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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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Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence

arXiv

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arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Ghassemi, Navid Heras, Jonathan Ling, Sai Ho Beheshti, Amin Zhang, Yu-Dong Wang, Shui-Hua Alizadehsani, Roohallah Gorriz, Juan M. Acharya, U. Rajendra Rokny, Hamid Alinejad Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Mathematics and Computer Science University of La Rioja La Rioja Spain Australia Data Analytics Lab Department of Computing Macquarie University SydneyNSW2109 Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Deakin University VIC3217 Australia Department of Psychiatry University of Cambridge United Kingdom School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia

Myocarditis is a significant cardiovascular disease (CVD) that poses a threat to the health of many individuals by causing damage to the myocardium. The occurrence of microbes and viruses, including the likes of HIV, plays a crucial role in the development of myocarditis disease (MCD). The images produced during cardiac magnetic resonance imaging (CMRI) scans are low contrast, which can make it challenging to diagnose cardiovascular diseases. In other hand, checking numerous CMRI slices for each CVD patient can be a challenging task for medical doctors. To overcome the existing challenges, researchers have suggested the use of artificial intelligence (AI)-based computer-aided diagnosis systems (CADS). The presented paper outlines a CADS for the detection of MCD from CMR images, utilizing deep learning (DL) methods. The proposed CADS consists of several steps, including dataset, preprocessing, feature extraction, classification, and post-processing. First, the Z-Alizadeh dataset was selected for the experiments. Subsequently, the CMR images underwent various preprocessing steps, including denoising, resizing, as well as data augmentation (DA) via CutMix and MixUp techniques. In the following, the most current deep pre-trained and transformer models are used for feature extraction and classification on the CMR images. The findings of our study reveal that transformer models exhibit superior performance in detecting MCD as opposed to pre-trained architectures. In terms of DL architectures, the Turbulence Neural Transformer (TNT) model exhibited impressive accuracy, reaching 99.73% utilizing a 10-fold cross-validation approach. Additionally, to pinpoint areas of suspicion for MCD in CMRI images, the Explainable-based Grad Cam method was employed. © 2022, CC BY-NC-ND.

关键词： Diseases

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The Resume Paradox: Greater Language Differences, Smaller Pay Gaps

arXiv

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arXiv 2023年

作者： Minot, Joshua R. Maier, Marc Demarest, Bradford Cheney, Nicholas Danforth, Christopher M. Dodds, Peter Sheridan Frank, Morgan R. Computational Story Lab The University of Vermont BurlingtonVT05401 United States Vermont Complex Systems Center The University of Vermont BurlingtonVT05401 United States Department of Computer Science The University of Vermont BurlingtonVT05401 United States MassMutual Data Science AmherstMA01002 United States Neurobotics Lab The University of Vermont BurlingtonVT05401 United States Department of Mathematics and Statistics The University of Vermont BurlingtonVT05401 United States Department of Informatics and Networked Systems University of Pittsburgh PittsburghPA15260 United States Digital Economy Lab Stanford University StanfordCA94305 United States Media Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States

Over the past decade, the gender pay gap has remained steady with women earning 84 cents for every dollar earned by men on average. Many studies explain this gap through demand-side bias in the labor market represented through employers' job postings. However, few studies analyze potential bias from the worker supply-side. Here, we analyze the language in millions of US workers' resumes to investigate how differences in workers' self-representation by gender compare to differences in earnings. Across US occupations, language differences between male and female resumes correspond to 11% of the variation in gender pay gap. This suggests that females' resumes that are semantically similar to males' resumes may have greater wage parity. However, surprisingly, occupations with greater language differences between male and female resumes have lower gender pay gaps. A doubling of the language difference between female and male resumes results in an annual wage increase of $2,797 for the average female worker. This result holds with controls for gender-biases of resume text and we find that per-word bias poorly describes the variance in wage gap. The results demonstrate that textual data and self-representation are valuable factors for improving worker representations and understanding employment inequities. Copyright © 2023, The Authors. All rights reserved.

关键词： Employment

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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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Coupling streaming AI and HPC ensembles to achieve 100-1000× faster biomolecular simulations

arXiv

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arXiv 2021年

作者： Brace, Alexander Yakushin, Igor Ma, Heng Trifan, Anda Munson, Todd Foster, Ian Ramanathan, Arvind Lee, Hyungro Turilli, Matteo Jha, Shantenu Data Science and Learning Division Mathematics and Computer Science Division Argonne National Laboratory Lemont United States Department of Computer Science University of Chicago Chicago United States Center For Biophysics University of Illinois Urbana-Champaign Champaign United States RADICAL-Lab Rutgers University New Brunswick United States Computational Science Initiative Brookhaven National Laboratory Upton United States

Machine learning (ML)-based steering can improve the performance of ensemble-based simulations by allowing for online selection of more scientifically meaningful computations. We present DeepDriveMD, a framework for ML-driven steering of scientific simulations that we have used to achieve orders-of-magnitude improvements in molecular dynamics (MD) performance via effective coupling of ML and HPC on large parallel computers. We discuss the design of DeepDriveMD and characterize its performance. We demonstrate that DeepDriveMD can achieve between 100–1000× acceleration for protein folding simulations relative to other methods, as measured by the amount of simulated time performed, while covering the same conformational landscape as quantified by the states sampled during a simulation. Experiments are performed on leadership-class platforms on up to 1020 nodes. The results establish DeepDriveMD as a high-performance framework for ML-driven HPC simulation scenarios, that supports diverse MD simulation and ML back-ends, and which enables new scientific insights by improving the length and time scales accessible with current computing capacity. © 2021, CC BY.

关键词： Molecular dynamics

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A Modified Clustering Using Representatives to Enhance and Optimize Tracking and Monitoring of Maritime Traffic in Real-time Using Automatic Identification System data

A Modified Clustering Using Representatives to Enhance and O...

引用

International Conference on computational science and computational Intelligence (CSCI)

作者： Cheronika Manyfield-Donald Tor A. Kwembe Jing-Ru C. Cheng Computational Data-Enabled Science and Engineering Jackson State University Jackson MS USA Department of Mathematics and Statistical Sciences Jackson State University Jackson MS USA Information Technology Lab. Engineer Research and Development Center U.S. Army Corps of Engineers Vicksburg MS USA

ISBN: (纸本)9781665458429

In this paper, we introduce a modification of the Clustering Using Representatives (CURE) algorithm to enhance and optimize the tracking and monitoring of maritime traffic in real-time using the Automatic Identification System (AIS) data. In doing so, we present a 2-D points data collection system for the utilization of a modified unsupervised machine learning clustering method of the CURE algorithm integrated with a data streaming algorithm to develop a more efficient method that will directly assist in adverting accidents and support in vessels avoiding dangerous environments. Results are presented that show tracking in inland as well as open sea waterways.

关键词： Machine learning algorithms Tracking Scientific computing Clustering methods Clustering algorithms Machine learning data collection

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Constrained multi-objective optimization of process design parameters in settings with scarce data: an application to adhesive bonding

arXiv

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arXiv 2021年

作者： Morales-Hernández, Alejandro Gonzalez, Sebastian Rojas Van Nieuwenhuyse, Inneke Couckuyt, Ivo Jordens, Jeroen Witters, Maarten Van Doninck, Bart Flanders Make@UHasselt and Data Science Institute Hasselt University Agoralaan Diepenbeek3590 Belgium Computational Mathematics Faculty of Sciences Hasselt University Agoralaan Diepenbeek3590 Belgium Surrogate Modeling Lab Ghent University-IMEC Technologiepark-Zwijnaarde Ghent9052 Belgium CoDesignS Flanders Make Oude Diestersebaan Lommel3920 Belgium

Adhesive joints are increasingly used in industry for a wide variety of applications because of their favorable characteristics such as high strength-to-weight ratio, design flexibility, limited stress concentrations, planar force transfer, good damage tolerance, and fatigue resistance. Finding the optimal process parameters for an adhesive bonding process is challenging: the optimization is inherently multi-objective (aiming to maximize break strength while minimizing cost), constrained (the process should not result in any visual damage to the materials, and stress tests should not result in failures that are adhesion-related), and uncertain (testing the same process parameters several times may lead to different break strengths). Real-life physical experiments in the lab are expensive to perform. Traditional evolutionary approaches (such as genetic algorithms) are then ill-suited to solve the problem, due to the prohibitive amount of experiments required for evaluation. Although Bayesian optimization-based algorithms are preferred to solve such expensive problems, few methods consider the optimization of more than one (noisy) objective and several constraints at the same time. In this research, we successfully applied specific machine learning techniques (Gaussian Process Regression) to emulate the objective and constraint functions based on a limited amount of experimental data. The techniques are embedded in a Bayesian optimization algorithm, which succeeds in detecting Pareto-optimal process settings in a highly efficient way (i.e., requiring a limited number of physical experiments). © 2021, CC BY-NC-ND.

关键词： Multiobjective optimization

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Hurricanes and hashtags: Characterizing online collective attention for natural disasters

arXiv

引用

arXiv 2020年

作者： Arnold, Michael V. Dewhurst, David Rushing Alshaabi, Thayer Minot, Joshua R. Adams, Jane L. Danforth, Christopher M. Dodds, Peter Sheridan MassMutual Center of Excellence in Complex Systems and Data Science Computational Story Lab Vermont Complex Systems Center Department of Mathematics and Statistics University of Vermont BurlingtonVT05405 United States MassMutual Data Science BostonMA02110 United States

We study collective attention paid towards hurricanes through the lens of n-grams on Twitter, a social media platform with global reach. Using hurricane name mentions as a proxy for awareness, we find that the exogenous temporal dynamics are remarkably similar across storms, but that overall collective attention varies widely even among storms causing comparable deaths and damage. We construct 'hurricane attention maps' and observe that hurricanes causing deaths on (or economic damage to) the continental United States generate substantially more attention in English language tweets than those that do not. We find that a hurricanes Saffir-Simpson wind scale category assignment is strongly associated with the amount of attention it receives. Higher category storms receive higher proportional increases of attention per proportional increases in number of deaths or dollars of damage, than lower category storms. The most damaging and deadly storms of the 2010s, Hurricanes Harvey and Maria, generated the most attention and were remembered the longest, respectively. On average, a category 5 storm receives 4.6 times more attention than a category 1 storm causing the same number of deaths and economic damage. Copyright © 2020, The Authors. All rights reserved.

关键词： Hurricanes

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Ousiometrics and Telegnomics: The essence of meaning conforms to a two-dimensional {powerful ⇔ weak} and {dangerous ⇔ safe} framework with diverse corpora presenting a safety bias

arXiv

引用

arXiv 2021年

作者： Dodds, Peter Sheridan Alshaabi, Thayer Fudolig, Mikaela Irene Zimmerman, Julia Witte Lovato, Juniper Beaulieu, Shawn Minot, Joshua R. Arnold, Michael V. Reagan, Andrew J. Danforth, Christopher M. Computational Story Lab Vermont Advanced Computing Core 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 Computer Science University of Vermont BurlingtonVT05405 United States Computational Ethics Lab University of Vermont BurlingtonVT05405 United States Advanced Bioimaging Center UC Berkeley BerkeleyCA94720 United States MassMutual Data Science AmherstMA01002 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05405 United States

We define ‘ousiometrics’ to be the study of essential meaning in whatever context that meaningful signals are communicated, and ‘telegnomics’ as the study of remotely sensed knowledge. From work emerging through the middle of the 20th century, the essence of meaning has become generally accepted as being well captured by the three orthogonal dimensions of evaluation, potency, and activation (EPA). By re-examining first types and then tokens for the English language, and through the use of automatically annotated histograms—‘ousiograms’—we find here that: 1. The essence of meaning conveyed by words is instead best described by a compass-like power-danger (PD) framework with orthogonal axes described by the semantic differentials of {powerful ⇔ weak} and {dangerous ⇔ safe};and 2. Analysis of a disparate collection of large-scale English language corpora—literature, news, Wikipedia, talk radio, and social media—shows that natural language exhibits a systematic bias toward safe, low-danger words—a reinterpretation of the Pollyanna principle’s positivity bias for written expression. We identify and explore a third dimension representing structure, S. To help justify our choice of dimension names and to help address the problems with representing observed ousiometric dimensions by bipolar adjective pairs, we introduce and explore ‘synousionyms’ and ‘antousionyms’—ousiometric counterparts of synonyms and antonyms. We further show that the PD framework revises the circumplex model of affect as a more general model of state of mind. Finally, we use our findings to construct and test a prototype ‘ousiometer’, a telegnomic instrument that measures ousiometric time series for temporal corpora. We contend that our power-danger ousiometric framework provides a complement for entropy-based measurements, and may be of value for the study of a wide variety of communication across biological and artificial life. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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