This paper introduces the Chebyshev Low-Pass Attention Filter (CLAF), a novel anomaly detection framework for multivariate time series in industrial sensor networks. The framework operates on Transformer attention weight matrices directly to address the limitation of raw data processing, which ignores multivariate dependencies and adapts poorly to pattern drift, within various existing offline static low-pass filters. Deployed only during testing as a plug-and-play component, CLAF constructs a dynamic normal baseline by performing spectral-domain smoothing on attention weight matrices adapted to non-stationary industrial data, and combines a hybrid anomaly score that integrates reconstruction error and Wasserstein distance to distinguish true anomalies from pattern drifts. Extensive experiments show that CLAF achieves state-of-the-art F1-scores of 0.9627 on SMD, 0.9570 on SMAP, 0.9541 on MSL, and 0.9139 on SWaT. CLAF maintains a recall above 0.98 and stable precision with high computational efficiency, offering it a practical solution for industrial intelligent maintenance.

Molecular machine learning has attracted much attention in molecular property prediction and AI-aided Drug Discovery. Recent advancements have significantly improved the efficiency and speed of searching for potential drug candidates. The primary challenge in molecular property prediction is how to transfer potential knowledge from known molecules to a large set of unseen candidates. To address this challenge, this work explores a metric distribution contrastive prediction method. This method establishes metric distributions among ordered categorical molecule populations, ensuring that all samples within the same class, whether known or unknown, share a common metric distribution. This technique comprises constructing metric distributions, extracting class prototypes as the foundational representations for each class, and employing a Bhattacharyya distribution transformation distance metric to distinguish pairwise metric distributions. Notably, this approach minimizes the distances between intra-class samples while increasing the distances between inter-class samples. This method integrates pre-training and subsequent prediction phases into a unified neural computation framework. Through thorough experiments on various diverse benchmark molecular datasets, the effectiveness of this method is validated, yielding notable improvements in prediction accuracy compared to existing models.

Graph embedding provides a feasible methodology to conduct pattern classification for graph-structured data by mapping each data into the vectorial space. Various pioneering works are essentially coding method that concentrates on a vectorial representation about the inner properties of a graph in terms of the topological constitution, node attributions, link relations, etc. However, the classification for each targeted data is a qualitative issue based on understanding the overall discrepancies within the dataset scale. From the statistical point of view, these discrepancies manifest a metric distribution over the dataset scale if the distance metric is adopted to measure the pairwise similarity or dissimilarity. Therefore, we present a novel embedding strategy named$\mathbf {MetricDistribution2vec}$to extract such distribution characteristics into the vectorial representation for each data. We demonstrate the application and effectiveness of our representation method in the supervised prediction tasks on extensive real-world structural graph datasets. The results have gained some unexpected increases compared with a surge of baselines on all the datasets, even if we take the lightweight models as classifiers. Moreover, the proposed method also conducts investigations in Few-Shot classification scenarios, and the results still show attractive discrimination in rare training samples based inference.

Molecular fingerprints are significant cheminformatics tools to map molecules into vectorial space according to their characteristics in diverse functional groups, atom sequences, and other topological structures. In this paper, we investigate a novel molecular fingerprint Anonymous-FP that possesses abundant perception about the underlying interactions shaped in small, medium, and large-scale atom chains. In detail, the possible atom chains from each molecule are sampled and extended as anonymous atom chains using an anonymous encoding manner. After that, the molecular fingerprint Anonymous-FP is embedded into vectorial space in virtue of the Natural Language Processing technique PV-DBOW. Anonymous-FP is studied on molecular property identification via molecule classification experiments on a series of molecule databases and has shown valuable advantages such as less dependence on prior knowledge, rich information content, full structural significance, and high experimental performance. During the experimental verification, the scale of the atom chain or its anonymous pattern is found significant to the overall representation ability of Anonymous-FP. Generally, the typical scale r = 8 could enhance the molecule classification performance, and specifically, Anonymous-FP gains the classification accuracy to above 93% on all NCI datasets.