Cape Breton University

In this paper, a learning framework is introduced which incorporates principles of probabilistic inference, variational optimization, and geometry-preserving operations inspired by quantum transformations. The central innovation of this quantum-inspired variational convolution (QiVC) lies in its quantum-inspired rotated ensemble (QiRE) mechanism. QiRE performs differentiable low-dimensional subspace rotations of convolutional weights. By drawing a mathematical analogy from unitary evolution, this approach enables structured uncertainty modeling that respects the intrinsic geometry of the parameter space. To demonstrate its practical potential, the concept is instantiated in a QiVC-based convolutional network (QiVC-Net) and evaluated in the context of biosignal classification, focusing on phonocardiogram (PCG) recordings. The proposed QiVC-Net integrates an architecture in which the QiVC layer does not introduce additional parameters, instead performing an ensemble rotation of the convolutional weights through a structured mechanism ensuring robustness without added highly computational burden. Experiments on two benchmark datasets, PhysioNet CinC 2016 and PhysioNet CirCor DigiScope 2022, show that QiVC-Net achieves high performance, reaching accuracies of 97.84% and 97.89%, respectively. These findings highlight the versatility of the QiVC framework and its promise for advancing uncertainty-aware modeling in real-world biomedical signal analysis. The implementation of the QiVConv layer is available in GitHub for public use.

Data on various air-quality metrics are collected repeatedly by numerous monitoring stations worldwide to closely assess the severity of pollution. Particle pollution from fine particulate matter PM2.5 is one such measure used as an indicator of whether air quality is unhealthy. PM2.5 is a specific form of air contamination that negatively impacts the environment and human health when levels are above a certain threshold. As data are collected repeatedly over time at multiple locations, there may be a temporal dependence among repeated outcomes and spatial dependence between neighboring stations. Thus, it is important to assess the impact of risk factors on trajectory risk prediction. However, due to the temporal and spatial dependencies, trajectory risk prediction for such data becomes complicated, as both types of dependences must be accounted for during model building. In this paper, we propose a modeling framework that accounts for both types of dependences by incorporating Markov chains and Markov regression, using autologistics for model fitting and trajectory risk prediction. The proposed model fitting and trajectory risk prediction are illustrated using PM2.5 outdoor air pollution data from the United States from 2000 to 2020.

The Adult-Oriented Sport Coaching Survey (AOSCS) measures perceptions of adult-oriented coaching practices known to enhance Masters athletes ' (MAs) sport experiences. However, beyond single case studies, little is known about MAs ' generalizable preferences for when and how often they want these practices from their coaches. This mixed-methods study explored MAs ' preferences for adult-oriented coaching and used these fi ndings to develop a practical guide for new and experienced coaches of adult and Masters sport. An online survey of 135 MAs used the AOSCS preference version (AOSCS-P) to assess how often they preferred adult-oriented coaching at fi ve points in the season. Open-ended questions provided deeper insights into their preferences. Factor-level curve estimation analyses indicated MAs have stable preferences during the season. A descriptive analysis of item-level modes revealed MAs ' preferences for AOSCS-P items to be used in high, moderate, or low frequencies by their coaches. Qualitative fi ndings provided the rationale for these item-level patterns, revealing important nuances for coaches regarding their contextual application. These fi ndings inform a novel coaching guide that illustrates MAs ' general preferences, providing an accessible, evidence-based resource for coaches to enhance and complement ongoing coach-adult athlete dialogue.

This meta-analysis investigates the impact of remittances on labor supply decisions in recipient households. Using 1811 estimates from 64 studies, we find that international remittances reduce labor force participation by 4.4 percentage points while domestic remittances reduce it by 2.2 percentage points. The decline in labor force participation is stronger in South Asia (−5.6 percentage points) than in other regions. In contrast, conditional on remaining in the labor force, the effect on hours worked is negligible. Although remittances lower participation across most employment types, they have no significant effect on the self-employed. These findings highlight the complex economic implications of remittances, emphasizing the need for policies that foster labor market engagement and entrepreneurship. We also outline key directions for future research on this topic.

The prevalence of large language models (LLMs) such as ChatGPT has wowed the world with its ability to generate text in a human-like manner. While educators evaluate how AI will impact the future of learning, we identify mistakes ChatGPT has made. We further extend this concern to nonfinancially sophisticated users seeking to improve their financial literacy who may not possess the financial acumen to determine when the AI is hallucinating. Using a longitudinal study, our analysis frames the prompts and subsequent findings within the four stages of the Dunning-Kruger effect to explore how users of varying expertise receive output from the LLMs. We find that ChatGPT cannot always fully distinguish between three different user groups. Our findings have important implications for accountants, educators, and students using LLMs as a tool in work and education and for the general population looking to bypass financial experts for their personal finance needs.