Electroantennogram Responses of the Armyworm (Lepidoptera: Noctuidae) and Cereal Leaf Beetle (Coleoptera: Chrysomelidae) to Volatile Chemicals of Seedling Oats

Armyworm, Pseudaletia unipuncta, eIectroantennogram (EAG) responses to 10 volatile chemicals of seedling oats and three of injured green plants were significantly different from each other while cereal leaf beetle, Oulema melallopus, EAG responses were not significantly different. The EAG responses of both species did not vary significantly with respect to sex, age, or between the antennae of the same specimen. (E)-2-hexenol, a compound extracted from injured green plants, yielded the highest peak response for the armyworm while more cereal leaf beetle antennae responded to this chemical than any other chemical. Armyworm antennallife averaged 38 + 20 min while those of the cereal leaf beetle averaged 6 + 14 min

Investigating the effect of auxiliary objectives for the automated grading of learner english speech transcriptions

We address the task of automatically grading the language proficiency of spontaneous speech based on textual features from automatic speech recognition transcripts. Motivated by recent advances in multi-task learning, we develop neural networks trained in a multi-task fashion that learn to predict the proficiency level of non-native English speakers by taking advantage of inductive transfer between the main task (grading) and auxiliary prediction tasks: morpho-syntactic labeling, language modeling, and native language identification (L1). We encode the transcriptions with both bi-directional recurrent neural networks and with bi-directional representations from transformers, compare against a feature-rich baseline, and analyse performance at different proficiency levels and with transcriptions of varying error rates. Our best performance comes from a transformer encoder with L1 prediction as an auxiliary task. We discuss areas for improvement and potential applications for text-only speech scoring.Cambridge Assessmen

Accurate modelling of language learning tasks and students using representations of grammatical proficiency

Adaptive learning systems aim to learn the relationship between curriculum content and students in order to optimise a student’s learning process. One form of such a system is content recommendation in which the system attempts to predict the most suitable content to next present to the student. In order to develop such a system, we must learn reliable representations of the curriculum content and the student. We consider this in the context of foreign language learning and present a novel neural network architecture to learn such representations. We also show that by incorporating grammatical error distributions as a feature in our neural architecture, we can substantially improve the quality of our representations. Different types of grammatical error are automatically detected in essays submitted by students to an online learning platform. We evaluate our model and representations by predicting student scores and grammatical error distributions on unseen language tasks. We also discuss further uses for our model beyond content recommendation such as inferring student knowledge components for a given domain and optimising spacing and repetition of content for efficient long term retention.Cambridge Assessmen

Skills embeddings: A neural approach to multicomponent representations of students and tasks

Educational systems use models of student skill to inform decision-making processes. Defining such a model manually is challenging due to the large number of relevant factors. We introduce an alternative approach by learning multidimensional representations (embeddings) from student activity data. Such embeddings are fixed-length real vectors with three desirable characteristics: co-location of similar students and items in a vector space; magnitude increases with skill, and that absence of a skill can be represented. Based on the Multicomponent Latent Trait Model, we use a neural network with complementary trainable weights to learn these embeddings by backpropagation in an unsupervised manner. We evaluate using synthetic student activity data that provides a ground-truth of student skills in order to understand the impact of number of students, question items and knowledge components in the domain. We find that our data-mined parameter values can recreate the synthetic datasets up to the accuracy of the model that generated them, for domains containing up to 10 simultaneously active knowledge components, which can be effectively mined using relatively small quantities of data (1000 students, 100 items). We describe a procedure to estimate the number of components in a domain, and propose a component-masking logic mechanism that improves performance on high-dimensional datasets.Cambridge Assessmen

CAMsterdam at SemEval-2019 task 6: Neural and graph-based feature extraction for the identification of offensive tweets

We describe the CAMsterdam team entry to the SemEval-2019 Shared Task 6 on offen-sive language identification in Twitter data.Our proposed model learns to extract tex-tual features using a multi-layer recurrent net-work, and then performs text classification us-ing gradient-boosted decision trees (GBDT). A self-attention architecture enables the model to focus on the most relevant areas in the text.We additionally learn globally optimised em-beddings for hashtags using node2vec, which are given as additional tweet features to the GBDT classifier.Our best model obtains78.79% macro F1-score on detecting offensive language (subtask A), 66.32% on categorising offence types (targeted/untargeted; subtask B),and 55.36% on identifying the target of of-fence (subtask C)

Effect of tanniniferous browse meal on nematode faecal egg counts and internal parasite burdens in sheep and goats

The effect of tanniniferous browse meal on faecal egg counts (FEC) and intestinal worm burdens was investigated in sheep and goats infested experimentally with gastrointestinal nematodes. Initially, leaves of different browse tree species were assayed for condensed tannin (CT) content using a colorimetric method to determine concentration and seasonal variations. The level of CT in the leaves ranged between 58 – 283 g/kg dry matter. Seasonal changes in CT levels were influenced by stage of leaf maturity with peak levels after the wet season in June. Leaves of Acacia polyacantha had the highest tannin concentration and were used to test their anthelmintic effect in goats and sheep infested with the nematodes in two separate feeding trials. In Trial 1 an acacia leaf meal supplement (AMS) was offered at 100 – 130 g/animal/day for 20 days to growing Small East African goats to investigate its effect on FEC and worm burden. Mean FEC and worm burden of the AMS-fed group were respectively 27% and 13% lower than in the control group. Trial 2 was similar to Trial 1 except that AMS was offered for 30 days to growing Black Head Persian sheep at 170 g/animal/day. The sheep receiving AMS showed a slight reduction in FEC (on average 19% lower than the control group) but had no effect on worm burden. The current results substantiated previous reports of a suppressing effect of CT on gastrointestinal nematodes of small ruminants. Although the observed anthelmintic activity of AMS was less than expected, such reductions can have practical epidemiological implications in reducing pasture larval contamination. Further studies are needed under field conditions to evaluate the feasibility of using locally available tanniniferous browse as an alternative to synthetic anthelmintics in reducing worm infestations in small ruminants. South African Journal of Animal Science Vol. 37 (2) 2007: pp. 97-10

Power requirements for electron cyclotron current drive and ion cyclotron resonance heating for sawtooth control in ITER

13MW of electron cyclotron current drive (ECCD) power deposited inside the q = 1 surface is likely to reduce the sawtooth period in ITER baseline scenario below the level empirically predicted to trigger neo-classical tearing modes (NTMs). However, since the ECCD control scheme is solely predicated upon changing the local magnetic shear, it is prudent to plan to use a complementary scheme which directly decreases the potential energy of the kink mode in order to reduce the sawtooth period. In the event that the natural sawtooth period is longer than expected, due to enhanced alpha particle stabilisation for instance, this ancillary sawtooth control can be provided from > 10MW of ion cyclotron resonance heating (ICRH) power with a resonance just inside the q = 1 surface. Both ECCD and ICRH control schemes would benefit greatly from active feedback of the deposition with respect to the rational surface. If the q = 1 surface can be maintained closer to the magnetic axis, the efficacy of ECCD and ICRH schemes significantly increases, the negative effect on the fusion gain is reduced, and off-axis negative-ion neutral beam injection (NNBI) can also be considered for sawtooth control. Consequently, schemes to reduce the q = 1 radius are highly desirable, such as early heating to delay the current penetration and, of course, active sawtooth destabilisation to mediate small frequent sawteeth and retain a small q = 1 radius.Comment: 29 pages, 16 figure