Session-Based Programming for Parallel Algorithms: Expressiveness and Performance

This paper investigates session programming and typing of benchmark examples to compare productivity, safety and performance with other communications programming languages. Parallel algorithms are used to examine the above aspects due to their extensive use of message passing for interaction, and their increasing prominence in algorithmic research with the rising availability of hardware resources such as multicore machines and clusters. We contribute new benchmark results for SJ, an extension of Java for type-safe, binary session programming, against MPJ Express, a Java messaging system based on the MPI standard. In conclusion, we observe that (1) despite rich libraries and functionality, MPI remains a low-level API, and can suffer from commonly perceived disadvantages of explicit message passing such as deadlocks and unexpected message types, and (2) the benefits of high-level session abstraction, which has significant impact on program structure to improve readability and reliability, and session type-safety can greatly facilitate the task of communications programming whilst retaining competitive performance

An evaluation of the Cygnet parenting support programme for parents of children with autism spectrum conditions

Parents of children on the autistic spectrum often struggle to understand the condition and, related to this, manage their child’s behaviour. Cygnet is a parenting intervention which aims to help parents address these difficulties, consequently improving parenting confidence. It is widely used in the United Kingdom (UK). Despite this, there have been few evaluations. This paper reports a small-scale pragmatic evaluation of Cygnet as it was routinely delivered in two English cities. A non-randomised controlled study of outcomes for parents (and their children) was conducted. Data regarding intervention fidelity and delivery costs were also collected. Parents either attending, or waiting to attend, Cygnet were recruited (intervention group: IG, n=35; comparator group: CG, n=32). Parents completed standardised measures of child behaviour and parenting sense of competence pre- and post-intervention, and at three-month follow-up (matched time points for CG). Longer-term outcomes were measured for the IG. IG parents also set specific child behaviour goals. Typically, the programme was delivered as specified by the manual. Attending Cygnet was associated with significant improvements in parenting satisfaction and the specific child behaviour goals. Findings regarding other outcomes were equivocal and further evaluation is required. We conclude that Cygnet is a promising intervention for parents of children with autism in terms of, at least, some outcomes

Estimating commitment in a digital market place environment

The future generation of mobile communication shall be a convergence of mobile telephony and information systems which promises to change people's lives by enabling them to access information when, where and how they want. It presents opportunities to offer multimedia applications and services that meet end-toend service requirements. The Digital Marketplace framework will enable users to have separate contracts for different services on a per call basis. In order for such a framework to function appropriately, there has to be some means for the network operator to know in advance if its network will be able to support the user requirements. This paper discusses the methods by which the network operator will be able to determine if the system will be able to support another user of a certain service class and therefore negotiate parameters like commitment, QoS and the associated cost for providing the service, thus making the Digital Marketplace wor

How do markets manage water resources?. An experiment on resource market (de) centralization with endogenous quality.

We test how a monopoly, a duopoly and a public monopoly manage and allocate water resources. Stock depletion for the public monopoly is fastest. However, it reaches the optimal stock level towards the end of the experimental sessions. The private monopoly and duopoly maintain inefficiently high levels of stock throughout the sessions. The average quality to price ratio offered by the public monopoly is substantially higher than that offered by the private monopoly or duopoly. A clear result from the experiments is that a public monopoly offers the highest (average) quality to price ratio and has the fastest rate of stock depletion compared to a private monopoly or duopoly

Using Remote Access for Sharing Experiences in a Machine Design Laboratory

A new Machine Design Laboratory at Marquette University has been created to foster student exploration and promote “hands-on” and “minds-on” learning. Laboratory experiments have been developed to give students practical experiences and expose them to physical hardware, actual tools, and design challenges. Students face a range of real-world tasks: identify and select components, measure parameters (dimensions, speed, force), distinguish between normal and used (worn) components and between proper and abnormal behavior, reverse engineer systems, and justify design choices. The experiments serve to motivate the theory, spark interest, and promote discovery learning in the subject of machine design. This paper presents details of the experiments in the Machine Design Laboratory and then explores the feasibility of sharing some of the experiences with students at other institutions through remote access technologies. The paper proposes steps towards achieving this goal and raises issues to be addressed for a pilot-study offering machine design experiences to students globally who have access to the internet

Adjustment with aphasia after stroke: study protocol for a pilot feasibility randomised controlled trial for SUpporting wellbeing through PEeR Befriending (SUPERB)

Background: Despite the high prevalence of mood problems after stroke, evidence on effective interventions particularly for those with aphasia is limited. There is a pressing need to systematically evaluate interventions aiming to improve wellbeing for people with stroke and aphasia. This study aims to evaluate the feasibility of a peer-befriending intervention. Methods/design: SUPERB is a single blind, parallel group feasibility trial of peer befriending for people with aphasia post-stroke and low levels of psychological distress. The trial includes a nested qualitative study and pilot economic evaluation and it compares usual care (n = 30) with usual care + peer befriending (n = 30). Feasibility outcomes include proportion screened who meet criteria, proportion who consent, rate of consent, number of missing/incomplete data on outcome measures, attrition rate at follow-up, potential value of conducting main trial using value of information analysis (economic evaluation), description of usual care, and treatment fidelity of peer befriending. Assessments and outcome measures (mood, wellbeing, communication, and social participation) for participants and significant others will be administered at baseline, with outcome measures re-administered at 4 and 10 months post-randomisation. Peer befrienders will complete outcome measures before training and after they have completed two cycles of befriending. The qualitative study will use semi-structured interviews of purposively sampled participants (n = 20) and significant others (n = 10) from both arms of the trial, and all peer befrienders to explore the acceptability of procedures and experiences of care. The pilot economic evaluation will utilise the European Quality of life measure (EQ-5D-5 L) and a stroke-adapted version of the Client Service Receipt Inventory (CSRI). Discussion: This study will provide information on feasibility outcomes and an initial indication of whether peer befriending is a suitable intervention to explore further in a definitive phase III randomised controlled trial. Trial registration: ClinicalTrials.gov identifier NCT02947776, registered 28th October 2016

Toward Reduced Poverty Across Generations: Early Findings from New York City's Conditional Cash Transfer Program

In 2007, New York City launched Opportunity NYC -- Family Rewards, an experimental, privately funded, conditional cash transfer (CCT) program to help families break the cycle of intergenerational poverty. CCT programs offer cash assistance to reduce immediate hardship and poverty but condition this assistance -- or cash transfers -- on families' efforts to improve their "human capital" (typically, children's educational achievement and family health) in the hope of reducing their poverty over the longer term. Such programs have grown rapidly across lower- and middle-income countries, and evaluations have found some important successes. Family Rewards is the first comprehensive CCT program in a developed country.Aimed at low-income families in six of New York City's highest-poverty communities, Family Rewards ties cash rewards to a pre-specified set of activities and outcomes in the areas of children's education, family preventive health care, and parents' employment. The program is available to 2,400 families for three years. Inspired by Mexico's pioneering Oportunidades program, Family Rewards' program effects are being measured via a randomized control trial.The Family Rewards demonstration is one of 40 initiatives sponsored by New York City's Center for Economic Opportunity (CEO), a unit within the Office of Mayor Michael R. Bloomberg that is responsible for testing innovative strategies to reduce the number of New Yorkers who are living in poverty. Two national, New York-based nonprofit organizations -- MDRC, a nonpartisan social policy research firm, and Seedco, a workforce and economic development organization -- worked in close partnership with CEO to design the demonstration. Seedco, together with a small network of local community-based organizations, is operating Family Rewards, and MDRC is conducting the evaluation and managing the overall demonstration. A consortium of private funders is supporting the project.1This report presents the initial findings from an ongoing and comprehensive evaluation of Family Rewards. It examines the program's implementation in the field and families' responses to it during the first two of its three years of operations. This evaluation period, beginning in September 2007 and ending in August 2009, encompasses a start-up phase as well as a stage when the program was beginning to mature. The report also presents early findings on the program's effects, or "impacts," on a wide range of outcome measures. For some measures, the results cover only the first program year, while for others they also cover part or all of the second year. No data are available yet on the third year. The evaluation findings are based on analyses of a wide variety of administrative records data, responses to a survey of parents that was administered about 18 months after random assignment, and qualitative in-depth interviews with program staff and families.Overall, this study shows that, despite an extraordinarily rapid start-up and early challenges, the program was operating largely as intended by its second year. Although many families struggled with the complexity of the program, most were substantially engaged with it and received a large amount of money for meeting the conditions it established. During the period covered by the report, Family Rewards reduced current poverty (its main short-term goal) and produced a range of effects on a variety of outcomes across all three program domains (children's education, family health care, and parents' work and training)

Real-time motion analytics during brain MRI improve data quality and reduce costs

Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional 'buffer data', an expensive practice that, by itself, does not guarantee sufficient high-quality MRI data for a given participant. Therefore, we developed an easy-to-setup, easy-to-use Framewise Integrated Real-time MRI Monitoring (FIRMM) software suite that provides scanner operators with head motion analytics in real-time, allowing them to scan each subject until the desired amount of low-movement data has been collected. Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more