Industry devices (ie, entities) such as server machines, spacecrafts, engines, etc., are typically
monitored with multivariate time series, whose anomaly detection is critical for an entity's …

Learning generative models for graph-structured data is challenging because graphs are
discrete, combinatorial, and the underlying data distribution is invariant to the ordering of nodes…

Internet-based service companies monitor a large number of KPIs (Key Performance
Indicators) to ensure their service quality and reliability. Correlating KPIs by fluctuations reveals …

The accurate estimation of mutual information (MI) plays a vital role in understanding
channel capacity and optimizing the performance of optical communications. While MI …

Objective Due to their special geological conditions and coal mining background, coal mining
subsidence areas are prone to suffer from ground cracks and subsidence, which tend to …

To improve the discharge performance of aluminum–air batteries, CeO 2 /Al6061 composites
were prepared as an anode using selective laser melting (SLM). Response surface …

High-resolution seismic models of sediment basins are critical inputs for earthquake ground
motion prediction and petroleum resource exploration. In this study we employed a newly …

Graph neural networks (GNNs) are emerging machine learning models on graphs. One key
property behind the expressiveness of existing GNNs is that the learned node …

For high-stakes online exams, it is important to detect potential rule violations to ensure the
security of the test. In this study, we investigate the task of detecting whether test takers are …

In situ stress measurement at seismogenic depth is critically important for deciphering fault
zone processes. In this study, we conducted a second active‐source crosswell field …