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Siamese neural network has been well investigated by tracking frameworks due to its fast speed and high accuracy. However, very few efforts were spent on background-extraction by those approaches. In this paper, a Pixel to Global Matching Network (PG-Net) is proposed to suppress t+he influence of background in search image while achieving state-of-the-art tracking performance. To achieve this purpose, each pixel on search feature is utilized to calculate the similarity with global template feature. This calculation method can appropriately reduce the matching area, thus introducing less background interference. In addition, we propose a new tracking framework to perform correlation-shared tracking and multiple losses for training, which not only reduce the computational burden but also improve the performance. We conduct comparison experiments on various public tracking datasets, which obtains state-of-the-art performance while running with fast speed.

Recently, the development of methods for the cleanup of large-scale oil spills or organic pollutants from water has been attracting global attention. However, traditional techniques suffer from the limits of low efficiency and high operation cost. Herein, we propose a high efficiency, large-scale and easy operation approach to fabricate a hydrophobic and oleophilic high density polyethylene microfiber bundles with interconnected microchannels for oil/water separation. The microfiber bundles with high porosity, excellent absorption capacity and large water contact angle are continuously fabricated by a melt extrusion and leaching technique. More importantly, they are readily cleaned by centrifugation and exhibit excellent shape-recoverable property, which are very promising for the cleanup of large-scale oil spills. This study provides a great promising way to fabricate multifunctional polymer materials by using common polymer molding method.

Vehicle taillight recognition has been well investigated recent years. However, few methods can be applied in practical because of the time consumption or less recognition accuracy. In this paper, we design a light-weight framework for vehicle taillight recognition in real-time, which is divided into detection and recognition stage. To achieve this purpose, Finer Detection Block and dense anchor regression are utilized to improve the robust of detection stage. In addition, a Siamese CNN-GRU network is proposed, which not only captures the short-time difference but also learns the long-time flash features. We conduct ablation experiments to verify our proposed method, which obtains a mean recall of 94.34% with 103 FPS on 1080Ti.

Impaired motor function, due to the dysfunction of the basal ganglia, is the most common syndrome of hepatic encephalopathy (HE), and its etiology remains poorly understood. Neural oxidative stress is shown to be the major cellular defects contributing to HE pathogenesis. Mitochondrial uncoupling protein 2 (UCP2) has been implicated in neuroprotection in several neurological disorders. We explored the neuroprotective role of UCP2 within the substantia nigra pars reticulate (SNr), the output structure of the basal ganglia, in HE. The toxin thioacetamide (TAA) induced HE mice showed hypolocomotion, which was associated with decreased ATP levels and loss of antioxidant substances SOD and GSH within the SNr. Stable overexpression of UCP2 via AAV-UCP2 under the control of the UCP2 promoter in bilateral SNr preserved local ATP level, increased antioxidant substances, and ameliorated locomotion defects after severe liver failure. Contrary to UCP2 overexpression, targeted knockdown of UCP2 within bilateral SNr via AAV-UCP2 shRNA exacerbated the impaired mitochondrial dysfunction and hypokinesia in HE mice. The modulatory effects of UCP2 was due to mediation of K+-Cl- cotransporter-2 (KCC2) expression on GABAergic neurons of SNr. Taken together, our results demonstrate that UCP2 exerts a neural protective role at the subcortical level by increasing the resistance of neurons to oxidative stress, which may offer a novel therapeutic target for the treatment of motor dysfunction diseases.

The vibration signal of a gear system is selected as the original information of fault diagnosis and the gear system vibration equipment is established. The vibration acceleration signals of the normal gear, gear with tooth root crack fault, gear with pitch crack fault, gear with tooth wear fault and gear with multi-fault (tooth root crack & tooth wear fault) is collected in four kinds of speed conditions such as 300 rpm, 900 rpm, 1200 rpm and 1500 rpm. Using the method of wavelet threshold de-noising to denoise the original signal and decomposing the denoising signal utilizing the wavelet packet transform, then 16 frequency bands of decomposed signal are got. After restructuring the decomposing signal and obtaining the signal energy in each frequency band, the signal energy of the 16 bands is as the shortlisted fault characteristic data. Based on this, using the methods of principal component analysis (short for PCA) and kernel principal component analysis (short for KPCA) to extract the feature from the fault features of shortlisted 16-dimensional data feature, then the effect of reducing dimension analysis are compared. The fault classifications are displayed through the information that got from the first and the second principal component and kernel principal component, and these demonstrate they have a different and good effect of classification. Meanwhile, the article discusses the effect of feature extraction and classification that caused by the kernel function and the different options of its parameters. These provide a new method for a gear system fault feature extraction and classification.