What if the earliest signs of skin cancer could be identified sooner — before a dermatology appointment?

Researchers at the University of Missouri are exploring how artificial intelligence could help detect melanoma — the most dangerous form of skin cancer — by evaluating images of suspicious skin abnormalities.

Due to their error-prone hardware, quantum computers have not yet found practical use. One promising solution is quantum error correction: special methods are used to find and correct errors in the calculations of quantum computers in order to achieve reliable results. In the snaQCs2025 project, neQxt GmbH, Fraunhofer IAF and Point 8 GmbH are working on the coordinated development of quantum error correction methods and quantum algorithms. The project aims at significantly advancing the practical applicability of quantum computers. The project kick-off took place in Cologne on January 14, 2026. The BMFTR is funding snaQCs with €2.5 million over three years.

Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known Heston option pricing model as a benchmark, researchers show that global interpretability tools based on Shapley values can meaningfully explain how neural networks work, outperforming local explanation methods and helping identify better-performing network architectures.

Researchers at the Nara Institute of Science and Technology and Osaka University have developed a computational model of how human emotions are formed. This system integrates body signals, sensory input, and language, forming emotional concepts that match the self-reported human emotional judgment with 75% accuracy. The findings highlight new ways of building emotionally aware artificial intelligence, with potential applications in mental health care, interactive robots, and assistive technologies.

Women are underrepresented in academia, especially in STEMM fields, at top institutions, and in senior positions. This study analyzes millions of biomedical and life science articles, revealing that female-authored articles spend longer under review than comparable male-authored articles, across most fields.