Sabrina Sea Floor Survey (IN2017-V01) Piston Core Images, Visual Logs and Grain Size Data Summaries IN2017-V01-C012-PC05

This document presents images, colour description, colour analysis profiles and summary grain size data for individual core sections from piston core IN2017-V01-C012-PC05 collected during the Sabrina Sea Floor Survey on the RV Investigator between January and March 2017. Detail of the survey can be obtained in the post-survey report (Armand et al., 2018).The authors wish to thank the CSIRO Marine National Facility (MNF) for its support in the form of sea time on RV Investigator, support personnel, scientific equipment and data management. All data and samples acquired on the voyage are made publicly available in accordance with MNF Policy. This Project is supported through funding from the Australian Government’s Australian Antarctic Science Grant Program (AAS #4333). We thank the Marine National Facility, the IN2017-V01 scientific party-led by the Chief Scientists L.K. Armand and P. O’Brien, MNF support staff and ASP crew members led by Capt. M. Watson for their help and support on board the RV Investigator. Grain size analyses were carried out by Aziah Williamson at Geoscience Australia. Alix Post publishes with permission of the CEO, Geoscience Australia under creative commons

Sabrina Sea Floor Survey (IN2017-V01) Piston Core Images, Visual Logs and grain size DATA summaries IN2017-V01-A005-PC01

This document presents images, colour description, colour analysis profiles and summary grain size data for individual core sections from piston core IN2017-V01-A005-PC01 collected during the Sabrina Sea Floor Survey on the RV Investigator between January and March 2017. Detail of the survey can be obtained in the post-survey report (Armand et al., 2018).The authors wish to thank the CSIRO Marine National Facility (MNF) for its support in the form of sea time on RV Investigator, support personnel, scientific equipment and data management. All data and samples acquired on the voyage are made publicly available in accordance with MNF Policy. This Project is supported through funding from the Australian Government’s Australian Antarctic Science Grant Program (AAS #4333). We thank the Marine National Facility, the IN2017-V01 scientific party-led by the Chief Scientists L.K. Armand and P. O’Brien, MNF support staff and ASP crew members led by Capt. M. Watson for their help and support on board the RV Investigator. Grain size analyses were carried out by Aziah Williamson at Geoscience Australia. Alix Post publishes with permission of the CEO, Geoscience Australia under creative commons

Oasis Tuba Quartet

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Changes in physical activity, sitting and sleep across the COVID-19 national lockdown period in Scotland

We examine the impact of the COVID-19 outbreak and concomitant restrictions (i.e., lockdown) on 24-hour movement behaviors (i.e., physical activity, sitting, sleep) in a purposive sample of people (n = 3230) reporting change recruited online. Participants’ self-reported time spent in moderate-to-vigorous physical activity (MVPA), walking, sitting and sleep prior to lockdown (T1), during the first national lockdown (T2) and as restrictions initially started to ease (T3). For each 24-hour movement behavior, category-shifts are reported (positive, negative or did not change), as well as the percentage of participants recording positive/negative changes across clusters of behaviors and the percentage of participants recording improvement or maintenance of change across time. From T1 to T2 walking decreased, whereas MVPA, sitting and sleep increased, from T2 to T3 levels returned to pre-lockdown for all but MVPA. Participants who changed one behavior positively were more likely to report a positive change in another and 50% of those who reported positive changes from T1 to T2 maintained or improved further when restrictions started to ease. The current study showed that a large proportion of the sample reported positive changes, most notably those displaying initially poor levels of each behavior. These findings will inform salutogenic intervention development.</p

What have we learned about positive changes experienced during COVID-19 lockdown? Evidence of the social patterning of change

Background Multiple studies have highlighted the negative impact of COVID-19 and its particular effects on vulnerable sub-populations. Complementing this work, here, we report on the social patterning of self-reported positive changes experienced during COVID-19 national lockdown in Scotland. Methods The CATALYST study collected data from 3342 adults in Scotland during weeks 9–12 of a national lockdown. Using a cross-sectional design, participants completed an online questionnaire providing data on key sociodemographic and health variables, and completed a measure of positive change. The positive change measure spanned diverse domains (e.g., more quality time with family, developing new hobbies, more physical activity, and better quality of sleep). We used univariate analysis and stepwise regression to examine the contribution of a range of sociodemographic factors (e.g., age, gender, ethnicity, educational attainment, and employment status) in explaining positive change. Results There were clear sociodemographic differences across positive change scores. Those reporting higher levels of positive change were female, from younger age groups, married or living with their partner, employed, and in better health. Conclusion Overall our results highlight the social patterning of positive changes during lockdown in Scotland. These findings begin to illuminate the complexity of the unanticipated effects of national lockdown and will be used to support future intervention development work sharing lessons learned from lockdown to increase positive health change amongst those who may benefit.</p

Senior Recital

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Senior Recital

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Junior Recital

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The economic trade-offs of large language models: A case study

Contacting customer service via chat is a common practice. Because employing customer service agents is expensive, many companies are turning to NLP that assists human agents by auto-generating responses that can be used directly or with modifications. Large Language Models (LLMs) are a natural fit for this use case; however, their efficacy must be balanced with the cost of training and serving them. This paper assesses the practical cost and impact of LLMs for the enterprise as a function of the usefulness of the responses that they generate. We present a cost framework for evaluating an NLP model's utility for this use case and apply it to a single brand as a case study in the context of an existing agent assistance product. We compare three strategies for specializing an LLM - prompt engineering, fine-tuning, and knowledge distillation - using feedback from the brand's customer service agents. We find that the usability of a model's responses can make up for a large difference in inference cost for our case study brand, and we extrapolate our findings to the broader enterprise space.Comment: Paper to be published at the Association for Computational Linguistics in the Industry Track 202

Tuba and Euphonium

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