The problem of direction of arrival (DOA) estimation with co-prime linear arrays is researched. An improved DOA estimation algorithm based on root-multiple signal classification (MUSIC) is proposed. The method can solve the matching error problem in Sun's algorithm. In addition, it can make full use of signal space and noise space to improve the estimation accuracy with low complexity. Experimental results verify the validity of the proposed algorithm.

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Triple-negative breast cancer is often aggressive and resistant to various cancer therapies, especially corresponding targeted drugs. It is shown that targeted delivery of chemotherapeutic drugs to tumor sites could enhance treatment outcome against triple-negative breast cancer. In this study, we exploited the active tumor-targeting capability of macrophages by loading doxorubicin-carrying liposomes on their surfaces via biotin−avidin interactions. Compared with conventional liposomes, this macrophage−liposome (MA-Lip) system further increased doxorubicin accumulation in tumor sites, penetrated deeper into tumor tissue, and enhanced antitumor immune response. As a result, the MA-Lip system significantly lengthened the survival rate of 4T1 cell-bearing mice with low toxicity. Besides, the MA-Lip system used highly biocompatible and widely approved materials, which ensured its long-term safety. This study provides a system for triple-negative breast cancer treatment and offers another macrophage-based strategy for tumor delivery.

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In recent years, visual recognition on challenging long-tailed distributions, where classes often exhibit extremely imbalanced frequencies, has made great progress mostly based on various complex paradigms (e.g., meta learning). Apart from these complex methods, simple refinements on training procedures also make contributions. These refinements, also called tricks, are minor but effective, such as adjustments in the data distribution or loss functions. However, different tricks might conflict with each other. If users apply these long-tail related tricks inappropriately, it could cause worse recognition accuracy than expected. Unfortunately, there has not been a scientific guideline of these tricks in the literature. In this paper, we first collect existing tricks in long-tailed visual recognition and then perform extensive and systematic experiments, in order to give a detailed experimental guideline and obtain an effective combination of these tricks. Furthermore, we also propose a novel data augmentation approach based on class activation maps for long-tailed recognition, which can be friendly combined with re-sampling methods and shows excellent results. By assembling these tricks scientifically, we can outperform state-of-the-art methods on four long-tailed benchmark datasets, including ImageNet-LT and iNaturalist 2018. Our code is open-source and available at https://github.com/zhangyongshun/BagofTricks-LT.