William Martin

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A system is presented for ray tracing trimmed NURBS surfaces. While approaches to components are drawn largely from existing literature, their combination within a single framework is novel. This paper also differs from prior work in that the details of an efficient implementation are fleshed out. Throughout, emphasis is placed on practical methods suitable(More)
We examine a rendering system that interactively ray traces an image on a conventional multiprocessor. The implementation is "brute force" in that it explicitly traces rays through every screen pixel, yet pays careful attention to system resources for acceleration. The design of the system is described, along with issues related to material models, lighting(More)
While traditional graphics techniques provide for the realistic display of three-dimensional objects, these methods often lack the flexibility to emulate expressive effects found in the works of artists such as Michelangelo and Cezanne. We introduce a technique for capturing custom artistic shading models from sampled art work. Our goal is to allow users to(More)
Our goal in this paper is to leverage traditional strengths from the geometric design and scientific visualization communities to produce a tool valuable to both. We present a method for representing and specifying attribute data across a trivariate NURBS volume. Some relevant attribute quantities include material composition and density, optical indices of(More)
Although ray tracing has been successfully applied to interactively render large datasets, su-persampling pixels will not be practical in interactive applications for some time. Because large datasets tend to have subpixel detail, one-sample-per-pixel ray tracing can produce visually distracting popping and scintillation. We present an algorithm that(More)
Many papers on App Store Mining are susceptible to the App Sampling Problem, which exists when only a subset of apps are studied, resulting in potential sampling bias. We introduce the App Sampling Problem, and study its effects on sets of user review data. We investigate the effects of sampling bias, and techniques for its amelioration in App Store Mining(More)
We present iRun, a system for interactively volume rendering large unstructured grids on commodity PCs. Rendering arbitrarily large datasets has been an active area of research for many years. However, the techniques required for polygonal data do not directly apply to the more complex problem of unstructured grids. In this paper, we describe the data(More)
This work presents a novel algorithm to quantify the relation between three factors that characterize a side channel adversary: the amount of observed side channel leakage, the workload of full key recovery, and its achievable success rate. The proposed algorithm can be used by security evaluators to derive a realistic bound on the capabilities of a side(More)
App developers would like to understand the impact of their own and their competitors’ software releases. To address this we introduce Causal Impact Release Analysis for app stores, and our tool, CIRA, that implements this analysis. We mined 38,858 popular Google Play apps, over a period of 12 months. For these apps, we identified 26,339 releases for(More)