Feature Pyramid U-Net with Attention for Semantic Segmentation of Forward-Looking Sonar Images.

Forward-looking sonar is a technique widely used for underwater detection. However, most sonar images have underwater noise and low resolution due to their acoustic properties. In recent years, the semantic segmentation model U-Net has shown excellent segmentation performance, and it has great potential in forward-looking sonar image segmentation. However, forward-looking sonar images are affected by noise, which prevents the existing U-Net model from segmenting small objects effectively. Therefore, this study presents a forward-looking sonar semantic segmentation model called Feature Pyramid U-Net with Attention (FPUA). This model uses residual blocks to improve the training depth of the network. To improve the segmentation accuracy of the network for small objects, a feature pyramid module combined with an attention structure is introduced. This improves the model's ability to learn deep semantic and shallow detail information. First, the proposed model is compared against other deep learning models and on two datasets, of which one was collected in a tank environment and the other was collected in a real marine environment. To further test the validity of the model, a real forward-looking sonar system was devised and employed in the lake trials. The results show that the proposed model performs better than the other models for small-object and few-sample classes and that it is competitive in semantic segmentation of forward-looking sonar images.

One-Shot Imitation Drone Filming of Human Motion Videos.

Imitation learning has recently been applied to mimic the operation of a cameraman in existing autonomous camera systems. To imitate a certain demonstration video, existing methods require users to collect a significant number of training videos with a similar filming style. Because the trained model is style-specific, it is challenging to generalize the model to imitate other videos with a different filming style. To address this problem, we propose a framework that we term "one-shot imitation filming", which can imitate a filming style by "seeing" only a single demonstration video of the target style without style-specific model training. This is achieved by two key enabling techniques: 1) filming style feature extraction, which encodes sequential cinematic characteristics of a variable-length video clip into a fixed-length feature vector; and 2) camera motion prediction, which dynamically plans the camera trajectory to reproduce the filming style of the demo video. We implemented the approach with a deep neural network and deployed it on a 6 degrees of freedom (DOF) drone system by first predicting the future camera motions, and then converting them into the drone's control commands via an odometer. Our experimental results on comprehensive datasets and showcases exhibit that the proposed approach achieves significant improvements over conventional baselines, and our approach can mimic the footage of an unseen style with high fidelity.

Glycyrrhiza Polysaccharide Alleviates Dextran Sulfate Sodium-Induced Ulcerative Colitis in Mice.

Background: Licorice is one of the most ubiquitous herbs in traditional Chinese medicine, with notable anti-inflammatory and antiulcerative effects as well as potent digestive disease therapeutic impacts; yet, its active components and mechanisms remain unclear. There is a lot of evidence that Glycyrrhiza polysaccharide (GPS) has antioxidants, improving intestinal flora, anti-inflammatory effects, etc. Hypothesis/Purpose. Here, we investigated the effects of GPS on dextran sulfate sodium (DSS)-induced acute ulcerative colitis (UC) mice and its possible mechanisms. Methods: GPS (100, 200, and 400 mg/kg) or the positive control drug sulfasalazine (SASP) (200 mg/kg) were orally administered to mice for 8 days. Body weight was recorded daily. Symptoms associated with UC, such as disease activity index (DAI), colon length, spleen weight, and mucosal damage were detected. The possible mechanism of GPS ameliorating enteritis symptoms was explored by detecting intestinal permeability and serum levels of inflammatory factors, and changes in intestinal permeability were expressed by serum concentration of FITC-dextran and D-lactic acid. Results: The results demonstrated that GPS administration alleviated UC symptoms in colitis mice, including weight loss, DAI index, shorting colon length, and mucosal damage. Mechanistic evaluation revealed that GPS treatment reduced intestinal permeability and serum levels of inflammatory factors: IL-1, IL-6, and TNF-α, while increasing serum levels of the anti-inflammatory factor IL-10, suggesting that GPS's mechanism in UC is related to reducing intestinal permeability and inhibiting the inflammatory response, with intestinal permeability implicated as the initiating mechanism. Conclusion: This study highlights GPS as a promising therapeutic agent, with high therapeutic efficacy and a good safety profile, for enteritis and beyond.