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528MO Cognitive phenotypes and its association with role functioning outcomes in Adolescents and Young Adults (AYAs) with cancer

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Annals of Oncology 2024年 35卷 S1596-S1596页

作者： P. Xiao Y. Wei W. Deng A.W.K. Leung C.K. Ngan H.H.F. Loong C-K. Li Y.T. Cheung School of Pharmacy Faculty of Medicine The Chinese University of Hong Kong Hong Kong SAR China Department of Pediatrics & Adolescent Medicine Hong Kong Children's Hospital Hong Kong SAR China Data Science WPI - Worcester Polytechnic Institute Worcester MA USA Department of Clinical Oncology The Chinese University of Hong Kong - Prince of Wales Hospital Hong Kong SAR China Department of Pediatrics Faculty of Medicine The Chinese University of Hong Kong Hong Kong SAR China

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IoT Environmental Analyzer using Sensors and Machine Learning for Migraine Occurrence Prevention

IoT Environmental Analyzer using Sensors and Machine Learnin...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Rosemarie J. Day Hassan Salehi Mahsa Javadi Data Science Program Worcester Polytechnic Institute Worcester MA U.S.A California State University Chico CA USA University of Connecticut Health Center Farmington CT USA

Everyday migraines are affecting more than one billion people worldwide. This headache disorder is classified as the sixth most disabling disease in the world. Migraines are just one chronic illness affected by environmental triggers due to changes that occur inside the home. Migraines share this characteristic with sinus headaches and thus are often misdiagnosed. In this research work, an iOS-based environmental analyzer was designed, implemented and evaluated for migraine sufferers with the use of sensors. After the data collection and cleaning, five machine learning model were used to estimate prediction accuracy of migraines in terms of the environment. The data was evaluated against the models using K-Fold cross validation. The algorithm accuracy comparison showed that Linear Discriminant Analysis (LDA) produced highest accuracy for the testing data at a mean of 0.938. Preliminary results demonstrate the feasibility of using machine learning algorithms to perform the automated recognition of migraine trigger areas in the environment.

关键词： Sensors Cloud computing Humidity Machine learning Standards Photonics databases

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Annals of Oncology 2022年 33卷 S1542-S1542页

作者： C.T.J. Ma H.H.F. Loong W.T. Ip J. Zhang A.W.K. Leung C.K. Ngan C-K. Li C. Yin Ting School of Pharmacy Faculty of Medicine The Chinese University of Hong Kong Hong Kong China Department of Clinical Oncology The Chinese University of Hong Kong Hong Kong China Department of Paediatrics The Chinese University of Hong Kong Hong Kong China Department of Data Science Worcester Polytechnic Institute Worcester MA USA

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Haptic display for virtual reality: progress and challenges

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Virtual Reality & Intelligent Hardware 2019年第2期1卷 136-162页

作者： Dangxiao WANG Yuan GUO Shiyi LIU Yuru ZHANG Weiliang XU Jing XIAO State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100083China Beijing Advanced Innovation Center for Biomedical Engineering Beihang UniversityBeijing 100083China Peng Cheng Laboratory Shenzhen 518055China Department of Mechanical Engineering University of AucklandAuckland 1142New Zealand School of Computer Science and the Robotics Engineering Program Worcester Polytechnic InstituteWorcesterMA 01609-2280USA

Immersion, interaction, and imagination are three features of virtual reality (VR). Existing VR systems possess fairly realistic visual and auditory feedbacks, and however, are poor with haptic feedback, by means of which human can perceive the physical world via abundant haptic properties. Haptic display is an interface aiming to enable bilateral signal communications between human and computer, and thus to greatly enhance the immersion and interaction of VR systems. This paper surveys the paradigm shift of haptic display occurred in the past 30 years, which is classified into three stages, including desktop haptics, surface haptics, and wearable haptics. The driving forces, key technologies and typical applications in each stage are critically reviewed. Toward the future high-fidelity VR interaction, research challenges are highlighted concerning handheld haptic device, multimodal haptic device, and high fidelity haptic rendering. In the end, the importance of understanding human haptic perception for designing effective haptic devices is addressed.

关键词： Haptic display Virtual reality Desktop haptics Surface haptics Wearable haptics Multimodal haptics

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Learning to Estimate Centers of Mass of Arbitrary Objects

Learning to Estimate Centers of Mass of Arbitrary Objects

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Sean McGovern Huitan Mao Jing Xiao Robotics Engineering Program Worcester Polytechnic Institute Department of Computer Science University of North Carolina at Charlotte

This paper introduces a reinforcement learning algorithm with robot manipulation to learn an arbitrary object's center of mass whose physical material composition is unknown. Robot learning is through manipulation of the object in a sequence of actions. The effectiveness of the algorithm is demonstrated in simulation to locate the centers of mass of rocks with complex shapes, with even or uneven mass distributions, and confirmed by vertically stacking the rocks along their learned centers of mass both in simulation and in real experiments.

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Handling Missing Values in Multivariate Time Series Classification

Handling Missing Values in Multivariate Time Series Classifi...

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2018 IEEE MIT Undergraduate Research Technology Conference, URTC 2018

作者： Friend, Julia Hauck, Alec Kurada, Sruthi Hartvigsen, Thomas Sen, Cansu Rundensteiner, Elke A. Oberlin College Department of Computer Science United States Valparaiso University United States Advanced Math and Science Academy Charter School Marlborough United Kingdom Data Science Program Worcester Polytechnic Institute Worcester Polytechnic Institute Computer Science Department United States

ISBN: (纸本)9781538693742

Multivariate time series data are littered with missing values across many domains. Often the patterns of missing data points reflect conscious decisions by the data collector, and thus may contain correlations that can be used to more accurately classify the data. Previous works propose machine learning models that are capable of detecting these correlations in order to achieve highly accurate results. We review the literature and identify a rich variety of strategies along the machine learning pipeline for handling missing values, including forward imputation, appending additional missing indicators, and others. We also study different deep learning methods for handling time series data, namely GRU and LSTM cells. We then design an experimental study for investigating the relative effectiveness of different state-of-the-art methods for handling missing values to more fully comprehend the intricacies of missing data. For the experimental evaluation study, we utilize MIMIC-III, a publicly available critical care dataset composed of Electronic Health Records (EHR) for over 58,000 hospital admissions collected at Beth Israel Deaconess Medical Center from 2001 to 2012. In particular, our experimental study compares variety of missingness-aware machine learning methods for predicting patient mortality, a benchmark task for this database. We validate in general that extracting information from missing values indeed improves predictive accuracy, and we observe that GRU and LSTM cells perform comparably with the forward imputation method further impacting the resulting accuracy for both models. © 2018 IEEE.

关键词： data handling

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Comparative photocatalytic performances towards acid yellow 17 (AY17) and direct blue 71 (DB71) degradation using Sn3O4 flower-like structure 1

Comparative photocatalytic performances towards acid yellow ...

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1st Sriwijaya International Conference on Basic and Applied sciences, SICBAS 2018

作者： Huda, A. Ichwani, R. Handoko, C.T. Bustan, M.D. Yudono, B. Gulo, F. Program Studi Ilmu Lingkungan Program Pascasarjana Universitas Sriwijaya Jalan Padang Selasa No. 524 Sumatera Selatan Palembang30121 Indonesia Department of Material Science and Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Program Studi Teknik Kimia Fakultas Teknik Universitas Sriwijaya Jalan Palembang Prabumulih Km. 32 Indralaya Ogan Ilir Sumatera Selatan30862 Indonesia

Microsphere Sn3O4 flower-like structure has been successfully synthesized using a novel microwave-assisted hydrothermal method and comprehensively characterized by X-ray diffraction (XRD), field emission gun scanning electron microscope (FEG-SEM) and UV Vis spectrophotometer equipped with diffuse reflectance spectroscopy (UV-Vis DRS). In order to examine its photocatalytic performance, two synthetic azo-based dyes, acid yellow 17 (AY17) and direct blue 71 (DB71), have been used as organic pollutant models degraded under visible-light illumination. The results show that the negative charges of Sn3O4 produce higher efficiency photocatalytic activity on DB71 degradation compared to that on AY17 degradation. Further investigation has confirmed that the adsorption capacities played the main role in determining photocatalytic performance and differentiated the quantum yield of dye degradation. Moreover, the different adsorption capacities are generated by the formation of electrostatic interaction and repulsion between surface charge of Sn3O4 and dyes functional groups. © Published under licence by IOP Publishing Ltd.

关键词： Scanning electron microscopy

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Improving the lifestyle behavior of type 2 diabetes mellitus patients using a mobile application

Improving the lifestyle behavior of type 2 diabetes mellitus...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Andreia Pinto João Viana Diogo Martinho Vítor Crista José Miguel Diniz Joana Reis João Matos David Greer Goreti Marreiros Alberto Freitas Department of Community Medicine Information and Health Decision Sciences (MEDCIDS) Faculty of Medicine Center for Health Technology and Services Research (CINTESIS) University of Porto Porto Portugal Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) Institute of Engineering Polytechnic of Porto Porto Portugal Health Data Science PhD Program Faculty of Medicine Center for Health Technology and Services Research (CINTESIS) University of Porto Lisboa Portugal CLOO - Behavioral Insights Unit Faculty of Psychology CICPSI - Research Center for Psychological Science University of Lisbon Lisboa Portugal Behavioral Insights Unit CLOO Porto Portugal Behavioral Insights Unit CLOO Lisboa Portugal

ISBN: (数字)9781665497923

ISBN: (纸本)9781665497930

Type 2 diabetes is an increasingly prevalent disease and patients do not always manage the disease properly. Therefore, creating tools that help diabetics self-manage their condition over time is of the utmost importance. Technological tools that include features meeting this population's needs, with an integrated personalized feedback system, in an environment of gamified incentives, may be the way for developing a sustained app's usage. This work aims to present a still in development, novel approach to managing diabetes type 2 with a focus on data analysis from user-inputs, gamification, and personalized coaching, with an accessible user interface. Thus, we will present some functionalities, in particular the theoretical and practical concept that is behind this development, namely the Transtheoretical model of behavior change to create different profiles, to provide customized feedback according to the user's behavioral stage, and also to ascertain if it induces any improvement at the behavioral level.

关键词： Computers data analysis Computational modeling User interfaces Diabetes Behavioral sciences Mobile applications

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Anomaly detection via graphical lasso

arXiv

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arXiv 2018年

作者： Liu, Haitao Paffenroth, Randy C. Zou, Jian Zhou, Chong Data Science Program Worcester Polytechnic Institute Mathematical Sciences Worcester Polytechnic Institute

Anomalies and outliers are common in real-world data, and they can arise from many sources, such as sensor faults. Accordingly, anomaly detection is important both for analyzing the anomalies themselves and for cleaning the data for further analysis of its ambient structure. Nonetheless, a precise definition of anomalies is important for automated detection and herein we approach such problems from the perspective of detecting sparse latent effects embedded in large collections of noisy data. Standard Graphical Lasso based techniques can identify the conditional dependency structure of a collection of random variables based on their sample covariance matrix. However, classic Graphical Lasso is sensitive to outliers in the sample covariance matrix. In particular, several outliers in a sample covariance matrix can destroy the sparsity of its inverse. Accordingly, we propose a novel optimization problem that is similar in spirit to Robust Principal Component Analysis (RPCA) and splits the sample covariance matrix M into two parts, M = F + S, where F is the cleaned sample covariance whose inverse is sparse and computable by Graphical Lasso, and S contains the outliers in M. We accomplish this decomposition by adding an additional `1 penalty to classic Graphical Lasso, and name it "Robust Graphical Lasso (Rglasso)". Moreover, we propose an Alternating Direction Method of Multipliers (ADMM) solution to the optimization problem which scales to large numbers of unknowns. We evaluate our algorithm on both real and synthetic datasets, obtaining interpretable results and outperforming the standard robust Minimum Covariance Determinant (MCD) method and Robust Principal Component Analysis (RPCA) regarding both accuracy and speed. Copyright © 2018, The Authors. All rights reserved.

关键词： Anomaly detection

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Bounded Manifold Completion

arXiv

引用

arXiv 2019年

作者： Gajamannage, Kelum Paffenroth, Randy Department of Mathematics and Statistics Texas A&M University–Corpus Christi Corpus ChristiTX78412 United States Department of Mathematical Sciences Department of Computer Science and Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States

Nonlinear dimensionality reduction or, equivalently, the approximation of high-dimensional data using a low-dimensional nonlinear manifold is an active area of research. In this paper, we will present a thematically different approach to detect the existence of a low-dimensional manifold of a given dimension that lies within a set of bounds derived from a given point cloud. A matrix representing the appropriately defined distances on a low-dimensional manifold is low-rank, and our method is based on current techniques for recovering a partially observed matrix from a small set of fully observed entries that can be implemented as a low-rank Matrix Completion (MC) problem. MC methods are currently used to solve challenging real-world problems, such as image inpainting and recommender systems, and we leverage extent efficient optimization techniques that use a nuclear norm convex relaxation as a surrogate for non-convex and discontinuous rank minimization. Our proposed method provides several advantages over current nonlinear dimensionality reduction techniques, with the two most important being theoretical guarantees on the detection of low-dimensional embeddings and robustness to non-uniformity in the sampling of the manifold. We validate the performance of this approach using both a theoretical analysis as well as synthetic and real-world benchmark *** Codes 68T05 Copyright © 2019, The Authors. All rights reserved.

关键词： Relaxation processes

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