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Register a new userOne size does not fit all: Evidence for a range of mixing efficiencies in stellar evolution calculations
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Context: Internal chemical mixing in intermediate- and high-mass stars represents an immense uncertainty in stellar evolution models.In addition to extending the main-sequence lifetime, chemical mixing also appreciably increases the mass of the stellar core. Several studies have made attempts to calibrate the efficiency of different convective boundary mixing mechanisms, with sometimes seemingly conflicting results. Aims: We aim to demonstrate that stellar models regularly under-predict the masses of convective stellar cores. Methods: We gather convective core mass and fractional core hydrogen content inferences from numerous independent binary and asteroseismic studies, and compare them to stellar evolution models computed with the MESA stellar evolution code. Results: We demonstrate that core mass inferences from the literature are ubiquitously more massive than predicted by stellar evolution models without or with little convective boundary mixing. Conclusions: Independent of the form of internal mixing, stellar models require an efficient mixing mechanism that produces more massive cores throughout the main sequence to reproduce high-precision observations. This has implications for the post-main sequence evolution of all stars which have a well developed convective core on the main sequence.
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As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their outputs. At the same time, these stakeholders, whether they be affected citizens, government regulators, domain experts, or system developers, present different requirements for explanations. Toward addressing these needs, we introduce AI Explainability 360 (http://aix360.mybluemix.net/), an open-source software toolkit featuring eight diverse and state-of-the-art explainability methods and two evaluation metrics. Equally important, we provide a taxonomy to help entities requiring explanations to navigate the space of explanation methods, not only those in the toolkit but also in the broader literature on explainability. For data scientists and other users of the toolkit, we have implemented an extensible software architecture that organizes methods according to their place in the AI modeling pipeline. We also discuss enhancements to bring research innovations closer to consumers of explanations, ranging from simplified, more accessibl
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Todays cloud service architectures follow a one size fits all deployment strategy where the same service version instantiation is provided to the end users. However, consumers are broad and different applications have different accuracy and responsiveness requirements, which as we demonstrate renders the one size fits all approach inefficient in practice. We use a production-grade speech recognition engine, which serves several thousands of users, and an open source computer vision based system, to explain our point. To overcome the limitations of the one size fits all approach, we recommend Tolerance Tiers where each MLaaS tier exposes an accuracy/responsiveness characteristic, and consumers can programmatically select a tier. We evaluate our proposal on the CPU-based automatic speech recognition (ASR) engine and cutting-edge neural networks for image classification deployed on both CPUs and GPUs. The results show that our proposed approach provides an MLaaS cloud service architecture that can be tuned by the end API user or consumer to outperform the conventional one size fits all approach.
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