Zobrazeno 1 - 10
of 520
pro vyhledávání: '"YAN Tianyi"'
Autor:
CAI Siyu, GUO Qiaohong, NING Xiaohong, LU Guijun, GUO Yanru, LIU Yin, QIN Xinyan, WANG Xianjing, YAN Tianyi, WANG Ruixin, ZHOU Xuan, PENG Xiaoxia, Pediatric Palliative Care Subspecialty Group of the Pediatrics Society of the Chinese Medical Association
Publikováno v:
Xiehe Yixue Zazhi, Vol 13, Iss 1, Pp 96-103 (2022)
Objective To describe the cross-cultural adaptation of the document of advance care planning (ACP) Voicing My CHOiCESTM in Chinese, and provide the basis for the practice of ACP in China. Methods The process of cross-cultural adaptation involved docu
Externí odkaz:
https://doaj.org/article/adf7d06ed002484187f894bcafcb4b7c
Autor:
Yan Tianyi, Ambar Farooq, Muhammad Mohiuddin, Asma Farooq, Norela C. T. Gonzalez, Asim Abbasi, Aiman Hina, Muhammad Irshad
Publikováno v:
Frontiers in Ecology and Evolution, Vol 11 (2023)
Iodine deficiency disorder (IDDs) is one of the most prevailing and common health issues in mountainous communities. An effective way to control the prevalence and emergence of IDDs in remote areas is to use iodized salt. However, recent studies indi
Externí odkaz:
https://doaj.org/article/1bc9a480bd20485ea8057c78a4735997
Autor:
Yan, Tianyi, Wu, Dongming, Han, Wencheng, Jiang, Junpeng, Zhou, Xia, Zhan, Kun, Xu, Cheng-zhong, Shen, Jianbing
Autonomous driving evaluation requires simulation environments that closely replicate actual road conditions, including real-world sensory data and responsive feedback loops. However, many existing simulations need to predict waypoints along fixed ro
Externí odkaz:
http://arxiv.org/abs/2411.11252
Contrast-enhanced magnetic resonance imaging (MRI) is pivotal in the pipeline of brain tumor segmentation and analysis. Gadolinium-based contrast agents, as the most commonly used contrast agents, are expensive and may have potential side effects, an
Externí odkaz:
http://arxiv.org/abs/2406.16074
Autor:
Wang, Fei, Fu, Xingyu, Huang, James Y., Li, Zekun, Liu, Qin, Liu, Xiaogeng, Ma, Mingyu Derek, Xu, Nan, Zhou, Wenxuan, Zhang, Kai, Yan, Tianyi Lorena, Mo, Wenjie Jacky, Liu, Hsiang-Hui, Lu, Pan, Li, Chunyuan, Xiao, Chaowei, Chang, Kai-Wei, Roth, Dan, Zhang, Sheng, Poon, Hoifung, Chen, Muhao
We introduce MuirBench, a comprehensive benchmark that focuses on robust multi-image understanding capabilities of multimodal LLMs. MuirBench consists of 12 diverse multi-image tasks (e.g., scene understanding, ordering) that involve 10 categories of
Externí odkaz:
http://arxiv.org/abs/2406.09411
Performance of large language models (LLMs) may vary with different prompts or instructions of even the same task. One commonly recognized factor for this phenomenon is the model's familiarity with the given prompt or instruction, which is typically
Externí odkaz:
http://arxiv.org/abs/2403.16038
Autor:
Yan, Tianyi Lorena, Wang, Fei, Huang, James Y., Zhou, Wenxuan, Yin, Fan, Galstyan, Aram, Yin, Wenpeng, Chen, Muhao
Instruction tuning has been used as a promising approach to improve the performance of large language models (LLMs) on unseen tasks. However, current LLMs exhibit limited robustness to unseen instructions, generating inconsistent outputs when the sam
Externí odkaz:
http://arxiv.org/abs/2402.11138
Structural magnetic resonance imaging (sMRI) provides accurate estimates of the brain's structural organization and learning invariant brain representations from sMRI is an enduring issue in neuroscience. Previous deep representation learning models
Externí odkaz:
http://arxiv.org/abs/2306.11378
Autor:
Wang, Gongshu, Jiang, Ning, Ma, Yunxiao, Liu, Tiantian, Chen, Duanduan, Wu, Jinglong, Li, Guoqi, Liang, Dong, Yan, Tianyi
Structural magnetic resonance imaging (sMRI) has shown great clinical value and has been widely used in deep learning (DL) based computer-aided brain disease diagnosis. Previous approaches focused on local shapes and textures in sMRI that may be sign
Externí odkaz:
http://arxiv.org/abs/2306.05297
Natural language understanding (NLU) models often suffer from unintended dataset biases. Among bias mitigation methods, ensemble-based debiasing methods, especially product-of-experts (PoE), have stood out for their impressive empirical success. Howe
Externí odkaz:
http://arxiv.org/abs/2305.17627