Treetrimmer: a method for phylogenetic dataset size reduction
نویسندگان
چکیده
منابع مشابه
A Reduction Method of Cogging Torque for Magnetic Gears
Nowadays, magnetic gears (MGs) have become an alternative choice for mechanical gears because of their low maintenance, improved durability, indirect contact between inner and outer rotors, no lubrication, and high efficiency. Generally, although these advantages, MGs suffer from inherent issues, mainly the cogging torque. Therefore, cogging torque mitigation has become an active research area....
متن کاملA Method for Automating Geospatial Dataset Metadata
Metadata have long been recognised as crucial to geospatial asset management and discovery, and yet undertaking their creation remains an unenviable task often to be avoided. This paper proposes a practical approach designed to address such concerns, decomposing various data creation, management, update and documentation process steps that are subsequently leveraged to contribute towards metada...
متن کاملSample size for a phylogenetic inference.
The objective of this work is to describe sample-size calculations for the inference of a nonzero central branch length in an unrooted four-species phylogeny. Attention is restricted to independent binary characters, such as might be obtained from an alignment of the purine-pyrimidine sequences of a nucleic acid molecule. A statistical test based on a multinomial model for character-state confi...
متن کاملRENOIR - A Benchmark Dataset for Real Noise Reduction Evaluation
In this paper we introduce a dataset of uncompressed color images taken with three digital cameras and exhibiting different levels of natural noise due to low-light conditions. For each scene there are on average two low-noise and two high noise images that are aligned at the pixel level both spatially and in intensity. The dataset contains over 100 scenes and more than 400 images, including bo...
متن کاملRENOIR - A dataset for real low-light image noise reduction
Many modern and popular state of the art image denoising algorithms are trained and evaluated using images corrupted by artificial noise. These trained algorithms and their evaluations on synthetic data may lead to incorrect conclusions about their performances on real noise. In this paper we introduce a benchmark dataset of uncompressed color images corrupted by natural noise due to low-light ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: BMC Research Notes
سال: 2013
ISSN: 1756-0500
DOI: 10.1186/1756-0500-6-145