arXiv subjects

Zhijie Zhou

Publications and source records attributed to Zhijie Zhou.

4 recordsLinked to original sources

arXiv2025

The origin of double-peaked narrow emission-line galaxies in MaNGA Survey

We select 36 double-peaked narrow emission-line galaxies (DPGs) from 10,010 unique galaxies in MaNGA survey. These DPGs show double-peaked Balmer lines and forbidden lines in the spectra. We use a double Gaussian model to separate the double-peaked profiles of each emission line into blue and red components ($λ_\text{blue}$ < $λ_\text{red}$), and analyze the spatially resolved kinematics and ionization mechanisms of each component. We find that in 35 out of 36 DPGs, the flux ratio between the blue and red components varies systematically along the major axes, while it keeps roughly a constant along the minor axes. The blue and red components of these DPGs exhibit similar distributions in both the value of line-of-sight velocity and the velocity dispersion. Additionally, 83.3% DPGs have both blue and red components located in the same ionization region in the [SII]-BPT diagram. Combining all these observational results, we suggest that the double-peaked emission line profiles in these 35 DPGs primarily originate from rotating discs. The remaining one galaxy shows clear outflow features. 8 out of 35 DPGs show symmetric line profiles that indicate undisturbed rotating discs, and the other 27 DPGs exhibit asymmetric profiles, suggesting dynamic disturbances in the rotating discs. Furthermore, we find that 58.3% DPGs experienced external processes, characterized by tidal features, companion galaxies, as well as gas-star misalignments. This fraction is about twice as much as that of the control sample, suggesting the origin of double-peaked emission line profiles is associated with external processes.

astro-ph.GA↗

arXiv2025

MedEthicsQA: A Comprehensive Question Answering Benchmark for Medical Ethics Evaluation of LLMs

While Medical Large Language Models (MedLLMs) have demonstrated remarkable potential in clinical tasks, their ethical safety remains insufficiently explored. This paper introduces $\textbf{MedEthicsQA}$, a comprehensive benchmark comprising $\textbf{5,623}$ multiple-choice questions and $\textbf{5,351}$ open-ended questions for evaluation of medical ethics in LLMs. We systematically establish a hierarchical taxonomy integrating global medical ethical standards. The benchmark encompasses widely used medical datasets, authoritative question banks, and scenarios derived from PubMed literature. Rigorous quality control involving multi-stage filtering and multi-faceted expert validation ensures the reliability of the dataset with a low error rate ($2.72\%$). Evaluation of state-of-the-art MedLLMs exhibit declined performance in answering medical ethics questions compared to their foundation counterparts, elucidating the deficiencies of medical ethics alignment. The dataset, registered under CC BY-NC 4.0 license, is available at https://github.com/JianhuiWei7/MedEthicsQA.

cs.CL↗

arXiv2025

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning

Medical Question-Answering (QA) encompasses a broad spectrum of tasks, including multiple choice questions (MCQ), open-ended text generation, and complex computational reasoning. Despite this variety, a unified framework for delivering high-quality medical QA has yet to emerge. Although recent progress in reasoning-augmented large language models (LLMs) has shown promise, their ability to achieve comprehensive medical understanding is still largely unexplored. In this paper, we present Med-U1, a unified framework for robust reasoning across medical QA tasks with diverse output formats, ranging from MCQs to complex generation and computation tasks. Med-U1 employs pure large-scale reinforcement learning with mixed rule-based binary reward functions, incorporating a length penalty to manage output verbosity. With multi-objective reward optimization, Med-U1 directs LLMs to produce concise and verifiable reasoning chains. Empirical results reveal that Med-U1 significantly improves performance across multiple challenging Med-QA benchmarks, surpassing even larger specialized and proprietary models. Furthermore, Med-U1 demonstrates robust generalization to out-of-distribution (OOD) tasks. Extensive analysis presents insights into training strategies, reasoning chain length control, and reward design for medical LLMs. Our code is available here.

cs.CL↗

arXiv2024

CA-BERT: Leveraging Context Awareness for Enhanced Multi-Turn Chat Interaction

Effective communication in automated chat systems hinges on the ability to understand and respond to context. Traditional models often struggle with determining when additional context is necessary for generating appropriate responses. This paper introduces Context-Aware BERT (CA-BERT), a transformer-based model specifically fine-tuned to address this challenge. CA-BERT innovatively applies deep learning techniques to discern context necessity in multi-turn chat interactions, enhancing both the relevance and accuracy of responses. We describe the development of CA-BERT, which adapts the robust architecture of BERT with a novel training regimen focused on a specialized dataset of chat dialogues. The model is evaluated on its ability to classify context necessity, demonstrating superior performance over baseline BERT models in terms of accuracy and efficiency. Furthermore, CA-BERT's implementation showcases significant reductions in training time and resource usage, making it feasible for real-time applications. The results indicate that CA-BERT can effectively enhance the functionality of chatbots by providing a nuanced understanding of context, thereby improving user experience and interaction quality in automated systems. This study not only advances the field of NLP in chat applications but also provides a framework for future research into context-sensitive AI developments.

cs.CL↗