Observation of higher-order exceptional points in a non-local acoustic metagrating

AbstractHigher-order exceptional points have attracted increased attention in recent years due to their enhanced sensitivity and distinct topological features. Here, we show that non-local acoustic metagratings enabling precise and simultaneous control over their multiple orders of diffraction can serve as a robust platform for investigating higher-order exceptional points in free space. The proposed metagratings, not only could advance the fundamental research of arbitrary order exceptional points, but could also empower unconventional free-space wave manipulation for applications related to sensing and extremely asymmetrical wave control.

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Enhanced sensitivity at higher-order exceptional points

Nature ◽

10.1038/nature23280 ◽

2017 ◽

Vol 548 (7666) ◽

pp. 187-191 ◽

Cited By ~ 405

Author(s):

Hossein Hodaei ◽

Absar U. Hassan ◽

Steffen Wittek ◽

Hipolito Garcia-Gracia ◽

Ramy El-Ganainy ◽

...

Keyword(s):

Higher Order ◽

Exceptional Points ◽

Enhanced Sensitivity

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Erratum: Enhanced sensitivity at higher-order exceptional points

Nature ◽

10.1038/nature24024 ◽

2017 ◽

Vol 551 (7682) ◽

pp. 658-658 ◽

Cited By ~ 2

Author(s):

Hossein Hodaei ◽

Absar U. Hassan ◽

Steffen Wittek ◽

Hipolito Garcia-Gracia ◽

Ramy El-Ganainy ◽

...

Keyword(s):

Higher Order ◽

Exceptional Points ◽

Enhanced Sensitivity

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Development and Performance Investigation of a Single-Channel 160 Gbps Free Space Optics Transmission Link Using Higher Order Modulation Scheme

Wireless Personal Communications ◽

10.1007/s11277-020-08038-y ◽

2021 ◽

Author(s):

Mehtab Singh ◽

Jyoteesh Malhotra

Keyword(s):

Free Space ◽

Single Channel ◽

Higher Order ◽

Free Space Optics ◽

Modulation Scheme ◽

Space Optics ◽

Transmission Link ◽

And Performance

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Determination of the velocity of short electromagnetic waves by interferometry

Proceedings of the Royal Society of London Series A - Mathematical and Physical Sciences ◽

10.1098/rspa.1952.0115 ◽

1952 ◽

Vol 213 (1112) ◽

pp. 123-141 ◽

Cited By ~ 26

Keyword(s):

Phase Velocity ◽

Electromagnetic Radiation ◽

Free Space ◽

Electromagnetic Waves ◽

Wave Beam ◽

Wave Length ◽

Wave Frequency ◽

Space Wave

A new measurement of the velocity of electromagnetic radiation is described. The result has been obtained, using micro-waves at a frequency of 24005 Mc/s ( λ = 1∙25 cm), with a form of interferometer which enables the free-space wave-length to be evaluated. Since the micro-wave frequency can also be ascertained, phase velocity is calculated from the product of frequency and wave-length. The most important aspect of the experiment is the application to the measured wave-length of a correction which arises from diffraction of the micro-wave beam. This correction is new to interferometry and is discussed in detail. The result obtained for the velocity, reduced to vacuum conditions, is c 0 = 299792∙6 ± 0∙7 km/s.

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Bot Detection on Social Networks Using Persistent Homology

Mathematical and Computational Applications ◽

10.3390/mca25030058 ◽

2020 ◽

Vol 25 (3) ◽

pp. 58

Author(s):

Minh Nguyen ◽

Mehmet Aktas ◽

Esra Akbas

Keyword(s):

Machine Learning ◽

Social Networks ◽

Social Media ◽

Persistent Homology ◽

Structural Features ◽

Higher Order ◽

Ego Networks ◽

Feature Extraction Method ◽

Topological Features ◽

Bot Detection

The growth of social media in recent years has contributed to an ever-increasing network of user data in every aspect of life. This volume of generated data is becoming a vital asset for the growth of companies and organizations as a powerful tool to gain insights and make crucial decisions. However, data is not always reliable, since primarily, it can be manipulated and disseminated from unreliable sources. In the field of social network analysis, this problem can be tackled by implementing machine learning models that can learn to classify between humans and bots, which are mostly harmful computer programs exploited to shape public opinions and circulate false information on social media. In this paper, we propose a novel topological feature extraction method for bot detection on social networks. We first create weighted ego networks of each user. We then encode the higher-order topological features of ego networks using persistent homology. Finally, we use these extracted features to train a machine learning model and use that model to classify users as bot vs. human. Our experimental results suggest that using the higher-order topological features coming from persistent homology is promising in bot detection and more effective than using classical graph-theoretic structural features.

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