Discovering Multidimensional Motifs in Physiological Signals for Personalized Healthcare.

Personalized diagnosis and therapy requires monitoring patient activity using various body sensors. Sensor data generated during personalized exercises or tasks may be too specific or inadequate to be evaluated using supervised methods such as classification. We propose multidimensional motif (MDM) discovery as a means for patient activity monitoring, since such motifs can capture repeating patterns across multiple dimensions of the data, and can serve as conformance indicators.

Fast l₁-minimization algorithms for robust face recognition.

l1-minimization refers to finding the minimum l1-norm solution to an underdetermined linear system [Formula: see text]. Under certain conditions as described in compressive sensing theory, the minimum l1-norm solution is also the sparsest solution. In this paper, we study the speed and scalability of its algorithms.

Mining pattern sequences in respiratory tumor motion data.

Management of respiration induced tumor motion during radiation therapy is crucial to effective treatment. Pattern sequences in the tumor motion signals can be valuable features in the analysis and prediction of irregular tumor motion. In this study, we put forward an approach towards mining pattern sequences in respiratory tumor motion data.