65,498 research outputs found
FIGURES 17A–R. Chilicola aenigma Packer, n in Fifteen new species of Chilicola (Hymenoptera: Apoidea; Colletidae)
No full textFIGURES 17A–R. Chilicola aenigma Packer, n. sp. Head of male: A. frontal view, B. lateral view. Head of female: C. lateral view, D. frontal view. E. male pronotum. F. Male hind femur and tibia. G. Female hind femur and tibia. H. Male T6, T7 and apex of S6. I. Lateral view of male metasomal venter. J–M. Male terminalia. J. S7. K. S8. L. genital capsule, dorsal views are at the left. M. Genital capsule, lateral view. N–R. Sting apparatus of female: N. hemitergite 7, O. hemitergite 8, P–Q. Furcula, dorsal and lateral views respectively. R. First and second valvifer with sting shaft. Stippling in A and B indicates pale colouration, in R a membranous region.Published as part of Packer, Laurence & Genaro, Julio A., 2007, Fifteen new species of Chilicola (Hymenoptera: Apoidea; Colletidae), pp. 1-55 in Zootaxa 1468 on page 53, DOI: 10.5281/zenodo.17662
Letter from J. R. Eakin to Arthur G. Ringland
No full textLetter (copy) from J. R. Eakin to Arthur C. Ringland about the alignment of 40 acres near the Buggeln ranch
Letter from Arno B. Cammerer to J. R. Eakin
No full textLetter from Arno B. Cammerer to J. R. Eakin describing the procedure for purchasing Bright Angel Trail
Letter from J. R. Eakin to Carl Hayden
No full textLetter from J. R. Eakin to Carl T. Hayden concerning access to Rowe Well and the canyon
Letter from J. R. Eakin to Stephen Mather
No full textLetter from J. R. Eakin to Stephen T. Mather about expenses and reconstruction of the Kaibab Trail
Letter from Carl Hayden to J. R. Eakin
No full textLetter from Carl T. Hayden to J. R. Eakin regarding changes to the Grand Canyon National Park boundaries and the purchase of lands from William Randolph Hearst
An on-line algorithm for semantic forgetting
No full textOntologies that evolve through use to support new domain tasks can grow extremely large. Moreover, large ontologies require more resources to use and have slower response times than small ones. To help address this problem, we present an on-line semantic forgetting algorithm that removes ontology fragments containing infrequently used or cheap to relearn concepts. We situate our algorithm in an extension of the widely used RoboCup Rescue platform, which provides simulated tasks to agents. We show that our agents send fewer messages and complete more tasks, and thus achieve a greater degree of success, than other state-of-the-art approaches
Cotton Packer.
No full textPatent for an automatic cotton packer that provides a new method to packing cotton. This mechanism can be controlled automatically or by hand. As the cotton leaves the gin, it is packed automatically
Letter from J. R. Eakin to Stephen Mather
No full textLetter from J. R. Eaking to the National Park Service director about changes to the Grand Canyon National Park boundaries, and access to water near the Buggeln property on Desert View road
Forgetting Fragments from Evolving Ontologies
No full textOntologies underpin the semantic web; they define the concepts and their relationships contained in a data source. An increasing number of ontologies are available on-line, but an ontology that combines information from many different sources can grow extremely large. As an ontology grows larger, more resources are required to use it, and its response time becomes slower. Thus, we present and evaluate an on-line approach that forgets fragments from an OWL ontology that are infrequently or no longer used, or are cheap to relearn, in terms of time and resources. In order to evaluate our approach, we situate it in a controlled simulation environment, RoboCup OWLRescue, which is an extension of the widely used RoboCup Rescue platform, which enables agents to build ontologies automatically based on the tasks they are required to perform. We benchmark our approach against other comparable techniques and show that agents using our approach spend less time forgetting concepts from their ontology, allowing them to spend more time deliberating their actions, to achieve a higher average score in the simulation environment
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