A.1 Network Architecture Here, we describe the architecture of the eVAE presented in Figure 1 of the main paper, in more detail. Event Context Network: We adapt the architecture proposed in [21] for the event context network, but without the feature transformation preprocessing steps. In our implementation, we use three Conv1d layers of 64, 128 and 1024 channels each followed by BatchNorm and a ReLU activation. At the end of the ECN, we add the temporal features (see Appendix A.2) to the N 1024 feature tensor, and execute the max operation to result in a context vector. The sizes of the intermediate features and the context feature are hyperparameters that can be varied based on the application, data complexity etc. Encoder: The encoder for the VAE is composed of two layers, of sizes 1024 and 256 respectively, resulting in two output vectors of 1 8 each, corresponding to the mean and standard deviation for the latent space vector.

The section explains detailed CipherNav network architecture in Table 4, 5 and 6. The view encoder E is shown in Table 4 and map encoder E is shown in Table 5. The encoders are trained end-to-end during plaintext training and freezed during ciphertext training. Each party has a copy of the encoder models and locally computes all forward passes in ciphertext training. The action classification network Gis shown in Table 6.

We consider the safe reinforcement learning (RL) problem of maximizing utility with extremely low constraint violation rates. Assuming no prior knowledge or pre-training of the environment safety model given a task, an agent has to learn, via exploration, which states and actions are safe. A popular approach in this line of research is to combine a model-free RL algorithm with the Lagrangian method to adjust the weight of the constraint reward relative to the utility reward dynamically. It relies on a single policy to handle the conflict between utility and constraint rewards, which is often challenging. We present SEditor, a two-policy approach that learns a safety editor policy transforming potentially unsafe actions proposed by a utility maximizer policy into safe ones.

Monocular Depth Estimation (MDE) enables the prediction of scene depths from a single RGB image, having been widely integrated into production-grade autonomous driving systems, e.g., Tesla Autopilot. Current adversarial attacks to MDE models focus on attaching an optimized adversarial patch to a designated obstacle. Although effective, this approach presents two inherent limitations: its reliance on specific obstacles and its limited malicious impact. In contrast, we propose a pioneering attack to MDE models that \textit{decouples obstacles from patches physically and deploys optimized patches on roads}, thereby extending the attack scope to arbitrary traffic participants. This approach is inspired by our groundbreaking discovery: \textit{various MDE models with different architectures, trained for autonomous driving, heavily rely on road regions} when predicting depths for different obstacles. Based on this discovery, we design the Adversarial Road Marking (AdvRM) attack, which camouflages patches as ordinary road markings and deploys them on roads, thereby posing a continuous threat within the environment. Experimental results from both dataset simulations and real-world scenarios demonstrate that AdvRM is effective, stealthy, and robust against various MDE models, achieving about 1.507 of Mean Relative Shift Ratio (MRSR) over 8 MDE models.

Drones aren't yet licensed to carry passengers, but some may already be airlifting wounded personnel off the battlefield and could be employed for smuggling people Still from a promotional video for Skysurfer, a US company that sells "ultralight aircraft" for personal, recreational use The first passenger-carrying drones may already be in use. These aren't sophisticated urban air taxis, but crudely modified cargo drones transporting combat casualties and criminals. Heavy-lift drones are essentially scaled-up versions of the familiar quadcopters. Hair-raising videos of hobbyists carried by home-made drones show that the basic technology is simple enough. But meeting aircraft safety requirements for passenger transport takes years, and drone-makers, including Volocopter, EHang and Eve Air Mobility, are all aiming to get vehicles certified this year or next.