Search

Your search keyword '"Cao, Difei"' showing total 17 results
Start Over Author "Cao, Difei"
17 results on '"Cao, Difei"'

7. Enhancing Reliability in Multimodal UAV Communication Based on Opportunistic Task Space

Author

Ren, Chao, Gong, Chao, Cao, Difei, Li, Yingqi, Zhang, Haijun, and Nallanathan, Arumugam

Abstract

An opportunistic task space-based model is proposed to enhance reliability in the execution of unmanned aerial vehicle (UAV) tasks. The task information consists of predefined basic information units, and multimodal communication is realized by sharing and transferring modality information against the interference from task-irrelevant information. The multimodal task information alignment and task-oriented multimodal non-intersecting mapping are also used to align and enhance the reliability of task-specific information from different modalities. Numerical results are provided to demonstrate the effectiveness of task space searching and its impact on task reliability.

Published
2024
Full Text
View/download PDF

8. Priority Aggregation Network With Integrated Computation and Sensation for Ultra Dense Artificial Intelligence of Things

Author

Ren, Chao, He, Zongrui, Li, Yingqi, Cao, Difei, Zhang, Haijun, and Nallanathan, Arumugam

Abstract

In the realm of ultra dense Intelligence of Things, efficient utilization of computation and sensation (CAS) resources remains a challenge due to their uneven temporal and spatial distribution. Priority Aggregation Network (PAN) is introduced to employ priority as an intermediary to restore, combine, and exchange idle CAS resources’ permissions for cross-temporal and cross-modal resource leasing. PAN awards priority to devices that lease their sensing and computing resources to others, facilitating efficient resource allocation. Numerical results indicate PAN maintains high cross-temporal task completion rates while conserving equipment resources, with increased willingness to pay for resources.

Published
2024
Full Text
View/download PDF

11. MicroEdge

Author

Cao, Difei, primary, Yoo, Jinsun, additional, Xu, Zhuangdi, additional, Saurez, Enrique, additional, Gupta, Harshit, additional, Krishna, Tushar, additional, and Ramachandran, Umakishore, additional

Published
2022
Full Text
View/download PDF

12. Investigating Neural Activation Effects on Deep Belief Echo-State Networks for Prediction Toward Smart Ocean Environment Monitoring

Author

Zhigang Li, Jiabo Zhang, Huixin Liu, Cao Difei, Xiaochuan Sun, Gang Wang, Yingqi Li, and Jialin Wang

Subjects
Data processing, Multidisciplinary, Series (mathematics), Computer science, 010102 general mathematics, Hyperbolic function, Activation function, Sigmoid function, 01 natural sciences, Benchmark (computing), State (computer science), 0101 mathematics, Time series, Algorithm
Abstract

Ocean sensor data prediction has become a promising means for smart ocean monitoring. In alternative solutions, deep neural networks (DNNs) are considered as a good choice. The determination of activation functions in DNNs has a significant effect on training speed and nonlinear approximation. In this paper, the effect of activation functions on a deep computing model called deep belief echo-state network (DBEN) is studied in the scenario of ocean time series prediction. Here, different forms, including hyperbolic tangent, rectified linear unit, exponential linear unit, swish, softplus and their variants, are considered. The purpose is to investigate, from the perspectives of accuracy and training efficiency, whether certain activation function in DBEN is completely universal for the different tasks of ocean sensor data processing or not. On a great deal of real-world ocean time series of different characteristics, the results show that the selection of activation functions in DBEN is task-related. Specially, these newly introduced activation functions are more beneficial to the accurate predictions for conventional and chemical data sets compared with sigmoid benchmark. The statistical analysis further verifies this finding.

Published
2021

16. Identification of spliceosome components pivotal to breast cancer survival.

Author

An, Jing, Luo, Zhehui, An, Weiwei, Cao, Difei, Ma, Jianli, and Liu, Zhaoliang

Subjects
SPLICEOSOMES, GENETICS of breast cancer, ALTERNATIVE RNA splicing, EPITHELIAL cells, GENE expression
Abstract

Cancer cells employ alternative splicing (AS) to acquire splicing isoforms favouring their survival. However, the causes of aberrant AS in breast cancer are poorly understood. In this study, the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) data were analysed with univariate feature selection. Of 122 analysed spliceosome components, U2SURP, PUF60, DDX41, HNRNPAB, EIF4A3, and PPIL3 were significantly associated with breast cancer survival. The top 4 four genes, U2SURP, PUF60, DDX41, and HNRNPAB, were chosen for further analyses. Their expression was significantly associated with cancer molecular subtype, tumour stage, tumour grade, overall survival (OS), and cancer-specific survival in the METABRIC data. These results were verifiable using other cohorts. The Cancer Genome Atlas data unveiled the elevated expression of PUF60, DDX41, and HNRNPAB in tumours compared with the normal tissue and confirmed the differential expression of the four genes among cancer molecular subtypes, as well as the associations of U2SURP, PUF60, and DDX41 expression with tumour stage. A meta-analysis data verified the associations of U2SURP, PUF60, and HNRNPAB expression with tumour grade, the associations of PUF60, DDX41, and HNRNPAB expression with OS and distant metastasis-free survival, and the associations of U2SURP and HNRNPAB expression with relapse-free survival. Experimentally, we demonstrated that inhibiting the expression of the four genes separately suppressed cell colony formation and slowed down cell growth considerably in breast cancer cells, but not in immortal breast epithelial cells. In conclusion, we have identified U2SURP, PUF60, DDX41, and HNRNPAB are spliceosome-related genes pivotal for breast cancer survival. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
View/download PDF

17. High-Quality Protein-Encoding Gene Design and Protein Analysis

Author

Zhao Jinhai, Cao Difei, Wang Dongkai, Huang Guoqing, Li Yao, Wu Qiong, and Wang Lei

Subjects
chemistry.chemical_classification, food.ingredient, biology, Food additive, Protein design, Genetically modified crops, biology.organism_classification, Amino acid, food, Biochemistry, chemistry, Gene, Protein secondary structure, Intracellular, Bacteria
Abstract

Rapid growth of global population should be demanded more high-quality food and this might be alleviated by improving protein nutrition levels in usual foodstuff. The expense associated with food additives used for animal production have led to the study of intracellular production of high-quality protein (HQP) by transgenic crops and other organisms, as a means of obtaining efficient and less costly sources of essential amino acids. One important approach is to extract high essential amino acids from bacteria or others. We designed a HQP gene encoding about 100 amino acids which had more than 80 % essential amino acids. GOR/Gibrat and Swiss-Pdb Viewer analysis were used to predict the secondary structure of HQP-1 in pI value, hydrophilic and hydrophobic properties. Then the HQP gene was transferred into E. coli BL21 (DE3), and the protein was extracted and identified by SDS-PAGE.

Published
2015

Catalog

Books, media, physical & digital resources
See catalog results