scholarly journals Unsupervised Heterogeneous Coupling Learning for Categorical Representation

Author(s):  
Chengzhang Zhu ◽  
Longbing Cao ◽  
Jianping Yin
2017 ◽  
Vol 69 (1) ◽  
pp. 107-129
Author(s):  
Masoud Kamgarpour

AbstractUnder the local Langlands correspondence, the conductor of an irreducible representation of Gln(F) is greater than the Swan conductor of the corresponding Galois representation. In this paper, we establish the geometric analogue of this statement by showing that the conductor of a categorical representation of the loop group is greater than the irregularity of the corresponding meromorphic connection.


2017 ◽  
Vol 17 (10) ◽  
pp. 281 ◽  
Author(s):  
Stefania Mattioni ◽  
Mohamed Rezk ◽  
Karen Cuculiza ◽  
Ceren Battal ◽  
Roberto Bottini ◽  
...  

2018 ◽  
Vol 97 (1) ◽  
Author(s):  
Andreas Otto ◽  
Günter Radons ◽  
Dániel Bachrathy ◽  
Gábor Orosz

Author(s):  
Ivan Tchalakov ◽  
Irina Popravko

Applying the notion of identity, the article analyses the role of real time observation of professional and amateur astronomers in the context of ongoing digitalization of research. Unveiling the importance of materiality and immediate relationships with instruments, we took a critical stance to the established research approaches to this subject, in particular the ethnography of profession and the actor-network theory (ANT). Bearing on of Julian Orr studies of professional culture and our own ANT notion of ‘heterogeneous coupling', an attempt was made to introduce a new language for analysing the two knowledge communities, based on the sociology of taste and attachment of Antoine Hennion and sociology of regimes of worth of Luck Boltanski, which allows to grasp both similarities and differences in the astronomers' identities.


2012 ◽  
Vol 22 (04) ◽  
pp. 1250074 ◽  
Author(s):  
J. TENREIRO MACHADO ◽  
ANTÓNIO C. COSTA ◽  
MARIA DULCE QUELHAS

This paper studies the DNA code of eleven mammals from the perspective of fractional dynamics. The application of Fourier transform and power law trendlines leads to a categorical representation of species and chromosomes. The DNA information reveals long range memory characteristics.


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