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51.
The purpose of this study was to identify self-advocacy group advisor activities, to examine advisor activities as they are linked to self-advocacy group development, and to examine how advisor activities change over time. A telephone survey was administered to 118 advisors randomly selected from a directory of self-advocacy groups (Longhurst, 1994). A total of 90 telephone surveys were completed representing a response rate of 76%. Major findings reveal a wide array of advisor activities, but perhaps most importantly, information on how advisors see their activities changing as the group changes. The results of this investigation provide important baseline information for individuals affiliated with the self-advocacy movement, university affiliated programs, developmental disability councils, and professionals in the field of intellectual impairment/developmental disabilities. The results can be used to help better meet the needs of advisors, self-advocacy group members, and to help readers to better understand how to support a self-advocacy group as it is evolving.  相似文献   
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A recent “third wave” of neural network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing. Because these modern NNs often comprise multiple interconnected layers, work in this area is often referred to as deep learning. Recent years have witnessed an explosive growth of research into NN-based approaches to information retrieval (IR). A significant body of work has now been created. In this paper, we survey the current landscape of Neural IR research, paying special attention to the use of learned distributed representations of textual units. We highlight the successes of neural IR thus far, catalog obstacles to its wider adoption, and suggest potentially promising directions for future research.  相似文献   
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This article presents results of a study of library-related posts on Yik Yak, a former social media app, and questions received via more traditional library-maintained virtual reference service platforms—namely, chat/instant messaging, SMS/text messaging, email, and LibAnswers' Query Spy—at College of Charleston's Addlestone Library. Based on an analysis of the results, suggestions are made for using social media tools similar to Yik Yak as a tool for interacting with patrons in academic library settings, specifically as a means to gather feedback about the library and to provide basic reference services.  相似文献   
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This article probes ways in which the school-choice marketplace as it developed via chartering has not worked as well in practice as many had hoped. It includes reflection on the profoundly different operating principles and theories of action that separate the district and charter sectors in their pure forms. It also offers market-strengthening suggestions that blend the 2 approaches, organizing them under 10 categories.  相似文献   
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We compared SenseWear Armband versions (v) 2.2 and 5.2 for estimating energy expenditure in healthy adults. Thirty-four adults (26 women), 30.1 ± 8.7 years old, performed two trials that included light-, moderate- and vigorous-intensity activities: (1) structured routine: seven activities performed for 8-min each, with 4-min of rest between activities; (2) semi-structured routine: 12 activities performed for 5-min each, with no rest between activities. Energy expenditure was measured by indirect calorimetry and predicted using SenseWear v2.2 and v5.2. Compared to indirect calorimetry (297.8 ± 54.2 kcal), the total energy expenditure was overestimated (P < 0.05) by both SenseWear v2.2 (355.6 ± 64.3 kcal) and v5.2 (342.6 ± 63.8 kcal) during the structured routine. During the semi-structured routine, the total energy expenditure for SenseWear v5.2 (275.2 ± 63.0 kcal) was not different than indirect calorimetry (262.8 ± 52.9 kcal), and both were lower (P < 0.05) than v2.2 (312.2 ± 74.5 kcal). The average mean absolute per cent error was lower for the SenseWear v5.2 than for v2.2 (P < 0.001). SenseWear v5.2 improved energy expenditure estimation for some activities (sweeping, loading/unloading boxes, walking), but produced larger errors for others (cycling, rowing). Although both algorithms overestimated energy expenditure as well as time spent in moderate-intensity physical activity (P < 0.05), v5.2 offered better estimates than v2.2.  相似文献   
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