This paper presents a minimally intrusive real-time gaze-tracking prototype to be used in several scenarios, including a laboratory stall and an in-vehicle system. The system requires specific infrared illumination to allow it to work with variable light conditions. However, it is minimally intrusive due to the use of a carefully configured switched infrared LED array. Although the perceived le...

We describe a distributed version of an advanced branch and bound algorithm over graphical models. The crucial issue of load balancing is addressed by estimating subproblem complexity through learning, yielding impressive speedups on various hard problems using hundreds of parallel CPUs.

In this paper we describe the rapid prototyping of an adaptive speech beamformer, using the Graphical Rapid Prototyping Environment: GRAPE-II [1]. We used a general purpose, commercially available multi-processor with 4 TMS320C40 DSP-processors. Our work proves the feasibility of real-time prototyping using a multiprocessor and a powerful programming

Handheld devices such as UMPC, though convenient, bear weakness of size constraint for display. To mitigate such a problem and enhance user experience for owners of small devices, in this paper we design a collaborative rendering platform. When running game graphic applications at a handheld, the generated OpenGL graphic commands are intercepted and then delivered to a device with a larger disp...

The use of probabilistic models of amino acid replacement is essential for the study of protein evolution, and programs like ProtTest implement different strategies to identify the best-fit model for the data at hand. For large protein alignments, this task can demand vast computational resources, preventing the justification of the model used in the analysis. We have implemented a High Perform...

High-end microprocessors are increasing in complexity to push the limits of speed and performance. As a result, analyzing these complex systems can be an arduous task. Architectural simulators, acting as software processors, are able to run programs and give statistics about the performance of the code on the design. While these statistics are valuable for identifying problems, they often do no...

Over the last decades, probabilistic graphical models have become the method of choice for representing uncertainty in machine learning. They are used in many research areas such as computer vision, speech processing, time-series and sequential data modelling, cognitive science, bioinformatics, probabilistic robotics, signal processing, communications and error-correcting coding theory, and in ...