Zobrazeno 1 - 10
of 316
pro vyhledávání: '"Liu Mingxuan"'
Publikováno v:
Xibei Gongye Daxue Xuebao, Vol 42, Iss 2, Pp 319-327 (2024)
With the help of lightweight virtualization technology such as containers, the spaceborne virtualization platform encapsulates computing tasks into containers to form task containers, so as to achieve efficient utilization of resources. However, the
Externí odkaz:
https://doaj.org/article/283b6be2f2a74eaf8d75765cd06d4eac
Remote photoplethysmography (rPPG) extracts PPG signals from subtle color changes in facial videos, showing strong potential for health applications. However, most rPPG methods rely on intensity differences between consecutive frames, missing long-te
Externí odkaz:
http://arxiv.org/abs/2411.15283
Organizing unstructured visual data into semantic clusters is a key challenge in computer vision. Traditional deep clustering (DC) approaches focus on a single partition of data, while multiple clustering (MC) methods address this limitation by uncov
Externí odkaz:
http://arxiv.org/abs/2410.05217
Autor:
Lukyanenko, Platon, Mayourian, Joshua, Liu, Mingxuan, Triedman, John K., Ghelani, Sunil J., La Cava, William G.
Several recent high-impact studies leverage large hospital-owned electrocardiographic (ECG) databases to model and predict patient mortality. MIMIC-IV, released September 2023, is the first comparable public dataset and includes 800,000 ECGs from a U
Externí odkaz:
http://arxiv.org/abs/2406.17002
Autor:
Yang, Rui, Ning, Yilin, Keppo, Emilia, Liu, Mingxuan, Hong, Chuan, Bitterman, Danielle S, Ong, Jasmine Chiat Ling, Ting, Daniel Shu Wei, Liu, Nan
Generative artificial intelligence (AI) has brought revolutionary innovations in various fields, including medicine. However, it also exhibits limitations. In response, retrieval-augmented generation (RAG) provides a potential solution, enabling mode
Externí odkaz:
http://arxiv.org/abs/2406.12449
Self-supervised monocular depth estimation aims to infer depth information without relying on labeled data. However, the lack of labeled information poses a significant challenge to the model's representation, limiting its ability to capture the intr
Externí odkaz:
http://arxiv.org/abs/2406.08928
Autor:
Liu, Mingxuan, Ning, Yilin, Teixayavong, Salinelat, Liu, Xiaoxuan, Mertens, Mayli, Shang, Yuqing, Li, Xin, Miao, Di, Xu, Jie, Ting, Daniel Shu Wei, Cheng, Lionel Tim-Ee, Ong, Jasmine Chiat Ling, Teo, Zhen Ling, Tan, Ting Fang, RaviChandran, Narrendar, Wang, Fei, Celi, Leo Anthony, Ong, Marcus Eng Hock, Liu, Nan
The ethical integration of Artificial Intelligence (AI) in healthcare necessitates addressing fairness-a concept that is highly context-specific across medical fields. Extensive studies have been conducted to expand the technical components of AI fai
Externí odkaz:
http://arxiv.org/abs/2405.17921
Open-vocabulary object detection (OvOD) has transformed detection into a language-guided task, empowering users to freely define their class vocabularies of interest during inference. However, our initial investigation indicates that existing OvOD de
Externí odkaz:
http://arxiv.org/abs/2405.10053
Autor:
Liu, Mingxuan, Ning, Yilin, Ke, Yuhe, Shang, Yuqing, Chakraborty, Bibhas, Ong, Marcus Eng Hock, Vaughan, Roger, Liu, Nan
The escalating integration of machine learning in high-stakes fields such as healthcare raises substantial concerns about model fairness. We propose an interpretable framework - Fairness-Aware Interpretable Modeling (FAIM), to improve model fairness
Externí odkaz:
http://arxiv.org/abs/2403.05235
Autor:
Wang, Ziwen, Lee, Jin Wee, Chakraborty, Tanujit, Ning, Yilin, Liu, Mingxuan, Xie, Feng, Ong, Marcus Eng Hock, Liu, Nan
Survival analysis is essential for studying time-to-event outcomes and providing a dynamic understanding of the probability of an event occurring over time. Various survival analysis techniques, from traditional statistical models to state-of-the-art
Externí odkaz:
http://arxiv.org/abs/2403.06999