PPT-Feature Emphasis and Contextual Cutaways for Multimodal Medical Visualization

Michael Burns Martin Haidacher Eduard Gröller Ivan Viola Wolfgang Wein Preface CT scan with embedded Ultrasound data Michael Burns Contextual Medical Visualization Visualization Scenario. Michael Kandefer and Stuart C. Shapiro. University at Buffalo. Department of Computer Science and Engineering. Center for Multisource Information Fusion. Center for Cognitive Science. {mwk3,shapiro}@. Norman Amundson. University of British Columbia. amundson@interchange.ubc.ca. . decision. trigger. external. influences. determining. contexts. ACTION. framing. Interactive Decision. Making Model. Mark Nelson. Office of Statewide Multimodal Planning. Reorganized to Support Multimodal Planning. A new Office of Statewide Multimodal Planning was created in February 2010. Goals for . Mn. /DOT:. Be structured to ensure multimodal planning . Ross . Girshick. , Jeff Donahue, Trevor Darrell, . Jitandra. Malik (UC Berkeley). Presenter: . Hossein. . Azizpour. Abstract. Can CNN improve . s.o.a. . object detection results?. Yes, it helps by learning rich representations which can then be combined with computer vision techniques.. ABOUT SGT.io. SGT.io. . offers . native advertising technology to monetize contextual . commerce.. We create new customers . by converting traffic and increasing order values across 25M+ users.. We increase ad-engagement . A Veprik, a TUITTO. Scd. , . imod. OUTLINE. Introduction and motivation. tuned dynamic absorber – how stuff works?. Multimodal tuned dynamic absorber . Concept. Equations of motion. Attainable performance. Sureyya Tarkan, Kostas . Pantazos. , Catherine . Plaisant. , Ben . Shneiderman. UMD Dept of Computer Science & HCIL. sureyya@cs.umd.edu. http://www.cs.umd.edu/hcil/sharp/. A Story. 1. Missed Lab Results. work. Helsingfors 150321. Staffan Selander. Stockholm University. Tack!. Thank. . you. !. Danielsson, K. & Selander, S. (In prep.) . Reading Multimodal Texts for Learning – A Model for Cultivating Multimodal Literacy. Faculty meeting. September 23, 2010. Satisfaction with goals and features. The survey results showed that faculty are satisfied overall with the goals and features of WRIT 1122. . In cases where goals/features were rated lower than others, they weren’t that much lower.. Michael Burns. Martin . Haidacher. Eduard . Gröller. Ivan Viola. Wolfgang . Wein. Preface. CT scan with embedded Ultrasound data. Michael Burns - Contextual Medical Visualization. Visualization Scenario. Mai H. El-. Shehaly. CS 6603: Reinventing CS education through the . eTextbook. Spring 2012. 1. Computer-based education relies on CS research. :. Image processing. Database systems. Massive Model Processing. Students: Gal Paikin, Nir Bachrach. Supervisor: Amir Kantor. Team . Gal Paikin – A student in his final year in . Bsc. Computer Science. Nir Bachrach – A student in his third year, in BSc Computer Science and Mathematics. . SWOT recommended:. closer industry ties as collaborations. connect to consumer base with technologies that also benefit the main . testbeds. Following a graduated . testbed. : “. neurogaming. ”. 50 K Intel grant to the CSNE. Week 7 Video 3. Thank you. Thank you to . Yiqiu. (Rachel) Zou for feedback and comments on this video. Multimodal Learning Analytics. “A set of techniques that can be used to collect multiple sources of data in high-frequency (video, logs, audio, gestures, biosensors), synchronize and code the data, and examine learning in realistic, ecologically valid, social, mixed-media learning environments.” (.