Boundary Layer: Exploring the Genius Between Worlds by Kem Luther

Review of Kem Luther\u27s Boundary Layer: Exploring the Genius Between Worlds

AMERICAN GOTHIC MAINSTREAM FICTION

This is my (Subhasis Chattopadhyay's) draft of PhD pre-submission. Dr. Scriver has (had) put it up online in her blog and I found it today, that is 1:06 pm, 28th May, 2017. I am grateful to her since intellectual ideas can otherwise be hijacked. She has done a wonderful editorial job

Who by Fire by Fred Stenson

Mary Scriver reviews Who by Fire by Fred Stenson

FINDbase: a relational database recording frequencies of genetic defects leading to inherited disorders worldwide

Frequency of INherited Disorders database (FINDbase) () is a relational database, derived from the ETHNOS software, recording frequencies of causative mutations leading to inherited disorders worldwide. Database records include the population and ethnic group, the disorder name and the related gene, accompanied by links to any corresponding locus-specific mutation database, to the respective Online Mendelian Inheritance in Man entries and the mutation together with its frequency in that population. The initial information is derived from the published literature, locus-specific databases and genetic disease consortia. FINDbase offers a user-friendly query interface, providing instant access to the list and frequencies of the different mutations. Query outputs can be either in a table or graphical format, accompanied by reference(s) on the data source. Registered users from three different groups, namely administrator, national coordinator and curator, are responsible for database curation and/or data entry/correction online via a password-protected interface. Databaseaccess is free of charge and there are no registration requirements for data querying. FINDbase provides a simple, web-based system for population-based mutation data collection and retrieval and can serve not only as a valuable online tool for molecular genetic testing of inherited disorders but also as a non-profit model for sustainable database funding, in the form of a ‘database-journal’

AMERICAN GOTHIC MAINSTREAM FICTION

This is my (Subhasis Chattopadhyay's) draft of PhD pre-submission. Dr. Scriver has (had) put it up online in her blog and I found it today, that is 1:06 pm, 28th May, 2017. I am grateful to her since intellectual ideas can otherwise be hijacked. She has done a wonderful editorial job. I want to make it clear that the author of the blog post is Dr. Scriver and not I. But in the Add Contributor here I cannot insert her name as the author so I have out her as an editor which is incorrect. Her blog-post though is in the public domain. Please see http://prairiemary.blogspot.in/2013/03/it-was-all-very-unexpected-and.htm

Edge of empire or edge of Asia?: 'Placing' Australia in the expanding mid-twentieth century discourse on modern architecture

Peter Scrive

Semantic Distance in WordNet: A Simplified and Improved Measure of Semantic Relatedness

Measures of semantic distance have received a great deal of attention recently in the field of computational lexical semantics. Although techniques for approximating the semantic distance of two concepts have existed for several decades, the introduction of the WordNet lexical database and improvements in corpus analysis have enabled significant improvements in semantic distance measures. In this study we investigate a special kind of semantic distance, called semantic relatedness. Lexical semantic relatedness measures have proved to be useful for a number of applications, such as word sense disambiguation and real-word spelling error correction. Most relatedness measures rely on the observation that the shortest path between nodes in a semantic network provides a representation of the relationship between two concepts. The strength of relatedness is computed in terms of this path. This dissertation makes several significant contributions to the study of semantic relatedness. We describe a new measure that calculates semantic relatedness as a function of the shortest path in a semantic network. The proposed measure achieves better results than other standard measures and yet is much simpler than previous models. The proposed measure is shown to achieve a correlation of r = 0. 897 with the judgments of human test subjects using a standard benchmark data set, representing the best performance reported in the literature. We also provide a general formal description for a class of semantic distance measures — namely, those measures that compute semantic distance from the shortest path in a semantic network. Lastly, we suggest a new methodology for developing path-based semantic distance measures that would limit the possibility of unnecessary complexity in future measures