Generalized bilinear model (GBM) has received extensive attention in the field of hyperspectral nonlinear unmixing. Traditional GBM unmixing methods are usually assumed to be degraded only by additive white Gaussian noise (AWGN), and the intensity of AWGN in each band of hyperspectral image (HSI) is assumed to be the same. However, the real HSIs are usually degraded by mixture of various kinds of noise, which include Gaussian noise, impulse noise, dead pixels or lines, stripes, and so on. Besides, the intensity of AWGN is usually different for each band of HSI. To address the above mentioned issues, we propose a novel nonlinear unmixing method based on the bandwise generalized bilinear model (NU-BGBM), which can be adapted to the presence of complex mixed noise in real HSI. Besides, the alternative direction method of multipliers (ADMM) is adopted to solve the proposed NU-BGBM. Finally, extensive experiments are conducted to demonstrate the effectiveness of the proposed NU-BGBM compared with some other state-of-the-art unmixing methods.Keywords: additive white Gaussian noise (AWGN); hyperspectral images (HSIs); mixed noise; bandwise generalized bilinear model (BGBM); alternative direction method of multipliers (ADMM) some flexible models based on signal processing, such as post-nonlinear model [22], neural network model [23] and kernel model [24]. The second category includes some physical based models, such as intimate mixture model [25], bilinear mixture model (BMM) [26][27][28][29][30][31][32][33] and multilinear mixing model [34][35][36]. Among them, the BMM only takes the second-order scattering into consideration, while the higher-order interactions of light are ignored [18]. The reason is that the interactions of orders larger than two, not only have minor contribution for improving the unmixing accuracy than that of second-order scattering, but also bring in tremendous computational costs [21]. Several representative models known as the family of BMM have been proposed. The Nascimento model (NM) [26] is an extended LMM with additional virtual endmembers, the Fan's model (FM) is the truncated Taylor expansion of nonlinear mixing function [27], and the GBM [28] can be seen as the generalization of LMM and FM, which can efficiently deal with the assumptions in BMM. Different methods have been proposed for GBM unmixing of HSI, Halimi et al. developed a Bayesian algorithm to estimate the abundance and noise variance of the GBM [28]. Besides, they also proposed a pixel-wise unmixing method based on the gradient descent algorithm (GDA) [29]. Moreover, Yokoya et al. proposed the semi-nonnegative matrix factorization (semi-NMF) as a new optimization method for GBM based HSI unmixing [30]. Furthermore, Li et al. developed the bound projected optimal gradient method (BPOGM) for GBM unmixng, and it can achieve the optimal convergence rate of O( 1 k 2 ), where k denotes the number of iteration in BPOGM [31].Most unmixing methods based on the GBM are implicitly developed for Gaussian noise, and the underlying assumptio...

LiteSing proposed in this paper is a high-quality singing voice synthesis (SVS) system, which is fast, lightweight and expressive. This model mainly stacks several non-autoregressive WaveNet blocks in the encoder and decoder under a generative adversarial architecture, predicts full conditions from the musical score, and generates acoustic features from these conditions. The full conditions in this paper consist of dynamic spectrogram energy, voiced/unvoiced (V/UV) decision and dynamic pitch curve, which are proven related to the expressiveness. We predict the pitch and the timbre features separately, avoiding the interdependence between these two features. Instead of neural network vocoders, a parametric WORLD vocoder is employed for the pitch curve consistency. Experiment results show that LiteSing outperforms the baseline model using feed-forward Transformer by 1.386 times faster on inference speed, 15 times smaller on training parameters number, and achieves a similar MOS on sound quality. Through an A/B test, LiteSing achieves 67.3% preference rate over baseline in pitch curve and dynamic spectrogram energy prediction. which demonstrates the advantage of LiteSing over the other compared models.

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The total sulfur rate of the coal sample was 4.973%, the inorganic sulfur content was more than 60%, which was the main component in the coal sample. The effect of grinding fineness, flotation pulp density, collector and frother on coal flotation desulfurization were investigated in this paper to remove the inorganic sulfur. The results showed that it reached to the optimum desulfurization rate 54.7% when the optimum grinding fineness was 39.20% -200 mesh rate, pulp concentration was 80 g/L, kerosene consumption was 1.4 kg/t, 2#oil loading was 100 g/t, and the total sulfur content reduced to 2.72%. The optimized flotation condition determined by orthogonal experiments was as follow: pulp density for 60 g/L, kerosene dosage of 1.4 kg/t, 2#oil consumption of 100 g/t. In this case, total sulfur content reduced to 2.19%, the total desulfurization rate and the inorganic desulfurization rate were 55.96% and 92.89%, respectively.