Multivariate time series anomaly detection holds critical application value in key domains such as industrial system monitoring, financial risk management, and medical surveillance. However, existing approaches face two major challenges: reconstruction-based or prediction-based models tend to adapt to anomalous patterns during training, thereby weakening the distinction between normal and abnormal samples; furthermore, the non-stationary nature of time series leads to distribution shifts between training and testing data, impairing model generalization. To address these issues, this paper proposes the TFCID model. The model innovatively leverages diffusion principles to effectively impute missing time series data while capturing significant frequency-domain features. In the temporal processing stream, an unconditional diffusion model combined with imputation masking is employed to achieve high-precision imputation of randomly missing values, effectively preventing anomalies from interfering with model training. In the frequency-domain processing stream, an amplitude-aware frequency-domain masked autoencoder is introduced to specifically capture periodic or trend-based pattern anomalies. The model mitigates distribution shift by constraining the discrepancy between temporal and frequency-domain representations via adversarial contrastive learning, and uses this discrepancy as a robust anomaly scoring metric. Experimental results on five public benchmark datasets show that TFCID significantly outperforms state-of-the-art methods in detection accuracy (F1-Score), validating its effectiveness in anomaly detection tasks.

In many board games, AlphaZero has demonstrated superhuman abilities. Dots-and-Boxes is a classic board game with simple rules but requiring skills to win. This paper proposes Dotsformer, which extracts chain-loop structures from the game board. These structures connect distant boxes, providing long-range relational information as input to the Transformer. We employ multiple convolutional kernels to generate Q, K, and V, and incorporate information about the box structure itself into the attention scores. We also incorporate auxiliary training tasks, including an initiative task and a classification task. These tasks determine whether to retain or relinquish the initiative in the current situation, and classify actions into forbidden, conceding, safe, and scoring moves. They provide additional supervisory signals and accelerate learning. The experimental results show that Dotsformer outperforms AlphaZero in both rollback speed and playing strength: it achieved a winning rate of 87.6% and an ELO rating lead of 340 points against the baseline. Additionally, ablation studies verify the effectiveness of each key module.