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Publication Metadata only Audio-driven human body motion analysis and synthesis(IEEE, 2008) Canton-Ferrer, C.; Tilmanne, J.; Bozkurt, E.; N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Department of Electrical and Electronics Engineering; Ofli, Ferda; Demir, Yasemin; Yemez, Yücel; Erzin, Engin; Tekalp, Ahmet Murat; PhD Student; Master Student; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; N/A; 107907; 34503; 26207This paper presents a framework for audio-driven human body motion analysis and synthesis. We address the problem in the context of a dance performance, where gestures and movements of the dancer are mainly driven by a musical piece and characterized by the repetition of a set of dance figures. The system is trained in a supervised manner using the multiview video recordings of the dancer. The human body posture is extracted from multiview video information without any human intervention using a novel marker-based algorithm based on annealing particle filtering. Audio is analyzed to extract beat and tempo information. The joint analysis of audio and motion features provides a correlation model that is then used to animate a dancing avatar when driven with any musical piece of the same genre. Results are provided showing the effectiveness of the proposed algorithm.Publication Metadata only Prediction of protein-protein interactions by combining structure and sequence conservation in protein interfaces(Oxford Univ Press, 2005) N/A; Department of Computer Engineering; Department of Chemical and Biological Engineering; Aytuna, Ali Selim; Gürsoy, Attila; Keskin, Özlem; Master Student; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 8745; 26605Motivation: Elucidation of the full network of protein- protein interactions is crucial for understanding of the principles of biological systems and processes. Thus, there is a need for in silico methods for predicting interactions. We present a novel algorithm for automated prediction of protein-protein interactions that employs a unique bottom-up approach combining structure and sequence conservation in protein interfaces. Results: Running the algorithm on a template dataset of 67 interfaces and a sequentially non-redundant dataset of 6170 protein structures, 62616 potential interactions are predicted. These interactions are compared with the ones in two publicly available interaction databases (Database of Interacting Proteins and Biomolecular Interaction Network Database) and also the Protein Data Bank. A significant number of predictions are verified in these databases. The unverified ones may correspond to (1) interactions that are not covered in these databases but known in literature, (2) unknown interactions that actually occur in nature and (3) interactions that do not occur naturally but may possibly be realized synthetically in laboratory conditions. Some unverified interactions, supported significantly with studies found in the literature, are discussed.