197,212 research outputs found
Failsafe distributed optimal routing in data-communication networks
No full textBibliography: p. 146-148.Research supported by ARPA Contract N00014-75-C-1183, ONR Contract ONR-N)))14-77-C-0532.by M. Sidi, A. Segall
The Interpretation of gravity changes and crustal deformation in active volcanic areas
No full textSimple models, like the well-known point source of dilation (Mogi’s source) in an elastic,
homogeneous and isotropic half-space, are widely used to interpret geodetic and gravity data in active
volcanic areas. This approach appears at odds with the real geology of volcanic regions, since the crust is
not a homogeneous medium and magma chambers are not spheres. In this paper, we evaluate several more
realistic source models that take into account the influence of self-gravitation effects, vertical
discontinuities in the Earth’s density and elastic parameters, and non-spherical source geometries. Our
results indicate that self-gravitation effects are second order over the distance and time scales normally
associated with volcano monitoring. For an elastic model appropriate to Long Valley caldera, we find only
minor differences between modeling the 1982–1999 caldera unrest using a point source in elastic,
homogeneous half-spaces, or in elasto-gravitational, layered half-spaces. A simple experiment of matching
deformation and gravity data from an ellipsoidal source using a spherical source shows that the standard
approach of fitting a center of dilation to gravity and uplift data only, excluding the horizontal
displacements, may yield estimates of the source parameters that are not reliable. The spherical source
successfully fits the uplift and gravity changes, overestimating the depth and density of the intrusion, but is
not able to fit the radial displacements
Computational Dimensionalities of Global Supercomputing
No full textThis Invited Paper pertains to subject of my Plenary Keynote Speech at the 17th World Multi-Conference on Systemics, Cybernetics and Informatics (WMSCI 2013) held in Orlando, Florida on July 9-12, 2013. The title of my Plenary Keynote Speech was: "Dimensionalities of Computation: from Global Supercomputing to Data, Text and Web Mining" but this Invited Paper will focus only on the "Computational Dimensionalities of Global Supercomputing" and is based upon a summary of the contents of several individual articles that have been previously written with myself as lead author and published in [75], [76], [77], [78], [79], [80] and [11]. The topics of these of the Plenary Speech included Overview of Current Research in Global Supercomputing [75], Open-Source Software Tools for Data Mining Analysis of Genomic and Spatial Images using High Performance Computing [76], Data Mining Supercomputing with SAS™ JMP® Genomics ([77], [79], [80]), and Visualization by Supercomputing Data Mining [81].
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[11.] Committee on the Future of Supercomputing, National Research Council (2003), The Future of Supercomputing: An Interim Report, ISBN-13: 978-0-309-09016- 2, http://www.nap.edu/catalog/10784.html
[75.] Segall, Richard S.; Zhang, Qingyu and Cook, Jeffrey S.(2013), "Overview of Current Research in Global Supercomputing", Proceedings of Forty- Fourth Meeting of Southwest Decision Sciences Institute (SWDSI), Albuquerque, NM, March 12-16, 2013.
[76.] Segall, Richard S. and Zhang, Qingyu (2010), "Open-Source Software Tools for Data Mining Analysis of Genomic and Spatial Images using High Performance Computing", Proceedings of 5th INFORMS Workshop on Data Mining and Health Informatics, Austin, TX, November 6, 2010.
[77.] Segall, Richard S., Zhang, Qingyu and Pierce, Ryan M.(2010), "Data Mining Supercomputing with SAS™ JMP®; Genomics: Research-in-Progress, Proceedings of 2010 Conference on Applied Research in Information Technology, sponsored by Acxiom Laboratory of Applied Research (ALAR), University of Central Arkansas (UCA), Conway, AR, April 9, 2010.
[78.] Segall, Richard S., Zhang, Qingyu and Pierce, Ryan M.(2009), "Visualization by Supercomputing Data Mining", Proceedings of the 4th INFORMS Workshop on Data Mining and System Informatics, San Diego, CA, October 10, 2009.
[79.] Segall, Richard S., Zhang, Qingyu, and Pierce, Ryan (2010), "Data Mining Supercomputing with SAS™ JMP® Genomics", Proceedings of 14th World Multi-Conference on Systemics, Cybernetics and Informatics: WMSCI 2010, Orlando, FL, June 29-July 2, 2010
[80.] Segall, Richard S., Zhang, Qingyu, and Pierce, Ryan (2010), "Data Mining Supercomputing with SAS™ JMP® Genomics", Journal of Systemics, Cybernetics and Informatics (JSCI), Vol. 9, No. 1, 2011, pp.28-33.
[81.] Segall, RS, Zhang, Q., and Pierce, RM (2009), Visualization by supercomputing data mining, Proceedings of the 4 th INFORMS Workshop on Data Mining and System Informatics, San Diego, CA, October 10, 200
A failsafe distributed routing protocol
No full textBibliography: p. 63."Sep. 1977, revised May 1978."Supported by the Advanced Research Project Agency (monitored by ONR) under Contract no. N00014-75-C-1183Philip M. Merlin and Adrian Segall
The mechanics of unrest at Long Valley caldera, California. 2. Constraining the nature of the source using geodetic and micro-gravity data
No full textResenha: Goethe, Fausto I, Trad. de J. Klabin Segall com notas de M. V. Mazzari
No full textRESENHA:GOETHE, Johann Wolfgang von: Fausto. Uma tragédia. Primeira parte. Tradução do original alemão de Jenny KLABIN SEGALL. Apresentação, comentários e notas de Marcus Vinícius MAZZARI. Ilustrações de Eugène Delacroix. Edição bilíngüe. São Paulo: editora 34, 2004.</p
A distributed shortest path protocol
No full textBibliography: p. 29."June, 1981."U.S. Department of Defense contract No. N00014-75-C-1183 Office of Naval Research Contract ONR/N00014-77-C-0532Francine B.M. Zerbib and Adrian Segall
Advanced Data Mining of Leukemia Cells Micro-Arrays
No full textThis paper provides continuation and extensions of previous research by Segall and Pierce (2009a) that discussed data mining for micro-array databases of Leukemia cells for primarily self-organized maps (SOM). As Segall and Pierce (2009a) and Segall and Pierce (2009b) the results of applying data mining are shown and discussed for the data categories of microarray databases of HL60, Jurkat, NB4 and U937 Leukemia cells that are also described in this article. First, a background section is provided on the work of others pertaining to the applications of data mining to micro-array databases of Leukemia cells and micro-array databases in general. As noted in predecessor article by Segall and Pierce (2009a), micro-array databases are one of the most popular functional genomics tools in use today. This research in this paper is intended to use advanced data mining technologies for better interpretations and knowledge discovery as generated by the patterns of gene expressions of HL60, Jurkat, NB4 and U937 Leukemia cells. The advanced data mining performed entailed using other data mining tools such as cubic clustering criterion, variable importance rankings, decision trees, and more detailed examinations of data mining statistics and study of other self-organized maps (SOM) clustering regions of workspace as generated by SAS Enterprise Miner version 4. Conclusions and future directions of the research are also presented
A recoverable protocol for loop-free distributed routing
No full textBibliography: leaf 25."September 1978." Suppported in part by Codex Corporation.Supported in part by the Advanced Research Project Agency (monitored by ONR) under Contract no. N00014-75-C-1183A. Segall, P.M. Merlin and R.G. Gallager
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