We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into ...
This paper presents a new model for mobile processes in occam-π. A process, embedded anywhere in a dynamically evolving network, may suspend itself mid-execution, be safely discon...
The concept of typed attributed graph transformation is most significant for modeling and meta modeling in software engineering and visual languages, but up to now there is no ade...
In this paper we articulate a new modeling paradigm for both local and global editing on complicated point set surfaces of arbitrary topology. In essence, the proposed technique l...
Data management in wireless sensor networks has been an area of significant research in recent years. Many existing sensor data management systems view sensor data as a continuou...