Representation in the (Artificial) Immune System

Much of contemporary research in Artificial Immune Systems (AIS) has partitioned into either algorithmic machine learning and optimisation, or, modelling biologically plausible dynamical systems, with little overlap between. We propose that this dichotomy is somewhat to blame for the lack of significant advancement of the field in either direction and demonstrate how a simplistic interpretation of Perelson’s shape-space formalism may have largely contributed to this dichotomy. In this paper, we motivate and derive an alternative representational abstraction. To do so we consider the validity of shape-space from both the biological and machine learning perspectives. We then take steps towards formally integrating these perspectives into a coherent computational model of notions such as life-long learning, degeneracy, constructive representations and contextual recognition—rhetoric that has long inspired work in AIS, while remaining largely devoid of operational definition

Mechanisms of dielectric relaxation of hexagonal ice

© 2020 Russian Academy of Natural Science. All rights reserved. The article presents the results of theoretical and experimental studies of the dielectric relaxation of hexagonal ice in a wide temperature range. The model of dielectric relaxation of ice was developed explaining the origin of the dynamic crossovers in relaxation time at high and low temperatures. The results of the model are in agreement with experimental studies and explain the irreproducibility of dielectric experiments at low temperatures