Rui Liu

Interpretable machine learning and mendelian randomization identify risk factors for lower extremity arterial embolism and thrombosis

X J Li, Guohao Wei, Rui Liu, Qiulin Jiang, Yarong Ma, Xiaolei Sun

Background Lower extremity arterial embolism and thrombosis lead to significant morbidity, but their risk factors are not fully characterized. Objectives To identify risk factors and develop an interpretable machine learning (ML) model for predicting lower extremity arterial embo…Read more

AuEmoChat: Authentic Emotion Understanding and Rendering for Conversational Speech Synthesis

Zhenqi Jia, Yuan Zhao, Aruukhan, Rui Liu, Haizhou Li

Conversational Speech Synthesis (CSS) aims to synthesize speech with human-like emotional expression and contextual consistency in user-agent interactions. Existing CSS methods struggle to render authentic human emotions due to limited predefined emotion label spaces (e.g., seven…Read more

Boogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget

Guoxuan Chen, Chufeng Xiao, Haoran Yang, Siyue Xie, Binxiao Huang, Ming Zhang, et al.

We introduce Boogu-Image-0.1, an open-source unified multimodal understanding and generation model family, comprising Base, Turbo, Edit, and Edit-Turbo variants. It delivers competitive performance in high-quality text-to-image generation, fast inference, instruction-based editin…Read more

Plug-and-Play Reweighting for Resilient Collaborative Decision-Making in Connected Autonomous Driving

Jiewen Liu, Rui Liu, Matthew Lee, Ming C. Lin, Xiaorui Liu, Peng Gao

Collaborative decision-making is a fundamental capability in multi-robot systems, such as connected autonomous vehicles. However, perceptual noise and adversarial attacks in collaborators can severely affect decision reliability. Overall, existing methods typically rely on retrai…Read more

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments

Deyao Zhu, Xin Zhou, Shengling Qin, Xuekai Zhu, Hangliang Ding, Shu Zhong, et al.

Pretraining scaling laws reveal that model capability improves predictably with data and compute. But learning from real world environments after deployment remains far less understood. Analyzing roughly 38,000 hours of agent interaction with the environment across 134 real world…Read more

GuideMe: Multi-Domain Task Guidance and Intervention in Streaming Video

Fang Liu, Jinpeng Chen, Ke Xu, Yuhao Liu, Huankang Guan, Xudong Lu, et al.

While multimodal Large Language Models (MLLMs) excel at offline video understanding, an interesting question of how far they are from serving as a real-time procedural coach remains unknown. Such a role typically requires an MLLM to continuously monitor the execution, detect mist…Read more

LISA: Likelihood Score Alignment for Visual-condition Controllable Generation

Yanghao Wang, Hongxu Chen, Jiazhen Liu, Zhenqi He, Rui Liu, Zhen Wang, et al.

The prevalent dual-branch paradigm, i.e., training a side network to encode visual conditions and fusing its intermediate-layer features to a frozen pretrained main network, has shown remarkable success in visual-condition controllable generation. Despite its widespread adoption,…Read more

Learning to Twin Wireless Channels: Conditional Synthesis for UAV-to-Vehicle Communication

Rui Liu, Lei Xiong, Dan Fei, Cheng Li, Liang Shen, Xiangbing Tan

Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping

Rui Liu, Xin Yang, Chong Xu, Liangshuai Wei, Xiangqiang Zeng

Landslide susceptibility mapping (LSM) is a useful tool to estimate the probability of landslide occurrence, providing a scientific basis for natural hazards prevention, land use planning, and economic development in landslide-prone areas. To date, a large number of machine learn…Read more