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A three-layer weighted fuzzy support vector regression (TLWFSVR) model is proposed for understanding human intention, and it is based on the emotion-identification information in human–robot interaction. The TLWFSVR model consists of three layers, including adjusted weighted kernel fuzzy c-means for data clustering, fuzzy support vector regressions (FSVR) for information understanding, and weighted fusion for intention understanding. It aims to guarantee the quick convergence and satisfactory performance of the local FSVR via adjusting the weights of each feature in each cluster, in such a way that importance of different emotion-identification information is represented. Moreover, smooth human-oriented interaction can be obtained by endowing robot with human intention understanding capability. Experimental results show that the proposed TLWFSVR model obtains higher intention understanding accuracy and less computational time than that of two-layer fuzzy support vector regression, support vector regression, and back propagation neural network (BPNN), respectively. Additionally, the preliminary application experiments are performed in the developing human–robot interaction system, called emotional social robot system, where 12 volunteers and 2 mobile robots experience a scenario of “drinking at a bar.” Application results indicate that the bartender robot is able to understand customers’ order intentions. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-Seq) data from complex human tissues have prevalent blood cell contamination due to the sample preparation process and may comprise cells of different genetic makeups. To reveal such complexity and annotate cells appropriately, we propose the first-of-its-kind computational framework, Originator, which deciphers single cells by genetic origin and separates blood cells from tissue-resident cells. We show that blood contamination is widely spread in scRNA-Seq data from a variety of tissues. We warn of the significant biases in downstream analysis without considering blood contamination and genetic contexts using pancreatic ductal adenocarcinoma and placenta data, respectively.