The rapid expansion of the Internet of Things (IoT) has created a growing demand for large-scale sensor deployment. However, the high cost of physical sensors limits the scalability and coverage of sensor networks, making fine-grained sensing difficult. Inductive Spatio-Temporal Kriging (ISK) addresses this challenge by introducing virtual sensors that infer measurements from physical sensors, typically using graph neural networks (GNNs) to model their relationships. Despite its promise, current ISK methods often rely on standard message-passing and generic architectures that fail to effectively capture spatio-temporal features or represent virtual nodes accurately. Additionally, existing graph construction techniques suffer from sparse and noisy connections, further hindering performance. To address these limitations, we propose DarkFarseer, a novel ISK framework with three key innovations. First, the Style-enhanced Temporal-Spatial architecture adopts a temporal-then-spatial processing scheme with a temporal style transfer mechanism to enhance virtual node representations. Second, Regional-semantic Contrastive Learning improves representation learning by aligning virtual nodes with regional component patterns. Third, the Similarity-Based Graph Denoising Strategy mitigates the influence of noisy edges by leveraging temporal similarity and regional structure. Extensive experiments on real-world datasets demonstrate that DarkFarseer significantly outperforms state-of-the-art ISK methods.

Missing values are prevalent in multivariate time series forecasting, especially when certain variables are entirely missing, posing a significant challenge to traditional methods. Two-stage models that combine imputation and forecasting methods are prone to introduce bias and lead to error accumulation. In contrast, existing end-to-end forecasting models with missing data recovery components fail to ensure data consistency when missing variables. Diffusion models, known for their robust generative capabilities, prefer to generate consistent results based on the available observations. Consequently, we propose a novel end-to-end denoising diffusion model named DiffMissing for multivariate time series forecasting with variable missing. DiffMissing employs a contextual conditional encoder to extract local and global contextual information from the observed variables, assisting the denoising network in generating missing variables that align with the true distribution. Meanwhile, the dynamic interaction between observed and missing variables is modeled through a carefully designed adaptive sparse attention, ensuring consistency among variables. Extensive experimental results show that DiffMissing outperforms existing methods in prediction performance on multiple real-world datasets, especially in the high missing rate scenario of 90%, where the MAE is improved by an average of 9.90% over the best baseline.

Few-shot named entity recognition (NER) is a challenging task that aims to recognize new named entities with only a limited amount of labeled examples. In this paper, we introduce SSF, which is a streamlined span-based factorization method that addresses the problem of few-shot NER. Our approach formulates few-shot NER as a span-level alignment problem between query and support instances. To achieve this goal, SSF decomposes the span-level alignment problem into several refined span-level procedures. The proposed approach encompasses several key modules such as the Span Boosting Module, Span Prototypical Module, Span Alignment Module, and Span Optimization Module. Our experimental results demonstrate a significant improvement over the previous state-of-the-art performance. Specifically, compared to previous methods, our proposed approach achieves an average F1 score improvement of 12 points on the FewNERD dataset and 10 points on the SNIPS dataset. Moreover, our approach has surpassed the latest state-of-the-art performance on both datasets.