30,588 research outputs found

    Applications of medical wireless LAN systems (MedLAN)

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    This is a post-peer-review, pre-copyedit version of an article published in Journal of Medical Marketing. The definitive publisher-authenticated version "Konstantinos A. Banitsas, R.S.H. Istepanian, Sapal Tachakra. Applications of medical Wireless LAN systems (MedLAN). Journal of Medical Marketing, Volume 2, Number 2, 1 January 2002 , pp. 136-142(7)" is available online at: http://www.ingentaconnect.com/content/pal/jomm/2002/00000002/00000002/art00008.In this paper the Wireless LAN (WLAN) networking principals are presented along with some of the implementation scenarios dedicated for Accidents and Emergencies wards. Preliminary simulation results of the MedLAN concept are also presented together with ongoing and future work in this area

    A Semantic Graph-Based Approach for Mining Common Topics From Multiple Asynchronous Text Streams

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    In the age of Web 2.0, a substantial amount of unstructured content are distributed through multiple text streams in an asynchronous fashion, which makes it increasingly difficult to glean and distill useful information. An effective way to explore the information in text streams is topic modelling, which can further facilitate other applications such as search, information browsing, and pattern mining. In this paper, we propose a semantic graph based topic modelling approach for structuring asynchronous text streams. Our model in- tegrates topic mining and time synchronization, two core modules for addressing the problem, into a unified model. Specifically, for handling the lexical gap issues, we use global semantic graphs of each timestamp for capturing the hid- den interaction among entities from all the text streams. For dealing with the sources asynchronism problem, local semantic graphs are employed to discover similar topics of different entities that can be potentially separated by time gaps. Our experiment on two real-world datasets shows that the proposed model significantly outperforms the existing ones

    Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary

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    Cross-lingual model transfer is a compelling and popular method for predicting annotations in a low-resource language, whereby parallel corpora provide a bridge to a high-resource language and its associated annotated corpora. However, parallel data is not readily available for many languages, limiting the applicability of these approaches. We address these drawbacks in our framework which takes advantage of cross-lingual word embeddings trained solely on a high coverage bilingual dictionary. We propose a novel neural network model for joint training from both sources of data based on cross-lingual word embeddings, and show substantial empirical improvements over baseline techniques. We also propose several active learning heuristics, which result in improvements over competitive benchmark methods.Comment: 5 pages with 2 pages reference. Accepted to appear in ACL 201

    Concrete modelling and materials behaviour in honor of Professor Klaas van Breugel

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