Denoising Autoencoders for fast Combinatorial Black Box Optimization

Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Autoencoders (AE) are generative stochastic networks with these desired properties. We integrate a special type of AE, the Denoising Autoencoder (DAE), into an EDA and evaluate the performance of DAE-EDA on several combinatorial optimization problems with a single objective. We asses the number of fitness evaluations as well as the required CPU times. We compare the results to the performance to the Bayesian Optimization Algorithm (BOA) and RBM-EDA, another EDA which is based on a generative neural network which has proven competitive with BOA. For the considered problem instances, DAE-EDA is considerably faster than BOA and RBM-EDA, sometimes by orders of magnitude. The number of fitness evaluations is higher than for BOA, but competitive with RBM-EDA. These results show that DAEs can be useful tools for problems with low but non-negligible fitness evaluation costs.Comment: corrected typos and small inconsistencie

Effect of antimony on the eutectic reaction of heavy section spheroidal graphite castings

There is a strong demand for heavy section castings made of spheroidal graphite with a fully ferritic matrix, e.g. for manufacturing hubs for windmills. Such castings with slow solidification process are prone to graphite degeneration that leads to a dramatic decrease of the mechanical properties of the cast parts. Chunky graphite is certainly the most difficult case of graphite degeneracy, though it has long been known that the limited and controlled addition of antimony may help eliminate it. The drawback of this remedy is that too large Sb additions lead to other forms of degenerate graphite, and also that antimony is a pearlite promoter. As part of an investigation aimed at mastering low level additions to cast iron melts before casting, solidification of large blocks with or without Sb added was followed by thermal analysis. Comparison of the cooling curves and of the microstructures of these different castings gives suggestions to understand the controlling nucleation and growth mechanisms for chunky graphite cells

An island based hybrid evolutionary algorithm for optimization

This is a post-print version of the article - Copyright @ 2008 Springer-VerlagEvolutionary computation has become an important problem solving methodology among the set of search and optimization techniques. Recently, more and more different evolutionary techniques have been developed, especially hybrid evolutionary algorithms. This paper proposes an island based hybrid evolutionary algorithm (IHEA) for optimization, which is based on Particle swarm optimization (PSO), Fast Evolutionary Programming (FEP), and Estimation of Distribution Algorithm (EDA). Within IHEA, an island model is designed to cooperatively search for the global optima in search space. By combining the strengths of the three component algorithms, IHEA greatly improves the optimization performance of the three basic algorithms. Experimental results demonstrate that IHEA outperforms all the three component algorithms on the test problems.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1

Enhanced Support for High Intensity Users of the Criminal Justice System – an evaluation of mental health nurse input into Integrated Offender Management Services in the North East of England

The current UK Government’s focus on the development of services to manage and support offenders with mental health problems has resulted in a number of innovative project developments. This research examines a service development in the North East of England which co-located Mental Health nurses with two Integrated Offender Management teams. While not solving all problems, the benefits of co-location were clear – although such innovations are now at risk from government changes which will make Integrated Offender Management the responsibility of new providers without compelling them to co-operate with health services

Digging into acceptor splice site prediction : an iterative feature selection approach

Feature selection techniques are often used to reduce data dimensionality, increase classification performance, and gain insight into the processes that generated the data. In this paper, we describe an iterative procedure of feature selection and feature construction steps, improving the classification of acceptor splice sites, an important subtask of gene prediction. We show that acceptor prediction can benefit from feature selection, and describe how feature selection techniques can be used to gain new insights in the classification of acceptor sites. This is illustrated by the identification of a new, biologically motivated feature: the AG-scanning feature. The results described in this paper contribute both to the domain of gene prediction, and to research in feature selection techniques, describing a new wrapper based feature weighting method that aids in knowledge discovery when dealing with complex datasets

Noncommutative Geometry Inspired Rotating Black Hole in Three Dimensions

We find a new rotating black hole in three-dimensional anti-de Sitter space using an anisotropic perfect fluid inspired by the noncommutative black hole. We deduce the thermodynamical quantities of this black hole and compare them with those of a rotating BTZ solution.Comment: 7 page

Surface modified Ni nanoparticles produced by the electrical explosion of wire

Spherical nickel nanoparticles were prepared by the electrical explosion of wire. The as prepared nanoparticles were modified immediately after fabrication at room temperature in order to provide tunable surface properties with focus on the development of composites filled with nanoparticles. Following liquid modificators were used: hexane, toluene and the solution of polystyrene in toluene. In one case the surface modification by carbon was made in gas phase as a result of hydrocarbon injection. The average size of the nanoparticles was about 50 nm and unit cell parameters were close to 0.351 nm. Detailed characterization was done by X-ray diffraction, transmission electron microscopy, and magnetization measurements. Sphericity was also checked using microwave resonant absorption. © (2015) Trans Tech Publications, Switzerland.Electromagnetics of Russian Academy of Sciences;et al;Institute for Theoretical and Applied;Japan Society for the Promotion of Science;Lomonosov Moscow State University;Russian Foundation for Basic Researc

Mouse p53-deficient cancer models as platforms for obtaining genomic predictors of human cancer clinical outcomes

Mutations in the TP53 gene are very common in human cancers, and are associated with poor clinical outcome. Transgenic mouse models lacking the Trp53 gene or that express mutant Trp53 transgenes produce tumours with malignant features in many organs. We previously showed the transcriptome of a p53-deficient mouse skin carcinoma model to be similar to those of human cancers with TP53 mutations and associated with poor clinical outcomes. This report shows that much of the 682-gene signature of this murine skin carcinoma transcriptome is also present in breast and lung cancer mouse models in which p53 is inhibited. Further, we report validated gene-expression-based tests for predicting the clinical outcome of human breast and lung adenocarcinoma. It was found that human patients with cancer could be stratified based on the similarity of their transcriptome with the mouse skin carcinoma 682-gene signature. The results also provide new targets for the treatment of p53-defective tumours

Active Mg Estimation Using Thermal Analysis: A Rapid Method to Control Nodularity in Ductile Cast Iron Production

Appropriate nodularity in ductile iron castings is strongly associated with the presence of high enough not combined Mg dissolved in the melt to cast. However, the residual Mg which is commonly measured for production control accounts for both dissolved Mg and Mg combined as oxides and sulfides. To account for the uncertainties associated with such a control, it is quite usual to over treat the melt with the risk of porosity appearance. A new methodology based on thermal analysis has been developed in the present work so as to estimate the amount of free Mg dissolved in the melt ready for pouring. A combination of Te mixture and a new “reactive mixture” composed of sulfur plus a commercial inoculant has been prepared for this purpose. This reactive mixture is able to transform the magnesium remaining dissolved in the melt to combined forms of this element. Experiments performed both during start of production (when Mg overtreatment is usual) and during normal mass production indicate that important variations of free Mg occur without relevant changes in residual Mg content as determined by spectrometry. The method developed in the present work has shown to be highly effective to detect those melt batches where active Mg content is not high enough for guaranteeing a correct nodularity of castings. Selection of proper active Mg thresholds and a correct inoculation process are critical to avoid “false”-negative results when using this new method