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Procedural models are a powerful tool for quickly creating a variety of computer graphics content. However, authoring them is challenging, requiring both programming and artistic expertise. In this paper, we present a method for learning procedural models from a small number of example objects. We focus on the modular design setting, where objects are constructed from a common library of parts. Our procedural representation is a probabilistic program that models both the discrete, hierarchical structure of the examples as well as the continuous variability in their spatial arrangements of parts. We develop an algorithm for learning such programs from examples, using combinatorial search over program structures and variational inference to estimate continuous program parameters. We evaluate our method by demonstrating its ability to learn programs from examples of ornamental designs, spaceships, space stations, and castles. Experiments suggest that our learned programs can reliably generate a variety of new objects that are perceptually indistinguishable from hand‐crafted examples.

Intermediate-length polyglutamine expansions in ataxin 2 are a risk factor for ALS. The polyglutamine tract is encoded by a trinucleotide repeat in a coding region of the ataxin 2 gene (ATXN2). Non-coding nucleotide repeat expansions in several genes are also associated with neurodegenerative and neuromuscular diseases. For example, hexanucleotide repeat expansions located in a non-coding region of C9ORF72 are the most common cause of ALS. We sought to assess a potential larger role of non-coding nucleotide repeat expansions in ALS. We analyzed the nucleotide repeat lengths of six genes (ATXN8, ATXN10, PPP2R2B, NOP56, DMPK and JPH3), which have been previously associated with neurological or neuromuscular disorders, in several hundred sporadic ALS patients and healthy controls. We report no association between ALS and repeat length in any of these genes, suggesting that variation in the non-coding repetitive regions in these genes does not contribute to ALS.

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The scaling of Silicon devices has exacerbated the unreliability of modern computer systems, and power constraints have necessitated the involvement of software in hardware error detection. At the same time, emerging workloads in the form of soft computing applications (e.g., multimedia applications) can tolerate most hardware errors as long as the erroneous outputs do not deviate significantly from error-free outcomes. We term outcomes that deviate significantly from the error-free outcomes as Egregious Data Corruptions (EDCs). In this study, we propose a technique to place detectors for selectively detecting EDC-causing errors in an application. We performed an initial study to formulate heuristics that identify EDC-causing data. Based on these heuristics, we developed an algorithm that identifies program locations for placing high coverage detectors for EDCs using static analysis. Our technique achieves an average EDC coverage of 82%, under performance overheads of 10%, while detecting 10% of the Non-EDC and benign faults. We also evaluate the error resilience of these applications under the 14 compiler optimizations.