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Publications
Multi-tract multi-symptom relationships in pediatric concussion
The heterogeneity of white matter damage and symptoms in concussions has been identified as a major obstacle to therapeutic innovation. In c… (voir plus)ontrast, the vast majority of diffusion MRI studies on concussion have traditionally employed group-comparison approaches. Such studies do not consider heterogeneity of damage and symptoms in concussion. To parse concussion heterogeneity, the present study combines diffusion MRI (dMRI) and multivariate statistics to investigate multi-tract multi-symptom relationships. Using dMRI data from a sample of 306 children ages 9 and 10 with a history of concussion from the Adolescent Brain Cognitive Development Study (ABCD study), we built connectomes weighted by classical and emerging diffusion measures. These measures were combined into two informative indices, the first capturing a mixture of patterns suggestive of microstructural complexity, the second representing almost exclusively axonal density. We deployed pattern-learning algorithms to jointly decompose these connectivity features and 19 behavioural measures that capture well-known symptoms of concussions. We found idiosyncratic symptom-specific multi-tract connectivity features, which would not be captured in traditional univariate analyses. Multivariable connectome-symptom correspondences were stronger than all single-tract/single-symptom associations. Multi-tract connectivity features were also expressed equally across different sociodemographic strata and their expression was not accounted for by injury-related variables. In a replication dataset, the expression of multi-tract connectivity features predicted adverse psychiatric outcomes after accounting for other psychopathology-related variables. By defining cross-demographic multi-tract multi-symptom relationships to parse concussion heterogeneity, the present study can pave the way for the development of improved stratification strategies that may contribute to the success of future clinical trials and the improvement of concussion management.
Abstract Designing hierarchical reinforcement learning algorithms that exhibit safe behaviour is not only vital for practical applications b… (voir plus)ut also facilitates a better understanding of an agent’s decisions. We tackle this problem in the options framework (Sutton, Precup & Singh, 1999), a particular way to specify temporally abstract actions which allow an agent to use sub-policies with start and end conditions. We consider a behaviour as safe that avoids regions of state space with high uncertainty in the outcomes of actions. We propose an optimization objective that learns safe options by encouraging the agent to visit states with higher behavioural consistency. The proposed objective results in a trade-off between maximizing the standard expected return and minimizing the effect of model uncertainty in the return. We propose a policy gradient algorithm to optimize the constrained objective function. We examine the quantitative and qualitative behaviours of the proposed approach in a tabular grid world, continuous-state puddle world, and three games from the Arcade Learning Environment: Ms. Pacman, Amidar, and Q*Bert. Our approach achieves a reduction in the variance of return, boosts performance in environments with intrinsic variability in the reward structure, and compares favourably both with primitive actions and with risk-neutral options.
Meta and transfer learning are two successful families of approaches to few-shot learning. Despite highly related goals, state-of-the-art ad… (voir plus)vances in each family are measured largely in isolation of each other. As a result of diverging evaluation norms, a direct or thorough comparison of different approaches is challenging. To bridge this gap, we perform a cross-family study of the best transfer and meta learners on both a large-scale meta-learning benchmark (Meta-Dataset, MD), and a transfer learning benchmark (Visual Task Adaptation Benchmark, VTAB). We find that, on average, large-scale transfer methods (Big Transfer, BiT) outperform competing approaches on MD, even when trained only on ImageNet. In contrast, meta-learning approaches struggle to compete on VTAB when trained and validated on MD. However, BiT is not without limitations, and pushing for scale does not improve performance on highly out-of-distribution MD tasks. In performing this study, we reveal a number of discrepancies in evaluation norms and study some of these in light of the performance gap. We hope that this work facilitates sharing of insights from each community, and accelerates progress on few-shot learning.
Classical machine learning algorithms often assume that the data are drawn i.i.d. from a stationary probability distribution. Recently, cont… (voir plus)inual learning emerged as a rapidly growing area of machine learning where this assumption is relaxed, i.e. where the data distribution is non-stationary and changes over time. This paper represents the state of data distribution by a context variable
All in This Together? A Preregistered Report on Deservingness of Government Aid During the COVID-19 Pandemic
Aengus Bridgman
Eric Roman Owen Merkley
Peter John Loewen
Taylor Reid Owen
Derek Ruths
Abstract The COVID-19 pandemic has placed unprecedented pressure on governments to engage in widespread cash transfers directly to citizens … (voir plus)to help mitigate economic losses. Major and near-universal redistribution efforts have been deployed, but there is remarkably little understanding of where the mass public believes financial support is warranted. Using experimental evidence, we evaluate whether considerations related to deservingness, similarity, and prejudicial attitudes structure support for these transfers. A preregistered experiment found broad, generous, and nondiscriminatory support for direct cash transfers related to COVID-19 in Canada. The second study, accepted as a preregistered report, further probes these dynamics by comparing COVID-19-related outlays with nonemergency ones. We find that COVID-19-related spending was more universal as compared to a more generic cash allocation program. Given that the results were driven by the income of hypothetical recipients, we find broad support for disaster relief that is not means-tested or otherwise constrained by pre-disaster income.
2021-03-31
Journal of Experimental Political Science (published)
Supplemental Digital Content is available in the text. Hypertension, elevated morning blood pressure (BP) surges, and circadian BP variabili… (voir plus)ty constitute risk factors for cerebrovascular events. Nevertheless, while evidence indicates that hypertension is associated with cognitive dysfunctions, the link between BP variability and cognitive performance during aging is not clear. The purpose of this study is to determine the interaction between relative morning BP, cerebral blood flow (CBF) levels, and cognitive performance in hypertensive older adults with controlled BP under antihypertensive treatment. Eighty-four participants aged between 60 and 75 years old were separated into normotensive (n=51) and hypertensive (n=33) groups and underwent 24-hour ambulatory BP monitoring. They were also examined for CBF in the gray matter (CBF-GM) by magnetic resonance imaging and 5 cognitive domains: global cognition, working memory, episodic memory, processing speed, and executive functions. There was no difference in cognitive performance and CBF between normotensive and controlled hypertensive participants. Through a sensitivity analysis, we identified that, among relative morning BP variables, the best fit for CBF values in this cohort was the morning-evening difference in BP. The relative morning BP was negatively associated with CBF-GM in these hypertensive older adults only. In turn, CBF-GM levels were negatively associated with working and episodic memory scores in hypertensive older adults. This is the first extended study demonstrating an association between high relative morning BP and lower levels of CBF-GM, including the further impact of CBF-GM levels on the cognitive performance of specific domains in a community-based cohort of older adults with hypertension.