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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Hu, Minjie Yang, Kuo Xu, Kuan Zhou, Xuezhong Beijing Jiaotong University Institute of Medical Intelligence Beijing Key Lab of Traffic Data Analysis and Mining School of Computer Science & Technology Beijing China

ISBN: (纸本)9798350386226

Accurately predicting drug-target binding affinity is crucial for advancing drug discovery. Recent molecular pre-training models and biological large models provide general molecular representations, but effectively leveraging these features for specific tasks like Drug-Target Affinity (DTA) prediction remains challenging. To address this, we propose LMDTA, a novel attention-based neural network combining molecular pre-training with interaction fine-tuning for affinity prediction. LMDTA learns complex drug and protein representations through pre-trained models, while fine-tuning an interaction-specific model for the DTA task. The two-side attention mechanism integrates pre-trained and fine-tuned features, capturing key drug-target interactions. Experiments on benchmark datasets show LMDTA achieves state-of-the-art performance, with ablation studies validating the model's design. © 2024 IEEE.

关键词： Molecular docking

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Exploring attractor reconstruction derived from encoder-decoder time series prediction

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Physical Review E 2025年第5期111卷 054214-054214页

作者： Yifei Chen Youfang Lin Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence School of Computer Science and Technology Beijing Jiaotong University Beijing 100044 People's Republic of China

Reconstructing the state space is a generic and basic step for studying nonlinear dynamics with partial observables. Recently, time series forecasting through an encoder-decoder artificial neural network is expected to implicitly realize this procedure. However, existing explanatory and comparative studies are insufficient to strongly support such an argument. This paper analyzes the feasibility of such a reconstruction from the predictive perspective. To produce the topologically equivalent attractor via the encoder, we generalize the conditions about the predictive range, measurement noise and model suitability. Based on these analyses, we explore an adapted reconstruction methodology from time series prediction. Moreover, we found that the inverted transformer network is more appealing for multivariate observations and the penalty function of noise amplification compensates for the deficiency of standard prediction error. We conduct an experimental comparison between the prediction-based reconstruction and typical delay coordinate reconstruction in multiple situations. The comparative outcomes demonstrate that sensible predictive designs can steadily reconstruct the attractor at the best level. Additionally, we recover the underlying dynamics from real meteorological data associated with El Niño–Southern Oscillation.

关键词： Forecasting

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Contour-aware Multi-expert Model for Ambiguous Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Wang, Jiangnan Zhou, Caixia Huang, Yaping Beijing Jiaotong University School of Computer Science and Technology Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence Beijing100044 China

Medical image segmentation is highly challenging due to the uncertainties caused by the inherent ambiguous regions and expert knowledge variations. Some recent works explore the uncertainties and produce multiple outputs to obtain more robust results. However, the quality of the boundary areas remains unsatisfactory. Unfortunately, the key differences among experts usually lie in these boundary areas, which are more critical in practical diagnosis. To tackle the above issues, different from previous pixel-wise segmentation approaches, we present a new perspective and formulate the task as a contour-based regression problem, and further propose a novel Contour-aware Multi-expert Segmentor, named ContourMS, which can provide diverse segmentation results with rich boundary details in a coarse-to-fine manner. Specifically, in the coarse stage, we use a SegmentNet to predict a region mask by leveraging the knowledge of multiple experts, and then the mask is converted to an initial contour shared by all experts. In the fine stage, we design a LatentNet to learn the expert-level latent space and a ContourNet to refine each expert contour, where the deformation guided by the expert style can gradually adjust the contour to match different annotations. Extensive experiments demonstrate that the proposed method can generate diverse segment variants and achieve competitive performance on multiple public multi-expert medical segmentation datasets. © 1982-2012 IEEE.

关键词： Image segmentation

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Spatial-Temporal Knowledge Distillation for Takeaway Recommendation 39

Spatial-Temporal Knowledge Distillation for Takeaway Recomme...

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39th Annual AAAI Conference on Artificial intelligence, AAAI 2025

作者： Zhao, Shuyuan Chen, Wei Shi, Boyan Zhou, Liyong Lin, Shuohao Wan, Huaiyu School of Computer Science & Technology Beijing Jiaotong University Beijing China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence Beijing China

ISBN: (纸本)157735897X

The takeaway recommendation system aims to recommend users' future takeaway purchases based on their historical purchase behaviors, thereby improving user satisfaction and boosting merchant sales. Existing methods focus on incorporating auxiliary information or leveraging knowledge graphs to alleviate the sparsity issue of user purchase sequences. However, two main challenges limit the performance of these approaches: (1) capturing dynamic user preferences on complex geospatial information and (2) efficiently integrating spatial-temporal knowledge from both graphs and sequence data with low computational costs. In this paper, we propose a novel spatial-temporal knowledge distillation model for takeaway recommendation (STKDRec) based on the two-stage training process. Specifically, during the first pre-training stage, a spatial-temporal knowledge graph (STKG) encoder is trained to extract high-order spatial-temporal dependencies and collaborative associations from the STKG. During the second spatial-temporal knowledge distillation (STKD) stage, a spatial-temporal Transformer (ST-Transformer) is employed to comprehensively model dynamic user preferences on various types of fine-grained geospatial information from a sequential perspective. Furthermore, the STKD strategy is introduced to transfer graph-based spatial-temporal knowledge to the ST-Transformer, facilitating the adaptive fusion of rich knowledge derived from both the STKG and sequence data while reducing computational overhead. Extensive experiments on three real-world datasets show that STKDRec significantly outperforms the state-of-the-art baselines. Copyright © 2025, Association for the Advancement of Artificial intelligence (***). All rights reserved.

关键词： Knowledge graph

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CoDe: Communication Delay-Tolerant Multi-Agent Collaboration via Dual Alignment of Intent and Timeliness

arXiv

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arXiv 2025年

作者： Song, Shoucheng Lin, Youfang Han, Sheng Yao, Chang Wu, Hao Wang, Shuo Lv, Kai School of Computer Science & Technology Beijing Jiaotong University Beijing China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence Beijing China

Communication has been widely employed to enhance multi-agent collaboration. Previous research has typically assumed delay-free communication, a strong assumption that is challenging to meet in practice. However, real-world agents suffer from channel delays, receiving messages sent at different time points, termed Asynchronous Communication, leading to cognitive biases and breakdowns in collaboration. This paper first defines two communication delay settings in MARL and emphasizes their harm to collaboration. To handle the above delays, this paper proposes a novel framework Communication Delay-tolerant Multi-Agent Collaboration (CoDe). At first, CoDe learns an intent representation as messages through future action inference, reflecting the stable future behavioral trends of the agents. Then, CoDe devises a dual alignment mechanism of intent and timeliness to strengthen the fusion process of asynchronous messages. In this way, agents can extract the long-term intent of others, even from delayed messages, and selectively utilize the most recent messages that are relevant to their intent. Experimental results demonstrate that CoDe outperforms baseline algorithms in three MARL benchmarks without delay and exhibits robustness under fixed and time-varying delays. © 2025, CC BY.

关键词： Delay tolerant networks

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CoDe: Communication Delay-Tolerant Multi-Agent Collaboration via Dual Alignment of Intent and Timeliness 39

CoDe: Communication Delay-Tolerant Multi-Agent Collaboration...

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39th Annual AAAI Conference on Artificial intelligence, AAAI 2025

ISBN: (纸本)157735897X

关键词： Delay tolerant networks

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STD-PLM: Understanding Both Spatial and Temporal Properties of Spatial-Temporal data with PLM 39

STD-PLM: Understanding Both Spatial and Temporal Properties ...

引用

39th Annual AAAI Conference on Artificial intelligence, AAAI 2025

作者： Huang, Yiheng Mao, Xiaowei Guo, Shengnan Chen, Yubin Shen, Junfeng Li, Tiankuo Lin, Youfang Wan, Huaiyu School of Computer and Information Technology Beijing Jiaotong University Beijing China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence Beijing China

ISBN: (纸本)157735897X

Spatial-temporal forecasting and imputation are important for real-world intelligent systems. Most existing methods are tailored for individual forecasting or imputation tasks but are not designed for both. Additionally, they are less effective for zero-shot and few-shot learning. While pre-trained language model (PLM) have exhibited strong pattern recognition and reasoning abilities across various tasks, including few-shot and zero-shot learning, their applications in spatial-temporal data understanding has been constrained by insufficient modeling of complex correlations such as the temporal correlations, spatial connectivity, non-pairwise and high-order spatial-temporal correlations within data. In this paper, we propose STD-PLM for understanding both spatial and temporal properties of Spatial-Temporal data with PLM, which is capable of implementing both spatial-temporal forecasting and imputation tasks. STD-PLM understands spatial-temporal correlations via explicitly designed spatial and temporal tokenizers. Topology-aware node embeddings are designed for PLM to comprehend and exploit the topology structure of data in inductive manner. Furthermore, to mitigate the efficiency issues introduced by the PLM, we design a sandglass attention module (SGA) combined with a specific constrained loss function, which significantly improves the model's efficiency while ensuring performance. Extensive experiments demonstrate that STD-PLM exhibits competitive performance and generalization capabilities across the forecasting and imputation tasks on various datasets. Moreover, STD-PLM achieves promising results on both few-shot and zero-shot tasks. Copyright © 2025, Association for the Advancement of Artificial intelligence (***). All rights reserved.

关键词： Zero-shot learning

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Enhancing Offline Safe Reinforcement Learning with Trajectory-Constrained Diffusion Planning 25

Enhancing Offline Safe Reinforcement Learning with Trajector...

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Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems

作者： Hengrui Zhang Youfang Lin Shuo Shen Hanfeng Lin Peng Cheng Sheng han Kai Lv Beijing Jiaotong University Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence Beijing China Interactive Entertainment Group Tencent ShangHai China

ISBN: (纸本)9798400714269

Recent approaches have utilized the RL via Supervised Learning (RvS) framework to model offline safe RL. However, these methods overlook the fundamental differences between reward maximization and constraint satisfaction, treating them identically with guidance sampling, and requiring different hyperparameters for different constraint conditions. To address these limitations, we propose a novel framework, the Trajectory-Constrained Diffusion Planner (TCDP), which reframes offline safe RL as a product of trajectory conditional probabilities and energy functions. Additionally, we introduce Cost-returns-To-Go relabeling with data Augmentation (CTGDA) and the Quantile Normalization (QN) technique, enabling the adaptation to various constraints without retraining or extensive hyperparameter adjustments.

关键词： diffusion planning

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Spatial-Temporal Knowledge Distillation for Takeaway Recommendation

arXiv

引用

arXiv 2024年

The takeaway recommendation system aims to recommend users’ future takeaway purchases based on their historical purchase behaviors, thereby improving user satisfaction and boosting merchant sales. Existing methods focus on incorporating auxiliary information or leveraging knowledge graphs to alleviate the sparsity issue of user purchase sequences. However, two main challenges limit the performance of these approaches: (1) capturing dynamic user preferences on complex geospatial information and (2) efficiently integrating spatial-temporal knowledge from both graphs and sequence data with low computational costs. In this paper, we propose a novel spatial-temporal knowledge distillation model for takeaway recommendation (STKDRec) based on the two-stage training process. Specifically, during the first pre-training stage, a spatial-temporal knowledge graph (STKG) encoder is trained to extract high-order spatial-temporal dependencies and collaborative associations from the STKG. During the second spatial-temporal knowledge distillation (STKD) stage, a spatial-temporal Transformer (ST-Transformer) is employed to comprehensively model dynamic user preferences on various types of fine-grained geospatial information from a sequential perspective. Furthermore, the STKD strategy is introduced to transfer graph-based spatial-temporal knowledge to the ST-Transformer, facilitating the adaptive fusion of rich knowledge derived from both the STKG and sequence data while reducing computational overhead. Extensive experiments on three real-world datasets show that STKDRec significantly outperforms the state-of-the-art baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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VGA: Vision and Graph Fused Attention Network for Rumor Detection

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ACM Transactions on Knowledge Discovery from data 2025年第4期19卷

作者： Bai, Lin Jia, Caiyan Song, Ziying Cui, Chaoqun Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence Beijing Jiaotong University Beijing China School of Computer Science and Technology Beijing Jiaotong University Beijing China

With the development of social media, rumors have been spread broadly on social media platforms, causing great harm to society. Beside textual information, many rumors also use manipulated images or conceal textual information within images to deceive people and avoid being detected, making multimodal rumor detection be a critical problem. The majority of multimodal rumor detection methods mainly concentrate on extracting features of source claims and their corresponding images, while ignoring the comments of rumors and their propagation structures. These comments and structures imply the wisdom of crowds and are proved to be crucial to debunk rumors. Moreover, these methods usually focus on basic visual feature extraction and rarely take into account image tampering or textual information. Therefore, in this study, we propose a novel Vision and Graph Fused Attention (VGA) Network for rumor detection to utilize propagation structures among posts so as to obtain the crowd opinions and further explore visual tampering features, as well as the textual information hidden in images. We conduct extensive experiments on three datasets, demonstrating that VGA can effectively detect multimodal rumors and outperform state-of-the-art methods significantly. Copyright © 2025 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Tweets

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