We describe a new method, Compass, for predicting the biological activities of molecules based on the activities and three-dimensional structures of other molecules. The method improves on previous techniques by representing only the surface of molecules, by incorporating a nonlinear statistical method, and by automatically choosing conformations and alignments of molecules. We use a benchmark problem of steroid binding affinity prediction to compare the performance of the method with that of two previous systems: CoMFA and a molecular similarity method. Compass predicts steroid affinities substantially more accurately than the others, which represent the state of the art. We present experiments showing that the improved performance depends on each of the technical innovations. IntroductionDrug discovery proceeds largely by trial and error. Typically, thousands of compounds are synthesized for each that finally becomes a drug. Each synthesis costs,
This paper describes two pilot studies, one completed and one ongoing, that evaluate the use of Tablet PCs and a Tablet-PCbased classroom presentation system in an introductory computer science class. The presentation system, Classroom Presenter [2, 3], supports student wireless submission of digital ink answers to in-class exercises. In these studies, we evaluate the hypothesis that the use of such a system increases student learning by: (1) increasing student focus and attentiveness in class, (2) providing immediate feedback to both students and instructor about student misunderstandings, (3) enabling the instructor to adjust course material in real-time based upon student answers to in-class exercises, (4) increasing student satisfaction. The studies evaluate each of the above four parameters by means of classroom observation, surveys, and interviews.
Location is a primary cue in many context-aware computing systems, and is often represented as a global coordinate, room number, or Euclidean distance various landmarks. A user's concept of location, however, is often defined in terms of regions in which common activities occur. We show how to partition a space into such regions based on patterns of observed user location and motion. These regions, which we call activity zones, represent regions of similar user activity, and can be used to trigger application actions, retrieve information based on previous context, and present information to users. We suggest that context-aware applications can benefit from a location representation learned from observing users. We describe an implementation of our system and present two example applications whose behavior is controlled by users' entry, exit, and presence in the zones.
The Problem: Location-based context is important for many applications. Previous systems offered only coarse room-level features or used manually specified room regions to determine fine-scale features. We propose a location context mechanism based on activity maps, which define regions of similar context based on observations of 3-D patterns of location and motion in an environment. We describe an algorithm for obtaining activity maps in real time using the spatio-temporal clustering of visual tracking data.Motivation: In many cases, fine grain location based information is preferred. One example would be to control lights and air conditioning, e.g. the desk lamp might light up and the air conditioning starts whenever a user is sitting at his desk. In addition the phone might become activated and the computer screen get invoked from stand-by mode. Similarly in a small group meeting the system could know where and how many people are in the room and could make appropriate settings for lights, air conditioning, and computer tools. For each of these tasks, location context information is important [3].
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