Deep learning-based edge caching for multi-cluster heterogeneous networks

© 2019, Springer-Verlag London Ltd., part of Springer Nature. In this work, we consider a time and space evolution cache refreshing in multi-cluster heterogeneous networks. We consider a two-step content placement probability optimization. At the initial complete cache refreshing optimization, the joint optimization of the activated base station density and the content placement probability is considered. And we transform this optimization problem into a GP problem. At the following partial cache refreshing optimization, we take the time–space evolution into consideration and derive a convex optimization problem subjected to the cache capacity constraint and the backhaul limit constraint. We exploit the redundant information in different content popularity using the deep neural network to avoid the repeated calculation because of the change in content popularity distribution at different time slots. Trained DNN can provide online response to content placement in a multi-cluster HetNet model instantaneously. Numerical results demonstrate the great approximation to the optimum and generalization ability

Angry Rats and Scaredy Cats: Lessons from Competing Cognitive Homologies

There have been several recent attempts to think about psychological kinds as homologies. Nevertheless, there are serious epistemic challenges for individuating homologous psychological kinds, or cognitive homologies. Some of these challenges are revealed when we look at competing claims of cognitive homology. This paper considers two competing homology claims that compare human anger with putative aggression systems of nonhuman animals. The competition between these hypotheses has been difficult to resolve in part because of what I call the boundary problem: boundaries between instances of psychological kinds (e.g., anger and fear) cannot be directly observed. Thus, there are distinctive difficulties for individuating psychological kinds across lineages. I draw four conclusions from this case study: First, recent evidence from the neuroscience of fear suggests that one of the proposed homologies involves a straightforward conflation of anger and fear. Second, this conflation arises because of the boundary problem. Third, there is an implicit constraint on the operational criteria that is easy to overlook in the psychological case. In this case, ignoring the constraint is part of the problem. Fourth, this is a clear case in which knowledge of homology cannot be accumulated piecemeal. Identifying homologs of human anger requires identifying homologs of fear

Distributed representations accelerate evolution of adaptive behaviours

Animals with rudimentary innate abilities require substantial learning to transform those abilities into useful skills, where a skill can be considered as a set of sensory - motor associations. Using linear neural network models, it is proved that if skills are stored as distributed representations, then within- lifetime learning of part of a skill can induce automatic learning of the remaining parts of that skill. More importantly, it is shown that this " free- lunch'' learning ( FLL) is responsible for accelerated evolution of skills, when compared with networks which either 1) cannot benefit from FLL or 2) cannot learn. Specifically, it is shown that FLL accelerates the appearance of adaptive behaviour, both in its innate form and as FLL- induced behaviour, and that FLL can accelerate the rate at which learned behaviours become innate

The self-organization of combinatoriality and phonotactics in vocalization systems

This paper shows how a society of agents can self-organize a shared vocalization system that is discrete, combinatorial and has a form of primitive phonotactics, starting from holistic inarticulate vocalizations. The originality of the system is that: (1) it does not include any explicit pressure for communication; (2) agents do not possess capabilities of coordinated interactions, in particular they do not play language games; (3) agents possess no specific linguistic capacities; and (4) initially there exists no convention that agents can use. As a consequence, the system shows how a primitive speech code may bootstrap in the absence of a communication system between agents, i.e. before the appearance of language

Optimizing the vertebrate vestibular semicircular canal: could we balance any better?

The fluid-filled semicircular canals (SCCs) of the vestibular system are used by all vertebrates to sense angular rotation. Despite masses spanning seven decades, all mammalian SCCs are nearly the same size. We propose that the SCC represents a sensory organ that evolution has `optimally designed'. Four geometric parameters are used to characterize the SCC, and `building materials' of given physical properties are assumed. Identifying physical and physiological constraints on SCC operation, we find that the most sensitive SCC has dimensions consistent with available data.Comment: 4 pages, 3 figure

A spherical Hopfield model

We introduce a spherical Hopfield-type neural network involving neurons and patterns that are continuous variables. We study both the thermodynamics and dynamics of this model. In order to have a retrieval phase a quartic term is added to the Hamiltonian. The thermodynamics of the model is exactly solvable and the results are replica symmetric. A Langevin dynamics leads to a closed set of equations for the order parameters and effective correlation and response function typical for neural networks. The stationary limit corresponds to the thermodynamic results. Numerical calculations illustrate our findings.Comment: 9 pages Latex including 3 eps figures, Addition of an author in the HTML-abstract unintentionally forgotten, no changes to the manuscrip