Publications

Noise covariance estimation in multi-task high-dimensional linear models
Kai Tan
Gabriel Romon
Lune P Bellec
2024-07-31
Bernoulli (published)
doi.org
arxiv.org
Socially Assistive Robots for patients with Alzheimer's Disease: A scoping review.
Vania Karami
Mark J. Yaffe
Genevieve Gore
AJung Moon
S. A. Rahimi
2024-07-31
Archives of Gerontology and Geriatrics (published)
doi.org
Understanding Decision-Time vs. Background Planning in Model-Based Reinforcement Learning
Safa Alver
Doina Precup
In model-based reinforcement learning, an agent can leverage a learned model to improve its way of behaving in different ways. Two prevalent… (see more) approaches are decision-time planning and background planning. In this study, we are interested in understanding under what conditions and in which settings one of these two planning styles will perform better than the other in domains that require fast responses. After viewing them through the lens of dynamic programming, we first consider the classical instantiations of these planning styles and provide theoretical results and hypotheses on which one will perform better in the pure planning, planning&learning, and transfer learning settings. We then consider the modern instantiations of these planning styles and provide hypotheses on which one will perform better in the last two of the considered settings. Lastly, we perform several illustrative experiments to empirically validate both our theoretical results and hypotheses. Overall, our findings suggest that even though decision-time planning does not perform as well as background planning in their classical instantiations, in their modern instantiations, it can perform on par or better than background planning in both the planning&learning and transfer learning settings.
2024-07-31
EWRL/2024/Workshop (accepted)
doi.org
openreview.net
Waves, patterns, bifurcations: A tutorial review on the vertebrate segmentation clock
Paul François
Victoria Mochulska
Proper vertebrae formation relies on a tissue-wide oscillator called the segmentation clock. Individual cellular oscillators in the presomit… (see more)ic mesoderm are modulated by intercellular coupling and external signals, leading to the propagation of oscillatory waves of genetic expression eventually stabilizing into a static pattern. Here, we review 4 decades of biophysical models of this process, starting from the pioneering Clock and Wavefront model by Cooke and Zeeman, and the reaction–diffusion model by Meinhardt. We discuss how modern descriptions followed advances in molecular description and visualization of the process, reviewing phase models, delayed models, systems-level, and finally geometric models. We connect models to high-level aspects of embryonic development from embryonic scaling to wave propagation, up to reconstructed stem cell systems. We provide new analytical calculations and insights into classical and recent models, leading us to propose a geometric description of somitogenesis organized along two primary waves of differentiation.
2024-07-31
Physics Reports (published)
doi.org
Differentially Private Linear Regression With Linked Data
Shurong Lin
Elliot Paquette
Eric D. Kolaczyk
There has been increasing demand for establishing privacy-preserving methodologies for modern statistics and machine learning. Differential … (see more)privacy, a mathematical notion from computer science, is a rising tool offering robust privacy guarantees. Recent work focuses primarily on developing differentially private versions of individual statistical and machine learning tasks, with nontrivial upstream pre-processing typically not incorporated. An important example is when record linkage is done prior to downstream modeling. Record linkage refers to the statistical task of linking two or more data sets of the same group of entities without a unique identifier. This probabilistic procedure brings additional uncertainty to the subsequent task. In this paper, we present two differentially private algorithms for linear regression with linked data. In particular, we propose a noisy gradient method and a sufficient statistics perturbation approach for the estimation of regression coefficients. We investigate the privacy-accuracy tradeoff by providing finite-sample error bounds for the estimators, which allows us to understand the relative contributions of linkage error, estimation error, and the cost of privacy. The variances of the estimators are also discussed. We demonstrate the performance of the proposed algorithms through simulations and an application to synthetic data.
2024-07-30
Harvard Data Science Review (published)
doi.org
arxiv.org
scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information
Yingfu Wu
Zhenqi Shi
Xiangfei Zhou
Pengyu Zhang
Xiuhui Yang
Jun Ding
Hao Wu
2024-07-30
Communications Biology (published)
doi.org
Diversity-aware Population Models: Quantifying Associations between Socio-Spatial Factors and Cognitive Development in the ABCD Cohort
Nicole Osayande
Justin Marotta
Shambhavi Aggarwal
Jakub Kopal
Avram Holmes
Sarah W. Yip
Danilo Bzdok
Population-level analyses are inherently complex due to a myriad of latent confounding effects that underlie the interdisciplinary topics of… (see more) research interest. Despite the mounting demand for generative population models, the limited generalizability to underrepresented groups hinders their widespread adoption in downstream applications. Interpretability and reliability are essential for clinicians and policymakers, while accuracy and precision are prioritized from an engineering standpoint. Thus, in domains such as population neuroscience, the challenge lies in determining a suitable approach to model population data effectively. Notably, the traditional strata-agnostic nature of existing methods in this field reveals a pertinent gap in quantitative techniques that directly capture major sources of population stratification. The emergence of population-scale cohorts, like the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study, provides unparalleled opportunities to explore and characterize neurobehavioral and sociodemographic relationships comprehensively. We propose diversity-aware population modeling, a framework poised to standardize systematic incorporation of diverse attributes, structured with respect to intrinsic population stratification to obtain holistic insights. Here, we leverage Bayesian multilevel regression and poststratification, to elucidate inter-individual differences in the relationships between socioeconomic status (SES) and cognitive development. We constructed 14 varying-intercepts and varying-slopes models to investigate 3 cognitive phenotypes and 5 sociodemographic variables (SDV), across 17 US states and 5 race subgroups. SDVs exhibited systemic socio-spatial effects that served as fundamental drivers of variation in cognitive outcomes. Low SES was disproportionately associated with cognitive development among Black and Hispanic children, while high SES was a robust predictor of cognitive development only among White and Asian children, consistent with the minorities’ diminished returns (MDRs) theory. Notably, adversity-susceptible subgroups demonstrated an expressive association with fluid cognition compared to crystallized cognition. Poststratification proved effective in correcting group attribution biases, particularly in Pennsylvania, highlighting sampling discrepancies in US states with the highest percentage of marginalized participants in the ABCD Study©. Our collective analyses underscore the inextricable link between race and geographic location within the US. We emphasize the importance of diversity-aware population models that consider the intersectional composition of society to derive precise and interpretable insights across applicable domains.
2024-07-29
Research Square (published)
doi.org
Strong Gravitational Lensing as a Probe of Dark Matter
S. Vegetti
S. Birrer
G. Despali
C.D. Fassnacht
D. Gilman
Y. Hezaveh
L.
L. Perreault Levasseur
J.P. McKean
D.M. Powell
C.M. O'Riordan
G.
G. Vernardos
Dark matter structures within strong gravitational lens galaxies and along their line of sight leave a gravitational imprint on the multiple… (see more) images of lensed sources. Strong gravitational lensing provides, therefore, a key test of different dark matter models in a way that is independent of the baryonic content of matter structures on subgalactic scales. In this chapter, we describe how galaxy-scale strong gravitational lensing observations are sensitive to the physical nature of dark matter. We provide a historical perspective of the field, and review its current status. We discuss the challenges and advances in terms of data, treatment of systematic errors and theoretical predictions, that will enable one to deliver a stringent and robust test of different dark matter models in the near future. With the advent of the next generation of sky surveys, the number of known strong gravitational lens systems is expected to increase by several orders of magnitude. Coupled with high-resolution follow-up observations, these data will provide a key opportunity to constrain the properties of dark matter with strong gravitational lensing.
2024-07-29
Space Science Reviews (published)
doi.org
arxiv.org
AAPM task group report 288: Recommendations for guiding radiotherapy event narratives
Bruce Thomadsen
Ajay Kapur
Bette Blankenship
Barrett Caldwell
Lindsey Claps
Joanne Cunningham
Jennifer Elee
Suzanne Evans
Eric Ford
Debbie Gilley
Sandra Hayden
Kathleen Hintenlang
Rishabh Kapoor
J. Kildea
Linda Kroger
Ksenija Kujundzic
Qing Liang
Sasa Mutic
Anita O'Donovan
Michael O'Hara … (see 6 more)
Zoubir Ouhib
Jatinder Palta
Todd Pawlicki
William Salter
Stacey Schmidt
Sugata Tripathi
2024-07-28
Medical Physics (Lancaster) (published)
doi.org
Implicitly Bayesian Prediction Rules in Deep Learning
Bruno Mlodozeniec
David M. Krueger
Richard E. Turner
The Bayesian approach leads to coherent updates of predictions under new data, which makes adhering to Bayesian principles appealing in deci… (see more)sion-making contexts. Traditionally, integrating Bayesian principles into models like deep neural networks involves setting priors on parameters and approximating posteriors. This is done despite the fact that, typically, priors on parameters reflect any prior beliefs only insofar as they dictate function space behaviour. In this paper, we rethink this approach and consider what properties characterise a prediction rule as being Bayesian. Algorithms meeting such criteria can be deemed implicitly Bayesian — they make the same predictions as some Bayesian model, without explicitly manifesting priors and posteriors. We argue this might be a more fruitful approach towards integrating Bayesian principles into deep learning. In this paper, we propose how to measure how close a general prediction rule is to being implicitly Bayesian, and empirically evaluate multiple prediction strategies using our approach. We also show theoretically that agents relying on non-implicitly Bayesian prediction rules can be easily exploited in adversarial betting settings.
2024-07-28
Proceedings of the 6th Symposium on Advances in Approximate Bayesian Inference (published)
proceedings.mlr.press
proceedings.mlr.press
scCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration
Xiuhui Yang
Koren K. Mann
Hao Wu
Jun Ding
Single-cell multi-omics data reveal complex cellular states, providing significant insights into cellular dynamics and disease. Yet, integra… (see more)tion of multi-omics data presents challenges. Some modalities have not reached the robustness or clarity of established transcriptomics. Coupled with data scarcity for less established modalities and integration intricacies, these challenges limit our ability to maximize single-cell omics benefits. We introduce scCross, a tool leveraging variational autoencoders, generative adversarial networks, and the mutual nearest neighbors (MNN) technique for modality alignment. By enabling single-cell cross-modal data generation, multi-omics data simulation, and in silico cellular perturbations, scCross enhances the utility of single-cell multi-omics studies. The online version contains supplementary material available at 10.1186/s13059-024-03338-z.
2024-07-28
Genome Biology (published)
doi.org
The report of AAPM task group 288: Recommendations for guiding radiotherapy event narratives.
Bruce Thomadsen
Ajay Kapur
Bette Blankenship
Barrett Caldwell
Lindsey Claps
Joanne Cunningham
Jennifer Elee
Suzanne Evans
Eric Ford
Debbie Gilley
Sandra Hayden
Kathleen Hintenlang
Rishabh Kapoor
J. Kildea
Linda Kroger
Ksenija Kujundzic
Qing Liang
Sasa Mutic
Anita O'Donovan
Michael O'Hara … (see 6 more)
Zoubir Ouhib
Jatinder Palta
Todd Pawlicki
William Salter
Stacey Schmidt
Sugata Tripathi
Incident reporting and learning systems provide an opportunity to identify systemic vulnerabilities that contribute to incidents and potenti… (see more)ally degrade quality. The narrative of an incident is intended to provide a clear, easy to understand description of an incident. Unclear, incomplete or poorly organized narratives compromise the ability to learn from them. This report provides guidance for drafting effective narratives, with particular attention to the use of narratives in incident reporting and learning systems (IRLS). Examples are given that compare effective and less than effective narratives. This report is mostly directed to organizations that maintain IRLS, but also may be helpful for individuals who desire to write a useful narrative for entry into such a system. Recommendations include the following: (1) Systems should allow a one- or two-sentence, free-text synopsis of an incident without guessing at causes; (2) Information included should form a sequence of events with chronology; and (3) Reporting and learning systems should consider using the headings suggested to guide the reporter through the narrative: (a) incident occurrences and actions by role; (b) prior circumstances and actions; (c) method by which the incident was identified; (d) equipment related details if relevant; (e) recovery actions by role; (f) relevant time span between responses; (g) and how individuals affected during or immediately after incident. When possible and appropriate, supplementary information including relevant data elements should be included using numerical scales or drop-down choices outside of the narrative. Information that should not be included in the narrative includes: (a) patient health information (PHI); (b) conjecture or blame; (c) jargon abbreviations or details without specifying their significance; (d) causal analysis.
2024-07-28
Medical Physics (Lancaster) (published)
doi.org