The widespread deployment of phasor measurement units (PMUs) in power transmission systems has accelerated the development of deep learning–based real-time monitoring solutions, such as event classification. Despite these advancements, recent research indicates that adversarial attacks pose a significant threat, as even minor perturbations in the input data can deceive well-trained models. In domains like computer vision, adversarial samples have been shown to transfer across different architectures, thus enabling black-box attacks via surrogate models. However, this transferability phenomenon remains largely unexplored in power system applications. In this work, we conduct a comprehensive study of adversarial transferability for power system event classification using machine learning models and a large-scale dataset. Drawing on the insights from this investigation, we propose a novel ensemble-based black-box adversarial attack that exploits transferability to achieve higher success rates and greater query efficiency. Furthermore, beyond using the Euclidean norm to measure perturbations, we incorporate signal-to-noise ratio (SNR) and maximum mean discrepancy (MMD) to enhance the robustness and depth of our perturbation analysis. Extensive experiments on a large-scale, real-world PMU dataset and state-of-the-art event classifiers highlight the effectiveness of our proposed approach.

Diffusion bridge models and stochastic interpolants enable high-quality image-to-image (I2I) translation by creating paths between distributions in pixel space. However, recent diffusion bridge models excel in image translation but suffer from restricted design flexibility and complicated hyperparameter tuning, whereas Stochastic Interpolants offer greater flexibility but lack essential refinements. We show that these complementary strengths can be unified by interpreting all existing methods within a single SI-based framework. In this work, we unify and expand the space of bridge models by extending Stochastic Interpolants (SIs) with preconditioning, endpoint conditioning, and an optimized sampling algorithm. These enhancements expand the design space of diffusion bridge models, leading to state-of-the-art performance in both image quality and sampling efficiency across diverse I2I tasks. Furthermore, we identify and address a previously overlooked issue of low sample diversity under fixed conditions. We introduce a quantitative analysis for output diversity and demonstrate how we can modify the base distribution for further improvements.

In StarCraft, buildings arrangement near the intersections is one of most the critical strategic decisions in the early stage. The high time complexity of the buildings arrangement makes it difficult for AI bot to make the real-time decision. This paper presents an approach to analyze the intersection in StarCraft II maps. We propose a radarlike algorithm to automatically detect the intersection and use a designed heuristic search algorithm to arrange the building for building the wall. Our method can obtain the optimal solution while meeting the real-time requirement.