Zobrazeno 1 - 10
of 416
pro vyhledávání: '"Liu Zhengliang"'
Autor:
Zhu Xiangyang, Qiu Song, Liu Tong, Ding You, Tang Ruoyu, Liu Zhengliang, Chen Xiaocen, Ren Yuan
Publikováno v:
Nanophotonics, Vol 12, Iss 12, Pp 2157-2169 (2023)
In most rotational Doppler effect (RDE) measurements, the optical axis and the rotating axis of the object are required to be aligned. However, the condition is very difficult to achieve in practical applications of rotation detection, which seriousl
Externí odkaz:
https://doaj.org/article/f532b626be1c462bbc7b85c4d736d4d3
Publikováno v:
Nanophotonics, Vol 11, Iss 6, Pp 1127-1135 (2022)
The simultaneous and independent measurement of multiple movement forms is a significant issue to be solved in research. In this paper, we proposed a method that combines the self-interference of conjugated optical vortices and external interference
Externí odkaz:
https://doaj.org/article/fecf0da5fd27471488128a41196c9767
Autor:
Yang, Zhenyuan, Lin, Xuhui, He, Qinyi, Huang, Ziye, Liu, Zhengliang, Jiang, Hanqi, Shu, Peng, Wu, Zihao, Li, Yiwei, Law, Stephen, Mai, Gengchen, Liu, Tianming, Yang, Tao
The emergence of Large Language Models (LLMs) and multimodal foundation models (FMs) has generated heightened interest in their applications that integrate vision and language. This paper investigates the capabilities of ChatGPT-4V and Gemini Pro for
Externí odkaz:
http://arxiv.org/abs/2408.12821
In recent years, the field of radiology has increasingly harnessed the power of artificial intelligence (AI) to enhance diagnostic accuracy, streamline workflows, and improve patient care. Large language models (LLMs) have emerged as particularly pro
Externí odkaz:
http://arxiv.org/abs/2408.11848
Autor:
Wang, Jiaqi, Jiang, Hanqi, Liu, Yiheng, Ma, Chong, Zhang, Xu, Pan, Yi, Liu, Mengyuan, Gu, Peiran, Xia, Sichen, Li, Wenjun, Zhang, Yutong, Wu, Zihao, Liu, Zhengliang, Zhong, Tianyang, Ge, Bao, Zhang, Tuo, Qiang, Ning, Hu, Xintao, Jiang, Xi, Zhang, Xin, Zhang, Wei, Shen, Dinggang, Liu, Tianming, Zhang, Shu
In an era defined by the explosive growth of data and rapid technological advancements, Multimodal Large Language Models (MLLMs) stand at the forefront of artificial intelligence (AI) systems. Designed to seamlessly integrate diverse data types-inclu
Externí odkaz:
http://arxiv.org/abs/2408.01319
Autor:
Zhang, Yutong, Pan, Yi, Zhong, Tianyang, Dong, Peixin, Xie, Kangni, Liu, Yuxiao, Jiang, Hanqi, Liu, Zhengliang, Zhao, Shijie, Zhang, Tuo, Jiang, Xi, Shen, Dinggang, Liu, Tianming, Zhang, Xin
Medical images and radiology reports are crucial for diagnosing medical conditions, highlighting the importance of quantitative analysis for clinical decision-making. However, the diversity and cross-source heterogeneity of these data challenge the g
Externí odkaz:
http://arxiv.org/abs/2407.05758
Autor:
Mukherjee, Subhabrata, Gamble, Paul, Ausin, Markel Sanz, Kant, Neel, Aggarwal, Kriti, Manjunath, Neha, Datta, Debajyoti, Liu, Zhengliang, Ding, Jiayuan, Busacca, Sophia, Bianco, Cezanne, Sharma, Swapnil, Lasko, Rae, Voisard, Michelle, Harneja, Sanchay, Filippova, Darya, Meixiong, Gerry, Cha, Kevin, Youssefi, Amir, Buvanesh, Meyhaa, Weingram, Howard, Bierman-Lytle, Sebastian, Mangat, Harpreet Singh, Parikh, Kim, Godil, Saad, Miller, Alex
We develop Polaris, the first safety-focused LLM constellation for real-time patient-AI healthcare conversations. Unlike prior LLM works in healthcare focusing on tasks like question answering, our work specifically focuses on long multi-turn voice c
Externí odkaz:
http://arxiv.org/abs/2403.13313
Autor:
Li, Yiwei, Wu, Zihao, Zhao, Huaqin, Yang, Tianze, Liu, Zhengliang, Shu, Peng, Sun, Jin, Parasuraman, Ramviyas, Liu, Tianming
To tackle the "reality gap" encountered in Sim-to-Real transfer, this study proposes a diffusion-based framework that minimizes inconsistencies in grasping actions between the simulation settings and realistic environments. The process begins by trai
Externí odkaz:
http://arxiv.org/abs/2403.11459
Autor:
Ma, Chong, Jiang, Hanqi, Chen, Wenting, Li, Yiwei, Wu, Zihao, Yu, Xiaowei, Liu, Zhengliang, Guo, Lei, Zhu, Dajiang, Zhang, Tuo, Shen, Dinggang, Liu, Tianming, Li, Xiang
In the medical multi-modal frameworks, the alignment of cross-modality features presents a significant challenge. However, existing works have learned features that are implicitly aligned from the data, without considering the explicit relationships
Externí odkaz:
http://arxiv.org/abs/2403.12416
Autor:
Xu, Shaochen, Wu, Zihao, Zhao, Huaqin, Shu, Peng, Liu, Zhengliang, Liao, Wenxiong, Li, Sheng, Sikora, Andrea, Liu, Tianming, Li, Xiang
In this study, we leverage LLM to enhance the semantic analysis and develop similarity metrics for texts, addressing the limitations of traditional unsupervised NLP metrics like ROUGE and BLEU. We develop a framework where LLMs such as GPT-4 are empl
Externí odkaz:
http://arxiv.org/abs/2402.11398