REACT Trend Estimation In Correlated Noise

ABSTRACT In this paper, we investigate the impact of correlated noise on fast radio burst (FRB) searching. We found that (1) the correlated noise significantly increases the false alarm probability; (2) the signal-to-noise ratios (S/N) of the false positives become higher; (3) the correlated noise also affects the pulse width distribution of false positives, and there will be more false positives with wider pulse width. We use 55-h observation for M82 galaxy carried out at Nanshan 26m radio telescope to demonstrate the application of the correlated noise modelling. The number of candidates and parameter distribution of the false positives can be reproduced with the modelling of correlated noise. We will also discuss a low S/N candidate detected in the observation, for which we demonstrate the method to evaluate the false alarm probability in the presence of correlated noise. Possible origins of the candidate are discussed, where two possible pictures, an M82-harboured giant pulse and a cosmological FRB, are both compatible with the observation.

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Forgery Detection in Digital Images by Multi-Scale Noise Estimation

Journal of Imaging ◽

10.3390/jimaging7070119 ◽

2021 ◽

Vol 7 (7) ◽

pp. 119

Author(s):

Marina Gardella ◽

Pablo Musé ◽

Jean-Michel Morel ◽

Miguel Colom

Keyword(s):

Noise Model ◽

Correlated Noise ◽

Forgery Detection ◽

Color Channel ◽

Image Block ◽

Multi Scale ◽

Compressed Images ◽

Inconsistency Analysis ◽

Highly Correlated ◽

The Moment

A complex processing chain is applied from the moment a raw image is acquired until the final image is obtained. This process transforms the originally Poisson-distributed noise into a complex noise model. Noise inconsistency analysis is a rich source for forgery detection, as forged regions have likely undergone a different processing pipeline or out-camera processing. We propose a multi-scale approach, which is shown to be suitable for analyzing the highly correlated noise present in JPEG-compressed images. We estimate a noise curve for each image block, in each color channel and at each scale. We then compare each noise curve to its corresponding noise curve obtained from the whole image by counting the percentage of bins of the local noise curve that are below the global one. This procedure yields crucial detection cues since many forgeries create a local noise deficit. Our method is shown to be competitive with the state of the art. It outperforms all other methods when evaluated using the MCC score, or on forged regions large enough and for colorization attacks, regardless of the evaluation metric.

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A local direct method for module identification in dynamic networks with correlated noise

IEEE Transactions on Automatic Control ◽

10.1109/tac.2020.3035634 ◽

2020 ◽

pp. 1-1

Author(s):

Karthik R. Ramaswamy ◽

Paul M.J. Vandenhof

Keyword(s):

Direct Method ◽

Dynamic Networks ◽

Correlated Noise ◽

Module Identification

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Extensive numerical simulations of surface growth with temporally correlated noise

Physical Review E ◽

10.1103/physreve.103.012121 ◽

2021 ◽

Vol 103 (1) ◽

Author(s):

Tianshu Song ◽

Hui Xia

Keyword(s):

Numerical Simulations ◽

Surface Growth ◽

Correlated Noise ◽

Temporally Correlated

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Escape dynamics of active particles in multistable potentials

Nature Communications ◽

10.1038/s41467-021-22647-6 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

A. Militaru ◽

M. Innerbichler ◽

M. Frimmer ◽

F. Tebbenjohanns ◽

L. Novotny ◽

...

Keyword(s):

Transition State Theory ◽

Silica Nanoparticle ◽

State Theory ◽

Biological Processes ◽

Correlated Noise ◽

Stochastic Force ◽

Escape Dynamics ◽

Active Particles ◽

Bistable Potential ◽

Quantitative Understanding

AbstractRare transitions between long-lived metastable states underlie a great variety of physical, chemical and biological processes. Our quantitative understanding of reactive mechanisms has been driven forward by the insights of transition state theory and in particular by Kramers’ dynamical framework. Its predictions, however, do not apply to systems that feature non-conservative forces or correlated noise histories. An important class of such systems are active particles, prominent in both biology and nanotechnology. Here, we study the active escape dynamics of a silica nanoparticle trapped in a bistable potential. We introduce activity by applying an engineered stochastic force that emulates self-propulsion. Our experiments, supported by a theoretical analysis, reveal the existence of an optimal correlation time that maximises the transition rate. We discuss the origins of this active turnover, reminiscent of the much celebrated Kramers turnover. Our work establishes a versatile experimental platform to study single particle dynamics in non-equilibrium settings.

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