We investigate the underwater propagation of multi-Gaussian correlated asymmetric Bessel beam with partial coherence in the condition of quadrature amplitude modulation. The oceanic turbulence optical power spectrum is used to characterize turbulence effects under variable temperature and salinity. Based on the derivation of orbital angular momentum mode distribution, the theoretical model of bit error rate (BER) is constructed. Numerical analyses show that the low-temperature oceanic channel is more beneficial to BER reduction than the low-salinity channel. Due to the better resistance to turbulence, low-order modulation is superior in BER performance. As for beam optimization, the increments in wavelength and source coherence width, or the decrements of topological charge and asymmetry factor, help to obtain a lower BER. The research is instructive for the construction of underwater transmission links based on vortex beams.

Aiming at the propagation of asymmetric vortex beam, we investigate the spreading and wander characteristics of asymmetric Bessel-Gaussian beam in oceanic turbulence. Based on the cross-spectral density and Wigner distribution function of beam field, the theoretical expressions of spreading factor and beam center displacement are established to evaluate spreading and wander. The anisotropic turbulent condition is adopted to analyze turbulence impacts with more generality. The turbulence dominated by salinity fluctuation with lower anisotropy, larger temperature dissipation and smaller kinetic energy dissipation induces more beam spreading and wander. As for asymmetric Bessel-Gaussian beam, the increment of asymmetry can improve beam directionality. Besides, smaller beam scale but larger Gaussian waist radius, vortex order and beam wavelength also mitigate spreading and wander. The investigations are instructive for the underwater applications of asymmetric vortex beams.

Exploiting label correlation is crucially important in multi-label learning, where each instance is associated with multiple labels simultaneously. Multi-label learning is more complex than single-label learning for that the labels tend to be correlated. Traditional multi-label learning algorithms learn independent classifiers for each label and employ ranking or threshold on the classification results. Most existing methods take label correlation as prior knowledge, which have worked well, but they failed to make full use of label dependency. As a result, the real relationship among labels may not be correctly characterized and the final prediction is not explicitly correlated. To address these problems, we propose a novel high-order multi-label learning algorithm of Label collAboration based Multi-laBel learning (LAMB). With regard to each label, LAMB utilizes collaboration between its own prediction and the prediction of other labels. Extensive experiments on various datasets demonstrate that our proposed LAMB algorithm achieves superior performance over existing state-of-the-art algorithms. In addition, one real-world dataset of channelrhodopsins chimeras is assessed, which would be of great value as pre-screen for membrane proteins function.