The learnware paradigm aims to enable users to leverage numerous existing well-trained models instead of building machine learning models from scratch.

Training machine learning and statistical models often involves optimizing a data-driven risk criterion.

Such investigations have demonstrated that attention layers are able to implement a wide range of different algorithms, even for the same task, using both positional and semantic attributes of the inputs.

Biodiversity plays a multifaceted role in sustaining ecosystems and supporting human well-being. Primarily, it serves as a cornerstone for ecosystem stability and resilience, providing a natural defence against disturbances such as climate change and invasive species (Cardinale et al., 2012).

Recent advances in multi-modal algorithms have driven and been driven by the increasing availability of large image-text datasets, leading to significant strides in various fields, including computational pathology.

These tokens serve as an effective protein structure representation.

In this paper, we propose a novel conceptual framework to detect outliers using optimal transport with a concave cost function. Conventional outlier detection approaches typically use a two-stage procedure: first, outliers are detected and removed, and then estimation is performed on the cleaned data.