Lossless compression is a classic technique for reducing data storage and transmission requirements. Asymmetric Numeral Systems (ANS) is a high-throughput, high-ratio lossless compression algorithm, but it lacks effective support for multi-byte data and cross-platform compatibility. To address this issue, we propose an Adaptive Data Mapping (ADM) scheme, which maps multi-byte integer data into single-byte space based on the data’s characteristics, improving the compression ratio of ANS while maintaining low encoding redundancy. We also optimize the ADM algorithm and the ANS encoder for GPU and CPU architectures, respectively, and combine them to create an efficient and portable ANS encoding method for multi-byte integer data, called MANS. Experimental results show that MANS improves compression ratios by an average of 1.24 ×, achieves 870.27MB/s throughput on CPUs, and delivers up to 288.45 × and 135.86 × speedups on an NVIDIA A100 and an AMD MI210 GPU compared to the CPU version—demonstrating its efficiency and portability across platforms.

Multivariate time series (MTS), whose patterns change dynamically, often have complex temporal and dimensional dependence. Most existing reconstruction-based MTS anomaly detection methods only learn the point-wise information while ignoring the overall trend of time series, resulting in their incompetence in extracting high-level semantic information. Although a few contrastive learning-based approaches have been proposed recently to solve this problem, they forcibly increase the difference between the features of normal data, leading to the loss of useful information. This paper proposes an adversarial contrastive autoencoder (ACAE) for MTS anomaly detection. ACAE conducts feature combination and decomposition as the contrastive learning proxy task, which introduces adversarial training to learn the transformation-invariant representation of data, achieving a robust representation of MTS. Firstly, ACAE constructs positive and negative sample pairs through the multi-scale timestamp mask and random sampling. Secondly, the features of the original samples are combined with those of the positive and negative samples to generate the positive and negative composite features. Finally, ACAE trains the encoder and discriminator to decompose the negative composite features cooperatively to decrease the similarity between the features of negative pairs. In contrast, it adversarially decomposes the positive composite features to increase the similarity between the features of positive pairs. Experimental results show that ACAE outperforms 14 state-of-the-art baselines on five real-world datasets from different fields.

Nowadays, the massive industrial data has effectively improved the performance of the data-driven deep learning Remaining Useful Life (RUL) prediction method. However, there are still problems of assigning fixed weights to features and only coarse-grained consideration at the sequence level. This paper proposes a Transformer-based end-to-end feature-level mask self-supervised learning method for RUL prediction. First, by proposing a fine-grained feature-level mask self-supervised learning method, the data at different time points under all features in a time window is sent to two parallel learning streams with and without random masks. The model can learn more fine-grained degradation information by comparing the information extracted by the two parallel streams. Instead of assigning fixed weights to different features, the abstract information extracted through the above process is invariable correlations between features, which has a good generalization to various situations under different working conditions. Then, the extracted information is encoded and decoded again using an asymmetric structure, and a fully connected network is used to build a mapping between the extracted information and the RUL. We conduct experiments on the public C-MAPSS datasets and show that the proposed method outperforms the other methods, and its advantages are more obvious in complex multi-working conditions.