Adversarial patches are widely used to evaluate the robustness of object detection systems in real-world scenarios. These patches were initially designed to deceive single-modal detectors (e.g., visible or infrared) and have recently been extended to target visible-infrared dual-modal detectors. However, existing dual-modal adversarial patch attacks have limited attack effectiveness across diverse physical scenarios. To address this, we propose CDUPatch, a universal cross-modal patch attack against visible-infrared object detectors across scales, views, and scenarios. Specifically, we observe that color variations lead to different levels of thermal absorption, resulting in temperature differences in infrared imaging. Leveraging this property, we propose an RGB-to-infrared adapter that maps RGB patches to infrared patches, enabling unified optimization of cross-modal patches. By learning an optimal color distribution on the adversarial patch, we can manipulate its thermal response and generate an adversarial infrared texture. Additionally, we introduce a multi-scale clipping strategy and construct a new visible-infrared dataset, MSDrone, which contains aerial vehicle images in varying scales and perspectives. These data augmentation strategies enhance the robustness of our patch in real-world conditions. Experiments on four benchmark datasets (e.g., DroneVehicle, LLVIP, VisDrone, MSDrone) show that our method outperforms existing patch attacks in the digital domain. Extensive physical tests further confirm strong transferability across scales, views, and scenarios. Attack demos are provided in the supplementary materials.

Adversarial transferability is a significant property of adversarial examples, which renders the adversarial example capable of attacking unknown models. However, the models with different architectures on the same task would concentrate on different information, which weakens adversarial transferability. To enhance the adversarial transferability, input transformation-based attacks perform random transformation over input to find a better result that can resist such transformations, but these methods ignore the model discrepancy; ensemble attacks fuse multiple models to shrink the search space to ensure that the found adversarial examples work on these models, but ensemble attacks are resource-intensive. In this article, we propose a simple but effective feature augmentation attack (FAUG) method to improve adversarial transferability. We dynamically add random noise to intermediate features of the target model during the generation of adversarial examples, thereby avoiding overfitting the target model. Specifically, we first explore the noise tolerance of the model and disclose the discrepancy under different layers and noise strengths. Then, based on that analysis, we devise a dynamic random noise generation method, which determines noise strength according to the produced features in the mini-batch. Finally, we exploit the gradient-based attack algorithm on featureaugmented models, resulting in better adversarial transferability without introducing extra computation costs. Extensive experiments conducted on the ImageNet dataset across CNN and Transformer models corroborate the efficacy of our method, e.g., we achieve improvement of +30.67% and +5.57% on input transformation-based attacks and combination methods, respectively.

The dissection of hyperspectral images into intrinsic components through hyperspectral intrinsic image decomposition (HIID) enhances the interpretability of hyperspectral data, providing a foundation for more accurate classification outcomes. However, the classification performance of HIID is constrained by the model’s representational ability. To address this limitation, this study rethinks hyperspectral intrinsic image decomposition for classification tasks by introducing deep feature embedding. The proposed framework, HyperDID, incorporates the Environmental Feature Module (EFM) and Categorical Feature Module (CFM) to extract intrinsic features. Additionally, a Feature Discrimination Module (FDM) is introduced to separate environment-related and category-related features. Experimental results across three commonly used datasets validate the effectiveness of HyperDID in improving hyperspectral image classification performance. This novel approach holds promise for advancing the capabilities of hyperspectral image analysis by leveraging deep feature embedding principles. The implementation of the proposed method could be accessed soon at https://github.com/shendu-sw/HyperDID for the sake of reproducibility.

For the temperature field reconstruction (TFR), a complex image-to-image regression problem, the convolutional neural network (CNN) is a powerful surrogate model due to the convolutional layer's good image feature extraction ability. However, a lot of labeled data is needed to train CNN, and the common CNN can not quantify the aleatoric uncertainty caused by data noise. In actual engineering, the noiseless and labeled training data is hardly obtained for the TFR. To solve these two problems, this paper proposes a deep Monte Carlo quantile regression (Deep MC-QR) method for reconstructing the temperature field and quantifying aleatoric uncertainty caused by data noise. On the one hand, the Deep MC-QR method uses physical knowledge to guide the training of CNN. Thereby, the Deep MC-QR method can reconstruct an accurate TFR surrogate model without any labeled training data. On the other hand, the Deep MC-QR method constructs a quantile level image for each input in each training epoch. Then, the trained CNN model can quantify aleatoric uncertainty by quantile level image sampling during the prediction stage. Finally, the effectiveness of the proposed Deep MC-QR method is validated by many experiments, and the influence of data noise on TFR is analyzed.

No doubt that the proper design of satellite system reliability is one of the most important issues in satellite system engineering. In order to optimize the satellite reliability, the reliability optimization method is studied in this paper based on compression algorithm, Bayesian Network (BN) and Particle Swarm Optimization (PSO). Firstly, the improved compression inference algorithm (ICIA) is proposed to model the BN reliability model of multistate system based on the existing compression and inference algorithms. Then, the reliability optimization problem is formulated, which is a typical discrete optimization problem with the discrete reliability measured in the number of each component's units as design variables. The process of optimization, based on the BN reliability model of multistate satellite system will be achieved by PSO. The efficacy of the proposed method is demonstrated in a microsatellite system reliability optimization, which verifies the importance of satellite reliability optimization in decreasing the cost of satellite development.

Abstract Load sharing in large, heterogeneous distributed systems allows users to access vast amounts of computing resources scattered around the system and may provide substantial performance improvements to applications. We discuss the design and implementation issues in Utopia, a load sharing facility specifically built for large and heterogeneous systems. The system has no restriction on the types of tasks that can be remotely executed, involves few application changes and no operating system change, supports a high degree of transparency for remote task execution, and incurs low overhead. The algorithms for managing resource load information and task placement take advantage of the clustering nature of large‐scale distributed systems; centralized algorithms are used within host clusters, and directed graph algorithms are used among the clusters to make Utopia scalable to thousands of hosts. Task placements in Utopia exploit the heterogeneous hosts and consider varying resource demands of the tasks. A range of mechanisms for remote execution is available in Utopia that provides varying degrees of transparency and efficiency. A number of applications have been developed for Utopia, ranging from a load sharing command interpreter, to parallel and distributed applications, to a distributed batch facility. For example, an enhanced Unix command interpreter allows arbitrary commands and user jobs to be executed remotely, and a parallel make facility achieves speed‐ups of 15 or more by processing a collection of tasks in parallel on a number of hosts.