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

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

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

Thermal, structural and degradation properties of an aromatic-aliphatic polyester built through ring-opening polymerisation

The novel biodegradable aromatic–aliphatic polyester, poly(2-(2-hydroxyethoxy)benzoate), was explored through thermal analysis, X-ray diffraction, dynamic mechanical analysis and comparative bio and catalysed degradation.</p

Simultaneous analysis of natural pigments and E-141i in olive oils by liquid chromatography-tandem mass spectrometry

This work describes the development of an ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method for the determination of carotenoids (β-carotene, lutein, β-criptoxanthin, neoxanthin, violaxanthin) and chlorophylls, as well as their related compounds (chlorophyll A and B, pheophytin A and B and the banned dyes Cu-pyropheophytin A, Cu-pheophytin A and B) in olive oils. For this purpose, the feasibility of electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI) and atmospheric pressure photoionization (APPI) for the ionization of these compounds was evaluated and compared. Tandem mass spectrometry (MS/MS) fragmentation was discussed for each family of compounds, and the most characteristic and abundant product ions were selected to propose a selective and sensitive UHPLC-MS/MS method. The best results were obtained using APCI and APPI, while ESI provided the worst signal-to-noise ratio (S/N) for all compounds. For the analysis of olive oils, a simple solid-phase extraction (SPE) with silica cartridges was applied before the determination by UHPLC-MS/MS (APCI and APPI) in multiple reaction monitoring (MRM) mode. Method quality parameters were stablished, and the results demonstrate the good performance of the new methods, providing low limits of detection (0.004-0.9 mg L−1), high extraction efficiencies (62-95%) and low matrix effects (< 25%). The developed UHPLC-API-MS/MS(APCI and APPI) methods were applied to the analysis of olive oil samples, and β-carotene, pheophytinA, pheophytin B and lutein were detected and quantified in all of them at concentrations ranging from 0.1 to 9.5 mg L−1

A probabilistic evolutionary optimization approach to compute quasiparticle braids

Topological quantum computing is an alternative framework for avoiding the quantum decoherence problem in quantum computation. The problem of executing a gate in this framework can be posed as the problem of braiding quasiparticles. Because these are not Abelian, the problem can be reduced to finding an optimal product of braid generators where the optimality is defined in terms of the gate approximation and the braid's length. In this paper we propose the use of different variants of estimation of distribution algorithms to deal with the problem. Furthermore, we investigate how the regularities of the braid optimization problem can be translated into statistical regularities by means of the Boltzmann distribution. We show that our best algorithm is able to produce many solutions that approximates the target gate with an accuracy in the order of $10^{-6}$, and have lengths up to 9 times shorter than those expected from braids of the same accuracy obtained with other methods.Comment: 9 pages,7 figures. Accepted at SEAL 201

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

Biomimetic lipid-based nanosystems for enhanced dermal delivery of drugs and bioactive agents

Clinical utility of conventional oral therapies is limited by their inability to deliver therapeutic molecules at the local or targeted site, causing a variety of side effects. Transdermal delivery has made a significant contribution in the management of skin diseases with enhanced therapeutic activities over the past two decades. In the modern era, various biomimetic and biocompatible polymer–lipid hybrid systems have been used to augment the transdermal delivery of therapeutics such as dermal patches, topical gels, iontophoresis, electroporation, sonophoresis, thermal ablation, microneedles, cavitational ultrasound, and nano or microlipid vesicular systems. Nevertheless, the stratum corneum still represents the main barrier to the delivery of vesicles into the skin. Lipid based formulations applied to the skin are at the center of attention and are anticipated to be increasingly functional as the skin offers many advantages for the direction of such systems. Accordingly, this review provides an overview of the development of conventional to advanced biomimetic lipid vesicles for skin delivery of a variety of therapeutics, with special emphasis on recent developments in this field including the development of transferosomes, niosomes, aquasomes, cubosomes, and other new generation lipoidal carriers