A SPECIFICATION OF A COMPLEX PROGRAMMING LANGUAGE STATEMENT

A formal specification of a complex programming language statement is presented. The subject matter was selected as being typical of the kind confronting a small software house. It is shown that formal specification notations may be applied, with benefit, to 'messy' problems. Emphasis is placed upon producing a specification which is readable by, and useful to a reader not familiar with formal notations.

Experiences of youth justice: Youth justice discourses and their multiple effects

Interventions within youth justice systems draw on a range of rationales and philosophies. Traditionally demarcated by a welfare/justice binary, the complex array of contemporary rationales meld different philosophies and practices, suggesting a mutability that gives this sphere a continued (re)productive and felt effect. While it may be increasingly difficult to ascertain which of these discourses is dominant in different jurisdictions in the UK, particular models of justice are perceived to be more prominent (Muncie, 2006). Traditionally it is assumed that Northern Ireland prioritises restoration, Wales prioritises rights, England priorities risk and Scotland welfare (McVie, 2011; Muncie, 2008, 2011). However, how these discourses are enacted in practice, how multiple and competing rationales circulate within them and most fundamentally how they are experienced by young people is less clear. This paper, based on research with young people who have experienced the full range of interventions in the youth justice system in Northern Ireland examines their narratives of ‘justice’. It considers how different discourses might influence the same intervention and how the deployment of multiple rationalities gives the experience of ‘justice’ its effect.

Utilising a computer game as a therapeutic intervention for youth in residential care: Some preliminary findings on use and acceptability

Mental illness is common amongst young people living in residential care, many of whom are reluctant to avail of therapeutic help. The potential value of computer games as therapeutic tools for these young people has received very little attention, despite indications of their potential for promoting engagement in therapeutic work and improving mental health outcomes. This study aimed to fill this research gap through the development, introduction, and preliminary evaluation of a therapeutic intervention in group care settings. The intervention incorporated a commercially available computer game (The SIMS Life Stories™) and emotion regulation skill coaching. Qualified residential social workers were trained to deliver it to young people in three children's homes in Northern Ireland, where therapeutic approaches to social work had been introduced. The research was framed as an exploratory case study which aimed to determine the acceptability and potential therapeutic value of this intervention. The evidence suggests that computer-game based interventions of this type may have value as therapeutic tools in group care settings and deserve further development and empirical investigation to determine their effectiveness in improving mental health outcomes.

Design patterns percolating to parallel programming framework implementation

Structured parallel programming is recognised as a viable and effective means of tackling parallel programming problems. Recently, a set of simple and powerful parallel building blocks RISC pb²l) has been proposed to support modelling and implementation of parallel frameworks. In this work we demonstrate how that same parallel building block set may be used to model both general purpose parallel programming abstractions, not usually listed in classical skeleton sets, and more specialized domain specific parallel patterns. We show how an implementation of RISC pb² l can be realised via the FastFlow framework and present experimental evidence of the feasibility and efficiency of the approach.

Prenatal hypoxia leads to increased muscle sympathetic nerve activity, sympathetic hyperinnervation, premature blunting of NPY signalling and hypertension in adult life

Adverse conditions prenatally increase the risk of cardiovascular disease, including hypertension. Chronic hypoxia in utero (CHU) causes endothelial dysfunction, but whether sympathetic vasoconstrictor nerve functioning is altered is unknown. We, therefore, compared in male CHU and control (N) rats muscle sympathetic nerve activity, vascular sympathetic innervation density, and mechanisms of sympathetic vasoconstriction. In young (Y)-CHU and Y-N rats (≈3 months), baseline arterial blood pressure was similar. However, tonic muscle sympathetic nerve activity recorded focally from arterial vessels of spinotrapezius muscle had higher mean frequency in Y-CHU than in Y-N rats (0.56±0.075 versus 0.33±0.036 Hz), and the proportions of single units with high instantaneous frequencies (1–5 and 6–10 Hz) being greater in Y-CHU rats. Sympathetic innervation density of tibial arteries was ≈50% greater in Y-CHU than in Y-N rats. Increases in femoral vascular resistance evoked by sympathetic stimulation at low frequency (2 Hz for 2 minutes) and bursts at 20 Hz were substantially smaller in Y-CHU than in Y-N rats. In Y-N only, the neuropeptide Y Y1-receptor antagonist BIBP3226 attenuated these responses. By contrast, baseline arterial blood pressure was higher in middle-aged (M)-CHU than in M-N rats (≈9 months; 139±3 versus 126±3 mmHg, respectively). BIBP3226 had no effect on femoral vascular resistance increases evoked by 2 Hz or 20 Hz bursts in M-N or M-CHU rats. These results indicate that fetal programming induced by prenatal hypoxia causes an increase in centrally generated muscle sympathetic nerve activity in youth and hypertension by middle age. This is associated with blunting of sympathetically evoked vasoconstriction and its neuropeptide Y component that may reflect premature vascular aging and contribute to increased risk of cardiovascular disease

Enhancing Linear Programming with Motion Modeling for Multi-target Tracking

In this paper we extend the minimum-cost network flow approach to multi-target tracking, by incorporating a motion model, allowing the tracker to better cope with longterm occlusions and missed detections. In our new method, the tracking problem is solved iteratively: Firstly, an initial tracking solution is found without the help of motion information. Given this initial set of tracklets, the motion at each detection is estimated, and used to refine the tracking solution.
Finally, special edges are added to the tracking graph, allowing a further revised tracking solution to be found, where distant tracklets may be linked based on motion similarity. Our system has been tested on the PETS S2.L1 and Oxford town-center sequences, outperforming the baseline system, and achieving results comparable with the current state of the art.

A Significance-Driven Programming Framework for Energy-Constrained Approximate Computing

Approximate execution is a viable technique for energy-con\-strained environments, provided that applications have the mechanisms to produce outputs of the highest possible quality within the given energy budget.
We introduce a framework for energy-constrained execution with controlled and graceful quality loss. A simple programming model allows users to express the relative importance of computations for the quality of the end result, as well as minimum quality requirements. The significance-aware runtime system uses an application-specific analytical energy model to identify the degree of concurrency and approximation that maximizes quality while meeting user-specified energy constraints. Evaluation on a dual-socket 8-core server shows that the proposed
framework predicts the optimal configuration with high accuracy, enabling energy-constrained executions that result in significantly higher quality compared to loop perforation, a compiler approximation technique.

A Programming Model and Runtime System for Significance-Aware Energy-Efficient Computing.

We introduce a task-based programming model and runtime system that exploit the observation that not all parts of a program are equally significant for the accuracy of the end-result, in order to trade off the quality of program outputs for increased energy-efficiency. This is done in a structured and flexible way, allowing for easy exploitation of different points in the quality/energy space, without adversely affecting application performance. The runtime system can apply a number of different policies to decide whether it will execute less-significant tasks accurately or approximately.

The experimental evaluation indicates that our system can achieve an energy reduction of up to 83% compared with a fully accurate execution and up to 35% compared with an approximate version employing loop perforation. At the same time, our approach always results in graceful quality degradation.

Approximate credal network updating by linear programming with applications to decision making

Credal nets are probabilistic graphical models which extend Bayesian nets to cope with sets of distributions. An algorithm for approximate credal network updating is presented. The problem in its general formulation is a multilinear optimization task, which can be linearized by an appropriate rule for fixing all the local models apart from those of a single variable. This simple idea can be iterated and quickly leads to accurate inferences. A transformation is also derived to reduce decision making in credal networks based on the maximality criterion to updating. The decision task is proved to have the same complexity of standard inference, being NPPP-complete for general credal nets and NP-complete for polytrees. Similar results are derived for the E-admissibility criterion. Numerical experiments confirm a good performance of the method.

Inference in Credal Networks Through Integer Programming

A credal network associates a directed acyclic graph with a collection of sets of probability measures; it offers a compact representation for sets of multivariate distributions. In this paper we present a new algorithm for inference in credal networks based on an integer programming reformulation. We are concerned with computation of lower/upper probabilities for a variable in a given credal network. Experiments reported in this paper indicate that this new algorithm has better performance than existing ones for some important classes of networks.

Inference in credal networks using multilinear programming

A credal network is a graphical tool for representation and manipulation of uncertainty, where probability values may be imprecise or indeterminate. A credal network associates a directed acyclic graph with a collection of sets of probability measures; in this context, inference is the computation of tight lower and upper bounds for conditional probabilities. In this paper we present new algorithms for inference in credal networks based on multilinear programming techniques. Experiments indicate that these new algorithms have better performance than existing ones, in the sense that they can produce more accurate results in larger networks.