Not all corporate tax avoiders will be snared by new rules

Hockey’s budget announcement of two major tax integrity measures was flagged before the budget was handed down, but even that came as no surprise. Integrity, or lack thereof, in our tax system is a hot topic and an easy target for a Treasurer looking to sell a federal budget. The first of the proposed changes is to our GST regime. No-one likes hearing that they will be paying more tax. But, the charging of GST on supplies of digital products and services in Australia by an off-shore supplier will at least make sense to the general public. With the inherent unfairness in the current system and a revenue raising prediction of A$350 million over the next four years, most are likely to accept the logic of such a measure. The second of the proposed changes are new laws to be included in Australia’s general anti-avoidance provision. New laws, which will apply from 1 January 2016, are aimed at multinational companies engaged in aggressive tax practices. The proposed anti-avoidance law is designed to stop multinationals that artificially avoid a taxable presence in Australia. It is difficult to see how this strategy of addressing specific behaviour through what is considered a general provision will work. And, it is these changes that are already causing confusion.

Data-aware task scheduling for all-to-all comparison problems in heterogeneous distributed systems

Solving large-scale all-to-all comparison problems using distributed computing is increasingly significant for various applications. Previous efforts to implement distributed all-to-all comparison frameworks have treated the two phases of data distribution and comparison task scheduling separately. This leads to high storage demands as well as poor data locality for the comparison tasks, thus creating a need to redistribute the data at runtime. Furthermore, most previous methods have been developed for homogeneous computing environments, so their overall performance is degraded even further when they are used in heterogeneous distributed systems. To tackle these challenges, this paper presents a data-aware task scheduling approach for solving all-to-all comparison problems in heterogeneous distributed systems. The approach formulates the requirements for data distribution and comparison task scheduling simultaneously as a constrained optimization problem. Then, metaheuristic data pre-scheduling and dynamic task scheduling strategies are developed along with an algorithmic implementation to solve the problem. The approach provides perfect data locality for all comparison tasks, avoiding rearrangement of data at runtime. It achieves load balancing among heterogeneous computing nodes, thus enhancing the overall computation time. It also reduces data storage requirements across the network. The effectiveness of the approach is demonstrated through experimental studies.

Editorial: In 2035, will all bacteria be multidrug-resistant? No

The spread of multidrug-resistant (MDR) bacteria has reached a threatening level. Extended-spectrum betalactamase- producing enterobacteriaceae (ESBLE) are now endemic in many hospitals worldwide as well as in the community, while resistance rates continue to rise steadily in Acinetobacter baumannii and Pseudomonas aeruginosa [1]. Even more alarming is the dissemination of carbapenemase-producing enterobacteriaceae (CPE), causing therapeutic and organizational problems in hospitals facing outbreaks or endemicity. This context could elicit serious concerns for the coming two decades; nevertheless, effective measures exist to stop the amplification of the problem and several axes of prevention remain to be fully exploited, leaving room for realistic hopes, at least for many parts of the world...