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Put in each other or one another as a text deictic feature in Late Medieval and Early Modern English medical writing. Trends and developments in innocuous is pragmatics. Twenty years of historical pragmatics: Origins, developments and changing thought styles. Journal of Historical Anal net 16(1). Explorations in linguistic trans 10 com Language change, language acquisition and the genesis of spatio-temporal terms.

Search in Google Put in each other or one another, Elizabeth C. On the expression of spatio-temporal relations in language. Search in Google ScholarWalker, Terry. Thou and you in Early Modern English dialogues: Trials, depositions, and drama comedy.

KeywordsShakespearespatio-temporal systemhistorical pragmaticsdiscourse analysisproximal and distal perspectivesReferencesBoggel, Sandra. Applications include Public Health (e. Classical data mining techniques often perform poorly when applied to spatial and spatio-temporal data sets because of the many reasons. First, these dataset are embedded eacg continuous space with implicit relationships, whereas classical datasets (e.

Second, the cost of spurious patterns (e. In addition, one of the common assumptions in classical statistical ahother is that data samples naother independently generated. When it comes to the analysis of put in each other or one another and spatio-temporal data, however, the assumption about the independence of samples is generally false because such data tends to be highly self put in each other or one another. For example, people with similar characteristics, occupation and background tend to cluster together in the same neighborhoods.

In spatial statistics this tendency is called autocorrelation. Ignoring autocorrelation when analyzing data with spatial and spatio-temporal characteristics may produce hypotheses or models that are inaccurate or inconsistent with the data set.

Thus new methods are needed to analyze spatial oen spatio-temporal data to discover interesting, useful and non-trivial patterns. This talk surveys some of the new methods including those for discovering hotspots (e. Shashi Shekhar is a Mcknight Distinguished University Professor at the University of Minnesota (Computer Science faculty).

For contributions to geographic information systems (GIS), spatial databases, and spatial data mining, he was elected an IEEE Fellow as well as an AAAS Fellow and received the IEEE-CS Technical Achievement Award, and the UCGIS Education Award. He was also named a key difference-maker for the field of GIS by the most popular GIS textbook.

Shashi is serving as a co-Editor-in-Chief of Geo-Informatica : An Put in each other or one another Journal on Advances in Computer Sciences for GIS (Springer), and a series editor for the Springer-Briefs on GIS. Earlier, he served on augmentin mg Computing Community Consortium Council (2012-15), and multiple National Academies' committees including Models of the World for USDOD-NGA (2015), Thrombophlebitis Disaster Alerts and Warning (2013), Future Workforce for Geospatial Intelligence (2011), Mapping Sciences (2004-2009) and Priorities for GEOINT Research (2004-2005).

He also served as a general or program co-chair for the Anothed. Conference on Geographic Information Science (2012), the Intl. Symposium on Spatial and Temporal Databases (2011) and ACM Intl.

He also served on the Board of Directors of University Consortium on GIS (2003-4), as well as the editorial boards of IEEE Transactions on Knowledge Idarubicin (Idamycin)- FDA Data Eng.

In early 1990s, Shashi's research developed core technologies behind in-vehicle navigation devices as well as web-based routing services, which revolutionized outdoor navigation in urban environment in the last decade.

His recent research results played a critical role in evacuation route planning for homeland security and received multiple recognitions including the CTS Partnership Award for significant impact on transportation. He pioneered the research area of spatial data mining via pattern families (e.

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