Child safety seat checks in Salt Lake County: protective and risk factors.

BACKGROUND: Older children are at an increased risk of injury due to less commonly being in an appropriate child safety seat (CSS). Proper installation and consistent use of CSSs can significantly reduce child and infant automobile injuries. While research exists around parent behaviours concerning CSS use (or lack), little research takes place at the county level to identify normative beliefs as they contribute to risk factors. METHODS: Through a mixed-methods approach, this evaluation retrospectively determines the Salt Lake County Health Department's impact on CSS usage, as well as identify normative parent behaviours that impact CSS usage. RESULTS: Results indicated that parents' level of education and being in the car with family/friends was significantly associated with overall CSS usage. DISCUSSION: More research is needed to specify parent normative beliefs around CSS use (or lack).

Predicting response to a smartphone-based cognitive-behavioral therapy for body dysmorphic disorder.

BACKGROUND: Body dysmorphic disorder (BDD) is a severe, chronic disorder if untreated. Smartphone cognitive behavioral therapy (CBT) for BDD is efficacious and can reduce key treatment barriers (e.g., lack of clinicians, cost, stigma). While promising, little is known about who is more or less likely to benefit from this approach. METHODS: This is a secondary data analysis of a randomized, waitlist-controlled trial of smartphone CBT for BDD. Participants (N = 80) were recruited nationally and randomized to receive a 12-week, coach-guided CBT for BDD app, either immediately or after a 12-week waitlist. The main outcome for this analysis was BDD severity (BDD-YBOCS) over time (baseline, week 6, week 12) during the active app use phase in each randomized group (n = 74). Secondary outcomes included treatment response (≥30 % reduction in BDD-YBOCS) and remission (total BDD-YBOCS ≤16) at end-of-treatment. RESULTS: Immediate (vs. delayed) CBT predicted better outcomes (symptom improvement), as did gender identity (symptom improvement), higher baseline treatment credibility and expectancy (response, remission), lower baseline BDD severity (remission), and sexual minority status (vs. heterosexual; response, remission). LIMITATIONS: Limitations include the relatively small sample, drop-out rate of 22 %, and limited gender and racial-ethnic diversity. CONCLUSIONS: These results highlight a potential advantage of smartphone CBT in historically marginalized populations, and the importance of efforts to hasten treatment access, bolster confidence in the treatment at treatment onset, and develop stratified care models to optimize treatment allocation and efficacy.

Tuned Compositional Feature Replays for Efficient Stream Learning.

Our brains extract durable, generalizable knowledge from transient experiences of the world. Artificial neural networks come nowhere close to this ability. When tasked with learning to classify objects by training on nonrepeating video frames in temporal order (online stream learning), models that learn well from shuffled datasets catastrophically forget old knowledge upon learning new stimuli. We propose a new continual learning algorithm, compositional replay using memory blocks (CRUMB), which mitigates forgetting by replaying feature maps reconstructed by combining generic parts. CRUMB concatenates trainable and reusable memory block vectors to compositionally reconstruct feature map tensors in convolutional neural networks (CNNs). Storing the indices of memory blocks used to reconstruct new stimuli enables memories of the stimuli to be replayed during later tasks. This reconstruction mechanism also primes the neural network to minimize catastrophic forgetting by biasing it toward attending to information about object shapes more than information about image textures and stabilizes the network during stream learning by providing a shared feature-level basis for all training examples. These properties allow CRUMB to outperform an otherwise identical algorithm that stores and replays raw images while occupying only 3.6% as much memory. We stress-tested CRUMB alongside 13 competing methods on seven challenging datasets. To address the limited number of existing online stream learning datasets, we introduce two new benchmarks by adapting existing datasets for stream learning. With only 3.7%-4.1% as much memory and 15%-43% as much runtime, CRUMB mitigates catastrophic forgetting more effectively than the state-of-the-art. Our code is available at https://github.com/MorganBDT/crumb.git.

Learning to Learn: How to Continuously Teach Humans and Machines.

Curriculum design is a fundamental component of education. For example, when we learn mathematics at school, we build upon our knowledge of addition to learn multiplication. These and other concepts must be mastered before our first algebra lesson, which also reinforces our addition and multiplication skills. Designing a curriculum for teaching either a human or a machine shares the underlying goal of maximizing knowledge transfer from earlier to later tasks, while also minimizing forgetting of learned tasks. Prior research on curriculum design for image classification focuses on the ordering of training examples during a single offline task. Here, we investigate the effect of the order in which multiple distinct tasks are learned in a sequence. We focus on the online class-incremental continual learning setting, where algorithms or humans must learn image classes one at a time during a single pass through a dataset. We find that curriculum consistently influences learning outcomes for humans and for multiple continual machine learning algorithms across several benchmark datasets. We introduce a novel-object recognition dataset for human curriculum learning experiments and observe that curricula that are effective for humans are highly correlated with those that are effective for machines. As an initial step towards automated curriculum design for online class-incremental learning, we propose a novel algorithm, dubbed Curriculum Designer (CD), that designs and ranks curricula based on inter-class feature similarities. We find significant overlap between curricula that are empirically highly effective and those that are highly ranked by our CD. Our study establishes a framework for further research on teaching humans and machines to learn continuously using optimized curricula. Our code and data are available through this link.