Computer Center Bulletin / July 5, 1990

This publication is published as required and is written by members of the staff, W. R. Church Computer Cente

Using algorithmic information theory and stochastic modeling to improve classification and evolutionary computation: a dissertation submitted to the department of computer science of the Universidad Autonoma de Madrid in partial fulfillment of the requirements for the degree of doctor of philosophy

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, junio de 200

The Split-Apply-Combine Strategy for Data Analysis

Many data analysis problems involve the application of a split-apply-combine strategy, where you break up a big problem into manageable pieces, operate on each piece independently and then put all the pieces back together. This insight gives rise to a new R package that allows you to smoothly apply this strategy, without having to worry about the type of structure in which your data is stored. The paper includes two case studies showing how these insights make it easier to work with batting records for veteran baseball players and a large 3d array of spatio-temporal ozone measurements

Knowledge Representation Using Petri Nets and Knowledge Tables

Computer Scienc

On environment difficulty and discriminating power

The final publication is available at Springer via http://dx.doi.org/10.1007/s10458-014-9257-1This paper presents a way to estimate the difficulty and discriminating power of any task instance. We focus on a very general setting for tasks: interactive (possibly multiagent) environments where an agent acts upon observations and rewards. Instead of analysing the complexity of the environment, the state space or the actions that are performed by the agent, we analyse the performance of a population of agent policies against the task, leading to a distribution that is examined in terms of policy complexity. This distribution is then sliced by the algorithmic complexity of the policy and analysed through several diagrams and indicators. The notion of environment response curve is also introduced, by inverting the performance results into an ability scale. We apply all these concepts, diagrams and indicators to two illustrative problems: a class of agent-populated elementary cellular automata, showing how the difficulty and discriminating power may vary for several environments, and a multiagent system, where agents can become predators or preys, and may need to coordinate. Finally, we discuss how these tools can be applied to characterise (interactive) tasks and (multi-agent) environments. These characterisations can then be used to get more insight about agent performance and to facilitate the development of adaptive tests for the evaluation of agent abilities.I thank the reviewers for their comments, especially those aiming at a clearer connection with the field of multi-agent systems and the suggestion of better approximations for the calculation of the response curves. The implementation of the elementary cellular automata used in the environments is based on the library 'CellularAutomaton' by John Hughes for R [58]. I am grateful to Fernando Soler-Toscano for letting me know about their work [65] on the complexity of 2D objects generated by elementary cellular automata. I would also like to thank David L. Dowe for his comments on a previous version of this paper. This work was supported by the MEC/MINECO projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02, GVA project PROMETEO/2008/051, the COST - European Cooperation in the field of Scientific and Technical Research IC0801 AT, and the REFRAME project, granted by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA), and funded by the Ministerio de Economia y Competitividad in Spain (PCIN-2013-037).José Hernández-Orallo (2015). On environment difficulty and discriminating power. Autonomous Agents and Multi-Agent Systems. 29(3):402-454. https://doi.org/10.1007/s10458-014-9257-1S402454293Anderson, J., Baltes, J., & Cheng, C. T. (2011). 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Gene analogue finder: a GRID solution for finding functionally analogous gene products

<p>Abstract</p> <p>Background</p> <p>To date more than 2,1 million gene products from more than 100000 different species have been described specifying their function, the processes they are involved in and their cellular localization using a very well defined and structured vocabulary, the gene ontology (GO). Such vast, well defined knowledge opens the possibility of compare gene products at the level of functionality, finding gene products which have a similar function or are involved in similar biological processes without relying on the conventional sequence similarity approach. Comparisons within such a large space of knowledge are highly data and computing intensive. For this reason this project was based upon the use of the computational GRID, a technology offering large computing and storage resources.</p> <p>Results</p> <p>We have developed a tool, G<b>EN</b>e Analo<b>G</b>ue F<b>IN</b>d<b>E</b>r (ENGINE) that parallelizes the search process and distributes the calculation and data over the computational GRID, splitting the process into many sub-processes and joining the calculation and the data on the same machine and therefore completing the whole search in about 3 days instead of occupying one single machine for more than 5 CPU years. The results of the functional comparison contain potential functional analogues for more than 79000 gene products from the most important species. 46% of the analyzed gene products are well enough described for such an analysis to individuate functional analogues, such as well-known members of the same gene family, or gene products with similar functions which would never have been associated by standard methods.</p> <p>Conclusion</p> <p>ENGINE has produced a list of potential functionally analogous relations between gene products within and between species using, in place of the sequence, the gene description of the GO, thus demonstrating the potential of the GO. However, the current limiting factor is the quality of the associations of many gene products from non-model organisms that often have electronic associations, since experimental information is missing. With future improvements of the GO, this limit will be reduced. ENGINE will manifest its power when it is applied to the whole GODB of more than 2,1 million gene products from more than 100000 organisms. The data produced by this search is planed to be available as a supplement to the GO database as soon as we are able to provide regular updates.</p

Factors Affecting Financial Inclusion: A Study in Rourkela

Financial services are a ubiquitous need, but the urban rich have easy and universal access with wider options, compared to the low-income group who are forced to accept informal, expensive and riskier means to fulfil their financial needs. While the need for a mix of financial products including credit, savings, insurance, remittance, social & welfare receipts, pension and so on, is well established, the demand for specific services can vary widely. Key influencers of demand and willingness to pay are demographics, literacy levels, social-dynamics, local enablers and inhibitors, availability of informal and alternate channels (together with their cost and convenience), adaptability to change, comfort with technology, and other exogenous and endogenous factors. At the same time, the demand and the supply of financial services for the poor is imbalanced, with supply being acutely constrained by lack of viability and sustainability of current business models. Evolving and newly emerging business models, rapid technological innovations and state initiatives have greatly facilitated supply conditions to improve and for the providers to consider building market-led self-sustaining alternatives to extend banking and other financial services to the excluded. The policy environment has evolved and (using a mix of loose and tight regulations and taking a controlling, direction setting or mentoring approach) provided suitable incentives and disincentives to promote financial inclusion. It enabled banks to extend outreach through third party agents and agent network managers. Financial inclusion confronts enormous barriers to adoption, some of which can be better dealt with by leveraging the wealth of knowledge and experience from diverse initiatives. The key guiding principles are to stay focussed on: –The consumer needs and expectations around - accessibility, proximity, simplicity, product relevance, ability to transact in low values, promise of adequate returns, pricing according to willingness to pay, establishing trust, ensuring portability, interoperability and safety. –The agent needs around viable returns, liquidity management, operational handholding, marketing, speed of response, security and keeping them motivated through a diverse range of incentives