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arXiv 2023年

作者： Choi, Junggu Kim, Kion Park, Soohyun Hur, Juyoen Yang, Hyunjung Kim, Younghoon Lee, Hakbae Han, Sanghoon Yonsei Graduate program in Cognitive Science Yonsei University Seoul03722 Korea Republic of Korea Quantum Computing© Seoul06164 Korea Republic of Department of Psychology Yonsei University Seoul03722 Korea Republic of University College Yonsei University Seoul03722 Korea Republic of Deparment of Applied Statistics Yonsei University Seoul03722 Korea Republic of Department of Statistics and Data science Yonsei University Seoul03722 Korea Republic of

Diverse cases regarding the impact, with its related factors, of the COVID-19 pandemic on mental health have been reported in previous studies. College student groups have been frequently selected as the target population in previous studies because they are easily affected by pandemics. In this study, multivariable datasets were collected from 751 college students based on the complex relationships between various mental health factors. We utilized quantum annealing (QA)-based feature selection algorithms that were executed by commercial D-Wave quantum computers to determine the changes in the relative importance of the associated factors before and after the pandemic. Multivariable linear regression (MLR) and XGBoost models were also applied to validate the QA-based algorithms. Based on the experimental results, we confirm that QA-based algorithms have comparable capabilities in factor analysis research to the MLR models that have been widely used in previous studies. Furthermore, the performance of the QA-based algorithms was validated through the important factor results from the algorithms. Pandemic-related factors (e.g., confidence in the social system) and psychological factors (e.g., decision-making in uncertain situations) were more important in post-pandemic conditions. We believe that our study will serve as a reference for researchers studying similar topics. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiple linear regression

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Sensor Selection for Activity Classification at Smart Home Environments

Sensor Selection for Activity Classification at Smart Home E...

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

作者： Nithish Bolleddula Geoffrey Yau Chun Hung Daren Ma Hoda Noorian Diane Myung-kyung Woodbridge MS in Data Science Program University of San Francisco

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

As the world's older population grows dramatically, the needs of continuing care retirement communities increases. Studies show that privacy can be a major concern for adopting technologies, while the older population prefers smart homes [1]. In order to minimize the number of sensors to be installed in each house, we performed Principal Component Analysis (PCA) to filter out the relatively unimportant sensors. We applied a machine learning model to classify residents' activity types, using a different set of sensors chosen by PCA. Then, we validated the trade-off between the classification model accuracy and the number of sensors used in classification. Our experiment shows that feature engineering helps reduce accuracy degradation for activity type classification when using fewer sensors in smart homes.

关键词： Feature extraction Principal component analysis Microsoft Windows Machine learning Smart homes Distributed computing

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Confidence Score for Source-Free Unsupervised Domain Adaptation

arXiv

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arXiv 2022年

作者： Lee, Jonghyun Jung, Dahuin Yim, Junho Yoon, Sungroh Data Science and AI Lab. Seoul National University Korea Republic of AIRS Company Hyundai Motor Group Seoul Korea Republic of Department of ECE Interdisciplinary Program in AI National University Seoul Korea Republic of

Source-free unsupervised domain adaptation (SFUDA) aims to obtain high performance in the unlabeled target domain using the pre-trained source model, not the source data. Existing SFUDA methods assign the same importance to all target samples, which is vulnerable to incorrect pseudo-labels. To differentiate between sample importance, in this study, we propose a novel sample-wise confidence score, the Joint Model-data Structure (JMDS) score for SFUDA. Unlike existing confidence scores that use only one of the source or target domain knowledge, the JMDS score uses both knowledge. We then propose a Confidence score Weighting Adaptation using the JMDS (CoWA-JMDS) framework for SFUDA. CoWA-JMDS consists of the JMDS scores as sample weights and weight Mixup that is our proposed variant of Mixup. Weight Mixup promotes the model make more use of the target domain knowledge. The experimental results show that the JMDS score outperforms the existing confidence scores. Moreover, CoWA-JMDS achieves state-of-the-art performance on various SFUDA scenarios: closed, open, and partial-set scenarios. Copyright © 2022, The Authors. All rights reserved.

关键词： Domain Knowledge

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Functional Connectome-Based Predictive Modeling in Autism

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BIOLOGICAL PSYCHIATRY 2022年第8期92卷 626-642页

作者： Horien, Corey Floris, Dorothea L. Greene, Abigail S. Noble, Stephanie Rolison, Max Tejavibulya, Link O'Connor, David McPartland, James C. Scheinost, Dustin Chawarska, Katarzyna Lake, Evelyn M. R. Constable, R. Todd Interdepartmental Neuroscience Program Yale School of Medicine New Haven Connecticut MD-PhD Program Yale School of Medicine New Haven Connecticut Methods of Plasticity Research Department of Psychology University of Zürich Zurich Switzerland Donders Center for Brain Cognition and Behavior Radboud University Nijmegen Nijmegen The Netherlands Department of Radiology and Biomedical Imaging Yale School of Medicine New Haven Connecticut Yale Child Study Center New Haven Connecticut Department of Biomedical Engineering Yale University New Haven Connecticut Department of Psychology Yale University New Haven Connecticut Department of Statistics and Data Science Yale University New Haven Connecticut Department of Pediatrics Yale School of Medicine New Haven Connecticut Department of Neurosurgery Yale School of Medicine New Haven Connecticut

Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.

关键词： Clinical translation Development Fingerprinting Individual differences Machine learning Resting-state fMRI

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Fabrication of PSf/P84-blended membranes with low P84 content: Characteristics and gas separation performance

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Case Studies in Chemical and Environmental Engineering 2024年 10卷

作者： Kayadoe, Victor Widiastuti, Nurul Gunawan, Triyanda Salleh, Wan Norhayati Wan Fansuri, Hamzah Romadiansyah, Taufik Qodar Pratama, Agus Wedi Department of Chemistry Faculty of Science and Data Analytical Institut Teknologi Sepuluh Nopember Chemistry Education Study Program Faculty of Teacher Training and Educational Pattimura University School of Chemical and Energy Engineering Faculty of Engineering Universiti Teknologi Malaysia Advanced Membrane Technology Research Center (AMTEC) Universiti Teknologi Malaysia

The demand for efficient and sustainable gas separation technologies is ever-increasing in various industries, including petrochemicals, natural gas processing, and carbon capture. Polymer-based membranes offer a promising solution due to their potential for high selectivity and energy efficiency. However, achieving optimal gas separation performance often requires overcoming the limitations of individual polymers through modifications such as polymer blending. In this study, PSf/P84 blended membranes with low P84 content were fabricated using the phase separation technique with a mixture of N-methyl-2-pyrrolidone and tetrahydrofuran as solvents. The effect of varying the mass ratio of PSf to P84 on membrane characteristics and gas separation performance was investigated. Characterization techniques included FTIR, XRD, SEM, TGA, Water Contact Angle (WCA) measurement, and mechanical property testing. Gas permeation tests were conducted with single gases at room temperature and 1 bar pressure. The results revealed that the addition of P84 increased membrane thickness, Young's modulus, and thermal stability, while decreasing d-spacing, dense layer thickness, hydrophilicity, tensile strength, and elongation. The F_index values, indicating the competence of the blended membranes compared to the ideal quality, demonstrated the positive impact of P84 addition on gas separation performance compared to pure PSf membranes. The optimal gas separation selectivity was achieved with the PSf/P84 1:0.025 blended membrane for H₂/CH₄ (11.28, 58 % increase), H₂/CO₂ (6.25, 193 % increase), and H₂/N₂ (5.25, 35 % increase). The highest N₂/CH₄ selectivity (6.29, 253 % increase) was observed with the 1:0.10 composition. Regarding commercially relevant separations based on the 2008 Robeson curve, the PSf/P84 1:0.20 blend showed promise for H₂/CO₂ separation, while the 1:0.

关键词： Findex Low P84 content PSf/P84 blended membrane Robeson curve

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Energy consumption of high-rise double skin façade buildings, a machine learning analysis

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Journal of Building Engineering 2024年 89卷

作者： Rezaie, Maedeh Kariminia, Shahab Band, Shahab S. Ameri, Rasoul Farokhi, Maryam Pai, Hao-Ting Gocer, Ozgur Rismanchi, Behzad Shooshtarian, Salman Department of Architecture Advancement in Architecture and Urban Planning Research Center Najafabad Branch Islamic Azad University Najafabad Iran Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Department of Information Management National Yunlin University of Science and Technology Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan School of Architecture Design and Planning The University of Sydney SydneyNSW Australia Renewable Energy and Energy Efficiency Group Department of Infrastructure Engineering Faculty of Engineering and Information Technology The University of Melbourne Australia School of Property Construction and Project Management RMIT University Melbourne Australia

Most energy exchanges take place through the building skin. The skin characteristics play a decisive role in the extent of these exchanges, but they are somewhat more varied in the double skin façade (DSF). Among these characteristics, cavity segmentation has a noticeable effect on the implementation of the DSF in different directions during the hot and cold seasons. The aim of this study was to investigate the role of DSF segmentation in energy consumption and natural ventilation of high-rise buildings in hot and dry climates. This study used DesignBuilder software to study sixty-four segmentation component scenarios in an eight-story residential building in Isfahan, Iran. Moreover, a proposed hybrid model utilizing the hybridization of the Hunger Game Search and Gradient Boosting (HGS-GB) algorithm was employed to estimate energy consumption in various scenarios involving lighting, heating, cooling, and total scenarios. The available outcomes revealed that the HGS-GB model had a better performance in comparison with other individual models, such as Gradient Boosting (GB), Random Forest (RF), and K-nearest neighbors (KNN). The R2 values for lighting, heating, cooling, and total energy estimation were 0.9993, 0.9958, 0.9991, and 0.9922, respectively. The findings of this study suggest the significance of DSF segmentation in energy consumption and natural ventilation in high-rise buildings in hot and dry climates. © 2024 Elsevier Ltd

关键词： Forestry

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A Machine Learning Approach to Detecting Low Medication State with Wearable Technologies

A Machine Learning Approach to Detecting Low Medication Stat...

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

作者： Andy Cheon Stephanie Yeoju Jung Collin Prather Matthew Sarmiento Kevin Wong Diane Myung-kyung Woodbridge MS in Data Science Program University of San Francisco

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Medication adherence is a critical component and implicit assumption of the patient life cycle that is often violated, incurring financial and medical costs to both patients and the medical system at large. As obstacles to medication adherence are complex and varied, approaches to overcome them must themselves be *** paper demonstrates one such approach using sensor data recorded by an Apple Watch to detect low counts of pill medication in standard prescription bottles. We use distributed computing on a cloud-based platform to efficiently process large volumes of high-frequency data and train a Gradient Boosted Tree machine learning model. Our final model yielded average cross-validated accuracy and F1 scores of 80.27% and 80.22%, *** conclude this paper with two use cases in which wearable devices such as the Apple Watch can contribute to efforts to improve patient medication adherence.

关键词： Microsoft Windows Machine learning Computational modeling Standards data models Containers Gyroscopes

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Unsupervised News Topic Modelling with Doc2Vec and Spherical Clustering

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Procedia Computer science 2021年 179卷 40-46页

作者： Arif Budiarto Reza Rahutomo Hendra Novyantara Putra Tjeng Wawan Cenggoro Muhamad Fitra Kacamarga Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Information System Department School of Information System Bina Nusantara University Jakarta Indonesia 11480 Eureka AI Singapore Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

In the digital and Internet era, companies are racing to profile their target users based on their online activities. One of the reliable sources is the news articles they read that can represent their interests. However, extracting latent information from the news articles is not an easy task for a human. In this paper, we introduced a practical model to automatically extract latent information from news articles with pre-determined topics. Our proposed model used unsupervised learning, thus alleviating the need for humans to label news items manually. Doc2vec was used to generate word vectors for each article. Afterward, a spectral clustering algorithm was applied to group the data based on the similarity. A supervised Long Short Term Memory (LSTM) model was built to compare the clustering performance. The best 1, best 3, and best 5 scores were used to evaluate our model. The result showed that our model could not outperformed LSTM model for the best 1 score. However, the best 5 score result indicated that our model was sufficiently robust to cluster the articles based on topic similarity. Additionally, the proposed unsupervised model was implemented in both an on-premise server, and a cloud server. Surprisingly, our proposed method could run faster in the cloud server despite its less number of CPU cores.

关键词： Topic Modelling Latent Information News Article Document Embedding Spherical Clustering

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Correction: How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment

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JMIR Medical Education 2024年 10卷 e57594页

作者： Gilson, Aidan Safranek, Conrad W. Huang, Thomas Socrates, Vimig Chi, Ling Taylor, Richard Andrew Chartash, David Section for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Department of Emergency Medicine Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States School of Medicine University College Dublin National University of Ireland Dublin Dublin Ireland Section for Biomedical Informatics and Data Science Yale University School of Medicine 300 George Street Suite 501 New Haven 06511 CT United States

In “How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge” (MIR Med Educ 2023;9:e45312) three additions were made to enhance discoverability. The title originally appeared as: How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment And has been changed to: How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge In the “Objective” section of the Abstract, the following sentence: This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination Step 1 and Step 2 exams, as well as to analyze responses for user interpretability. Has been changed to read as: This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination (USMLE) Step 1 and Step 2 exams, as well as to analyze responses for user interpretability. Finally, the abbreviation “USMLE” has been added to the Keywords section. The correction will appear in the online version of the paper on the JMIR Publications website on February 27, 2024 together with the publication of this correction notice. Because this was made after submission to PubMed, PubMed Central, and other full-text repositories, the corrected article has also been resubmitted to those repositories. © 2024 JMIR Publications Inc.. All rights reserved.

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Integrin αEβ7+ T cells direct intestinal stem cell fate decisions via adhesion signaling

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细胞研究（英文版） 2021年第12期31卷 1291-1307页

作者： Shiyang Chen Yajuan Zheng Xiaojuan Ran Hui Du Hua Feng Lei Yang Yating Wen Changdong Lin Shihui Wang Mengwen Huang Zhanjun Yan Dianqing Wu Hongyan Wang Gaoxiang Ge An Zeng Yi Arial Zeng Jianfeng Chen School of Life Science Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhouZhejiangChina State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell ScienceShanghai Institute of Biochemistry and Cell BiologyChinese Academy of SciencesUniversity of Chinese Academy of SciencesShanghaiChina State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell ScienceShanghai Institute of Biochemistry and Cell BiologyChinese Academy of SciencesUniversity of Chinese Academy of SciencesShanghaiChina Omics Core Bio-Med Big Data CenterShanghai Institute of Nutrition and HealthChinese Academy of SciencesShanghaiChina Department of Orthopedics Suzhou Ninth Peoples HospitalSoochow UniversitySuzhouJiangsuChina Department of Pharmacology Vascular Biology and Therapeutic ProgramYale School of MedicineNew HavenCTUSA

Intestinal stem cell (ISC) differentiation is regulated precisely by a niche in the crypt,where lymphocytes may interact with stem and transient amplifying (TA) ***,whether and how lymphocyte-stem/TA cell contact affects ISC differentiation is largely ***,we uncover a novel role of T cell-stem/TA cell contact in ISC fate *** show that intestinal lymphocyte depletion results in skewed ISC differentiation in mice,which can be rescued by T cell ***,integrin αEβ7 expressed on T cells binds to E-cadherin on ISCs and TA cells,triggering E-cadherin endocytosis and the consequent Wnt and Notch signaling *** αEβ7-E-cadherin adhesion suppresses Wnt signaling and promotes Notch signaling in ISCs and TA cells,leading to defective ISC ***,αEβ7+ T cells regulate ISC differentiation at single-cell level through cell-cell contact-mediated αEβ7-E-cadherin adhesion signaling,highlighting a critical role of the T cell-stem/TA cell contact in maintaining intestinal homeostasis.

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