Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Daqing Yun"'
Publikováno v:
Applied Sciences, Vol 14, Iss 3, p 1119 (2024)
Recent advances in artificial intelligence (AI) technologies such as deep learning open up new opportunities for various industries, such as cement manufacturing, to transition from traditional human-aided manually controlled production processes to
Externí odkaz:
https://doaj.org/article/33efa26ac835476fbb5b66e2edb775ff
Autor:
Sujoy Roy, Daqing Yun, Behrouz Madahian, Michael W. Berry, Lih-Yuan Deng, Daniel Goldowitz, Ramin Homayouni
Publikováno v:
Frontiers in Bioengineering and Biotechnology, Vol 5 (2017)
In this study, we developed and evaluated a novel text-mining approach, using non-negative tensor factorization (NTF), to simultaneously extract and functionally annotate transcriptional modules consisting of sets of genes, transcription factors (TFs
Externí odkaz:
https://doaj.org/article/5e82b53fa2bc4036aa444b1a916a4d7b
Autor:
Ming Li, Shijie Dai, Yinghao Wu, Lingling Zheng, Yuhang Cai, Shaokun Ma, Xinyu Zhu, Dichun Chen, Bo Tang, Daqing Yun
Publikováno v:
Chinese Science Bulletin. 67:4395-4404
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2cbabffe7a36d29869f462edd0f77cd7
https://doi.org/10.1360/tb-2022-0497
https://doi.org/10.1360/tb-2022-0497
Publikováno v:
GLOBECOM 2022 - 2022 IEEE Global Communications Conference.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a4acef474c96bf615d732f13d5be7cb2
https://doi.org/10.1109/globecom48099.2022.10001709
https://doi.org/10.1109/globecom48099.2022.10001709
Publikováno v:
IEEE/ACM Transactions on Networking. 27:2280-2293
Big data transfer in next-generation scientific applications is now commonly carried out over dedicated channels in high-performance networks (HPNs), where transport protocols play a critical role in maximizing application-level throughput. Optimizin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::62567e106aa4b69aa8c6a5033a2ea914
https://doi.org/10.1109/tnet.2019.2943884
https://doi.org/10.1109/tnet.2019.2943884
Publikováno v:
2020 IEEE Cloud Summit.
Recent advances in hardware capability and machine learning techniques enable convenient monitoring of wildlife and their living environments. In this work, we apply Deep Learning (DL) methods to detect and recognize wildlife in digital images and re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f438d181c4fd0906b1789cc0ab85ed35
https://doi.org/10.1109/ieeecloudsummit48914.2020.00033
https://doi.org/10.1109/ieeecloudsummit48914.2020.00033
Publikováno v:
IEEE Transactions on Parallel and Distributed Systems. 28:3656-3670
High-performance networks (HPNs) are being increasingly developed and deployed to support the transfer of big data. However, such HPN-based technologies and services have not been fully utilized as their use often requires considerable networking and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e59673007409873c4eedea597851732e
https://doi.org/10.1109/tpds.2017.2732987
https://doi.org/10.1109/tpds.2017.2732987
Publikováno v:
Engineering Applications of Artificial Intelligence. 102:104285
Big data transfer in large-scale scientific and business applications is increasingly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) via advance bandwidth reservation. Provisioning agents need t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::65822c7a60ae9ab7cd566d0f6a822547
https://doi.org/10.1016/j.engappai.2021.104285
https://doi.org/10.1016/j.engappai.2021.104285
Autor:
Chase Q. Wu, Daqing Yun
High-performance networks featuring advance bandwidth reservation have been developed and deployed to support big data transfer in extreme-scale scientific applications. The performance of such big data transfer largely depends on the transport proto
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ff5ab61074f880712d0ee900eebce0d7
https://doi.org/10.4018/978-1-5225-5045-7.ch005
https://doi.org/10.4018/978-1-5225-5045-7.ch005
