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Publications
Why do sleep disorders belong to mental disorder classifications? A network analysis of the "Sleep-Wake Disorders" section of the DSM-5.
Recent studies report that socioeconomic status (SES) correlates with brain structure. Yet, such findings are variable and little is known a… (voir plus)bout underlying causes. We present a well-powered voxel-based analysis of grey matter volume (GMV) across levels of SES, finding many small SES effects widely distributed across the brain, including cortical, subcortical and cerebellar regions. We also construct a polygenic index of SES to control for the additive effects of common genetic variation related to SES, which attenuates observed SES-GMV relations, to different degrees in different areas. Remaining variance, which may be attributable to environmental factors, is substantially accounted for by body mass index, a marker for lifestyle related to SES. In sum, SES affects multiple brain regions through measurable genetic and environmental effects. One-sentence Summary Socioeconomic status is linked with brain anatomy through a varying balance of genetic and environmental influences.
Recent research analyzing the sensitivity of natural language understanding models to word-order perturbations has shown that neural models … (voir plus)are surprisingly insensitive to the order of words.In this paper, we investigate this phenomenon by developing order-altering perturbations on the order of words, subwords, and characters to analyze their effect on neural models’ performance on language understanding tasks.We experiment with measuring the impact of perturbations to the local neighborhood of characters and global position of characters in the perturbed texts and observe that perturbation functions found in prior literature only affect the global ordering while the local ordering remains relatively unperturbed.We empirically show that neural models, invariant of their inductive biases, pretraining scheme, or the choice of tokenization, mostly rely on the local structure of text to build understanding and make limited use of the global structure.
Background: There is a growing recognition that strategies to reduce SARS-CoV-2 transmission should be responsive to local transmission dyna… (voir plus)mics. Studies have revealed inequalities along social determinants of health, but little investigation was conducted surrounding geographic concentration within cities. We quantified social determinants of geographic concentration of COVID-19 cases across sixteen census metropolitan areas (CMA) in four Canadian provinces. Methods: We used surveillance data on confirmed COVID-19 cases at the level of dissemination area. Gini (co-Gini) coefficients were calculated by CMA based on the proportion of the population in ranks of diagnosed cases and each social determinant using census data (income, education, visible minority, recent immigration, suitable housing, and essential workers) and the corresponding share of cases. Heterogeneity was visualized using Lorenz (concentration) curves. Results: Geographic concentration was observed in all CMAs (half of the cumulative cases were concentrated among 21-35% of each city's population): with the greatest geographic heterogeneity in Ontario CMAs (Gini coefficients, 0.32-0.47), followed by British Columbia (0.23-0.36), Manitoba (0.32), and Quebec (0.28-0.37). Cases were disproportionately concentrated in areas with lower income, education attainment, and suitable housing; and higher proportion of visible minorities, recent immigrants, and essential workers. Although a consistent feature across CMAs was concentration by proportion visible minorities, the magnitude of concentration by social determinants varied across CMAs. Interpretation: The feature of geographical concentration of COVID-19 cases was consistent across CMAs, but the pattern by social determinants varied. Geographically-prioritized allocation of resources and services should be tailored to the local drivers of inequalities in transmission in response to SARS-CoV-2's resurgence.
Learning reward-agnostic representations is an emerging paradigm in reinforcement learning. These representations can be leveraged for sever… (voir plus)al purposes ranging from reward shaping to skill discovery. Nevertheless, in order to learn such representations, existing methods often rely on assuming uniform access to the state space. With such a privilege, the agent’s coverage of the environment can be limited which hurts the quality of the learned representations. In this work, we introduce a method that explicitly couples representation learning with exploration when the agent is not provided with a uniform prior over the state space. Our method learns representations that constantly drive exploration while the data generated by the agent’s exploratory behavior drives the learning of better representations. We empirically validate our approach in goal-achieving tasks, demonstrating that the learned representation captures the dynamics of the environment, leads to more accurate value estimation, and to faster credit assignment, both when used for control and for reward shaping. Finally, the exploratory policy that emerges from our approach proves to be successful at continuous navigation tasks with sparse rewards.
