Section A.1 outlines the generation process of the SynRS3D dataset, including the tools and It also covers the licenses for these plugins. Section A.4 describes the experimental setup and the selection of hyperparameters for the RS3DAda method. Section A.5 presents the ablation study results and analysis for the RS3DAda method. Section A.6 provides supplementary experimental The generation workflow of SynRS3D involves several key steps, from initializing sensor and sunlight parameters to generating the layout, geometry, and textures of the scene. Initialization: Set up the sensor and sunlight parameters using uniform and normal distributions to simulate various conditions.

This task combines land cover mapping, which is closely related to semantic segmentation in computer vision, and height estimation.

The ability to promptly respond to environmental changes is crucial for the perception system of autonomous driving.

Graph Contrastive Learning (GCL) has emerged as a powerful approach for generating graph representations without the need for manual annotation. Most advanced GCL methods fall into three main frameworks: node discrimination, group discrimination, and bootstrapping schemes, all of which achieve comparable performance. However, the underlying mechanisms and factors that contribute to their effectiveness are not yet fully understood.

By enhancing these capabilities, humans can more effectively utilize data, leading to better comprehension and learning outcomes.

Current approaches to mitigate these representational harms balance the number of retrieved items across population groups defined by a small number of (often binary) attributes. However, most existing methods overlook intersectional groups determined by combinations of group attributes, such as gender, race, and ethnicity.

Consequently, it is uncertain whether the generated modalities sufficiently represent the diversity of future behaviors.

To ensure quality, all charts and questions are handpicked, curated, and verified by human experts.

Large Language Models (LLMs) are widely used in today's tasks of natural language processing.