| CS-2024-01 | ||||
|---|---|---|---|---|
| Title | A Study on Sand Ripple Visualization | |||
| Authors | Andy Yu, Gladimir V. G. Baranoski | |||
| Abstract | This report presents a comprehensive study on the visualization of sand ripple phenomena, focusing on aeolian ripples rather than subaqueous ones. We begin with an overview of the phenomena and categorization of sand ripples, driven by fluid dynamics and granular physics. Pioneering approaches, from procedural texture generation to physically-based and real-time simulations, are examined. Our discussion presents a comparison of existing algorithms, highlighting their strengths and limitations. The report concludes with perspectives on future research directions, exploring the potential of deep learning and physically inspired methods. | |||
| Date | January 15, 2024 | |||
| Report | CS-2024-01 (4.8 MB PDF) | |||
| CS-2024-02 | ||||
| Title | A Study on Rendering Techniques to Visually Represent Sparkles | |||
| Authors | Frank Fan, Gladimir V. G. Baranoski | |||
| Abstract | Advances in rendering have allowed researchers to produce physically-based and realistic images of surfaces with a sparkly appearance. In this report, we provide a comprehensive survey that examines the phenomenon of sparkles and the different methods used to render them. We then highlight the current limitations of existing works and outline future research opportunities. | |||
| Date | January 19, 2024 | |||
| Report | CS-2024-02 (16.8 MB PDF) | |||
| CS-2024-03 | ||||
| Title | Compiling Probabilistic Programs for Variable Elimination with Information Flow (Extended Version) | |||
| Authors | Jianlin Li, Eric Wang, Yizhou Zhang | |||
| Abstract | A key promise of probabilistic programming is the ability to specify rich models using an expressive programming language. However, the expressive power that makes probabilistic programming languages enticing also poses challenges to inference, so much so that specialized approaches to inference ban language features such as recursion. We present an approach to variable elimination and marginal inference for probabilistic programs featuring bounded recursion, discrete distributions, and sometimes continuous distributions. A compiler eliminates probabilistic side effects, using a novel information-flow type system to factorize probabilistic computations and hoist independent subcomputations out of sums or integrals. For a broad class of recursive programs with dynamically recurring substructure, the compiler effectively decomposes a global marginal-inference problem, which may otherwise be intractable, into tractable subproblems. We prove the compilation correct by showing that it preserves denotational semantics. Experiments show that the compiled programs subsume widely used PTIME algorithms for recursive models and that the compilation time scales with the size of the inference problems. As a separate contribution, we develop a denotational, logical-relations model of information-flow types in the novel measure-theoretic setting of probabilistic programming; we use it to prove noninterference and consequently the correctness of variable elimination. This technical report is the extended version of a paper published at PLDI 2024. | |||
| Date | June 24, 2024 | |||
| Report | CS-2024-03 (954 kB PDF) | |||
