Zobrazeno 1 - 10
of 2 632
pro vyhledávání: '"Cheng Kuang"'
Publikováno v:
The Journal of Critical Care Medicine, Vol 10, Iss 3, Pp 266-270 (2024)
Malposition is a relatively rare complication associated with peripherally inserted central catheters (PICCs), particularly in cases of superficial femoral vein (SFV) catheterization. To the best of our knowledge, we are the first to report this rare
Externí odkaz:
https://doaj.org/article/240d2b63caa344e5bc18d5eeb7e421bd
Autor:
Lori-jon C. Waugh, Iselle Flores Ruiz, Cheng Kuang, Jian Guo, Jay T. Cullen, Maria T. Maldonado
Publikováno v:
Frontiers in Marine Science, Vol 9 (2022)
The Strait of Georgia (SoG) is a semi-enclosed, urban basin with seasonally dependent estuarine water circulation, dominantly influenced by Northeast Pacific waters and the Fraser River. To establish a baseline and understand the fate and potential t
Externí odkaz:
https://doaj.org/article/578581c2d4554e588fca162be0b41072
Autor:
Yung-Tai Chen, Chih-Chin Yu, Hsin-Chih Yeh, Hsiang-Ying Lee, Yuan-Hong Jiang, Yu-Khun Lee, Chia-Hao Kuei, Chia-Chang Wu, Chao-Yuan Huang, Wei-Yu Lin, Cheng Kuang Yang, Yao Chou Tsai
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
Abstract Our aim was to analyze the clinical and survival differences among patients who underwent the two main treatment modalities, endoscopic ablation and radical nephroureterectomy. This study examined all patients who had undergone endoscopic ma
Externí odkaz:
https://doaj.org/article/edb0342392fb438b8c253c7d3999360a
Structured generation, the process of producing content in standardized formats like JSON and XML, is widely utilized in real-world applications to extract key output information from large language models (LLMs). This study investigates whether such
Externí odkaz:
http://arxiv.org/abs/2408.02442
This study explores the proactive ability of LLMs to seek user support. We propose metrics to evaluate the trade-off between performance improvements and user burden, and investigate whether LLMs can determine when to request help under varying infor
Externí odkaz:
http://arxiv.org/abs/2407.14767
Autor:
Tseng, Liang-Hsuan, Chen, Zih-Ching, Chang, Wei-Shun, Lee, Cheng-Kuang, Huang, Tsung-Ren, Lee, Hung-yi
Recent advances in automatic speech recognition (ASR) often rely on large speech foundation models for generating high-quality transcriptions. However, these models can be impractical due to limited computing resources. The situation is even more sev
Externí odkaz:
http://arxiv.org/abs/2407.10603
Recent efforts in Spoken Dialogue Modeling aim to synthesize spoken dialogue without the need for direct transcription, thereby preserving the wealth of non-textual information inherent in speech. However, this approach faces a challenge when speaker
Externí odkaz:
http://arxiv.org/abs/2407.01911
Recent works have shown that large language model (LLM) agents are able to improve themselves from experience, which is an important ability for continuous enhancement post-deployment. However, existing benchmarks primarily evaluate their innate capa
Externí odkaz:
http://arxiv.org/abs/2406.08747
In this paper, we investigate the phenomena of "selection biases" in Large Language Models (LLMs), focusing on problems where models are tasked with choosing the optimal option from an ordered sequence. We delve into biases related to option order an
Externí odkaz:
http://arxiv.org/abs/2406.03009
Existing Maximum-Entropy (MaxEnt) Reinforcement Learning (RL) methods for continuous action spaces are typically formulated based on actor-critic frameworks and optimized through alternating steps of policy evaluation and policy improvement. In the p
Externí odkaz:
http://arxiv.org/abs/2405.13629