Xuan Liu

AAAI Conference 2026 Conference Paper

DAPrompt: Dual Alignment Prompt of Structure and Semantics for Few-shot Graph Learning

• Lifan Jiang
• Mengying Zhu
• Yangyang Wu
• Xuan Liu
• Xiaolin Zheng
• Shenglin Ben

Few-shot graph learning remains a fundamental yet challenging problem, especially under heterophilic graph settings where connected nodes are likely to belong to different classes. In such scenarios, two key challenges arise: (1) unreliable or noisy graph structures that hinder effective message passing, and (2) semantic inconsistency: in heterophilic graphs, aggregating messages from neighbors of different classes entangles representations and introduces misleading semantics. These issues are further exacerbated by the limited labeled data inherent to few-shot learning, making it difficult to adaptively repair structure or disentangle semantics. To address these challenges, we propose DAPrompt, a Dual Alignment Prompt framework that jointly calibrates graph structure and semantic representations across the learning pipeline. In the pretraining stage, DAPrompt incorporates a graph structure learning module to denoise and repair the underlying topology, enhancing structural reliability. In the prompt tuning stage, we introduce two coordinated modules: a structure-aware prompt learner, which employs prompt tokens to repair unreliable graph structures and capture structure-level alignment, and a semantics-aligned prompt learner, which enhances the graph using target node semantics to mitigate representation noise caused by class-mismatched propagation. Extensive experiments on both node-level and graph-level few-shot benchmarks validate its effectiveness, achieving state-of-the-art performance and highlighting the value of structure-semantic dual alignment in heterophilic few-shot graph learning.

EAAI Journal 2026 Journal Article

OPHEP-Miner: One-phase high-efficiency pattern mining utilizing tree structures

• Xuan Liu
• Genlang Chen
• Fangyu Wu
• Wanli Zuo
• Youxi Wu

JBHI Journal 2026 Journal Article

Order-Aware Deep Learning for Drug Combination Benefit Prediction in Cancer Cell Lines

• Xuan Liu
• Jian Zhang
• Jie Yang
• Shichao Liu
• Wen Zhang

Drug combination therapy has exhibited favorable effects in treating cancer patients, with less toxicity and adverse reactions compared to monotherapy. To accelerate the discovery of therapeutic drug combinations, numerous computational methods have been developed to predict drug synergy in cancer cell lines, typically modeling the task as binary classification (synergistic vs non-synergistic) or regression (continuous synergy scores). Yet, a recent study proposes categorizing drug combination benefits into multiple ordered classes (e. g. , synergy, bliss additivity, independent actions) based on clinical activities, and suggests that drug combinations remain valuable if they reduce cancer cell viability, even without defined synergy. To distinguish various levels of combination benefits, we present a novel order-aware deep learning model, called OrderCombo. Specifically, OrderCombo extracts the drug representation via a pretrained chemical language model and the cell line representation via an omics-oriented linear network. Then, these representations are fused into a unified embedding for each drug-drug-cell line triplet, by leveraging a hybrid encoder that combines concatenation-based dependencies and attention-based interactions. Finally, an ordinal contrastive loss is designed to promote a discriminative embedding space and maintain class ordinality, thereby improving the predictions of drug combination benefits. We evaluate OrderCombo on a large-scale combination benefit dataset, and in silico results show that our method outperforms the state-of-the-art baselines in terms of prediction accuracy, while maintaining robust generalization to unseen drug pairs and cell lines. Substantial case studies further demonstrate OrderCombo's potential value in discovering novel anticancer drug combinations across different therapeutic levels.

EAAI Journal 2026 Journal Article

Super principal component analysis with spatial-spectral multiscale tokenization and unmixing transformer for hyperspectral image classification

• Xuan Liu
• Wangyou Chen
• Shenming Qu

NeurIPS Conference 2025 Conference Paper

FGBench: A Dataset and Benchmark for Molecular Property Reasoning at Functional Group-Level in Large Language Models

• Xuan Liu
• Siru Ouyang
• Xianrui Zhong
• Jiawei Han
• Huimin Zhao

Large language models (LLMs) have gained significant attention in chemistry. However, most existing datasets center on molecular-level property prediction and overlook the role of fine-grained functional group (FG) information. Incorporating FG-level data can provide valuable prior knowledge that links molecular structures with textual descriptions, which can be used to build more interpretable, structure-aware LLMs for reasoning on molecule-related tasks. Moreover, LLMs can learn from such fine-grained information to uncover hidden relationships between specific functional groups and molecular properties, thereby advancing molecular design and drug discovery. Here, we introduce FGBench, a dataset comprising 625K molecular property reasoning problems with functional group information. Functional groups are precisely annotated and localized within the molecule, which ensures the dataset's interoperability thereby facilitating further multimodal applications. FGBench includes both regression and classification tasks on 245 different functional groups across three categories for molecular property reasoning: (1) single functional group impacts, (2) multiple functional group interactions, and (3) direct molecular comparisons. In the benchmark of state-of-the-art LLMs on 7K curated data, the results indicate that current LLMs struggle with FG-level property reasoning, highlighting the need to enhance reasoning capabilities in LLMs for chemistry tasks. We anticipate that the methodology employed in FGBench to construct datasets with functional group-level information will serve as a foundational framework for generating new question–answer pairs, enabling LLMs to better understand fine-grained molecular structure–property relationships. The dataset and evaluation code are available at this \href{https: //github. com/xuanliugit/FGBench}{link}.

AAAI Conference 2025 Conference Paper

Knowledge-Guided Domain Adaptation Model for Transferring Drug Response Prediction from Cell Lines to Patients

• Xuan Liu
• Menglu Li

Drug response prediction (DRP) is a longstanding challenge in modern oncology that underpins personalized treatment. Early DRP methods, trained on label-rich cell line samples, suffer from performance degradation when applied to label-scarce patient samples due to the distribution shift. Recently, a few transfer learning efforts have addressed this issue by aligning cell line (source domain) and patient (target domain) data via unsupervised domain adaptation (UDA). However, these efforts often treat each drug's response prediction as an isolated task, requiring model retraining when the drug changes; and focus only on aligning data distributions as a whole, neglecting the category (e.g., different cancers or tissues) confusion problem. To address these limitations, we propose a knowledge-guided domain adaptation model to transfer the DRP from cell lines to patients, named TransDRP. Specifically, TransDRP operates in two phases: pre-training and adaptation. In the first phase, we pre-train a multi-label graph neural network using molecular knowledge, to simultaneously predict responses for various drugs and capture their interdependencies. In the second phase, we implement a global-local domain adversarial strategy with clinical knowledge, to encourage representation alignment within same cancer categories and separation among different cancer categories across domains. Extensive experiments demonstrate that TransDRP outperforms state-of-the-art UDA methods in both transfer efficiency and precision for the patient DRP.

JBHI Journal 2025 Journal Article

MBSCLoc: Multi-Label Subcellular Localization Predict Based on Cluster Balanced Subspace Partitioning Method and Multi-Class Contrastive Representation Learning

• Bangyi Zhang
• Yun Zuo
• Zhiqiang Dai
• Sifan Zhu
• Xuan Liu
• Zhaohong Deng

mRNA subcellular localization is a prevalent and essential mechanism that precisely regulates protein translation and significantly impacts various cellular processes. mRNA subcellular localization has advanced the understanding of mRNA function, yet existing methods face limitations, including imbalanced data, suboptimal model performance, and inadequate generalization, particularly in multi-label localization scenarios where solutions are scarce. This study introduces MBSCLoc, a predictor for mRNA multi-label subcellular localization. MBSCLoc predicts mRNA locations across multiple cellular compartments simultaneously, overcoming challenges like single-location prediction, incomplete feature extraction, and imbalanced data. MBSCLoc leverages UTR-LM model for feature extraction, followed by multi-class contrastive representation learning and Clustering Balanced Subspace Partitioning to construct balanced subspaces. It then optimizes sample distribution to tackle severe data imbalance and uses multiple XGBoost classifiers, integrated through voting, to enhance accuracy and generalization. Five-fold cross-validation and independent testing results show that MBSCLoc significantly outperforms other methods. Additionally, MBSCLoc offers superior pixel-level interpretability, strongly supporting mRNA multi-label subcellular localization research. Crucially, the importance of the 5' UTR and 3' UTR regions has been preliminarily confirmed using traditional biological analysis and Tree-SHAP, with most mRNA sequences showing significant relevance in these regions, especially the 3' UTR where about 80% of specific sites reach peak significance.

AAAI Conference 2025 Conference Paper

Mjölnir: Breaking the Shield of Perturbation-Protected Gradients via Adaptive Diffusion

• Xuan Liu
• Siqi Cai
• Qihua Zhou
• Song Guo
• Ruibin Li
• Kaiwei Lin

Perturbation-based mechanisms, such as differential privacy, mitigate gradient leakage attacks by introducing noise into the gradients, thereby preventing attackers from reconstructing clients' private data from the leaked gradients. However, can gradient perturbation protection mechanisms truly defend against all gradient leakage attacks? In this paper, we present the first attempt to break the shield of gradient perturbation protection in Federated Learning for the extraction of private information. We focus on common noise distributions, specifically Gaussian and Laplace, and apply our approach to DNN and CNN models. We introduce Mjölnir, a perturbation-resilient gradient leakage attack that is capable of removing perturbations from gradients without requiring additional access to the original model structure or external data. Specifically, we leverage the inherent diffusion properties of gradient perturbation protection to develop a novel diffusion-based gradient denoising model for Mjölnir. By constructing a surrogate client model that captures the structure of perturbed gradients, we obtain crucial gradient data for training the diffusion model. We further utilize the insight that monitoring disturbance levels during the reverse diffusion process can enhance gradient denoising capabilities, allowing Mjölnir to generate gradients that closely approximate the original, unperturbed versions through adaptive sampling steps. Extensive experiments demonstrate that Mjölnir effectively recovers the protected gradients and exposes the Federated Learning process to the threat of gradient leakage, achieving superior performance in gradient denoising and private data recovery.

ICRA Conference 2025 Conference Paper

Retinex-BEVFormer: Using Retinex to Enhance Multi-View Image-Based BEV Detector in Low Light Scenes

• Xuan Liu
• Zhongxia Xiong
• Ziying Yao
• Xinkai Wu

Multi-view image-based BEV (Bird's Eye View) 3D perception is gaining attention as an alternative to highcost LiDAR systems and has achieved notable success. However, there is a significant safety concern for future image-based BEV autonomous driving in low-light conditions (such as nighttime) while the limited research on BEV detectors for these scenes. In this paper, we attempt to enhance low-light BEV perception with illumination-guided feature fusion. We propose Retinex-BEVFormer, which uses illumination information generated by the Retinex theory to enhance the model's robustness to varying lighting conditions and improve detection performance in low-light scenes. Additionally, to address the illumination estimation discontinuity from multi-view images that can adversely affect detection, we propose the MVB-Retinex module, which balances illumination estimation by leveraging overlapping regions between adjacent images. Notably, our proposed method is a plug-and-play module that can be applied to any image-based BEV detector method and does not require any additional ground truth supervision. We conduct extensive experiments on the nuScenes dataset, validating our algorithm in nighttime and daytime scenes. Compared to the baseline, our algorithm achieves a 2. 9% increase in mAP on the validation set with minimal computational cost, especially showing a 3. 6% improvement in the nighttime scene. The experiments demonstrate that our Retinex-BEVFormer effectively improves detection performance under low light conditions and enhances performance under normal illumination, indicating increased robustness of the BEV detector.

NeurIPS Conference 2025 Conference Paper

Virus Infection Attack on LLMs: Your Poisoning Can Spread "VIA" Synthetic Data

• Zi Liang
• Qingqing Ye
• Xuan Liu
• Yanyun Wang
• Jianliang Xu
• Haibo Hu

Synthetic data refers to artificial samples generated by models. While it has been validated to significantly enhance the performance of large language models (LLMs) during training and has been widely adopted in LLM development, potential security risks it may introduce remain uninvestigated. This paper systematically evaluates the resilience of synthetic-data-integrated training paradigm for LLMs against mainstream poisoning and backdoor attacks. We reveal that such a paradigm exhibits strong resistance to existing attacks, primarily thanks to the different distribution patterns between poisoning data and queries used to generate synthetic samples. To enhance the effectiveness of these attacks and further investigate the security risks introduced by synthetic data, we introduce a novel and universal attack framework, namely, Virus Infection Attack (VIA), which enables the propagation of current attacks through synthetic data even under purely clean queries. Inspired by the principles of virus design in cybersecurity, VIA conceals the poisoning payload within a protective “shell” and strategically searches for optimal hijacking points in benign samples to maximize the likelihood of generating malicious content. Extensive experiments on both data poisoning and backdoor attacks show that VIA significantly increases the presence of poisoning content in synthetic data and correspondingly raises the attack success rate (ASR) on downstream models to levels comparable to those observed in the poisoned upstream models.

AAAI Conference 2024 Conference Paper

A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide Generation

• Yongkang Wang
• Xuan Liu
• Feng Huang
• Zhankun Xiong
• Wen Zhang

Therapeutic peptides represent a unique class of pharmaceutical agents crucial for the treatment of human diseases. Recently, deep generative models have exhibited remarkable potential for generating therapeutic peptides, but they only utilize sequence or structure information alone, which hinders the performance in generation. In this study, we propose a Multi-Modal Contrastive Diffusion model (MMCD), fusing both sequence and structure modalities in a diffusion framework to co-generate novel peptide sequences and structures. Specifically, MMCD constructs the sequence-modal and structure-modal diffusion models, respectively, and devises a multi-modal contrastive learning strategy with inter-contrastive and intra-contrastive in each diffusion timestep, aiming to capture the consistency between two modalities and boost model performance. The inter-contrastive aligns sequences and structures of peptides by maximizing the agreement of their embeddings, while the intra-contrastive differentiates therapeutic and non-therapeutic peptides by maximizing the disagreement of their sequence/structure embeddings simultaneously. The extensive experiments demonstrate that MMCD performs better than other state-of-the-art deep generative methods in generating therapeutic peptides across various metrics, including antimicrobial/anticancer score, diversity, and peptide-docking.

ICRA Conference 2024 Conference Paper

A Vision-Centric Approach for Static Map Element Annotation

• Jiaxin Zhang 0014
• Shiyuan Chen
• Haoran Yin
• Ruohong Mei
• Xuan Liu
• Cong Yang
• Qian Zhang 0009
• Wei Sui

The recent development of online static map element (a. k. a. HD Map) construction algorithms has raised a vast demand for data with ground truth annotations. However, available public datasets currently cannot provide high-quality training data regarding consistency and accuracy. To this end, we present CAMA: a vision-centric approach for Consistent and Accurate Map Annotation. Without LiDAR inputs, our proposed framework can still generate high-quality 3D annotations of static map elements. Specifically, the annotation can achieve high reprojection accuracy across all surrounding cameras and is spatial-temporal consistent across the whole sequence. We apply our proposed framework to the popular nuScenes dataset to provide efficient and highly accurate annotations. Compared with the original nuScenes static map element, models trained with annotations from CAMA achieve lower reprojection errors (e. g. , 4. 73 vs. 8. 03 pixels).

IJCAI Conference 2024 Conference Paper

Advancing Generalized Transfer Attack with Initialization Derived Bilevel Optimization and Dynamic Sequence Truncation

• Yaohua Liu
• Jiaxin Gao
• Xuan Liu
• Xianghao Jiao
• Xin Fan
• Risheng Liu

Transfer attacks generate significant interest for real-world black-box applications by crafting transferable adversarial examples through surrogate models. Whereas, existing works essentially directly optimize the single-level objective w. r. t. the surrogate model, which always leads to poor interpretability of attack mechanism and limited generalization performance over unknown victim models. In this work, we propose the BilEvel Transfer AttacK (BETAK) framework by establishing an initialization derived bilevel optimization paradigm, which explicitly reformulates the nested constraint relationship between the Upper-Level (UL) pseudo-victim attacker and the Lower-Level (LL) surrogate attacker. Algorithmically, we introduce the Hyper Gradient Response (HGR) estimation as an effective feedback for the transferability over pseudo-victim attackers, and propose the Dynamic Sequence Truncation (DST) technique to dynamically adjust the back-propagation path for HGR and reduce computational overhead simultaneously. Meanwhile, we conduct detailed algorithmic analysis and provide convergence guarantee to support non-convexity of the LL surrogate attacker. Extensive evaluations demonstrate substantial improvement of BETAK (e. g. , 53. 41% increase of attack success rates against IncRes-v2_ens victim) against different victims and defense methods in targeted and untargeted attack scenarios.

AAAI Conference 2024 Conference Paper

Cautiously-Optimistic Knowledge Sharing for Cooperative Multi-Agent Reinforcement Learning

• Yanwen Ba
• Xuan Liu
• Xinning Chen
• Hao Wang
• Yang Xu
• Kenli Li
• Shigeng Zhang

While decentralized training is attractive in multi-agent reinforcement learning (MARL) for its excellent scalability and robustness, its inherent coordination challenges in collaborative tasks result in numerous interactions for agents to learn good policies. To alleviate this problem, action advising methods make experienced agents share their knowledge about what to do, while less experienced agents strictly follow the received advice. However, this method of sharing and utilizing knowledge may hinder the team's exploration of better states, as agents can be unduly influenced by suboptimal or even adverse advice, especially in the early stages of learning. Inspired by the fact that humans can learn not only from the success but also from the failure of others, this paper proposes a novel knowledge sharing framework called Cautiously-Optimistic kNowledge Sharing (CONS). CONS enables each agent to share both positive and negative knowledge and cautiously assimilate knowledge from others, thereby enhancing the efficiency of early-stage exploration and the agents' robustness to adverse advice. Moreover, considering the continuous improvement of policies, agents value negative knowledge more in the early stages of learning and shift their focus to positive knowledge in the later stages. Our framework can be easily integrated into existing Q-learning based methods without introducing additional training costs. We evaluate CONS in several challenging multi-agent tasks and find it excels in environments where optimal behavioral patterns are difficult to discover, surpassing the baselines in terms of convergence rate and final performance.

IJCAI Conference 2024 Conference Paper

Selective Learning for Sample-Efficient Training in Multi-Agent Sparse Reward Tasks (Extended Abstract)

• Xinning Chen
• Xuan Liu
• Yanwen Ba
• Shigeng Zhang
• Bo Ding
• Kenli Li

Learning effective strategies in sparse reward tasks is one of the fundamental challenges in reinforcement learning. This becomes extremely difficult in multi-agent environments, as the concurrent learning of multiple agents induces the non-stationarity problem and a sharply increased joint state space. Existing works have attempted to promote multi-agent cooperation through experience sharing. However, learning from a large collection of shared experiences is inefficient as there are only a few high-value states in sparse reward tasks, which may instead lead to the curse of dimensionality in large-scale multi-agent systems. This paper focuses on sparse-reward multi-agent cooperative tasks and proposes an effective experience-sharing method, Multi-Agent Selective Learning (MASL), to boost sample-efficient training by reusing valuable experiences from other agents. MASL adopts a retrogression-based selection method to identify high-value traces of agents from the team rewards, based on which some recall traces are generated and shared among agents to motivate effective exploration. Moreover, MASL selectively considers information from other agents to cope with the non-stationarity issue while enabling efficient training for large-scale agents. Experimental results show that MASL significantly improves sample efficiency compared with state-of-the-art MARL algorithms in cooperative tasks with sparse rewards.

EAAI Journal 2024 Journal Article

Self-supervised multi-magnification feature enhancement for segmentation of hepatocellular carcinoma region in pathological images

• Songhui Diao
• Xiang Liu
• Xuan Liu
• Boyun Zheng
• Jiahui He
• Yaoqin Xie
• Wenjian Qin

EAAI Journal 2024 Journal Article

SU-VPDN: A scene understanding method for vehicle part detection

• Yongjie Zhai
• Nianhao Chen
• Zhenqi Zhang
• Xunqi Zhou
• Xuan Liu
• Xinying Wang
• Qianming Wang

JBHI Journal 2024 Journal Article

Subgraph-Aware Graph Kernel Neural Network for Link Prediction in Biological Networks

• Menglu Li
• Zhiwei Wang
• Luotao Liu
• Xuan Liu
• Wen Zhang

Identifying links within biological networks is important in various biomedical applications. Recent studies have revealed that each node in a network may play a unique role in different links, but most link prediction methods overlook distinctive node roles, hindering the acquisition of effective link representations. Subgraph-based methods have been introduced as solutions but often ignore shared information among subgraphs. To address these limitations, we propose a Subgraph-aware Graph Kernel Neural Network (SubKNet) for link prediction in biological networks. Specifically, SubKNet extracts a subgraph for each node pair and feeds it into a graph kernel neural network, which decomposes each subgraph into a combination of trainable graph filters with diversity regularization for subgraph-aware representation learning. Additionally, node embeddings of the network are extracted as auxiliary information, aiding in distinguishing node pairs that share the same subgraph. Extensive experiments on five biological networks demonstrate that SubKNet outperforms baselines, including methods especially designed for biological networks and methods adapted to various networks. Further investigations confirm that employing graph filters to subgraphs helps to distinguish node roles in different subgraphs, and the inclusion of diversity regularization further enhances its capacity from diverse perspectives, generating effective link representations that contribute to more accurate link prediction.

IJCAI Conference 2024 Conference Paper

ZeroDDI: A Zero-Shot Drug-Drug Interaction Event Prediction Method with Semantic Enhanced Learning and Dual-modal Uniform Alignment

• Ziyan Wang
• Zhankun Xiong
• Feng Huang
• Xuan Liu
• Wen Zhang

Drug-drug interactions (DDIs) can result in various pharmacological changes, which can be categorized into different classes known as DDI events (DDIEs). In recent years, previously unobserved/unseen DDIEs have been emerging, posing a new classification task when unseen classes have no labelled instances in the training stage, which is formulated as a zero-shot DDIE prediction (ZS-DDIE) task. However, existing computational methods are not directly applicable to ZS-DDIE, which has two primary challenges: obtaining suitable DDIE representations and handling the class imbalance issue. To overcome these challenges, we propose a novel method named ZeroDDI for the ZS-DDIE task. Specifically, we design a biological semantic enhanced DDIE representation learning module, which emphasizes the key biological semantics and distills discriminative molecular substructure-related semantics for DDIE representation learning. Furthermore, we propose a dual-modal uniform alignment strategy to distribute drug pair representations and DDIE semantic representations uniformly in unit sphere and align the matched ones, which can mitigate the issue of class imbalance. Extensive experiments showed that ZeroDDI surpasses the baselines and indicate that it is a promising tool for detecting unseen DDIEs. Our code has been released in https: //github. com/wzy-Sarah/ZeroDDI.

NeurIPS Conference 2023 Conference Paper

Learning Dictionary for Visual Attention

• Yingjie Liu
• Xuan Liu
• Hui Yu
• Xuan Tang
• Xian Wei

Recently, the attention mechanism has shown outstanding competence in capturing global structure information and long-range relationships within data, thus enhancing the performance of deep vision models on various computer vision tasks. In this work, we propose a novel dictionary learning-based attention (\textit{Dic-Attn}) module, which models this issue as a decomposition and reconstruction problem with the sparsity prior, inspired by sparse coding in the human visual perception system. The proposed \textit{Dic-Attn} module decomposes the input into a dictionary and corresponding sparse representations, allowing for the disentanglement of underlying nonlinear structural information in visual data and the reconstruction of an attention embedding. By applying transformation operations in the spatial and channel domains, the module dynamically selects the dictionary's atoms and sparse representations. Finally, the updated dictionary and sparse representations capture the global contextual information and reconstruct the attention maps. The proposed \textit{Dic-Attn} module is designed with plug-and-play compatibility, allowing for integration into deep attention encoders. Our approach offers an intuitive and elegant means to exploit the discriminative information from data, promoting visual attention construction. Extensive experimental results on various computer vision tasks, e. g. , image and point cloud classification, validate that our method achieves promising performance, and shows a strong competitive comparison with state-of-the-art attention methods.

AAAI Conference 2023 Short Paper

MGIA: Mutual Gradient Inversion Attack in Multi-Modal Federated Learning (Student Abstract)

• Xuan Liu
• Siqi Cai
• Lin Li
• Rui Zhang
• Song Guo

Recent studies have demonstrated that local training data in Federated Learning can be recovered from gradients, which are called gradient inversion attacks. These attacks display powerful effects on either computer vision or natural language processing tasks. As it is known that there are certain correlations between multi-modality data, we argue that the threat of such attacks combined with Multi-modal Learning may cause more severe effects. Different modalities may communicate through gradients to provide richer information for the attackers, thus improving the strength and efficiency of the gradient inversion attacks. In this paper, we propose the Mutual Gradient Inversion Attack (MGIA), by utilizing the shared labels between image and text modalities combined with the idea of knowledge distillation. Our experimental results show that MGIA achieves the best quality of both modality data and label recoveries in comparison with other methods. In the meanwhile, MGIA verifies that multi-modality gradient inversion attacks are more likely to disclose private information than the existing single-modality attacks.

AAAI Conference 2023 Conference Paper

Multi-Relational Contrastive Learning Graph Neural Network for Drug-Drug Interaction Event Prediction

• Zhankun Xiong
• Shichao Liu
• Feng Huang
• Ziyan Wang
• Xuan Liu
• Zhongfei Zhang
• Wen Zhang

Drug-drug interactions (DDIs) could lead to various unexpected adverse consequences, so-called DDI events. Predicting DDI events can reduce the potential risk of combinatorial therapy and improve the safety of medication use, and has attracted much attention in the deep learning community. Recently, graph neural network (GNN)-based models have aroused broad interest and achieved satisfactory results in the DDI event prediction. Most existing GNN-based models ignore either drug structural information or drug interactive information, but both aspects of information are important for DDI event prediction. Furthermore, accurately predicting rare DDI events is hindered by their inadequate labeled instances. In this paper, we propose a new method, Multi-Relational Contrastive learning Graph Neural Network, MRCGNN for brevity, to predict DDI events. Specifically, MRCGNN integrates the two aspects of information by deploying a GNN on the multi-relational DDI event graph attributed with the drug features extracted from drug molecular graphs. Moreover, we implement a multi-relational graph contrastive learning with a designed dual-view negative counterpart augmentation strategy, to capture implicit information about rare DDI events. Extensive experiments on two datasets show that MRCGNN outperforms the state-of-the-art methods. Besides, we observe that MRCGNN achieves satisfactory performance when predicting rare DDI events.

IJCAI Conference 2023 Conference Paper

Multi-view Contrastive Learning Hypergraph Neural Network for Drug-Microbe-Disease Association Prediction

• Luotao Liu
• Feng Huang
• Xuan Liu
• Zhankun Xiong
• Menglu Li
• Congzhi Song
• Wen Zhang

Identifying the potential associations among drugs, microbes and diseases is of great significance in exploring the pathogenesis and improving precision medicine. There are plenty of computational methods for pair-wise association prediction, such as drug-microbe and microbe-disease associations, but few methods focus on the higher-order triple-wise drug-microbe-disease (DMD) associations. Driven by the advancement of hypergraph neural networks (HGNNs), we expect them to fully capture high-order interaction patterns behind the hypergraph formulated by DMD associations and realize sound prediction performance. However, the confirmed DMD associations are insufficient due to the high cost of in vitro screening, which forms a sparse DMD hypergraph and thus brings in suboptimal generalization ability. To mitigate the limitation, we propose a Multi-view Contrastive Learning Hypergraph Neural Network, named MCHNN, for DMD association prediction. We design a novel multi-view contrastive learning on the DMD hypergraph as an auxiliary task, which guides the HGNN to learn more discriminative representations and enhances the generalization ability. Extensive computational experiments show that MCHNN achieves satisfactory performance in DMD association prediction and, more importantly, demonstrate the effectiveness of our devised multi-view contrastive learning on the sparse DMD hypergraph.

EAAI Journal 2022 Journal Article

Effective algorithms to mine skyline frequent-utility itemsets

• Xuan Liu
• Genlang Chen
• Wanli Zuo

IJCAI Conference 2022 Conference Paper

Goal Consistency: An Effective Multi-Agent Cooperative Method for Multistage Tasks

• Xinning Chen
• Xuan Liu
• Shigeng Zhang
• Bo Ding
• Kenli Li

Although multistage tasks involving multiple sequential goals are common in real-world applications, they are not fully studied in multi-agent reinforcement learning (MARL). To accomplish a multi-stage task, agents have to achieve cooperation on different subtasks. Exploring the collaborative patterns of different subtasks and the sequence of completing the subtasks leads to an explosion in the search space, which poses great challenges to policy learning. Existing works designed for single-stage tasks where agents learn to cooperate only once usually suffer from low sample efficiency in multi-stage tasks as agents explore aimlessly. Inspired by human’s improving cooperation through goal consistency, we propose Multi-Agent Goal Consistency (MAGIC) framework to improve sample efficiency for learning in multi-stage tasks. MAGIC adopts a goal-oriented actor-critic model to learn both local and global views of goal cognition, which helps agents understand the task at the goal level so that they can conduct targeted exploration accordingly. Moreover, to improve exploration efficiency, MAGIC employs two-level goal consistency training to drive agents to formulate a consistent goal cognition. Experimental results show that MAGIC significantly improves sample efficiency and facilitates cooperation among agents compared with state-of-art MARL algorithms in several challenging multistage tasks.

AAAI Conference 2022 Short Paper

The Psychology of Semantic Spaces: Experiments with Positive Emotion (Student Abstract)

• Xuan Liu
• Kokil Jaidka
• Niyati Chayya

Psychological concepts can help computational linguists to better model the latent semantic spaces of emotions, and understand the underlying states motivating the sharing or suppressing of emotions. This abstract applies the understanding of agency and social interaction in the happiness semantic space to its role in positive emotion. BERT-based fine-tuning yields an expanded seed set to understand the vocabulary of the latent space. Results benchmarked against many emotion datasets suggest that the approach is valid, robust, offers an improvement over direct prediction, and is useful for downstream predictive tasks related to psychological states.

IROS Conference 2020 Conference Paper

Learning to Locomote with Artificial Neural-Network and CPG-based Control in a Soft Snake Robot

• Xuan Liu
• Renato Gasoto
• Ziyi Jiang
• Cagdas D. Onal
• Jie Fu 0002

In this paper, we present a new locomotion control method for soft robot snakes. Inspired by biological snakes, our control architecture is composed of two key modules: A reinforcement learning (RL) module for achieving adaptive goal-tracking behaviors with changing goals, and a central pattern generator (CPG) system with Matsuoka oscillators for generating stable and diverse locomotion patterns. The two modules are interconnected into a closed-loop system: The RL module, analogizing the locomotion region located in the midbrain of vertebrate animals, regulates the input to the CPG system given state feedback from the robot. The output of the CPG system is then translated into pressure inputs to pneumatic actuators of the soft snake robot. Based on the fact that the oscillation frequency and wave amplitude of the Matsuoka oscillator can be independently controlled under different time scales, we further adapt the option-critic framework to improve the learning performance measured by optimality and data efficiency. The performance of the proposed controller is experimentally validated with both simulated and real soft snake robots.

ICRA Conference 2019 Conference Paper

A Validated Physical Model For Real-Time Simulation of Soft Robotic Snakes

• Renato Gasoto
• Miles Macklin
• Xuan Liu
• Yinan Sun
• Kenny Erleben
• Cagdas D. Onal
• Jie Fu 0002

In this work we present a framework that is capable of accurately representing soft robotic actuators in a multiphysics environment in real-time. We propose a constraint-based dynamics model of a 1-dimensional pneumatic soft actuator that accounts for internal pressure forces, as well as the effect of actuator latency and damping under inflation and deflation and demonstrate its accuracy a full soft robotic snake with the composition of multiple 1D actuators. We verify our model's accuracy in static deformation and dynamic locomotion open-loop control experiments. To achieve real-time performance we leverage the parallel computation power of GPUs to allow interactive control and feedback.