Machine learning topological invariants of non-Hermitian systems

Graph theory is a discrete branch of mathematics for designing and predicting a network. Some topological invariants are mathematical tools for the analysis of connection properties of a particular network. The Cellular Neural Network (CNN) is a computer paradigm in the field of machine learning and computer science. In this article we have given a close expression to dominating invariants computed by the dominating degree for a cellular neural network. Moreover, we have also presented a 3D comparison between dominating invariants and classical degree-based indices to show that, in some cases, dominating invariants give a better correlation on the cellular neural network as compared to classical indices.

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Machine Learning Topological Invariants with Neural Networks

Physical Review Letters ◽

10.1103/physrevlett.120.066401 ◽

2018 ◽

Vol 120 (6) ◽

Cited By ~ 79

Author(s):

Pengfei Zhang ◽

Huitao Shen ◽

Hui Zhai

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Topological Invariants

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Prediction of Topological Invariants in Photonic Crystals Using Machine Learning

2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) ◽

10.1109/piers-fall48861.2019.9021393 ◽

2019 ◽

Author(s):

Bei Wu ◽

Kun Ding ◽

C. T. Chan ◽

Yuntian Chen

Keyword(s):

Machine Learning ◽

Photonic Crystals ◽

Topological Invariants

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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