Paranoplocephala sciuri (Cestoda: Anoplocephalidae), a Parasite of the Northern Flying Squirrel (Glaucomys sabrinus), with a Discussion of Its Systematic Status

This study redescribes Andrya sciuri Rausch, 1947 (Anoplocephalidae) from the northern flying squirrel, Glaucomys sabrinus (Shaw), in North America, to redefine the morphology and generic position of this poorly known cestode. Andrya sciuri is shown to belong unambiguously to the genus Paranoplocephala Lühe, 1910 sensu Haukisalmi and Wickström (2005). Paranoplocephala sciuri is compared with four species that resemble it morphologically, and features that can be used in its identification are presented. It is suggested that P. sciuri has speciated through a shift from arvicoline rodents (voles and lemmings) to G. sabrinus.

Managing Large Data Sets Using Support Vector Machines

Hundreds of Terabytes of CMS (Compact Muon Solenoid) data are being accumulated for storage day by day at the University of Nebraska-Lincoln, which is one of the eight US CMS Tier-2 sites. Managing this data includes retaining useful CMS data sets and clearing storage space for newly arriving data by deleting less useful data sets. This is an important task that is currently being done manually and it requires a large amount of time. The overall objective of this study was to develop a methodology to help identify the data sets to be deleted when there is a requirement for storage space. CMS data is stored using HDFS (Hadoop Distributed File System). HDFS logs give information regarding file access operations. Hadoop MapReduce was used to feed information in these logs to Support Vector Machines (SVMs), a machine learning algorithm applicable to classification and regression which is used in this Thesis to develop a classifier. Time elapsed in data set classification by this method is dependent on the size of the input HDFS log file since the algorithmic complexities of Hadoop MapReduce algorithms here are O(n). The SVM methodology produces a list of data sets for deletion along with their respective sizes. This methodology was also compared with a heuristic called Retention Cost which was calculated using size of the data set and the time since its last access to help decide how useful a data set is. Accuracies of both were compared by calculating the percentage of data sets predicted for deletion which were accessed at a later instance of time. Our methodology using SVMs proved to be more accurate than using the Retention Cost heuristic. This methodology could be used to solve similar problems involving other large data sets.