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arXiv 2024年

作者： Agazie, Gabriella Anumarlapudi, Akash Archibald, Anne M. Arzoumanian, Zaven Baier, Jeremy G. Baker, Paul T. Bécsy, Bence Blecha, Laura Brazier, Adam Brook, Paul R. Burke-Spolaor, Sarah Casey-Clyde, J. Andrew Charisi, Maria Chatterjee, Shami Cohen, Tyler Cordes, James M. Cornish, Neil J. Crawford, Fronefield Cromartie, H. Thankful Crowter, Kathryn DeCesar, Megan E. Demorest, Paul B. Deng, Heling Dey, Lankeswar Dolch, Timothy Ferrara, Elizabeth C. Fiore, William Fonseca, Emmanuel Freedman, Gabriel E. Gardiner, Emiko C. Garver-Daniels, Nate Gentile, Peter A. Gersbach, Kyle A. Glaser, Joseph Good, Deborah C. Guertin, Lydia Gültekin, Kayhan Hazboun, Jeffrey S. Jennings, Ross J. Johnson, Aaron D. Jones, Megan L. Kaiser, Andrew R. Kaplan, David L. Kelley, Luke Zoltan Kerr, Matthew Key, Joey S. Laal, Nima Lam, Michael T. Lamb, William G. Larsen, Bjorn Lazio, T.W. Joseph Lewandowska, Natalia Liu, Tingting Lorimer, Duncan R. Luo, Jing Lynch, Ryan S. Ma, Chung-Pei Madison, Dustin R. McEwen, Alexander McKee, James W. McLaughlin, Maura A. McMann, Natasha Meyers, Bradley W. Meyers, Patrick M. Middleton, Hannah Mingarelli, Chiara M.F. Mitridate, Andrea Moore, Christopher J. Ng, Cherry Nice, David J. Ocker, Stella Koch Olum, Ken D. Pennucci, Timothy T. Perera, Benetge B.P. Pol, Nihan S. Radovan, Henri A. Ransom, Scott M. Ray, Paul S. Romano, Joseph D. Runnoe, Jessie C. Saffer, Alexander Sardesai, Shashwat C. Schmiedekamp, Ann Schmiedekamp, Carl Schmitz, Kai Shapiro-Albert, Brent J. Siemens, Xavier Simon, Joseph Siwek, Magdalena S. Sosa Fiscella, Sophia V. Stairs, Ingrid H. Stinebring, Daniel R. Stovall, Kevin Susobhanan, Abhimanyu Swiggum, Joseph K. Taylor, Stephen R. Turner, Jacob E. Unal, Caner Vallisneri, Michele Vecchio, Alberto Vigeland, Sarah J. Wahl, Haley M. Witt, Caitlin A. Wright, David Young, Olivia Center for Gravitation Cosmology and Astrophysics Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 MilwaukeeWI53201 United States Newcastle University NE1 7RU United Kingdom X-Ray Astrophysics Laboratory NASA Goddard Space Flight Center Code 662 GreenbeltMD20771 United States Department of Physics Oregon State University CorvallisOR97331 United States Department of Physics and Astronomy Widener University One University Place ChesterPA19013 United States Physics Department University of Florida GainesvilleFL32611 United States Cornell Center for Astrophysics and Planetary Science Department of Astronomy Cornell University IthacaNY14853 United States Cornell Center for Advanced Computing Cornell University IthacaNY14853 United States Institute for Gravitational Wave Astronomy School of Physics and Astronomy University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States Department of Physics University of Connecticut 196 Auditorium Road U-3046 StorrsCT06269-3046 United States Department of Physics and Astronomy Vanderbilt University 2301 Vanderbilt Place NashvilleTN37235 United States Department of Physics New Mexico Institute of Mining and Technology 801 Leroy Place SocorroNM87801 United States Department of Physics Montana State University BozemanMT59717 United States Department of Physics and Astronomy Franklin & Marshall College P.O. Box 3003 LancasterPA17604 United States National Research Council Research Associate National Academy of Sciences WashingtonDC20001 United States Naval Research Laboratory WashingtonDC20375 United States Department of Physics and Astronomy University of British Columbia 6224 Agricultural Road VancouverBCV6T 1Z1 Cana

Evidence has emerged for a stochastic signal correlated among 67 pulsars within the 15-year pulsar-timing data set compiled by the NANOGrav collaboration. Similar signals have been found in data from the European, Indian, Parkes, and Chinese PTAs. This signal has been interpreted as indicative of the presence of a nanohertz stochastic gravitational wave background. To explore the internal consistency of this result we investigate how the recovered signal strength changes as we remove the pulsars one by one from the data set. We calculate the signal strength using the (noise-marginalized) optimal statistic, a frequentist metric designed to measure correlated excess power in the residuals of the arrival times of the radio pulses. We identify several features emerging from this analysis that were initially unexpected. The significance of these features, however, can only be assessed by comparing the real data to synthetic data sets. After conducting identical analyses on simulated data sets, we do not find anything inconsistent with the presence of a stochastic gravitational wave background in the NANOGrav 15-year data. The methodologies developed here can offer additional tools for application to future, more sensitive data sets. While this analysis provides an internal consistency check of the NANOGrav results, it does not eliminate the necessity for additional investigations that could identify potential systematics or uncover unmodeled physical phenomena in the data. © 2024, CC BY.

关键词： Gravitational effects

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Building the next generation of climate modelers: Scale-aware physics parameterization and the "grey zone" challenge

Building the next generation of climate modelers: Scale-awar...

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作者： Frassoni, Ariane Castilho, Dayana Rixen, Michel Ramirez, Enver De Mattos, João Gerd Z. Kubota, Paulo Calheiros, Alan James Peixoto Reed, Kevin A. Da Silva Dias, Maria Assunção F. Da Silva Dias, Pedro L. De Campos Velho, Haroldo Fraga De Roode, Stephan R. Doblas-Reyes, Francisco Eiras, Denis Ek, Michael Figueroa, Silvio N. Forbes, Richard Freitas, Saulo R. Grell, Georg A. Herdies, Dirceu L. Lauritzen, Peter H. Machado, Luiz Augusto T. Manzi, Antonio O. Martins, Guilherme Oliveira, Gilvan S. Rosário, Nilton E. Sales, Domingo C. Wedi, Nils Yamada, Bárbara Center for Weather Forecasting and Climate Studies Brazilian National Institute for Space Research Cachoeira Paulista Sao Paulo Brazil World Climate Research Programme World Meteorological Organization Geneva Switzerland Associate Laboratory for Computing and Applied Mathematics Brazilian National Institute for Space Research Sao Jose dos Campos Sao Paulo Brazil Stony Brook University State University of New York Stony BrookNY United States Institute of Astronomy Geophysics and Atmospheric Sciences University of Sao Paulo Sao Paulo Sao Paulo Brazil Delft University of Technology Delft Netherlands ICREA Barcelona Supercomputing Center Barcelona Spain National Center for Atmospheric Research BoulderCO United States European Centre for Medium-Range Weather Forecasts Reading United Kingdom USRA/GESTAR/Global Modeling and Assimilation Office National Aeronautics and Space Administration Goddard Space Flight Center GreenbeltMD United States NOAA Earth Systems Research Laboratory BoulderCO United States Federal University of Sao Paulo Diadema Sao Paulo Brazil

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Synchronization in networks with strongly delayed couplings

arXiv

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arXiv 2017年

作者： Maia, Daniel M. N. Macau, Elbert E.N. Pereira, Tiago Yanchuk, Serhiy Associate Laboratory of Applied Computing and Mathematics - LAC National Institute for Space Research - INPE Av. dos Astronautas São José dos Campos-SP12227-010 Brazil Institute of Mathematics and Computer Sciences - ICMC University of São Paulo - USP Av. do Trabalhador São-Carlense 400 São Carlos-SP13566-590 Brazil Institute of Mathematics Technical University of Berlin Strasse des 17. Juni 136 Berlin10623 Germany

We investigate the stability of synchronization in networks of dynamical systems with strongly delayed connections. We obtain strict conditions for synchronization of periodic and equilibrium solutions. In particular, we show the existence of a critical coupling strength κc, depending only on the network structure, isolated dynamics and coupling function, such that for large delay and coupling strength κ c, the network possesses stable synchronization. The critical coupling κccan be chosen independently of the delay for the case of equilibria, while for the periodic solution, κcdepends essentially on the delay and vanishes as the delay increases. We observe that, for random networks, the synchronization interval is maximal when the network is close to the connectivity threshold. We also derive scaling of the coupling parameter that allows for a synchronization of large networks for different network topologies. Copyright © 2017, The Authors. All rights reserved.

关键词： Complex networks

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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Recent developments in the PySCF program package

arXiv

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arXiv 2020年

作者： Sun, Qiming Zhang, Xing Banerjee, Samragni Bao, Peng Barbry, Marc Blunt, Nick S. Bogdanov, Nikolay A. Booth, George H. Chen, Jia Cui, Zhi-Hao Eriksen, Janus Juul Gao, Yang Guo, Sheng Hermann, Jan Hermes, Matthew R. Koh, Kevin Koval, Peter Lehtola, Susi Li, Zhendong Liu, Junzi Mardirossian, Narbe McClain, James D. Motta, Mario Mussard, Bastien Pham, Hung Q. Pulkin, Artem Purwanto, Wirawan Robinson, Paul J. Ronca, Enrico Sayfutyarova, Elvira Scheurer, Maximilian Schurkus, Henry F. Smith, James E.T. Sun, Chong Sun, Shi-Ning Upadhyay, Shiv Wagner, Lucas K. Wang, Xiao White, Alec Whitfield, James Daniel Williamson, Mark J. Wouters, Sebastian Yang, Jun Yu, Jason M. Zhu, Tianyu Berkelbach, Timothy C. Sharma, Sandeep Sokolov, Alexander Yu. Chan, Garnet Kin-Lic AxiomQuant Investment Management LLC Shanghai200120 China Division of Chemistry and Chemical Engineering California Institute of Technology PasadenaCA91125 United States Department of Chemistry and Biochemistry The Ohio State University ColumbusOH43210 United States Beijing National Laboratory for Molecular Sciences State Key Laboratory for Structural Chemistry of Unstable and Stable Species Institute of Chemistry Chinese Academy of Sciences Beijing100190 China Simbeyond B.V. P.O. Box 513 EindhovenNL-5600 MB Netherlands Department of Chemistry Lensfield Road CambridgeCB2 1EW United Kingdom Max Planck Institute for Solid State Research Heisenbergstraße 1 Stuttgart70569 Germany Department of Physics King's College London Strand LondonWC2R 2LS United Kingdom Department of Physics University of Florida GainesvilleFL32611 United States Quantum Theory Project University of Florida GainesvilleFL32611 United States School of Chemistry University of Bristol Cantock's Close BristolBS8 1TS United Kingdom Division of Engineering and Applied Science California Institute of Technology PasadenaCA91125 United States Google Inc. Mountain ViewCA94043 United States FU Berlin Department of Mathematics and Computer Science Arnimallee 6 Berlin14195 Germany TU Berlin Machine Learning Group Marchstr. 23 Berlin10587 Germany Department of Chemistry Chemical Theory Center Supercomputing Institute University of Minnesota 207 Pleasant Street SE MinneapolisMN55455 United States Department of Chemistry and Biochemistry The University of Notre Dame du Lac 251 Nieuwland Science Hall Notre DameIN46556 United States Simune Atomistics S.L. Avenida Tolosa 76 Donostia-San Sebastian Spain HelsinkiFI-00014 Finland Key Laboratory of Theoretical and Computational Photochemistry Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Chemistry The Johns Hopkins University BaltimoreMD21218 United States AMGEN

PYSCF is a Python-based general-purpose electronic structure platform that both supports first-principles simulations of molecules and solids, as well as accelerates the development of new methodology and complex computational workflows. The present paper explains the design and philosophy behind PYSCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PYSCF as a development environment. We then summarize the capabilities of PYSCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PYSCF across the domains of quantum chemistry, materials science, machine learning and quantum information science. Copyright © 2020, The Authors. All rights reserved.

关键词： Python

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Synchronization versus neighborhood similarity in complex networks of nonidentical oscillators

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Physical Review E 2015年第3期92卷 032901-032901页

作者： Celso Freitas Elbert Macau Ricardo Luiz Viana []Associate Laboratory for Computing and Applied Mathematics—LAC National Institute for Space Research—INPE 12245-970 S?o José dos Campos SP Brazil

Does the assignment order of a fixed collection of slightly distinct subsystems into given communication channels influence the overall ensemble behavior? We discuss this question in the context of complex networks of nonidentical interacting oscillators. Three types of connection configurations are considered: Similar, Dissimilar, and Neutral patterns. These different groups correspond, respectively, to oscillators alike, distinct, and indifferent relative to their neighbors. To construct such scenarios we define a vertex-weighted graph measure, the total dissonance, which comprises the sum of the dissonances between all neighbor oscillators in the network. Our numerical simulations show that the more homogeneous a network, the higher tend to be both the coupling strength required for phase locking and the associated final phase configuration spread over the circle. On the other hand, the initial spread of partial synchronization occurs faster for Similar patterns in comparison to Dissimilar ones, while neutral patterns are an intermediate situation between both extremes.

关键词： SYNCHRONIZATION NONLINEAR oscillators GRAPH theory COMPUTER simulation MATHEMATICAL optimization

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Correction: White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group

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Molecular psychiatry 2024年第4期29卷 1216页

作者： Bo-Gyeom Kim Gakyung Kim Yoshinari Abe Pino Alonso Stephanie Ameis Alan Anticevic Paul D Arnold Srinivas Balachander Nerisa Banaj Nuria Bargalló Marcelo C Batistuzzo Francesco Benedetti Sara Bertolín Jan Carl Beucke Irene Bollettini Silvia Brem Brian P Brennan Jan K Buitelaar Rosa Calvo Miguel Castelo-Branco Yuqi Cheng Ritu Bhusal Chhatkuli Valentina Ciullo Ana Coelho Beatriz Couto Sara Dallaspezia Benjamin A Ely Sónia Ferreira Martine Fontaine Jean-Paul Fouche Rachael Grazioplene Patricia Gruner Kristen Hagen Bjarne Hansen Gregory L Hanna Yoshiyuki Hirano Marcelo Q Höxter Morgan Hough Hao Hu Chaim Huyser Toshikazu Ikuta Neda Jahanshad Anthony James Fern Jaspers-Fayer Selina Kasprzak Norbert Kathmann Christian Kaufmann Minah Kim Kathrin Koch Gerd Kvale Jun Soo Kwon Luisa Lazaro Junhee Lee Christine Lochner Jin Lu Daniela Rodriguez Manrique Ignacio Martínez-Zalacaín Yoshitada Masuda Koji Matsumoto Maria Paula Maziero Jose M Menchón Luciano Minuzzi Pedro Silva Moreira Pedro Morgado Janardhanan C Narayanaswamy Jin Narumoto Ana E Ortiz Junko Ota Jose C Pariente Chris Perriello Maria Picó-Pérez Christopher Pittenger Sara Poletti Eva Real Y C Janardhan Reddy Daan van Rooij Yuki Sakai João Ricardo Sato Cinto Segalas Roseli G Shavitt Zonglin Shen Eiji Shimizu Venkataram Shivakumar Noam Soreni Carles Soriano-Mas Nuno Sousa Mafalda Machado Sousa Gianfranco Spalletta Emily R Stern S Evelyn Stewart Philip R Szeszko Rajat Thomas Sophia I Thomopoulos Daniela Vecchio Ganesan Venkatasubramanian Chris Vriend Susanne Walitza Zhen Wang Anri Watanabe Lidewij Wolters Jian Xu Kei Yamada Je-Yeon Yun Mojtaba Zarei Qing Zhao Xi Zhu Paul M Thompson Willem B Bruin Guido A van Wingen Federica Piras Fabrizio Piras Dan J Stein Odile A van den Heuvel Helen Blair Simpson Rachel Marsh Jiook Cha Department of Psychology College of Social Sciences Seoul National University Seoul Republic of Korea. Department of Brain and Cognitive Sciences College of Natural Sciences Seoul National University Seoul Republic of Korea. Graduate School of Medical Science Kyoto Prefectural University of Medicine Department of Psychiatry Kyoto City Japan. Bellvitge Biomedical Research Insitute-IDIBELL Bellvitge University Hospital Barcelona Spain. CIBER of Mental Health (CIBERSAM) Carlos III Health Institute Madrid Spain. Department of Clinical Sciences University of Barcelona Barcelona Spain. The Margaret and Wallace McCain Centre for Child Youth & Family Mental Health and Campbell Family Mental Health Research Institute Centre for Addiction and Mental Health Toronto ON Canada. Department of Psychiatry University of Toronto Toronto ON Canada. Program in Neurosciences and Mental Health The Hospital for Sick Children Toronto ON Canada. Department of Psychiatry Yale University School of Medicine New Haven CT 06510 USA. The Mathison Centre for Mental Health Research & Education Hotchkiss Brain Institute Cumming School of Medicine University of Calgary Calgary AB Canada. Departments of Psychiatry and Medical Genetics Cumming School of Medicine University of Calgary Calgary AB Canada. OCD clinic Department of Psychiatry National Institute of Mental Health And Neurosciences (NIMHANS) Bangalore India. Laboratory of Neuropsychiatry Department of Clinical and Neuroscience and Neurorehabilitation IRCCS Santa Lucia Foundation Rome Italy. Center of Image Diagnostic Hospital Clínic de Barcelona Barcelona Spain. Magnetic Resonance Image Core Facility Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) Barcelona Spain. Departamento e Instituto de Psiquiatria do Hospital das Clinicas IPQ HCFMUSP Faculdade de Medicina Universidade de Sao Paulo Sao Paulo SP Brazil. Department of Methods and Techniques in Psychology Pontifical Catholic University São Paulo SP

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On Proposing a Test Oracle Generator Based on Static and Dynamic Source Code Analysis

On Proposing a Test Oracle Generator Based on Static and Dyn...

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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Alessandro Oliveira Arantes Valdivino Alexandre de Santiago Nandamudi Lankalapalli Vijaykumar Aerospace Technology and Science Department (DCTA) Institute for Advanced Space Studies (IEAv) São Paulo Brazil Instituto Nacional de Pesquisas Espaciais Sao Jose dos Campos SP BR National Institute for Space Research (INPE) Laboratory of Computing and Applied Mathematics (LAC) São Paulo Brazil

Test oracles have become a welcoming approach as a very efficient support for testing any type of application. Since oracle's generation can be a very laborious work, several techniques have been studied in order to perform an automatic or semi-automatic generation. However, the construction of an automated test oracle is not a trivial task, especially in terms of the inference of verdicts and detecting defects. Therefore, this research proposes a strategy to construct an automated test oracle based only on a model generated from reverse engineering, and to detect defects by a combination of static and dynamic source code analysis with a minimum manual labor from testers. We have applied our methodology to some interesting case studies and compared with other tools in order to show usefulness of our approach.

关键词： Inductors Unified modeling language Input variables Reverse engineering Standards Programming Software

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Modelling and performance evaluation of wireless networks

Modelling and performance evaluation of wireless networks

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11th International Conference on Telecommunications, ICT 2004

作者： Carvalho, G. Rodrigues, R.M. FrancÊs, C. Costa, J. Carvalho, S.V. Electrical and Computing Engineering Department Universidade Federal do Pará-UFPA BelémPA Brazil Laboratory of Computing and Applied Mathematics-LAC Instituto Nacional de Pesquisas Espaciais-INPE São José dos CamposSP Brazil

ISBN: (纸本)3540225714

Integration between voice services and data services brought about new concepts in modelling and performance evaluation of wireless networks. In this paper it is proposed a new approach to deal with that putting together a formal method called Statecharts and a Continuous Time Markov Chain (CTMC)1. From the specification of the system made in Statecharts it is automatically generated a CTMC, and then evaluated the performance measures. The advantages of this technique are better representation of the behavior of the system without ambiguity and with consistency. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Wireless networks

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A Committee of MLP with Adaptive Slope Parameter Trained by the quasi-Newton Method to Solve Problems in Hydrologic Optics

A Committee of MLP with Adaptive Slope Parameter Trained by ...

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International Joint Conference on Neural Networks

作者： Fabio Dall Cortivo Ezzat S. Chalhoub Haroldo F. Campos Velho Applied Computing Graduate Program (CAP) National Institute for Space Research (INPE) Sao Jose dos Campos Associated Laboratory for Computing and Applied Mathematics (LAC) National Institute for Space Research (INPE)

ISBN: (纸本)9781467314886

Artificial Neural Networks (ANNs) can be used to solve problems in Hydrologic Optics. A relevant problem is the estimation of the single scattering albedo and the phase function parameters, from the emitted radiation at the surface of natural waters. In this work we use a committee of ANNs of Multilayer Perceptron type to perform the estimation of the two mentioned parameters. The training of each network is formulated as a nonlinear optimization problem subject to constraints. In addition, each activation function has a distinct slope parameter, that is initially chosen by a random number generator function. This set of parameter (slopes) was included within the free variables network set in order to be adjusted to reach "optimal values", together with the weights and biases, during the network training. This procedure (slope parameters inclusion) makes each one of the activation functions to have a different slope. Each network that composes the committee was trained independently, in order to become expert for the estimation of only one of the hydrologic parameters. For the networks training, we used the quasi-Newton method that is implemented in E04UCF subroutine, in the NAG library, developed by the Numerical Algorithms Group - NAG. The use of the quasi-Newton method to train the networks together with the distinct slope parameters resulted in a network with a fast learning and excellent generalization. Once the networks were trained, they were grouped so to share the input patterns, but remained independent from one another. For the validation/generalization test we used two distinct sets. For all considered noise levels, we obtained 100% of correct answers for the first set, and above 90% of correct answers for the second set.

关键词： Artificial Neural Network Multilayer Perceptron Backpropagation Quasi-Newton Method Hydrologic Optics Phase Function Single Scattering Albedo Inverse Problems SLOPE FACTOR Newton method Slope parameter Artificial neural networks CSRP3 gene Network Optics hydrologic parameters Hydrology functional parameter Random Number Generators Activating function input mode

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