86,627 research outputs found

    Informed selection and use of training examples for knowledge refinement.

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    Knowledge refinement tools seek to correct faulty rule-based systems by identifying and repairing faults indicated by training examples that provide evidence of faults. This thesis proposes mechanisms that improve the effectiveness and efficiency of refinement tools by the best use and selection of training examples. The refinement task is sufficiently complex that the space of possible refinements demands a heuristic search. Refinement tools typically use hill-climbing search to identify suitable repairs but run the risk of getting caught in local optima. A novel contribution of this thesis is solving the local optima problem by converting the hill-climbing search into a best-first search that can backtrack to previous refinement states. The thesis explores how different backtracking heuristics and training example ordering heuristics affect refinement effectiveness and efficiency. Refinement tools rely on a representative set of training examples to identify faults and influence repair choices. In real environments it is often difficult to obtain a large set of training examples, since each problem-solving task must be labelled with the expert's solution. Another novel aspect introduced in this thesis is informed selection of examples for knowledge refinement, where suitable examples are selected from a set of unlabelled examples, so that only the subset requires to be labelled. Conversely, if a large set of labelled examples is available, it still makes sense to have mechanisms that can select a representative set of examples beneficial for the refinement task, thereby avoiding unnecessary example processing costs. Finally, an experimental evaluation of example utilisation and selection strategies on two artificial domains and one real application are presented. Informed backtracking is able to effectively deal with local optima by moving search to more promising areas, while informed ordering of training examples reduces search effort by ensuring that more pressing faults are dealt with early on in the search. Additionally, example selection methods achieve similar refinement accuracy with significantly fewer examples

    Data-driven discovery of coordinates and governing equations

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    The discovery of governing equations from scientific data has the potential to transform data-rich fields that lack well-characterized quantitative descriptions. Advances in sparse regression are currently enabling the tractable identification of both the structure and parameters of a nonlinear dynamical system from data. The resulting models have the fewest terms necessary to describe the dynamics, balancing model complexity with descriptive ability, and thus promoting interpretability and generalizability. This provides an algorithmic approach to Occam's razor for model discovery. However, this approach fundamentally relies on an effective coordinate system in which the dynamics have a simple representation. In this work, we design a custom autoencoder to discover a coordinate transformation into a reduced space where the dynamics may be sparsely represented. Thus, we simultaneously learn the governing equations and the associated coordinate system. We demonstrate this approach on several example high-dimensional dynamical systems with low-dimensional behavior. The resulting modeling framework combines the strengths of deep neural networks for flexible representation and sparse identification of nonlinear dynamics (SINDy) for parsimonious models. It is the first method of its kind to place the discovery of coordinates and models on an equal footing.Comment: 25 pages, 6 figures; added acknowledgment

    Systems for technical refinement in experienced performers: The case from expert-level golf

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    This paper provides an overview of current golf coaching practices employed with experts, when attempting to make changes to (i.e., refine) a player’s existing technique. In the first of two studies, European Tour golfers (n = 5) and coaches (n = 5) were interviewed to establish the prevalence of any systematic processes, and whether facilitation of resistance to competitive pressure (hereafter termed “pressure resistance”) was included. Study 2 employed an online survey, administered to 89 PGA Professionals and amateur golfers (mostly amateurs; n = 83). Overall, results suggested no standardized, systematic, or theoretically considered approach to implementing technical change, with pressure resistance being considered outside of the change process itself; if addressed at all. In conclusion, there is great scope for PGA professionals to increase their coaching efficacy relating to skill refinement; however, this appears most likely to be achieved through a collaborative approach between coach education providers, researchers, and coaches

    Goals/questions/metrics method and SAP implementation projects

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    During the last years some researchers have studied the critical success factors (CSFs) in ERP implementations. However, until now, no one has studied how these CSFs should be put in practice to help organizations achieve success in ERP implementations. This technical research report attempts to define the usage of Goals/Questions/Metrics (GQM) approach in the definition of a measurement system for ERP implementation projects. GQM approach is a mechanism for defining and interpreting operational, measurable goals. Lately, because of its intuitive nature the approach has gained widespread appeal. We present a metrics overview and a description of GQM approach. Then we provide an example of GQM application for monitoring sustained management support in ERP implementations. Sustained management support is the most cited critical success factor in ERP implementation projects.Postprint (published version

    Ethics, space, and somatic sensibilities: comparing relationships between scientific researchers and their human and animal experimental subjects

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    Drawing on geographies of affect and nature-society relations, we propose a radical rethinking of how scientists, social scientists, and regulatory agencies conceptualise human and animal participants in scientif ic research. The scientific rationale for using animal bodies to simulate what could be done in human bodies emphasises shared somatic capacities that generate comparable responses to clinical interventions. At the same time, regulatory guidelines and care practices stress the differences between human and animal subjects. In this paper we consider the implications of this differentiation between human and animal bodies in ethical and welfare protocols and practices. We show how the bioethical debates around the use of human subjects tend to focus on issues of consent and language, while recent work in animal welfare reflects an increasing focus on the affectual dimensions of ethical practice. We argue that this attention to the more-than-representational dimensions of ethics and welfare might be equally important for human subjects. We assert that paying attention to these somatic sensibilities can offer insights into how experimental environments can both facilitate and restrict the development of more care-full and response-able relations between researchers and their experimental subjects. <br/

    PoseAgent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning

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    State-of-the-art computer vision algorithms often achieve efficiency by making discrete choices about which hypotheses to explore next. This allows allocation of computational resources to promising candidates, however, such decisions are non-differentiable. As a result, these algorithms are hard to train in an end-to-end fashion. In this work we propose to learn an efficient algorithm for the task of 6D object pose estimation. Our system optimizes the parameters of an existing state-of-the art pose estimation system using reinforcement learning, where the pose estimation system now becomes the stochastic policy, parametrized by a CNN. Additionally, we present an efficient training algorithm that dramatically reduces computation time. We show empirically that our learned pose estimation procedure makes better use of limited resources and improves upon the state-of-the-art on a challenging dataset. Our approach enables differentiable end-to-end training of complex algorithmic pipelines and learns to make optimal use of a given computational budget
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