Abstract

Abstract

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. © 2017 Copyright held by the owner/author(s).

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.

Abstract
This paper introduces a novel control design method for stabilization of input constrained non-holonomic wheeled systems. Important classes of mobile robots can be controlled by the proposed method, namely differential drive robots and car like systems where certain constraints are imposed on the system inputs and states. The proposed control is based on the recently developed Constrained Directions Method (CDM). CDM guarantees stabilization and preservation of the constraints on the inputs and provides control over the transient performance of robot. Moreover, it has been shown that CDM has a built-in preventive measure against wheel slip due to the inverse proportionality of robot forward velocity to the curvature of the path. Simulation results are used to show the validity of the proposed stabilizing control and to compare the performance of CDM with several well-known methods from the literature.