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A spiking neural network model of rodent head direction calibrated with landmark free learning (2022)
Journal Article
Stentiford, R., Knowles, T. C., & Pearson, M. J. (2022). A spiking neural network model of rodent head direction calibrated with landmark free learning. Frontiers in Neurorobotics, 16, -. https://doi.org/10.3389/fnbot.2022.867019

Maintaining a stable estimate of head direction requires both self-motion (idiothetic) information and environmental (allothetic) anchoring. In unfamiliar or dark environments idiothetic drive can maintain a rough estimate of heading but is subject t... Read More about A spiking neural network model of rodent head direction calibrated with landmark free learning.

Inter-annotator agreement using the Conversation Analysis Modelling Schema, for dialogue (2022)
Journal Article
Duran, N., Battle, S., & Smith, J. (in press). Inter-annotator agreement using the Conversation Analysis Modelling Schema, for dialogue. Communication Methods and Measures, https://doi.org/10.1080/19312458.2021.2020229

We present the Conversation Analysis Modeling Schema (CAMS), a novel dialogue labeling schema that combines the Conversation Analysis concept of Adjacency Pairs, with Dialogue Acts. The aim is to capture both the semantic and syntactic structure of d... Read More about Inter-annotator agreement using the Conversation Analysis Modelling Schema, for dialogue.

Evolutionary n-level hypergraph partitioning with adaptive coarsening (2019)
Journal Article
Preen, R., & Smith, J. (2019). Evolutionary n-level hypergraph partitioning with adaptive coarsening. IEEE Transactions on Evolutionary Computation, 23(6), 962-971. https://doi.org/10.1109/TEVC.2019.2896951

Hypergraph partitioning is an NP-hard problem that occurs in many computer science applications where it is necessary to reduce large problems into a number of smaller, computationally tractable sub-problems. Current techniques use a multilevel appro... Read More about Evolutionary n-level hypergraph partitioning with adaptive coarsening.