In this article, we study the problem of differentially private multi-party data sharing, where the involved parties assisted by a semi-honest curator collectively generate a shared dataset while satisfying differential privacy. Inspired by the success of data synthesis with the generative adversarial network (GAN), we propose a novel GAN-based differentially private multi-party data sharing approach named ATLAS. In ATLAS, we extend the original GAN to multiple discriminators, and let each party hold a discriminator while the curator holds a generator. To update the generator without compromising each party's privacy, we decompose the calculation of the generator's gradient and selectively sanitize the discriminators’ responses . Additionally, we propose two methods to improve the utility of shared data, i.e., the collaborative discriminator filtering (CDF) method and the adaptive gradient perturbation (AGP) method. Specifically, the CDF method utilizes trained discriminators to refine synthetic records, while the AGP method adaptively adjusts the noise scale during training to reduce the impact of deferentially private noise on the final shared data. Extensive experiments on real-world datasets validate the superiority of our ATLAS approach.

Reinforcement learning (RL) has become an essential tool in optimizing transportation systems, offering promising solutions to dynamic and complex decision-making challenges. This paper explores the application of RL in four representative domains of transportation systems, including road-level traffic signal control, low-altitude unmanned aerial vehicle (UAV) navigation, high-altitude air traffic management (ATM), and maritime autonomous ship (AMS) navigation. These domains span land, low airspace, high airspace, and sea, reflecting key operational environments where multi-agent coordination is crucial. While significant advances have been made in the development of RL-based systems, traditional models often face limitations when applied to diverse, real-world environments due to their reliance on single-scenario training. To address this, we focus on cross-scenario multi-agent reinforcement learning (MARL), which aims to enhance the adaptability and scalability of RL models across multiple, distinct scenarios. Specifically, we examine offline RL and meta RL techniques, which enable agents to transfer policies learned in one environment to new, unseen scenarios. Despite the progress, challenges such as policy transferability, data efficiency, and scalability remain, which need to be addressed for broader application in transportation systems. This paper provides an overview of these challenges, reviews the state-of-the-art methods, and discusses future directions for advancing RL in transportation systems.

A study on the time-domain characteristics of two ultra-wideband disc monopole antennas with different feeding structures have been presented. First, the transfer functions and group delays of the two antenna systems are obtained in the measurements. Then their respective impulse responses are investigated through a convolution approach. It is shown that by properly selecting the input source signal the spectrum of the transmitted signal can meet the Federal Communication Commission-defined emission mask. It is also demonstrated that the received signal waveforms generally follow the shape of source pulse with slight distortion and ringing.

Existing multi-agent deep reinforcement learning (MADRL) methods for multi-UAV navigation face challenges in generalization, particularly when applied to unseen complex environments. To address these limitations, we propose a Dual-Transformer Encoder-Based Proximal Policy Optimization (DTPPO) method. DTPPO enhances multi-UAV collaboration through a Spatial Transformer, which models inter-agent dynamics, and a Temporal Transformer, which captures temporal dependencies to improve generalization across diverse environments. This architecture allows UAVs to navigate new, unseen environments without retraining. Extensive simulations demonstrate that DTPPO outperforms current MADRL methods in terms of transferability, obstacle avoidance, and navigation efficiency across environments with varying obstacle densities. The results confirm DTPPO’s effectiveness as a robust solution for multi-UAV navigation in both known and unseen scenarios.