We employ a total of 26 annotators, consisting of 14 females and 12 males.

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.

Recent evidence suggests that, in some problems, NeSy models can achieve high accuracy on the reasoning task by learning concepts with incorrect semantics .

We introduce Image2Struct, a benchmark to evaluate vision-language models (VLMs) on extracting structure from images.

We develop new conformal inference methods for obtaining validity guarantees on the output of large language models (LLMs). Prior work in conformal language modeling identifies a subset of the text that satisfies a high-probability guarantee of correctness. These methods work by filtering claims from the LLM's original response if a scoring function evaluated on the claim fails to exceed a threshold calibrated via split conformal prediction. Existing methods in this area suffer from two deficiencies. First, the guarantee stated is not conditionally valid.

This supplementary document follows the Datasheets for Datasets template of (8) to document the Global Flood Forecasting (GFF) dataset and its creation. Further resources are provided: in the accompanying publication https://arxiv.org/abs/2409.18591 in the GitHub repository https://github.com/Multihuntr/GFF

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.