We present SurfaceLink, a system where users can make natural surface gestures to control association and information transfer among a set of devices placed on a mutually shared surface (e.g., a table). SurfaceLink uses a combination of on-device accelerometers, vibration motors, speakers, and microphones (and, optionally, an off-device contact microphone for greater sensitivity) to sense gestures performed on the shared surface. In a controlled evaluation with 10 participants, SurfaceLink detected the presence of devices on the same surface with 97.7% accuracy, their relative arrangement with 89.4% accuracy, and a set of single-and multi-touch surface gestures with an average accuracy of 90.3%. A usability analysis showed that SurfaceLink has advantages over current multi-device interaction techniques in a number of situations.

In this paper, we present efficient algorithms for finding indeterminate Arc-Annotated patterns in indeterminate Arc-Annotated references. Our algorithms run in Oem +�) time where n and m are respectively the w length of our reference and pattern strings and w is the size of our target machine word size. Here we have assumed the alphabet size to be constant, because, indeterminate Arc-Annotated sequences are used to model biological sequences. Clearly, for short patterns, our algorithms run in linear time and efficient algorithms for matching short patterns to reference genomes have huge applications in practical settings. We also perform some preliminary experiments that suggest that our algorithms run very fast in practice.