7.9k citations
- University of AberdeenGB11 papers
- Northwestern Polytechnical UniversityCN8 papers
- Xi'an Jiaotong UniversityCN7 papers
- Xi’an UniversityCN6 papers
- Xidian UniversityCN6 papers
- Universidade Estadual de Ponta GrossaBR5 papers
- Xi'an Technological UniversityCN5 papers
- Xi'an University of Science and TechnologyCN5 papers
- Chinese Academy of SciencesCN4 papers
- National Institute for Materials ScienceJP4 papers
- State Key Laboratory of Solidification Processing4 papers
- Tohoku UniversityJP4 papers
8 papers · 1 filter
Mathematical model of brain tumour growth with drug resistance
José Trobia, Kun Tian, Antonio Marcos Batista +9
Brain tumours are masses of abnormal cells that can grow in an uncontrolled way in the brain. There are different types of malignant brain tumours. Gliomas are malignant brain tumo…
An exploratory study on machine learning to couple numerical solutions of partial differential equations
H. S. Tang, L. Li, M. Grossberg +4
As further progress in the accurate and efficient computation of coupled partial differential equations (PDEs) becomes increasingly difficult, it has become highly desired to devel…
Angle-dependence of interlayer coupling in twisted transition metal dichalcogenide heterobilayers
W. T. Geng, V. Wang, J. B. Lin +2
We reveal by first-principles calculations that the interlayer binding in a twisted MoS2/MoTe2 heterobilayer decreases with increasing twist angle, due to the increase of the inter…
Effects of drug resistance in the tumour-immune system with chemotherapy treatment
José Trobia, Enrique C Gabrick, Evandro G Seifert +10
Cancer is a term used to refer to a large set of diseases. The cancerous cells grow and divide and, as a result, they form tumours that grow in size. The immune system recognise th…
FedBoosting: Federated Learning with Gradient Protected Boosting for Text Recognition
Hanchi Ren, Jingjing Deng, Xianghua Xie +2
Typical machine learning approaches require centralized data for model training, which may not be possible where restrictions on data sharing are in place due to, for instance, pri…
Deep graph learning for semi-supervised classification
Guangfeng Lin, Xiaobing Kang, Kaiyang Liao +2
Graph learning (GL) can dynamically capture the distribution structure (graph structure) of data based on graph convolutional networks (GCN), and the learning quality of the graph…