Extreme learning machines for feature learning by Liyanaarachchi Lekamalage, Chamara Kasun

“…Hierarchal features consist of multiple levels and the low level features represent edges of an image such as lines and curves while high level features represent parts of the image such as nose, eyes of a face. …”
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渺小的伟大 ：以电视剧《大英雄》探讨新加坡小市民的邻里关系 = Building Singapore's neighbourhood relations : a case study of drama series Hero by 林殷如 Lim, Esther Yin Ru

“…Nonetheless, they were able to understand and identify themselves with the beautiful neighbourly relations portrayed in the drama and this is something that they longed for.…”
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Unsupervised bayesian generative methods by Li, Shaohua

“…This finding bridges the two very different lines of model selection methods. The third task is about Word Embedding, a technique that maps words in a natural language to continuous vectors which encode the semantic/syntactic regularities among the words. …”
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Graph representation learning by Zhang, Xinyi

“…Besides different techniques used in graph representation learning, according to the fineness of the objects for embedding, the graph representation learning methods can be mainly divided into node-level, edge-level, and graph-level representation learning methods. …”
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Next generation design of floating systems for coastal solar farms by Bi, Cheng

“…Experiments on a novel adaptive barrier-mooring system (ABMS) including perimeter pontoons, submerged barriers, clump weights, mooring lines and anchors were conducted to examine the adaptivity of ABMS to a large tidal range as well as its performance on platform stabilization under wave action. …”
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Antidiabetic activity of Curculigo latifolia extracts in vitro and in vivo studies by Ishak, Nur Akmal

“…In the present study, antidiabetic properties of C. latifolia in cell lines (in vitro) and in diabetic-induced rats were determined. …”
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Computational analysis for infectious diseases surveillance and host-pathogen interactions (in the context of Influenza A viruses) by Beyene, Biruhalem Taye

“…We used host gene expression signatures of cell lines from three species (A549 (human), CEF (chicken), and MDCK (canine) in response to six IAVs (H1N1/WSN, H5N2/F59, H5N2/F118, H5N2/F189, H5N3 and H9N2 viruses). …”
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