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arXiv 2023年

作者： Izumi, Natsuko Sanhueza, Patricio Koch, Patrick M. Lu, Xing Li, Shanghuo Sabatini, Giovanni Olguin, Fernando A. Zhang, Qizhou Nakamura, Fumitaka Tatematsu, Ken'ichi Morii, Kaho Sakai, Takeshi Tafoya, Daniel Institute of Astronomy and Astrophysics Academia Sinica No. 1 Section 4 Roosevelt Road Taipei10617 Taiwan National Astronomical Observatory of Japan National Institutes of Natural Sciences 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Astronomical Science Program The Graduate University for Advanced Studies SOKENDAI 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Shanghai Astronomical Observatory Chinese Academy of Sciences 80 Nandan Road Shanghai200030 China Max Planck Institute for Astronomy Konigstuhl 17 HeidelbergD-69117 Germany INAF Osservatorio Astrofisico di Arcetri Largo E. Fermi 5 FirenzeI-50125 Italy Institute of Astronomy National Tsing Hua University Hsinchu30013 Taiwan Center for Astrophysics |Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Department of Astronomy Graduate School of Science The University of Tokyo Bunkyo-ku Tokyo113-0033 Japan Graduate School of Informatics and Engineering The University of Electro-Communications Chofu Tokyo182-8585 Japan Department of Space Earth and Environment Chalmers University of Technology Onsala Space Observatory OnsalaSE-439 92 Sweden

Massive infrared dark clouds (IRDCs) are considered to host the earliest stages of high-mass star formation. In particular, 70 µm dark IRDCs are the colder and more quiescent clouds. At a scale of about 5000 au using formaldehyde (H2CO) emission, we investigate the kinetic temperature of dense cores in 12 IRDCs obtained from the pilot ALMA Survey of 70 µm dark High-mass clumps in Early Stages (ASHES). Compared to 1.3 mm dust continuum and other molecular lines, such as C18O and deuterated species, we find that H2CO is mainly sensitive to low-velocity outflow components rather than to quiescent gas expected in the early phases of star formation. The kinetic temperatures of these components range from 26 to 300 K. The Mach number reaches about 15 with an average value of about 4, suggesting that the velocity distribution of gas traced by H2CO is significantly influenced by a supersonic non-thermal component. In addition, we detect warm line emission from HC3N and OCS in 14 protostellar cores, which requires high excitation temperatures (Eu/k ∼ 100 K). These results show that some of the embedded cores in the ASHES fields are in an advanced evolutionary stage, previously unexpected for 70 µm dark IRDCs. Copyright © 2023, The Authors. All rights reserved.

关键词： Giant stars

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Big Data Governance for Building A Smart Cities 10

Big Data Governance for Building A Smart Cities

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10th International Conference on ICT for Smart Society, ICISS 2019

作者： Zulkarnain, Novan Kosala, Raymond Ranti, Benny Supangkat, Suhono Harso Bina Nusantara University Computer Science Department BINUS Graduate Program Doctor of Computer Science Information Systems Department Jakarta11480 Indonesia Universitas Indonesia Computer Science Department Depok16424 Indonesia Institute of Technology Bandung School of Electrical and Informatics Engineering Bandung40132 Indonesia

ISBN: (纸本)9781728148809

There have been many studies conducted related to Smart City, IT Governance and Big Data. In this study aims to find out how the relationship between the three and how to form a framework to explain it. The methodology used is qualitative by looking for some literature on smart city framework, IT Governance framework, and a Big Data framework. From these results, an overall picture of the relationship between the three is concluded, where Big Data has a role in IT Governance. and also the relationship of IT Governance to the realization of Smart City. And the final results of this study produce a framework to explain the relationship between Smart City, IT Governance and Big Data. © 2019 IEEE.

关键词： Big data

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Parallel simulation of pattern formation in a reaction-diffusion system of FitzHugh-Nagumo using GPU CUDA

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AIP Conference Proceedings 2020年第1期2217卷

作者： Alfredo Gormantara Pranowo 1Informatics Engineering Graduate Program Universitas Atma Jaya Yogyakarta Yogyakarta 55281 Indonesia

One of the reaction-diffusion type differential equations is the formation of the Turing pattern. This paper describes the numerical simulation of the reaction-diffusion equations of FitzHugh-Nahumo model. This model is one of Turing systems, which can be used as a mathematical model of pattern formation in animal skin. The model is solved numerically using a finite difference method while the time evolution is discretized by using explicit Euler scheme. For reduction, the elapsed time, the parallel algorithm based on Graphical Processing Unit (GPU) with CUDA was used. This study compares the elapsed time of the CPU and GPU, numerical experiments were carried out using various mesh sizes. The results show that parallel implementation of GPU achieves acceleration up to 12 times faster than the serial CPU implementation. The greater the computing resources needed will make GPU use more optimal.

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Metrics for benchmarking and uncertainty quantification: Quality, applicability, and a path to best practices for machine learning in chemistry

arXiv

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arXiv 2020年

作者： Vishwakarma, Gaurav Sonpal, Aditya Hachmann, Johannes Department of Chemical and Biological Engineering University at Buffalo State University of New York BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Graduate Program University at Buffalo State University of New York BuffaloNY14260 United States New York State Center of Excellence in Materials Informatics BuffaloNY14203 United States

This review aims to draw attention to two issues of concern when we set out to make machine learning work in the chemical and materials domain, i.e., statistical loss function metrics for the validation and benchmarking of data-derived models, and the uncertainty quantification of predictions made by them. They are often overlooked or underappreciated topics as chemists typically only have limited training in statistics. Aside from helping to assess the quality, reliability, and applicability of a given model, these metrics are also key to comparing the performance of different models and thus for developing guidelines and best practices for the successful application of machine learning in chemistry. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

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Comparison of Potential Telemarketing Customers Predictions with a Data Mining Approach using the MLPNN and RBFNN Methods

Comparison of Potential Telemarketing Customers Predictions ...

引用

International Conference on Information and Communications Technology (ICOIACT)

作者： Annisa Nurul Puteri Dewiani Zulkifli Tahir Post-graduate Program of Electrical Engineering Hasanuddin University Makassar Indonesia Dept. Electrical Engineering Hasanuddin University Makassar Indonesia Dept. Informatics Engineering Hasanuddin University Makassar Indonesia

Bank as a company that has many customers who conduct transactions every day, of course, has data that is increased continuously. In this paper, customer transaction data has been predicted to find potential customers in deposit offer. Data mining approach has been performed to classify potential customers for marketing through telemarketing. This data analysis can be used as a consideration in determining marketing strategy decisions for marketing managers. 15,713 data with 13 class attributes and 1 target class were obtained from UCI Machine Learning repository. The data was divided into 70% training data and 30% test data. Feedforward method of Artificial Neural Network was used to classify customer data. Multilayer Perceptron Neural Network and Radial Basis Function Neural Network were used to obtain optimal classification results. The result of this classification predicted potential customers to subscribe deposits. The result of this study indicated that the Radial Basis Function Neural Network method with 95.3% accuracy and 96.4% sensitivity was a better method compared to the Multilayer Perceptron Neural Network method with 88.0% accuracy and 99.4% sensitivity.

关键词： Artificial neural networks Data mining Multilayer perceptrons Support vector machines Radial basis function networks Entropy

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Needle Insertion Control Method for Minimizing Both Deflection and Tissue Damage

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Journal of Medical Robotics Research 2019年第1期4卷

作者： Tsumura, Ryosuke Takishita, Yusuke Iwata, Hiroyasu Department of Creative and Science Engineering Waseda University Tokyo Japan Graduate Program of Embodiment Informatics Waseda University Tokyo Japan

Because fine needles can easily be deflected, accurate needle insertion is often difficult. Lower abdominal insertion is particularly difficult because of less imaging feedback;thus, an approach for allowing a straight insertion path by minimizing deflection is beneficial in cases of lower abdominal insertion. Although insertion with axial rotation can minimize deflection, the rotational insertion may cause tissue damage. Therefore, we established a novel insertion method for minimizing both deflection and tissue damage by combining rotation and vibration. Using layered tissues, we evaluated the effect of a combination of rotation and vibration in terms of deflection and tissue damage, which were measured by the insertion force and torque, and the area of the hole created by the needle using histological tissue sections to measure tissue damage. The experimental results demonstrated that insertion with unidirectional rotation is risky in terms of tissue wind-up, while insertion with bidirectional rotation can decrease deflection and avoid wind-up. We also found that insertion with vibration can decrease the insertion force and torque. Therefore, insertion with a combination of bidirectional rotation and vibration can minimize needle deflection and tissue damage, including the insertion force and torque and the hole area. © 2019 World Scientific Publishing Company.

关键词： deflection Needle insertion tissue damage

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Developmental stage classification of Embryos using two-stream neural network with linear-chain conditional random field

arXiv

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arXiv 2021年

作者： Lukyanenko, Stanislav Jang, Won-Dong Wei, Donglai Struyven, Robbert Kim, Yoon Leahy, Brian Yang, Helen Rush, Alexander Ben-Yosef, Dalit Needleman, Daniel Pfister, Hanspeter Department of Informatics Technical University of Munich Germany School of Engineering and Applied Sciences Harvard University United States Department of Molecular and Cellular Biology Harvard University United States Graduate Program in Biophysics Harvard University United States University College London United Kingdom MIT United States Cornell University United States Center for Computational Biology Flatiron Institute United States Lis Maternity Hospital Tel-Aviv Sourasky Medical Center Israel Cell and Developmental Biology Tel-Aviv University Israel

The developmental process of embryos follows a monotonic order. An embryo can progressively cleave from one cell to multiple cells and finally transform to morula and blastocyst. For time-lapse videos of embryos, most existing developmental stage classification methods conduct per-frame predictions using an image frame at each time step. However, classification using only images suffers from overlapping between cells and imbalance between stages. Temporal information can be valuable in addressing this problem by capturing movements between neighboring frames. In this work, we propose a two-stream model for developmental stage classification. Unlike previous methods, our two-stream model accepts both temporal and image information. We develop a linear-chain conditional random field (CRF) on top of neural network features extracted from the temporal and image streams to make use of both modalities. The linear-chain CRF formulation enables tractable training of global sequential models over multiple frames while also making it possible to inject monotonic development order constraints into the learning process explicitly. We demonstrate our algorithm on two time-lapse embryo video datasets: i) mouse and ii) human embryo datasets. Our method achieves 98.1% and 80.6% for mouse and human embryo stage classification, respectively. Our approach will enable more profound clinical and biological studies and suggests a new direction for developmental stage classification by utilizing temporal information. Copyright © 2021, The Authors. All rights reserved.

关键词： Classification (of information)

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Correction: Characterizing country-specific human and ecosystem health impact and damage cost of agricultural pesticides: the case for Thailand

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The International Journal of Life Cycle Assessment 2022年第12期27卷 1377-1377页

作者： Mankong, Phatchari Fantke, Peter Phenrat, Tanapon Mungkalasiri, Jitti Gheewala, Shabbir H. Prapaspongsa, Trakarn Graduate Program in Environmental and Water Resources Engineering Department of Civil and Environmental Engineering Faculty of Engineering Mahidol University Nakhon Pathom Thailand Quantitative Sustainability Assessment Department of Environmental and Resource Engineering Technical University of Denmark Kongens Lyngby Denmark Department of Civil Engineering Faculty of Engineering Naresuan University Phitsanulok Thailand Technology and Informatics Institute for Sustainability (TIIS) National Metal and Materials Technology Center (MTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand The Joint Graduate School of Energy and Environment (JGSEE) King Mongkut’s University of Technology Thonburi Bangkok Thailand Centre of Excellence On Energy Technology and Environment Ministry of Higher Education Science Research and Innovation Bangkok Thailand

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

引用

arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Application of factor analysis method to support the users acceptance model of ERP systems implementation

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Journal of Physics: Conference Series 2021年第1期1836卷

作者： W Sardjono J Sudirwan W Priatna G R Putra Information Systems Management Department BINUS Graduate Program -Master of Information Systems Management Bina Nusantara University Jakarta Indonesia – 11480 School of Information Systems Department BINUS Undergraduate Program Bina Nusantara University Jakarta Indonesia 11480 Informatics Engineering Engineering Bhayangkara University Jakarta Raya Indonesia Computer Science Study Program Faculty of Mathematics & Natural Sciences Pakuan University Bogor Indonesia

An e-Commerce company has been using an Enterprise Resource Planning (ERP) system for several years, but is still constrained in its implementation, this is reflected in the number of issue/change request tickets submitted by users who have not been able to reach ideal conditions. To overcome this problem, we need an appropriate reference model, the ERP system user acceptance model in companies. The study conducted factors that influence the level of acceptance of SAP ERP users in the XYZ group to then determine a model that is able to explain practically the level of ERP system user acceptance, which in turn can produce concrete proposals to increase ERP system user acceptance. Research is quantitative descriptive using factor analysis and regression analysis methods. The research method uses a questionnaire with a total of 107 valid respondents coming from 8 company names under the XYZ group in 6 Southeast Asian countries. The conclusion of the study is that there are 24 indicators and 4 factors that influence user acceptance of the SAP ERP system. These four factors are the low contribution of the ERP system, the quality of ERP systems and data, the development of ERP systems, and also the low quality of ERP system supporters. It is hoped that by optimizing the value of the 4 factors, the XYZ group can increase user acceptance of the SAP ERP system so that the system can provide benefits expected by the company to be competitive advantage.

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