Maximin design on non hypercube domain and kernel interpolation

In the paradigm of computer experiments, the choice of an experimental design is an important issue. When no information is available about the black-box function to be approximated, an exploratory design have to be used. In this context, two dispersion criteria are usually considered: the minimax and the maximin ones. In the case of a hypercube domain, a standard strategy consists of taking the maximin design within the class of Latin hypercube designs. However, in a non hypercube context, it does not make sense to use the Latin hypercube strategy. Moreover, whatever the design is, the black-box function is typically approximated thanks to kernel interpolation. Here, we first provide a theoretical justification to the maximin criterion with respect to kernel interpolations. Then, we propose simulated annealing algorithms to determine maximin designs in any bounded connected domain. We prove the convergence of the different schemes.Comment: 3 figure

Relational visual cluster validity

The assessment of cluster validity plays a very important role in cluster analysis. Most commonly used cluster validity methods are based on statistical hypothesis testing or finding the best clustering scheme by computing a number of different cluster validity indices. A number of visual methods of cluster validity have been produced to display directly the validity of clusters by mapping data into two- or three-dimensional space. However, these methods may lose too much information to correctly estimate the results of clustering algorithms. Although the visual cluster validity (VCV) method of Hathaway and Bezdek can successfully solve this problem, it can only be applied for object data, i.e. feature measurements. There are very few validity methods that can be used to analyze the validity of data where only a similarity or dissimilarity relation exists – relational data. To tackle this problem, this paper presents a relational visual cluster validity (RVCV) method to assess the validity of clustering relational data. This is done by combining the results of the non-Euclidean relational fuzzy c-means (NERFCM) algorithm with a modification of the VCV method to produce a visual representation of cluster validity. RVCV can cluster complete and incomplete relational data and adds to the visual cluster validity theory. Numeric examples using synthetic and real data are presente

The role of C-terminal amidation in the membrane interactions of the anionic antimicrobial peptide, maximin H5

Maximin H5 is an anionic antimicrobial peptide from amphibians, which carries a C-terminal amide moiety, and was found to be moderately haemolytic (20%). The α-helicity of the peptide was 42% in the presence of lipid mimics of erythrocyte membranes and was found able to penetrate (10.8mNm(-1)) and lyse these model membranes (64 %). In contrast, the deaminated peptide exhibited lower levels of haemolysis (12%) and α-helicity (16%) along with a reduced ability to penetrate (7.8mNm(-1)) and lyse (55%) lipid mimics of erythrocyte membranes. Taken with molecular dynamic simulations and theoretical analysis, these data suggest that native maximin H5 primarily exerts its haemolytic action via the formation of an oblique orientated α-helical structure and tilted membrane insertion. However, the C-terminal deamination of maximin H5 induces a loss of tilted α-helical structure, which abolishes the ability of the peptide's N-terminal and C-terminal regions to H-bond and leads to a loss in haemolytic ability. Taken in combination, these observations strongly suggest that the C-terminal amide moiety carried by maximin H5 is required to stabilise the adoption of membrane interactive tilted structure by the peptide. Consistent with previous reports, these data show that the efficacy of interaction and specificity of maximin H5 for membranes can be attenuated by sequence modification and may assist in the development of variants of the peptide with the potential to serve as anti-infective

The True Destination of EGO is Multi-local Optimization

Efficient global optimization is a popular algorithm for the optimization of expensive multimodal black-box functions. One important reason for its popularity is its theoretical foundation of global convergence. However, as the budgets in expensive optimization are very small, the asymptotic properties only play a minor role and the algorithm sometimes comes off badly in experimental comparisons. Many alternative variants have therefore been proposed over the years. In this work, we show experimentally that the algorithm instead has its strength in a setting where multiple optima are to be identified