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    Associations between social factors and school belonging among newcomer and non-newcomer youth in Sweden
    (Public Library of Science, 2023-02-03) McDiarmid, Serena; Osman, Fatumo; Sarkadi, Anna; Durbeej, Natalie
    Feeling a sense of belonging at school is associated with important positive outcomes for youth and requires youth to engage in positive social relationships. Yet there is a limited understanding of the social factors most associated with youths’ school belonging and limited evidence about whether correlates of school belonging vary for marginalized groups like newcomers compared to majority groups. Sweden provides an important context for investigation of these issues because, over the past two decades, the country has experienced an influx of asylum seekers and educational reforms that have altered the composition and functioning of Swedish secondary schools. This study addresses these gaps by (1) investigating which of eight social factors are associated with school belonging among diverse Swedish youth, and (2) examining whether newcomer status moderates the relationship between social factors and school belonging. Hierarchical regression and moderation analyses were used to analyze data from 14 to 19 year-old (n = 233) newcomers and non-newcomers in Sweden. An exploratory factor analysis revealed that the school belonging measure contained two factors: positive perceptions and negative perceptions (reverse coded). For both, stronger school belonging was associated with lower perceived ethnic discrimination. Positive perceptions of school belonging were also associated with more prosocial behaviours and lower emotional problems. Negative perceptions of school belonging were associated with more peer problems. Notably, quantity and quality of peer relationships were not associated with school belonging. There was no consistent evidence of newcomer status moderating the relationship between social factors and school belonging. These results highlight factors associated with school belonging which are modifiable and amenable to intervention or impact by policy—ethnic discrimination, prosocial behaviour, and emotional and peer problems. The absence of moderation by newcomer status suggests that school belonging interventions or related policies are likely to affect newcomer and non-newcomer students similarly.
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    Proactive Contract Tracing
    (Public Library of Science, 2023-03-13) Gupta, Prateek; Maharaj, Tegan; Weiss, Martin; Rahaman, Nasim; Alsdurf, Hannah; Minoyan, Nanor; Harnois-Leblanc, Soren; Merckx, Joanna; Williams, Andrew; Schmidt, Victor; St-Charles, Pierre-Luc; Patel, Akshay; Zhang, Yang; Buckeridge, David L.; Pal, Christopher; Scholkopf, Bernhard; Bengio, Yoshua
    The COVID-19 pandemic has spurred an unprecedented demand for interventions that can reduce disease spread without excessively restricting daily activity, given negative impacts on mental health and economic outcomes. Digital contact tracing (DCT) apps have emerged as a component of the epidemic management toolkit. Existing DCT apps typically recommend quarantine to all digitally-recorded contacts of test-confirmed cases. Over-reliance on testing may, however, impede the effectiveness of such apps, since by the time cases are confirmed through testing, onward transmissions are likely to have occurred. Furthermore, most cases are infectious over a short period; only a subset of their contacts are likely to become infected. These apps do not fully utilize data sources to base their predictions of transmission risk during an encounter, leading to recommendations of quarantine to many uninfected people and associated slowdowns in economic activity. This phenomenon, commonly termed as “pingdemic,” may additionally contribute to reduced compliance to public health measures. In this work, we propose a novel DCT framework, Proactive Contact Tracing (PCT), which uses multiple sources of information (e.g. self-reported symptoms, received messages from contacts) to estimate app users’ infectiousness histories and provide behavioral recommendations. PCT methods are by design proactive, predicting spread before it occurs. We present an interpretable instance of this framework, the Rule-based PCT algorithm, designed via a multi-disciplinary collaboration among epidemiologists, computer scientists, and behavior experts. Finally, we develop an agent-based model that allows us to compare different DCT methods and evaluate their performance in negotiating the trade-off between epidemic control and restricting population mobility. Performing extensive sensitivity analysis across user behavior, public health policy, and virological parameters, we compare Rule-based PCT to i) binary contact tracing (BCT), which exclusively relies on test results and recommends a fixed-duration quarantine, and ii) household quarantine (HQ). Our results suggest that both BCT and Rule-based PCT improve upon HQ, however, Rule-based PCT is more efficient at controlling spread of disease than BCT across a range of scenarios. In terms of cost-effectiveness, we show that Rule-based PCT pareto-dominates BCT, as demonstrated by a decrease in Disability Adjusted Life Years, as well as Temporary Productivity Loss. Overall, we find that Rule-based PCT outperforms existing approaches across a varying range of parameters. By leveraging anonymized infectiousness estimates received from digitally-recorded contacts, PCT is able to notify potentially infected users earlier than BCT methods and prevent onward transmissions. Our results suggest that PCT-based applications could be a useful tool in managing future epidemics.
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    Microbially mediated carbon dioxide removal for sustainable mining
    (Public Library of Science, 2023-03-21) McCutcheon, Jenine; Power, Ian M.
    The climate crisis and rising demand for critical minerals necessitate the development of novel carbon dioxide removal and ore processing technologies. Microbial processes can be harnessed to recover metals from and store carbon dioxide within mine tailings to transform the mining industry for a greener and more sustainable future.
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    Synthesis of Ta-doped Li7La3Zr2O12 for application in solid-state electrolyte
    (University of Waterloo, 2026-04-30) Yan, Xinmei
    Solid-state batteries have emerged as a major focus in rechargeable battery research. Among them, cubic-phase of lithium lanthanum zirconium oxide (LLZO) demonstrates excellent room-temperature ionic conductivity, low activation energy, and high thermal stability against lithium metal. Conventional LLZO synthesis struggles with particle size control and large-scale uniform production, limiting commercial applications. Thus, developing an efficient synthesis route for high-quality LLZO is critical. In this work, a refined standard operating procedure (SOP) for fabricating dense pellets from powder was established using commercial powder. Systematic optimization of green body properties and sintering yielded ceramics with 95.8% relative density, 0.49 mS cm⁻¹ conductivity at 20 °C and 0.25 eV activation energy (20–40 °C). Furthermore, the study introduces a spray-drying synthesis approach for cubic-phase Ta-doped LLZO powder. Compared to commercial powders, the synthesized LLZTO produced similarly dense pellets (up to 96.4%) with comparable electrochemical performance. The best sample reached an ionic conductivity of 0.36 mS cm⁻¹ at 20 °C and 0.52 mS cm⁻¹ at 40 °C, with a minimum activation energy of 0.23 eV. Preliminary tests integrated synthesized LLZTO into 3D printing inks. After de binding and sintering, phase stability or crystallite size were unaffected, but mechanical fragility prevented reliable electrochemical testing. Overall, this study demonstrates both an effective spray drying route for scalable LLZTO synthesis and the feasibility of fabricating oxide-based solid electrolytes via 3D printing. Further optimization is needed to improve the mechanical strength and reproducibility of printed structures before achieving consistent electrochemical characterization.
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    Efficient Inference-time Control and Alignment
    (University of Waterloo, 2026-04-30) Rashid, Ahmad
    Modern foundation models are typically trained in three broad stages. First, large-scale pre-training is performed using self-supervised learning on massive corpora. Second, models are adapted through mid-training using supervised fine-tuning or instruction tuning on labeled datasets. Finally, a post-training stage is often applied using preference data and reinforcement learning in order to align the model and improve its safety, reliability, and usefulness. Although effective, post-training methods can be computationally expensive and inflexible once large models are deployed. This thesis explores an alternative paradigm: enforcing behavioral objectives at inference time rather than modifying model parameters during post-training. In this approach, smaller modular control models are combined with a base model to shape predictions during the decision process. Our aim is to design alignment mechanisms that are both mathematically grounded and empirically strong while remaining computationally efficient and easy to deploy. We apply this perspective of inference-time control to three problems. First, we address reliability in neural classifiers. We introduce PreLoad, an inference-time mechanism that mitigates arbitrarily high confidence on inputs that lie outside the training support while preserving accuracy and training efficiency. Second, we study reward-guided text generation (RGTG) in large language models as a form of inference-time alignment. We show that stable reward-guided decoding requires carefully designed token-level reward models and propose two algorithms, PARGS and FaRMA, that enable effective reward-guided generation. Third, we address the computational cost of RGTG and propose an efficient algorithm that adds only a minor overhead during inference while preserving the performance and benefits of reward-guided decoding. Together, these results demonstrate that inference-time control provides a flexible and computationally efficient framework for shaping the behavior of modern neural systems. By decoupling representation learning from the decision-time objectives, this work introduces new tools for improving the reliability, alignment, and efficiency of large-scale machine learning models without retraining them.