Band selection, by choosing a set of representative bands in hyperspectral image (HSI), is an effective method to reduce the redundant information without compromising the original contents. Recently, various unsupervised band selection methods have been proposed, but most of them are based on approximation algorithms which can only obtain suboptimal solutions toward a specific objective function. This paper focuses on clustering-based band selection, and proposes a new framework to solve the above dilemma, claiming the following contributions: 1) An optimal clustering framework (OCF), which can obtain the optimal clustering result for a particular form of objective function under a reasonable constraint. 2) A rank on clusters strategy (RCS), which provides an effective criterion to select bands on existing clustering structure. 3) An automatic method to determine the number of the required bands, which can better evaluate the distinctive information produced by certain number of bands. In experiments, the proposed algorithm is compared to some state-of-the-art competitors. According to the experimental results, the proposed algorithm is robust and significantly outperform the other methods on various data sets.

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Background/Aims: The role of ZFX in tumourigenesis is unclear. We aimed to study ZFX expression, regulation, and function and the clinical implications of this protein in human pancreatic cancer (PCa). Methods: One hundred and twenty patients with histologically confirmed PCa who underwent surgery were recruited for this study. Tumour samples and PCa cell lines were used to examine ZFX. Various cell functions related to tumourigenesis were assessed. In vivo mouse tumour xenografts were used to confirm the in vitro results. Results: Patients with ZFX-positive tumours had worse overall survival than patients with ZFX-negative tumours. The depletion of ZFX using lentiviral shRNAs significantly inhibited cell proliferation by inducing cell cycle arrest in G0/G1 phase and resulted in increased cell apoptosis and invasive repression. In vivo studies confirmed that ZFX promoted tumour growth. Mechanistically, MAPK pathway activation was involved in the oncogenic functions of ZFX. Conclusions: ZFX acts as a putative oncogene in PCa and could be a novel therapeutic target for this disease.

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Few-shot deep learning is a topical challenge area for scaling visual recognition to open-ended growth in the space of categories to recognise. A promising line work towards realising this vision is deep networks that learn to match queries with stored training images. However, methods in this paradigm usually train a deep embedding followed by a single linear classifier. Our insight is that effective generalpurpose matching requires discrimination with regards to features at multiple abstraction levels. We therefore propose a new framework termed Deep Comparison Network (DCN) that decomposes embedding learning into a sequence of modules, and pairs each with a relation module. The relation modules compute a non-linear metric to score the match using the corresponding embedding module's representation. To ensure that all embedding module's features are used, the relation modules are deeply supervised. Finally generalisation is further improved by a learned noise regulariser. The resulting network achieves state of the art performance on both miniImageNet and tieredImageNet, while retaining the appealing simplicity and efficiency of deep metric learning approaches.

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