50,885 research outputs found

    The pp-Center Problem in Tree Networks Revisited

    Get PDF
    We present two improved algorithms for weighted discrete pp-center problem for tree networks with nn vertices. One of our proposed algorithms runs in O(nlogn+plog2nlog(n/p))O(n \log n + p \log^2 n \log(n/p)) time. For all values of pp, our algorithm thus runs as fast as or faster than the most efficient O(nlog2n)O(n\log^2 n) time algorithm obtained by applying Cole's speed-up technique [cole1987] to the algorithm due to Megiddo and Tamir [megiddo1983], which has remained unchallenged for nearly 30 years. Our other algorithm, which is more practical, runs in O(nlogn+p2log2(n/p))O(n \log n + p^2 \log^2(n/p)) time, and when p=O(n)p=O(\sqrt{n}) it is faster than Megiddo and Tamir's O(nlog2nloglogn)O(n \log^2n \log\log n) time algorithm [megiddo1983]

    The Steiner tree problem revisited through rectifiable G-currents

    Full text link
    The Steiner tree problem can be stated in terms of finding a connected set of minimal length containing a given set of finitely many points. We show how to formulate it as a mass-minimization problem for 11-dimensional currents with coefficients in a suitable normed group. The representation used for these currents allows to state a calibration principle for this problem. We also exhibit calibrations in some examples

    A neural network architecture for implementation of expert systems for real time monitoring

    Get PDF
    Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered

    Bird's tree allocations revisited

    Get PDF
    Game Theory;Cost Allocation

    Combining Local Appearance and Holistic View: Dual-Source Deep Neural Networks for Human Pose Estimation

    Full text link
    We propose a new learning-based method for estimating 2D human pose from a single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN). Recently, many methods have been developed to estimate human pose by using pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective. In this paper, we propose to integrate both the local (body) part appearance and the holistic view of each local part for more accurate human pose estimation. Specifically, the proposed DS-CNN takes a set of image patches (category-independent object proposals for training and multi-scale sliding windows for testing) as the input and then learns the appearance of each local part by considering their holistic views in the full body. Using DS-CNN, we achieve both joint detection, which determines whether an image patch contains a body joint, and joint localization, which finds the exact location of the joint in the image patch. Finally, we develop an algorithm to combine these joint detection/localization results from all the image patches for estimating the human pose. The experimental results show the effectiveness of the proposed method by comparing to the state-of-the-art human-pose estimation methods based on pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective.Comment: CVPR 201
    corecore