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The application of deep learning in time-series prediction has developed gradually. In this paper, we propose a deep generative network model for feature extraction of multivariate time series, namely, mutual information variational autoencoders (MI-VAE). In the architecture of the proposed model, we use the latent space of VAE for feature learning, which can extract the essential features of multivariate time-series data effectively. The latent space employed directly as a feature extractor can avoid poor interpretability of model. In addition, we introduce a mutual information term into the loss function, which improves the expression capability and accuracy of model. The proposed model, combining the merits of VAE and mutual information, extracts features for multivariate time-series data from a new perspective. The Lorenz system and Beijing air quality time series are used to test performance of the proposed model and comparative models. Results show that the proposed model is superior to other similar models in terms of accuracy and expression capability of latent space. [ABSTRACT FROM AUTHOR]

Beijing You Chicken, a valuable local chicken breed from Beijing, China, was once listed as an endangered breed. From the point of view of conservation, the preservation of this breed is an important task for the local researchers. Semen cryopreservation is a popular method to maintain valuable species. However, during cryopreservation, semen is susceptible to oxidative damage. Melatonin is a potent antioxidant and free radical scavenger, so it has been selected to improve the efficiency of sperm cryopreservation. In this study, the chicken semen was treated with different concentrations of melatonin in the cryopreservation solution. The results showed that melatonin at concentrations of 10−3 M and 10−5 M significantly improved sperm progressive motility and total motility, respectively, compared to the control (P < 0.05). Melatonin at 10−3 M also significantly improved the plasma membrane and acrosome integrity of spermatozoa compared to the control. The mechanisms are that melatonin significantly reduces the level of ROS and preserves sperm mitochondrial membrane potential. Most importantly, the melatonin-treated cryopreserved chicken sperm after artificial insemination significantly increased the hatching rate of chicks compared to the control (p < 0.05). The results show that melatonin has a positive effect on the quality of the cryopreserved spermatozoa. These results provide the theoretical and practical basis for using melatonin to improve Beijing You Chicken conservation, and they may also be applicable to poultry as a whole. [ABSTRACT FROM AUTHOR]

• An imputation model imputes specific missing values in multivariate time series. • Our method outperforms the comparison method when specific values are missing. • Our experiments provide selection principles of hyperparameters. • Our experiment determines the best training method with introduction of noise. Data missing is a ubiquitous phenomenon in multivariate time series analysis because of failure measurements, improper installation or other unfavorable factors. Most imputation methods, such as statistical methods and machine learning methods, are mainly for dealing with random or continuous missing, but do not take into consideration the non-random specific missing situation. This paper proposes an imputation model based on the variational auto-encoders (VAE) and shift correction for specific missing values, which is also extended to the β -VAE model. The shift correction is used to correct the original probability distribution deviation caused by specific values concentrated missing. Then taking the meteorological and air quality data sets in Beijing as the example, this paper mainly explores two common specific missing situations, including large values missing and small values missing. The experimental results show that the proposed model can effectively impute specific missing values, which has high imputation accuracy and robustness. [ABSTRACT FROM AUTHOR]