40,081 research outputs found
Enabling Factor Analysis on Thousand-Subject Neuroimaging Datasets
The scale of functional magnetic resonance image data is rapidly increasing
as large multi-subject datasets are becoming widely available and
high-resolution scanners are adopted. The inherent low-dimensionality of the
information in this data has led neuroscientists to consider factor analysis
methods to extract and analyze the underlying brain activity. In this work, we
consider two recent multi-subject factor analysis methods: the Shared Response
Model and Hierarchical Topographic Factor Analysis. We perform analytical,
algorithmic, and code optimization to enable multi-node parallel
implementations to scale. Single-node improvements result in 99x and 1812x
speedups on these two methods, and enables the processing of larger datasets.
Our distributed implementations show strong scaling of 3.3x and 5.5x
respectively with 20 nodes on real datasets. We also demonstrate weak scaling
on a synthetic dataset with 1024 subjects, on up to 1024 nodes and 32,768
cores
Fast algorithms for computing isogenies between elliptic curves
We survey algorithms for computing isogenies between elliptic curves defined
over a field of characteristic either 0 or a large prime. We introduce a new
algorithm that computes an isogeny of degree ( different from the
characteristic) in time quasi-linear with respect to . This is based in
particular on fast algorithms for power series expansion of the Weierstrass
-function and related functions
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