Shallow donors in semiconductor nanoparticles: limit of the effective mass approximation

The spatial distribution of the electronic wave function of a shallow, interstitial Li donor in a ZnO semiconductor nanocrystal has been determined in the regime of quantum confinement by using the nuclear spins as probes. Hyperfine interactions as monitored by electron nuclear double resonance spectroscopy quantitatively reveal the transition from semiconductor to molecular properties upon reduction of the size of the nanoparticles.     

Multilayer brain network combined with deep convolutional neural network for detecting major depressive disorder

As a global and grievous mental disease, major depressive disorder (MDD) has received much attention. Accurate detection of MDD via physiological signals represents an urgent research topic. Here, a frequency-dependent multilayer brain network, combined with deep convolutional neural network (CNN), is developed to detect the MDD. Multivariate pseudo Wigner distribution is firstly introduced to extract the time-frequency characteristics from the multi-channel EEG signals. Then multilayer brain network is constructed, with each layer corresponding to a specific frequency band. Such multilayer framework is in line with the nature of the workings of the brain, and can effectively characterize the brain state. Further, a multilayer deep CNN architecture is designed to study the brain network topology features, which is finally used to accurately detect MDD. The experimental results on a publicly available MDD dataset show that the proposed approach is able to detect MDD with state-of-the-art accuracy of 97.27%. Our approach, combining multilayer brain network and deep CNN, enriches the multivariate time series analysis theory and helps to better characterize and recognize the complex brain states.