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A heterogeneous information network is a network composed of multiple types of objects and links. Recently, it has been recognized that strongly-typed heterogeneous information networks are prevalent in the real world. Sometimes, label information is available for some objects. Learning from such labeled and unlabeled data via transductive classification(More)
BACKGROUND It is a public health recommendation to accumulate at least 150 minutes per week of moderate intensity physical activity. Although pedometers are widely used as a physical activity-monitoring tool, they are unable to measure activity intensity. Translating current physical activity recommendations into a pedometer-based guideline could increase(More)
With the maturity and wide availability of GPS, wireless, telecommunication, and Web technologies, massive amounts of object movement data have been collected from various moving object targets, such as animals, mobile devices, vehicles, and climate radars. Analyzing such data has deep implications in many applications, such as, ecological study, traffic(More)
The Hippo pathway controls tissue growth and tumorigenesis by inhibiting cell proliferation and promoting apoptosis. Recent genetic studies in Drosophila identified Kibra as a novel regulator of Hippo signaling. Human KIBRA has been associated with memory performance and cell migration. However, it is unclear whether or how KIBRA is connected to the Hippo(More)
It has been recently recognized that heterogeneous information networks composed of multiple types of nodes and links are prevalent in the real world. Both classification and ranking of the nodes (or data objects) in such networks are essential for network analysis. However, so far these approaches have generally been performed separately. In this paper, we(More)
In many information processing tasks, one is often confronted with very high-dimensional data. Feature selection techniques are designed to find the meaningful feature subset of the original features which can facilitate clustering, classification, and retrieval. In this paper, we consider the feature selection problem in unsupervised learning scenarios,(More)
We consider the problem of lossy image compression from machine learning perspective. Typical image compression algorithms first transform the image from its spatial domain representation to frequency domain representation using some transform technique, such as discrete cosine transform and discrete wavelet transform, and then code the transformed values.(More)
As the Internet grows explosively, search engines play a more and more important role for users in effectively accessing online information. Recently, it has been recognized that a query is often triggered by a search task that the user wants to accomplish. Similarly, many web pages are specifically designed to help accomplish a certain task. Therefore,(More)
PURPOSE To determine the prevalence of disordered eating (DE) attitudes and behaviors in a multi-racial/ethnic sample of female high-school athletes. METHODS The Eating Disorders Examination Questionnaire (EDE-Q) was administered to 453 suburban female high-school athletes (277 Caucasian, 103 Latina, and 73 African American; aged 15.7 +/- 1.2 years)(More)