| 1. |
- Duncanson, Laura, et al.
(author)
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Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission
- 2022
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In: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 270
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Journal article (peer-reviewed)abstract
- NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.
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| 2. |
- Chen, C. C., et al.
(author)
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Drift-preserving numerical integrators for stochastic Hamiltonian systems
- 2020
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In: Advances in Computational Mathematics. - : Springer Science and Business Media LLC. - 1019-7168 .- 1572-9044. ; 46:2
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Journal article (peer-reviewed)abstract
- The paper deals with numerical discretizations of separable nonlinear Hamiltonian systems with additive noise. For such problems, the expected value of the total energy, along the exact solution, drifts linearly with time. We present and analyze a time integrator having the same property for all times. Furthermore, strong and weak convergence of the numerical scheme along with efficient multilevel Monte Carlo estimators are studied. Finally, extensive numerical experiments illustrate the performance of the proposed numerical scheme.
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| 3. |
- Cohen, David, et al.
(author)
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Drift-preserving numerical integrators for stochastic Poisson systems
- 2021
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In: International Journal of Computer Mathematics. - : Informa UK Limited. - 0020-7160 .- 1029-0265. ; 99:1
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Journal article (peer-reviewed)abstract
- We perform a numerical analysis of a class of randomly perturbed Hamiltonian systems and Poisson systems. For the considered additive noise perturbation of such systems, we show the long-time behaviour of the energy and quadratic Casimirs for the exact solution. We then propose and analyse a drift-preserving splitting scheme for such problems with the following properties: exact drift preservation of energy and quadratic Casimirs, mean-square order of convergence 1, weak order of convergence 2. These properties are illustrated with numerical experiments.
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| 4. |
- Fireaizen, Tomer, et al.
(author)
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Intelligent Reflecting Surface OFDM Communication with Deep Neural Prior
- 2022
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In: IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538683477 - 9781538683484 ; , s. 2645-2650
-
Conference paper (peer-reviewed)abstract
- An Intelligent Reflecting Surface (IRS) is an emerging technology for improving the data rate over wireless channels by controlling the underlying channel. In this paper, we describe a novel solution for IRS configuration to maximize the data rate over wideband channels. The optimization is obtained by online training of a deep generative neural network. Inspired by related works in image processing, this network is randomly initialized and acts as a regularization term for the optimization process since the structure of the generator is sufficient to capture a great deal of IRS statistics prior to any learning. In contrast to recent deep learning techniques for IRS configuration, the proposed technique does not require an offline training stage and can adapt quickly to any environment. Compared to the previous state-of-the-art algorithm, the proposed method is significantly faster and obtains IRS configurations that achieve higher data transmission rates.
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| 5. |
- Gorgolewski, Krzysztof J., et al.
(author)
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BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods
- 2017
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In: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 13:3
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Journal article (peer-reviewed)abstract
- The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.
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| 6. |
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| 7. |
- Baer, Donald R., et al.
(author)
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XPS guide: Charge neutralization and binding energy referencing for insulating samples
- 2020
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In: Journal of Vacuum Science & Technology. A. Vacuum, Surfaces, and Films. - : A V S AMER INST PHYSICS. - 0734-2101 .- 1520-8559. ; 38:3
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Journal article (peer-reviewed)abstract
- This guide deals with methods to control surface charging during XPS analysis of insulating samples and approaches to extracting useful binding energy information. The guide summarizes the causes of surface charging, how to recognize when it occurs, approaches to minimize charge buildup, and methods used to adjust or correct XPS photoelectron binding energies when charge control systems are used. There are multiple ways to control surface charge buildup during XPS measurements, and examples of systems on advanced XPS instruments are described. There is no single, simple, and foolproof way to extract binding energies on insulating material, but advantages and limitations of several approaches are described. Because of the variety of approaches and limitations of each, it is critical for researchers to accurately describe the procedures that have been applied in research reports and publications.
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| 8. |
- Berg, André, et al.
(author)
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APPROXIMATED EXPONENTIAL INTEGRATORS FOR THE STOCHASTIC MANAKOV EQUATION
- 2023
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In: Journal of Computational Dynamics. - : American Institute of Mathematical Sciences (AIMS). - 2158-2491 .- 2158-2505. ; 10:2, s. 323-344
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Journal article (peer-reviewed)abstract
- . This article presents and analyzes an approximated exponential integrator for the (inhomogeneous) stochastic Manakov system. This system of SPDE occurs in the study of pulse propagation in randomly birefringent optical fibers. For a globally Lipschitz-continuous nonlinearity, we prove that the strong order of the time integrator is 1/2. This is then used to prove that the approximated exponential integrator has convergence order 1/2 in probability and almost sure order 1/2-, in the case of the cubic nonlinear coupling which is relevant in optical fibers. Finally, we present several numerical experiments in order to support our theoretical findings and to illustrate the efficiency of the approximated exponential integrator as well as a modified version of it.
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| 9. |
- Berg, André, et al.
(author)
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Lie-Trotter Splitting for the Nonlinear Stochastic Manakov System
- 2021
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In: Journal of Scientific Computing. - : Springer Science and Business Media LLC. - 0885-7474 .- 1573-7691. ; 88:1
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Journal article (peer-reviewed)abstract
- This article analyses the convergence of the Lie-Trotter splitting scheme for the stochastic Manakov equation, a system arising in the study of pulse propagation in randomly birefringent optical fibers. First, we prove that the strong order of the numerical approximation is 1/2 if the nonlinear term in the system is globally Lipschitz. Then, we show that the splitting scheme has convergence order 1/2 in probability and almost sure order 1/2- in the case of a cubic nonlinearity. We provide several numerical experiments illustrating the aforementioned results and the efficiency of the Lie-Trotter splitting scheme. Finally, we numerically investigate the possible blowup of solutions for some power-law nonlinearities.
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| 10. |
- Brehier, C. E., et al.
(author)
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Analysis of a splitting scheme for a class of nonlinear stochastic Schrodinger equations
- 2023
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In: Applied Numerical Mathematics. - : Elsevier BV. - 0168-9274. ; 186, s. 57-83
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Journal article (peer-reviewed)abstract
- We analyze the qualitative properties and the order of convergence of a splitting scheme for a class of nonlinear stochastic Schrodinger equations driven by additive noise. The class of nonlinearities of interest includes nonlocal interaction cubic nonlinearities. We show that the numerical solution is symplectic and preserves the expected mass for all times (trace formula). On top of that, for the convergence analysis, some exponential moment bounds for the exact and numerical solutions are proved. This enables us to provide strong orders of convergence as well as orders of convergence in probability and almost surely. Finally, extensive numerical experiments illustrate the performance of the proposed numerical scheme. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of IMACS. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/).
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