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Yuankai Wu

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13 papers

2 author rows

AAAI Conference 2026 Conference Paper

Revitalizing Canonical Pre-Alignment for Irregular Multivariate Time Series Forecasting

Ziyu Zhou
Yiming Huang
Yanyun Wang
Yuankai Wu
James Kwok
Yuxuan Liang

Irregular multivariate time series (IMTS), characterized by uneven sampling and inter-variate asynchrony, fuel many forecasting applications yet remain challenging to model efficiently. Canonical Pre-Alignment (CPA) has been widely adopted in IMTS modeling by padding zeros at every global timestamp, thereby alleviating inter-variate asynchrony and unifying the series length, but its dense zero-padding inflates the pre-aligned series length, especially when numerous variates are present, causing prohibitive compute overhead. Recent graph-based models with patching strategies sidestep CPA, but their local message passing struggles to capture global inter-variate correlations. Therefore, we posit that CPA should be retained, with the pre-aligned series properly handled by the model, enabling it to outperform state-of-the-art graph-based baselines that sidestep CPA. Technically, we propose KAFNet, a compact architecture grounded in CPA for IMTS forecasting that couples (1) a Pre-Convolution module for sequence smoothing and sparsity mitigation, (2) a Temporal Kernel Aggregation module for learnable compression and modeling of intra-series irregularity, and (3) Frequency Linear Attention blocks for low-cost inter-series correlation modeling in the frequency domain. Experiments on multiple IMTS datasets show that KAFNet achieves state-of-the-art forecasting performance, with a 7.2× parameter reduction and an 8.4× training–inference acceleration.

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NeurIPS Conference 2025 Conference Paper

Aeolus: A Multi-structural Flight Delay Dataset

Lin Xu
Xinyun Yuan
Yuxuan Liang
Suwan Yin
Yuankai Wu

We introduce Aeolus, a large-scale Multi-modal Flight Delay Dataset designed to advance research on flight delay prediction and support the development of foundation models for tabular data. Existing datasets in this domain are typically limited to flat tabular structures and fail to capture the spatiotemporal dynamics inherent in delay propagation. Aeolus addresses this limitation by providing three aligned modalities: (i) a tabular dataset with rich operational, meteorological, and airportlevel features for over 50 million flights; (ii) a flight chain module that models delay propagation along sequential flight legs, capturing upstream and downstream dependencies; and (iii) a flight network graph that encodes shared aircraft, crew, and airport resource connections, enabling cross-flight relational reasoning. The dataset is carefully constructed with temporal splits, comprehensive features, and strict leakage prevention to support realistic and reproducible machine learning evaluation. Aeolus supports a broad range of tasks, including regression, classification, temporal structure modeling, and graph learning, serving as a unified benchmark across tabular, sequential, and graph modalities. We release baseline experiments and preprocessing tools to facilitate adoption. Aeolus fills a key gap for both domain-specific modeling and general-purpose structured data research. Our source code and data can be accessed at https: //github. com/Flnny/Delay-data

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AAAI Conference 2025 Conference Paper

AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks

Qiongyan Wang
Yutong Xia
Siru Zhong
Weichuang Li
Yuankai Wu
Shifen Cheng
Junbo Zhang
Yu Zheng

Monitoring real-time air quality is essential for safeguarding public health and fostering social progress. However, the widespread deployment of air quality monitoring stations is constrained by their significant costs. To address this limitation, we introduce AirRadar, a deep neural network designed to accurately infer real-time air quality in locations lacking monitoring stations by utilizing data from existing ones. By leveraging learnable mask tokens, AirRadar reconstructs air quality features in unmonitored regions. Specifically, it operates in two stages: first capturing spatial correlations and then adjusting for distribution shifts. We validate AirRadar’s efficacy using a year-long dataset from 1,085 monitoring stations across China, demonstrating its superiority over multiple baselines, even with varying degrees of unobserved data.

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IROS Conference 2025 Conference Paper

Multi-Modal Graph Convolutional Network with Sinusoidal Encoding for Robust Human Action Segmentation

Hao Xing
Kai Zhe Boey
Yuankai Wu
Darius Burschka
Gordon Cheng

Accurate temporal segmentation of human actions is critical for intelligent robots in collaborative settings, where a precise understanding of sub-activity labels and their temporal structure is essential. However, the inherent noise in both human pose estimation and object detection often leads to over-segmentation errors, disrupting the coherence of action sequences. To address this, we propose a Multi-Modal Graph Convolutional Network (MMGCN) that integrates low-frame-rate (e. g. , 1 fps) visual data with high-frame-rate (e. g. , 30 fps) motion data (skeleton and object detections) to mitigate fragmentation. Our framework introduces three key contributions. First, a sinusoidal encoding strategy that maps 3D skeleton coordinates into a continuous sin-cos space to enhance spatial representation robustness. Second, a temporal graph fusion module that aligns multi-modal inputs with differing resolutions via hierarchical feature aggregation, Third, inspired by the smooth transitions inherent to human actions, we design SmoothLabelMix, a data augmentation technique that mixes input sequences and labels to generate synthetic training examples with gradual action transitions, enhancing temporal consistency in predictions and reducing over-segmentation artifacts. Extensive experiments on the Bimanual Actions Dataset, a public benchmark for human-object interaction understanding, demonstrate that our approach outperforms state-of-the-art methods, especially in action segmentation accuracy, achieving F1@10: 94. 5% and F1@25: 92. 8%.

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IJCAI Conference 2025 Conference Paper

Reinforcement Learning for Hybrid Charging Stations Planning and Operation Considering Fixed and Mobile Chargers

Yanchen Zhu
Honghui Zou
Chufan Liu
Yuyu Luo
Yuankai Wu
Yuxuan Liang

efficient and adaptable charging infrastructure. Fixed-location charging stations often suffer from underutilization or congestion due to fluctuating demand, while mobile chargers offer flexibility by relocating as needed. This paper studies the optimal planning and operation of hybrid charging infrastructures that combine both fixed and mobile chargers within urban road networks. We formulate the Hybrid Charging Station Planning and Operation (HCSPO) problem, jointly optimizing the placement of fixed stations and the scheduling of mobile chargers. A charging demand prediction model based on Model Predictive Control (MPC) supports dynamic decision-making. To solve the HCSPO problem, we propose a deep reinforcement learning approach enhanced with heuristic scheduling. Experiments on real-world urban scenarios show that our method improves infrastructure availability—achieving up to 244. 4% increase in coverage—and reduces user inconvenience with up to 79. 8% shorter waiting times, compared to existing solutions.

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ICLR Conference 2024 Conference Paper

DeepSPF: Spherical SO(3)-Equivariant Patches for Scan-to-CAD Estimation

Driton Salihu
Adam Misik
Yuankai Wu
Constantin Patsch
Fabián Seguel
Eckehard G. Steinbach

Recently, SO(3)-equivariant methods have been explored for 3D reconstruction via Scan-to-CAD. Despite significant advancements attributed to the unique characteristics of 3D data, existing SO(3)-equivariant approaches often fall short in seamlessly integrating local and global contextual information in a widely generalizable manner. Our contributions in this paper are threefold. First, we introduce Spherical Patch Fields, a representation technique designed for patch-wise, SO(3)-equivariant 3D point clouds, anchored theoretically on the principles of Spherical Gaussians. Second, we present the Patch Gaussian Layer, designed for the adaptive extraction of local and global contextual information from resizable point cloud patches. Culminating our contributions, we present Learnable Spherical Patch Fields (DeepSPF) – a versatile and easily integrable backbone suitable for instance-based point networks. Through rigorous evaluations, we demonstrate significant enhancements in Scan-to-CAD performance for point cloud registration, retrieval, and completion: a significant reduction in the rotation error of existing registration methods, an improvement of up to 17\% in the Top-1 error for retrieval tasks, and a notable reduction of up to 30\% in the Chamfer Distance for completion models, all attributable to the incorporation of DeepSPF.

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IROS Conference 2024 Conference Paper

Enhanced Robotic Assistance for Human Activities through Human-Object Interaction Segment Prediction

Yuankai Wu
Rayene Messaoud
Arne-Christoph Hildebrandt
Marco Baldini
Driton Salihu
Constantin Patsch
Eckehard G. Steinbach

Robotic assistance is a current research topic with high application value and multiple challenges. Assistive robots are used in various scenarios, such as production lines, operating tables, and elderly care. While providing effective assistance, most of the assistance tasks that current robots can perform are limited to predefined tasks. This limitation arises from the insufficiency of the current robot perception system to forecast future human activities. To address this issue, we propose a novel 2-stage robotic assistant for human activities through future human-object interaction (HOI) segment prediction. Unlike previous work focusing on predefined or short-term tasks, our robotic assistant can make predictions for future assistance according to human habits. In the first stage, we propose a visual-based human-object interaction segment prediction method to predict human activities, which enables the robotic system to infer human intention. Moreover, we define the robotic executable tasks as an interactive tuple to keep the robotic assistance normatively consistent with human activity. Meanwhile, a graph convolutional network with geometric features that can predict human-object interaction segments is proposed to provide target manipulation and target object for the assistive robot. In the second stage, we present a mobile task completion process including visual navigation, object localization and grasping. The perception stage is evaluated on the MPHOI dataset and custom-collected SPHOI dataset. Finally, we evaluate our comprehensive framework through real-time experimentation.

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AAAI Conference 2024 Conference Paper

MSGNet: Learning Multi-Scale Inter-series Correlations for Multivariate Time Series Forecasting

Wanlin Cai
Yuxuan Liang
Xianggen Liu
Jianshuai Feng
Yuankai Wu

Multivariate time series forecasting poses an ongoing challenge across various disciplines. Time series data often exhibit diverse intra-series and inter-series correlations, contributing to intricate and interwoven dependencies that have been the focus of numerous studies. Nevertheless, a significant research gap remains in comprehending the varying inter-series correlations across different time scales among multiple time series, an area that has received limited attention in the literature. To bridge this gap, this paper introduces MSGNet, an advanced deep learning model designed to capture the varying inter-series correlations across multiple time scales using frequency domain analysis and adaptive graph convolution. By leveraging frequency domain analysis, MSGNet effectively extracts salient periodic patterns and decomposes the time series into distinct time scales. The model incorporates a self-attention mechanism to capture intra-series dependencies, while introducing an adaptive mixhop graph convolution layer to autonomously learn diverse inter-series correlations within each time scale. Extensive experiments are conducted on several real-world datasets to showcase the effectiveness of MSGNet. Furthermore, MSGNet possesses the ability to automatically learn explainable multi-scale inter-series correlations, exhibiting strong generalization capabilities even when applied to out-of-distribution samples.

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IROS Conference 2024 Conference Paper

Rethinking 3D Geometric Object Features for Enhancing Skeleton-based Action Recognition

Yuankai Wu
Chi Wang
Driton Salihu
Constantin Patsch
Marsil Zakour
Eckehard G. Steinbach

Human action recognition is crucial for intelligent robots, especially in the realm of human-robot collaboration research. Recent advancements in human pose estimation algorithms have shifted the focus of action recognition towards skeleton-based models, which exhibit robustness to changes in background and illumination. However, many state-of-the-art action recognition models rely on 2D skeleton data, neglecting object features. This limitation becomes obvious in complex scenarios where human interactions with objects are crucial, potentially compromising the reliability of assistive robots in understanding human behavior in their environment. To address this issue, we propose a method that effectively integrates 3D geometric object features into skeleton data using graph convolutional neural networks (GCNs). In addition to analyzing the effectiveness of information from different dimensions such as object center position, category, translation, and rotation, we explore various adjacency matrix designs for graph networks. Our model performance is evaluated on two challenging datasets: IKEA ASM and Bimanual Actions. The results demonstrate a significant improvement in action recognition by integrating object features into skeleton-based models. Specifically, on the IKEA-ASM dataset, our approach achieves a frame-wise Top-1 score improvement of 10. 8% and an average F1@k improvement of 13. 3%, while on the Bimanual Actions dataset, it achieves a frame-wise Top-1 score improvement of 11. 4% and an average F1@k improvement of 5. 3%, with negligible increases in model complexity.

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IROS Conference 2024 Conference Paper

Sim-to-Real Domain Shift in Online Action Detection

Constantin Patsch
Wael Torjmene
Marsil Zakour
Yuankai Wu
Driton Salihu
Eckehard G. Steinbach

Human reasoning comprises the ability to understand and reason about the current action solely based on past information. To provide effective assistance in an eldercare or household environment an assistive robot or intelligent assistive system has to assess human actions correctly. Based on this presumption, the task of online action detection determines the current action solely based on the past without access to future information. During inference, the performance of the model is largely impacted by the attributes of the underlying training dataset. However, as high costs and ethical concerns are associated with the real-world data collection process, synthetically created data provides a way to mitigate these problems while providing additional data for the training process of the underlying action detection model to improve performanceDue to the inherent domain shift between the synthetic and real data, we introduce a new egocentric dataset called Human Kitchen Interactions (HKI) to investigate the sim-to-real gap. Our dataset contains in total 100 synthetic and real videos in which 21 different actions are executed in a kitchen environment. The synthetic data is acquired in an egocentric virtual reality (VR) setup while capturing the virtual environment in a game engine. We evaluate state-of-the-art online action detection models on our dataset and provide insights into sim-to-real domain shift. Upon acceptance, we will release our dataset and the corresponding features at https://c-patsch.github.io/HKI/.

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NeurIPS Conference 2024 Conference Paper

Terra: A Multimodal Spatio-Temporal Dataset Spanning the Earth

Wei Chen
Xixuan Hao
Yuankai Wu
Yuxuan Liang

Since the inception of our planet, the meteorological environment, as reflected through spatio-temporal data, has always been a fundamental factor influencing human life, socio-economic progress, and ecological conservation. A comprehensive exploration of this data is thus imperative to gain a deeper understanding and more accurate forecasting of these environmental shifts. Despite the success of deep learning techniques within the realm of spatio-temporal data and earth science, existing public datasets are beset with limitations in terms of spatial scale, temporal coverage, and reliance on limited time series data. These constraints hinder their optimal utilization in practical applications. To address these issues, we introduce Terra, a multimodal spatio-temporal dataset spanning the earth. This dataset encompasses hourly time series data from 6, 480, 000 grid areas worldwide over the past 45 years, while also incorporating multimodal spatial supplementary information including geo-images and explanatory text. Through a detailed data analysis and evaluation of existing deep learning models within earth sciences, utilizing our constructed dataset. we aim to provide valuable opportunities for enhancing future research in spatio-temporal data mining, thereby advancing towards more spatio-temporal general intelligence. Our source code and data can be accessed at https: //github. com/CityMind-Lab/NeurIPS24-Terra.

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IROS Conference 2023 Conference Paper

Modeling Action Spatiotemporal Relationships Using Graph-Based Class-Level Attention Network for Long-Term Action Detection

Yuankai Wu
Xin Su
Driton Salihu
Hao Xing
Marsil Zakour
Constantin Patsch

In recent years, Action Detection has become an active research topic in various fields such as human-robot interaction and assistive robots. Most of the previous methods in this field focus on temporally processing the action representation, without considering the dependencies among the action classes. However, actions that occur in a video are constantly related, and this correlation could offer effective clues for detection tasks. In this work, we propose to exploit the information of related action classes with the help of a graph neural network in conjunction with temporal modeling. We introduce the attention-based temporal class module (ATC), which models the inherent action dependencies on the graph and learns action-specific features among temporal dimensions with a dual-branch attention mechanism. Further, we present the Graph-based Class-level Attention Network (GCAN), which is built upon ATC modules with increasing temporal receptive fields to handle actions instances in complex untrimmed videos. Our network is evaluated on two challenging benchmark datasets with dense annotations: Charades and MultiTHUMOS. Experimental results show that our approach demonstrates highly competitive results with a significantly reduced model complexity.

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AAAI Conference 2021 Conference Paper

Inductive Graph Neural Networks for Spatiotemporal Kriging

Yuankai Wu
Dingyi Zhuang
Aurelie Labbe
Lijun Sun

Time series forecasting and spatiotemporal kriging are the two most important tasks in spatiotemporal data analysis. Recent research on graph neural networks has made substantial progress in time series forecasting, while little attention has been paid to the kriging problem—recovering signals for unsampled locations/sensors. Most existing scalable kriging methods (e. g. , matrix/tensor completion) are transductive, and thus full retraining is required when we have a new sensor to interpolate. In this paper, we develop an Inductive Graph Neural Network Kriging (IGNNK) model to recover data for unsampled sensors on a network/graph structure. To generalize the effect of distance and reachability, we generate random subgraphs as samples and the corresponding adjacency matrix for each sample. By reconstructing all signals on each sample subgraph, IGNNK can effectively learn the spatial message passing mechanism. Empirical results on several real-world spatiotemporal datasets demonstrate the effectiveness of our model. In addition, we also find that the learned model can be successfully transferred to the same type of kriging tasks on an unseen dataset. Our results show that: 1) GNN is an efficient and effective tool for spatial kriging; 2) inductive GNNs can be trained using dynamic adjacency matrices; 3) a trained model can be transferred to new graph structures and 4) IGNNK can be used to generate virtual sensors.

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