Qianli Xing

Yali Fu, Jindong Li, Jiahong Liu et al.

Unsupervised graph-level anomaly detection (UGAD) has garnered increasing attention in recent years due to its significance. Most existing methods that rely on traditional GNNs mainly consider pairwise relationships between first-order neighbors, which is insufficient to capture the complex high-order dependencies often associated with anomalies. This limitation underscores the necessity of exploring high-order node interactions in UGAD. In addition, most previous works ignore the underlying properties (e.g., hierarchy and power-law structure) which are common in real-world graph datasets and therefore are indispensable factors in the UGAD task. In this paper, we propose a novel Dual Hyperbolic Contrastive Learning for Unsupervised Graph-Level Anomaly Detection (HC-GLAD in short). To exploit high-order node group information, we construct hypergraphs based on pre-designed gold motifs and subsequently perform hypergraph convolution. Furthermore, to preserve the hierarchy of real-world graphs, we introduce hyperbolic geometry into this field and conduct both graph and hypergraph embedding learning in hyperbolic space with the hyperboloid model. To the best of our knowledge, this is the first work to simultaneously apply hypergraph with node group information and hyperbolic geometry in this field. Extensive experiments on 13 real-world datasets of different fields demonstrate the superiority of HC-GLAD on the UGAD task. The code is available at https://github.com/Yali-F/HC-GLAD.

Jindong Li, Qianli Xing, Qi Wang et al.

Unsupervised graph-level anomaly detection (UGAD) has received remarkable performance in various critical disciplines, such as chemistry analysis and bioinformatics. Existing UGAD paradigms often adopt data augmentation techniques to construct multiple views, and then employ different strategies to obtain representations from different views for jointly conducting UGAD. However, most previous works only considered the relationship between nodes/graphs from a limited receptive field, resulting in some key structure patterns and feature information being neglected. In addition, most existing methods consider different views separately in a parallel manner, which is not able to explore the inter-relationship across different views directly. Thus, a method with a larger receptive field that can explore the inter-relationship across different views directly is in need. In this paper, we propose a novel Simplified Transformer with Cross-View Attention for Unsupervised Graph-level Anomaly Detection, namely, CVTGAD. To increase the receptive field, we construct a simplified transformer-based module, exploiting the relationship between nodes/graphs from both intra-graph and inter-graph perspectives. Furthermore, we design a cross-view attention mechanism to directly exploit the view co-occurrence between different views, bridging the inter-view gap at node level and graph level. To the best of our knowledge, this is the first work to apply transformer and cross attention to UGAD, which realizes graph neural network and transformer working collaboratively. Extensive experiments on 15 real-world datasets of 3 fields demonstrate the superiority of CVTGAD on the UGAD task. The code is available at \url{https://github.com/jindongli-Ai/CVTGAD}.

Henrik Johansson, Qianli Xing, Nathaniel Taylor et al.

Grid-forming (GFM) inverters are expected in future inverter-dominated grids. In such grids, time-domain protection schemes, for example those based on instantaneous incremental quantities (IQs), are being advocated as potential solutions to the challenges faced by traditional phasor-based protection schemes, due to their ability to process nonlinear data. However, IQ-based protection uses the superposition principle; thus, linearity is still assumed in their application, while GFM inverters are nonlinear sources during faults. This paper proposes an analytical model to study the impact of GFM inverters on the relay-measured IQs. The model is validated with PSCAD/EMTDC simulations, and is used to investigate the interoperability of time-domain IQ-based distance protection with GFM inverters employing different current limiters. Results show that time-domain IQ-based distance protection demonstrates superior dependability for close-in faults compared to that of quadrilateral distance protection with GFM inverters, and it has the possibility to be secure for external faults when quadrilateral distance protection overreaches; however, tuning of its settings is hard to generalize for various sources and faults. Taking the observed interoperability issues into account, a trip criterion for dependable and secure time-domain IQ-based distance protection is proposed, which facilitates easy-to-tune and general settings for applications with GFM inverters.

Yali Fu, Jindong Li, Qi Wang et al.

Unsupervised graph-level anomaly detection (UGLAD) is a critical and challenging task across various domains, such as social network analysis, anti-cancer drug discovery, and toxic molecule identification. However, existing methods often struggle to capture the long-range dependencies efficiently and neglect the spectral information. Recently, selective State Space Models (SSMs), particularly Mamba, have demonstrated remarkable advantages in capturing long-range dependencies with linear complexity and a selection mechanism. Motivated by their success across various domains, we propose GLADMamba, a novel framework that adapts the selective state space model into UGLAD field. We design View-Fused Mamba (VFM) with a Mamba-Transformer-style architecture to efficiently fuse information from different views with a selective state mechanism. We also design Spectrum-Guided Mamba (SGM) with a Mamba-Transformer-style architecture to leverage the Rayleigh quotient to guide the embedding refining process. GLADMamba can dynamically focus on anomaly-related information while discarding irrelevant information for anomaly detection. To the best of our knowledge, this is the first work to introduce Mamba and explicit spectral information to UGLAD. Extensive experiments on 12 real-world datasets demonstrate that GLADMamba outperforms existing state-of-the-art methods, achieving superior performance in UGLAD. The code is available at https://github.com/Yali-F/GLADMamba.

Zengjue Chen, Runliang Niu, He Kong et al.

Visual-Language-Action (VLA) models have demonstrated strong cross-scenario generalization capabilities in various robotic tasks through large-scale pre-training and task-specific fine-tuning. However, their training paradigm mainly relies on manually collected successful demonstrations, making it difficult to adapt to complex environments when encountering out-of-distribution (OOD) scenarios or execution biases. While Reinforcement Learning (RL) provides a closed-loop optimization framework via active trial-and-error mechanism, it suffers from sparse rewards, high variance, and unstable optimization in long-horizon robotic tasks. To address these limitations, we propose Trajectory-based Group Relative Policy Optimization (TGRPO), an online RL-based training framework for VLA models. TGRPO leverages task analysis generated by a large language model to automatically construct dense reward functions, providing fine-grained feedback to accelerate convergence and improve credit assignment. The core of our method is a group-based strategy that samples and normalizes multiple trajectories in parallel, reducing variance through relative comparison. By integrating trajectory-level and step-level advantage estimation, TGRPO captures both global and local optimization signals without relying on a value network. Experiments on four task categories of the LIBERO benchmark demonstrate that TGRPO achieves an average success rate of 80.7\%, which is 4.2\% higher than that of Supervised Fine-Tuning (SFT) and outperforms other representative RL-based post-training methods.

Jiao Huang, Qianli Xing, Jinglong Ji et al.

Property prediction is a fundamental task in crystal material research. To model atoms and structures, structures represented as graphs are widely used and graph learning-based methods have achieved significant progress. Bond angles and bond distances are two key structural information that greatly influence crystal properties. However, most of the existing works only consider bond distances and overlook bond angles. The main challenge lies in the time cost of handling bond angles, which leads to a significant increase in inference time. To solve this issue, we first propose a crystal structure modeling based on dual scale neighbor partitioning mechanism, which uses a larger scale cutoff for edge neighbors and a smaller scale cutoff for angle neighbors. Then, we propose a novel Atom-Distance-Angle Graph Neural Network (ADA-GNN) for property prediction tasks, which can process node information and structural information separately. The accuracy of predictions and inference time are improved with the dual scale modeling and the specially designed architecture of ADA-GNN. The experimental results validate that our approach achieves state-of-the-art results in two large-scale material benchmark datasets on property prediction tasks.

Zhiyi Duan, Xiangren Wang, Hongyu Yuan et al.

Teachers' emotional states are critical in educational scenarios, profoundly impacting teaching efficacy, student engagement, and learning achievements. However, existing studies often fail to accurately capture teachers' emotions due to the performative nature and overlook the critical impact of instructional information on emotional expression.In this paper, we systematically investigate teacher sentiment analysis by building both the dataset and the model accordingly. We construct the first large-scale teacher multimodal sentiment analysis dataset, T-MED.To ensure labeling accuracy and efficiency, we employ a human-machine collaborative labeling process.The T-MED dataset includes 14,938 instances of teacher emotional data from 250 real classrooms across 11 subjects ranging from K-12 to higher education, integrating multimodal text, audio, video, and instructional information.Furthermore, we propose a novel asymmetric attention-based multimodal teacher sentiment analysis model, AAM-TSA.AAM-TSA introduces an asymmetric attention mechanism and hierarchical gating unit to enable differentiated cross-modal feature fusion and precise emotional classification. Experimental results demonstrate that AAM-TSA significantly outperforms existing state-of-the-art methods in terms of accuracy and interpretability on the T-MED dataset.

Yongguang Li, Jindong Li, Qi Wang et al.

Source-Free Unsupervised Open-Set Domain Adaptation (SF-OSDA) methods using CLIP face significant issues: (1) while heavily dependent on domain-specific threshold selection, existing methods employ simple fixed thresholds, underutilizing CLIP's zero-shot potential in SF-OSDA scenarios; and (2) overlook intrinsic class tendencies while employing complex training to enforce feature separation, incurring deployment costs and feature shifts that compromise CLIP's generalization ability. To address these issues, we propose CLIPXpert, a novel SF-OSDA approach that integrates two key components: an adaptive thresholding strategy and an unknown class feature filtering module. Specifically, the Box-Cox GMM-Based Adaptive Thresholding (BGAT) module dynamically determines the optimal threshold by estimating sample score distributions, balancing known class recognition and unknown class sample detection. Additionally, the Singular Value Decomposition (SVD)-Based Unknown-Class Feature Filtering (SUFF) module reduces the tendency of unknown class samples towards known classes, improving the separation between known and unknown classes. Experiments show that our source-free and training-free method outperforms state-of-the-art trained approach UOTA by 1.92% on the DomainNet dataset, achieves SOTA-comparable performance on datasets such as Office-Home, and surpasses other SF-OSDA methods. This not only validates the effectiveness of our proposed method but also highlights CLIP's strong zero-shot potential for SF-OSDA tasks.

Jiao Huang, Qianli Xing, Jinglong Ji et al.

Crystal material representation is the foundation of crystal material research. Existing works consider crystal molecules as graph data with different representation methods and leverage the advantages of techniques in graph learning. A reasonable crystal representation method should capture the local and global information. However, existing methods only consider the local information of crystal molecules by modeling the bond distance and bond angle of first-order neighbors of atoms, which leads to the issue that different crystals will have the same representation. To solve this many-to-one issue, we consider the global information by further considering dihedral angles, which can guarantee that the proposed representation corresponds one-to-one with the crystal material. We first propose a periodic complete representation and calculation algorithm for infinite extended crystal materials. A theoretical proof for the representation that satisfies the periodic completeness is provided. Based on the proposed representation, we then propose a network for predicting crystal material properties, PerCNet, with a specially designed message passing mechanism. Extensive experiments are conducted on two real-world material benchmark datasets. The PerCNet achieves the best performance among baseline methods in terms of MAE. In addition, our results demonstrate the importance of the periodic scheme and completeness for crystal representation learning.

Lanbo Zhang, Yi Zhang, Qianli Xing

Existing document filtering systems learn user profiles based on user relevance feedback on documents. In some cases, users may have prior knowledge about what features are important. For example, a Spanish speaker may only want news written in Spanish, and thus a relevant document should contain the feature "Language: Spanish"; a researcher focusing on HIV knows an article with the medical subject "Subject: AIDS" is very likely to be relevant to him/her. Semi-structured documents with rich metadata are increasingly prevalent on the Internet. Motivated by the well-adopted faceted search interface in e-commerce, we study the exploitation of user prior knowledge on faceted features for semi-structured document filtering. We envision two faceted feedback mechanisms, and propose a novel user profile learning algorithm that can incorporate user feedback on features. To evaluate the proposed work, we use two data sets from the TREC filtering track, and conduct a user study on Amazon Mechanical Turk. Our experiment results show that user feedback on faceted features is useful for filtering. The proposed user profile learning algorithm can effectively learn from user feedback on both documents and features, and performs better than several existing methods.