947 resultados para Iterative determinant maximization


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Our objective was to assess the contribution of lean body mass (LBM) and fat body mass (FBM) to areal bone mineral density (aBMD) in women during the years surrounding menopause. We used a 12-year observational design. Participants included 75 Caucasian women who were premenopausal, 53 of whom were available for follow-up. There were two measurement periods: baseline and 12-year follow-up. At both measurement periods, bone mineral content and aBMD of the proximal femur, posterior-anterior lumbar spine, and total body was assessed using dual-energy X-ray absorptiometry (DXA). LBM and FBM were derived from the total-body scans. General health, including current menopausal status, hormone replace therapy use, medication use, and physical activity, was assessed by questionnaires. At the end of the study, 44% of the women were postmenopausal. After controlling for baseline aBMD, current menopausal status, and current hormone replacement therapy, we found that change in LBM was independently associated with change in aBMD of the proximal femur (P = 0.001). The cross-sectional analyses also indicated that LBM was a significant determinant of aBMD of all three DXA-scanned sites at both baseline and follow-up. These novel longitudinal data highlight the important contribution of LBM to the maintenance of proximal femur bone mass at a key time in women's life span, the years surrounding menopause.

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The fundamental failure of current approaches to ontology learning is to view it as single pipeline with one or more specific inputs and a single static output. In this paper, we present a novel approach to ontology learning which takes an iterative view of knowledge acquisition for ontologies. Our approach is founded on three open-ended resources: a set of texts, a set of learning patterns and a set of ontological triples, and the system seeks to maintain these in equilibrium. As events occur which disturb this equilibrium, actions are triggered to re-establish a balance between the resources. We present a gold standard based evaluation of the final output of the system, the intermediate output showing the iterative process and a comparison of performance using different seed input. The results are comparable to existing performance in the literature.

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An unsupervised learning procedure based on maximizing the mutual information between the outputs of two networks receiving different but statistically dependent inputs is analyzed (Becker S. and Hinton G., Nature, 355 (1992) 161). By exploiting a formal analogy to supervised learning in parity machines, the theory of zero-temperature Gibbs learning for the unsupervised procedure is presented for the case that the networks are perceptrons and for the case of fully connected committees.

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In recent years there has been an increased interest in applying non-parametric methods to real-world problems. Significant research has been devoted to Gaussian processes (GPs) due to their increased flexibility when compared with parametric models. These methods use Bayesian learning, which generally leads to analytically intractable posteriors. This thesis proposes a two-step solution to construct a probabilistic approximation to the posterior. In the first step we adapt the Bayesian online learning to GPs: the final approximation to the posterior is the result of propagating the first and second moments of intermediate posteriors obtained by combining a new example with the previous approximation. The propagation of em functional forms is solved by showing the existence of a parametrisation to posterior moments that uses combinations of the kernel function at the training points, transforming the Bayesian online learning of functions into a parametric formulation. The drawback is the prohibitive quadratic scaling of the number of parameters with the size of the data, making the method inapplicable to large datasets. The second step solves the problem of the exploding parameter size and makes GPs applicable to arbitrarily large datasets. The approximation is based on a measure of distance between two GPs, the KL-divergence between GPs. This second approximation is with a constrained GP in which only a small subset of the whole training dataset is used to represent the GP. This subset is called the em Basis Vector, or BV set and the resulting GP is a sparse approximation to the true posterior. As this sparsity is based on the KL-minimisation, it is probabilistic and independent of the way the posterior approximation from the first step is obtained. We combine the sparse approximation with an extension to the Bayesian online algorithm that allows multiple iterations for each input and thus approximating a batch solution. The resulting sparse learning algorithm is a generic one: for different problems we only change the likelihood. The algorithm is applied to a variety of problems and we examine its performance both on more classical regression and classification tasks and to the data-assimilation and a simple density estimation problems.

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Iterative multiuser joint decoding based on exact Belief Propagation (BP) is analyzed in the large system limit by means of the replica method. It is shown that performance can be improved by appropriate power assignment to the users. The optimum power assignment can be found by linear programming in most technically relevant cases. The performance of BP iterative multiuser joint decoding is compared to suboptimum approximations based on Interference Cancellation (IC). While IC receivers show a significant loss for equal-power users, they yield performance close to BP under optimum power assignment.

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The evolutionarily conserved apical determinant Crumbs (Crb) is essential for maintaining apicobasal polarity and integrity of many epithelial tissues [1]. Crb levels are crucial for cell polarity and homeostasis, yet strikingly little is known about its trafficking or the mechanism of its apical localization. Using a newly established, liposome-based system described here, we determined Crb to be an interaction partner and cargo of the retromer complex. Retromer is essential for the retrograde transport of numerous transmembrane proteins from endosomes to the trans-Golgi network (TGN) and is conserved between plants, fungi, and animals [2]. We show that loss of retromer function results in a substantial reduction of Crb in Drosophila larvae, wing discs, and the follicle epithelium. Moreover, loss of retromer phenocopies loss of crb by preventing apical localization of key polarity molecules, such as atypical protein kinase C (aPKC) and Par6 in the follicular epithelium, an effect that can be rescued by overexpression of Crb. Additionally, loss of retromer results in multilayering of the follicular epithelium, indicating that epithelial integrity is severely compromised. Our data reveal a mechanism for Crb trafficking by retromer that is vital for maintaining Crb levels and localization. We also show a novel function for retromer in maintaining epithelial cell polarity.

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This paper formulates a logistics distribution problem as the multi-depot travelling salesman problem (MDTSP). The decision makers not only have to determine the travelling sequence of the salesman for delivering finished products from a warehouse or depot to a customer, but also need to determine which depot stores which type of products so that the total travelling distance is minimised. The MDTSP is similar to the combination of the travelling salesman and quadratic assignment problems. In this paper, the two individual hard problems or models are formulated first. Then, the problems are integrated together, that is, the MDTSP. The MDTSP is constructed as both integer nonlinear and linear programming models. After formulating the models, we verify the integrated models using commercial packages, and most importantly, investigate whether an iterative approach, that is, solving the individual models repeatedly, can generate an optimal solution to the MDTSP. Copyright © 2006 Inderscience Enterprises Ltd.

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In spite of increasing significance of Broadband Internet, there are not many research papers explicitly addressing issues pertaining to its deployment and continuance. Previous research on Broadband has mainly focused on the supply side aspect at the national level, ignoring the importance of the demand side which may involve looking more deeply into the factors impacting organizational and individual uptake. In an attempt to fill this gap, the current study empirically verifies the IS continuance model to examine factors influencing Broadband Internet post-adoption behavior of some 1,500 organizations in Singapore. Strong support for the model has been manifested by our results, providing insight into influential factors. Results of the study suggest that that perceived usefulness is the strongest predictor of users' continuance intention, followed by satisfaction with Broadband Internet usage as a significant but weaker predictor.

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Recently, there has been a considerable research activity in extending topographic maps of vectorial data to more general data structures, such as sequences or trees. However, the representational capabilities and internal representations of the models are not well understood. We rigorously analyze a generalization of the Self-Organizing Map (SOM) for processing sequential data, Recursive SOM (RecSOM [1]), as a non-autonomous dynamical system consisting off a set of fixed input maps. We show that contractive fixed input maps are likely to produce Markovian organizations of receptive fields o the RecSOM map. We derive bounds on parameter $\beta$ (weighting the importance of importing past information when processing sequences) under which contractiveness of the fixed input maps is guaranteed.

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The global market has become increasingly dynamic, unpredictable and customer-driven. This has led to rising rates of new product introduction and turbulent demand patterns across product mixes. As a result, manufacturing enterprises were facing mounting challenges to be agile and responsive to cope with market changes, so as to achieve the competitiveness of producing and delivering products to the market timely and cost-effectively. This paper introduces a currency-based iterative agent bidding mechanism to effectively and cost-efficiently integrate the activities associated with production planning and control, so as to achieve an optimised process plan and schedule. The aim is to enhance the agility of manufacturing systems to accommodate dynamic changes in the market and production. The iterative bidding mechanism is executed based on currency-like metrics; each operation to be performed is assigned with a virtual currency value and agents bid for the operation if they make a virtual profit based on this value. These currency values are optimised iteratively and so does the bidding process based on new sets of values. This is aimed at obtaining better and better production plans, leading to near-optimality. A genetic algorithm is proposed to optimise the currency values at each iteration. In this paper, the implementation of the mechanism and the test case simulation results are also discussed. © 2012 Elsevier Ltd. All rights reserved.