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Summary Variation in leaf venation network architecture may reflect trade‐offs among multiple functions including efficiency, resilience, support, cost, and resistance to drought and herbivory. However, our knowledge about architecture‐function trade‐offs is mostly based on studies examining a small number of functional axes, so we still lack a more integrative picture of multidimensional trade‐offs.Here, we measured architecture and functional traits on 122 ferns and angiosperms species to describe how trade‐offs vary across phylogenetic groups and vein spatial scales (small, medium, and large vein width) and determine whether architecture traits at each scale have independent or integrated effects on each function.We found that generalized architecture‐function trade‐offs are weak. Architecture strongly predicts leaf support and damage resistance axes but weakly predicts efficiency and resilience axes. Architecture traits at different spatial scales contribute to different functional axes, allowing plants to independently modulate different functions by varying network properties at each scale.This independence of vein architecture traits within and across spatial scales may enable evolution of multiple alternative leaf network designs with similar functioning.
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Practical Random Access to SLP-Compressed Texts
Grammar-based compression is a popular and powerful approach to compressing repetitive texts but until recently its relatively poor time-space trade-offs during real-life construction made it impractical for truly massive datasets such as genomic databases. In a recent paper (SPIRE 2019) we showed how simple pre-processing can dramatically improve those trade-offs, and in this paper we turn our attention to one of the features that make grammar-based compression so attractive: the possibility of supporting fast random access. This is an essential primitive in many algorithms that process grammar-compressed texts without decompressing them and so many theoretical bounds have been published about it, but experimentation has lagged behind. We give a new encoding of grammars that is about as small as the practical state of the art (Maruyama et al., SPIRE 2013) but with significantly faster queries.
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- Award ID(s):
- 2029552
- PAR ID:
- 10278822
- Date Published:
- Journal Name:
- SPIRE 2020
- Volume:
- 12303
- Page Range / eLocation ID:
- 221-231
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-59212-7_16
