One More Revolution to Make: Free Scientific Publishing

Computer scientists are in the position to create new, free high-quality journals. So what would it take?Comment: Taken from http://www.acm.org/pubs/citations/journals/cacm/2001-44-5/p25-apt/ Posted with permission of the AC

Bayesian robot Programming

We propose a new method to program robots based on Bayesian inference and learning. The capacities of this programming method are demonstrated through a succession of increasingly complex experiments. Starting from the learning of simple reactive behaviors, we present instances of behavior combinations, sensor fusion, hierarchical behavior composition, situation recognition and temporal sequencing. This series of experiments comprises the steps in the incremental development of a complex robot program. The advantages and drawbacks of this approach are discussed along with these different experiments and summed up as a conclusion. These different robotics programs may be seen as an illustration of probabilistic programming applicable whenever one must deal with problems based on uncertain or incomplete knowledge. The scope of possible applications is obviously much broader than robotics

Multi-objective optimisation of machine tool error mapping using automated planning

Error mapping of machine tools is a multi-measurement task that is planned based on expert knowledge. There are no intelligent tools aiding the production of optimal measurement plans. In previous work, a method of intelligently constructing measurement plans demonstrated that it is feasible to optimise the plans either to reduce machine tool downtime or the estimated uncertainty of measurement due to the plan schedule. However, production scheduling and a continuously changing environment can impose conflicting constraints on downtime and the uncertainty of measurement. In this paper, the use of the produced measurement model to minimise machine tool downtime, the uncertainty of measurement and the arithmetic mean of both is investigated and discussed through the use of twelve different error mapping instances. The multi-objective search plans on average have a 3% reduction in the time metric when compared to the downtime of the uncertainty optimised plan and a 23% improvement in estimated uncertainty of measurement metric when compared to the uncertainty of the temporally optimised plan. Further experiments on a High Performance Computing (HPC) architecture demonstrated that there is on average a 3% improvement in optimality when compared with the experiments performed on the PC architecture. This demonstrates that even though a 4% improvement is beneficial, in most applications a standard PC architecture will result in valid error mapping plan

Seeing and not seeing populism in Latin America

I suggest a different, if complementary approach to understanding populism by turning to the specificity and complexity of Latin American politics in the 20th and 21st century histories. First, I view populism in the context of Latin American nations’ failures to achieve equality and inclusion as they modernized. In so doing, I consider together what I call “the first coming of the people on the scene,” between the Mexican Revolution and the military governments of the 1960s and 70s, and the “second coming of the people on the scene,” between the 1980s and the present. I suggest that we are seeing today a repetition of mid-20th century experiences and that the present should be seen as a replay, with key differences, of the 1960s and 1970s.Accepted manuscrip