10,000 publications from this institution
Auto-generated open-source dataset of 19,744 chemical reactions retrieved from 53,538 solid-state synthesis paragraphs. The data are collected using an automated extraction pipeline which converts unstructured scientific paragraphs describing inorganic materials synthesis into so-called "codified recipe" of synthesis. The pipeline utilizes a variety of text mining and NLP approaches to find information about target materials, starting compounds, synthesis steps and conditions in the text, and to process them into chemical equation. Submitted Data Descriptor in Scientific Data: SDATA-19-00539
Abstract not Available.
Isotropic tracer techniques have been used to characterize the dynamics of carbon deposition on the surface of a Ru/TiO/sub 2/ catalyst during Fischer-Tropsch synthesis and /sup 13/C-NMR spectroscopy has been used to characterize the structure of the deposited carbon. Elemental carbon, designated C/sub ..cap alpha../ is formed very rapidly, whereas alkyl carbon, designated C/sub ..beta../ accumulates much more slowly. The influence of catalyst reduction on temperature, reaction conditions, and time under reaction conditions on the surface concentrations and reactivity of C/sub ..cap alpha../ and C/sub ..beta../ will be discussed. It will be shown that C/sub ..beta../ progressively becomes less reactive and may be the precursor to the formation of graphitic carbon.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
No abstract is provided for this article.
Gibbs sampling is a workhorse for Bayesian inference but has several limitations when used for parameter estimation, and is often much slower than non-sampling inference methods. SAME (State Augmentation for Marginal Estimation) \cite{Doucet99,Doucet02} is an approach to MAP parameter estimation which gives improved parameter estimates over direct Gibbs sampling. SAME can be viewed as cooling the posterior parameter distribution and allows annealed search for the MAP parameters, often yielding very high quality (lower loss) estimates. But it does so at the expense of additional samples per iteration and generally slower performance. On the other hand, SAME dramatically increases the parallelism in the sampling schedule, and is an excellent match for modern (SIMD) hardware. In this paper we explore the application of SAME to graphical model inference on modern hardware. We show that combining SAME with factored sample representation (or approximation) gives throughput competitive with the fastest symbolic methods, but with potentially better quality. We describe experiments on Latent Dirichlet Allocation, achieving speeds similar to the fastest reported methods (online Variational Bayes) and lower cross-validated loss than other LDA implementations. The method is simple to implement and should be applicable to many other models.
