Zobrazeno 1 - 10
of 244
pro vyhledávání: '"Andrew Lumsdaine"'
Publikováno v:
IET Computer Vision, Vol 8, Iss 3, Pp 207-215 (2014)
To achieve more complete and more uniformly highlighted salient object regions, this study presents a computational saliency enhancement model that incorporates the properties of multi‐scale and logarithmic response into the local and global contra
Externí odkaz:
https://doaj.org/article/df56f867453c4690934e5aec697ff02e
Publikováno v:
PLoS ONE, Vol 6, Iss 11, p e27431 (2011)
Transfer entropy (TE) is an information-theoretic measure which has received recent attention in neuroscience for its potential to identify effective connectivity between neurons. Calculating TE for large ensembles of spiking neurons is computational
Externí odkaz:
https://doaj.org/article/9d1101e0b15a48e8a1ece4aec2a5c688
Publikováno v:
Logical Methods in Computer Science, Vol Volume 6, Issue 3 (2010)
The call-by-need lambda calculus provides an equational framework for reasoning syntactically about lazy evaluation. This paper examines its operational characteristics. By a series of reasoning steps, we systematically unpack the standard-order redu
Externí odkaz:
https://doaj.org/article/288b247bbaed4145a323bd6e6e50330a
Publikováno v:
ACM Journal of Experimental Algorithmics. 27:1-31
Label Propagation is not only a well-known machine learning algorithm for classification but also an effective method for discovering communities and connected components in networks. We propose a new Direction-optimizing Label Propagation Algorithm
Autor:
Andrei Karavanov, Andrew Lumsdaine, Sungchan Park, Matthew Cinnamon, David T.L. Liu, Thorne Garvin, Darius Strobeck
Publikováno v:
IEEE Transactions on Parallel and Distributed Systems. 32:2639-2642
One of the tasks for the SC19 Student Cluster Competition is to reproduce the results in the reproducibility challenge article “Computing Planetary Interior Normal Modes with a Highly Parallel Polynomial Filtering Eigensolver”, by J. Shi et al. ,
Autor:
Jeremy Kepner, Peter Aaltonen, David Bader, Aydin Buluc, Franz Franchetti, John Gilbert, Shana Hutchison, Manoj Kumar, Andrew Lumsdaine, Henning Meyerhenke, Scott McMillan, Jose Moreira, John D. Owens, Carl Yang, Marcin Zalewski, Timothy G. Mattson
Publikováno v:
Massive Graph Analytics ISBN: 9781003033707
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::89bfa1cce8975749250693395f185723
https://doi.org/10.1201/9781003033707-24
https://doi.org/10.1201/9781003033707-24
Autor:
Nicholas Edmonds, Andrew Lumsdaine
Publikováno v:
Massive Graph Analytics ISBN: 9781003033707
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a05859797f2d7fabe6d7aabbdb79b2b5
https://doi.org/10.1201/9781003033707-21
https://doi.org/10.1201/9781003033707-21
Publikováno v:
2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW).
Autor:
Xu T. Liu, Jesun Firoz, Sinan Aksoy, Ilya Amburg, Andrew Lumsdaine, Cliff Joslyn, Brenda Praggastis, Assefaw H. Gebremedhin
Hypergraphs offer flexible and robust data representations for many applications, but methods that work directly on hypergraphs are not readily available and tend to be prohibitively expensive. Much of the current analysis of hypergraphs relies on fi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::563bcb178b33f0425f53960d3601965d
http://arxiv.org/abs/2201.11326
http://arxiv.org/abs/2201.11326
Autor:
Xu T. Liu, Jesun Firoz, Andrew Lumsdaine, Cliff Joslyn, Sinan Aksoy, Brenda Praggastis, Assefaw H. Gebremedhin
Publikováno v:
2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC).