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COVID-19 mRNA Vaccine Degradation Prediction Using LR and LGBM Algorithms

Journal of Physics: Conference Series 2021年第1期1997卷

作者： Soon Hwai Ing Azian Azamimi Abdullah Nor Hazlyna Harun Shigehiko Kanaya Faculty of Electronic Engineering Technology Universiti Malaysia Perlis (UniMAP) Perlis Malaysia. Medical Devices and Life Sciences Cluster Sport Engineering Research Centre Centre of Excellence (SERC) Universiti Malaysia Perlis (UniMAP) Perlis Malaysia. Data Science Research Lab School of Computing School of Computing Universiti Utara Malaysia Sintok Kedah. Computational Systems Biology Lab Graduate School of Information Science Nara Institute of Science and Technology Ikoma Nara Japan.

The threatening Coronavirus which was assigned as the global pandemic concussed not only the public health but society, economy and every walks of life. Some measurements are taken to stifle the spread and one of the best ways is to carry out some precautions to prevent the contagion of SARS-CoV-2 virus to uninfected populaces. Injecting prevention vaccines is one of the precaution steps under the grandiose blueprint. Among all vaccines, it is found that mRNA vaccine which shows no side effect with marvellous effectiveness is the most preferable candidates to be considered. However, degradation had become its biggest drawback to be implemented. Hereby, this study is held with desideratum to develop prediction models specifically to predict the degradation rate of mRNA vaccine for COVID-19. Two machine learning algorithms, which are, Linear Regression (LR) and Light Gradient Boosting Machine (LGBM) are proposed for models development using Python language. dataset comprises of thousands of RNA molecules that holds degradation rates at each position from Eterna platform is extracted, pre-processed and encoded with label encoding before loaded into algorithms. The results show that LGBM (0.2447) performs better than LR (0.3957) for this study when evaluated with the RMSE metric.

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Monitoring the Impacts of Government Interventions to Contain COVID-19: A Quantitative Approach

arXiv

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arXiv 2020年

作者： Wang, Shuo Yang, Xian Huang, Yuan Li, Ling Teng, Zhongzhao Guo, Yike Data Science Institute Imperial College London United Kingdom Department Computer Science Hong Kong Baptist University Hong Kong Special Administrative Region China Department of Pure Mathematics and Mathematical Statistics University of Cambridge United Kingdom School of Computing University of Kent United Kingdom Department of Radiology University of Cambridge United Kingdom Hong Kong Baptist University Hong Kong Special Administrative Region China

Monitoring the evolving impacts of non-pharmaceutical intervention measures requires fine-grained estimation of transmission dynamics. We propose a framework to estimate instantaneous reproduction number R! using Bayesian inference upon a renewal process, disentangling the R! reduction into mitigation and suppression factors for quantifying their impacts at a finer granularity. Investigating the impacts of intervention measures of European countries, the United States and Wuhan with the framework, we reveal the effects of interventions in Europe and alert that 30 states in the United States are facing resurgence risks. Copyright © 2020, The Authors. All rights reserved.

关键词： Bayesian networks

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FAIR for AI: An interdisciplinary and international community building perspective

arXiv

引用

arXiv 2022年

作者： Huerta, E.A. Blaiszik, Ben Brinson, L. Catherine Bouchard, Kristofer E. Diaz, Daniel Doglioni, Caterina Duarte, Javier M. Emani, Murali Foster, Ian Fox, Geoffrey Harris, Philip Heinrich, Lukas Jha, Shantenu Katz, Daniel S. Kindratenko, Volodymyr Kirkpatrick, Christine R. Lassila-Perini, Kati Madduri, Ravi K. Neubauer, Mark S. Psomopoulos, Fotis E. Roy, Avik Rübel, Oliver Zhao, Zhizhen Zhu, Ruike Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Globus University of Chicago ChicagoIL60637 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Biological Systems & Engineering Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Helen Wills Neuroscience Institute University of California Berkeley BerkeleyCA94720 United States Department of Physics University of California La Jolla San DiegoCA92093 United States Lund University Department of Physics Box 118 Lund221 00 Sweden School of Physics & Astronomy The University of Manchester ManchesterM13 9PL United Kingdom Leadership Computing Facility Argonne National Laboratory LemontIL60439 United States Biocomplexity Institute Department of Computer Science University of Virginia CharlottesvilleVA22904 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Technical University Munich Arcisstraße 21 München80333 Germany Computational Science Initiative Brookhaven National Laboratory UptonNY11973 United States Electrical and Computer Engineering Rutgers The State University of New Jersey PiscatawayNJ08854 United States National Center for Supercomputing Applications University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States San Diego Supercomputer Center University of California La Jolla San DiegoCA

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been reinterpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022. Copyright © 2022, The Authors. All rights reserved.

关键词： Information management

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Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers

arXiv

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arXiv 2023年

作者： Huppenkothen, Daniela Ntampaka, Michelle Ho, Matthew Fouesneau, Morgan Nord, Brian Peek, J.E.G. Walmsley, Mike Wu, John F. Avestruz, C. Buck, Tobias Brescia, Massimo Finkbeiner, Douglas P. Goulding, Andy D. Kacprzak, T. Melchior, Peter Pasquato, Mario Ramachandra, Nesar Ting, Yuan-Sen van de Ven, Glenn Villar, Soledad Villar, V.A. Zinger, Elad SRON Netherlands Institute for Space Research Niels Bohrweg 4 Leiden2333CA Netherlands Anton Pannekoek Institute for Astronomy University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Space Telescope Science Institute BaltimoreMD21218 United States Department of Physics & Astronomy Johns Hopkins University BaltimoreMD21218 United States UMR 7095 98 bis bd Arago ParisF-75014 France Königstuhl 17 HeidelbergD-69117 Germany Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Department of Astronomy and Astrophysics University of Chicago ChicagoIL60637 United States Jodrell Bank Centre for Astrophysics Department of Physics & Astronomy University of Manchester ManchesterM13 9PL United Kingdom Dunlap Institute for Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada Leinweber Center for Theoretical Physics University of Michigan Ann ArborMI48109 United States Department of Physics University of Michigan Ann ArborMI48109 United States Universität Heidelberg Interdisziplinäres Zentrum für Wissenschaftliches Rechnen Im Neuenheimer Feld 205 Heidelberg69120 Germany Universität Heidelberg Zentrum für Astronomie Institut für Theoretische Astrophysik Albert-Ueberle-Straße 2 Heidelberg69120 Germany Department of Physics "E. Pancini " University Federico II of Napoli Via Cinthia 21 NapoliI-80126 Italy INAF Astronomical Observatory of Capodimonte Salita Moiariello 16 NapoliI-80131 Italy Department of Physics Harvard University 17 Oxford St. CambridgeMA02138 United States Harvard-Smithsonian Center for Astrophysics 60 Garden St. CambridgeMA02138 United States Department of Astrophysical Sciences Princeton University PrincetonNJ08544 United States Swiss Data Science Center Paul Scherrer Institute Villigen5303 Switzerland Center for Statistics & Machine Learn

Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best practices, challenges, and drawbacks, which, at present, are often reported on incompletely in the astrophysical literature. With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Synchronizing Genesio Chaotic System by Zhang-Dynamics Controller without or with Noise Perturbation

Synchronizing Genesio Chaotic System by Zhang-Dynamics Contr...

引用

第三十八届中国控制会议

作者： Yihong Ling Deyang Zhang Jianxin Zhang Binin Qiu Yunong Zhang School of Data and Computer Science Sun Yat-sen University (SYSU) Research Institute of Sun Yat-sen University in Shenzhen Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

This paper investigates the synchronization control of Genesio chaotic system. For solving the synchronization problem of Genesio chaotic system, Zhang dynamics(ZD) method is firstly presented and utilized, and the corresponding ZD controller is designed and investigated for synchronizing control. On the one hand, the ZD controller for the synchronization of Genesio chaotic system without noise perturbation is considered, and the synchronization error between the drive Genesio chaotic subsystem and the response Genesio chaotic subsystem globally exponentially converges to zero. On the other hand,the ZD controller for the synchronization of Genesio chaotic system with noise perturbation is also considered, and detailed theoretical analyses show that the synchronization error globally converges to a small error bound or its inside. Note that the ZD controller provided here is not only effective but also robust for the synchronization of Genesio chaotic system. Finally,the simulations and comparisons with different methods for synchronizing Genesio chaotic system substantiate the efficacy,robustness and advantage of the presented ZD controller in the synchronization control.

关键词： Synchronization control Genesio chaotic system Zhang dynamics method Noise perturbation Robustness

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Cake cutting on graphs: A discrete and bounded proportional protocol

arXiv

引用

arXiv 2019年

作者： Bei, Xiaohui Sun, Xiaoming Wu, Hao Zhang, Jialin Zhang, Zhijie Zi, Wei School of Physical and Mathematical Sciences Nanyang Technological University CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences

The classical cake cutting problem studies how to find fair allocations of a heterogeneous and divisible resource among multiple agents. Two of the most commonly studied fairness concepts in cake cutting are proportionality and envy-freeness. It is well known that a proportional allocation among n agents can be found efficiently via simple protocols [16]. For envy-freeness, in a recent breakthrough, Aziz and Mackenzie [5] proposed a discrete and bounded envy-free protocol for any number of players. However, the protocol suffers from high multiple-exponential query complexity and it remains open to find simpler and more efficient envy-free protocols. In this paper we consider a variation of the cake cutting problem by assuming an underlying graph over the agents whose edges describe their acquaintance relationships, and agents evaluate their shares relatively to those of their neighbors. An allocation is called locally proportional if each agent thinks she receives at least the average value over her neighbors. Local proportionality generalizes proportionality and is in an interesting middle ground between proportionality and envy-freeness: its existence is guaranteed by that of an envy-free allocation, but no simple protocol is known to produce such a locally proportional allocation for general graphs. Previous works showed locally proportional protocols for special classes of graphs, and it is listed in both [1] and [8] as an open question to design simple locally proportional protocols for more general classes of graphs. In this paper we completely resolved this open question by presenting a discrete and bounded locally proportional protocol for any given graph. Our protocol has a query complexity of only single exponential, which is significantly smaller than the six towers of n query complexity of the envy-free protocol given in [5]. Copyright © 2019, The Authors. All rights reserved.

关键词： Graphic methods

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data-Driven Modeling of Telluric Features and Stellar Variability with ***

arXiv

引用

arXiv 2024年

作者： Gilbertson, Christian Ford, Eric B. Halverson, Samuel Fitzmaurice, Evan Blake, Cullen H. Stefánsson, Guðmundur Mahadevan, Suvrath Wright, Jason T. Luhn, Jacob K. Ninan, Joe P. Robertson, Paul Roy, Arpita Schwab, Christian Terrien, Ryan C. Center for Computing Research Sandia National Laboratories 1450 Innovation Pkwy SE AlbuquerqueNM87123 United States Department of Astronomy & Astrophysics Penn State University 525 Davey Laboratory 251 Pollock Road University ParkPA16802 United States Center for Exoplanets and Habitable Worlds Penn State University 525 Davey Laboratory 251 Pollock Road University ParkPA16802 United States Penn State Astrobiology Research Center University ParkPA16802 United States Center for Astrostatistics Penn State University 525 Davey Laboratory 251 Pollock Road University ParkPA16802 United States Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States Jet Propulsion Laboratory 4800 Oak Grove Drive PasadenaCA91109 United States Institute for Computational and Data Sciences Penn State University 224B Computer Building University ParkPA16802 United States Department of Physics & Astronomy University of Pennsylvania 209 South 33rd Street PhiladelphiaPA19104 United States NASA Sagan United States Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08540 United States ETH Zurich Institute for Particle Physics & Astrophysics Zurich Switzerland Penn State Extraterrestrial Intelligence Center Penn State University 525 Davey Laboratory 251 Pollock Road University ParkPA16802 United States Department of Physics & Astronomy The University of California Irvine IrvineCA92697 United States Department of Astronomy and Astrophysics Tata Institute of Fundamental Research Homi Bhabha Road Colaba Mumbai400005 India Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Department of Physics and Astronomy Johns Hopkins University 3400 N Charles St BaltimoreMD21218 United States School of Mathematical and Physical Sciences Macquarie University Balaclava Road North RydeNSW2109 Australia Carleton College One North College St. Nor

A significant barrier to achieving the radial velocity (RV) measurement accuracy and precision required to characterize terrestrial mass exoplanets is the existence of time-variable features in the measured spectra, from both telluric absorption and stellar variability, which affect measured line shapes and can cause apparent RV shifts. Reaching the desired accuracy using traditional techniques often requires avoiding lines contaminated by stellar variability and/or changing tellurics, and thus discarding a large fraction of the spectrum, lowering precision. New data-driven methods can help achieve extremely precise and accurate RVs by enabling the use of a larger fraction of the available data. While there exist methods for modeling telluric features or the stellar variability individually, there is a need for additional tools that are capable of modeling them simultaneously at the spectral level. Here we present *** (SSOF), a Julia package for measuring Doppler shifts and creating data-driven models (with fast, physically-motivated Gaussian Process regularization) for the time-variable spectral features for both the telluric transmission and stellar spectrum, while accounting for the wavelength-dependent instrumental line-spread function. We demonstrate SSOF's state-of-the-art performance on data from the NEID RV spectrograph on the WIYN 3.5m Telescope for multiple stars. We show SSOF's,ability to accurately identify and characterize spectral variability and provide ∼2-6x smaller photon-limited errors over the NEID CCF-based pipeline and match the performance of SERVAL, a leading template-based pipeline, using only observed EPRV spectra. © 2024, CC BY.

关键词： Stars

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A data Analytic Elucidation on the Spatial Agglomeration of Singapore Maritime Industry

International Journal on Smart and Sustainable Cities

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International Journal on Smart and Sustainable Cities 2023年第1期1卷

作者： Lock Yue Chew Ning Ning Chung Wen Xuan Sia Hoai Nguyen Huynh Glenn Sim Alvin Chua Zhongwen Huang https://***/0000-0003-1366-8205 School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore Data Science & Artificial Intelligence Research Centre Nanyang Technological University Singapore 637335 Singapore Centre for University Core Singapore University of Social Sciences Singapore 599494 Singapore Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) 1 Fusionopolis Way Connexis North Tower #16-16 Singapore 138632 Singapore School of Global Affairs Faculty of Social Science & Public Policy King’s College London London WC2R 2LS UK Design & Planning Lab Urban Redevelopment Authority Singapore 179369 Singapore

In this paper, we create three new statistical measures to characterize the spatial patterns and structure of firms and subsectors of industry. The new measures give a direct quantification of the tendency of subsectors to spatially agglomerate, the likelihood of cross-subsector spatial co-agglomeration, and the potential of leading and following behaviors between firms. Unlike the conventional measures which deduce possible agglomeration through deviations from the baseline distribution or distance density, our measures study agglomeration in terms of clustering and co-location behaviors to provide a clear physical picture on spatial agglomeration. With each measure quantifying one of the three different ways in which firms agglomerate spatially, their collective use provides a combined perspective on the agglomeration behavior and location choice of firms that is not available from the conventional measures. We employ this joint application of measures to show that Singapore maritime firms have a propensity to distribute themselves spatially in a manner that allows them to reap the benefits of localization externalities from the agglomeration economy.

关键词： Spatial morphology Agglomeration Co-location Co-occurrence Urban complexity Maritime industry

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Applications of Game Theory in Vehicular Networks: A Survey

arXiv

引用

arXiv 2021年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Li, Guofa Anil, Carie Li, Keqiang Guo, Xinyu Sun, Geng Tian, Daxin Cao, Dongpu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Institute of Human Factors and Ergonomics College of Mechatronics and Control Engineering Shenzhen University Guangdong Shenzhen518060 China School of Computer Science and Engineering VIT-AP Andhra Pradesh Amaravati522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China State Key Laboratory of Automotive Safety and Energy Department of Automotive Engineering Tsinghua University Beijing100084 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

In the Internet of things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming vehicular networks (VNs) that provide efficient and safe traffic and ubiquitous access to various applications. However, as the number of nodes in an ITS increases, it is challenging to satisfy a varied and large number of service requests with different quality of service (QoS) and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve the objective for either an individual or a group. Game theory (GT), a theoretical framework designed for strategic interactions among rational decision makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including the basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs that are integrated with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions of applying GT in VNs are outlined. Copyright © 2021, The Authors. All rights reserved.

关键词： Quality of service

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Template bank for compact binary mergers in the fourth observing run of Advanced LIGO, Advanced Virgo, and KAGRA

arXiv

引用

arXiv 2022年

作者： Sakon, Shio Tsukada, Leo Fong, Heather Kennington, James Niu, Wanting Hanna, Chad Adhicary, Shomik Baral, Pratyusava Baylor, Amanda Cannon, Kipp Caudill, Sarah Cousins, Bryce Creighton, Jolien D.E. Ewing, Becca George, Richard N. Godwin, Patrick Harada, Reiko Huang, Yun-Jing Huxford, Rachael Joshi, Prathamesh Kuwahara, Soichiro Li, Alvin K.Y. Magee, Ryan Meacher, Duncan Messick, Cody Morisaki, Soichiro Mukherjee, Debnandini Pace, Alex Posnansky, Cort Ray, Anarya Sachdev, Surabhi Singh, Divya Tapia, Ron Tsutsui, Takuya Ueno, Koh Viets, Aaron Wade, Leslie Wade, Madeline Wang, Jonathan Department of Physics The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Department of Physics and Astronomy University of British Columbia VancouverBCV6T 1Z4 Canada RESCEU The University of Tokyo Tokyo113-0033 Japan Graduate School of Science The University of Tokyo Tokyo113-0033 Japan Department of Astronomy and Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States Leonard E. Parker Center for Gravitation Cosmology and Astrophysics University of Wisconsin-Milwaukee MilwaukeeWI53201 United States Department of Physics University of Massachusetts DartmouthMA02747 United States Center for Scientific Computing and Data Science Research University of Massachusetts DartmouthMA02747 United States Department of Physics University of Illinois UrbanaIL61801 United States Center for Gravitational Physics University of Texas at Austin AustinTX78712 United States LIGO Laboratory California Institute of Technology PasadenaCA91125 United States MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan NASA Marshall Space Flight Center HuntsvilleAL35811 United States Center for Space Plasma and Aeronomic Research University of Alabama in Huntsville HuntsvilleAL35899 United States School of Physics Georgia Institute of Technology AtlantaGA30332 United States Concordia University Wisconsin MequonWI53097 United States Department of Physics Hayes Hall Kenyon College GambierOH43022 United States Department of Physics University of Michigan Ann ArborMI48109 United States

Matched-filtering gravitational wave search pipelines identify gravitational wave signals by computing correlations, i.e., signal-to-noise ratios, between gravitational wave detector data and gravitational wave template waveforms. Intrinsic parameters, the component masses and spins, of the gravitational wave waveforms are often stored in "template banks", and the construction of a densely populated template bank is essential for some gravitational wave search pipelines. This paper presents a template bank that is currently being used by the GstLAL-based compact binary search pipeline in the fourth observing run of the LIGO, Virgo, and KAGRA collaboration, and was generated with a new binary tree approach of placing templates, manifold. The template bank contains 1.8×106 sets of template parameters covering plausible neutron star and black hole systems up to a total mass of 400 Mo with component masses between 1-200 Mo and mass ratios between 1 and 20 under the assumption that each component object’s angular momentum is aligned with the orbital angular momentum. We validate the template bank generated with our new method, manifold, by comparing it with a template bank generated with the previously used stochastic template placement method. We show that both template banks have similar effectualness. The GstLAL search pipeline performs singular value decomposition (SVD) on the template banks to reduce the number of filters used. We describe a new grouping of waveforms that improves the computational efficiency of SVD by nearly 5 times as compared to previously reported SVD sorting schemes. Copyright © 2022, The Authors. All rights reserved.

关键词： Singular value decomposition

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