Quantitative remote sensing retrieval algorithms help understanding the dynamic aspects of Digital ***,the Big Data and complex models in Digital Earth pose grand challenges for computation *** this article,taking the...
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Quantitative remote sensing retrieval algorithms help understanding the dynamic aspects of Digital ***,the Big Data and complex models in Digital Earth pose grand challenges for computation *** this article,taking the aerosol optical depth(AOD)retrieval as a study case,we exploit parallel computing methods for high efficient geophysical parameter *** present an efficient geocomputation workflow for the AOD calculation from the Moderate Resolution Imaging Spectroradiometer(MODIS)satellite *** to their individual potential for parallelization,several procedures were adapted and implemented for a successful parallel execution on multicore processors and Graphics Processing Units(GPUs).The benchmarks in this paper validate the high parallel performance of the retrieval workflow with speedups of up to 5.x on a multi-core processor with 8 threads and 43.x on a *** specifically address the time-consuming model retrieval part,hybrid parallel patterns which combine the multicore processor’s and the GPU’s compute power were implemented with static and dynamic workload distributions and evaluated on two systems with different CPU–GPU *** is shown that only the dynamic hybrid implementation leads to a greatly enhanced overall exploitation of the heterogeneous hardware environment in varying circumstances.
Alignments of frequency profiles against frequency profiles have a wide scope of applications in currently used bioinformatic analysis tools ranging from multiple alignment methods based on the progressive alignment a...
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In this paper the numerical and parallel efficiency of the adaptive parallel strategy employed in a multigrid-solver package LiSS, developed in GMD-scai, are discussed. Two main aspects in these issues are adaptive cr...
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In this paper we treat the cell-centred multigrid approach, which distinguishes itself from the classical vertex-centred multigrid by a non-nested hierarchy of grid nodes and the use of constant, problem-independent t...
In this paper we treat the cell-centred multigrid approach, which distinguishes itself from the classical vertex-centred multigrid by a non-nested hierarchy of grid nodes and the use of constant, problem-independent transfer operators even in complicated situations. We demonstrate, that the tool of Local Fourier Analysis can also be profitably applied in this setting. We consider in detail the standard transfer operators from literature and their respective polynomial and Fourier orders, paying special attention to the combination of piecewise constant interpolation and its adjoint. Furthermore, we give several numerical examples for model problems and an application from biomedical engineering.
Knowledge graphs have been shown to play an important role in recent knowledge mining and discovery, for example in the field of life sciences or bioinformatics. Although a lot of research has been done on the field o...
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Contextual information is widely considered for NLP and knowledge discovery in life sciences since it highly influences the exact meaning of natural language. The scientific challenge is not only to extract such conte...
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Access to relevant information and knowledge is essential for all steps of the drug discovery process. However, keeping track of relevant information in publications and patents becomes a real challenge for scientists...
Alkaline methanol oxidation is an electrochemical process, perspective for the design of efficient high energy density fuel cells. The process involves a large number of elementary reactions, forming a complex reactio...
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This book broadly covers the given spectrum of disciplines in Computational Life Sciences, transforming it into a strong helping hand for teachers, students, practitioners and researchers. In Life Sciences, problem-so...
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ISBN:
(数字)9783031084119
ISBN:
(纸本)9783031084102;9783031084133
This book broadly covers the given spectrum of disciplines in Computational Life Sciences, transforming it into a strong helping hand for teachers, students, practitioners and researchers. In Life Sciences, problem-solving and data analysis often depend on biological expertise combined with technical skills in order to generate, manage and efficiently analyse big data. These technical skills can easily be enhanced by good theoretical foundations, developed from well-chosen practical examples and inspiring new strategies. This is the innovative approach of Computational Life Sciences-Data Engineering and Data Mining for Life Sciences: We present basic concepts, advanced topics and emerging technologies, introduce algorithm design and programming principles, address data mining and knowledge discovery as well as applications arising from real projects. Chapters are largely independent and often flanked by illustrative examples and practical advise.
Generalized Method of Moments (GMM) estimators in their various forms, including the popular Maximum Likelihood (ML) estimator, are frequently applied for the evaluation of complex econometric models with not analytic...
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