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Blind video quality assessment (BVQA) is of great importance for video-related applications, yet still challenging even in this deep learning era. The difficulty lies in the shortage of large-scale labeled data, thus making it hard to train a robust spatiotemporal encoder for BVQA. To relieve such difficulty, we first build a video dataset, which contains over 320K samples suffering from various compression and transmission artifacts. While manually annotating the dataset with subjective perception is much labor-intensive and time-consuming, we adopt reference-based VQA algorithms to weakly label the data automatically. We consider that single weak label is derived from single knowledge, which is deficient and incomplete for VQA. To alleviate the bias from single weak label (i.e., single knowledge) in the weakly labeled dataset, we propose HEterogeneous Knowledge Ensemble (HEKE) for spatiotemporal representation learning. Compared to learning from single knowledge, learning with HEKE is thought to achieve a lower infimum theoretically, and obtain richer representation. On the basis of the built dataset and the HEKE methodology, a feature encoder specific to BVQA is formed, and directly extract spatiotemporal representation from videos. Then, the video quality can be either acquired in a completely BVQA manner without ground truth, or via a finetuning-based regressor with labels. Extensive experiments on various VQA databases show that our BVQA model with the pretrained encoder achieves the state-of-the-art performance. More surprisingly, even trained on the synthetic data, our model still shows competitive performance on authentic databases. The data and source code will be available at https://github.com/Sissuire/BVQA-HEKE.

Video quality assessment (VQA) is much more challenging than image quality assessment,due to the difficulty of modeling temporal influence among frames. Most of the existing VQA methods usually isolate each moment within the video (i.e.,it neglects the sequential nature),leading to a large gap from the subjective perception. Recent research on neuroscience suggests a serially dependent perception (SDP) mechanism in the human visual system (HVS). Namely,the HVS tends to incorporate the recent past visual experience to predict the present perception. Inspired by the SDP,we suggest that the HVS prefers stable and continuous degradations in videos due to their predictability,and exhibits less tolerance to interrupted and unpredictable disturbances. Thus,we introduce a novel serial dependence modeling (SDM) framework for full-reference VQA in this paper. Firstly,the instantaneous degradation is measured on both the static appearance and motion information for each glimpse of scenes. Since motion plays an important role in videos,two types of structures are extracted for motion representation,namely,an explicit content-based 3D structure and an implicit feature-based 2D structure. Next,an assessment-directed long-short term memory (A-LSTM) is proposed to capture the serial dependence among instantaneous degradations. With the consideration of the perceptual effect from the previous moment on the current one,especially the effect from the perceptually worst moment,the serially dependent degradation is characterized. Finally,by mimicking the subjective rating for video-viewing,an attention-based quality decision procedure is presented to acquire the final video quality. Experimental results on publicly available VQA databases demonstrate that the proposed method maintains good consistency with the subjective perception.

Due to the formation of excessive brittle compounds, the joint of Cf/C composites and single crystal Ni-based superalloy (DD3) has relatively low mechanical properties, greatly degrading the reliability of joints. In this work, an active Cu interlayer is introduced to adjust the interfacial reaction and microstructure of the joint. Effects of the Cu interlayer on the interfacial reaction and microstructure of joints are investigated in detail. Especially, the effect of the Cu interlayer on the joining stability of the joint is first analyzed, and its enhancement mechanism is further clarified. Adding an active Cu interlayer prevents the intense diffusion of Ni and Al from the DD3 side to the Cf/C side by a reactive adjusting process. Therefore, brittle AlNi2Ti phases only can be formed in the region nearby the DD3. Besides, the content of the Cu solid solution in the brazed seam is increased, significantly improving the plastic deformation ability of the joint. The maximum shear strength of Cf/C-DD3 joints with the Cu interlayer reaches to 54 MPa, approximately 50% higher than that of the joint without the Cu interlayer. Furthermore, a stable joint strength is obtained in a broader brazing temperature range from 880 °C to 940 °C.

BACKGROUND Rex rabbits are important fur rabbits. Heat stress severely reduces the fur quality of Rex rabbits. The aim of this study was to experimentally investigate the effect of dietary vitamin A (VA) addition on hair follicle development and related signal pathways in Rex rabbits under heat stress. RESULTS In the experiment, 90 Rex rabbits were randomly divided into three groups: control group (20-25 °C, fed basic diet), heat stress group (30-34 °C, fed basic diet), and heat stress + VA group (20-25 °C, fed 12 000 IU/kg VA in addition to the basic diet). VA could significantly increase the hair follicle density (P

Herein, the natural extract of garlic, allicin (Alli), was added into chitosan (CS)/polyvinyl alcohol (PVA)/graphene oxide (GO) composites to develop the nanofibrous membranes with strong antibacterial activity and sustained-release properties by electrospinning technology. Vitro Alli release test showed that the release rate and amount of Alli could be regulated by the content of GO in the nanofibrous membrane. The antibacterial activity test against Staphylococcus aureus was performed and revealed the antibacterial activity of nanofibrous membranes loading with Alli. Compared with the nanofibrous membrane without GO, the CS/PVA/Alli nanofibrous membrane with 0.1 wt% GO still had nice antibacterial activity after 48 h. The water contact angle of nanofibrous membranes dropped significantly with the addition of GO and Alli, which showed the nanofibrous membrane had highly hydrophobic. The CS/PVA/GO nanofibrous membrane loading with Alli had great hygroscopicity and moisture-retention capacity. The essential characteristics of nanofibrous membranes were evaluated by SEM, FTIR, XRD. The above results indicate that the membrane has a strong antimicrobial activity and long-lasting efficacy, so the developed natural nanofibrous membranes hold potential as promising antibacterial wound dressing and tissue engineering.

With the rapid growth of digital video through the Internet, a reliable objective video-quality assessment (VQA) algorithm is in great demand for video management. Motion information plays a dominant role for video perception, and the human visual system (HVS) is able to track moving objects effectively with eye movement. Moreover, the middle temporal area of the brain is selective for moving objects with particular velocities. In other words, visual contents that are along the motion trajectories will automatically attract our attention for dedicated processing. Inspired by the motion-related process in the HVS, we suggest analyzing the degradation along attended motion trajectories for VQA. The characteristic of motion velocity along each trajectory is analyzed for temporal quality measurement. Meanwhile, visual information along each trajectory is extracted for joint spatial-temporal quality measurement. Finally, considering the spatial-quality degradation from each frame, a novel full-reference assessor along salient trajectories (FAST) for VQA (which combines the spatial, temporal, and joint spatial-temporal quality degradations) is introduced. Experimental results on five publicly available VQA databases demonstrate that the proposed FAST VQA model performs consistently with the subjective perception. The source code of the proposed method is available at http://web.xidian.edu.cn/wjj/paper.html .