Anomaly detection is a critical component for ensuring data quality in data management; however, as continuously collected data introduces unseen normal and anomalous classes, the performance of traditional methods often deteriorates markedly. While some approaches attempt to mitigate this challenge by simulating unseen anomaly distributions, they are constrained by the quality of the generated pseudo-anomalies and fail to solve the core problem of misidentifying unseen normal instances as anomalous. We address these limitations from a novel perspective of normal invariant representation learning by proposing WAlign, which introduces a misclassification-aware weighting mechanism for the normal distribution alignment process. This mechanism mitigates the detrimental influence of misclassified instances and unlabeled anomalies on representation learning for normal instances. As a plug-and-play module, WAlign can be seamlessly integrated into two well-established anomaly detection paradigms. For each paradigm, we instantiate a lightweight base model and conduct extensive experiments on five real-world datasets. Experimental results demonstrate that integrating WAlign improves the AUC-PR by up to 3.7
Incremental Named Entity Recognition (INER) aims to learn new entity types over time while preserving performance on previously seen types, addressing the challenges of catastrophic forgetting and semantic shift. Existing methods mostly combine pseudo-labeling and knowledge distillation to protect old knowledge, achieving promising results. However, they typically randomly initialize the classification head for new entity types, which creates a significant misalignment between the new classifier and features extracted by the backbone model. This misalignment leads to highly unstable gradient updates during early training, slowing convergence and, more critically, increasing the risk of distorting the shared feature space-ultimately harming performance. To address this issue, we propose New Classifier Pre-IniTialization (NCPT), a strategy that generates well-informed initial parameters for new classifiers before formal incremental training begins. Instead of random initialization, NCPT learns a structured transformation from the weights of old classifiers to initialize new classifiers. This ensures that the new classifier starts from a point that is semantically coherent with the existing feature space, enabling smoother optimization and better integration of new knowledge. Furthermore, we introduce a cross-step class similarity mechanism that leverages semantic relatedness computed from old prototypes and new entity types to guide the transformation. These allow the model to balance stability and plasticity. Experiments on three datasets demonstrate that NCPT significantly enhances performance and training stability across incremental steps when integrated into state-of-the-art methods, offering a principled solution to classifier initialization.
Spatio-temporal forecasting plays a vital role in various domains such as transportation, meteorology, and urban management. However, existing approaches often struggle to model complex spatio-temporal dependencies effectively while maintaining computational efficiency, particularly when handling full spatio-temporal sequences. The quadratic time-space complexity of the traditional self-attention mechanism significantly limits its scalability to long-sequence tasks. To address these challenges, we propose LOSTFormer (Linear Orthogonal Spatio-Temporal Transformer) for efficient and accurate spatio-temporal forecasting. LOSTFormer constructs hierarchical embeddings across temporal, spatial, and joint spatio-temporal dimensions to enhance representation learning. Linear attention framework, a novel Learnable Orthogonal Rotation Attention (Lor-Attention) is introduced, which employs the Cayley transform to learn orthogonal rotation matrices and adaptively refine the mapping of Positive Orthogonal Random Features. Our approach can achieve linear computational complexity and effectively capture long-range and cross-dimensional dependencies in spatio-temporal data. Extensive experiments on spatio-temporal datasets demonstrate that LOSTFormer consistently outperforms state-of-the-art methods in prediction accuracy while maintaining efficiency. The code can be found on our GitHub repository( https://github.com/QAQYYC/LOSTFormer .).
Detecting sparse anomalies in long time series is a fundamental challenge in time series anomaly detection, where anomalous events are extremely rare, subtle, and occupy only a minute fraction of the sequence. Excessive false positives in such sparse settings can severely undermine system usability and erode user trust. Most deep learning models are constrained by fixed window scopes and single-mode decision mechanisms, lacking explicit connections to historical windows and sufficient anomaly evaluation criteria, which leads to frequent false alarms. To tackle these limitations, we propose a self-supervised method named C ^3 DR (Cross-window Contrast and Cross-level Discriminative Reconstruction), which unifies representation quality and reconstruction deviation as joint multi-mode evaluation criteria within a single algorithm. Specifically, C ^3 DR models long-range cross-window consistency during encoding, and introduces a discriminative bidirectional cross-attention mechanism during decoding to mitigate overfitting on anomalous regions. On the high-quality UCR benchmark, four-protocol testing shows C ^3 DR reliably outperforms leading deep learning and distance-based detectors, achieving superior event-level accuracy, robust regional detection, and reduced false alarms.
Complex event processing faces significant challenges in real-world scenarios where noisy event streams containing missing events, misordered sequences, and erroneous attribute values are prevalent. Existing approaches address only specific aspects of this problem, with approximate matching techniques handling missing events and out-of-order processing methods addressing sequence misalignment, but none provide a comprehensive solution. This paper introduces K-NFA, a novel fault-tolerant automaton that systematically handles all major error types in complex event matching. Our approach features a compact automaton structure with specialized transitions for event deletion and swap operations, coupled with a dynamic matching algorithm that efficiently processes noisy streams while respecting temporal and attribute constraints. Experimental results show our method improves fault-tolerant matching capacity by 32.91
