A Case Study in Refactoring Functional Programs

Refactoring is the process of redesigning existing code without changing its functionality. Refactoring has recently come to prominence in the OO community. In this paper we explore the prospects for refactoring functional programs. Our paper centres on the case study of refactoring a 400 line Haskell program written by one of our students. The case study illustrates the type and variety of program manipulations involved in refactoring. Similarly to other program transformations, refactorings are based on program equivalences, and thus ultimately on language semantics. In the context of functional languages, refactorings can be based on existing theory and program analyses. However, the use of program transformations for program restructuring emphasises a different kind of transformation from the more traditional derivation or optimisation: characteristically, they often require wholesale changes to a collection of modules, and although they are best controlled by programmers, their application may require nontrivial semantic analyses. The paper also explores the background to refactoring, provides a taxonomy for describing refactorings and draws some conclusions about refactoring for functional programs

Media discourse on jihadist terrorism in Europe

This article analyzes the manner in which European print media discuss jihadist terrorism in Europe. It presents key results from a qualitative analysis of media discourse following three selected attacks in seven European countries in 2010: the attack on the cartoonist Westergaard, the Yemen cargo plane plot, and the Stockholm suicide attack. The article finds that attack type is a factor shaping media discourse across different media in Europe. Considering that terrorists also aim to impact discourse for their own agenda, the article presents implications for policy reactions on the basis of attack type, and not as desired by terrorists.Publisher PD

Refactoring Functional Programs

Refactoring is the process of redesigning existing code without changing its functionality. Refactoring has recently come to prominence in the OO community. In this paper we explore the prospects for refactoring functional programs. Our paper centres on the case study of refactoring a 400 line Haskell program written by one of our students. The case study illustrates the type and variety of program manipulations involved in refactoring. Similarly to other program transformations, refactorings are based on program equivalences, and thus ultimately on language semantics. In the context of functional languages, refactorings can be based on existing theory and program analyses. However, the use of program transformations for program restructuring emphasises a different kind of transformation from the more traditional derivation or optimisation: characteristically, they often require wholesale changes to a collection of modules, and although they are best controlled by programmers, their application may require nontrivial semantic analyses. The paper also explores the background to refactoring, provides a taxonomy for describing refactorings and draws some conclusions about refactoring for functional programs

BMP Signaling Goes Posttranscriptional in a microRNA Sort of Way

Aberrant microRNA (miRNA) expression correlates with human diseases such as cardiac disorders and cancer. Treatment of such disorders using miRNA-targeted therapeutics requires a thorough understanding of miRNA regulation in vivo. A recent paper in Nature by Davis et al. expands our understanding of miRNA biogenesis and maturation, elucidating a mechanism by which extracellular signaling directs cell differentiation via posttranscriptional regulation of miRNA expression

Household Income and Vehicle Fuel Economy in California

This white paper presents the findings from an analysis of the fiscal implications for vehicle owners of changing from the current statewide fuel tax to a “road user charge” (RUC) based on vehicle-miles traveled (VMT). Since 1923, California’s motor vehicle fuel tax has provided revenue used to plan, construct, and maintain the state’s publicly funded transportation systems. Over time, improvements in vehicle fuel efficiency and the effects of inflation have reduced both the revenue from the fuel tax and its purchasing power. Thus, there is growing interest among policy makers for replacing the state’s per-gallon fuel tax with an RUC based on VMT. This study analyzes the 2010-2011California Household Travel Survey (CHTS) to identify the potential effects this policy change would be likely to have on households across the state. The analysis found that while daily household fuel consumption and VMT both appear to increase with household income, urban and rural households show roughly the same amount of fuel consumption and VMT. No statistically significant difference in cost was found between the two programs in any income group. This suggests that an RUC designed to collect the same amount of revenues statewide as the current fuel tax would not place a significant financial burden on California households

A Factor Graph Approach to Multi-Camera Extrinsic Calibration on Legged Robots

Legged robots are becoming popular not only in research, but also in industry, where they can demonstrate their superiority over wheeled machines in a variety of applications. Either when acting as mobile manipulators or just as all-terrain ground vehicles, these machines need to precisely track the desired base and end-effector trajectories, perform Simultaneous Localization and Mapping (SLAM), and move in challenging environments, all while keeping balance. A crucial aspect for these tasks is that all onboard sensors must be properly calibrated and synchronized to provide consistent signals for all the software modules they feed. In this paper, we focus on the problem of calibrating the relative pose between a set of cameras and the base link of a quadruped robot. This pose is fundamental to successfully perform sensor fusion, state estimation, mapping, and any other task requiring visual feedback. To solve this problem, we propose an approach based on factor graphs that jointly optimizes the mutual position of the cameras and the robot base using kinematics and fiducial markers. We also quantitatively compare its performance with other state-of-the-art methods on the hydraulic quadruped robot HyQ. The proposed approach is simple, modular, and independent from external devices other than the fiducial marker.Comment: To appear on "The Third IEEE International Conference on Robotic Computing (IEEE IRC 2019)

Examination of the Monoamine Oxidase a Gene Promoter on Motivation to Exercise and Levels of Voluntary Physical Activity

Purpose: Monoamine oxidase A (MAO-A) is an enzyme that causes inactivation of monoamine neurotransmitters, such as dopamine. Polymorphisms in the promoter region of the MAO-A gene can change transcriptional activity and the amount of MAO-A produced, leading to alterations in available dopamine levels. MAO-A polymorphisms have been associated with physical activity level. This study examined whether motivation to exercise, and levels of voluntary physical activity are associated with MAO-A gene polymorphisms. Methods: Seventy-one participants (18-24 years, 13 males & 58 females) completed the Behavioral Regulation in Exercise Questionaire-2 (BREQ-2) to assess their motivation to exercise and the International Physical Activity Questionnaire (IPAQ) to assess their level of physical activity. DNA was isolated from a cheek cell sample. MAO-A 3/3 and 4/4 genotype individuals were used for analysis. Results: External motivation to exercise was significantly higher (p \u3c 0.01) in the high transcription 4/4 genotype (ave 1.17 ± 0.7) compared to the low transcription 3/3 genotype (ave 0.42 ± 0.5). Internal motivation to exercise, body mass index, and weekly MET minutes were comparable between genotypes. Conclusion: The results suggest a polymorphism in this monoamine pathway may play a role in increasing sensitivity to external factors that motivate individuals to exercise

Progressive growing of self-organized hierarchical representations for exploration

Designing agent that can autonomously discover and learn a diversity of structures and skills in unknown changing environments is key for lifelong machine learning. A central challenge is how to learn incrementally representations in order to progressively build a map of the discovered structures and re-use it to further explore. To address this challenge, we identify and target several key functionalities. First, we aim to build lasting representations and avoid catastrophic forgetting throughout the exploration process. Secondly we aim to learn a diversity of representations allowing to discover a "diversity of diversity" of structures (and associated skills) in complex high-dimensional environments. Thirdly, we target representations that can structure the agent discoveries in a coarse-to-fine manner. Finally, we target the reuse of such representations to drive exploration toward an "interesting" type of diversity, for instance leveraging human guidance. Current approaches in state representation learning rely generally on monolithic architectures which do not enable all these functionalities. Therefore, we present a novel technique to progressively construct a Hierarchy of Observation Latent Models for Exploration Stratification, called HOLMES. This technique couples the use of a dynamic modular model architecture for representation learning with intrinsically-motivated goal exploration processes (IMGEPs). The paper shows results in the domain of automated discovery of diverse self-organized patterns, considering as testbed the experimental framework from Reinke et al. (2019)