Abstract
Window functions are extremely useful and have become increasingly popular, allowing ranking, cumulative sums and other analytic aggregations to be computed over a highly flexible and configurable sliding window. This powerful expressiveness comes naturally at the expense of heavy computational requirements which, so far, have been addressed through optimizations around centralized approaches by works both from the industry and academia. Distribution and parallelization has the potential to improve performance, but introduces several challenges associated with data distribution that may harm data locality. In this paper, we show how data similarity can be employed across partitions during the distributed execution of these operators to improve data co-locality between instances of a Distributed Query Engine and the associated data storage nodes. Our contribution can attain network gains in the average of 3 times and it is expected to scale as the number of instances increase. In the scenario with 8 nodes, we were to able attain bandwidth and time savings of 7.3 times and 2.61 times respectively.

Abstract
In nights at Cerro Paranal where good seeing and low wind conditions are present, the PSF delivered to the focal plane of the SPHERE instrument has been shown to have significant errors, and have been aptly described as the Low Wind Effect' (LWE). We demonstrate here a method to quantify the LWE using experimental and on-sky data. We find single image phase diversity is a useful tool in quantifying the LWE and can be used to monitor this effect over the course of the night.

Abstract
Programming models for building large-scale distributed applications assist the developer in reasoning about consistency and distribution. However, many of the programming models for weak consistency, which promise the largest scalability gains, have little in the way of evaluation to demonstrate the promised scalability. We present an experience report on the implementation and largescale evaluation of one of these models, Lasp, originally presented at PPDP '15, which provides a declarative, functional programming style for distributed applications. We demonstrate the scalability of Lasp's prototype runtime implementation up to 1024 nodes in the Amazon cloud computing environment. It achieves high scalability by uniquely combining hybrid gossip with a programming model based on convergent computation. We report on the engineering challenges of this implementation and its evaluation, specifically related to operating research prototypes in a production cloud environment.

Abstract
The complexity and subjectivity of the judgement task conceals the existence of biases that undermines the quality of the process. This paper presents a weighted aggregation function that attempts to reduce the influence of biased judgements on the final score. We also discuss a set of desirable properties. The proposed weighted aggregation function is able to correct the “nationalism bias” found by Emerson et al. (Am Stat 63(2):124–131, 2009) in the 2000 Olympic Games diving competition and suggest the possibility of a “reputation bias”. Our results can be applied to judgement sports and other activities that require the aggregation of several personal evaluations. © 2016, Springer Science+Business Media New York.

Abstract
This paper presents a study of the combined influence of battery models and sizing strategy for hybrid and battery-based electric vehicles. In particular, the aim is to find the number of battery (and super capacitor) cells to propel a light vehicle to run two different standard driving cycles. Three equivalent circuit models are considered to simulate the battery electrical performance: linear static, non-linear static and non-linear with first-order dynamics. When dimensioning a battery-based vehicle, less complex models may lead to a solution with more battery cells and higher costs. Despite the same tendency, when a hybrid vehicle is taken into account, the influence of the battery models is dependent on the sizing strategy. In this work, two sizing strategies are evaluated: dynamic programming and filter based. For the latter, the complexity of the battery model has a clear influence on the result of the sizing problem. On the other hand, a modest influence is observed when a dynamic programming strategy is followed.

Abstract
Theory Revision from Examples is the process of repairing incorrect theories and/or improving incomplete theories from a set of examples. This process usually results in more accurate and comprehensible theories than purely inductive learning. However, so far, progress on the use of theory revision techniques has been limited by the large search space they yield. In this article, we argue that it is possible to reduce the search space of a theory revision system by introducing stochastic local search. More precisely, we introduce a number of stochastic local search components at the key steps of the revision process, and implement them on a state-of-the-art revision system that makes use of the most specific clause to constrain the search space. We show that with the use of these SLS techniques it is possible for the revision system to be executed in a feasible time, while still improving the initial theory and in a number of cases even reaching better accuracies than the deterministic revision process. Moreover, in some cases the revision process can be faster and still achieve better accuracies than an ILP system learning from an empty initial hypothesis or assuming an initial theory to be correct.