Learning to generate one-sentence biographies from Wikidata

We investigate the generation of one-sentence Wikipedia biographies from facts derived from Wikidata slot-value pairs. We train a recurrent neural network sequence-to-sequence model with attention to select facts and generate textual summaries. Our model incorporates a novel secondary objective that helps ensure it generates sentences that contain the input facts. The model achieves a BLEU score of 41, improving significantly upon the vanilla sequence-to-sequence model and scoring roughly twice that of a simple template baseline. Human preference evaluation suggests the model is nearly as good as the Wikipedia reference. Manual analysis explores content selection, suggesting the model can trade the ability to infer knowledge against the risk of hallucinating incorrect information

Optimising Selective Sampling for Bootstrapping Named Entity Recognition

Training a statistical named entity recognition system in a new domain requires costly manual annotation of large quantities of in-domain data. Active learning promises to reduce the annotation cost by selecting only highly informative data points. This paper is concerned with a real active learning experiment to bootstrap a named entity recognition system for a new domain of radio astronomical abstracts. We evaluate several committee-based metrics for quantifying the disagreement between classifiers built using multiple views, and demonstrate that the choice of metric can be optimised in simulation experiments with existing annotated data from different domains. A final evaluation shows that we gained substantial savings compared to a randomly sampled baseline. 1

Alien Registration- Hachey, Fred (Augusta, Kennebec County)

https://digitalmaine.com/alien_docs/19035/thumbnail.jp

Neurotoxic Effects Of Anesthesia On The Developing Brain

Each year, thousands of neonates receive anesthesia and/or sedation for various surgical procedures. With advancements in neonatal care and surgical techniques, the number of infants receiving anesthesia globally will continue to increase. A relatively large and growing body of literature suggests that exposure to general anesthetics can be detrimental to the developing brain. Based upon various animal studies, it is thought that exposure of the immature brain to anesthetic agents may result in apoptosis, neurodegeneration and ultimately long-term cognitive deficiencies (Walters & Paule, 2016). This information presents a dilemma for practitioners when caring for a neonate requiring a surgical procedure, knowing that exposure to the very agents that will alleviate pain, provide adequate sedation and maintain anesthesia, may also result in adverse neurological outcomes. Further compounding this issue, there is currently no known safe alternatives for children undergoing surgery. Although various literature exists suggesting that general anesthesia (GA) has negative effects on neurodevelopment (ND) outcomes, it is unclear as to what extent. It is also unclear as to what other treatments or health related factors during the neonatal period may contribute to long-term outcomes. The following literature review will provide an examination of various retrospective cohort studies as well as one recent randomized controlled trial, all of which sought to determine the association between exposure to GA during the neonatal or early childhood period with ND outcomes. Variations between the reviewed studies include, type of surgical procedure, age and method of ND assessment, and duration of GA. The goal of this review is to provide a description of what is currently known about the effects of GA on the developing brain and what further research is required