Many-core compiler fuzzing

We address the compiler correctness problem for many-core systems through novel applications of fuzz testing to OpenCL compilers. Focusing on two methods from prior work, random differential testing and testing via equivalence modulo inputs (EMI), we present several strategies for random generation of deterministic, communicating OpenCL kernels, and an injection mechanism that allows EMI testing to be applied to kernels that otherwise exhibit little or no dynamically-dead code. We use these methods to conduct a large, controlled testing campaign with respect to 21 OpenCL (device, compiler) configurations, covering a range of CPU, GPU, accelerator, FPGA and emulator implementations. Our study provides independent validation of claims in prior work related to the effectiveness of random differential testing and EMI testing, proposes novel methods for lifting these techniques to the many-core setting and reveals a significant number of OpenCL compiler bugs in commercial implementations

Alien Registration- Aho, Arvo C. (Dedham, Hancock County)

https://digitalmaine.com/alien_docs/19726/thumbnail.jp

Construct, Merge, Solve and Adapt: Application to the repetition-free longest common subsequence problem

In this paper we present the application of a recently proposed, general, algorithm for combinatorial optimization to the repetition-free longest common subsequence problem. The applied algorithm, which is labelled Construct, Merge, Solve & Adapt, generates sub-instances based on merging the solution components found in randomly constructed solutions. These sub-instances are subsequently solved by means of an exact solver. Moreover, the considered sub-instances are dynamically changing due to adding new solution components at each iteration, and removing existing solution components on the basis of indicators about their usefulness. The results of applying this algorithm to the repetition-free longest common subsequence problem show that the algorithm generally outperforms competing approaches from the literature. Moreover, they show that the algorithm is competitive with CPLEX for small and medium size problem instances, whereas it outperforms CPLEX for larger problem instances.Peer ReviewedPostprint (author's final draft

A Computational Interpretation of Context-Free Expressions

We phrase parsing with context-free expressions as a type inhabitation problem where values are parse trees and types are context-free expressions. We first show how containment among context-free and regular expressions can be reduced to a reachability problem by using a canonical representation of states. The proofs-as-programs principle yields a computational interpretation of the reachability problem in terms of a coercion that transforms the parse tree for a context-free expression into a parse tree for a regular expression. It also yields a partial coercion from regular parse trees to context-free ones. The partial coercion from the trivial language of all words to a context-free expression corresponds to a predictive parser for the expression

Luminescence quenching of the triplet excimer state by air traces in gaseous argon

While developing a liquid argon detector for dark matter searches we investigate the influence of air contamination on the VUV scintillation yield in gaseous argon at atmospheric pressure. We determine with a radioactive alpha-source the photon yield for various partial air pressures and different reflectors and wavelength shifters. We find for the fast scintillation component a time constant tau1= 11.3 +- 2.8 ns, independent of gas purity. However, the decay time of the slow component depends on gas purity and is a good indicator for the total VUV light yield. This dependence is attributed to impurities destroying the long-lived argon excimer states. The population ratio between the slowly and the fast decaying excimer states is determined for alpha-particles to be 5.5 +-0.6 in argon gas at 1100 mbar and room temperature. The measured mean life of the slow component is tau2 = 3.140 +- 0.067 microsec at a partial air pressure of 2 x 10-6 mbar.Comment: 7 pages submitted to NIM

Signatures of cross-modal alignment in children's early concepts

Whether supervised or unsupervised, human and machine learning is usually characterized as event-based. However, learning may also proceed by systems alignment in which mappings are inferred between entire systems, such as visual and linguistic systems. Systems alignment is possible because items that share similar visual contexts, such as a car and a truck, will also tend to share similar linguistic contexts. Because of the mirrored similarity relationships across systems, the visual and linguistic systems can be aligned at some later time absent either input. In a series of simulation studies, we considered whether children's early concepts support systems alignment. We found that children's early concepts are close to optimal for inferring novel concepts through systems alignment, enabling agents to correctly infer more than 85% of visual-word mappings absent supervision. One possible explanation for why children's early concepts support systems alignment is that they are distinguished structurally by their dense semantic neighborhoods. Artificial agents using these structural features to select concepts proved highly effective, both in environments mirroring children's conceptual world and those that exclude the concepts that children commonly acquire. For children, systems alignment and event-based learning likely complement one another. Likewise, artificial systems can benefit from incorporating these developmental principles

Correction: Relative abundance of and composition within fungal orders differ between cheatgrass (Bromus tectorum) and sagebrush (Artemisia tridentata)-associated soils

Nonnative Bromus tectorum (cheatgrass) is decimating sagebrush steppe, one of the largest ecosystems in the Western United States, and is causing regional-scale shifts in the predominant plant-fungal interactions. Sagebrush, a native perennial, hosts arbuscular mycorrhizal fungi (AMF), whereas cheatgrass, a winter annual, is a relatively poor host of AMF. This shift is likely intertwined with decreased carbon (C)-sequestration in cheatgrass-invaded soils and alterations in overall soil fungal community composition and structure, but the latter remain unresolved. We examined soil fungal communities using high throughput amplicon sequencing (ribosomal large subunit gene) in the 0-4 cm and 4-8 cm depth intervals of six cores from cheatgrass- and six cores from sagebrush-dominated soils. Sagebrush core surfaces (0-4 cm) contained higher nitrogen and total C than cheatgrass core surfaces; these differences mirrored the presence of glomalin related soil proteins (GRSP), which has been associated with AMF activity and increased C-sequestration. Fungal richness was not significantly affected by vegetation type, depth or an interaction of the two factors. However, the relative abundance of seven taxonomic orders was significantly affected by vegetation type or the interaction between vegetation type and depth. Teloschistales, Spizellomycetales, Pezizales and Cantharellales were more abundant in sagebrush libraries and contain mycorrhizal, lichenized and basal lineages of fungi. Only two orders (Coniochaetales and Sordariales), which contain numerous economically important pathogens and opportunistic saprotrophs, were more abundant in cheatgrass libraries. Pleosporales, Agaricales, Helotiales and Hypocreales were most abundant across all libraries, but the number of genera detected within these orders was as much as 29 times lower in cheatgrass relative to sagebrush libraries. These compositional differences between fungal communities associated with cheatgrass- and sagebrush-dominated soils warrant future research to examine soil fungal community composition across more sites and time points as well as in association with native grass species that also occupy cheatgrass-invaded ecosystems