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Among the rarest is a 1916 Modigliani portrait, "Almaïsa," depicting one of his favorite models, with large almond-shaped eyes and an elongated nose that recall the facial distortions of an African mask.
The shift toward bigger cars and S.U.V.'s, despite punitive taxes of as much as 40 percent on models with large engine displacements (more than 4 liters) is prompting concern in Beijing.
Thus, there is a recognizable benefit to using dimension reduction techniques when building regression-type models with large survey datasets.
Creating such mixed models will be crucial for developing forecast models with large enough probability gains to have societal impacts.
Key practical aspects of training these models with large data sets are discussed, along with the role of GPU computing.
We introduce a new method for screening inputs in mathematical or computational models with large numbers of inputs.
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The northern gradient approach provides an efficient means to detect coseismic signals and potentially constrain fault slip models with large-scale gravitational changes using limited time span of monthly GRACE solutions.
So far, GRACE observations have been applied to constrain fault slip models with large-scale gravity changes, but mostly from the direct use of level 1B (range and range-rate) data (e.g., Han et al. 2010, 2011, 2013).
This gradient approach particularly applies for the detection of coseismic signals with limited time span (e.g., a few months after the earthquake) of monthly GRACE solutions and potentially provides a means to constrain fault slip models with large-scale gravitational changes from GRACE observations.
It is further acknowledged that models with larger numbers of variables are not necessarily better.
Higher airflow rates were found in scale models with larger ventilation opening surface areas.
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