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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 Hits 1 – 20 of 73 1 Probing language identity encoded in pre-trained multilingual models: a typological view Zheng, Jianyu; Liu, Ying In: PeerJ Comput Sci (2022) BASE Show details 2 Multi-indicators decision for product design solutions: a TOPSIS-MOGA integrated model Yu, Zeyuan; Zhao, Wu; Guo, Xin. - : MDPI, 2022 BASE Show details 3 A data-driven approach for integrating hedonic quality and pragmatic quality in user experience modelling Tong, Yanzhang; Liang, Yan; Spasic, Irena. - : American Society of Mechanical Engineers, 2022 BASE Show details 4 Additional file 4 of Social calls influence the foraging behavior in wild big-footed myotis ... Dongge Guo; Jianan Ding; Liu, Heng. - : figshare, 2021 BASE Show details 5 Additional file 4 of Social calls influence the foraging behavior in wild big-footed myotis ... Dongge Guo; Jianan Ding; Liu, Heng. - : figshare, 2021 BASE Show details 6 Social calls influence the foraging behavior in wild big-footed myotis ... Dongge Guo; Jianan Ding; Liu, Heng. - : figshare, 2021 BASE Show details 7 Social calls influence the foraging behavior in wild big-footed myotis ... Dongge Guo; Jianan Ding; Liu, Heng. - : figshare, 2021 BASE Show details 8 Nursing resources and patient outcomes in intensive care units: A protocol for systematic review and meta-analysis Xu, Xiaoyan; Zhang, Haiyan; Ding, Jin... In: Medicine (Baltimore) (2021) BASE Show details 9 Social calls influence the foraging behavior in wild big-footed myotis Guo, Dongge; Ding, Jianan; Liu, Heng... In: Front Zool (2021) BASE Show details 10 Cross-linguistic influence of L2 on L1 in late Chinese-English bilinguals : the case of subject realisation Liu, Ying (S32823); Qi, Ruying (R10565); Di Biase, Bruno (R7581). - : Netherlands, John Benjamins Publishing, 2021 BASE Show details 11 A graph convolutional network-based sensitive information detection algorithm Liu, Ying; Yang, Chao Yu; Yang, Jie In: Test Series for Scopus Harvesting 2021 (2021) BASE Show details 12 Industrial Internet of Learning (IIoL): IIoT based pervasive knowledge network for LPWAN—concept, framework and case studies [<Journal>] Qin, Jian Verfasser] DNB Subject Category Language Show details 13 Writing Lived Experience – A Melancholy Elegy Liu, Ying. - : The University of Edinburgh, 2020 BASE Show details 14 SPEAKING THE UNSPOKEN AND UNSPEAKABLE: LIVING WITH THE AFTERMATH OF SIBLING ABORTION UNDER CHINA'S ONE-CHILD POLICY Liu, Ying. - : European Network Qualitative Inquiry, 2020 BASE Show details 15 L2 Influence on L1 : Chinese subject realisation in Chinese-English bilinguals Liu, Ying. - 2020 BASE Show details 16 Comparing context-dependent call sequences employing machine learning methods: an indication of syntactic structure of greater horseshoe bats Zhang, Kangkang; Liu, Tong; Xiao, Yanhong. - : The Company of Biologists Ltd, 2019 BASE Show details 17 A Context Free Gramma for Key Noun-Phrase Extraction from Text Liu, Ying. - 2019 BASE Show details 18 Representation of Deep Features using Radiologist defined Semantic Features Paul, Rahul; Liu, Ying; Li, Qian; Hall, Lawrence; Goldgof, Dmitry; Balagurunathan, Yoganand; Schabath, Matthew; Gillies, Robert. - 2018 Abstract: Semantic features are common radiological traits used to characterize a lesion by a trained radiologist. These features have been recently formulated, quantified on a point scale in the context of lung nodules by our group. Certain radiological semantic traits have been shown to extremely predictive of malignancy [26]. Semantic traits observed by a radiologist at examination describe the nodules and the morphology of the lung nodule shape, size, border, attachment to vessel or pleural wall, location and texture etc. Deep features are numeric descriptors often obtained from a convolutional neural network (CNN) which are widely used for classification and recognition. Deep features may contain information about texture and shape, primarily. Lately, with the advancement of deep learning, convolutional neural networks (CNN) are also being used to analyze lung nodules. In this study, we relate deep features to semantic features by looking for similarity in ability to classify. Deep features were obtained using a transfer learning approach from both an ImageNet pre-trained CNN and our trained CNN architecture. We found that some of the semantic features can be represented by one or more deep features. In this process, we can infer that some deep feature(s) have similar discriminatory ability as semantic features. Keyword: Article URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6233304/ BASE Hide details 19 A Comparative Study of Sino-U.S. Business Negotiation Strategy From the Perspective of Cultural Dimensions Theory CAO, Shuo; LIU, Ying; GAO, Jingyang In: Cross-Cultural Communication; Vol 14, No 4 (2018): Cross-Cultural Communication; 1-11 ; 1923-6700 ; 1712-8358 (2018) BASE Show details 20 To Exhaust or Not to Exhaust: An Experimental Study on Mandarin SHI-Clefts Liu, Ying; Yang, Yu'an In: Proceedings of GLOW in Asia XI (2017), S. 103-117 Leibniz-Zentrum Allgemeine Sprachwissenschaft Show details Page: 1 2 3 4

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