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Self-control tasks appear to deplete a limited resource resulting in reduced subsequent self-control performance; a state of ego depletion.
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To improve the system performance, a state-of-the-art solution is to dynamically adapting the allocation of system resources at run time for each execution scenario based on pre-determined resource schemes that have been optimized at design time.
These models also proved to yield best performance, out- performing a state-of-the-art search engine (Lucene).
It evaluates the performance of a state-of-art industry solution with that obtainable with adaptive and sliding mode positioning controls.
DAC demonstrated better performance than a state-of-the-art technique, a Random Forest, both in terms of quality of the prediction and execution time.
To compare the BWLT's performance to a state-of-the-art method, Figure 9 depicts the results when the "Portrait" and "Cafeteria" images are transcoded to codestreams with different sizes using the CoRD method introduced in [4].
The main objective of this paper was to improve the overall RD performance of a state-of-the-art DVC codec using a more powerful SI creation framework, notably exploiting perspective transform motion modeling to better characterize more complex video motions.
Evaluated on a Mandarin/English detection task, the proposed integrated LVCSR-LRE system using frame-expanded N-best phone lattice achieves comparable performance to a state-of-the-art phone recognition-vector space modeling (PRVSM) system, but with an added computational cost three times lower than that of a separate PRVSM system.
We have evaluated the performance of a state-of-the-art Eulerian-path based sequence assembler on simulated metagenomic datasets using a read length of 36 base pairs (bp), as produced by the Solexa/Illumina sequencing technology.
We test our image analysis system on representative locomotive environments (Fig. 1) and compare performances with a state-of-the-art thresholding method (see Results).
In this study, performance of a state of the art learning algorithm called extreme learning machine (ELM), was evaluated through comparing with the other calibration methods proposed in the literature for predicting lead (Pb) and Zinc (Zn) concentrations.
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