Author name cluster

Jun Liao

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8 papers

2 author rows

AAAI Conference 2026 Conference Paper

Cross-Field Interface-Aware Neural Operators for Multiphase Flow Simulation

Zhenzhong Wang
Xin Zhang
Jun Liao
Min Jiang

Multiphase flow simulation is critical in science and engineering but incurs high computational costs due to complex field discontinuities and the need for high-resolution numerical meshes. While Neural Operators (NOs) offer an efficient alternative for solving Partial Differential Equations (PDEs), they struggle with two core challenges unique to multiphase systems: spectral bias caused by spatial heterogeneity at phase interfaces, and the persistent scarcity of expensive, high-resolution field data. This work introduces the Interface Information Aware Neural Operator (IANO), a novel architecture that mitigates these issues by leveraging readily obtainable interface data (e.g., topology and position). Interface data inherently contains the high-frequency features not only necessary to complement the physical field data, but also help with spectral bias. IANO incorporates an interface-aware function encoding mechanism to capture dynamic coupling, and a geometry-aware positional encoding method to enhance spatial fidelity for pointwise super-resolution. Empirical results across multiple multiphase flow cases demonstrate that IANO achieves significant accuracy improvements (up to ~10%) over existing NO baselines. Furthermore, IANO exhibits superior generalization capabilities in low-data and noisy settings, confirming its utility for practical, data-efficient AI-based multiphase flow simulations.

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AAAI Conference 2025 Conference Paper

Decomposing and Fusing Intra- and Inter-Sensor Spatio-Temporal Signal for Multi-Sensor Wearable Human Activity Recognition

Haoyu Xie
Haoxuan Li
Chunyuan Zheng
Haonan Yuan
Guorui Liao
Jun Liao
Li Liu

Wearable Human Activity Recognition (WHAR) is a prominent research area within ubiquitous computing. Multi-sensor synchronous measurement has proven to be more effective for WHAR than using a single sensor. However, existing WHAR methods use shared convolutional kernels for indiscriminate temporal feature extraction across each sensor variable, which fails to effectively capture spatio-temporal relationships of intra-sensor and inter-sensor variables. We propose the DecomposeWHAR model consisting of a decomposition phase and a fusion phase to better model the relationships between modality variables. The decomposition creates high-dimensional representations of each intra-sensor variable through the improved Depth Separable Convolution to capture local temporal features while preserving their unique characteristics. The fusion phase begins by capturing relationships between intra-sensor variables and fusing their features at both the channel and variable levels. Long-range temporal dependencies are modeled using the State Space Model (SSM), and later cross-sensor interactions are dynamically captured through a self-attention mechanism, highlighting inter-sensor spatial correlations. Our model demonstrates superior performance on three widely used WHAR datasets, significantly outperforming state-of-the-art models while maintaining acceptable computational efficiency.

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AAAI Conference 2025 Conference Paper

HiPoser: 3D Human Pose Estimation with Hierarchical Shared Learning at Parts-Level Using Inertial Measurement Units

Guorui Liao
Chunyuan Zheng
Li Cheng
Haoyu Xie
Shanshan Huang
Jun Liao
Haoxuan Li
Li Liu

This paper considers the challenging problem of 3D Human Pose Estimation (HPE) from a sparse set of Inertial Measurement Units (IMUs). Existing efforts typically reconstruct a pose sequence by either directly tackling whole-body motions or focusing on distinctive spatio-temporal features of local body parts. Unfortunately, these methods ignore existing interdependent motor synergies amongst body parts, which may lead to pose estimation with ambiguous local parts. This observation motivates us to propose a hierarchical learning-based approach, HiPoser, which utilizes a hierarchical shared structure using Mamba blocks as the backbone to focus on the following estimation tasks, involving: 1) torso pose, 2) lower limbs pose, 3) upper limbs pose, and finally 4) global translation. These tasks selectively incorporate body motion states and are to be carried out sequentially in reconstructing part-based poses, which are amalgamated to estimate the final full-body pose with the global translation that satisfies inter-part consistencies. Our hierarchical structure allows HiPoser the flexibility in prioritizing different aspects of pose estimation, to emphasize more on detail or stability. Empirical evaluations over three benchmark datasets demonstrate the superiority of HiPoser over existing state-of-the-art models, suggesting that analyzing the synergistic movement of body parts is indeed important for advancing IMU-based 3D HPE.

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EAAI Journal 2024 Journal Article

A survey of causal discovery based on functional causal model

Lei Wang
Shanshan Huang
Shu Wang
Jun Liao
Tingpeng Li
Li Liu

Causal discovery finds widespread applications, ranging from estimating treatment effectiveness in medicine, analyzing policy impacts in economics, to constructing predictive models in machine learning—all of which rely on the study and discovery of causal relationships. In recent years, as causal learning has progressed, causal discovery has been classified into different categories depending on assumptions and learning strategies. In this paper, we undertake an exploration of causal discovery methods based on the functional causal model (FCM). We commence by introducing essential terminology associated with causal discovery and laying out the foundational assumptions underpinning FCM-based methods. Following this, we conduct a comprehensive exploration of classical FCM algorithms that have gained prominence in recent years. Furthermore, we scrutinize the performance of these FCM methods across a selection of benchmark datasets. Finally, we deliberate on unresolved issues within this category of methodologies and outline potential avenues for future research.

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AAAI Conference 2023 Conference Paper

Preserving Structural Consistency in Arbitrary Artist and Artwork Style Transfer

Jingyu Wu
Lefan Hou
Zejian Li
Jun Liao
Li Liu
Lingyun Sun

Deep generative models are effective in style transfer. Previous methods learn one or several specific artist-style from a collection of artworks. These methods not only homogenize the artist-style of different artworks of the same artist but also lack generalization for the unseen artists. To solve these challenges, we propose a double-style transferring module (DSTM). It extracts different artist-style and artwork-style from different artworks (even untrained) and preserves the intrinsic diversity between different artworks of the same artist. DSTM swaps the two styles in the adversarial training and encourages realistic image generation given arbitrary style combinations. However, learning style from single artwork can often cause over-adaption to it, resulting in the introduction of structural features of style image. We further propose an edge enhancing module (EEM) which derives edge information from multi-scale and multi-level features to enhance structural consistency. We broadly evaluate our method across six large-scale benchmark datasets. Empirical results show that our method achieves arbitrary artist-style and artwork-style extraction from a single artwork, and effectively avoids introducing the style image’s structural features. Our method improves the state-of-the-art deception rate from 58.9% to 67.2% and the average FID from 48.74 to 42.83.

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ICML Conference 2006 Conference Paper

Totally corrective boosting algorithms that maximize the margin

Manfred K. Warmuth
Jun Liao
Gunnar Rätsch

We consider boosting algorithms that maintain a distribution over a set of examples. At each iteration a weak hypothesis is received and the distribution is updated. We motivate these updates as minimizing the relative entropy subject to linear constraints. For example AdaBoost constrains the edge of the last hypothesis w.r.t. the updated distribution to be at most γ = 0. In some sense, AdaBoost is "corrective" w.r.t. the last hypothesis. A cleaner boosting method is to be "totally corrective": the edges of all past hypotheses are constrained to be at most γ, where γ is suitably adapted.Using new techniques, we prove the same iteration bounds for the totally corrective algorithms as for their corrective versions. Moreover with adaptive γ, the algorithms provably maximizes the margin. Experimentally, the totally corrective versions return smaller convex combinations of weak hypotheses than the corrective ones and are competitive with LPBoost, a totally corrective boosting algorithm with no regularization, for which there is no iteration bound known.

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NeurIPS Conference 2001 Conference Paper

Active Learning in the Drug Discovery Process

Manfred K. Warmuth
Gunnar Rätsch
Michael Mathieson
Jun Liao
Christian Lemmen

We investigate the following data mining problem from Computational Chemistry: From a large data set of compounds, ﬁnd those that bind to a target molecule in as few iterations of biological testing as possible. In each iteration a comparatively small batch of compounds is screened for binding to the target. We apply active learning techniques for selecting the successive batches. One selection strategy picks unlabeled examples closest to the maximum margin hyperplane. Another produces many weight vectors by running perceptrons over multiple permutations of the data. Each weight vector prediction and we pick the unlabeled examples for which votes with its the prediction is most evenly split between. For a third selec- tion strategy note that each unlabeled example bisects the version space of consistent weight vectors. We estimate the volume on both sides of the split by bouncing a billiard through the version space and select un- labeled examples that cause the most even split of the version space. We demonstrate that on two data sets provided by DuPont Pharmaceu- ticals that all three selection strategies perform comparably well and are much better than selecting random batches for testing. and

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EAAI Journal 2000 Journal Article

Application of a system for the automatic generation of fuzzy neural networks

Jun Liao
Meng Joo Er
Jianya Lin

To facilitate the transfer of technology emerging from theoretical research of fuzzy neural networks into industrial applications, a fuzzy neural networks system for the automatic generation (FNNAGS) is proposed in this paper. In FNNAGS, the fuzzy model constructed by the system can be expressed as either a Mamdani model or a Takagi–Sugeno model, according to the preference of the user. Off-line design and on-line applications are incorporated into an interactive software system. In the stage of off-line design, only the training data need to be provided in order to construct a process model. Users do not need to give the initial fuzzy partitions, membership functions or fuzzy logic rules. These initial parameters will be set up automatically by the FNNAGS, in accordance with the properties of the training data. After off-line design has been completed, the model can be expressed as a fuzzy rule base, which can be used to control, estimate, identify or predict a process or plant through an application interface between FNNAGS and the external world.

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