A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound.

Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning, leveraging large amounts of unlabelled data. This review summarizes recent research into its usage in X-ray, computed tomography, magnetic resonance, and ultrasound imaging, concentrating on studies that compare self-supervised pretraining to fully supervised learning for diagnostic tasks such as classification and segmentation. The most pertinent finding is that self-supervised pretraining generally improves downstream task performance compared to full supervision, most prominently when unlabelled examples greatly outnumber labelled examples.

User Perception of Smart Home Surveillance Among Adults Aged 50 Years and Older: Scoping Review.

Background: Smart home technology (SHT) can be useful for aging in place or health-related purposes. However, surveillance studies have highlighted ethical issues with SHTs, including user privacy, security, and autonomy.

Objective: As digital technology is most often designed for younger adults, this review summarizes perceptions of SHTs among users aged 50 years and older to explore their understanding of privacy, the purpose of data collection, risks and benefits, and safety.

Post-laryngectomy pulmonary and related symptom changes following adoption of an optimal day-and-night heat and moisture exchanger (HME) regimen.

Background: This study examined post-laryngectomy pulmonary and related symptom changes following establishment of an optimal day/night regimen (all day/night wear of devices with improved humidification) using a new generation range of heat and moisture exchanger (HME) devices.

Methods: In Phase 1 (6 weeks), 42 post-laryngectomy HME users transitioned from their usual HME regime to equivalent new device/s (i.e.

Disruption of Social Orders in Societal Transitions as Affective Control of Uncertainty.

Bayesian affect control theory is a model of affect-driven social interaction under conditions of uncertainty. In this paper, we investigate how the operationalization of uncertainty in the model can be related to the disruption of social orders-societal pressures to adapt to ongoing environmental and technological change. First, we study the theoretical tradeoffs between three kinds of uncertainty as groups navigate external problems: validity (the predictability of the environment, including of other agents), coherence (the predictability of interpersonal affective dynamics), and dependence (the predictability of affective meanings).