It is increasingly interesting to model the relationship between two sets of highdimensional measurements with potentially high correlations. Canonical correlation analysis (CCA) is a classical tool that explores the dependency of two multivariate random variables and extracts canonical pairs of highly correlated linear combinations. Driven by applications in genomics, text mining, and imaging research, among others, many recent studies generalize CCA to high-dimensional settings. However, most of them either rely on strong assumptions on covariance matrices, or do not produce nested solutions. We propose a new sparse CCA (SCCA) method that recasts high-dimensional CCA as an iterative penalized least squares problem. Thanks to the new iterative penalized least squares formulation, our method directly estimates the sparse CCA directions with efficient algorithms. Therefore, in contrast to some existing methods, the new SCCA does not impose any sparsity assumptions on the covariance matrices. The proposed SCCA is also very flexible in the sense that it can be easily combined with properly chosen penalty functions to perform structured variable selection and incorporate prior information. Moreover, our proposal of SCCA produces nested solutions and thus provides great convenient in practice. Theoretical results show that SCCA can consistently estimate the true canonical pairs with an overwhelming probability in ultra-high dimensions. Numerical results also demonstrate the competitive performance of SCCA.

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BackgroundRabies virus is the causative agent of rabies, a central nervous system disease that is almost invariably fatal. Currently vaccination is the most effective strategy for preventing rabies, and vaccines are most commonly produced from cultured cells. Although the vaccine strains employed in China include CTN, aG, PM and PV, there are no reports of strains that are adapted to primary chick embryo cells for use in human rabies prevention in China.ResultsRabies virus strain CTN-1 V was adapted to chick embryo cells by serial passage to obtain the CTNCEC25 strain. A virus growth curve demonstrated that the CTNCEC25 strain achieved high titers in chick embryo cells and was nonpathogenic to adult mice by intracerebral inoculation. A comparison of the structural protein genes of the CTNCEC25 strain and the CTN-1 V strain identified eight amino acid changes in the mature M, G and L proteins. The immunogenicity of the CTNCEC25 strain increased with the adaptation process in chick embryo cells and conferred high protective efficacy. The inactivated vaccine induced high antibody responses and provided full protection from an intramuscular challenge in adult mice.ConclusionsThis is the first description of a CTNCEC25 strain that was highly adapted to chick embryo cells, and both its in vitro and in vivo biological properties were characterized. Given the high immunogenicity and good propagation characteristics of the CTNCEC25 strain, it has excellent potential to be a candidate for development into a human rabies vaccine with high safety and quality characteristics for controlling rabies in China.

Deep neural networks (DNNs) are being prototyped for a variety of artificial intelligence (AI) tasks including computer vision, data analytics, robotics, etc. The efficacy of DNNs coincides with the fact that they can provide state-ofthe-art inference accuracy for these applications. However, this advantage comes from the high computational complexity of the DNNs in use. Hence, it is becoming increasingly important to scale these DNNs so that they can fit on resource-constrained hardware and edge devices. The main goal is to allow efficient processing of the DNNs on low-power micro-AI platforms without compromising hardware resources and accuracy. In this work, we aim to provide a comprehensive survey about the recent developments in the domain of energy-efficient deployment of DNNs on micro-AI platforms. To this extent, we look at different neural architecture search strategies as part of micro-AI model design, provide extensive details about model compression and quantization strategies in practice, and finally elaborate on the current hardware approaches towards efficient deployment of the micro-AI models on hardware. The main takeaways for a reader from this article will be understanding of different search spaces to pinpoint the best micro-AI model configuration, ability to interpret different quantization and sparsification techniques, and the realization of the micro-AI models on resource-constrained hardware and different design considerations associated with it.

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