scholarly journals Comments on "Data assimilation as a deep learning tool to infer ODE representations of dynamical models" by Bocquet et al.

2019 ◽  
Author(s):  
Anonymous
Author(s):  
Humberto Farias ◽  
Mauricio Solar ◽  
Daniel Ortiz

2019 ◽  
Vol 167 ◽  
pp. 172-179 ◽  
Author(s):  
Bo Mao ◽  
Li-Guo Han ◽  
Qiang Feng ◽  
Yu-Chen Yin

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 201450-201457 ◽  
Author(s):  
Zeeshan Abbas ◽  
Hilal Tayara ◽  
Kil to Chong
Keyword(s):  

2020 ◽  
Vol 36 (10) ◽  
pp. 3248-3250
Author(s):  
Marta Lovino ◽  
Maria Serena Ciaburri ◽  
Gianvito Urgese ◽  
Santa Di Cataldo ◽  
Elisa Ficarra

Abstract Summary In the last decade, increasing attention has been paid to the study of gene fusions. However, the problem of determining whether a gene fusion is a cancer driver or just a passenger mutation is still an open issue. Here we present DEEPrior, an inherently flexible deep learning tool with two modes (Inference and Retraining). Inference mode predicts the probability of a gene fusion being involved in an oncogenic process, by directly exploiting the amino acid sequence of the fused protein. Retraining mode allows to obtain a custom prediction model including new data provided by the user. Availability and implementation Both DEEPrior and the protein fusions dataset are freely available from GitHub at (https://github.com/bioinformatics-polito/DEEPrior). The tool was designed to operate in Python 3.7, with minimal additional libraries. Supplementary information Supplementary data are available at Bioinformatics online.


2020 ◽  
Vol 135 ◽  
pp. 110921 ◽  
Author(s):  
Yi-Wei Wang ◽  
Lei Huang ◽  
Si-Wen Jiang ◽  
Kan Li ◽  
Jun Zou ◽  
...  

Author(s):  
Jin Yang

The chapter explores the use of social media in educational settings and assesses its potential as a learning tool in facilitating deep learning and knowledge development. Guided by Vygotsky and Bakhtin's theory of dialogic learning, the chapter argues, by discussion, that social media may facilitate deep learning and knowledge development due to social media's convenient discursive space and heightened interactivity. Specifically, social media's discursive space may provide a platform that is egalitarian and democratic to all who have access to it. The breakdown of traditional communication barriers in this discursive space can be significant in engaging students in dialogic learning. Social media's heightened interactivity embodied in social, procedural, expository, explanatory, and cognitive dimensions may shorten psychological distances, lighten class-managing load, expedite learning materials' delivery, expand the learning space without time constraint, and encourage cross-pollination of ideas and viewpoints. The chapter discusses the profound opportunity that social media may have to enhance knowledge development.


2019 ◽  
Vol 286 ◽  
pp. 07013
Author(s):  
N. Kumar ◽  
F. Kerhervé ◽  
L. Cordier

The design of active model-based flow controllers requires the knowledge of a dynamical model of the flow. However, real-time and robust estimation of the flow state remains a challenging task when only limited spatial and temporal discrete measurements are available. In this study, the objective is to draw upon the methodologies implemented in meteorology to develop dynamic observers for flow control applications. Well established data assimilation (DA) method using Kalman filter is considered. These approaches are extended to both estimate model states and parameters. Simple non-linear dynamical models are first considered to establish quantitative comparisons between the different algorithms. An experimental demonstration for the particular case of a plane mixing layer is then proposed.


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