Being a giant bulk Rashba semiconductor, the ambient-pressure phase of BiTeI was predicted to transform into a topological insulator under pressure at 1.7−4.1 GPa [Nat. Commun. 2012, 3, 679]. Because the structure governs the new quantum state of matter, it is essential to establish the high-pressure phase transitions and structures of BiTeI for better understanding its topological nature. Here, we report a joint theoretical and experimental study up to 30 GPa to uncover two orthorhombic high-pressure phases of Pnma and P4/nmm structures named phases II and III, respectively. Phases II (stable at 8.8−18.9 GPa) and III (stable at >18.9 GPa) were first predicted by our first-principles structure prediction calculations based on the calypso method and subsequently confirmed by our high-pressure powder X-ray diffraction experiment. Phase II can be regarded as a partially ionic structure, consisting of positively charged (BiTe) + ladders and negatively charged I − ions. Phase III is a typical ionic structure characterized by interconnected cubic building blocks of Te−Bi−I stacking. Application of pressures up to 30 GPa tuned effectively the electronic properties of BiTeI from a topological insulator to a normal semiconductor and eventually a metal having a potential of superconductivity.

Local quantum capacitance of graphene is imaged with plasmonics-based electrical impedance microscopy, from which the local density and polarity of charged impurities, electron and hole puddles associated with the charged impurities, and the density of the impurity states are determined.

Active contour models are popular and widely used for a variety of image segmentation applications with promising accuracy, but they may suffer from limited segmentation performances due to the presence of intensity inhomogeneity. To overcome this drawback, a novel region-based active contour model based on two different local fitted images is proposed by constructing a novel local hybrid image fitting energy, which is minimized in a variational level set framework to guide the evolving of contour curves toward the desired boundaries. The proposed model is evaluated and compared with several typical active contour models to segment synthetic and real images with different intensity characteristics. Experimental results demonstrate that the proposed model outperforms these models in terms of accuracy in image segmentation.

We employed a multi-scale clustering methodology known as “data cloud geometry” to extract functional connectivity patterns derived from functional magnetic resonance imaging (fMRI) protocol. The method was applied to correlation matrices of 106 regions of interest (ROIs) in 29 individuals with autism spectrum disorders (ASD), and 29 individuals with typical development (TD) while they completed a cognitive control task. Connectivity clustering geometry was examined at both “fine” and “coarse” scales. At the coarse scale, the connectivity clustering geometry produced 10 valid clusters with a coherent relationship to neural anatomy. A supervised learning algorithm employed fine scale information about clustering motif configurations and prevalence, and coarse scale information about intra- and inter-regional connectivity; the algorithm correctly classified ASD and TD participants with sensitivity of and specificity of . Most of the predictive power of the logistic regression model resided at the level of the fine-scale clustering geometry, suggesting that cellular versus systems level disturbances are more prominent in individuals with ASD. This article provides validation for this multi-scale geometric approach to extracting brain functional connectivity pattern information and for its use in classification of ASD.