Zobrazeno 1 - 10
of 889
pro vyhledávání: '"Wang Junxiang"'
Autor:
Wang, Junxiang, Barragan, Juan Antonio, Ishida, Hisashi, Guo, Jingkai, Ku, Yu-Chun, Kazanzides, Peter
Telesurgery is an effective way to deliver service from expert surgeons to areas without immediate access to specialized resources. However, many of these areas, such as rural districts or battlefields, might be subject to different problems in commu
Externí odkaz:
http://arxiv.org/abs/2411.13449
Autor:
Lin, Minhua, Chen, Zhengzhang, Liu, Yanchi, Zhao, Xujiang, Wu, Zongyu, Wang, Junxiang, Zhang, Xiang, Wang, Suhang, Chen, Haifeng
Time series data is ubiquitous across various domains, including manufacturing, finance, and healthcare. High-quality annotations are essential for effectively understanding time series and facilitating downstream tasks; however, obtaining such annot
Externí odkaz:
http://arxiv.org/abs/2410.17462
Autor:
Deng, Chengyuan, Chen, Zhengzhang, Zhao, Xujiang, Wang, Haoyu, Wang, Junxiang, Chen, Haifeng, Gao, Jie
Change point detection aims to identify abrupt shifts occurring at multiple points within a data sequence. This task becomes particularly challenging in the online setting, where different types of changes can occur, including shifts in both the marg
Externí odkaz:
http://arxiv.org/abs/2407.09698
Autor:
Wang, Junxiang, Zhao, Liang
We introduce GraphSL, a new library for studying the graph source localization problem. graph diffusion and graph source localization are inverse problems in nature: graph diffusion predicts information diffusions from information sources, while grap
Externí odkaz:
http://arxiv.org/abs/2405.03724
Our work addresses the challenges older adults face with commercial Voice Assistants (VAs), notably in conversation breakdowns and error handling. Traditional methods of collecting user experiences-usage logs and post-hoc interviews-do not fully capt
Externí odkaz:
http://arxiv.org/abs/2403.02421
Time series domain adaptation stands as a pivotal and intricate challenge with diverse applications, including but not limited to human activity recognition, sleep stage classification, and machine fault diagnosis. Despite the numerous domain adaptat
Externí odkaz:
http://arxiv.org/abs/2312.12276
Autor:
Zhang, Zheng, Li, Sirui, Zhou, Jingcheng, Wang, Junxiang, Angirekula, Abhinav, Zhang, Allen, Zhao, Liang
Spatial networks are networks whose graph topology is constrained by their embedded spatial space. Understanding the coupled spatial-graph properties is crucial for extracting powerful representations from spatial networks. Therefore, merely combinin
Externí odkaz:
http://arxiv.org/abs/2312.10808
Publikováno v:
2022 International Symposium on Medical Robotics (ISMR), GA, USA, 2022, pp. 1-6
Positron Emission Tomography (PET) enables functional imaging of deep brain structures, but the bulk and weight of current systems preclude their use during many natural human activities, such as locomotion. The proposed long-term solution is to cons
Externí odkaz:
http://arxiv.org/abs/2311.17863
Graph Neural Networks (GNNs) have achieved great success in representing data with dependencies by recursively propagating and aggregating messages along the edges. However, edges in real-world graphs often have varying degrees of difficulty, and som
Externí odkaz:
http://arxiv.org/abs/2310.18735
Conventional Voice Assistants (VAs) rely on traditional language models to discern user intent and respond to their queries, leading to interactions that often lack a broader contextual understanding, an area in which Large Language Models (LLMs) exc
Externí odkaz:
http://arxiv.org/abs/2309.13879