Detecting dependencies among variables is a fundamental task across scientific disciplines.

Districting is a complex combinatorial problem that consists in partitioning a geographical area into small districts.

Figure 1: The Slice-100K dataset consists of STL files and their G-code counterparts. However, we do foresee some potential negative societal impacts. We provide additional visualizations to understand the distribution of STL models in Slice-100K. Slicing: We utilize Prusa's Slicer for generating G-code from STL files. Finetuning implementation: For finetuning our translation model, we use a batch size of 32 with 8 gradient accumulation steps.

Temporal Domain Generalization (TDG) addresses the challenge of training predictive models under temporally varying data distributions. Traditional TDG approaches typically focus on domain data collected at fixed, discrete time intervals, which limits their capability to capture the inherent dynamics within continuous-evolving and irregularly-observed temporal domains.

A core task in multi-modal learning is to integrate information from multiple feature spaces (e.g., text and audio), offering modality-invariant essential representations of data.

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We provide a persistent dereferenceable identifier DOI: https://doi.org/10.57967/hf/2530. RK retrieval datasets are under license CC-BY -4.0 as stated in our website. We will maintain our GitHub repository will pull requests and open issues. Code: We have provided the complete codebase in our GitHub repository. Evaluation Procedures: All evaluation procedures are thoroughly documented.