Guoxing Yu

Guangya Yu, Yanhao Li, Zongying Jiang et al.

Medical quality control indicators are essential to assess the qualifications of healthcare institutions for medical services. With the impressive performance of large language models (LLMs) like GPT-4 in the medical field, leveraging these technologies for the Medical Quality Control Indicator Calculation (MQCIC) presents a promising approach. In this work, (1) we introduce a real-world task MQCIC and propose an open-source Chinese electronic medical records (EMRs)-based dataset (CMQCIC-Bench) comprising 785 instances and 76 indicators. (2) We propose a semi-automatic method to enhance the rule representation. Then we propose the Clinical Facts-based Inferential Rule (CF-IR) method that disentangles the clinical fact verification and inferential rule reasoning actions. (3) We conduct comprehensive experiments on 20 representative LLMs, covering general and medical models. Our findings reveal that CF-IR outperforms Chain-of-Thought methods in MQCIC tasks. (4) We conduct an error analysis and investigate the capabilities of clinical fact verification and inferential rule reasoning, providing insights to improve performance in the MQCIC further. The dataset and code is available in this repository https://github.com/YuY-2001/C-MQCIC.

Qi Wang, Shituo Ma, Guoxin Yu et al.

Voice cloning for Text-to-Speech (TTS) aims to generate expressive and personalized speech from text using limited data from a target speaker. Federated Learning (FL) offers a collaborative and privacy-preserving framework for this task, but existing approaches suffer from high communication costs and tend to suppress stylistic heterogeneity, resulting in insufficient personalization. To address these issues, we propose Fed-PISA, which stands for Federated Personalized Identity-Style Adaptation. To minimize communication costs, Fed-PISA introduces a disentangled Low-Rank Adaptation (LoRA) mechanism: the speaker's timbre is retained locally through a private ID-LoRA, while only a lightweight style-LoRA is transmitted to the server, thereby minimizing parameter exchange. To harness heterogeneity, our aggregation method, inspired by collaborative filtering, is introduced to create custom models for each client by learning from stylistically similar peers. Experiments show that Fed-PISA improves style expressivity, naturalness, and speaker similarity, outperforming standard federated baselines with minimal communication costs.

Zhuming Zhang, Yongtao Hu, Guoxing Yu et al.

A fiducial marker system usually consists of markers, a detection algorithm, and a coding system. The appearance of markers and the detection robustness are generally limited by the existing detection algorithms, which are hand-crafted with traditional low-level image processing techniques. Furthermore, a sophisticatedly designed coding system is required to overcome the shortcomings of both markers and detection algorithms. To improve the flexibility and robustness in various applications, we propose a general deep learning based framework, DeepTag, for fiducial marker design and detection. DeepTag not only supports detection of a wide variety of existing marker families, but also makes it possible to design new marker families with customized local patterns. Moreover, we propose an effective procedure to synthesize training data on the fly without manual annotations. Thus, DeepTag can easily adapt to existing and newly-designed marker families. To validate DeepTag and existing methods, beside existing datasets, we further collect a new large and challenging dataset where markers are placed in different view distances and angles. Experiments show that DeepTag well supports different marker families and greatly outperforms the existing methods in terms of both detection robustness and pose accuracy. Both code and dataset are available at https://herohuyongtao.github.io/research/publications/deep-tag/.