4,136 research outputs found
Focused Proofreading: Efficiently Extracting Connectomes from Segmented EM Images
Identifying complex neural circuitry from electron microscopic (EM) images
may help unlock the mysteries of the brain. However, identifying this circuitry
requires time-consuming, manual tracing (proofreading) due to the size and
intricacy of these image datasets, thus limiting state-of-the-art analysis to
very small brain regions. Potential avenues to improve scalability include
automatic image segmentation and crowd sourcing, but current efforts have had
limited success. In this paper, we propose a new strategy, focused
proofreading, that works with automatic segmentation and aims to limit
proofreading to the regions of a dataset that are most impactful to the
resulting circuit. We then introduce a novel workflow, which exploits
biological information such as synapses, and apply it to a large dataset in the
fly optic lobe. With our techniques, we achieve significant tracing speedups of
3-5x without sacrificing the quality of the resulting circuit. Furthermore, our
methodology makes the task of proofreading much more accessible and hence
potentially enhances the effectiveness of crowd sourcing
Automorphism Group of : Applications to the Bosonic String
This paper is concerned with the formulation of a non-pertubative theory of
the bosonic string. We introduce a formal group which we propose as the
``universal moduli space'' for such a formulation. This is motivated because
establishes a natural link between representations of the Virasoro algebra
and the moduli space of curves. Among other properties of it is shown that
a ``local'' version of the Mumford formula holds on .Comment: 29 page
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