Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Stellar angular momentum distribution linked to galaxy morphology

We study the spatially-resolved stellar specific angular momentum j∗ in a high-quality sample of 24 CALIFA galaxies covering a broad range of visual morphology, accounting for stellar velocity and velocity dispersion. The shape of the spaxel-wise probability density function of normalised s = j∗/j∗mean, PDF(s), deviates significantly from the near-universal initial distribution expected of baryons in a dark matter halo and can be explained by the expected baryonic effects in galaxy formation that remove and redistribute angular momentum. Further we find that the observed shape of the PDF(s) correlates significantly with photometric morphology, where late-type galaxies have a PDF(s) that is similar to a normal distribution, whereas early types have a strongly-skewed PDF(s) resulting from an excess of low-angular momentum material. Galaxies that are known to host pseudobulges (bulge S´ersic index nb < 2.2) tend to have less skewed bulge PDF(s), with skewness (b1r b ) . 0.8. The PDF(s) encodes both kinematic and photometric information and appears to be a robust tracer of morphology. Its use is motivated by the desire to move away from traditional component-based classifications which are subject to observer bias, to classification on a galaxy’s fundamental (stellar mass, angular momentum) properties. In future, PDF(s) may also be useful as a kinematic decomposition tool