Zhe Li

Machine learning quantifies dew formation mechanisms across China’s climate gradients

Zhe Li, Lin Zhao, Hanbo Ren, Ranmao Yang, Xinxin Li, Zhijiang Zhang, et al.

Integrating surface and borehole sensing for accurate microseismic localization: A velocity-model-free deep learning approach

Rui Xu, Kai Zhan, Jiajun Chen, Zhe Li

Systematic Position Mapping of Split CRISPR‐Cas12a Activators Enables Highly Sensitive miRNA Detection and Cancer Cell Stratification

Xiaoyan Tang, Zhe Li, Yuning Lu, Ma My, Zijian Mo, Jiajun Ke, et al.

Amplification-free Cas12a diagnostics with split crRNA enable rapid and programmable target recognition, yet insufficient understanding of DNA activator architecture prevents predictable control over trans-cleavage activity and sensitivity. Here we systematically map over 200 spl…Read more

EmoEUS: Uncertainty Supervision for Multimodal Emotion Recognition in Conversation

Zilong Huang, Kong Aik Lee, Junjie Li, Zhe Li, Man-Wai Mak

Multimodal emotion recognition in conversation (MERC) can leverage multimodal and contextual cues to boost recognition performance. However, existing fusion approaches in MERC often ignore modality-specific uncertainty across utterances caused by conflicting cues, varying noise,…Read more

Open-AoE: An Open Egocentric Manipulation Dataset and Toolchain for Embodied Learning

Zishuo Li, Bowen Yang, Changtao Miao, Kai Zhu, Hao Chen, Qingze Guan, et al.

Egocentric videos of human manipulation provide scalable supervision for embodied intelligence, yet existing resources rarely combine low-cost continuous capture, manipulation-level structured annotations, and reusable tools for robot learning. We present Open-AoE, an open, commu…Read more

DeSeG: Decoupling Semantic Intent and Geometric Constraints for Physically Plausible Human-Scene Interaction

Jiakun Li, Zhe Li, Wenqiang Wu, Zheng Chang, Mingqi Gao, Jinyu Yang, et al.

Synthesizing physically plausible human-scene interactions (HSI) remains a critical challenge in computer vision and the development of human avatars. Although recent generative models enable diverse motion synthesis, they suffer from an inductive bias referred to as semantic-geo…Read more

DT-Guard: Intent-Driven Reasoning-Active Training for Reasoning-Free LLM Safety Guardrail

He Liu, Changtao Miao, Xinjie Yang, Tianle Song, Yin Wu, Junchi Chen, et al.

Large language models deployed in open-world applications require safety guardrails that are both robust to complex risks and efficient enough for low-latency runtime moderation. Existing guardrails face a practical trade-off between lightweight classification-based models, which…Read more