Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Xingxing Xing"'
Autor:
Pisek Kultavewuti, Eric Y. Zhu, Xingxing Xing, Li Qian, Vincenzo Pusino, Marc Sorel, J. Stewart Aitchison
Publikováno v:
Scientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
Abstract Photonic-based qubits and integrated photonic circuits have enabled demonstrations of quantum information processing (QIP) that promises to transform the way in which we compute and communicate. To that end, sources of polarization-entangled
Externí odkaz:
https://doaj.org/article/c008e6c73a0f4cd4b112e0c85459c671
Publikováno v:
International Journal of Intelligent Systems. 37:1157-1185
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0d46bb6ebd8bb93624e57c53fcb81dc6
https://doi.org/10.1002/int.22664
https://doi.org/10.1002/int.22664
Autor:
Huifang Xu, Zhenpu Shi, Hongyun Yue, Xingxing Xing, Yanhong Yin, Shuting Yang, Kai Xiao, Lan Wang, Kexin Zhang
Publikováno v:
ACS Applied Energy Materials. 4:7743-7750
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::47ccfe72691687dd133e26c845218398
https://doi.org/10.1021/acsaem.1c01078
https://doi.org/10.1021/acsaem.1c01078
Autor:
Junjie Yao, Jian Liu, Runlan Zhang, Xingxing Xing, Shanxin Xiong, Xiaoying Kang, Xiaoqin Wang, Chunxia Yu
Publikováno v:
Integrated Ferroelectrics. 206:105-111
Multiferroic YFeO3 nanofibers were prepared by electrospinning. The as-prepared nanofibers were characterized by XRD, TG-DTA, SEM and Raman spectrum. The magnetic property and photocatalytic activi...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::44d7cdd08f435313b72ded153ddae9d3
https://doi.org/10.1080/10584587.2020.1728631
https://doi.org/10.1080/10584587.2020.1728631
Publikováno v:
CIKM
Graph contrastive representation learning aims to learn discriminative node representations by contrasting positive and negative samples. It helps models learn more generalized representations to achieve better performances on downstream tasks, which
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4f61d59803cc5784ddee181c8dd8d420
https://doi.org/10.1145/3459637.3482478
https://doi.org/10.1145/3459637.3482478
Publikováno v:
IEEE transactions on neural networks and learning systems.
Graph convolutional networks (GCNs) have achieved great success in many applications and have caught significant attention in both academic and industrial domains. However, repeatedly employing graph convolutional layers would render the node embeddi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b50e4c315c6896562dfde476a1841c34
https://pubmed.ncbi.nlm.nih.gov/34637383
https://pubmed.ncbi.nlm.nih.gov/34637383
Autor:
Yaomin Chang, Lin Shu, Erxin Du, Chuan Chen, Ziyang Zhang, Zibin Zheng, Yuzhao Huang, Xingxing Xing
Publikováno v:
Knowledge-Based Systems. 251:109187
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8f5e53b98f082670cf1e7d546f2b95b1
https://doi.org/10.1016/j.knosys.2022.109187
https://doi.org/10.1016/j.knosys.2022.109187
Publikováno v:
Neurocomputing. 259:76-84
Learning spatio-temporal dependency structure is meaningful to characterize causal or statistical relationships. In many real-world applications, dependency structure is often characterized by time-lag between variables. For example, traffic system a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::caaa8e28efc6399d1f52286f4e2a11cf
https://doi.org/10.1016/j.neucom.2016.06.084
https://doi.org/10.1016/j.neucom.2016.06.084
Publikováno v:
ITSC
This paper assesses the predictability of individual vehicle's mobility in Beijing expressway system using Electronic Toll Collection (ETC) data records. By examining the uncertainties of movements using entropy, considering both the frequencies and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3a72d57a08c908f9862ecd2b1759854d
https://doi.org/10.1109/itsc.2016.7795972
https://doi.org/10.1109/itsc.2016.7795972
Autor:
Yu Lei, Xiabing Zhou, Xingxing Xing, Wenhao Huang, Kunqing Xie, Haikun Hong, Kaigui Bian, Fei Chen
Publikováno v:
ICMLA
Nearest neighbor based nonparametric regression is a classic data-driven method for traffic flow prediction in intelligent transportation systems (ITS). Performances of those models depend heavily on the similarity or distance metric used to search n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::65e273bf0ce979627df2504fe4574d3c
https://doi.org/10.1109/icmla.2015.188
https://doi.org/10.1109/icmla.2015.188