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To assimilate satellite-based passive microwave observation over heavy clouds and precipitation into numerical weather prediction (NWP) systems and thus to improve the performance of it has become an intensely studied topic. These attempts rely on the development of a radiative transfer (RT) model that accounts for particle scattering and accurately simulates the observation process at an acceptable...
Spark has grown both in popularity and complexity in recent years. In order to use available resources in an efficient way, users need to understand how the behavior of their applications is affected by the size of the datasets and various configuration settings. Indeed, Spark allows users to specify many configuration parameters and understanding the impact of these choices with respect to the application...
With the rapid growth of data in various domains, models for data processing become more and more complicated and require higher and higher computing power of local machines. In our opinion, it is a good solution to put models into the "cloud". Integrating and categorizing these models in different domains is convenient for users. They don't have to establish similar systems for different...
Higher-order Singular Value Decomposition (HOSVD) for tensor decomposition is widely used in multi-variate data analysis, and has shown applications in several areas in computer vision in the last decade. Conventional multi-linear assumption in HOSVD is not translation invariant — translation in different tensor modes can yield different decomposition results. The translation is difficult to remove...
When auditing the large enterprise groups with many accounting subjects, in order to find. the doubtful auditing points quickly in the mass electronic data, we designed and developed a accounting report procedure for single subject according to the logic of balance sheet with the parallel simulation; adopted the associative rules to mine the audit features of electronic data, and combined with the...
The linear regression model based on the ordinary least square (OLS) estimation is a commonly used method for crop yield predicting. But it is not adequate in many cases because spatial autocorrelation among variables may violate the underlying assumption that observations are independent. In this study, we compared the OLS regression model and the spatial autoregressive model for predicting corn...
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