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DISCRETE EVENT SIMULATION TO EVALUATE SHELTER CAPACITY EXPANSION OPTIONS FOR LGBTQ+ HOMELESS YOUTH

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arXiv 2022年

作者： Kaya, Yaren Bilge Mantell, Sophia Lee Maass, Kayse Konrad, Renata Trapp, Andrew C. Dimas, Geri L. Dank, Meredith Mechanical and Industrial Engineering Northeastern University 360 Huntington Ave BostonMA02115 United States School of Business Worcester Polytechnic Institute 100 Institute Rd WorcesterMA01609 United States Data Science Program Worcester Polytechnic Institute 100 Institute Rd WorcesterMA01609 United States Marron Institute of Urban Management New York University 60 5th Ave 2nd floor New YorkNY10011 United States

The New York City (NYC) youth shelter system provides housing, counseling, and other support services to runaway and homeless youth and young adults (RHY). These resources reduce RHY's vulnerability to human trafficking, yet most shelters are unable to meet demand. This paper presents a Discrete Event Simulation (DES) model of a crisis-emergency and drop-in center for LGBTQ+ youth in NYC, which aims to analyze the current operations and test potential capacity expansion interventions. The model uses data from publicly available resources and interviews with service providers and key stakeholders. The simulated shelter has 66 crisis-emergency beds, offers five different support services, and serves on average 1,399 LGBTQ+ RHY per year. The capacity expansion interventions examined in this paper are adding crisis-emergency beds and psychiatric therapists. This application of DES serves as a tool to communicate with policymakers, funders, and service providers-potentially having a strong humanitarian impact. © 2022, CC BY-NC-SA.

关键词： Discrete event simulation

Towards Detecting Cascades of Biased Medical Claims on Twitter

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arXiv 2023年

作者： Tiderman, Libby Mercedes, Juan Sanchez Romanoschi, Fiona Murai, Fabricio Dept. of Mathematics University of Arizona TucsonAZ United States Dept. of Information Systems & Analytics Bryant University SmithfieldRI United States School of Information Univerisity of Texas at Austin AustinTX United States Data Science & Computer Science Worcester Polytechnic Institute WorcesterMA United States

Social media may disseminate medical claims that highlight misleading correlations between social identifiers and diseases due to not accounting for structural determinants of health. Our research aims to identify biased medical claims on Twitter and measure their spread. We propose a machine learning framework that uses two models in tandem: RoBERTa to detect medical claims and DistilBERT to classify bias. After identifying original biased medical claims, we conducted a retweet cascade analysis, computing their individual reach and rate of spread. Tweets containing biased claims were found to circulate faster and further than unbiased claims. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Points below a parabola in affine planes of prime order

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arXiv 2024年

作者： Adriaensen, Sam Weiner, Zsuzsa Department of Mathematics and Data Science Vrije Universiteit Brussel Pleinlaan 2 Elsene1050 Belgium Department of Mathematical Sciences Worcester Polytechnic Institute 100 Institute Road WorcesterMA01605 United States HUN-REN-ELTE Geometric and Algebraic Combinatorics Research Group Pázmány Péter sétány 1/C BudapestH-1117 Hungary

MSC Codes 51E15The elements of a finite field of prime order canonically correspond to the integers in an interval. This induces an ordering on the elements of the field. Using this ordering, Kiss and Somlai recently proved interesting properties of the set of points below the diagonal line. In this paper, we investigate the set of points lying below a parabola. We prove that in some sense, this set of points looks the same from all but two directions, despite having only one non-trivial automorphism. In addition, we study the sizes of these sets, and their intersection numbers with respect to lines. © 2024, CC BY.

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Blind image denoising and inpainting using robust Hadamard autoencoders

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arXiv 2021年

作者： Karkare, Rasika Paffenroth, Randy Mahindre, Gunjan Data Science Worcester Polytechnic Institute WorcesterMA01609 United States Mathematical Sciences Comp. Science and Data Science Worcester Polytechnic Institute WorcesterMA01609 United States Electrical and Computer Engineering Colorado State University Fort CollinsCO80523 United States

In this paper, we demonstrate how deep autoencoders can be generalized to the case of inpainting and denoising, even when no clean training data is available. In particular, we show how neural networks can be trained to perform all of these tasks simultaneously. While, deep autoencoders implemented by way of neural networks have demonstrated potential for denoising and anomaly detection, standard autoencoders have the drawback that they require access to clean data for training. However, recent work in Robust Deep Autoencoders (RDAEs) shows how autoencoders can be trained to eliminate outliers and noise in a dataset without access to any clean training data. Inspired by this work, we extend RDAEs to the case where data are not only noisy and have outliers, but also only partially observed. Moreover, the dataset we train the neural network on has the properties that all entries have noise, some entries are corrupted by large mistakes, and many entries are not even known. Given such an algorithm, many standard tasks, such as denoising, image inpainting, and unobserved entry imputation can all be accomplished simultaneously within the same framework. Herein we demonstrate these techniques on standard machine learning tasks, such as image inpainting and denoising for the MNIST and CIFAR10 datasets. However, these approaches are not only applicable to image processing problems, but also have wide ranging impacts on datasets arising from real-world problems, such as manufacturing and network processing, where noisy, partially observed data naturally arise. Copyright © 2021, The Authors. All rights reserved.

关键词： Anomaly detection

A pre-training oracle for predicting distances in social networks

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arXiv 2021年

作者： Mahindre, Gunjan Paffenroth, Randy Jayasumana, Anura Karkare, Rasika Electrical and Computer Engineering Colorado State University Fort Collins United States Mathematical Sciences Worcester Polytechnic Institute Worcester United States Data Science Worcester Polytechnic Institute Worcester United States

In this paper, we propose a novel method to make distance predictions in real-world social networks. As predicting missing distances is a difficult problem, we take a two-stage approach. Structural parameters for families of synthetic networks are first estimated from a small set of measurements of a real-world network and these synthetic networks are then used to pre-train the predictive neural networks. Since our model first searches for the most suitable synthetic graph parameters which can be used as an "oracle" to create arbitrarily large training data sets, we call our approach "Oracle Search Pre-training" (OSP). For example, many real-world networks exhibit a Power law structure in their node degree distribution, so a Power law model can provide a foundation for the desired oracle to generate synthetic pre-training networks, if the appropriate Power law graph parameters can be estimated. Accordingly, we conduct experiments on real-world Facebook, Email, and Train Bombing networks and show that OSP outperforms models without pre-training, models pre-trained with inaccurate parameters, and other distance prediction schemes such as Low-rank Matrix Completion. In particular, we achieve a prediction error of less than one hop with only 1% of sampled distances from the social network. OSP can be easily extended to other domains such as random networks by choosing an appropriate model to generate synthetic training data, and therefore promises to impact many different network learning problems. Unpublished w Not for © 2021, CC BY.

关键词： Forecasting

Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck Equations

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arXiv 2024年

作者： Hu, Zheyuan Zhang, Zhongqiang Em Karniadakis, George Kawaguchi, Kenji Department of Computer Science National University of Singapore Singapore119077 Singapore Department of Mathematical Sciences Worcester Polytechnic Institute WorcesterMA01609 United States Division of Applied Mathematics Brown University USA ProvidenceRI02912 United States Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA United States

The Fokker-Planck (FP) equation is a foundational partial differential equation (PDE) in stochastic processes involving Brownian motions. However, the curse of dimensionality (CoD) poses a formidable challenge when dealing with high-dimensional FP equations. Although Monte Carlo simulation and (vanilla) Physics-Informed Neural Networks (PINNs) have shown the potential to tackle CoD, both methods exhibit significant numerical errors in high dimensions when dealing with the probability density function (PDF) associated with Brownian motion. The point-wise PDF values tend to decrease exponentially as dimensionality increases, surpassing the precision of numerical simulations and resulting in substantial errors. Moreover, due to its massive sampling, Monte Carlo fails to offer fast sampling. Modeling the logarithm likelihood (LL) via vanilla PINNs transforms the FP equation into a notoriously difficult Hamilton-Jacobi-Bellman (HJB) equation, which is impractical for PINN learning, whose error grows rapidly with dimension. To this end, we propose a novel approach utilizing a score-based solver to fit the score function in stochastic differential equations (SDEs). The score function, defined as the gradient of the LL, plays a fundamental role in inferring LL and PDF and enables fast SDE sampling, offering an effective means to overcome the CoD. Three fitting methods, Score Matching (SM), Sliced Score Matching (SSM), and Score-PINN, are introduced, each contributing unique advantages in computational complexity, accuracy, and generality. The proposed score-based SDE solver operates in two stages: first, employing score matching or Score-PINN to acquire the score function;and second, solving the LL via an ordinary differential equation (ODE) using the obtained score function. Comparative evaluations across these methods showcase varying trade-offs. The proposed methodology is evaluated across diverse SDEs, including anisotropic Ornstein-Uhlenbeck processes, geometric Browni

关键词： Fokker Planck equation

Social inequality and cultural factors impact the awareness and reaction during the cryptic transmission period of pandemic

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arXiv 2025年

作者： Jiang, Zhuoren Liu, Xiaozhong Kang, Yangyang Sun, Changlong Ahn, Yong-Yeol Bollen, Johan School of Public Affairs Zhejiang University 866 Yuhangtang Rd Zhejiang Hangzhou310058 China Data Science Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States Alibaba Group 969 West Wen Yi Road Zhejiang Hangzhou310030 China College of Computer Science and Technology Zhejiang University Yuquan Campus Zhejiang Hangzhou310013 China School of Informatics Computing and Engineering Indiana University Bloomington 107 S. Indiana Avenue BloomingtonIN47405 United States Polytechnic Institute Zhejiang University 866 Yuhangtang Rd Zhejiang Hangzhou310058 China

The World Health Organization (WHO) declared the COVID-19 outbreak a Public Health Emergency of International Concern (PHEIC) on January 31, 2020. However, rumors of a "mysterious virus" had already been circulating in China in December 2019, possibly preceding the first confirmed COVID-19 case. Understanding how awareness about an emerging pandemic spreads through society is vital not only for enhancing disease surveillance, but also for mitigating demand shocks and social inequities, such as shortages of personal protective equipment (PPE) and essential supplies. Here we leverage a massive e-commerce dataset comprising 150 billion online queries and purchase records from 94 million people to detect the traces of early awareness and public response during the cryptic transmission period of COVID-19. Our analysis focuses on identifying information gaps across different demographic cohorts, revealing significant social inequities and the role of cultural factors in shaping awareness diffusion and response behaviors. By modeling awareness diffusion in heterogeneous social networks and analyzing online shopping behavior, we uncover the evolving characteristics of vulnerable populations. Our findings expand the theoretical understanding of awareness spread and social inequality in the early stages of a pandemic, highlighting the critical importance of e-commerce data and social network data in effectively and timely addressing future pandemic challenges. We also provide actionable recommendations to better manage and mitigate dynamic social inequalities in public health crises. © 2025, CC BY.

关键词： COVID-19

Mobile Communication Log Time Series to Detect Depressive Symptoms

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Mobile Communication Log Time Series to Detect Depressive Sy...

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： M L Tlachac Miranda Reisch Michael Heinz Department of Information Systems and Analytics The Center for Health and Behavioral Sciences Bryant University Smithfield RI USA Department of Data Science Worcester Polytechnic Institute (WPI) Worcester MA USA Departments of Quantitative Biomedical Sciences and Epidemiology Dartmouth College Department of Psychiatry Dartmouth Health Hanover NH USA

Major Depressive Disorder (MDD) is highly prevalent and characterized by often debilitating behavioral and cognitive symptoms. MDD is poorly understood, likely due to considerable heterogeneity and self-report-driven symptomatology. While researchers have been exploring the ability of machine learning to screen for MDD, much less attention has been paid to individual symptoms. We posit that understanding the relationship between objective data streams and individual depression symptoms is important for understanding the considerable heterogeneity in MDD. Thus, we conduct a comprehensive comparative study to explore the ability of machine learning to predict nine self-reported depressive symptoms with call and text logs. We created time series from the logs of over 300 participants by aggregating communication attributes– average length, count, or contacts– every 4, 6, 12, or 24 hours. We were most successful predicting movement irregularities with a balanced accuracy of 0.70. Further, we predicted suicidal ideation with a balanced accuracy of 0.67. Outgoing texts proved to be the most useful log type. This study provides valuable insights for future mobile health research aimed at personalizing assessment and intervention for MDD.

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TWEET-FID: An Annotated dataset for Multiple Foodborne Illness Detection Tasks

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arXiv 2022年

作者： Hu, Ruofan Zhang, Dongyu Tao, Dandan Hartvigsen, Thomas Feng, Hao Rundensteiner, Elke Data Science Program Worcester Polytechnic Institute WorcesterMA United States Vanke School of Public Health Tsinghua University Beijing China CSAIL Massachusetts Insititute of Technology CambridgeMA United States Dept. of Food Science & Human Nutrition Univ. of Illinois Urbana-Champaign UrbanaIL United States

Foodborne illness is a serious but preventable public health problem – with delays in detecting the associated outbreaks resulting in productivity loss, expensive recalls, public safety hazards, and even loss of life. While social media is a promising source for identifying unreported foodborne illnesses, there is a dearth of labeled datasets for developing effective outbreak detection models. To accelerate the development of machine learning-based models for foodborne outbreak detection, we thus present TWEET-FID (TWEET-Foodborne Illness Detection), the first publicly available annotated dataset for multiple foodborne illness incident detection tasks. TWEET-FID collected from Twitter is annotated with three facets: tweet class, entity type, and slot type, with labels produced by experts as well as by crowdsource workers. We introduce several domain tasks leveraging these three facets: text relevance classification (TRC), entity mention detection (EMD), and slot filling (SF). We describe the end-to-end methodology for dataset design, creation, and labeling for supporting model development for these tasks. A comprehensive set of results for these tasks leveraging state-of-the-art single- and multi-task deep learning methods on the TWEET-FID dataset are provided. This dataset opens opportunities for future research in foodborne outbreak detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Crowdsourcing