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Westlake University

Academic institutionasia · cn
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Research library716linked papers
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Selected work

Representative Papers

Enhancing efficiency and propulsion in bio-mimetic robotic fish through end-to-end deep reinforcement learning

Mar 01, 2024The Physics of Fluids

Bionic robotic fish suffer from low propulsion efficiency and high energy consumption. Method: This study proposes an end-to-end deep reinforcement learning (DRL) control framework, introducing— for the first time in underwater bionic robotics—extended pressure sensing combined with temporal Transformer modeling, integrated with a policy transfer mechanism to enhance training stability and environmental adaptability. Training achieves autonomous, stable, and rapid convergence within CFD simulations (Re = 6000). Contribution/Results: The DRL policy improves propulsion efficiency by 37% and reduces specific energy consumption per unit thrust by 29% over conventional pre-programmed gaits. Flow-field analysis reveals that efficiency stems from embodied regulation of body deformation and vortex–body interactions. The core contribution is a novel bio-inspired locomotion control paradigm unifying perception, spatiotemporal modeling, and decision-making.

9 citationsRead paper

Federated modality-specific encoders and partially personalized fusion decoder for multimodal brain tumor segmentation

Aug 18, 2025Medical Image Anal.

This work addresses the challenge of simultaneously handling missing modalities and personalization demands in federated multi-modal medical image segmentation. To this end, the authors propose FedMEPD, a novel framework that assigns a dedicated encoder to each modality to accommodate heterogeneous modality availability across clients, and introduces a partially personalized fusion decoder. This decoder leverages global multi-modal representation anchors and cross-attention mechanisms to effectively compensate for missing modality information. As the first approach to jointly tackle modality heterogeneity and personalization under the federated learning paradigm, FedMEPD demonstrates significant performance gains over existing methods on the BraTS 2018 and 2020 datasets, validating its effectiveness and superiority in personalized federated multi-modal learning.

4 citationsRead paper

Dynamics-inspired Structure Hallucination for Protein-protein Interaction Modeling

Jan 08, 2026Trans. Mach. Learn. Res.

Accurately predicting the effects of mutations on protein–protein interactions (PPIs) remains challenging due to the absence of mutant structures and inadequate modeling of dynamic interaction mechanisms. To address these limitations, this work proposes Refine-PPI, a novel framework that first generates high-quality mutant protein structures using a structure refinement module guided by a Masked Mutation Modeling (MMM)-driven structural hallucination strategy. Subsequently, it introduces a Probability Density Cloud Network (PDC-Net), which leverages geometric deep learning to model the three-dimensional dynamics of PPIs and atomic-level uncertainty. Evaluated on the SKEMPI v2 benchmark, Refine-PPI significantly outperforms existing methods in predicting binding free energy changes, demonstrating the effectiveness and innovation of integrating structural hallucination with dynamic uncertainty modeling.

2 citations1 influentialRead paper

MToP: A MATLAB Optimization Platform for Evolutionary Multitasking

Dec 13, 2023arXiv.org

The multi-task optimization (MTO) community lacks a unified, open-source evaluation platform, hindering reproducibility, fair benchmarking, and practical validation of evolutionary multitasking (EMT) algorithms. Method: This paper introduces EMT-Platform—the first open-source MATLAB platform dedicated to EMT research—featuring a modular architecture, plugin-based algorithm interfaces, a graphical user interface, and built-in knowledge transfer mechanisms. It integrates over 50 multitasking algorithms (including systematically adapted single-task baselines), 200+ standardized benchmark problem instances, and 20+ performance metrics. Contribution/Results: EMT-Platform enables standardized algorithm development, rigorous cross-algorithm evaluation, and intuitive result visualization. It significantly enhances reproducibility, accelerates empirical research, and supports real-world application validation across diverse domains. Widely adopted by the EMT research community, it serves as a foundational infrastructure for advancing both theoretical and applied multitasking optimization.

2 citations1 influentialRead paper

AutoFigure: Generating and Refining Publication-Ready Scientific Illustrations

Feb 03, 2026

This work addresses the time-consuming and labor-intensive process of manually creating high-quality scientific illustrations, a common bottleneck in both academic research and industry. We propose AutoFigure, the first agent-based framework capable of automatically generating and optimizing publication-ready scientific figures from long-form scientific text. Employing an end-to-end architecture, AutoFigure is the first to produce structurally coherent and aesthetically refined illustrations directly from extended textual inputs, enhanced by integrated mechanisms for reasoning, reorganization, and validation to ensure output quality. To support evaluation and future research, we introduce FigureBench, a benchmark dataset comprising 3,300 figure–text pairs. Experimental results demonstrate that AutoFigure significantly outperforms existing baselines, consistently generating figures that meet publication standards. The code, dataset, and demonstration platform are publicly released.

1 citationsRead paper
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