A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications

This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers

Domain wall description of superconductivity

In the present work we shall address the issue of electrical conductivity in superconductors in the perspective of superconducting domain wall solutions in the realm of field theory. We take our set up made out of a dynamical complex scalar field coupled to gauge field to be responsible for superconductivity and an extra scalar real field that plays the role of superconducting domain walls. The temperature of the system is interpreted through the fact that the soliton following accelerating orbits is a Rindler observer experiencing a thermal bath.Comment: 9 pages, 5 figures, Latex. Version to appear in PL