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The reflectance function of a scene point captures the appearance of that point as a function of lighting direction. We present an approach to printing the reflectance functions of an object or scene so that its appearance is modified correctly as a function of the lighting conditions when viewing the print. For example, such a “photograph” of a statue printed with our approach appears to cast shadows to the right when the “photograph” is illuminated from the left. Viewing the same print with lighting from the right will cause the statue's shadows to be cast to the left. Beyond shadows, all effects due to the lighting variation, such as Lambertian shading, specularity, and inter-reflection can be reproduced. We achieve this ability by geometrically and photometrically controlling specular highlights on the surface of the print. For a particular viewpoint, arbitrary reflectance functions can be built up at each pixel by controlling only the specular highlights and avoiding significant diffuse reflections. Our initial binary prototype uses halftoning to approximate continuous grayscale reflectance functions.

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Predicting access times is a crucial part of predicting hard disk drive performance. Existing approaches use white-box modeling and require intimate knowledge of the internal layout of the drive, which can take months to extract. Automatically learning this behavior is a much more desirable approach, requiring less expert knowledge, fewer assumptions, and less time. Others have created behavioral models of hard disk drive performance, but none have shown low per-request errors. A barrier to machine learning of access times has been the existence of periodic behavior with high, unknown frequencies. We show how hard disk drive access times can be predicted to within 0.83 ms using a neural net after these frequencies are found using Fourier analysis.

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