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There is a familiar problem that attaches to any attempt to survey the English constitution at pretty much any moment in history. And it comes down to this: was there one? The reason for the confusion is of course just as familiar. Whatever passes for an English constitution is “unwritten.” The same question might be asked in other jurisdictions. But the presence of a document that claims to be a “constitution” at least prejudices the response. However, in England there is no written constitution, just what the later Victorian commentator Mountstuart Grant Duff would term a “strange abstraction.”1 The problem was given famous expression by Alexis de Tocqueville in his Democracy in America, published in 1840. In England, he observed, “the Constitution can change constantly, or rather it does not exist at all.”2 Tocqueville rather evidently preferred the American way, a written constitution enumerating lots of rights. In this he was not alone. Successive revolutions, in America and then France, at the end of the eighteenth century had stimulated considerable debate as to how best to reform and perhaps write an English constitution. It was a conversation that Mary Wollstonecraft entered with a characteristic vigor. We will shortly take a closer look at what she had to say. But first we need to set the scene, to revisit the larger conversation.
Facilitating the application of machine learning (ML) to materials science problems requires enhancing the data ecosystem to enable discovery and collection of data from many sources, automated dissemination of new data across the ecosystem, and the connecting of data with materials-specific ML models. Here, we present two projects, the Materials Data Facility (MDF) and the Data and Learning Hub for Science (DLHub), that address these needs. We use examples to show how MDF and DLHub capabilities can be leveraged to link data with ML models and how users can access those capabilities through web and programmatic interfaces.
Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the trade-off between accuracy and computational cost. Here, we assess multiple ML approaches for predicting the atomization energy of organic molecules. Our resulting models learn the difference between low-fidelity, B3LYP, and high-accuracy, G4MP2, atomization energies and predict the G4MP2 atomization energy to 0.005 eV (mean absolute error) for molecules with less than nine heavy atoms (training set of 117,232 entries, test set 13,026) and 0.012 eV for a small set of 66 molecules with between 10 and 14 heavy atoms. Our two best models, which have different accuracy/speed trade-offs, enable the efficient prediction of G4MP2-level energies for large molecules and are available through a simple web interface.
Australian Politics in the Twenty-First Century brings to life traditional institutions, theories and concepts by considering the key question: how are Australia's political institutions holding up in the face of the new challenges, dynamics and turbulence that have emerged and intensified in the new millennium? This approach encourages students to critically examine the complex interplay between a centuries' old system and a diverse, modern Australian society. This text presents the many moving parts of Australia's political system from an institutional perspective: the legislative and judiciary bodies, as well as lobby groups, the media, minor parties and independents, and the citizenry - institutions not often considered but whose influence is rapidly increasing. Student learning is supported through learning objectives, key terms, discussion questions, further readings and breakout boxes that highlight key theories, events and individuals. The extensive resources available in the VitalSource interactive eBook reaffirm comprehension and extend learning.