Time series anomaly detection in real-world Industrial Internet of Things (IIoT) systems is pivotal for identifying unsafe conditions and implementing timely preventive measures. While diffusion models are popular for capturing complex patterns, they often struggle to balance diversity and fidelity across scenarios due to limited exploration of context-window logical inference relationships and trend-pattern cues. To address these challenges, we propose ICAD, a novel method that rethinks the role of inference and cues in IIoT time series anomaly detection. ICAD defines trend patterns and uncertainties in textual form and utilizes a fine-tuned large language model to encode these descriptions as conditions for the diffusion model, thereby enhancing its generalization across diverse data distributions. Additionally, a “reasoning network for contextual window” mechanism is designed to capture temporal dependencies between adjacent windows, complemented by multi-scale and spatial feature adaptive fusion modules to further enhance the predictive performance. Empirical evaluations across four benchmark datasets and a large-scale ethylene oxide production process demonstrate that ICAD consistently outperforms state-of-the-art baselines, confirming its effectiveness and practicality in overcoming current anomaly detection model limitations.

In general knowledge base question answering (KBQA) models, subject recognition (SR) is usually a precondition of finding an answer, and it is a common way to employ a general named entity recognition (NER) model such as BERT‐CRF to recognize the subject. However, in previous researches, the difference between a NER task and a SR task is usually ignored, and a wrong entity recognized by the NER model will certainly lead to a wrong answer in the KBQA task, which is one bottleneck for KBQA performance. In this paper, a multigranularity pruning model (MGPM) is proposed to answer a question when general models fail to recognize a subject. In MGPM, the set of all possible subjects in the Knowledge Base (KB) is pruned by 4 multigranularity pruning submodels successively based on the constraint of relation (domain and tuple), string similarity, and semantic similarity. Experimental results show that our model is compatible with various KBQA models for both single‐relation and complex questions answering. The integrated MGPM model (with the BERT‐CRF model) achieves a SR accuracy of 94.4% on the SimpleQuestions dataset, 68.6% on the WebQuestionsSP dataset, and 63.7% on the WebQuestions dataset, which outperforms the original model by a margin of 3.6%, 8.6%, and 5.3%, respectively.

Predicting treatment medicines is a key task in many intelligent healthcare systems. Prediction of treatment medicines can assist doctors in making informed prescription decisions for patients according to their Electronic Health Records (EHRs). However, predicting treatment medicines is a challenging task due to the following reasons: (1) heterogeneous nature of EHR data that typically includes laboratory results, treatment records, disease conditions, and demographic information; (2) complex correlations among EHR sequences, including inter-correlations between sequences and temporal intra-correlations within each sequence; (3) temporal dynamics of these correlations changing with disease progression. In this paper, we predict treatment medicines for patients with dual adaptive sequential networks (DASNet). Specifically, DASNet is designed with three components. First, a decomposed adaptive long short-term memory network (DA-LSTM) is designed to capture the intra- and inter-correlations in multiple heterogeneous temporal sequences. Then, we develop an attentive meta learning network (AT-MetaNet) to learn dynamic weight parameters for DA-LSTM, thus enabling it to model various correlation structures. Finally, we employ an attentive fusion network (AT-FuNet) to incorporate historical information and collectively fuse representation embeddings of heterogeneous data to predict treatment medicines. Our results on the public MIMIC-III dataset covering 11 medical conditions demonstrate that the proposed end-to-end model can achieve the state-of-the-art prediction performance while providing clinically useful insights.

Game theory has become a standard tool for depicting and demonstrating various game-like phenomena by providing appropriate mathematical models and for analyzing and predicting agents' behaviors and their decisions by formalizing solution concepts. The conventional game model mainly concerns ideal systems that would always guarantee optimal responses, which appears unrealistic for practical game scenarios since decision-making usually entails resource costs. Therefore, this study considers players' decision-making in extensive games when the computational cost of searching the strategy space is limited. We start with a new mathematical model of extensive games that features a bound on computational resources during players' decision-making process such that they can only foresee a part of the available alternatives in the future. This model is more appropriate in predicting players' strategies than the conventional model, under which we investigate the effects of computational costs on players' strategies as well as the computational complexity. Furthermore, a simulation experiment is performed to seek the connection between the amount of resources and the goodness of the outcomes. This study is expected to provide a foundation for players' rational decision-making with computational costs.

Partial correlation-based connectivity networks can describe the direct connectivity between features while avoiding spurious effects, and hence they can be implemented in diagnosing complex dynamic multivariate systems. However, existing studies mainly focus on single systems that are ill-equipped for incremental learning. Moreover, related methods estimate temporal connectivity network by imposing only sparse regularization without integrating pattern priors (e.g., inter-system shared pattern and intra-system intrinsic pattern), which have been proven effective in limiting noise interference. To this end, we develop an adaptive connectivity estimation model that incorporates prior patterns, namely Sparse Adaptive Meta-Learning Connectivity Network (SAMCN). Specifically, our model extends ideas of the gradient-based meta-learning to capture inter-system shared prior information by generating fast adaptive initialization parameters for the connectivity matrix. Then, a sparse variational autoencoder is proposed to generate a weight matrix for sparse regularization penalty in reweighted LASSO, which helps extract intra-system intrinsic patterns (local manifold structure). Experimental results on both synthetic data and real-world datasets demonstrate that our method is capable of adequately capturing the aforementioned pattern priors. Further, experiments from corresponding classification tasks validate the strength of the prior pattern-aware features connectivity network in resulting in better classification performance.