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Collaborative perception enables connected vehicles to share information, overcoming occlusions and extending the limited sensing range inherent in single-agent (non-collaborative) systems. Existing vision-only methods for 3D semantic occupancy prediction commonly rely on dense 3D voxels, which incur high communication costs, or 2D planar features, which require accurate depth estimation or additional supervision, limiting their applicability to collaborative scenarios. To address these challenges, we propose the first approach leveraging sparse 3D semantic Gaussian splatting for collaborative 3D semantic occupancy prediction. By sharing and fusing intermediate Gaussian primitives, our method provides three benefits: a neighborhood-based cross-agent fusion that removes duplicates and suppresses noisy or inconsistent Gaussians; a joint encoding of geometry and semantics in each primitive, which reduces reliance on depth supervision and allows simple rigid alignment; and sparse, object-centric messages that preserve structural information while reducing communication volume. Extensive experiments demonstrate that our approach outperforms single-agent perception and baseline collaborative methods by +8.42 and +3.28 points in mIoU, and +5.11 and +22.41 points in IoU, respectively. When further reducing the number of transmitted Gaussians, our method still achieves a +1.9 improvement in mIoU, using only 34.6% communication volume, highlighting robust performance under limited communication budgets. 

With the growing ubiquity of video content, efficient video analytics has become essential for applications such as surveillance, autonomous driving, and augmented reality. Yet, deploying video analytics models on resource-constrained edge devices and in lowbandwidth environments remains challenging. A dominant method for handling demanding video analytics tasks on edge devices has been to offload computation strategically from the edge device to servers. However, all prior solutions fail to offload under severely constrained, real-world network conditions (such as, a few-Mbps satellite network) due to the much higher data rates associated with video tasks. We introduce ApproxBit, a system to optimize shared edge-to-cloud processing for video analytics tasks; the two that we experiment with are video action recognition and video question answering. ApproxBit integrates an encoder within the video model, uses learned binary codes to effectively compress and offload data, and adaptively decides on the offloading point depending on the network bandwidth. ApproxBit’s adaptive and efficient data compression, which reduces the original feature map size by up to 2142.4×, makes it an ideal solution for video analytics on edge devices, especially with constrained networks. We evaluate ApproxBit on the two video tasks, across different model architectures (e.g., convolution- and Transformer-based) and multiple datasets (e.g., Something-Something-v2, Kinetics, and MSVD). Our results of latency and accuracy are superior over baselines: edge-only processing, server-only processing, DNN Surgery [ToCC ’23], full offloading of H.264-encoded videos, DeepCOD [SenSys ’20], neural video compression DCVC-FM [CVPR ’24], and Limit- Net [MobiSys ’24]. We also demonstrate ApproxBit’s adaptivity to changing network conditions, and generalization in a real-world user study. 

Resilience in cyber-physical systems (CPS) is the fundamental ability to maintain safety and critical functionality despite adverse "perturbations," which includes security attacks, environmental disruptions, and hardware or software failures. This survey provides a comprehensive review of CPS resilience, framing the field through five interconnected themes that are required in an integrated whole to achieve real-world resilience. The article first posits that resilience is a system-wide property emerging from interactions between hardware, software, and human users. Second, it addresses the challenges of learning-enabled CPS, which often operate in data-scarce environments characterized by imbalanced or noisy data, requiring innovative solutions like synthetic data generation and foundation model adaptation. Third, the survey examines proactive measures for resilience, which include distinctive aspects of verification, testing, and redundancy. Fourth, it explores recovery mechanisms, moving beyond traditional fault models to design "just good enough" recovery strategies that prioritize safety-critical functions during perturbations. Finally, it highlights the central role of the human, focusing on the different levels of human intervention, the necessity of trust calibration, and the requirement for explainable AI to support human-CPS teaming. These themes are illustrated through representative application domains, primarily Connected and Autonomous Transportation Systems (CATS) and Medical CPS (MCPS). By integrating the five interconnected themes, this survey provides a systematic roadmap for achieving the resilient CPS in increasingly complex and adversarial environments.