103,951 research outputs found
Recoverable DTN Routing based on a Relay of Cyclic Message-Ferries on a MSQ Network
An interrelation between a topological design of network and efficient
algorithm on it is important for its applications to communication or
transportation systems. In this paper, we propose a design principle for a
reliable routing in a store-carry-forward manner based on autonomously moving
message-ferries on a special structure of fractal-like network, which consists
of a self-similar tiling of equilateral triangles. As a collective adaptive
mechanism, the routing is realized by a relay of cyclic message-ferries
corresponded to a concatenation of the triangle cycles and using some good
properties of the network structure. It is recoverable for local accidents in
the hierarchical network structure. Moreover, the design principle is
theoretically supported with a calculation method for the optimal service rates
of message-ferries derived from a tandem queue model for stochastic processes
on a chain of edges in the network. These results obtained from a combination
of complex network science and computer science will be useful for developing a
resilient network system.Comment: 6 pages, 12 figures, The 3rd Workshop on the FoCAS(Fundamentals of
Collective Adaptive Systems) at The 9th IEEE International Conference on
SASO(Self-Adaptive and Self-Organizing systems), Boston, USA, Sept.21, 201
Simulating Problem Difficulty in Arithmetic Cognition Through Dynamic Connectionist Models
The present study aims to investigate similarities between how humans and
connectionist models experience difficulty in arithmetic problems. Problem
difficulty was operationalized by the number of carries involved in solving a
given problem. Problem difficulty was measured in humans by response time, and
in models by computational steps. The present study found that both humans and
connectionist models experience difficulty similarly when solving binary
addition and subtraction. Specifically, both agents found difficulty to be
strictly increasing with respect to the number of carries. Another notable
similarity is that problem difficulty increases more steeply in subtraction
than in addition, for both humans and connectionist models. Further
investigation on two model hyperparameters --- confidence threshold and hidden
dimension --- shows higher confidence thresholds cause the model to take more
computational steps to arrive at the correct answer. Likewise, larger hidden
dimensions cause the model to take more computational steps to correctly answer
arithmetic problems; however, this effect by hidden dimensions is negligible.Comment: 7 pages; 15 figures; 5 tables; Published in the proceedings of the
17th International Conference on Cognitive Modelling (ICCM 2019
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