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Both studies improved on previous work.
We have also improved on previous work by introducing a more realistic amino acid distance measure d W that is derived from maximum likelihood fitting of real protein sequence data.
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Our approach improves on previous work in three important ways.
Together, all these changes allowed us to train a model that significantly improves on previous work.
We improve on previous work by using large scale administrative datasets to investigate factors associated with LOS.
It improves on previous work (e.g. [ 24]) in that a longer cost period is included and the distribution of costs is more appropriately reflected in the regression models.
We improve on previous works by proposing an adequate implementation of the EKF-SLAM algorithm on a multiprocessing architecture (ARM processor, SIMD NEON coprocessor, DSP core).
This new framework, however, improves greatly on previous work through the addition of shape and texture generation based on data obtained from an extensive database of segmented cells, the inclusion of more debris models, a more versatile method for object distribution, and an accurate approximation of the image depth of field, allowing for simulated focus stacks to be created.
We propose a uniform algebraic approach that improves on previous formal work in the area due to the following characteristics.
Those eager to grow are often able to hire relatively cheaply well-trained workers who are eager to improve on their previous work experiencheaply well-trained
Our study substantially improves on this previous work by incorporating a much longer follow-up period, using a larger cohort representative of both the male and female U.S. population, and examining both cognitive and memory impairment.
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