Anomaly detection using spatial and temporal information in multivariate time series.

Real-world industrial systems contain a large number of interconnected sensors that generate a significant amount of time series data during system operation. Performing anomaly detection on these multivariate time series data can timely find faults, prevent malicious attacks, and ensure these systems safe and reliable operation. However, the rarity of abnormal instances leads to a lack of labeled data, so the supervised machine learning methods are not applicable. Furthermore, most current techniques do not take full advantage of the spatial and temporal dependencies implied among multiple variables to detect anomalies. Hence, we propose STADN, a novel Anomaly Detection Network Using Spatial and Temporal Information. STADN models the relationship graph between variables for a graph attention network to capture the spatial dependency between variables and utilizes a long short-term memory network to mine the temporal dependency of time series to fully use the spatial and temporal information of multivariate time series. STADN predicts the future behavior of each sensor by combining the historical behavior of the sensor and its neighbors, then detects and locates anomalies according to the prediction error. Furthermore, we improve the proposed model's ability to discriminate anomaly and regularity and expand the prediction error gap between normal and abnormal instances by reconstructing the prediction errors. We conduct experiments on two real-world datasets, and the experimental results suggested that STADN achieves state-of-the-art outperformance.

Review on Main Gate Characteristics of P-Type GaN Gate High-Electron-Mobility Transistors.

As wide bandgap semiconductors, gallium nitride (GaN) lateral high-electron-mobility transistors (HEMTs) possess high breakdown voltage, low resistance and high frequency performance. PGaN gate HEMTs are promising candidates for high-voltage, high-power applications due to the normally off operation and robust gate reliability. However, the threshold and gate-breakdown voltages are relatively low compared with Si-based and SiC-based power MOSFETs. The epitaxial layers and device structures were optimized to enhance the main characteristics of pGaN HEMTs. In this work, various methods to improve threshold and gate-breakdown voltages are presented, such as the top-layer optimization of the pGaN cap, hole-concentration enhancement, the low-work-function gate electrode, and the MIS-type pGaN gate. The discussion of the main gate characteristic enhancement of p-type GaN gate HEMTs would accelerate the development of GaN power electronics to some extent.

Survey on security issues of routing and anomaly detection for space information networks.

Space information networks is network systems that can receive, transmit, and process spatial information lively. It uses satellites, stratosphere airships, Unmanned Aerial Vehicles, and other platforms as the carrier. It supports high-dynamic, real-time broadband transmission of earth observations and ultra-long-distance, long-delay reliable transmission of deep space exploration. The deeper the network integration, the higher the system's security concerns and the more likely SINs will be controlled and destroyed in terms of cybersecurity. How to integrate new IT technologies such as artificial intelligence, digital twins, and blockchain to diverse application scenarios of SINs while maintaining SIN cybersecurity will be a long-term critical technical issue. This study is a review of the security issues for space information networks. First, this paper examines space information networks' security issues and figures out the relationship between the main security threats, services, and mechanisms. Then, this article selects secure routing and anomaly detection from many security technologies to conduct a detailed overview from two perspectives of traditional methods and artificial intelligence. Subsequently, this paper investigates anomaly detection schemes for spatial information networks and proposes a deep learning-based anomaly detection scheme. Finally, we suggest the potential research directions and opening problems of space information network security. Overall, this paper aims to give readers an overview of the newly emerging technologies in space information networks' security issues and provide inspiration for future exploration.

Delayed but not loss of gliogenesis in Rbpj-deficient trigeminal ganglion.

Somatosensory ganglia including dorsal root ganglion (DRG) and trigeminal ganglion (TG) are derived from a common pool of neural crest stem cells (NCCs), and are good systems to study the mechanisms of neurogenesis and gliogenesis. Previous studies have reported that deletion of Rbpj, a critical integrator of activation signals from all Notch receptors, in NCCs and their derived cells resulted in the delayed gliogenesis at early stage and a loss of glial cells at later stage in the DRG. But the phenotypes in the TG have not been described. Here we reported although the gliogenesis was also delayed initially in Rbpj-deficient TG, it was recovered as the development progressed, as shown by the presence of large number of glial cells in the TG at later stages. However, neuronal reduction was observed in Rbpj-deficient TG, which is similar to what observed in Rbpj-deficient DRG. Taken together, our data indicate the function of Rbpj is diversified and context dependent in the gliogenesis of somatosensory ganglia.