Liwei Zhang

AAAI Conference 2025 Conference Paper

4D Diffusion for Dynamic Protein Structure Prediction with Reference and Motion Guidance

• Kaihui Cheng
• Ce Liu
• Qingkun Su
• Jun Wang
• Liwei Zhang
• Yining Tang
• Yao Yao
• Siyu Zhu

Protein structure prediction is pivotal for understanding the structure-function relationship of proteins, advancing biological research, and facilitating pharmaceutical development and experimental design. While deep learning methods and the expanded availability of experimental 3D protein structures have accelerated structure prediction, the dynamic nature of protein structures has received limited attention. This study introduces an innovative 4D diffusion model incorporating molecular dynamics (MD) simulation data to learn dynamic protein structures. Our approach is distinguished by the following components: (1) a unified diffusion model capable of generating dynamic protein structures, including both the backbone and side chains, utilizing atomic grouping and side-chain dihedral angle predictions; (2) a reference network that enhances structural consistency by integrating the latent embeddings of the initial 3D protein structures; and (3) a motion alignment module aimed at improving temporal structural coherence across multiple time steps. To our knowledge, this is the first diffusion-based model aimed at predicting protein trajectories across multiple time steps simultaneously. Validation on benchmark datasets demonstrates that our model exhibits high accuracy in predicting dynamic 3D structures of proteins containing up to 256 amino acids over 32 time steps, effectively capturing both local flexibility in stable states and significant conformational changes.

JMLR Journal 2023 Journal Article

Statistical Robustness of Empirical Risks in Machine Learning

• Shaoyan Guo
• Huifu Xu
• Liwei Zhang

This paper studies convergence of empirical risks in reproducing kernel Hilbert spaces (RKHS). A conventional assumption in the existing research is that empirical training data are generated by the unknown true probability distribution but this may not be satisfied in some practical circumstances. Consequently the existing convergence results may not provide a guarantee as to whether the empirical risks are reliable or not when the data are potentially corrupted (generated by a distribution perturbed from the true). In this paper, we fill out the gap from robust statistics perspective (Krätschmer, Schied and Zähle (2012); Krätschmer, Schied and Zähle (2014); Guo and Xu (2020). First, we derive moderate sufficient conditions under which the expected risk changes stably (continuously) against small perturbation of the probability distributions of the underlying random variables and demonstrate how the cost function and kernel affect the stability. Second, we examine the difference between laws of the statistical estimators of the expected optimal loss based on pure data and contaminated data using Prokhorov metric and Kantorovich metric, and derive some asymptotic qualitative and non-asymptotic quantitative statistical robustness results. Third, we identify appropriate metrics under which the statistical estimators are uniformly asymptotically consistent. These results provide theoretical grounding for analysing asymptotic convergence and examining reliability of the statistical estimators in a number of regression models. [abs] [ pdf ][ bib ] &copy JMLR 2023. ( edit, beta )

YNIMG Journal 2019 Journal Article

Enhanced high-frequency precuneus-cortical effective connectivity is associated with decreased sensory gating following total sleep deprivation

• Liwei Zhang
• Yue Huang
• Ying Zhang
• Wei Xin
• Yongcong Shao
• Yebing Yang

IROS Conference 2016 Conference Paper

Vision-based real-time 3D mapping for UAV with laser sensor

• Jinqiao Shi
• Bingwei He
• Liwei Zhang
• Jianwei Zhang 0001

Real-time 3D mapping with MAV (Micro Aerial Vehicle) in GPS-denied environment is a challenging problem. In this paper, we present an effective vision-based 3D mapping system with 2D laser-scanner. All algorithms necessary for this system are on-board. In this system, two cameras work together with the laser-scanner for motion estimation. The distance of the points detected by laser-scanner are transformed and treated as the depth of image features, which improves the robustness and accuracy of the pose estimation. The output of visual odometry is used as an initial pose in the Iterative Closest Point (ICP) algorithm and the motion trajectory is optimized by the registration result. We finally get the MAV's state by fusing IMU with the pose estimation from mapping process. This method maximizes the utility of the point clouds information and overcomes the scale problem of lacking depth information in the monocular visual odometry. The results of the experiments prove that this method has good characteristics in real-time and accuracy.

IROS Conference 2015 Conference Paper

A novel optical tracking based tele-control system for tabletop object manipulation tasks

• Haiyang Jin
• Liwei Zhang
• Sebastian Rockel
• Jun Zhang
• Ying Hu 0001
• Jianwei Zhang 0001

For a robot serving in a complex environment such as in a restaurant, it is difficult to perform a task like tabletop object manipulation completely by itself, in that some information may be missing. An approach to deal with this is to use a tele-control system and method to control the robot or demonstrate. In this paper, a LeapMotion sensor based non-contact tele-control method is developed for a robot to perform tabletop object manipulation tasks. A coordinate system for mapping from the operation space of the LeapMotion sensor to the workspace of the robot is established. A gesture recognition and action generating algorithm is proposed for control or to demonstrate the motion to the robot. To evaluate the performance of the LeapMotion sensor and proposed method for tele-control of a robot, a comprehensive assessment index based on entropy weighting is proposed. Three common tele-control modes, including demonstration mode, teleoperation mode and semi-teleoperation mode, are developed on a PR2 robot. The experimental results show that the proposed tele-control system is more appropriate for use in task demonstration.

ICRA Conference 2014 Conference Paper

An hyperreality imagination based reasoning and evaluation system (HIRES)

• Sebastian Rockel
• Denis Klimentjew
• Liwei Zhang
• Jianwei Zhang 0001

In this work we ask whether an integrated system based on the concept of human imagination and realized as a hyperreal setup can improve system robustness and autonomy. In particular we focus on how non-nominal failures in a planning-based system can be detected before actual failure. To investigate, we integrated a system combining an accurate physics-based simulation, robust object recognition and a symbolic planner to achieve realistic prediction of robot actions. A Gazebo simulation was used to reason about and evaluate situations before and during plan execution. The simulation enabled re-planning to take place in advance of actual plan failure. We present a restaurant scenario in which our system prevents plan failure and successfully lets the robot serve a drink on a table cluttered with objects. The results give us confidence in our approach to improving situations where unavoidable abstractions of robot action planning meet the real world.