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We apply these inequalities to obtain point-wise estimates on the associated heat kernel, improving upon earlier results.
Proteases can increase essential yeast nutrients in the form of aminoacids, peptides and free amino nitrogen, as well as promote the hydrolysis of protein substrates, like corn kernel, improving the rate and yield of ethanol.
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Placing an application's runtime routines in the kernel improves performance.
We then demonstrate that the resulting ability to locally tilt, rotate or scale the kernel improves filtering performance in various applications over traditional spatially invariant Gaussian filters, without incurring a significant penalty in computational expense.
Since the side lobes of a Lanczos kernel improved reconstruction performance along the edge regions, the used kernel size severely impacted the performance of the reconstructed image.
Further, reduction of width of the Gaussian kernel improves the performance of the proposed technique as some finer details are also selected as edges.
Receiver Operating Characteristic (ROC) curves show that integrating information coming from different sources into a single kernel improves the performance of the corresponding classifier (Figure 2).
On average, Raw kernel improves the predictive accuracy by 7.3% over 12 datasets compared to linear kernel.
Overall, incorporating global information through the graph diffusion kernel improves the performance in identifying synthetic lethal pairs.
Our experimental results show that the Raw kernel improves the classification accuracy by 7.3% on average over twelve datasets.
The transition kernel improves the convergence properties of the Markov chain substantially, so that fewer initial samples must be discarded as "burn-in".
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