Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution

It is not uncommon that meta-heuristic algorithms contain some intrinsic parameters, the optimal configuration of which is crucial for achieving their peak performance. However, evaluating the effectiveness of a configuration is expensive, as it involves many costly runs of the target algorithm. Perhaps surprisingly, it is possible to build a cheap-to-evaluate surrogate that models the algorithm's empirical performance as a function of its parameters. Such surrogates constitute an important building block for understanding algorithm performance, algorithm portfolio/selection, and the automatic algorithm configuration. In principle, many off-the-shelf machine learning techniques can be used to build surrogates. In this paper, we take the differential evolution (DE) as the baseline algorithm for proof-of-concept study. Regression models are trained to model the DE's empirical performance given a parameter configuration. In particular, we evaluate and compare four popular regression algorithms both in terms of how well they predict the empirical performance with respect to a particular parameter configuration, and also how well they approximate the parameter versus the empirical performance landscapes

Zooming in to Massive Star Birth

We present high resolution (0.2", 1000 AU) 1.3 mm ALMA observations of massive infrared dark cloud clump, G028.37+00.07-C1, thought to harbor the early stages of massive star formation. Using $\rm N_2D^+$(3-2) we resolve the previously identified C1-S core, separating the bulk of its emission from two nearby protostellar sources. C1-S is thus identified as a massive ($\sim50\:M_\odot$), compact ($\sim0.1\:$pc diameter) starless core, e.g., with no signs of outflow activity. Being highly deuterated, this is a promising candidate for a pre-stellar core on the verge of collapse. An analysis of its dynamical state indicates a sub-virial velocity dispersion compared to a trans-Alfv\'enic turbulent core model. However, virial equilibrium could be achieved with sub-Alfv\'enic conditions involving $\sim2\:$mG magnetic field strengths.Comment: 19 pages, 15 figures, 4 tables, accepted by Ap

The sound absorption coefficient and noise reduction coefficient of rice husk silica

This paper presents the acoustical behavior of rice husk silica samples in various composition and thickness by using epoxy and methyl ethyl ketone peroxide (MEKP) as binder. Rice husk is one of agriculture waste materials that can be obtained in a large quantity which have the potential in the application of sound absorber. Rice husk silica samples were fabricated in various composition and thickness by using epoxy and methyl ethyl ketone peroxide (MEKP) as binder. Impedance tube testing and physical test were conducted to obtain the properties of rice husk silica. The testing results found that specimens with higher composition of rice husk silica and using MEKP as binder have the lowest density, highest porosity and give a better sound absorption coefficient. The increment of thickness can produce a greater sound absorption at lower frequency. The results also demonstrate 20 mm is the best thickness for a better noise reduction coefficient