The contributions of qqqqqˉqqqq\bar{q} components to the axial charges of proton and its resonances

We calculate the axial charges of the proton and its resonances in the framework of the constituent quark model, which is extended to include the $qqqq\bar{q}$ components. If 20% admixtures of the $qqqq\bar{q}$ components in the proton are assumed, the theoretical value for the axial charge in our model is in good agreement with the empirical value, which can not be well reproduced in the traditional constituent quark model even though the $SU(6) \bigotimes O(3)$ symmetry breaking or relativistic effect is taken into account. We also predict an unity axial charge for $N^{*}(1440)$ with 30% $qqqq\bar{q}$ components constrained by the strong and electromagnetic decays.Comment: 4 pages, 4 table

Entropy and Its Quantum Thermodynamical Implication for Anomalous Spectral Systems

The state function entropy and its quantum thermodynamical implication for two typical dissipative systems with anomalous spectral densities are studied by investigating on their low-temperature quantum behavior. In all cases it is found that the entropy decays quickly and vanishes as the temperature approaches zero. This reveals a good conformity with the third law of thermodynamics and provides another evidence for the validity of fundamental thermodynamical laws in the quantum dissipative region.Comment: 10 pages, 3 figure

Topological Pattern Recognition of Severe Alzheimer's Disease via Regularized Supervised Learning of EEG Complexity

Alzheimer's disease (AD) is a progressive brain disorder with gradual memory loss that correlates to cognitive deficits in the elderly population. Recent studies have shown the potentials of machine learning algorithms to identify biomarkers and functional brain activity patterns across various AD stages using electroencephalography (EEG). In this study, we aim to discover the altered spatio-temporal patterns of EEG complexity associated with AD pathology in different severity levels. We employed the multiscale entropy (MSE), a complexity measure of time series signals, as the biomarkers to characterize the nonlinear complexity at multiple temporal scales. Two regularized logistic regression methods were applied to extracted MSE features to capture the topographic pattern of MSEs of AD cohorts compared to healthy baseline. Furthermore, canonical correlation analysis was performed to evaluate the multivariate correlation between EEG complexity and cognitive dysfunction measured by the Neuropsychiatric Inventory scores. 123 participants were recruited and each participant was examined in three sessions (length = 10 seconds) to collect resting-state EEG signals. MSE features were extracted across 20 time scale factors with pre-determined parameters (m = 2, r = 0.15). The results showed that comparing to logistic regression model, the regularized learning methods performed better for discriminating severe AD cohort from normal control, very mild and mild cohorts (test accuracy ~ 80%), as well as for selecting significant biomarkers arcoss the brain regions. It was found that temporal and occipitoparietal brain regions were more discriminative in regard to classifying severe AD cohort vs. normal controls, but more diverse and distributed patterns of EEG complexity in the brain were exhibited across individuals in early stages of AD

Proton strangeness form factors in (4,1) clustering configurations

We reexamine a recent result within a nonrelativistic constituent quark model (NRCQM) which maintains that the uuds\bar s component in the proton has its uuds subsystem in P state, with its \bar s in S state (configuration I). When the result are corrected, contrary to the previous result, we find that all the empirical signs of the form factors data can be described by the lowest-lying uuds\bar s configuration with \bar s in P state that has its uuds subsystem in $S$ state (configuration II). Further, it is also found that the removal of the center-of-mass (CM) motion of the clusters will enhance the contributions of the transition current considerably. We also show that a reasonable description of the existing form factors data can be obtained with a very small probability P_{s\bar s}=0.025% for the uuds\bar s component. We further see that the agreement of our prediction with the data for G_A^s at low-q^2 region can be markedly improved by a small admixture of configuration I. It is also found that by not removing CM motion, P_{s\bar s} would be overestimated by about a factor of four in the case when transition dominates over direct currents. Then, we also study the consequence of a recent estimate reached from analyzing the existing data on quark distributions that P_{s\bar s} lies between 2.4-2.9% which would lead to a large size for the five-quark (5q) system, as well as a small bump in both G^s_E+\eta G^s_M and G^s_E in the region of q^2 =< 0.1 GeV^2.Comment: Prepared for The Fifth Asia-Pacific Conference on Few-Body Problems in Physics 2011 in Seoul, South Korea, 22-26 August 201