There are many methods of exploring an HMD-based virtual environment such as using a game controller, physically walking, walking in place, teleporting, flying, leaning, etc. The purpose of this work is to introduce a simple method of implementing "walking in place" using a simple inexpensive accelerometer sensor. We then evaluate this method of walking in place by comparing it to normal walking and another previously published inexpensive method of exploration, arm swinging. In an experiment that compares the spatial awareness, we show that walking in place is not as good as walking on foot, but it is is better than arm swinging. Subjects also complete blind locomotion distance estimation trials in each of the locomotion conditions.

Journalists act as gatekeepers to the scientific world, controlling what information reaches the public eye and how it is presented. Analyzing the kinds of research that typically receive more media attention is vital to understanding issues such as the “science of science communication” (National Academies of Sciences, Engineering, and Medicine 2017), patterns of misinformation, and the “cycle of hype.” We track the coverage of 91,997 scientific articles published in 2016 across various disciplines, publishers, and news outlets using metadata and text data from a leading tracker of scientific coverage in social and traditional media, Altmetric. We approach the problem as one of ranking each day’s, or week’s, papers by their likely level of media attention, using the learning-to-rank model lambdaMART (Burges 2010). We find that ngram features from the title, abstract and press release significantly improve performance over the metadata features journal, publisher, and subjects.

While composing a new document, anything from a news article to an email or essay, authors often utilize direct quotes from a variety of sources. Although an author may know what point they would like to make, selecting an appropriate quote for the specific context may be time-consuming and difficult. We therefore propose a novel context-aware quote recommendation system which utilizes the content an author has already written to generate a ranked list of quotable paragraphs and spans of tokens from a given source document. We approach quote recommendation as a variant of open-domain question answering and adapt the state-of-the-art BERT-based methods from open-QA to our task. We conduct experiments on a collection of speech transcripts and associated news articles, evaluating models' paragraph ranking and span prediction performances. Our experiments confirm the strong performance of BERT-based methods on this task, which outperform bag-of-words and neural ranking baselines by more than 30% relative across all ranking metrics. Qualitative analyses show the difficulty of the paragraph and span recommendation tasks and confirm the quotability of the best BERT model's predictions, even if they are not the true selected quotes from the original news articles.

Neural Architecture Search (NAS) methods, which automatically learn entire neural model or individual neural cell architectures, have recently achieved competitive or state-of-theart (SOTA) performance on variety of natural language processing and computer vision tasks, including language modeling, natural language inference, and image classification. In this work, we explore the applicability of a SOTA NAS algorithm, Efficient Neural Architecture Search (ENAS) (Pham et al., 2018) to two sentence pair tasks, paraphrase detection and semantic textual similarity. We use ENAS to perform a microlevel search and learn a task-optimized RNN cell architecture as a drop-in replacement for an LSTM. We explore the effectiveness of ENAS through experiments on three datasets (MRPC, SICK, STS-B), with two different models (ESIM, BiLSTM-Max), and two sets of embeddings (Glove, BERT). In contrast to prior work applying ENAS to NLP tasks, our results are mixed -we find that ENAS architectures sometimes, but not always, outperform LSTMs and perform similarly to random architecture search.

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