose the no double occupancy constraint by
employing the Gutzwiller approximation. The phase diagrams as functions of
doping and are studied. Using the standard value of and
in the large limit, we show that the antiferromagnetic (AF) order emerges
and coexists with the dSC in the underdoped region below the doping
. The dSC order parameter increases from zero as the doping
increases and reaches a maximum near the optimal doping . In
the small limit, only the dSC order survives while the AF order disappears.
As 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 and becomes an AF insulator for large . 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
and , and upon increasing doping the other ones will
form around . 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
AbstractâWe 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)
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