Several prominent phenomena of condensed matter, exemplified by nonlinear transport, flat-band superconductivity, and fractional Chern insulators, are systematically reviewed with a focus on their quantum geometric origins.

Researchers at Concordia’s Gina Cody School of Engineering and Computer Science have developed a new approach to identifying fake news. And they say it will be able to find hidden patterns that reveal whether a particular item is likely fake or not.

The model, called SmoothDetector, integrates a probabilistic algorithm with a deep neural network. It’s designed to capture the uncertainties and key patterns in the shared latent representations of texts and images in a multimodal setting. The model uses annotated text and image data from the United States–based social media platform X and the China-based Weibo to learn. The researchers are currently looking into ways to eventually incorporate functionalities to detect fake audio and video content as well, leveraging every medium to counter misinformation.