We present We Feel Fine, an emotional search engine and web-based artwork whose mission is to collect the world's emotions to help people better understand themselves and others. We Feel Fine continuously crawls blogs, microblogs, and social networking sites, extracting sentences that include the words "I feel" or "I am feeling", as well as the gender, age, and location of the people authoring those sentences. The We Feel Fine search interface allows users to search or browse over the resulting sentence-level index, asking questions such as "How did young people in Ohio feel when Obama was elected?" While most research in sentiment analysis focuses on algorithms for extraction and classification of sentiment about given topics, we focus instead on building an interface that provides an engaging means of qualitative exploration of emotional data, and a flexible data collection and serving architecture that enables an ecosystem of data analysis applications. We use our observations on the usage of We Feel Fine to suggest a class of visualizations called Experiential Data Visualization, which focus on immersive item-level interaction with data. We also discuss the implications of such visualizations for crowdsourcing qualitative research in the social sciences.

We describe the development of built-in self-test (BIST) for a generic SoC consisting of a field programmable gate array (FPGA) core for application specific logic along with a processor and several memory cores. Our target device was the Atmel AT94K series system-on-chip (SoC), also known as a field programmable system level integrated circuit (FPSLIC). The original goal for this project was to develop BIST configurations to completely test the programmable logic and routing resources of the FPGA core and then to use the FPGA core to test the other cores. We found that the FPGA can provide only limited testing of the some memory cores and even less testing of the processor. The processor, on the other hand, provides more effective testing of some memory cores than the FPGA core. In addition, the ability of the processor to write the FPGA configuration memory provides an improved and more efficient method of testing the FPGA core. As a result, the processor core was the primary testing resource instead of the FPGA.

This paper describes extensions to OpenMP that implemen data placemen features needed for NUMA architectures. OpenMP is a collection of compiler directives and library routines used to write portable parallel programs for shared-memory architectures. Writing efficient parallel programs for NUMA architectures, which have characteristics of both shared-memory and distributed-memory architectures, requires that a programmer control the placement of data in memory and the placement of computations that operate on that data. Optimal performance is obtained when computations occur on processors that have fast access to the data needed by those computations. OpenMP-designed for shared-memory architectures-does not by itself address these issues. The extensions to OpenMP Fortran presented here have been mainly taken from High Performance Fortran. The paper describes some of the techniques that the Compaq Fortran compiler uses to generate efficient code based on these extensions. I also describes some additional compiler optimizations, and concludes with some preliminary results.