Medical images exhibit latent anatomical groupings, such as organs, tissues, and pathological regions, that standard Vision Transformers (ViTs) fail to exploit. While recent work like SBM-Transformer attempts to incorporate such structures through stochastic binary masking, they suffer from non-differentiability, training instability, and the inability to model complex community structure. We present DCMM-Transformer, a novel ViT architecture for medical image analysis that incorporates a Degree-Corrected Mixed-Membership (DCMM) model as an additive bias in self-attention. Unlike prior approaches that rely on multiplicative masking and binary sampling, our method introduces community structure and degree heterogeneity in a fully differentiable and interpretable manner. Comprehensive experiments across diverse medical imaging datasets, including brain, chest, breast, and ocular modalities, demonstrate the superior performance and generalizability of the proposed approach. Furthermore, the learned group structure and structured attention modulation substantially enhance interpretability by yielding attention maps that are anatomically meaningful and semantically coherent.

The emergence of networked electrical machines has increased susceptibility to anomalies, including cyber-attack and physical faults, potentially leading to significant operational disruptions. In this article, we propose an online adaptive anomaly detection algorithm, adaptive enveloped singular spectrum transformation (AdaESST), which aims to identify hard-to-detect anomalies effectively. AdaESST first extracts informative components of signals by embedding the waveform data into subspaces using singular value decomposition, and then calculates anomalous score based on the subspace distance between two subsequence time series. AdaESST outperforms traditional detection methods by its capacity to adjust to new operational scenarios, thereby offering persistent protection in dynamic industrial environments. Throughout all numerical experiments simulating real-world industrial conditions, AdaESST exhibits high detection accuracy in monitoring motor and point of common coupling (PCC) currents, demonstrating its capability to safeguard against sophisticated anomalies. The detection accuracy for PCC currents is on par with that for motor currents. In essence, AdaESST has the potential to reduce the requirements for sensors, thereby lowering maintenance costs while maintaining high data integrity and security. The work contributes to enhancing the security of networked electrical machines, presenting a resilient and cost-efficient strategy in the face of emerging anomalies.

As cyber-physical systems are becoming more wide spread, it is imperative to secure these systems. In the real world these systems produce large amounts of data. However, it is generally impractical to test security techniques on operational cyber-physical systems. Thus, there exists a need to have realistic systems and data for testing security of cyber-physical systems [1]. This is often done in testbeds and cyber ranges. Most cyber ranges and testbeds focus on traditional network systems and few incorporate cyber-physical components. When they do, the cyber-physical components are often simulated. In the systems that incorporate cyber-physical components, generally only the network data is analyzed for attack detection and diagnosis. While there is some study in using physical signals to detect and diagnosis attacks, this data is not incorporated into current testbeds and cyber ranges. This study surveys currents testbeds and cyber ranges and demonstrates a prototype testbed that includes cyber-physical components and sensor data in addition to traditional cyber data monitoring.

Rapid and accurate detection and localization of electronic disturbances simultaneously are important for preventing its potential damages and determining potential remedies. The existing anomaly detection methods are severely limited by the low accuracy, expensive computational cost, and the need for highly trained personnel. There is an urgent need for a scalable online algorithm for the in-field analysis of large-scale power electronics networks. In this article, we propose a fast and accurate algorithm for anomaly detection and localization of power electronics networks: the stratified colored-node graph (CONGO). This algorithm hierarchically models the change of correlated waveforms and then correlated sensors using the CONGO. By aggregating the change of each sensor with its neighbors’ inputs, we can spontaneously identify and localize the anomaly that cannot be detected by data collected from a single sensor. As our proposed method only focuses on the changes within a short time frame, it is highly computational efficient and only needs small data storage. Thus, our method is ideal for online and reliable anomaly detection and localization of large-scale power electronic networks. Compared to the existing anomaly detection methods, our method is entirely data driven without training data, highly accurate and reliable for wide-spectrum anomalies detection, and more importantly, capable of both detection and localization. Thus, it is ideal for the in-field deployment for large-scale power electronic networks. As illustrated by a distributed energy resources (DERs) power grid with 37-node, our method can effectively detect and localize various cyber and physical attacks.