Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Allon G. Percus"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Quantum algorithms provide an exponential speedup for solving certain classes of linear systems, including those that model geologic fracture flow. However, this revolutionary gain in efficiency does not come without difficulty. Quantum algo
Externí odkaz:
https://doaj.org/article/ccdc1e367ce546d3919b65820efa8850
Publikováno v:
Communications Physics, Vol 4, Iss 1, Pp 1-6 (2021)
The scientific ecosystem is characterized by complex multifaceted relationships between institutions, researchers, and their collaborators. In this work, the authors find common patterns in these relationships expressed through superlinear scaling, H
Externí odkaz:
https://doaj.org/article/9f02cfaf337e4cb6a2fc04743f321298
Real-world networks are rarely static. Recently, there has been increasing interest in both network growth and network densification, in which the number of edges scales superlinearly with the number of nodes. Less studied but equally important, howe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af8d38bf1e7d7767c5b86a420e2fefe6
http://arxiv.org/abs/2304.03479
http://arxiv.org/abs/2304.03479
Publikováno v:
Communications Physics, Vol 4, Iss 1, Pp 1-6 (2021)
Research institutions provide the infrastructure for scientific discovery, yet their role in the production of knowledge is not well characterized. To address this gap, we analyze interactions of researchers within and between institutions from milli
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e6fccc4c15de9aac686d686f6f440bc
http://arxiv.org/abs/2001.08734
http://arxiv.org/abs/2001.08734
Publikováno v:
Discrete Mathematics & Theoretical Computer Science, Vol Vol. 12 no. 3, Iss Graph and Algorithms (2010)
Graphs and Algorithms
Externí odkaz:
https://doaj.org/article/08c87effa1a245fda95ccf2012dd1126
Publikováno v:
Proc Math Phys Eng Sci
Network topologies can be non-trivial, due to the complex underlying behaviors that form them. While past research has shown that some processes on networks may be characterized by low-order statistics describing nodes and their neighbors, such as de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bfb92e4e8da223d10f4a8ef6a8828991
https://pubmed.ncbi.nlm.nih.gov/32523411
https://pubmed.ncbi.nlm.nih.gov/32523411
Publikováno v:
Scientific Reports, Vol 8, Iss 1, Pp 1-1 (2018)
A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c0432032f25056174c772d6bf63c7a1
https://doi.org/10.1038/s41598-018-36018-7
https://doi.org/10.1038/s41598-018-36018-7
Publikováno v:
ACM Transactions on Knowledge Discovery from Data. 11:1-26
Real-world networks are often organized as modules or communities of similar nodes that serve as functional units. These networks are also rich in content, with nodes having distinguishing features or attributes. In order to discover a network's modu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e9059ab3c18a6f3b97bbd13772a3d39
https://doi.org/10.1145/2968451
https://doi.org/10.1145/2968451
Autor:
Zhengming Song, Bryce Rogan, Esteban Rougier, Viet T. Chau, Allon G. Percus, Yadong Ruan, Diana Y. Lee, Gowri Srinivasan, Hari S. Viswanathan, Bryan A. Moore, Max Schwarzer
We propose a machine learning approach to address a key challenge in materials science: predicting how fractures propagate in brittle materials under stress, and how these materials ultimately fail. Our methods use deep learning and train on simulati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::09c5620061bda7a77d779db2154f7b4e
http://arxiv.org/abs/1810.06118
http://arxiv.org/abs/1810.06118
Networks facilitate the spread of cascades, allowing a local perturbation to percolate via interactions between nodes and their neighbors. We investigate how network structure affects the dynamics of a spreading cascade. By accounting for the joint d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8640078a18cfc2ac9ea3854aad9cfded
http://arxiv.org/abs/1807.05472
http://arxiv.org/abs/1807.05472