"To infinity and beyond!" - A genre-specific film analysis of movie success mechanisms - Kaimann, Daniel
The objective of this study is the analysis of movie success mechanisms in a genre-specific context. Instead of the examination of all time box office champions, we focus on the two film genres of computer animated and comic book based films. By introducing the concept of the motion-picture marketing mix, which represents a set of tactical marketing tools in order to strengthen a company’s strategic customer orientation, we are able to systematically identify key movie success factors. We conduct a cross-sectional empirical analysis across regional distinctions based on dataset that covers a time horizon of more than 30 years. We find empirical evidence that actors with ex ante popularity, award nominations and the production budget represent key movie success mechanisms and significantly influence a movie’s commercial appeal. Additionally, word-of-mouth creates reputation effects that also significantly affects box office gross.

Measuring and Predicting Temperature Distributions on FPGAs at Run-Time - Happe, Markus; Agne, Andreas; Plessl, Christian (Proceedings of the 2011 International Conference on Reconfigurable Computing and FPGAs (ReConFig))
In the next decades, hybrid multi-cores will be the predominant architecture for reconfigurable FPGA-based systems. Temperature-aware thread mapping strategies are key for providing dependability in such systems. These strategies rely on measuring the temperature distribution and redicting the thermal behavior of the system when there are changes to the hardware and software running on the FPGA. While there are a number of tools that use thermal models to predict temperature distributions at design time, these tools lack the flexibility to autonomously adjust to changing FPGA configurations. To address this problem we propose a temperature-aware system that empowers FPGA-based reconfigurable multi-cores to autonomously predict the on-chip temperature distribution for pro-active thread remapping. Our system obtains temperature measurements through a self-calibrating grid of sensors and uses area constrained heat-generating circuits in order to generate spatial and temporal temperature gradients. The generated temperature variations are then used to learn the free parameters of the system's thermal model. The system thus acquires an understanding of its own thermal characteristics. We implemented an FPGA system containing a net of 144 temperature sensors on a Xilinx Virtex-6 LX240T FPGA that is aware of its thermal model. Finally, we show that the temperature predictions vary less than 0.72 degree C on average compared to the measured temperature distributions at run-time.