13,237 research outputs found

    Twisted atrioventricular connections in double inlet right ventricle: evaluation by magnetic resonance imaging

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    Twisted atrioventricular connections occur almost exclusively in the hearts with biventricular atrioventricular connections. Only one example of double inlet left ventricle has been illustrated in which the axes of the two atrioventricular valves crossed each other. We describe herein three patients, and one autopsied specimen, with double inlet right ventricle in which magnetic resonance imaging clearly demonstrated twisted atrioventricular connections

    Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking

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    The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. These limitations add significantly to development costs and make cross-domain, multi-lingual dialogue systems intractable. Moreover, human languages are context-aware. The most natural response should be directly learned from data rather than depending on predefined syntaxes or rules. This paper presents a statistical language generator based on a joint recurrent and convolutional neural network structure which can be trained on dialogue act-utterance pairs without any semantic alignments or predefined grammar trees. Objective metrics suggest that this new model outperforms previous methods under the same experimental conditions. Results of an evaluation by human judges indicate that it produces not only high quality but linguistically varied utterances which are preferred compared to n-gram and rule-based systems.Comment: To be appear in SigDial 201

    Online Primal-Dual Algorithms with Configuration Linear Programs

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    In this paper, we present primal-dual algorithms for online problems with non-convex objectives. Problems with convex objectives have been extensively studied in recent years where the analyses rely crucially on the convexity and the Fenchel duality. However, problems with non-convex objectives resist against current approaches and non-convexity represents a strong barrier in optimization in general and in the design of online algorithms in particular. In our approach, we consider configuration linear programs with the multilinear extension of the objectives. We follow the multiplicative weight update framework in which a novel point is that the primal update is defined based on the gradient of the multilinear extension. We introduce new notions, namely (local) smoothness, in order to characterize the competitive ratios of our algorithms. The approach leads to competitive algorithms for several problems with convex/non-convex objectives

    Improving the connectivity of heterogeneous multi-hop wireless networks

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    Heterogeneous conditions can occur in multi-hop wireless networks due to a variety of factors such as variations in transmission power and signal propagation environments. Directed links can occur when the environment and/or the nodes are heterogeneous. In this paper, we examine the network connectivity for heterogeneous multi-hop wireless networks and propose an algorithm to identify the connectivity of the network. We follow this with a numerical study of the connectivity in random topologies. Lastly, we propose two schemes for constructing additional links to enhance the connectivity of the network. Our proposed schemes identify the links to be improved or created via a cluster based approach. © 2011 IEEE