Secrets of Success: Identifying Success Factors in Institutional Repositories

4th International Conference on Open RepositoriesThis presentation was part of the session : Conference PresentationsDate: 2009-05-19 08:30 AM – 09:30 AMThere is little agreement on which factors lead to successful institutional repositories. Researchers primarily cite content recruitment and services as key factors; however, there has also been discussion of measuring IR success in terms of how well the IR furthers the overall goals of the library. This paper examines the topic of IRs and success. Our findings are based on a comparative case study of five IRs in colleges and universities. We argue that success should be measured by both internal (e.g., content or services) as well as external factors - how well the IR fulfills or brings the library closer to achieving its long-term goals in terms of service to the academic community.Institute of Museum and Library Service

Impacts of exercise intervention on various diseases in rats.

BackgroundExercise is considered as an important intervention for treatment and prevention of several diseases, such as osteoarthritis, obesity, hypertension, and Alzheimer's disease. This review summarizes decadal exercise intervention studies with various rat models across 6 major systems to provide a better understanding of the mechanisms behind the effects that exercise brought.MethodsPubMed was utilized as the data source. To collect research articles, we used the following terms to create the search: (exercise [Title] OR physical activity [Title] OR training [Title]) AND (rats [Title/Abstract] OR rat [Title/Abstract] OR rattus [Title/Abstract]). To best cover targeted studies, publication dates were limited to "within 11 years." The exercise intervention methods used for different diseases were sorted according to the mode, frequency, and intensity of exercise.ResultsThe collected articles were categorized into studies related to 6 systems or disease types: motor system (17 articles), metabolic system (110 articles), cardiocerebral vascular system (171 articles), nervous system (71 articles), urinary system (2 articles), and cancer (21 articles). Our review found that, for different diseases, exercise intervention mostly had a positive effect. However, the most powerful effect was achieved by using a specific mode of exercise that addressed the characteristics of the disease.ConclusionAs a model animal, rats not only provide a convenient resource for studying human diseases but also provide the possibility for exploring the molecular mechanisms of exercise intervention on diseases. This review also aims to provide exercise intervention frameworks and optimal exercise dose recommendations for further human exercise intervention research

Unleashing the potential of GNNs via Bi-directional Knowledge Transfer

Based on the message-passing paradigm, there has been an amount of research proposing diverse and impressive feature propagation mechanisms to improve the performance of GNNs. However, less focus has been put on feature transformation, another major operation of the message-passing framework. In this paper, we first empirically investigate the performance of the feature transformation operation in several typical GNNs. Unexpectedly, we notice that GNNs do not completely free up the power of the inherent feature transformation operation. By this observation, we propose the Bi-directional Knowledge Transfer (BiKT), a plug-and-play approach to unleash the potential of the feature transformation operations without modifying the original architecture. Taking the feature transformation operation as a derived representation learning model that shares parameters with the original GNN, the direct prediction by this model provides a topological-agnostic knowledge feedback that can further instruct the learning of GNN and the feature transformations therein. On this basis, BiKT not only allows us to acquire knowledge from both the GNN and its derived model but promotes each other by injecting the knowledge into the other. In addition, a theoretical analysis is further provided to demonstrate that BiKT improves the generalization bound of the GNNs from the perspective of domain adaption. An extensive group of experiments on up to 7 datasets with 5 typical GNNs demonstrates that BiKT brings up to 0.5% - 4% performance gain over the original GNN, which means a boosted GNN is obtained. Meanwhile, the derived model also shows a powerful performance to compete with or even surpass the original GNN, enabling us to flexibly apply it independently to some other specific downstream tasks.Comment: 13 pages, 9 figure