scholarly journals Deep structure learning using feature extraction in trained projection space

2021 ◽  
Vol 92 ◽  
pp. 107097
Author(s):  
Christoph Angermann ◽  
Markus Haltmeier
Author(s):  
Haibo Wang ◽  
Chuan Zhou ◽  
Jia Wu ◽  
Weizhen Dang ◽  
Xingquan Zhu ◽  
...  

Author(s):  
Joanne Nakonechny ◽  
Shona Ellis

Throughout this chapter, the authors trace how the theoretical and practical understanding, interpretation, and interactions with e-portfolios and their implementation support, both individually and through group work, students’ abilities to engage in deeper structure learning, and their resulting growth as authentic science scholars. The bryofolio, an individual and group course e-portfolio, begins this online journey to facilitate deeper structure learning for 31 students in Biology 321, Bryophytes: Mosses, Hornworts and Liverworts. (“Bryfolio” is a contraction of “bryophytes” and “e-portfolio.”) Initially, the authors give a short introduction to science education and how constructivist learning theory can include the use of e-portfolios as a teaching method. Following this, e-portfolios are situated within the learning context by providing a definition, a condition, and discussion on the key e-portfolio element,of critical reflection. The authors continue by introducing the bryofolio, its major components, and our analysis of how the bryofolio encourages deep structure learning at both individual and group levels.


2017 ◽  
Vol 17 (4-5) ◽  
pp. 245-289 ◽  
Author(s):  
Jeffrey S. Morris ◽  
Veerabhadran Baladandayuthapani

The advent of high-throughput multi-platform genomics technologies providing whole- genome molecular summaries of biological samples has revolutionalized biomedical research. These technologiees yield highly structured big data, whose analysis poses significant quantitative challenges. The field of bioinformatics has emerged to deal with these challenges, and is comprised of many quantitative and biological scientists working together to effectively process these data and extract the treasure trove of information they contain. Statisticians, with their deep understanding of variability and uncertainty quantification, play a key role in these efforts. In this article, we attempt to summarize some of the key contributions of statisticians to bioinformatics, focusing on four areas: (1) experimental design and reproducibility, (2) preprocessing and feature extraction, (3) unified modelling and (4) structure learning and integration. In each of these areas, we highlight some key contributions and try to elucidate the key statistical principles underlying these methods and approaches. Our goals are to demonstrate major ways in which statisticians have contributed to bioinformatics, encourage statisticians to get involved early in methods development as new technologies emerge, and to stimulate future methodological work based on the statistical principles elucidated in this article and utilizing all available information to uncover new biological insights.


2005 ◽  
Vol 33 (4) ◽  
pp. 403-410 ◽  
Author(s):  
Rebecca L. Bordt

This article suggests ways in which a current research article on employment discrimination from the American Sociological Review can be used in the undergraduate classroom to facilitate deep structure learning (Roberts 1986, 2001, 2002). The exercises are designed for different levels of the undergraduate curriculum and adopt the strategies of benign disruption, inquiry-based learning, and role-taking so students can accomplish higher intellectual development.


Author(s):  
Shibo Shen ◽  
Rongpeng Li ◽  
Zhifeng Zhao ◽  
Qing Liu ◽  
Jing Liang ◽  
...  

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