检索结果-内蒙古大学图书馆

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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In-Situ Integration of a Hydrophobic and Fast-Zn2+-Conductive Inorganic Interphase to Stabilize Zn Metal Anodes

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Angewandte Chemie 2023年第27期135卷

作者： Mengyu Liu Wentao Yuan Dr. Guoqiang Ma Kaiyue Qiu Dr. Xueyu Nie Prof. Yongchang Liu Prof. Shigang Shen Prof. Ning Zhang College of Chemistry and Materials Science Key Laboratory of Analytical Science and Technology of Hebei Province Hebei University Baoding 071002 P. R. China Contribution: Conceptualization (equal) Data curation (lead) Formal analysis (lead) Investigation (lead) Methodology (equal) Writing - original draft (equal) Contribution: Data curation (equal) Formal analysis (equal) Investigation (equal) Methodology (equal) Software (lead) Validation (equal) Contribution: Formal analysis (equal) Validation (equal) Contribution: Formal analysis (equal) Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and Technology State Key Laboratory for Advanced Metals and Materials University of Science and Technology Beijing Beijing 100083 P. R. China Contribution: Conceptualization (equal) Formal analysis (equal) Funding acquisition (supporting) Resources (equal) Supervision (equal) Writing - review & editing (equal) Contribution: Formal analysis (equal) Resources (equal) Supervision (supporting) Contribution: Conceptualization (lead) Formal analysis (equal) Funding acquisition (lead) Investigation (equal) Methodology (equal) Resources (lead) Supervision (lead) Writing - original draft (lead) Writing - review & editing (lead)

The irreversible issues of Zn anode stemming from dendrite growth and water-induced erosion have severely hindered the commercialization of rechargeable aqueous Zn batteries. Herein, a hydrophobic and fast-Zn 2+ -conductive zinc hexacyanoferrate (HB-ZnHCF) interphase layer is in situ integrated on Zn by a rapid room-temperature wet-chemistry method to address these dilemmas. Different from currently proposed hydrophilic inorganic cases, the hydrophobic and compact HB-ZnHCF interphase effectively prevents the access of water molecules to Zn surface, thus avoiding H 2 evolution and Zn corrosion. Moreover, the HB-ZnHCF with large internal ion channels, strong zincophilicity, and high Zn 2+ transference number (0.86) permits fast Zn 2+ transport and enables smooth Zn deposition. Remarkably, the resultant HB-ZnHCF@Zn electrode delivers unprecedented reversibility with 99.88 % Coulombic efficiency over 3000 cycles, realizes long-term cycling over 5800 h (>8 months, 1 mA cm −2 ) and 1000 h (10 mA cm −2 ), and assures the stable operation of full Zn battery with both coin- and pouch-type configurations.

关键词： Aqueous Zn Battery Hydrophobic Interphase Inorganic Interphase Zinc Hexacyanoferrate Zn Metal Anode

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engineering Fluorine-rich Double Protective Layer on Zn Anode for Highly Reversible Aqueous Zinc-ion Batteries

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Angewandte Chemie 2023年第51期135卷

作者： Titi Li Sanlue Hu Chenggang Wang Dun Wang Minwei Xu Caiyun Chang Xijin Xu Cuiping Han School of Physics and Technology University of Jinan Shandong 250022 China Faculty of Materials Science and Energy Engineering/Institute of Technology for Carbon Neutrality Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Contribution: Conceptualization (lead) Data curation (lead) Investigation (lead) Resources (lead) Writing - original draft (lead) Shenzhen Key Laboratory of Energy Materials for Carbon Neutrality Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Contribution: Methodology (lead) Software (lead) Writing - review & editing (equal) Contribution: Supervision (lead) Writing - review & editing (equal) Contribution: Data curation (lead) Formal analysis (lead) Investigation (lead) Methodology (lead) Contribution: Resources (lead) Supervision (lead) Contribution: Conceptualization (lead) Data curation (lead) Investigation (lead) Methodology (lead) Resources (equal) Writing - review & editing (equal) Contribution: Resources (lead) Supervision (lead) Writing - review & editing (lead) Contribution: Methodology (lead) Resources (lead) Supervision (lead) Writing - review & editing (lead)

The high thermodynamic instability and side reactions of Zn-metal anode (ZMA), especially at high current densities, greatly impede the commercialization of aqueous zinc-ion batteries (AZIBs). Herein, a fluorine-rich double protective layer strategy is proposed to obtain the high reversibility of AZIBs through the introduction of a versatile tetradecafluorononane-1,9-diol (TDFND) additive in aqueous electrolyte. TDFND molecule with large adsorption energy (−1.51 eV) preferentially absorbs on the Zn anode surface to form a Zn(OR) 2 − (R=−CH 2 −(CF 2 ) 7 −CH 2 −) cross-linking complex network, which balances space electric field and controls the Zn 2+ ion flux, thus enabling the uniform and compact deposition of Zn (002) crystal planes. Meanwhile, TDFND with low Lowest unoccupied molecular orbital (LUMO, 0.10 eV) energy level is priorly decomposed to regulate the interfacial chemistry of ZMA by building a ZnF 2 -rich solid electrode/electrolyte interface (SEI) layer. It is found that a 14 nm-thick SEI layer delivers excellent structural integrity to suppress parasitic reactions by blocking the direct contact of active water and ZMA. Consequently, the Zn electrode exhibits a superior cycling life over 430 h at 10 mA cm −2 and a high average Coulombic efficiency of 99.8 % at 5 mA cm −2 . Furthermore, a 68 mAh pouch cell delivers 80.3 % capacity retention for 1000 cycles.

关键词： Cross-Linking Network Solid-Electrolyte Interface Zinc Anode Zinc-Ion Battery ZnF2

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Multi-Selenophene Incorporated Thiazole Imide-Based n-Type Polymers for High-Performance Organic Thermoelectrics

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Angewandte Chemie 2023年第3期136卷

作者： Yongchun Li Wenchang Wu Yimei Wang Enmin Huang Sang Young Jeong Prof. Han Young Woo Prof. Xugang Guo Dr. Kui Feng Department of Materials Science and Engineering Southern University of Science and Technology (SUSTech) Shenzhen Guangdong 518055 China Contribution: Data curation (lead) Investigation (lead) Writing - original draft (lead) Contribution: Formal analysis (equal) Investigation (equal) Contribution: Investigation (supporting) Department of Chemistry Korea University Seoul 136-713 South Korea Songshan Lake Materials Laboratory Dongguan Guangdong 523808 China Contribution: Funding acquisition (lead) Writing - review & editing (lead) Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology (SUSTech) Shenzhen Guangdong 518055 China Contribution: Conceptualization (lead) Funding acquisition (equal) Investigation (lead) Supervision (lead) Writing - review & editing (lead)

Developing polymers with high electrical conductivity ( σ ) after n-doping is a great challenge for the advance of the field of organic thermoelectrics (OTEs). Herein, we report a series of thiazole imide-based n-type polymers by gradually increasing selenophene content in polymeric backbone. Thanks to the strong intramolecular noncovalent N⋅⋅⋅S interaction and enhanced intermolecular Se⋅⋅⋅Se interaction, with the increase of selenophene content, the polymers show gradually lowered LUMOs, more planar backbone, and improved film crystallinity versus the selenophene-free analogue. Consequently, polymer PDTzSI−Se with the highest selenophene content achieves a champion σ of 164.0 S cm −1 and a power factor of 49.0 μW m −1 K −2 in the series when applied in OTEs after n-doping. The σ value is the highest one for n-type donor-acceptor OTE materials reported to date. Our work indicates that selenophene substitution is a powerful strategy for developing high-performance n-type OTE materials and selenophene incorporated thiazole imides offer an excellent platform in enabling n-type polymers with high backbone coplanarity, deep-lying LUMO and enhanced mobility/conductivity.

关键词： Electrical Conductivity Organic Thermoelectrics Selenophene Substitution Thiazole Imide n-Type Polymer Semiconductors

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Multi-Layer Graph Generative Model Using AutoEncoder for Recommendation Systems

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Journal on Big data 2019年第1期1卷 1-7页

作者： Syed Falahuddin Quadri Xiaoyu Li Desheng Zheng Muhammad Umar Aftab Yiming Huang School of Information and Software Engineering University of Electronics Science and Technology of ChinaChengdu610054China School of Computer Science Southwest Petroleum University8 Xindu AvenueChengdu610500China University of Maryland Institute for Advanced Computer Studies Bldg 224 Stadium Dr Room 3100College ParkMaryland20742USA

Given the glut of information on the web,it is crucially important to have a system,which will parse the information appropriately and recommend users with relevant information,this class of systems is known as Recommendation Systems(RS)-it is one of the most extensively used systems on the web ***,Deep Learning(DL)models are being used to generate recommendations,as it has shown state-of-the-art(SoTA)results in the field of Speech Recognition and Computer Vision in the last ***,the RS is a much harder problem,as the central variable in the recommendation system’s environment is the chaotic nature of the human’s purchasing/consuming behaviors and their *** user-item interactions cannot be fully represented in the Euclidean-Space,as it will trivialize the interaction and undermine the implicit interactions *** to preserve the implicit as well as explicit interactions of user and items,we propose a new graph based recommendation *** fundamental idea behind this framework is not only to generate the recommendations in the unsupervised fashion but to learn the dynamics of the graph and predict the short and long term interest of the *** this paper,we propose the first step,a heuristic multi-layer high-dimensional graph which preserves the implicit and explicit interactions between users and items using SoTA Deep Learning models such as *** generate recommendation from this generated graph a new class of neural network architecture-Graph Neural Network-can be used.

关键词： Recommendation systems autoencoder knowledge representation representation learning graph-structured data

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Accumulative time-based ranking method to reputation evaluation in information networks

arXiv

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arXiv 2019年

作者： Liao, Hao Liu, Qi-Xin Huang, Ze-Cheng Yeung, Chi Ho Zhang, Yi-Cheng National Engineering Laboratory on Big Data System Computing Technology Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Institute of Big Data Intelligent Management and Decision Shenzhen University Shenzhen518060 China Department of Science and Environmental Studies The Education University of Hong Kong Hong Kong Hong Kong Department of Physics University of Fribourg Fribourg Switzerland

With the rapid development of modern technology, the Web has become an important platform for users to make friends and acquire information. However, since information on the Web is over-abundant, information filtering becomes a key task for online users to obtain relevant suggestions. As most Websites can be ranked according to users’ rating and preferences, relevance to queries, and recency, how to extract the most relevant item from the over-abundant information is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed accumulative time-based ranking (ATR) algorithm, we incorporate two behavioral weighting factors which are updated when users select or rate items, to reflect the evolution of user reputation and item quality over time. We showed that our algorithm outperforms state-of-the-art ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Copyright © 2019, The Authors. All rights reserved.

关键词： Information filtering

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Rapid Charge Transfer Endowed by Interfacial Ni-O Bonding in S-scheme Heterojunction for Efficient Photocatalytic H2and Imine Production

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Angewandte Chemie 2023年第50期135卷

作者： Dr. Bowen He Dr. Peng Xiao Dr. Sijie Wan Dr. Jianjun Zhang Prof. Tao Chen Prof. Liuyang Zhang Prof. Jiaguo Yu Laboratory of Solar Fuel Faculty of Materials Science and Chemistry China University of Geosciences 68 Jincheng Street Wuhan 430078 P. R. China Contribution: Conceptualization (lead) Data curation (lead) Formal analysis (lead) Methodology (lead) Writing - original draft (equal) Hefei National Laboratory for Physical Sciences at Microscale CAS Key Laboratory of Materials for Energy Conversion Department of Materials Science and Engineering University of Science and Technology of China Hefei Anhui 230026 China Contribution: Data curation (equal) Formal analysis (equal) State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology 122 Luoshi Road Wuhan 430070 P. R. China Contribution: Formal analysis (supporting) Methodology (supporting) Software (lead) Contribution: Methodology (supporting) Validation (supporting) Contribution: Methodology (supporting) Resources (supporting) Contribution: Formal analysis (equal) Funding acquisition (equal) Supervision (supporting) Writing - review & editing (lead)

Cooperative coupling of H 2 evolution with oxidative organic synthesis is promising in avoiding the use of sacrificial agents and producing hydrogen energy with value-added chemicals simultaneously. Nonetheless, the photocatalytic activity is obstructed by sluggish electron-hole separation and limited redox potentials. Herein, Ni-doped Zn 0.2 Cd 0.8 S quantum dots are chosen after screening by DFT simulation to couple with TiO 2 microspheres, forming a step-scheme heterojunction. The Ni-doped configuration tunes the highly active S site for augmented H 2 evolution, and the interfacial Ni−O bonds provide fast channels at the atomic level to lower the energy barrier for charge transfer. Also, DFT calculations reveal an enhanced built-in electric field in the heterojunction for superior charge migration and separation. Kinetic analysis by femtosecond transient absorption spectra demonstrates that expedited charge migration with electrons first transfer to Ni 2+ and then to S sites. Therefore, the designed catalyst delivers drastically elevated H 2 yield (4.55 mmol g −1 h −1 ) and N-benzylidenebenzylamine production rate (3.35 mmol g −1 h −1 ). This work provides atomic-scale insights into the coordinated modulation of active sites and built-in electric fields in step-scheme heterojunction for ameliorative photocatalytic performance.

关键词： Benzylamine Coupling Oxidation Hydrogen Production Interfacial Charge Transfer S-Scheme Photocatalyst

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Master stability functions for complete, intralayer, and interlayer synchronization in multiplex networks of coupled Rössler oscillators

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Physical Review E 2019年第1期99卷 012304-012304页

作者： Longkun Tang Xiaoqun Wu Jinhu Lü Jun-an Lu Raissa M. D'Souza Fujian Province University Key Laboratory of Computation Science School of Mathematical Science Huaqiao University Quanzhou 362021 China School of Mathematics and Statistics Wuhan University Wuhan 430072 China Department of Computer Science University of California Davis California 95616 USA School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China

Synchronization phenomena are of broad interest across disciplines and increasingly of interest in a multiplex network setting. For the multiplex network of coupled Rössler oscillators, here we show how the master stability function, a celebrated framework for analyzing synchronization on a single network, can be extended to certain classes of multiplex networks with different intralayer and interlayer coupling functions. We derive three master stability equations that determine, respectively, the necessary regions of complete synchronization, intralayer synchronization, and interlayer synchronization. We calculate these three regions explicitly for the case of a two-layer network of Rössler oscillators and show that the overlap of the regions determines the type of synchronization achieved. In particular, if the interlayer or intralayer coupling function is such that the interlayer or intralayer synchronization region is empty, complete synchronization cannot be achieved regardless of the coupling strength. Furthermore, for any network structure, the occurrence of intralayer and interlayer synchronization depends mainly on the coupling functions of nodes within a layer and across layers, respectively. Our mathematical analysis requires that the intralayer and interlayer supra-Laplacians commute. But, we show this is only a sufficient, and not necessary, condition and that the results can be applied more generally.

关键词： Collective dynamics Synchronization Synchronization transition

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Confidence intervals of prediction accuracy measures for multivariable prediction models based on the bootstrap-based optimism correction methods

arXiv

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arXiv 2020年

作者： Noma, Hisashi Shinozaki, Tomohiro Iba, Katsuhiro Teramukai, Satoshi Furukawa, Toshi A. Department of Data Science The Institute of Statistical Mathematics Tokyo Japan Department of Information and Computer Technology Faculty of Engineering Tokyo University of Science Tokyo Japan Department of Statistical Science School of Multidisciplinary Sciences The Graduate University for Advanced Studies Tokyo Japan Office of Biostatistics Department of Biometrics Headquarters of Clinical Development Otsuka Pharmaceutical Co. Ltd. Tokyo Japan Department of Biostatistics Graduate School of Medical Science Kyoto Prefectural University of Medicine Kyoto Japan Department of Health Promotion and Human Behavior and of Clinical Epidemiology Kyoto University Graduate School of Medicine School of Public Health Kyoto Japan

In assessing prediction accuracy of multivariable prediction models, optimism corrections are essential for preventing biased results. However, in most published papers of clinical prediction models, the point estimates of the prediction accuracy measures are corrected by adequate bootstrap-based correction methods, but their confidence intervals are not corrected, e.g., the DeLong's confidence interval is usually used for assessing the Cstatistic. These naïve methods do not adjust for the optimism bias and do not account for statistical variability in the estimation of parameters in the prediction models. Therefore, their coverage probabilities of the true value of the prediction accuracy measure can be seriously below the nominal level (e.g., 95%). In this article, we provide two generic bootstrap methods, namely (1) location-shifted bootstrap confidence intervals and (2) two-stage bootstrap confidence intervals, that can be generally applied to the bootstrap-based optimism correction methods, i.e., the Harrell's bias correction, 0.632, and 0.632+ methods. In addition, they can be widely applied to various methods for prediction model development involving modern shrinkage methods such as the ridge and lasso regressions. Through numerical evaluations by simulations, the proposed confidence intervals showed favourable coverage performances. Besides, the current standard practices based on the optimism-uncorrected methods showed serious undercoverage properties. To avoid erroneous results, the optimism-uncorrected confidence intervals should not be used in practice, and the adjusted methods are recommended instead. We also developed the R package predboot for implementing these methods (https://***/nomahi/predboot). The effectiveness of the proposed methods are illustrated via applications to the GUSTO-I clinical trial. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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