Author name cluster
Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.
EAAI Journal 2026 Journal Article
AAAI Conference 2026 Conference Paper
Understanding how chemical perturbations propagate through biological systems is essential for robust molecular property prediction. While most existing methods focus on chemical structures alone, recent advances highlight the crucial role of cellular responses such as morphology and gene expression in shaping drug effects. However, current cell-aware approaches face two key limitations: (1) modality incompleteness in external biological data, and (2) insufficient modeling of hierarchical dependencies across molecular, cellular, and genomic levels. We propose CHMR (Cell-aware Hierarchical Multi-Modal Representations), a robust framework that jointly models local-global dependencies between molecules and cellular responses and captures latent biological hierarchies via a novel tree-structured vector quantization module. Evaluated on public benchmarks spanning 696 tasks, CHMR outperforms state-of-the-art baselines, yielding average improvements of 3.6% on classification and 17.2% on regression tasks. These results demonstrate the advantage of hierarchy-aware, multi-modal learning for reliable and biologically grounded molecular representations, offering a generalizable framework for integrative biomedical modeling.
EAAI Journal 2026 Journal Article
Text data are difficult for computers to process directly due to their unstructured nature. In traditional text information extraction systems, predefined structural templates are typically used to extract structured data, such as event schema or attribute-entity pairs. However, such template-based designs limit the adaptability of models when dealing with unseen domains and weaken their ability to capture the complex semantics of natural language. In this study, we propose a deep learning-driven schema-free structured information extraction paradigm to eliminate the dependence on manually designed templates. Unlike traditional slot-filling methods, our framework first extracts entities appearing in unstructured text, and then employs a deep neural text generation model to dynamically infer their semantic roles or structured attributes based on contextual semantics. This paradigm provides a unified and data-driven approach to representing unstructured text in a structured form, and can be effectively applied to complex event extraction tasks. Comprehensive experiments conducted on two benchmark datasets demonstrate that the proposed deep learning-based method not only achieves higher accuracy than the baseline models, but also significantly alleviates information omission and semantic fragmentation, exhibiting strong generalisation and robustness.
IROS Conference 2006 Conference Paper
In this paper the vehicle structure, performance parameters and dynamic model of JLUIV5-CyberCar are discussed. An optimal controller is designed to achieve the line path tracking ability, while the curve path tracking ability is guaranteed by a fuzzy controller. To improve the tracking performance of the vehicle steering system in different conditions, controller output collections of JLUIV5-CyberCar are established combined with variable structure control theory. Design the state observer based on Kalman filter theory to estimate the lateral velocity and yaw rate. Simulations and experimental results have confirmed the effectiveness of the proposed method.