Zobrazeno 1 - 10
of 9 538
pro vyhledávání: '"Shu, Wei"'
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
Digital quantum simulation of many-body dynamics relies on Trotterization to decompose the target time evolution into elementary quantum gates operating at a fixed equidistant time discretization. Recent advances have outlined protocols enabling more
Externí odkaz:
http://arxiv.org/abs/2406.06198
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
This paper explores the integration of cross-polarized stimulated Brillouin scattering (XP-SBS) with Kerr and quadratic nonlinearities in lithium niobate (LN) to enhance photonic device performance. Three novel applications are demonstrated: (i) a re
Externí odkaz:
http://arxiv.org/abs/2405.15888
Autor:
Tan, Ting Fang, Elangovan, Kabilan, Jin, Liyuan, Jie, Yao, Yong, Li, Lim, Joshua, Poh, Stanley, Ng, Wei Yan, Lim, Daniel, Ke, Yuhe, Liu, Nan, Ting, Daniel Shu Wei
Purpose: To assess the alignment of GPT-4-based evaluation to human clinician experts, for the evaluation of responses to ophthalmology-related patient queries generated by fine-tuned LLM chatbots. Methods: 400 ophthalmology questions and paired answ
Externí odkaz:
http://arxiv.org/abs/2402.10083
Autor:
Ong, Jasmine Chiat Ling, Jin, Liyuan, Elangovan, Kabilan, Lim, Gilbert Yong San, Lim, Daniel Yan Zheng, Sng, Gerald Gui Ren, Ke, Yuhe, Tung, Joshua Yi Min, Zhong, Ryan Jian, Koh, Christopher Ming Yao, Lee, Keane Zhi Hao, Chen, Xiang, Chng, Jack Kian, Than, Aung, Goh, Ken Junyang, Ting, Daniel Shu Wei
Importance: We introduce a novel Retrieval Augmented Generation (RAG)-Large Language Model (LLM) framework as a Clinical Decision Support Systems (CDSS) to support safe medication prescription. Objective: To evaluate the efficacy of LLM-based CDSS in
Externí odkaz:
http://arxiv.org/abs/2402.01741
Autor:
Ke, YuHe, Jin, Liyuan, Elangovan, Kabilan, Abdullah, Hairil Rizal, Liu, Nan, Sia, Alex Tiong Heng, Soh, Chai Rick, Tung, Joshua Yi Min, Ong, Jasmine Chiat Ling, Ting, Daniel Shu Wei
Purpose: Large Language Models (LLMs) hold significant promise for medical applications. Retrieval Augmented Generation (RAG) emerges as a promising approach for customizing domain knowledge in LLMs. This case study presents the development and evalu
Externí odkaz:
http://arxiv.org/abs/2402.01733
Autor:
Ke, Yu He, Yang, Rui, Lie, Sui An, Lim, Taylor Xin Yi, Abdullah, Hairil Rizal, Ting, Daniel Shu Wei, Liu, Nan
Background: Cognitive biases in clinical decision-making significantly contribute to errors in diagnosis and suboptimal patient outcomes. Addressing these biases presents a formidable challenge in the medical field. Objective: This study explores the
Externí odkaz:
http://arxiv.org/abs/2401.14589
Autor:
Chou-Chen, Shu Wei, Barboza, Luis A.
Respiratory diseases represent one of the most significant economic burdens on healthcare systems worldwide. The variation in the increasing number of cases depends greatly on climatic seasonal effects, socioeconomic factors, and pollution. Therefore
Externí odkaz:
http://arxiv.org/abs/2401.03101
Autor:
Kung, Chi-Hsi, Yang, Chieh-Chi, Pao, Pang-Yuan, Lu, Shu-Wei, Chen, Pin-Lun, Lu, Hsin-Cheng, Chen, Yi-Ting
Intelligent driving systems aim to achieve a zero-collision mobility experience, requiring interdisciplinary efforts to enhance safety performance. This work focuses on risk identification, the process of identifying and analyzing risks stemming from
Externí odkaz:
http://arxiv.org/abs/2312.01659