Idiom token classification is the task of deciding for a set of potentially idiomatic phrases whether each occurrence of a phrase is a literal or idiomatic usage of the phrase. In this work we explore the use of Skip-Thought Vectors to create distributed representations that encode features that are predictive with respect to idiom token classification. We show that classifiers using these representations have competitive performance compared with the state of the art in idiom token classification. Importantly, however, our models use only the sentence containing the target phrase as input and are thus less dependent on a potentially inaccurate or incomplete model of discourse context. We further demonstrate the feasibility of using these representations to train a competitive general idiom token classifier.

This paper describes an experiment to evaluate the impact of idioms on Statistical Machine Translation (SMT) process using the language pair English/BrazilianPortuguese. Our results show that on sentences containing idioms a standard SMT system achieves about half the BLEU score of the same system when applied to sentences that do not contain idioms. We also provide a short error analysis and outline our planned work to overcome this limitation.

No abstract

Some of the most important pathogens affecting wildlife are transmitted indirectly via the environment. Yet the environmental stages of pathogens are often poorly understood, relative to infection in the host, making this an important research frontier. Sarcoptic mange is a globally widespread disease caused by the parasitic mite Sarcoptes scabiei . The bare-nosed wombat ( Vombatus ursinus ) is particularly susceptible, and their solitary nature and overlapping use of burrows strongly indicate the importance of environmental transmission. However, due to the challenge of accessing and monitoring within wombat burrows, there has been limited research into their suitability for off-host mite survival and environmental transmission (i.e., to serve as a fomite). We created a model using published laboratory data to predict mite survival times based on temperature and humidity. We then implemented innovative technologies (ground-penetrating radar and a tele-operated robotic vehicle) to map and access wombat burrows to record temperature and relative humidity. We found that the stable conditions within burrows were conducive for off-host survival of S. scabiei, particularly in winter (estimated mite survival of 16.41 ± 0.34 days) and less so in warmer and drier months (summer estimated survival of 5.96 ± 0.37 days). We also compared two areas with higher and lower average mange prevalence in wombats (13.35% and 4.65%, respectively), finding estimated mite survival was slightly higher in the low prevalence area (10.10 and 12.12 days, respectively), contrary to our expectations, suggesting other factors are also important for population prevalence. Our study is the first to demonstrate the suitability of the bare-nosed wombat burrow for off-host mite survival and environmental transmission. Our findings have implications for understanding observed patterns of mange, disease dynamics and disease management for not only bare-nosed wombats, but also other burrow or den-obligate species exposed to S. scabiei via environmental transmission.

Animal pests notoriously cause billions of dollars of damage by spoiling crops, damaging infrastructure and spreading disease. Pest control companies try to mitigate this damage by implementing pest management approaches to respond to and prevent infestations. However, these approaches are labour intensive, as pest control technicians must regularly visit the affected areas for monitoring and evaluation. Current remote sensing technologies can allow for decision making based on real-time data remotely uploaded from pest traps. This reduces the frequency of field visits and improves the pest management process. In this paper, we survey a variety of modern data-driven pest management approaches. We also evaluate wireless communication infrastructures which can be used to facilitate data transfer between pest traps and cloud servers.