An Exploration of Auditory Brainstem Encoding of Stop Consonants in Infants and Implications for Language Outcomes

Current trends in speech-language pathology focus on early intervention as the preferred tool for promoting the best possible outcomes in children with language disorders. Neuroimaging techniques are being studied as promising tools for flagging at-risk infants. In this study, the auditory brainstem response (ABR) to the syllables /ba/ and /ga/ was examined in 41 infants between 3 and 12 months of age as a possible tool to predict language development in toddlerhood. The MacArthur-Bates Communicative Development Inventory (MCDI) was used to assess language development at 18 months of age. The current study compared the periodicity of the responses to the stop consonants and phase differences between /ba/ and /ga/ in both at-risk and low-risk groups. The study also examined whether there are correlations among ABR measures (periodicity and phase differentiation) and language development. The study found that these measures predict language development at 18 months.

Developing Numeric Nutrient Criteria for Streams on the Delmarva Peninsula

To better address stream impairments due to excess nitrogen and phosphorus and to accomplish the goals of the Clean Water Act, the U.S. Environmental Protection Agency (EPA) is requiring states to develop numeric nutrient criteria. An assessment of nutrient concentrations in streams on the Delmarva Peninsula showed that nutrient levels are mostly higher than numeric criteria derived by EPA for the Eastern Coastal Plain, indicating widespread water quality degradation. Here, various approaches were used to derive numeric nutrient criteria from a set of 52 streams sampled across Delmarva. Results of the percentile and y-intercept methods were similar to those obtained elsewhere. Downstream protection values show that if numeric nutrient criteria were implemented for Delmarva streams they would be protective of the Choptank River Estuary, meeting the goals of the Chesapeake Bay Total Maximum Daily Load (TMDL).

Microblogging Temporal Summarization: Filtering Important Twitter Updates for Breaking News

While news stories are an important traditional medium to broadcast and consume news, microblogging has recently emerged as a place where people can dis- cuss, disseminate, collect or report information about news. However, the massive information in the microblogosphere makes it hard for readers to keep up with these real-time updates. This is especially a problem when it comes to breaking news, where people are more eager to know “what is happening”. Therefore, this dis- sertation is intended as an exploratory effort to investigate computational methods to augment human effort when monitoring the development of breaking news on a given topic from a microblog stream by extractively summarizing the updates in a timely manner. More specifically, given an interest in a topic, either entered as a query or presented as an initial news report, a microblog temporal summarization system is proposed to filter microblog posts from a stream with three primary concerns: topical relevance, novelty, and salience. Considering the relatively high arrival rate of microblog streams, a cascade framework consisting of three stages is proposed to progressively reduce quantity of posts. For each step in the cascade, this dissertation studies methods that improve over current baselines. In the relevance filtering stage, query and document expansion techniques are applied to mitigate sparsity and vocabulary mismatch issues. The use of word embedding as a basis for filtering is also explored, using unsupervised and supervised modeling to characterize lexical and semantic similarity. In the novelty filtering stage, several statistical ways of characterizing novelty are investigated and ensemble learning techniques are used to integrate results from these diverse techniques. These results are compared with a baseline clustering approach using both standard and delay-discounted measures. In the salience filtering stage, because of the real-time prediction requirement a method of learning verb phrase usage from past relevant news reports is used in conjunction with some standard measures for characterizing writing quality. Following a Cranfield-like evaluation paradigm, this dissertation includes a se- ries of experiments to evaluate the proposed methods for each step, and for the end- to-end system. New microblog novelty and salience judgments are created, building on existing relevance judgments from the TREC Microblog track. The results point to future research directions at the intersection of social media, computational jour- nalism, information retrieval, automatic summarization, and machine learning.