9 research outputs found

    Learning to Behave: Internalising Knowledge

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    A complex systems approach to education in Switzerland

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    The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance

    Optimal mapping of graph homomorphism onto self organising Hopfield network

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    In a recent paper by us, a novel programming strategy was proposed to obtain homomorphic graph matching using the Hopfield network. Subsequently a self-organisation scheme was also proposed to adaptively learn the constraint parameter which is required to generate the desired homomorphic mapping for every pair of model and scene data. In this paper, an augmented weighted model attributed relational graph (WARG) representation scheme is proposed. The representation scheme incorporates a distinct weighting factor and tolerance parameter for every model attribute. To estimate the parameters in a simplified form of the model WARG representation, learning schemes are presented. A heuristic learning scheme is employed to estimate suitable values for threshold parameters. The computation of weighting factors is formulated as an optimisation problem and solved using the quadratic programming algorithm. The formulation implicitly evaluates ambiguity, robustness and discriminatory power of the relational attributes chosen for graph matching and assigns weighting factors appropriately to the chosen attributes. Experimental results are presented to demonstrate that the parameter learning schemes are essential when the models have intra-model ambiguity and the optimal set of parameters always generates a better mapping

    Image analysis for the study of chromatin distribution in cell nuclei with application to cervical cancer screening

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    PSA 2018

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    These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2018

    PSA 2018

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    These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2018
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