The roles of agricultural biodiversity in the McLaren Vale landscape

Douglas K. Bardsley, Elisa Palazzo, Nathanael D. Wiseman, and Randy Stringe

Engineering a static verification tool for GPU kernels

We report on practical experiences over the last 2.5 years related to the engineering of GPUVerify, a static verification tool for OpenCL and CUDA GPU kernels, plotting the progress of GPUVerify from a prototype to a fully functional and relatively efficient analysis tool. Our hope is that this experience report will serve the verification community by helping to inform future tooling efforts. © 2014 Springer International Publishing

A Solution to Weed Control in Grassland Containing White Clover

Productive grass with white clover can lead to advantages both in forage quantity and quality, economics and in meeting wider expectations detailed in recent EU and UK policy. The ability to achieve this agronomic success is currently difficult due to a lack of options for broad spectrum weed control that also allow establishment or preservation of a white clover population. The aim of this paper is to demonstrate that 3730XL, developed by Corteva Agriscience, is a solution to this critical success factor. Data is presented from 16 efficacy trials (10 from established grassland and 5 from newly sown) where white clover cover of plots treated with 3730XL was recorded relative to an untreated plot. Data is also presented from 106 weed control trials, against a selection of species, demonstrating the efficacy of 3730XL split by grassland scenario. The evidence presented highlights the capability of 3730XL to both control a broad spectrum of weed species and allow the establishment or preservation of white clover. Consequently, growers are able to cultivate the associated benefits that this confers

Specification and verification of atomic operations in GPGPU programs

We propose a specification and verification technique based on separation logic to reason about data race freedom and functional correctness of GPU kernels that use atomic operations as synchronisation mechanism. Our approach exploits the notion of resource invariant from Concurrent Separation Logic (CSL) to capture the behaviour of atomic operations. However, because of the different memory levels in the GPU architecture, we adapt this notion of resource invariant to these memory levels, i.e., group resource invariants capture the behaviour of atomic operations that access locations in local memory, while kernel resource invariants capture the behaviour of atomic operations that access locations in global memory. We show soundness of our approach and we provide tool support that enables us to verify kernels from standard benchmarks suites

Coordination when there are restricted and unrestricted options

One might expect that, in pure coordination games, coordination would become less frequent as the number of options increases. Contrary to this expectation, we report an experiment which found more frequent coordination when the option set was unrestricted than when it was restricted. To try to explain this result, we develop a method for eliciting the general rules that subjects use to identify salient options in restricted and unrestricted sets. We find that each such rule, if used by all subjects, would generate greater coordination in restricted sets. However, subjects tend to apply different rules to restricted and unrestricted sets

An approximate empirical Bayesian method for large-scale linear-Gaussian inverse problems

We study Bayesian inference methods for solving linear inverse problems, focusing on hierarchical formulations where the prior or the likelihood function depend on unspecified hyperparameters. In practice, these hyperparameters are often determined via an empirical Bayesian method that maximizes the marginal likelihood function, i.e., the probability density of the data conditional on the hyperparameters. Evaluating the marginal likelihood, however, is computationally challenging for large-scale problems. In this work, we present a method to approximately evaluate marginal likelihood functions, based on a low-rank approximation of the update from the prior covariance to the posterior covariance. We show that this approximation is optimal in a minimax sense. Moreover, we provide an efficient algorithm to implement the proposed method, based on a combination of the randomized SVD and a spectral approximation method to compute square roots of the prior covariance matrix. Several numerical examples demonstrate good performance of the proposed method