Face recognition (FR) with single sample per person (SSPP) is a challenge in computer vision. Since there is only one sample to be trained, it makes facial variation such as pose, illumination, and disguise difficult to be predicted. To overcome this problem, this paper proposes a scheme combined traditional and deep learning (TDL) method to process the task. First, it proposes an expanding sample method based on traditional approach. Compared with other expanding sample methods, the method can be used easily and conveniently. Besides, it can generate samples such as disguise, expression, and mixed variation. Second, it uses transfer learning and introduces a well-trained deep convolutional neural network (DCNN) model and then selects some expanding samples to fine-tune the DCNN model. Third, the fine-tuned model is used to implement experiment. Experimental results on AR face database, Extend Yale B face database, FERET face database, and LFW database demonstrate that TDL achieves the state-of-the-art performance in SSPP FR.

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In the central nervous system (CNS), a large group of glial cells called astrocytes play important roles in both physiological and disease conditions. Astrocytes participate in the formation of neurovascular units and interact closely with other cells of the CNS, such as microglia and neurons. Stroke is a global disease with high mortality and disability rate, most of which are ischemic stroke. Significant strides in understanding astrocytes have been made over the past few decades. Astrocytes respond strongly to ischemic stroke through a process known as activation or reactivity. Given the important role played by reactive astrocytes (RAs) in different spatial and temporal aspects of ischemic stroke, there is a growing interest in the potential therapeutic role of astrocytes. Currently, interventions targeting astrocytes, such as mediating astrocyte polarization, reducing edema, regulating glial scar formation, and reprogramming astrocytes, have been proven in modulating the progression of ischemic stroke. The aforementioned potential interventions on astrocytes and the crosstalk between astrocytes and other cells of the CNS will be summarized in this review.

A training scheme based on combination of virtual substation primary equipment real secondary devices is proposed for smart substation accident management. The primary equipment simulator is implemented through communication between 3D cartoon server and simulated switch controller. Electronic current and voltage transformer is simulated by EMTDC transient model and FPGA+DSP terminal controller. A smart substation on-site training environment is established for the substation O&M personnel.