Researchers have introduced a comprehensive dataset and evaluation framework to better assess the ability of artificial intelligence to understand and interpret financial credit information from both visual and textual sources.
New research reveals that the areas of dermoscopic images that AI algorithms prioritize directly impact their ability to accurately diagnose melanoma.
![The fitted spectral-NN estimator, parameterized with [latex]M=L=10[/latex], a depth of 4, a width of 20, and [latex]q=20[/latex], demonstrates a capacity for modeling complex relationships within three-dimensional fMRI data, yet its very structure-like any theoretical framework-risks being swallowed by the inherent limitations of its own design.](https://arxiv.org/html/2601.00284v1/x1.png)
A new deep learning approach offers a computationally efficient way to analyze complex, high-dimensional time series data.
A new deep learning approach leveraging advanced neural networks is showing promising results in the automated and accurate identification of skin conditions.
New research demonstrates that incorporating non-traditional data sources-like climate risks and textual reports-can significantly enhance the accuracy of credit default predictions.
New research reveals consistent patterns of financial network reorganization during the pandemic, indicating heightened systemic risk and lingering vulnerabilities across global economies.
A new review challenges the assumption that artificial intelligence automatically improves regional climate projections, suggesting established methods remain competitive and crucial for reliable future assessments.
A new deep learning model analyzes full-day electrocardiograms to forecast heart failure risk up to five years before traditional diagnosis.
![The predicted melting curve of iron, derived from two-phase simulations, aligns with and refines existing data from diverse experimental studies [emcAnzellini2013,emcSinmyo2019,emcLi2020,emcKraus2022,emcBalugani2024] and computational investigations [aimcAlfe2009,aimcGonzalez2023,aimcSun2022,aimcSun2023,aimcBelonoshko2021,aimcStixrude2014,aimcAlfe2002,aimcBelonosko2000,aimcWu2024,aimcSola2009], demonstrating the model’s capacity to synthesize and potentially transcend established knowledge of iron’s behavior under extreme conditions.](https://arxiv.org/html/2512.25061v1/x3.png)
A new physics-informed machine learning approach is unlocking the secrets of iron’s behavior at the extreme temperatures and pressures of Earth’s core.
![Decomposition reveals distinct components-a background term [latex] \widetilde{\mathcal{M}} [/latex] and a perturbation term [latex] g [/latex]-each assessed against reference Maxwellian distributions characterized by varying anisotropy and isotropy, ultimately demonstrating how moment-matching techniques refine the approximation of complex systems as they evolve.](https://arxiv.org/html/2512.24205v1/x8.png)
A new framework leverages neural networks and advanced mathematical techniques to efficiently predict the behavior of complex plasmas under uncertainty.