Dust Formation in the Ejecta of Common Envelope Systems

The material that is ejected in a common-envelope (CE) phase in a close binary system provides an ideal environment for dust formation. By constructing a simple toy model to describe the evolution of the density and the temperature of CE ejecta and using the \emph{AGBDUST} code to model dust formation, we show that dust can form efficiently in this environment. The actual dust masses produced in the CE ejecta depend strongly on their temperature and density evolution. We estimate the total dust masses produced by CE evolution by means of a population synthesis code and show that, compared to dust production in AGB stars, the dust produced in CE ejecta may be quite significant and could even dominate under certain circumstances.Comment: 11pages, 7 figures, accepted for publication by Ap

TasselNet: Counting maize tassels in the wild via local counts regression network

Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have experienced a significant breakthrough due to the emergence of large-scale datasets and increased computational resources. Naturally image-based approaches have also received much attention in plant-related studies. Yet a fact is that most image-based systems for plant phenotyping are deployed under controlled laboratory environment. When transferring the application scenario to unconstrained in-field conditions, intrinsic and extrinsic variations in the wild pose great challenges for accurate counting of maize tassels, which goes beyond the ability of conventional image processing techniques. This calls for further robust computer vision approaches to address in-field variations. This paper studies the in-field counting problem of maize tassels. To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment.Comment: 14 page