Arthur Lindo Patterson, his function and element preferences in early crystal structures

In 1934, Arthur Lindo Patterson showed that a map of interatomic vectors is obtainable from measured X-ray diffraction data without phase information. Such maps were interpretable for simple crystal structures, but proliferation and overlapping of peaks caused confusion as the number of atoms increased. Since the peak height of a vector between two particular atoms is related to the product of their atomic numbers, a complicated structure could effectively be reduced to a simple one by including just a few heavy atoms (of high atomic number) since their interatomic vectors would stand out from the general clutter. Once located, these atoms provide approximate phases for Fourier syntheses that reveal the locations of additional atoms. Surveys of small-molecule structures in the Cambridge Structural Database during the periods 1936-1969, when Patterson methods were commonly used, and 1980-2013, dominated by direct methods, demonstrate large differences in the abundance of certain elements. The moderately heavy elements K, Rb, As and Br are the heaviest elements in the structure more than 3 times as often in the early period than in the recent period. Examples are given of three triumphs of the heavy atom method and two initial failures that had to be overcome. © 2014 © 2014 Taylor & Francis.

Topic extraction from microblog posts using conversation structures

Conventional topic models are ineffective for topic extraction from microblog messages since the lack of structure and context among the posts renders poor message-level word co-occurrence patterns. In this work, we organize microblog posts as conversation trees based on reposting and replying relations, which enrich context information to alleviate data sparseness. Our model generates words according to topic dependencies derived from the conversation structures. In specific, we differentiate messages as leader messages, which initiate key aspects of previously focused topics or shift the focus to different topics, and follower messages that do not introduce any new information but simply echo topics from the messages that they repost or reply. Our model captures the different extents that leader and follower messages may contain the key topical words, thus further enhances the quality of the induced topics. The results of thorough experiments demonstrate the effectiveness of our proposed model.