8,036 research outputs found
Quartet consistency count method for reconstructing phylogenetic trees
Among the distance based algorithms in phylogenetic tree reconstruction, the
neighbor-joining algorithm has been a widely used and effective method. We
propose a new algorithm which counts the number of consistent quartets for
cherry picking with tie breaking. We show that the success rate of the new
algorithm is almost equal to that of neighbor-joining. This gives an
explanation of the qualitative nature of neighbor-joining and that of
dissimilarity maps from DNA sequence data. Moreover, the new algorithm always
reconstructs correct trees from quartet consistent dissimilarity maps.Comment: 11 pages, 5 figure
Font Representation Learning via Paired-glyph Matching
Fonts can convey profound meanings of words in various forms of glyphs.
Without typography knowledge, manually selecting an appropriate font or
designing a new font is a tedious and painful task. To allow users to explore
vast font styles and create new font styles, font retrieval and font style
transfer methods have been proposed. These tasks increase the need for learning
high-quality font representations. Therefore, we propose a novel font
representation learning scheme to embed font styles into the latent space. For
the discriminative representation of a font from others, we propose a
paired-glyph matching-based font representation learning model that attracts
the representations of glyphs in the same font to one another, but pushes away
those of other fonts. Through evaluations on font retrieval with query glyphs
on new fonts, we show our font representation learning scheme achieves better
generalization performance than the existing font representation learning
techniques. Finally on the downstream font style transfer and generation tasks,
we confirm the benefits of transfer learning with the proposed method. The
source code is available at https://github.com/junhocho/paired-glyph-matching.Comment: Accepted to BMVC202
Unsupervised Hyperbolic Representation Learning via Message Passing Auto-Encoders
Most of the existing literature regarding hyperbolic embedding concentrate
upon supervised learning, whereas the use of unsupervised hyperbolic embedding
is less well explored. In this paper, we analyze how unsupervised tasks can
benefit from learned representations in hyperbolic space. To explore how well
the hierarchical structure of unlabeled data can be represented in hyperbolic
spaces, we design a novel hyperbolic message passing auto-encoder whose overall
auto-encoding is performed in hyperbolic space. The proposed model conducts
auto-encoding the networks via fully utilizing hyperbolic geometry in message
passing. Through extensive quantitative and qualitative analyses, we validate
the properties and benefits of the unsupervised hyperbolic representations.
Codes are available at https://github.com/junhocho/HGCAE
Utilizing University-based Enterprise to Foster Industry-Academia Collaboration in the Field of Product Development
University-based enterprise (UBE) system has been adopted and implemented in diverse fields focusing on practice-based learning. The aim of the presentation is to introduce the UBE system integrated into product development course and active collaborations with regional government and other companies to enhance studentsā practical abilities. Presented are the UBE system and two cases utilizing the system for product development education
Plant Location Selection for Food Production by Considering the Regional and Seasonal Supply Vulnerability of Raw Materials
A production capacity analysis considering market demand and raw materials is very important to design a new plant. However, in the food processing industry, the supply uncertainty of raw materials is very high, depending on the production site and the harvest season, and further, it is not straightforward to analyze too complex food production systems by using an analytical optimization model. For these reasons, this study presents a simulation-based decision support model to select the right location for a new food processing plant. We first define three supply vulnerability factors from the standpoint of regional as well as seasonal instability and present an assessment method for supply vulnerability based on fuzzy quantification. The evaluated vulnerability scores are then converted into raw material supply variations for food production simulation to predict the quarterly production volume of a new food processing plant. The proposed selection procedure is illustrated using a case study of semiprocessed kimchi production. The best plant location is proposed where we can reduce and mitigate risks when supplying raw material, thereby producing a target production volume steadily
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