ObjectivesPAX-Good Behaviour Game (PAX-GBG) is associated with improved mental health among youth. First Nations community members decided on a whole school approach to facilitate PAX-GBG implementation, by offering intervention training to all staff members in their schools. Our objective is to gain a greater understanding of how this approach was viewed by school personnel, in order to improve implementation in remote and northern First Nations communities.DesignWe conducted a qualitative case study using semi-structured interviews.SettingInterviews were conducted in First Nations schools located in northern Manitoba, Canada, in February 2018.ParticipantsWe used purposive sampling in selecting the 23 school staff from First Nations communities.InterventionPAX-GBG is a mental health promotion intervention that teachers deliver in the classroom alongside normal instructional activities. It was implemented school-wide over 4 months from October 2017 to February 2018.Outcome measuresWe inquired about the participants’ perception of PAX-GBG and the whole school approach. We applied an iterative coding system, identified recurring ideas and classified the ideas into major categories.ResultsImplementing the PAX-GBG whole school approach improved students’ behaviour and created a positive school environment. Students were learning self-regulation, had quieter voices and demonstrated awareness of the PAX-GBG strategies. All teachers interviewed had used the programme. Support from school administrators and having all school personnel use the programme consistently were facilitators to successful implementation. Challenges included the timing of training, lack of clarity in how to implement and implementing among students in older grades and those with special needs.ConclusionsThe whole school approach to implementing PAX-GBG was viewed as an acceptable and feasible way to extend the reach of PAX-GBG in order to promote the mental health of First Nations youth. Recommendations included ensuring school leadership support, changes to the training and cultural and literacy adaptations.

Abstract-These days, many traditional end-user applications are said to "run fast enough" on existing machines, so the search continues for novel applications that can leverage the new capabilities of our evolving hardware. Foremost of these potential applications are those that are clustered around information processing capabilities that humans have today but are lacking in computers. The fact that brains can perform these computations serves as an existence proof that these applications are realizable. At the same time, we often discover that the human nervous system, with its 80 billion neurons, on some metrics, is more powerful and energy-efficient than today's machines. Both of these aspects make this class of applications a desirable target for an architectural benchmark suite, because there is evidence that these applications are both useful and computationally challenging.This paper details CortexSuite, a Synthetic Brain Benchmark Suite, which seeks to capture this workload. We classify and identify benchmarks within CortexSuite by analogy to the human neural processing function. We use the major lobes of the cerebral cortex as a model for the organization and classification of data processing algorithms. To be clear, our goal is not to emulate the brain at the level of the neuron, but rather to collect together synthetic, man-made algorithms that have similar function and have met with success in the real world. We consulted six worldclass machine learning and computer vision researchers, who collectively hold 83,091 citations across their distinct subareas, asking them to identify newly emerging computationally-intensive algorithms or applications that are going to have a large impact over the next ten years. This is coupled with datasets that reflect the philosophy of practical use algorithms and are coded in "clean C" so as to make them accessible, analyzable, and usable for parallel and approximate compiler and architecture researchers alike.

Burst-parallel serverless applications invoke thousands of short-lived distributed functions to complete complex jobs such as data analytics, video encoding, or compilation. While these tasks execute in seconds, starting and configuring the virtual network they rely on is a major bottleneck that can consume up to 84% of total startup time. In this paper we characterize the magnitude of this network cold start problem in three popular overlay networks, Docker Swarm, Weave, and Linux Overlay. We focus on end-to-end startup time that encompasses both the time to boot a group of containers as well as interconnecting them. Our primary observation is that existing overlay approaches for serverless networking scale poorly in short-lived serverless environments. Based on our findings we develop Particle, a network stack tailored for multi-node serverless overlay networks that optimizes network creation without sacrificing multi-tenancy, generality, or throughput. When integrated into a serverless burst-parallel video processing pipeline, Particle improves application runtime by 2.4-3× over existing overlays. CCS Concepts • Computer systems organization → Cloud computing.

Research experiences for undergraduates (REUs) have many positive outcomes on students' perception of and retention in Computer Science (CS). Yet nearly all REUs are aimed at late-college students, well into a CS program. We present the Early Research Scholars Program (ERSP), a 4 quarter program designed to engage early-college (first or second year) CS students in high-quality research experiences in active research groups at a large research university. ERSP's structured course-supported group-apprentice model and its unique dual advising structure make it possible to vastly increase number of early-career CS students who participate in high-quality research experiences with little additional burden on individual faculty mentors. ERSP's focus on community building and support makes it particularly appropriate for students from groups who are traditionally underrepresented in CS. This paper reports the structure of the program and observations and learning thus-far with ERSP, with the goal of enabling others to implement this program at other large research-focused universities.