Composition of Biochemical Networks using Domain Knowledge

Graph composition has applications in a variety of practical applications. In drug development, for instance, in order to understand possible drug interactions, one has to merge known networks and examine topological variants arising from such composition. Similarly, the design of sensor nets may use existing network infrastructures, and the superposition of one network on another can help with network design and optimisation. The problem of network composition has not received much attention in algorithm and database research. Here, we work with biological networks encoded in Systems Biology Markup Language (SBML), based on XML syntax. We focus on XML merging and examine the algorithmic and performance challenges we encountered in our work and the possible solutions to the graph merge problem. We show that our XML graph merge solution performs well in practice and improves on the existing toolsets. This leads us into future work directions and the plan of research which will aim to implement graph merging primitives using domain knowledge to perform composition and decomposition on specific graphs in the biological domain

Biochemical network matching and composition

This paper looks at biochemical network matching and compositio

reSearch : enhancing information retrieval with images

Combining image and text search is an open research question. The main issues are what technologies to base this solution on, and what measures of relevance to employ. Our reSearch prototype mashes up papers indexed using information retrieval techniques (Terrier) with Google image search for faces and Google book search. The user can interactively employ query expansion with additional terms suggested by Terrier, and use those terms to expand both the text and image search. We test this solution with a selection of recent publications and queries concerning people engaged in research. We report on the effectiveness of this solution. It seems that the combination works to a large extent, as testified by our observations

Algebraic incremental maintenance of XML views

International audienceMaterialized views can bring important performance benefits when querying XML documents. In the presence of XML document changes, materialized views need to be updated to faithfully reflect the changed document. In this work, we present an algebraic approach for propagating source updates to XML materialized views expressed in a powerful XML tree pattern formalism. Our approach differs from the state of the art in the area in two important ways. First, it relies on set-oriented, algebraic operations, to be contrasted with node-based previous approaches. Second, it exploits state-of-the-art features of XML stores and XML query evaluation engines, notably XML structural identifiers and associated structural join algorithms. We present algorithms for determining how updates should be propagated to views, and highlight the benefits of our approach over existing algorithms through a series of experiments

Tips for effective blended learning for computer science education

Blended learning is the combination of in-person teaching and online activities. For example, combining face-to-face lectures/tutorials with online videos and assessments. With the adoption of non-traditional, not fully on-campus courses, such as degree and graduate apprenticeships in the UK, CPD, re/upskilling, and alternative teaching methods being required due to the pandemic, many different teaching strategies have been explored. Blended learning is one of these strategies and has proven popular for both universities and students. Based on experience, we present tips for blended learning within the teaching of computer science

Stakeholder perspectives on the cost requirements of Small Modular Reactors

This paper is in closed access until 11th Dec 2019.© 2018 Elsevier Ltd The cost of a nuclear power plant (NPP) is an important influence on the future commercial success of Small Modular Reactors (SMRs). At the early design stage, the cost requirements of SMRs can be derived from an analysis of the factors driving the Levelized Cost of Electricity (LCOE). It is often much later into the development process before customers are engaged and their cost requirements are known, by which time key design decisions which influence the lifecycle cost have already been locked-in. A clear understanding is required of the cost priorities for the key stakeholders who are to invest in the SMR. This paper presents a novel approach to ranking the relative importance of different cost factors used to calculate the LCOE. Using a dynamic stakeholder analysis, the key decision-makers for each stage of the SMR product lifecycle are identified. The Analytic Hierarchy Process (AHP) with pair-wise comparisons obtained from nuclear cost experts is employed to rank the different factors in terms of their relative importance on the commercial success of a near-term deployable SMR. Each expert provides a different set of rankings, although project financing cost is consistently the most important for the successful commercial deployment of the SMR. The approach presented in this paper can be used as a verification method for any power generation technology to provide confidence that cost requirements are adequately captured to design for life cycle cost competitiveness from the perspective of different stakeholders

COEGAN: Evaluating the Coevolution Effect in Generative Adversarial Networks

Generative adversarial networks (GAN) present state-of-the-art results in the generation of samples following the distribution of the input dataset. However, GANs are difficult to train, and several aspects of the model should be previously designed by hand. Neuroevolution is a well-known technique used to provide the automatic design of network architectures which was recently expanded to deep neural networks. COEGAN is a model that uses neuroevolution and coevolution in the GAN training algorithm to provide a more stable training method and the automatic design of neural network architectures. COEGAN makes use of the adversarial aspect of the GAN components to implement coevolutionary strategies in the training algorithm. Our proposal was evaluated in the Fashion-MNIST and MNIST dataset. We compare our results with a baseline based on DCGAN and also with results from a random search algorithm. We show that our method is able to discover efficient architectures in the Fashion-MNIST and MNIST datasets. The results also suggest that COEGAN can be used as a training algorithm for GANs to avoid common issues, such as the mode collapse problem.Comment: Published in GECCO 2019. arXiv admin note: text overlap with arXiv:1912.0617

Stakeholder perspectives on the cost requirements of small modular reactors

© 2018 Elsevier Ltd The cost of a nuclear power plant (NPP) is an important influence on the future commercial success of Small Modular Reactors (SMRs). At the early design stage, the cost requirements of SMRs can be derived from an analysis of the factors driving the Levelized Cost of Electricity (LCOE). It is often much later into the development process before customers are engaged and their cost requirements are known, by which time key design decisions which influence the lifecycle cost have already been locked-in. A clear understanding is required of the cost priorities for the key stakeholders who are to invest in the SMR. This paper presents a novel approach to ranking the relative importance of different cost factors used to calculate the LCOE. Using a dynamic stakeholder analysis, the key decision-makers for each stage of the SMR product lifecycle are identified. The Analytic Hierarchy Process (AHP) with pair-wise comparisons obtained from nuclear cost experts is employed to rank the different factors in terms of their relative importance on the commercial success of a near-term deployable SMR. Each expert provides a different set of rankings, although project financing cost is consistently the most important for the successful commercial deployment of the SMR. The approach presented in this paper can be used as a verification method for any power generation technology to provide confidence that cost requirements are adequately captured to design for life cycle cost competitiveness from the perspective of different stakeholders

Fission possible: understanding the cost of nuclear power

The cost of nuclear power has been debated ever since the build of the first plant at Calder Hall. Despite crippling construction delays in the 1970s and 80s, nuclear new build is again considered to meet both future demand growth and CO2 reduction targets. UK suppliers could produce around 45% of the high value components, with the potential to enter international export markets. Initially estimated at £9bn, to £16bn after Fukushima, with the most recent estimate at £24.5bn, Hinkley Point C will be the pilot build for new nuclear. The question remains, can the UK build a nuclear power station economically? The research aims to provide a methodology for estimating the cost of future nuclear build projects. This paper will review cost drivers for historic nuclear build, prior to and after their construction. Based on this analysis the paper will critique the current methodology and provide direction for the research