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The Two Fundamental Shapes of Sleep Heart Rate Dynamics and Their Connection to Mental Health in College Students

Digital Biomarkers 2024年第1期8卷 120-131页

作者： Fudolig, Mikaela Irene Bloomfield, Laura S.P. Price, Matthew Bird, Yoshi M. Hidalgo, Johanna E. Kim, Julia N. Llorin, Jordan Lovato, Juniper McGinnis, Ellen W. McGinnis, Ryan S. Ricketts, Taylor Stanton, Kathryn Dodds, Peter Sheridan Danforth, Christopher M. Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT United States Department of Mathematics and Statistics University of Vermont BurlingtonVT United States Computational Story Lab MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT United States Gund Institute for Environment University of Vermont BurlingtonVT United States Department of Psychological Science University of Vermont BurlingtonVT United States Project Lemurs University of Vermont BurlingtonVT United States Department of Electrical and Biomedical Engineering University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States Wake Forest University School of Medicine Winston-SalemNC United States Rubenstein School of Environment and Natural Resources University of Vermont BurlingtonVT United States Santa Fe Institute Santa FeNM United States

Introduction: Wearable devices are rapidly improving our ability to observe health-related processes for extended durations in an unintrusive manner. In this study, we use wearable devices to understand how the shape of the heart rate curve during sleep relates to mental health. Methods: As part of the Lived Experiences Measured Using Rings Study (LEMURS), we collected heart rate measurements using the Oura ring (Gen3) for over 25,000 sleep periods and self-reported mental health indicators from roughly 600 first-year university students in the USA during the fall semester of 2022. Using clustering techniques, we find that the sleeping heart rate curves can be broadly separated into two categories that are mainly differentiated by how far along the sleep period the lowest heart rate is reached. Results: Sleep periods characterized by reaching the lowest heart rate later during sleep are also associated with shorter deep and REM sleep and longer light sleep, but not a difference in total sleep duration. Aggregating sleep periods at the individual level, we find that consistently reaching the lowest heart rate later during sleep is a significant predictor of (1) self-reported impairment due to anxiety or depression, (2) a prior mental health diagnosis, and (3) firsthand experience in traumatic events. This association is more pronounced among females. Conclusion: Our results show that the shape of the sleeping heart rate curve, which is only weakly correlated with descriptive statistics such as the average or the minimum heart rate, is a viable but mostly overlooked metric that can help quantify the relationship between sleep and mental health. © 2024 The Author(s). Published by S. Karger AG, Basel.

关键词： Wearable technology

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Towards understanding residual and dilated dense neural networks via convolutional sparse coding

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National science Review 2021年第3期8卷 127-139页

作者： Zhiyang Zhang Shihua Zhang National Center for Mathematics and Interdisciplinary Sciences Center for Excellence in Mathematical Science Key Laboratory of Random Complex Structures and Data ScienceAcademy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences Key Laboratory of Systems Biology Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Chinese Academy of Sciences

Convolutional neural network(CNN) and its variants have led to many state-of-the-art results in various ***,a clear theoretical understanding of such networks is still ***,a multilayer convolutional sparse coding(ML-CSC) model has been proposed and proved to equal such simply stacked networks(plain networks).Here,we consider the initialization,the dictionary design and the number of iterations to be factors in each layer that greatly affect the performance of the ML-CSC *** by these considerations,we propose two novel multilayer models:the residual convolutional sparse coding(Res-CSC) model and the mixed-scale dense convolutional sparse coding(MSD-CSC) *** are closely related to the residual neural network(ResNet) and the mixed-scale(dilated) dense neural network(MSDNet),***,we derive the skip connection in the ResNet as a special case of a new forward propagation rule for the ML-CSC *** also find a theoretical interpretation of dilated convolution and dense connection in the MSDNet by analyzing the MSD-CSC model,which gives a clear mathematical understanding of *** implement the iterative soft thresholding algorithm and its fast version to solve the Res-CSC and MSD-CSC *** unfolding operation can be employed for further ***,extensive numerical experiments and comparison with competing methods demonstrate their effectiveness.

关键词： convolutional neural network convolutional sparse coding residual neural network mixed-scale dense neural network dilated convolution dense connection

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Curating corpora with classifiers: A case study of clean energy sentiment online

arXiv

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arXiv 2023年

作者： Arnold, Michael V. Dodds, Peter Sheridan Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States Department of Mathematics & Statistics University of Vermont BurlingtonVT United States

Well curated, large-scale corpora of social media posts containing broad public opinion offer an alternative data source to complement traditional surveys. While surveys are effective at collecting representative samples and are capable of achieving high accuracy, they can be both expensive to run and lag public opinion by days or weeks. Both of these drawbacks could be overcome with a real-time, high volume data stream and fast analysis pipeline. A central challenge in orchestrating such a data pipeline is devising an effective method for rapidly selecting the best corpus of relevant documents for analysis. Querying with keywords alone often includes irrelevant documents that are not easily disambiguated with bag-of-words natural language processing methods. Here, we explore methods of corpus curation to filter irrelevant tweets using pre-trained transformer-based models, fine-tuned for our binary classification task on hand-labeled tweets. We are able to achieve F1 scores of up to 0.95. The low cost and high performance of fine-tuning such a model suggests that our approach could be of broad benefit as a pre-processing step for social media datasets with uncertain corpus boundaries. © 2023, CC BY.

关键词： Tweets

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A blind spot for large language models: Supradiegetic linguistic information

arXiv

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arXiv 2023年

作者： Zimmerman, Julia Witte Hudon, Denis Cramer, Kathryn Onge, Jonathan St. Fudolig, Mikaela Trujillo, Milo Z. Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab Vermont Advanced Computing Center University of Vermont BurlingtonVT05405 United States Vermont Complex Systems Center MassMutual Center of Excellence in Complex Systems and Data Science University of Vermont BurlingtonVT05405 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05405 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States

Large Language Models (LLMs) like ChatGPT reflect profound changes in the field of Artificial Intelligence, achieving a linguistic fluency that is impressively, even shockingly, human-like. The extent of their current and potential capabilities is an active area of investigation by no means limited to scientific researchers. It is common for people to frame the training data for LLMs as "text" or even "language". We examine the details of this framing using ideas from several areas, including linguistics, embodied cognition, cognitive science, mathematics, and history. We propose that considering what it is like to be an LLM like ChatGPT, as Nagel might have put it, can help us gain insight into its capabilities in general, and in particular, that its exposure to linguistic training data can be productively reframed as exposure to the diegetic information encoded in language, and its deficits can be reframed as ignorance of extradiegetic information, including supradiegetic linguistic information. Supradiegetic linguistic information consists of those arbitrary aspects of the physical form of language that are not derivable from the one-dimensional relations of context—frequency, adjacency, proximity, co-occurrence—that LLMs like ChatGPT have access to. Roughly speaking, the diegetic portion of a word can be thought of as its function, its meaning, as the information in a theoretical vector in a word embedding, while the supradiegetic portion of the word can be thought of as its form, like the shapes of its letters or the sounds of its syllables. We use these concepts to investigate why LLMs like ChatGPT have trouble handling palindromes, the visual characteristics of symbols, translating Sumerian cuneiform, and continuing integer sequences. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

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Hollywood’s misrepresentation of death: A comparison of overall and by-gender mortality causes in film and the real world

arXiv

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arXiv 2024年

作者： Beauregard, Calla Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab Vermont Advanced Computing Center University of Vermont BurlingtonVT05405 United States Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT05405 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05405 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States Santa Fe Institute 1399 Hyde Park Rd Santa FeNM87501 United States

The common phrase ‘representation matters’ asserts that media has a measurable and important impact on civic society’s perception of self and others. The representation of health in media, in particular, may reflect and perpetuate a society’s disease burden. Here, for the top 10 major causes of death in the United States, we examine how cinematic representation of overall and by-gender mortality diverges from reality. Using crowd-sourced data on film deaths from Cinemorgue Wiki, we employ natural language processing (NLP) techniques to analyze shifts in representation of deaths in movies versus the 2021 National Vital Statistic Survey (NVSS) top ten mortality causes. Overall, movies strongly overrepresent suicide and, to a lesser degree, accidents. In terms of gender, movies overrepresent men and underrepresent women for nearly every major mortality cause, including heart disease and cerebrovascular disease. The two exceptions for which women are overrepresented are suicide and accidents. We discuss the implications of under- and over-representing causes of death overall and by gender, as well as areas of future research. © 2024, CC BY-SA.

关键词： Natural language processing systems

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Collective sleep and activity patterns of college students from wearable devices

arXiv

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arXiv 2024年

作者： Fudolig, Mikaela Irene Bloomfield, Laura S.P. Price, Matthew Bird, Yoshi M. Hidalgo, Johanna E. Kim, Julia Llorin, Jordan Lovato, Juniper McGinnis, Ellen W. McGinnis, Ryan S. Ricketts, Taylor Stanton, Kathryn Dodds, Peter Sheridan Danforth, Christopher M. Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT05405 United States Department of Mathematics and Statistics University of Vermont BurlingtonVT05405 United States Computational Story Lab MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT05405 United States Gund Institute for Environment University of Vermont BurlingtonVT05405 United States Department of Psychological Science University of Vermont BurlingtonVT05405 United States Project LEMURS University of Vermont BurlingtonVT05405 United States Department of Electrical and Biomedical Engineering University of Vermont BurlingtonVT05405 United States Wake Forest University School of Medicine Winston-SalemNC27101 United States Rubenstein School of Environment and Natural Resources University of Vermont BurlingtonVT05405 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States Santa Fe Institute New Mexico87501 United States

To optimize interventions for improving wellness, it is essential to understand habits, which wearable devices can measure with greater precision. Using high temporal resolution biometric data taken from the Oura Gen3 ring, we examine daily and weekly sleep and activity patterns of a cohort of young adults (N = 582) in their first semester of college. A high compliance rate is observed for both daily and nightly wear, with slight dips in wear compliance observed shortly after waking up and also in the evening. Most students have a late-night chronotype with a median midpoint of sleep at 5AM, with males and those with mental health impairment having more delayed sleep periods. Social jetlag, or the difference in sleep times between free days and school days, is prevalent in our sample. While sleep periods generally shift earlier on weekdays and later on weekends, sleep duration on both weekdays and weekends is shorter than during prolonged school breaks, suggesting chronic sleep debt when school is in session. Synchronized spikes in activity consistent with class schedules are also observed, suggesting that walking in between classes is a widespread behavior in our sample that substantially contributes to physical activity. Lower active calorie expenditure is associated with weekends and a delayed but longer sleep period the night before, suggesting that for our cohort, active calorie expenditure is affected less by deviations from natural circadian rhythms and more by the timing associated with activities. Our study shows that regular sleep and activity routines may be inferred from consumer wearable devices if high temporal resolution and long data collection periods are available. Copyright © 2024, The Authors. All rights reserved.

关键词： Sleep research

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An assessment of measuring local levels of homelessness through proxy social media signals

arXiv

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arXiv 2023年

作者： Bird, Yoshi Meke Grobe, Sarah E. Arnold, Michael V. Rogers, Sean P. Fudolig, Mikaela I. Zimmerman, Julia Witte Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Center University of Vermont BurlingtonVT05405 United States University of Vermont BurlingtonVT05405 United States Department of Mathematics and Statistics University of Vermont BurlingtonVT05405 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States Santa Fe Institute 1399 Hyde Park Rd Santa FeNM87501 United States

Although nearly 600,000 people experience homelessness in the United States every year, efforts to address this public health crisis are limited by the underperformance of standard methods to estimate localized and nationwide homelessness. Recent studies suggest social media activity can function as a proxy for measures of state-level public health, detectable through straightforward applications of natural language processing. We present results of our efforts to apply this approach to estimate homelessness at the state level throughout the US during the period 2010-2019 and 2022 using a dataset of roughly 1 million geotagged tweets containing the substring "homeless." Correlations between homelessness-related tweet counts and ranked per capita homelessness volume, but not general-population densities, suggest a relationship between the likelihood of Twitter users to personally encounter or observe homelessness in their everyday lives and their likelihood to communicate about it online. An increase to the log-odds of the word "homeless" appearing in an English-language tweet, as well as an acceleration in the increase in average tweet sentiment, suggest that tweets about homelessness are also affected by trends at the nation-scale. Additionally, changes to the lexical content of tweets over time suggest that reversals to the polarity of national or state-level trends may be detectable through an increase in political or service-sector language over the semantics of charity or direct appeals. Although tweet sentiment does not correlate to changes in homelessness volume, an analysis of user account type undertaken to explain nationwide sentiment dynamics revealed changes to Twitter-use patterns by accounts authored by individuals versus entities that may provide an additional signal to confirm changes to homelessness density in a given jurisdiction. While a computational approach to social media analysis may provide a low-cost, real-time dataset rich with information

关键词： Public health

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A decomposition of book structure through ousiometric fluctuations in cumulative word-time

arXiv

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arXiv 2022年

作者： Fudolig, Mikaela Irene Alshaabi, Thayer Cramer, Kathryn Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT United States Advanced Bioimaging Center UC Berkeley BerkeleyCA United States Department of Mathematics & Statistics University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States

While quantitative methods have been used to examine changes in word usage in books, studies have focused on overall trends, such as the shapes of narratives, which are independent of book length. We instead look at how words change over the course of a book as a function of the number of words, rather than the fraction of the book, completed at any given point;we define this measure as "cumulative word-time". Using ousiometrics, a reinterpretation of the valence-arousal-dominance framework of meaning obtained from semantic differentials, we convert text into time series of power and danger scores, with time corresponding to cumulative word-time. Each time series is then decomposed using empirical mode decomposition into a sum of constituent oscillatory modes and a non-oscillatory trend. By comparing the decomposition of the original power and danger time series with those derived from shuffled text, we find that shorter books exhibit only a general trend, while longer books have fluctuations in addition to the general trend. These fluctuations typically have a period of a few thousand words regardless of the book length or library classification code, but vary depending on the content and structure of the book. Our findings suggest that, in the ousiometric sense, longer books are not expanded versions of shorter books, but rather are more similar in structure to a concatenation of shorter texts. Further, they are consistent with editorial practices that require longer texts to be broken down into sections, such as chapters. Our method also provides a data-driven denoising approach that works for texts of various lengths, in contrast to the more traditional approach of using large window sizes that may inadvertently smooth out relevant information, especially for shorter texts. All together, these results open up avenues for future work in computational literary analysis, particularly the possibility of measuring a basic unit of narrative. Copyright © 2022, The Auth

关键词： Empirical mode decomposition

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Noncooperative dynamics in election interference

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Physical Review E 2020年第2期101卷 022307-022307页

作者： David Rushing Dewhurst Christopher M. Danforth Peter Sheridan Dodds MassMutual Center of Excellence in Complex Systems and Data Science Computational Story Lab Vermont Complex Systems Center and Department of Mathematics and Statistics University of Vermont Burlington Vermont 05405 USA

Foreign power interference in domestic elections is an existential threat to societies. Manifested through myriad methods from war to words, such interference is a timely example of strategic interaction between economic and political agents. We model this interaction between rational game players as a continuous-time differential game, constructing an analytical model of this competition with a variety of payoff structures. All-or-nothing attitudes by only one player regarding the outcome of the game lead to an arms race in which both countries spend increasing amounts on interference and counterinterference operations. We then confront our model with data pertaining to the Russian interference in the 2016 United States presidential election contest. We introduce and estimate a Bayesian structural time-series model of election polls and social media posts by Russian Twitter troll accounts. Our analytical model, while purposefully abstract and simple, adequately captures many temporal characteristics of the election and social media activity. We close with a discussion of our model's shortcomings and suggestions for future research.

关键词： Econophysics Brownian dynamics data analysis Fokker–Planck equation Monte Carlo methods

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Blending search queries with social media data to improve forecasts of economic indicators

arXiv

引用

arXiv 2021年

作者： Li, Yi Ahani, Asieh Zhan, Haimao Foley, Kevin Alshaabi, Thayer Linnell, Kelsey Dodds, Peter Sheridan Danforth, Christopher M. Fox, Adam MassMutual Data Science AmherstMA01002 United States Vermont Complex Systems Center University of Vermont BurlingtonVT05401 United States MassMutual Center of Excellence for Complex Systems & Data Science BurlingtonVT05401 United States Department of Computer Science University of Vermont BurlingtonVT05401 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States

The forecasting of political, economic, and public health indicators using internet activity has demonstrated mixed results. For example, while some measures of explicitly surveyed public opinion correlate well with social media proxies, the opportunity for profitable investment strategies to be driven solely by sentiment extracted from social media appears to have expired. Nevertheless, the internet’s space of potentially predictive input signals is combinatorially vast and will continue to invite careful exploration. Here, we combine unemployment related search data from Google Trends with economic language on Twitter to attempt to nowcast and forecast: 1. State and national unemployment claims for the US, and 2. Consumer confidence in G7 countries. Building off of a recently developed search-query-based model, we show that incorporating Twitter data improves forecasting of unemployment claims, while the original method remains marginally better at nowcasting. Enriching the input signal with temporal statistical features (e.g., moving average and rate of change) further reduces errors, and improves the predictive utility of the proposed method when applied to other economic indices, such as consumer confidence. Copyright © 2021, The Authors. All rights reserved.

关键词： Employment

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