Optical transitions between Landau levels: AA-stacked bilayer graphene

The low-frequency optical excitations of AA-stacked bilayer graphene are investigated by the tight-binding model. Two groups of asymmetric LLs lead to two kinds of absorption peaks resulting from only intragroup excitations. Each absorption peak obeys a single selection rule similar to that of monolayer graphene. The excitation channel of each peak is changed as the field strength approaches a critical strength. This alteration of the excitation channel is strongly related to the setting of the Fermi level. The peculiar optical properties can be attributed to the characteristics of the LL wave functions of the two LL groups. A detailed comparison of optical properties between AA-stacked and AB-stacked bilayer graphenes is also offered. The compared results demonstrate that the optical properties are strongly dominated by the stacking symmetry. Furthermore, the presented results may be used to discriminate AABG from MG, which can be hardly done by STM

A concurrent task facilitates, not impedes, the heel-to-toe standing balance inchildren: The case of a dual-task benefit

Performance in a dual task is typically worse than performance in a single task due to the sharing of limited cognitivecapacity. The present study found the opposite results when the task involved postural control in non-typical standing. Thirty-six children aged 4-9 years stood on a force plate for 10 seconds with a normal or heel-to-toe stance. In the dual-task condition,they also performed an auditory or a visuospatial task. They were instructed to achieve high accuracy on the concurrent taskwhile maintaining balance. Standing balance, expressed in terms of the velocity and the trajectory of the center of pressureon the force plate, was significantly better in the dual-task than in the single-task condition. Performances on the concurrenttasks were also better in the dual-task condition. The overall dual-task benefits are attributed to the increased deployment ofcognitive capacity specially called for by the balance challenge in non-typical standing

Gender differences in ankylosing spondylitis-associated cumulative healthcare utilization: a population-based cohort study

BACKGROUND: Ankylosing spondylitis (AS) is one of the most common rheumatic diseases with gender differences in prevalence and clinical presentation. This study aimed to examine whether such gender differences are correlated with cumulative healthcare utilization in Taiwan. METHODS: The National Health Insurance Research Database supplied claim records of one million individuals from 1996 to 2007. Selected cases included patients aged >16 years. Certified rheumatologists diagnosed the patients in three or more visits and gave prescriptions for AS. Multivariate adjusted logistic regression analyses were used to calculate the influence of gender on cumulative healthcare utilization associated with AS. RESULTS: The study included 228 women and 636 men. After adjustment for potential confounding factors, men had more cumulative outpatient visits associated with AS (odds ratio, 1.59; 95% confidence interval, 1.13 -2.23; p = 0.008). Men also exhibited a trend for higher frequency of AS-related hospitalization (p = 0.054). CONCLUSION: Men are more likely to have high cumulative AS-associated healthcare utilization than women. Further investigation of the causal factors is warranted

Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation

While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated. However, one cannot easily address this task without observing ground truth annotation for the training data. To address this problem, we propose a novel deep learning model of Cross-Domain Representation Disentangler (CDRD). By observing fully annotated source-domain data and unlabeled target-domain data of interest, our model bridges the information across data domains and transfers the attribute information accordingly. Thus, cross-domain joint feature disentanglement and adaptation can be jointly performed. In the experiments, we provide qualitative results to verify our disentanglement capability. Moreover, we further confirm that our model can be applied for solving classification tasks of unsupervised domain adaptation, and performs favorably against state-of-the-art image disentanglement and translation methods.Comment: CVPR 2018 Spotligh