1 |
Probing language identity encoded in pre-trained multilingual models: a typological view
|
|
|
|
In: PeerJ Comput Sci (2022)
|
|
BASE
|
|
Show details
|
|
2 |
Multi-indicators decision for product design solutions: a TOPSIS-MOGA integrated model
|
|
|
|
BASE
|
|
Show details
|
|
3 |
A data-driven approach for integrating hedonic quality and pragmatic quality in user experience modelling
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Additional file 4 of Social calls influence the foraging behavior in wild big-footed myotis ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Additional file 4 of Social calls influence the foraging behavior in wild big-footed myotis ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Social calls influence the foraging behavior in wild big-footed myotis ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Social calls influence the foraging behavior in wild big-footed myotis ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
Nursing resources and patient outcomes in intensive care units: A protocol for systematic review and meta-analysis
|
|
|
|
In: Medicine (Baltimore) (2021)
|
|
BASE
|
|
Show details
|
|
9 |
Social calls influence the foraging behavior in wild big-footed myotis
|
|
|
|
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
|
|
|
|
BASE
|
|
Show details
|
|
11 |
A graph convolutional network-based sensitive information detection algorithm
|
|
|
|
In: Test Series for Scopus Harvesting 2021 (2021)
|
|
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
|
|
|
|
BASE
|
|
Show details
|
|
16 |
Comparing context-dependent call sequences employing machine learning methods: an indication of syntactic structure of greater horseshoe bats
|
|
|
|
BASE
|
|
Show details
|
|
17 |
A Context Free Gramma for Key Noun-Phrase Extraction from Text
|
|
|
|
BASE
|
|
Show details
|
|
18 |
Representation of Deep Features using Radiologist defined Semantic Features
|
|
|
|
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
|
|
|
|
In: Cross-Cultural Communication; Vol 14, No 4 (2018): Cross-Cultural Communication; 1-11 ; 1923-6700 ; 1712-8358 (2018)
|
|
BASE
|
|
Show details
|
|
|
|