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This paper introduces Mako, a highly available, highthroughput, and horizontally scalable transactional key-value store. Mako performs strongly consistent geo-replication to maintain availability despite entire datacenter failures, uses multi-core machines for fast serializable transaction processing, and shards data to scale out. To achieve these properties, especially to overcome the overheads of distributed transactions in geo-replicated settings, Mako decouples transaction execution and replication. This enables Mako to run transactions speculatively and very fast, and replicate transactions in the background to make them fault-tolerant. The key innovation in Mako is the use of two-phase commit (2PC) speculatively to allow distributed transactions to proceed without having to wait for their decisions to be replicated, while also preventing unbounded cascading aborts if shards fail prior to the end of replication. Our experimental evaluation on Azure shows that Mako processes 3.66M TPC-C transactions per second when data is split across 10 shards, each of which runs with 24 threads. This is an 8.6× higher throughput than state-of-the-art systems optimized for geo-replication. 

This paper introduces Mako, a highly available, high- throughput, and horizontally scalable transactional key-value store. Mako performs strongly consistent geo-replication to maintain availability despite entire datacenter failures, uses multi-core machines for fast serializable transaction process- ing, and shards data to scale out. To achieve these properties, especially to overcome the overheads of distributed transac- tions in geo-replicated settings, Mako decouples transaction execution and replication. This enables Mako to run transactions speculatively and very fast, and replicate transactions in the background to make them fault-tolerant. The key innovation in Mako is the use of two-phase commit (2PC) speculatively to allow distributed transactions to proceed without having to wait for their decisions to be replicated, while also preventing unbounded cascading aborts if shards fail prior to the end of replication. Our experimental evaluation on Azure shows that Mako processes 3.66M TPC-C transactions per second when data is split across 10 shards, each of which runs with 24 threads. This is an 8.6×higher throughput than state-of-the-art systems optimized for geo-replication. 

ABSTRACT BackgroundEducational technologies typically provide teachers with analytics regarding student proficiency, but few digital tools provide teachers with process‐based information about students' variable problem‐solving strategies as they solve problems. Utilising design thinking and co‐designing with teachers can provide insight to researchers about what educators need to make instructional decisions based on student problem‐solving data. ObjectivesThis case study presents a collaboration where researchers and teachers co‐designed MathFlowLens, a teacher‐facing dashboard that provides analytics and visualisations about students' diverse problem‐solving strategies and behaviours used when solving online math problems in the classroom. MethodsOver several sessions, teachers discussed, mocked up, and were provided with behavioural data and strategy visualisations from students' math problem‐solving that demonstrated the variability of strategic approaches. Throughout this process, the team documented, transcribed, and used these conversations and artefacts to inform the design and development of the teacher tool. Results and ConclusionsTeachers discussed and designed prototypes of data dashboards and provided the research team with ongoing feedback to inform the iteration of the tool development. 

Abstract Near the ends of their lives, supernova remnants (SNRs) enter a “radiative phase,” when efficient cooling of the postshock gas slows expansion. Understanding SNR evolution at this stage is crucial for estimating feedback in galaxies, as SNRs are expected to release energy and momentum into the interstellar medium near the ends of their lives. A standard prediction of SNR evolutionary models is that the onset of the radiative stage precipitates the formation of a dense shell behind the forward shock. In Paper I, we showed that such shell formation yields detectable nonthermal radiation from radio toγ-rays, most notably emission brightening by nearly 2 orders of magnitude. However, there remains no observational evidence for such brightening, suggesting that this standard prediction needs to be investigated. In this paper, we perform magnetohydrodynamic simulations of SNR evolution through the radiative stage, including cosmic rays (CRs) and magnetic fields to assess their dynamical roles. We find that both sources of nonthermal pressure impede shell formation, reducing shell densities by a factor of a few to more than an order of magnitude. We also use a self-consistent model of particle acceleration to estimate the nonthermal emission from these modified SNRs and demonstrate that, for reasonable CR acceleration efficiencies and magnetic field strengths, the nonthermal signatures of shell formation can all but disappear. We therefore conclude that the absence of observational signatures of shell formation represents strong evidence that nonthermal pressures from CRs and magnetic fields play a critical dynamical role in late-stage SNR evolution.