Abstract
As the scale and complexity of future High Performance Computing systems continues to grow, the rising frequency of faults and errors and their impact on HPC applications will make it increasingly difficult to accomplish useful computation. Traditional means of fault detection and correction are either hardware based or use software based redundancy. Redundancy based approaches usually entail complete replication of the program state or the computation and therefore incurs substantial overhead to application performance. Therefore, the wide-scale use of full redundancy in future exascale class systems is not a viable solution for error detection and correction. In this paper we present an application level fault detection approach that is based on adaptive redundant multithreading. Through a language level directive, the programmer can define structured code blocks. When these blocks are executed by multiple threads and their outputs compared, we can detect errors in specific parts of the program state that will ultimately determine the correctness of the application outcome. The compiler outlines such code blocks and a runtime system reasons whether their execution by redundant threads should enabled/disabled by continuously observing and learning about the fault tolerance state of the system. By providing flexible building blocks for application specific fault detection, our approach makes possible more reasonable performance overheads than full redundancy. Our results show that the overheads to application performance are in the range of 4% to 70% due to runtime system being continuously aware of the rate and source of system faults, rather than the usual overhead in the excess of 100% that is incurred by complete replication. © 2014 IEEE.

Abstract
This paper presents a successful application of operations research techniques in guiding the decision making process to achieve a superior operational efficiency in core activities. We focus on a rich vehicle routing problem faced by a Portuguese food distribution company on a daily basis. This problem can be described as a heterogeneous fleet site dependent vehicle routing problem with multiple time windows. We use the adaptative large neighbourhood search framework, which has proven to be effective to solve a variety of different vehicle routing problems. Our plans are compared against those of the company and the impact that the proposed decision support tool may have in terms of cost savings is shown. The algorithm converges quickly giving the planner considerably more time to focus on value-added tasks, rather than manually correct the routing schedule. Moreover, contrarily to the necessary adaptation time of the planner, the tool is quite flexible in following market changes, such as the introduction of new customers or new products.

Abstract

Abstract
The No-Limit Texas Hold'em variant of Poker is the game that is most frequently used to assess new developments in incomplete information problems, through the development of game playing agents. For this particular game, current state-ofthe-art techniques consist in the pre-computation of a set of strategies that are in a Nash-Equilibrium state. However, due to the game's decision tree size, current algorithms only work in an abstracted version of No-Limit Poker. Moreover, since these strategies are static, they ignore the opponents' playing style thus being unable to maximize profit against certain kinds of opponents. This makes these strategies unusable when playing in an online environment against human players. In this paper we present a rule-based strategy approach for a No-Limit Poker agent that was developed to play online, against human players and in online multiplayer matches. This strategy is based on a popular technique used by human players short stack playing which consists of playing in tables with up to 6 players and low initial resources. Using domain specific opponent modeling techniques and limiting the decisions to the first round of the game, the agent was able to make a good profit margin of 11.5% per game when playing against human players. The significance of our results resides in the fact that, for the first time in the Computer Poker literature, we present a game playing agent that can match human players in multiplayer games.

Abstract

Abstract
This paper presents an interactive apparatus to didactically explore Alberti's treatise on architecture, De re aedificatoria, as generative design systems, namely shape grammars. This apparatus allows users to interactively explore such architectonical knowledge in both appealing and informal ways, by enabling them to visualize and manipulate in real-time different design solutions. The authors identify the difficulties on encoding the architectural knowledge of a parametric design model into an interactive apparatus to be used by laypeople. At last, the authors discuss the results of a survey conducted to users that interacted with the prototype in order to assess its ability to communicate the knowledge of an architectural language.