Publications

Advanced Diffusion MR Imaging for Multiple Sclerosis in the Brain and Spinal Cord
Masaaki Hori
Tomoko Maekawa
Kouhei Kamiya
Akifumi Hagiwara
Masami Goto
Mariko Y. Takemura
Shohei Fujita
Christina Andica
Koji Kamagata
Shigeki Aoki
Diffusion tensor imaging (DTI) has been established its usefulness in evaluating normal-appearing white matter (NAWM) and other lesions that… (see more) are difficult to evaluate with routine clinical MRI in the evaluation of the brain and spinal cord lesions in multiple sclerosis (MS), a demyelinating disease. With the recent advances in the software and hardware of MRI systems, increasingly complex and sophisticated MRI and analysis methods, such as q-space imaging, diffusional kurtosis imaging, neurite orientation dispersion and density imaging, white matter tract integrity, and multiple diffusion encoding, referred to as advanced diffusion MRI, have been proposed. These are capable of capturing in vivo microstructural changes in the brain and spinal cord in normal and pathological states in greater detail than DTI. This paper reviews the current status of recent advanced diffusion MRI for assessing MS in vivo as part of an issue celebrating two decades of magnetic resonance in medical sciences (MRMS), an official journal of the Japanese Society of Magnetic Resonance in Medicine.
Advanced Diffusion MR Imaging for Multiple Sclerosis in the Brain and Spinal Cord
Masaaki Hori
Tomoko Maekawa
Kouhei Kamiya
Akifumi Hagiwara
Masami Goto
Mariko Yoshida Takemura
Shohei Fujita
Christina Andica
Koji Kamagata
Shigeki Aoki
Diffusion tensor imaging (DTI) has been established its usefulness in evaluating normal-appearing white matter (NAWM) and other lesions that… (see more) are difficult to evaluate with routine clinical MRI in the evaluation of the brain and spinal cord lesions in multiple sclerosis (MS), a demyelinating disease. With the recent advances in the software and hardware of MRI systems, increasingly complex and sophisticated MRI and analysis methods, such as q-space imaging, diffusional kurtosis imaging, neurite orientation dispersion and density imaging, white matter tract integrity, and multiple diffusion encoding, referred to as advanced diffusion MRI, have been proposed. These are capable of capturing in vivo microstructural changes in the brain and spinal cord in normal and pathological states in greater detail than DTI. This paper reviews the current status of recent advanced diffusion MRI for assessing MS in vivo as part of an issue celebrating two decades of magnetic resonance in medical sciences (MRMS), an official journal of the Japanese Society of Magnetic Resonance in Medicine.
Halting Time is Predictable for Large Models: A Universality Property and Average-Case Analysis
Bart van Merriënboer
Fabian Pedregosa
Geographic concentration of SARS-CoV-2 cases by social determinants of health in metropolitan areas in Canada: a cross-sectional study
Yiqing Xia
Huiting Ma
Gary Moloney
Héctor A. Velásquez García
Monica Sirski
Naveed Janjua
David Vickers
Tyler Williamson
Alan Katz
Kristy Yu
K. Yiu
Rafal Kustra
Marc Brisson
Stefan Baral
Sharmistha Mishra
Mathieu Maheu-Giroux
Robust Policy Learning over Multiple Uncertainty Sets
Annie Xie
Shagun Sodhani
Chelsea Finn
Amy Zhang
Reinforcement learning (RL) agents need to be robust to variations in safety-critical environments. While system identification methods prov… (see more)ide a way to infer the variation from online experience, they can fail in settings where fast identification is not possible. Another dominant approach is robust RL which produces a policy that can handle worst-case scenarios, but these methods are generally designed to achieve robustness to a single uncertainty set that must be specified at train time. Towards a more general solution, we formulate the multi-set robustness problem to learn a policy robust to different perturbation sets. We then design an algorithm that enjoys the benefits of both system identification and robust RL: it reduces uncertainty where possible given a few interactions, but can still act robustly with respect to the remaining uncertainty. On a diverse set of control tasks, our approach demonstrates improved worst-case performance on new environments compared to prior methods based on system identification and on robust RL alone.
Erratum to: Rapid simultaneous acquisition of macromolecular tissue volume, susceptibility, and relaxometry maps (Magn Reson Med. 2022;87:781‐790.)
Fang Frank Yu
Susie Y. Huang
Ashwin S. Kumar
T. Witzel
Congyu Liao
Tanguy Duval
Berkin Bilgic
Erratum to: Rapid simultaneous acquisition of macromolecular tissue volume, susceptibility, and relaxometry maps (Magn Reson Med. 2022;87:781‐790.)
Fang Frank Yu
Susie Yi Huang
Ashwin Kumar
Thomas Witzel
Congyu Liao
Tanguy Duval
Berkin Bilgic
Multivariate, Transgenerational Associations of the COVID-19 Pandemic Across Minoritized and Marginalized Communities.
S. Yip
Ayana Jordan
Robert J. Kohler
Avram J. Holmes
Importance The experienced consequences of the COVID-19 pandemic have diverged across individuals, families, and communities, resulting in i… (see more)nequity within a host of factors. There is a gap of quantitative evidence about the transgenerational impacts of these experiences and factors. Objective To identify baseline predictors of COVID-19 experiences, as defined by child and parent report, using a multivariate pattern-learning framework from the Adolescent Brain and Cognitive Development (ABCD) cohort. Design, Setting, and Participants ABCD is an ongoing prospective longitudinal study of child and adolescent development in the United States including 11 875 youths, enrolled at age 9 to 10 years. Using nationally collected longitudinal profiling data from 9267 families, a multivariate pattern-learning strategy was developed to identify factor combinations associated with transgenerational costs of the ongoing COVID-19 pandemic. ABCD data (release 3.0) collected from 2016 to 2020 and released between 2019 and 2021 were analyzed in combination with ABCD COVID-19 rapid response data from the first 3 collection points (May-August 2020). Exposures Social distancing and other response measures imposed by COVID-19, including school closures and shutdown of many childhood recreational activities. Main Outcomes and Measures Mid-COVID-19 experiences as defined by the ABCD's parent and child COVID-19 assessments. Results Deep profiles from 9267 youth (5681 female [47.8%]; mean [SD] age, 119.0 [7.5] months) and their caregivers were quantitatively examined. Enabled by a pattern-learning analysis, social determinants of inequity, including family structure, socioeconomic status, and the experience of racism, were found to be primarily associated with transgenerational impacts of COVID-19, above and beyond other candidate predictors such as preexisting medical or psychiatric conditions. Pooling information across more than 17 000 baseline pre-COVID-19 family indicators and more than 280 measures of day-to-day COVID-19 experiences, non-White (ie, families who reported being Asian, Black, Hispanic, other, or a combination of those choices) and/or Spanish-speaking families were found to have decreased resources (mode 1, canonical vector weight [CVW] = 0.19; rank 5 of 281), escalated likelihoods of financial worry (mode 1, CVW = -0.20; rank 4), and food insecurity (mode 1, CVW = 0.21; rank 2), yet were more likely to have parent-child discussions regarding COVID-19-associated health and prevention issues, such as handwashing (mode 1, CVW = 0.14; rank 9), conserving food or other items (mode 1, CVW = 0.21; rank 1), protecting elderly individuals (mode 1, CVW = 0.11; rank 21), and isolating from others (mode 1, CVW = 0.11; rank 23). In contrast, White families (mode 1, CVW = -0.07; rank 3), those with higher pre-COVID-19 income (mode 1, CVW = -0.07; rank 5), and presence of a parent with a postgraduate degree (mode 1, CVW = -0.06; rank 14) experienced reduced COVID-19-associated impact. In turn, children from families experiencing reduced COVID-19 impacts reported longer nighttime sleep durations (mode 1, CVW = 0.13; rank 14), less difficulties with remote learning (mode 2, CVW = 0.14; rank 7), and decreased worry about the impact of COVID-19 on their family's financial stability (mode 1, CVW = 0.134; rank 13). Conclusions and Relevance The findings of this study indicate that community-level, transgenerational intervention strategies may be needed to combat the disproportionate burden of pandemics on minoritized and marginalized racial and ethnic populations.
The Brain-Computer Metaphor Debate Is Useless: A Matter of Semantics
Timothy P. Lillicrap
On learning Whittle index policy for restless bandits with scalable regret
Nima Akbarzadeh
Reinforcement learning is an attractive approach to learn good resource allocation and scheduling policies based on data when the system mod… (see more)el is unknown. However, the cumulative regret of most RL algorithms scales as
Tackling Climate Change with Machine Learning
Priya L. Donti
Lynn H. Kaack
Kelly Kochanski
Alexandre Lacoste
Kris Sankaran
Andrew Slavin Ross
Nikola Milojevic-Dupont
Natasha Jaques
Anna Waldman-Brown
Alexandra Luccioni
Evan David Sherwin
S. Karthik Mukkavilli
Konrad Paul Kording
Carla P. Gomes
Andrew Y. Ng
Demis Hassabis
John C. Platt
Felix Creutzig … (see 2 more)
Jennifer T Chayes
Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here… (see more) we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.
TACTiS: Transformer-Attentional Copulas for Time Series
The estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. However, t… (see more)he practical utility of such estimates is limited by how accurately they quantify predictive uncertainty. In this work, we address the problem of estimating the joint predictive distribution of high-dimensional multivariate time series. We propose a versatile method, based on the transformer architecture, that estimates joint distributions using an attention-based decoder that provably learns to mimic the properties of non-parametric copulas. The resulting model has several desirable properties: it can scale to hundreds of time series, supports both forecasting and interpolation, can handle unaligned and non-uniformly sampled data, and can seamlessly adapt to missing data during training. We demonstrate these properties empirically and show that our model produces state-of-the-art predictions on multiple real-world datasets.