Search results for " code"

showing 10 items of 636 documents

Efficient generation of restricted growth words

2013

A length n restricted growth word is a word w=w"1w"2...w"n over the set of integers where w"1=0 and each w"i, i>1, lies between 0 and the value of a word statistics of the prefix w"1w"2...w"i"-"1 of w, plus one. Restricted growth words simultaneously generalize combinatorial objects as restricted growth functions, staircase words and ascent or binary sequences. Here we give a generic generating algorithm for restricted growth words. It produces a Gray code and runs in constant average time provided that the corresponding statistics has some local properties.

010102 general mathematicsBinary numberValue (computer science)0102 computer and information sciences[ MATH.MATH-CO ] Mathematics [math]/Combinatorics [math.CO]01 natural sciencesComputer Science ApplicationsTheoretical Computer SciencePrefixCombinatoricsGray code010201 computation theory & mathematics[MATH.MATH-CO]Mathematics [math]/Combinatorics [math.CO]Signal ProcessingPartial word0101 mathematicsConstant (mathematics)ComputingMilieux_MISCELLANEOUSWord (group theory)Information SystemsMathematicsInformation Processing Letters
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Restricted compositions and permutations: from old to new Gray codes

2011

Any Gray code for a set of combinatorial objects defines a total order relation on this set: x is less than y if and only if y occurs after x in the Gray code list. Let @? denote the order relation induced by the classical Gray code for the product set (the natural extension of the Binary Reflected Gray Code to k-ary tuples). The restriction of @? to the set of compositions and bounded compositions gives known Gray codes for those sets. Here we show that @? restricted to the set of bounded compositions of an interval yields still a Gray code. An n-composition of an interval is an n-tuple of integers whose sum lies between two integers; and the set of bounded n-compositions of an interval si…

0102 computer and information sciences02 engineering and technologyInterval (mathematics)[ MATH.MATH-CO ] Mathematics [math]/Combinatorics [math.CO]01 natural sciencesTheoretical Computer ScienceCombinatoricsGray codePermutationsymbols.namesakeInteger020204 information systems[MATH.MATH-CO]Mathematics [math]/Combinatorics [math.CO]0202 electrical engineering electronic engineering information engineeringComputingMilieux_MISCELLANEOUSMathematicsDiscrete mathematicsExtension (predicate logic)Composition (combinatorics)Cartesian productComputer Science Applications010201 computation theory & mathematicsComputer Science::Computer Vision and Pattern RecognitionBounded functionSignal ProcessingsymbolsInformation Systems
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THE GYROTRON STARTUP SCENARIO IN THE SINGLE MODE TIME DEPENDENT APPROACH

2019

The paper explains how to solve the Gyrotron equation system in the Single Mode Time Dependent Approach. In particular, we point out problems encountered when solving these well-known equations. The starting current estimation approach a using time model is suggested. The solution has been implemented in the Matlab code, which is attached to the article.

010302 applied physicsPhysicstime dependent approachgyrotronNuclear engineeringSingle-mode optical fiberMatlab code01 natural sciences010305 fluids & plasmaslaw.inventiondifferential equationlawModeling and SimulationGyrotron0103 physical sciencesQA1-939MathematicsAnalysisMathematical Modelling and Analysis
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Online Management of Hybrid DRAM-NVMM Memory for HPC

2019

Non-volatile main memories (NVMMs) offer a comparable performance to DRAM, while requiring lower static power consumption and enabling higher densities. NVMM therefore can provide opportunities for improving both energy efficiency and costs of main memory. Previous hybrid main memory management approaches for HPC either do not consider the unique characteristics of NVMMs, depend on high profiling costs, or need source code modifications. In this paper, we investigate HPC applications' behaviors in the presence of NVMM as part of the main memory. By performing a comprehensive study of HPC applications and based on several key observations, we propose an online hybrid memory architecture for …

010302 applied physicsProfiling (computer programming)Source codebusiness.industryComputer sciencemedia_common.quotation_subject02 engineering and technology01 natural sciences020202 computer hardware & architectureNon-volatile memoryMemory managementEmbedded system0103 physical sciencesMemory architecture0202 electrical engineering electronic engineering information engineeringKey (cryptography)businessDrammedia_common2019 IEEE 26th International Conference on High Performance Computing, Data, and Analytics (HiPC)
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Joint Gaussian processes for inverse modeling

2017

Solving inverse problems is central in geosciences and remote sensing. Very often a mechanistic physical model of the system exists that solves the forward problem. Inverting the implied radiative transfer model (RTM) equations numerically implies, however, challenging and computationally demanding problems. Statistical models tackle the inverse problem and predict the biophysical parameter of interest from radiance data, exploiting either in situ data or simulated data from an RTM. We introduce a novel nonlinear and nonparametric statistical inversion model which incorporates both real observations and RTM-simulated data. The proposed Joint Gaussian Process (JGP) provides a solid framework…

010504 meteorology & atmospheric sciencesComputer science0211 other engineering and technologiesNonparametric statisticsInverseInversion (meteorology)Statistical model02 engineering and technologyInverse problem01 natural sciencesData modelingNonlinear systemsymbols.namesakeAtmospheric radiative transfer codesRadiancesymbolsGaussian processAlgorithm021101 geological & geomatics engineering0105 earth and related environmental sciences
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Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: Evaluation over an agricultural area with …

2011

International audience; Neural networks trained over radiative transfer simulations constitute the basis of several operational algorithms to estimate canopy biophysical variables from satellite reflectance measurements. However, only little attention was paid to the training process which has a major impact on retrieval performances. This study focused on the several modalities of the training process within neural network estimation of LAI, FCOVER and FAPAR biophysical variables. Performances were evaluated over both actual experimental observations and model simulations. The SAIL and PROSPECT radiative transfer models were used here to simulate the training and the synthetic test dataset…

010504 meteorology & atmospheric sciencesComputer scienceGaussian0211 other engineering and technologiesSoil ScienceCANOPY BIOPHYSICAL CHARACTERISTICS02 engineering and technologyNEURAL NETWORK01 natural sciencesTransfer functionsymbols.namesakeAtmospheric radiative transfer codesRadiative transferRange (statistics)Sensitivity (control systems)Computers in Earth Sciences021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingArtificial neural networkGeologySigmoid functionRELATION SOL-PLANTE-ATMOSPHEREMODEL INVERSION[SDE]Environmental SciencessymbolsINDICE FOLIAIRE
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Gaussian Processes Retrieval of LAI from Sentinel-2 Top-of-Atmosphere Radiance Data

2020

Abstract Retrieval of vegetation properties from satellite and airborne optical data usually takes place after atmospheric correction, yet it is also possible to develop retrieval algorithms directly from top-of-atmosphere (TOA) radiance data. One of the key vegetation variables that can be retrieved from at-sensor TOA radiance data is leaf area index (LAI) if algorithms account for variability in atmosphere. We demonstrate the feasibility of LAI retrieval from Sentinel-2 (S2) TOA radiance data (L1C product) in a hybrid machine learning framework. To achieve this, the coupled leaf-canopy-atmosphere radiative transfer models PROSAIL-6SV were used to simulate a look-up table (LUT) of TOA radi…

010504 meteorology & atmospheric sciencesMean squared errorComputer science0211 other engineering and technologiesAtmospheric correctionFOS: Physical sciences02 engineering and technology15. Life on land01 natural sciencesAtomic and Molecular Physics and OpticsArticleComputer Science ApplicationsPhysics - Atmospheric and Oceanic PhysicsAtmospheric radiative transfer codesKrigingAtmospheric and Oceanic Physics (physics.ao-ph)RadianceSatelliteComputers in Earth SciencesLeaf area indexScale (map)Engineering (miscellaneous)021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensing
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Empirical and physical estimation of Canopy Water Content from CHRIS/PROBA data

2013

20 páginas, 4 tablas, 7 figuras.

010504 meteorology & atmospheric sciencesMean squared errorScience0211 other engineering and technologies02 engineering and technologyCHRIS/PROBA01 natural sciencescanopy water content;model inversion;neural networks;look up tables;empirical up-scalingmodel inversionEmpirical up-scalingAtmospheric radiative transfer codeslook up tablesRadiative transferModel inversion021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematicsRemote sensingArtificial neural networkCanopy water contentQHyperspectral imagingInversion (meteorology)Sigmoid functionSpectral bandsempirical up-scaling15. Life on landneural networks[SDE]Environmental SciencesGeneral Earth and Planetary SciencesLook up tablescanopy water contentNeural networkscanopy water content; model inversion; neural networks; look up tables; empirical up-scaling; CHRIS/PROBA
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Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

2017

This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Res…

010504 meteorology & atmospheric sciencesMean squared errorScienceleaf area index (LAI)0211 other engineering and technologies02 engineering and technology01 natural sciencesCropAtmospheric radiative transfer codesConsistency (statistics)KrigingSpatial consistencyArròs Malalties i plaguesSentinel-1ALeaf area indexmappingSentinel021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensing2. Zero hungerLeaf Area IndexSentinel-2AQCiències de la terrarice mapGeneral Earth and Planetary SciencesEnvironmental sciencerice map; leaf area index (LAI); Sentinel-1A; Sentinel-2A; Gaussian process regressionRice cropGaussian process regressionRemote Sensing
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Public discussion on a proposed revision of the International Code of Nomenclature of Prokaryotes

2021

The Editorial Board for the International Code of Nomenclature of Prokaryotes (ICNP) has compiled proposed revisions of the ICNP. As outlined previously (Oren et al., Int J Syst Evol Microbiol 2021;71:004598; https://doi.org/10.1099/ijsem.0.004598) and to comply with Articles 13(b)(4) and 4(d) of the statutes of the International Committee on Systematics of Prokaryotes, a public discussion of the document will start on 1 July 2021, to last for 6 months. Here, we present the procedure for this discussion.

0106 biological sciences0301 basic medicineEcology (disciplines)C100Library scienceInternational Committee on Systematics of ProkaryotesGeneral MedicineEditorial boardC500BiologyInternational Code of Nomenclature of ProkaryotesClassification010603 evolutionary biology01 natural sciencesMicrobiologyProkaryotic CodeInternational codeStatute03 medical and health sciences030104 developmental biologyPublic discussionProkaryotic CellsTerminology as TopicNomenclatureEcology Evolution Behavior and Systematics
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