Blood lactate clearance during active recovery after an intense running bout depends on the intensity of the active recovery

High-intensity exercise training contributes to the production and accumulation of blood lactate, which is cleared by active recovery. However, there is no commonly agreed intensity or mode for clearing accumulated blood lactate. We studied clearance of accumulated blood lactate during recovery at various exercise intensities at or below the lactate threshold after high-intensity interval runs that prompted lactate accumulation. Ten males repeated 5-min running bouts at 90% of maximal oxygen uptake ([Vdot]O2max), which increased blood lactate concentration from 1.0 ± 0.1 to 3.9 ± 0.3 mmol · l-1. This was followed by recovery exercises ranging from 0 to 100% of lactate threshold. Repeated blood lactate measurements showed faster clearance of lactate during active versus passive recovery, and that the decrease in lactate was more rapid during higher (60-100% of lactate threshold) than lower (0-40% of lactate threshold) (P < 0.05) intensities. The more detailed curve and rate analyses showed that active recovery at 80-100% of lactate threshold had shorter time constants for 67% lactate clearance and higher peak clearance rates than 40% of lactate threshold or passive recovery (P < 0.05). Finally, examination of self-regulated intensities showed enhanced lactate clearance during higher versus lower intensities, further validating the intensity dependence of clearance of accumulated blood lactate. Therefore, active recovery after strenuous exercise clears accumulated blood lactate faster than passive recovery in an intensity-dependent manner. Maximum clearance occurred at active recovery close to the lactate threshold

Is One Hyperparameter Optimizer Enough?

Hyperparameter tuning is the black art of automatically finding a good combination of control parameters for a data miner. While widely applied in empirical Software Engineering, there has not been much discussion on which hyperparameter tuner is best for software analytics. To address this gap in the literature, this paper applied a range of hyperparameter optimizers (grid search, random search, differential evolution, and Bayesian optimization) to defect prediction problem. Surprisingly, no hyperparameter optimizer was observed to be `best' and, for one of the two evaluation measures studied here (F-measure), hyperparameter optimization, in 50\% cases, was no better than using default configurations. We conclude that hyperparameter optimization is more nuanced than previously believed. While such optimization can certainly lead to large improvements in the performance of classifiers used in software analytics, it remains to be seen which specific optimizers should be applied to a new dataset.Comment: 7 pages, 2 columns, accepted for SWAN1

Photo-Disintegration of Light Nuclei

Abstract Not Provided

Weak Values and Continuous-Variable Entanglement Concentration

We demonstrate a general weak measurement model which allows Gaussian preserving entanglement concentration of the two mode squeezed vacuum. The power of this simple and elegant protocol is through the constraints it places on possible ancilla states and measurement strategies that will allow entanglement concentration. In particular, it is shown how previously discovered protocols of this kind emerge as special examples of the general model described here. Finally, as evidence of its utility, we use it to provide another novel example of such a protocol.Comment: 4 pages, 1 figure, Final version to appear in Phys. Rev.