The plausibility machine commonsense (PMC) dataset: A massively crowdsourced human-annotated dataset for studying plausibility in large language models.

Commonsense reasoning has emerged as a challenging problem in Artificial Intelligence (AI). However, one area of commonsense reasoning that has not received nearly as much attention in the AI research community is , which focuses on determining the likelihood of commonsense statements. Human-annotated benchmarks are essential for advancing research in this nascent area, as they enable researchers to develop and evaluate AI models effectively.

Multi-Source Data Integration for Segmentation of Unannotated MRI Images.

Automatic semantic segmentation of magnetic resonance imaging (MRI) images using deep neural networks greatly assists in evaluating and planning treatments for various clinical applications. However, training these models is conditioned on the availability of abundant annotated data. Even if we annotate enough data, MRI images display considerable variability due to factors such as differences among patients, MRI scanners, and imaging protocols.