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As the smartphones are widely used in daily life, people are habituated to take photos in anywhere at anytime. The character photos, which contain both people and landscapes, are the most extensive ones in all kinds of photos. However, it is usually time-consuming and laborious for people to select and manage desired character photos manually. Therefore, automatic photo selection became extremely significant. Most of the existing methods select best character photos by assigning a absolute score or binary label, regardless of the photo’s content. The selected character photos are hence not satisfied. In this paper, we propose an effective automatic framework to select best character photos, which first automatically eliminates photos with unattractive character, then ranks photos and assists users to select the desired ones. Moreover, our framework is especially useful for mobile camera in practical application scenarios. To reduce the burden of photo selection and improve the efficiency of automatic selection from a large number of photos on mobile platform, we divide the photo selection into two stages: eliminating of unattractive photos based on designed efficient and effective features related to faces and human postures, and ranking the remaining photos using a two-stream convolution neural network. To ensure that informative features are selected and less useful ones are suppressed, we design and utilize the attention mechanism module in network. Experiments demonstrate the effectiveness of our method on the automatic selection of the satisfied photos from a large number of photos.

Copper (Cu) is an important microelement for the maintenance of normal metabolism of dairy cows and a variety of enzymic composition. When Cu deficiency is occurred in dairy cows, it’ll be accompanied by many diseases. The main objective of the present study was to explore the correlation between Cu deficiency and oxidative stress of lactating dairy cows and evaluate early-warning index for Cu deficiency. There were divided into two groups based on serum Cu levels: a Cu deficiency group and a control group. Serum levels of ceruloplasmin (CP), superoxide dismutase (SOD), total antioxidant capacity (T-AOC), glutathione peroxidase (GSH-PX), and Cu were analysed. The results showed that CP levels in the Cu deficiency group were significantly lower than in the control group, while T-AOC levels were significantly higher, but there were no significant differences between SOD and GSH-PX levels. Pearson correlation analysis showed that serum CP levels were very significantly positively correlated with Cu deficiency, while serum T-AOC levels were very significantly negatively correlated and SOD and GSH-PX serum levels showed no correlations. Logistic regression analysis showed that CP was a risk factor for Cu deficiency, ROC analysis showed that CP had diagnostic value for Cu deficiency, with a critical value of 13.7 U/l. Hence, CP and T-AOC levels in lactating cows showed strong correlations to Cu deficiency, indicating that serum CP can be used to predict Cu deficiency.