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Given the growing attention on citizen involvement in local sustainability, this study explores how citizens evaluate government sustainability performance stemming from exploitation (established policies) and exploration strategies (pioneering initiatives). Our survey experiment finds that positive sustainability performance resulting from exploitation achieves more favourable citizen evaluations compared to exploration. Negative sustainability performance does not moderate the associations between sustainability strategies and public assessments. Furthermore, Republicans, individuals with low climate beliefs, Hispanics, and low-income citizens prefer exploitation over exploration. As an early attempt to examine citizen preferences for organizational strategies, this study extends performance management research by linking organizational strategies with performance. 

As Cloud's adoption surges across industries, the limitations of its default scheduler, particularly on large scales or for jobs outside of its initial design scope, have become increasingly prominent. While the default schedulers in various cloud platforms were primarily engineered to focus on simple and predictable tasks, reinforcement learning (RL)-based schedulers are attracting attention as they can predict a larger and more diverse cloud environment. Nevertheless, there are practical constraints to the use of RL. Retraining for adaptation is necessary for each new environment, and exploration taken during each training may lead to unexpected performance degradation at runtime. To address these issues, this paper presents Dejavu which combines reinforcement learning with neural networks to learn and resolve scheduling problems more effectively. To tackle the extended training time and performance degradation by unexpected explorations, we apply pretraining using Demonstrations from existing heuristics. This guides the RL agent to explore in a safe and efficient manner. Furthermore, we design a robust reward function to push Dejavu to compete with and eventually outperform, the exploited heuristics and other baselines. The experimental results demonstrate the efficacy of Dejavu, showing remarkable improvements in key metrics. Compared to the default scheduler, it boosts resource utilization by 6 % and shortens scheduling time by 3% during the scheduling period. 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.