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Topological descriptors for Parkinson⇔s disease classification and regression analysis

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arXiv 2020年

作者： Nawar, Afra Rahman, Farhan Krishnamurthi, Narayanan Som, Anirudh Turaga, Pavan School of Electrical Computer and Energy Engineering Arizona State University School of Computing Informatics and Decision Systems Engineering Arizona State University Edson College of Nursing and Health Innovation Arizona State University School of Arts Media and Engineering Arizona State University

At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson’s would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson’s disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson’s disease dataset comprised of healthy-elderly, healthy-young and Parkinson’s disease patients. Our code is available at https://***/itsmeafra/Sublevel-Set-TDA. Copyright © 2020, The Authors. All rights reserved.

关键词： Hospital data processing

Use of Bayesian nonparametric methods for estimating the measurements in high clutter

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arXiv 2020年

作者： Moraffah, Bahman Richmond, Christ Moraffah, Raha Papandreou-Suppappola, Antonia Department of Electrical Computer and Energy Arizona State University TempeAZ United States School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States

Robust tracking of a target in a clutter environment is an important and challenging task. In recent years, the nearest neighbor methods and probabilistic data association filters were proposed. However, the performance of these methods diminishes as number of measurements increases. In this paper, we propose a robust generative approach to effectively model multiple sensor measurements for tracking a moving target in an environment with high clutter. We assume a time-dependent number of measurements that include sensor observations with unknown origin, some of which may only contain clutter with no additional information. We robustly and accurately estimate the trajectory of the moving target in high clutter environment with unknown number of clutters by employing Bayesian nonparametric modeling. In particular, we employ a class of joint Bayesian nonparametric models to construct the joint prior distribution of target and clutter measurements such that the conditional distributions follow a Dirichlet process. The marginalized Dirichlet process prior of the target measurements is then used in a Bayesian tracker to estimate the dynamically-varying target state. We show through experiments that the tracking performance and effectiveness of our proposed framework are increased by suppressing high clutter measurements. In addition, we show that our proposed method outperforms existing methods such as nearest neighbor and probability data association filters. © 2020, CC BY.

关键词： Radar clutter

Unveiling Coordinated Groups Behind White Helmets Disinformation

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arXiv 2020年

作者： Pacheco, Diogo Flammini, Alessandro Menczer, Filippo Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Bloomington

Propaganda, disinformation, manipulation, and polarization are the modern illnesses of a society increasingly dependent on social media as a source of news. In this paper, we explore the disinformation campaign, sponsored by Russia and allies, against the Syria Civil Defense (a.k.a. the White Helmets). We unveil coordinated groups using automatic retweets and content duplication to promote narratives and/or accounts. The results also reveal distinct promoting strategies, ranging from the small groups sharing the exact same text repeatedly, to complex "news website factories" where dozens of accounts synchronously spread the same news from multiple sites. Copyright © 2020, The Authors. All rights reserved.

关键词： Social networking (online)

Focused digital cohort selection from social media using the metric backbone of biomedical knowledge graphs

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Journal of biomedical informatics 2025年 168卷 104847页

作者： Ziqi Guo Jack Felag Jordan C Rozum Rion Brattig Correia Xuan Wang Luis M Rocha School of Systems Science & Industrial Engineering Binghamton University Binghamton NY USA. School of Informatics Computing & Engineering Indiana University Bloomington IN USA School of Systems Science & Industrial Engineering Binghamton University Binghamton NY USA Universidade Católica Portuguesa Católica Biomedical Research Centre Lisbon Portugal. Electronic address: rocha@binghamton.edu.

Social media data allows researchers to construct large digital cohorts-groups of users who post health-related content--to study the interplay between human behavior and medical treatment. Identifying the users most relevant to a specific health problem is, however, a challenge in that social media sites vary in the generality of their discourse. While X (formerly Twitter), Instagram, and Facebook cater to wide ranging topics, Reddit subgroups and dedicated patient advocacy forums trade in much more specific, biomedically-relevant discourse. To filter relevant users on any social media, we have developed a general method and tested it on epilepsy discourse. We analyzed the text from posts by users who mention epilepsy drugs at least once in the general-purpose social media sites X and Instagram, the epilepsy-focused Reddit subgroup (r/Epilepsy), and the Epilepsy Foundation of America (EFA) forums. We used a curated medical terminology dictionary to generate a knowledge graph (KG) from each social media site, whereby nodes represent terms, and edge weights denote the strength of association between pairs of terms in the collected text. Our method is based on computing the metric backbone of each KG, which yields the (sparsified) subgraph of edges that participate in shortest paths. By comparing the subset of users who contribute to the backbone to the subset who do not, we show that epilepsy-focused social media users contribute to the KG backbone in much higher proportion than do general-purpose social media users. Furthermore, using human annotation of Instagram posts, we demonstrate that users who do not contribute to the backbone are much more likely to use dictionary terms in a manner inconsistent with their biomedical meaning and are rightly excluded from the cohort of interest. Our metric backbone approach, thus, has several benefits: it yields focused user cohorts who engage in discourse relevant to a targeted biomedical problem; unlike engagement-based appr

关键词： Epilepsy Network science Network sparsification Patient cohort selection Social media mining

What we should learn from pandemic publishing

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arXiv 2024年

作者： Sikdar, Satyaki Venturini, Sara Charpignon, Marie-Laure Kumar, Sagar Rinaldi, Francesco Tudisco, Francesco Fortunato, Santo Majumder, Maimuna S. Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Computer Science Loyola University Chicago ChicagoIL United States Senseable City Laboratory Massachusetts Institute of Technology CambridgeMA United States Department of Mathematics "Tullio Levi-Civita" University of Padova Padova Italy Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Network Science Institute Northeastern University BostonMA United States School of Mathematics The University of Edinburgh Edinburgh United Kingdom School of Mathematics Gran Sasso Science Institute L’Aquila Italy Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children’s Hospital BostonMA United States

Since the emergence of COVID-19, discussions of ongoing pandemic-related research have accounted for an unprecedented share of media coverage and debate in the public sphere1. The urgency of the pandemic forced researchers to operate on an accelerated timeline, as both policymakers and the public relied on the most current evidence to guide their decisions and behaviors. With high demand for rapid pandemic-related insights and lower barriers to entry via preprint servers, the volume of COVID-19 articles skyrocketed2. The pressing need for research triggered the participation of many researchers with expertise in the science of infectious disease outbreaks (‘outbreak scientists’), who were joined by researchers from other disciplines (‘bellwethers’) and more junior researchers still in training (‘newcomers’) with the common goal of advancing the frontiers of pandemic science and informing policy decisions3. Please see Box 1 for details of this taxonomy. © 2024, CC BY-NC-ND.

关键词： COVID-19

Imitation learning of robot policies by combining language, vision and demonstration

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arXiv 2019年

作者： Stepputtis, Simon Campbell, Joseph Phielipp, Mariano Baral, Chitta Amor, Heni Ben School of Computing Informatics and Decision Systems Engineering ASU Intel AI Lab

In this work we propose a novel end-to-end imitation learning approach which combines natural language, vision, and motion information to produce an abstract representation of a task, which in turn is used to synthesize specific motion controllers at run-time. This multimodal approach enables generalization to a wide variety of environmental conditions and allows an end-user to direct a robot policy through verbal communication. We empirically validate our approach with an extensive set of simulations and show that it achieves a high task success rate over a variety of conditions while remaining amenable to probabilistic interpretability. Copyright © 2019, The Authors. All rights reserved.

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A Generic Deep Learning-Based computing Algorithm in Support of the Development of Instrumented Bikes

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ASCE Open: Multidisciplinary Journal of Civil Engineering

ASCE Open: Multidisciplinary Journal of Civil engineering 2024年第1期2卷

作者： Ho, Chun-Hsing Qiu, Peijie Zhang, Yifei Ren, Kewei Durham School of Architectural Engineering and Construction Univ. of Nebraska Ͽ Lincoln 113 Nebraska Hall P.O. Box 880500 900 N. 16th St. LincolnNE68588-0500 United States Dept. of Computer Science and Engineering Washington Univ. at St. Louis One Brookings Dive St. LouisMO63130 United States School of Informatics Computing and Cyber Systems Northern Arizona Univ. 1295 S. Knoles Dr. P.O. Box 5693 FlagstaffAZ8601 United States

The paper introduces a generic deep learning-based method using a sliding window computing algorithm based on long short-term memory (LSTM) networks for the classification of potential anomalies (e.g., cracks, potholes, bumps, and uneven surfaces) in support of the development of an instrumented bike. The instrumented bike provides a real-time platform to sense, store, transmit, and analyze cycling information through a sensor logger, smartphone, and the proposed LSTM-based sliding window computing algorithm. The paper is to address concerns with respect to existing factors such as weight of cyclists, speeds, types of bikes, and threshold setting that have an impact on the accuracy of identification of potential anomalies during instrumented cycling activities. The LSTM-based sliding window computing algorithm is designed in a way that it analyzes and localizes anomalies without any human-controlled supervision (threshold setting) while achieving human-level perception. Two bike routes were selected to validate the effectiveness of the sliding window computing algorithm in the identification of anomalies involving four cyclists. Based on the computing results from the two field tests, the numbers of distressed pavement areas from the four cyclists were 53, 51, 46, and 48, respectively. The follow-up p-value of ANOVA test result is 0.98, indicating the difference in detected anomalies among the four cyclists is not significant. Therefore, the paper concludes that the LSTM-based sliding window computing algorithm has the ability to effectively detect anomalies of cycling trails and it also provides an effective and efficient technique to replace the human-made threshold setting in support of the development of instrumented bikes and promote cycling as a daily mode of transportation. © 2024 This work is made available under the terms of the Creative Commons Attribution 4.0 International license,.

关键词： Long short-term memory

Search-based Test-Case Generation by Monitoring Responsibility Safety Rules

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arXiv 2020年

作者： Hekmatnejad, Mohammad Hoxha, Bardh Fainekos, Georgios School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States Toyota Research Institute of North America Ann ArborMI48187 United States

The safety of Automated Vehicles (AV) as Cyber-Physical systems (CPS) depends on the safety of their consisting modules (software and hardware) and their rigorous integration. Deep Learning is one of the dominant techniques used for perception, prediction and decision making in AVs. The accuracy of predictions and decision-making is highly dependant on the tests used for training their underlying deep-learning. In this work, we propose a method for screening and classifying simulation-based driving test data to be used for training and testing controllers. Our method is based on monitoring and falsification techniques, which lead to a systematic automated procedure for generating and selecting qualified test data. We used Responsibility Sensitive Safety (RSS) rules as our qualifier specifications to filter out the random tests that do not satisfy the RSS assumptions. Therefore, the remaining tests cover driving scenarios that the controlled vehicle does not respond safely to its environment. Our framework is distributed with the publicly available S-TALIRO and Sim-ATAV tools. Copyright © 2020, The Authors. All rights reserved.

关键词： decision making

HCI Across Borders: Building a Collective Vision for the Future

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HCI Across Borders: Building a Collective Vision for the Fut...

2025 CHI Conference on Human Factors in computing systems, CHI EA 2025

作者： Moin, Sara Cannanure, Vikram Kamath Mthoko, Hafeni Rivera-Loaiza, Cuauhtémoc Kano, Tsuyoshi Varghese, Delvin Till, Sarina C. Gautam, Aakash Wong-Villacres, Marisol Gaytán-Lugo, Laura Sanely Dray, Susan Sturm, Christian Mélange Lab. IIITD Delhi India Saarland University Saarland Informatics Campus Saarbrücken Germany The Independent Institute of Education?s Varsity College Sandton South Africa Universidad Michoacana Morelia Mexico Department of Management Systems Kanazawa Institute of Technology Ishikawa Kanazawa Japan Action Lab. Monash University Melbourne VIC Australia University of Cape Town Western Cape Cape Town South Africa School of Computing and Information University of Pittsburgh Pittsburgh PA United States Facultad de Ingeniería en Electricidad y Computación Escuela Superior Politécnica del Litoral Guayaquil Ecuador School of Mechanical and Electrical Engineering Universidad de Colima Coquimatlan Mexico Dray & Associate Minneapolis MN United States Hamm-Lippstadt University of Applied Sciences Hamm Germany

ISBN: (纸本)9798400713958

The HCI Across Borders (HCIxB) workshop at CHI 2025 will focus on "Building a Collective Vision for the Future." HCIxB has gathered a diverse audience annually by conducting workshops and symposia since CHI 2016. This year, we hope to regroup as a community to reflect on the growth of HCIxB over the past ten years, engage in mentoring sessions for present early career researchers, and collectively form a vision for the future. This full-day hybrid workshop aims to gather researchers and practitioners to explore how we can collectively shape the future of HCI across diverse cultures and geographies. We will have a panel discussion featuring invited speakers sharing insights from a recent HCIxB Book Chapter summarizing our future. Attendees will showcase their research through posters, highlighting HCIxB’s trends worldwide, particularly in the Global South. Lastly, we will collaborate to create a vision for our community’s future and provide initial resources to future HCIxB organizers. © 2025 Copyright held by the owner/author(s).

关键词： Cultural Diversity Geographic Diversity Global Development HCI HCI Across Borders