scholarly journals Searching for signatures of chaos in γ-ray light curves of selected Fermi-LAT blazars

2021 ◽  
Vol 502 (2) ◽  
pp. 2750-2756
Author(s):  
O Ostapenko ◽  
M Tarnopolski ◽  
N Żywucka ◽  
J Pascual-Granado
Keyword(s):  
Light Curves ◽  
High Dimensional ◽  
Nonlinear Time Series Analysis ◽  
Emission Mechanism ◽  
Large Area ◽  
The Past ◽  
Analysis Phase ◽  
Γ Ray ◽  
Low Dimensional ◽  
Unambiguous Detection

ABSTRACT Blazar variability appears to be stochastic in nature. However, a possibility of low-dimensional chaos was considered in the past, but with no unambiguous detection so far. If present, it would constrain the emission mechanism by suggesting an underlying dynamical system. We rigorously searched for signatures of chaos in Fermi-Large Area Telescope light curves of 11 blazars. The data were comprehensively investigated using the methods of nonlinear time-series analysis: phase-space reconstruction, fractal dimension, and maximal Lyapunov exponent (mLE). We tested several possible parameters affecting the outcomes, in particular the mLE, in order to verify the spuriousness of the outcomes. We found no signs of chaos in any of the analysed blazars. Blazar variability is either truly stochastic in nature or governed by high-dimensional chaos that can often resemble randomness.

scholarly journals Diversity of γ-ray and radio variability of bright blazars and implications for γ-ray emission location

2020 ◽  
Vol 72 (3) ◽  
Author(s):  
Hai-Ming Zhang ◽  
Zhen-Jie Wang ◽  
Jin Zhang ◽  
Ting-Feng Yi ◽  
Liang Chen ◽  
...  
Keyword(s):  
Time Lag ◽  
Light Curves ◽  
Emission Region ◽  
Large Area ◽  
Central Engine ◽  
Variation Patterns ◽  
Multi Wavelength ◽  
Γ Ray ◽  
Fast Spike

Abstract Violent multi-wavelength variabilities are observed in γ-ray-selected blazars. We present an analysis of long-term light curves for eight bright blazars to explore the co-variation pattern in the γ-ray and radio bands. We extract their γ-ray light curves and spectra with data observed by the Fermi Large Area Telescope (LAT) since 2008. We find diverse co-variation patterns between the γ-ray and radio (at 43 GHz) fluxes in these sources. The γ-ray and radio fluxes of 3C 454.3 and PKS 1633+382 are correlated without any time lag, suggesting that they are from the same radiation region. Similar correlation is also observed in 3C 273 and PKS 1222+216, but the radio flux lags behind the γ-ray flux by approximately ∼160 d and ∼290 d, respectively. This likely suggests that their γ-ray emission regions are located the upstream of their radio cores at 43 GHz. The γ-ray and radio fluxes of the other four blazars are not correlated, implying that the γ-ray and radio emission may be from different regions in their jets. The γ-ray light curves of the eight blazars can be decomposed into some components with long-timescale variability and some fast spike flares. We propose that they may be attributed to the central engine activity and the magnetic reconnection process or turbulence in the local emission region, respectively.

scholarly journals Stochastic approach to modeling the γ-ray variability of Fermi/LAT blazars

2014 ◽  
Vol 10 (S313) ◽  
pp. 21-26
Author(s):  
M. Sobolewska ◽  
A. Siemiginowska ◽  
B. Kelly ◽  
K. Nalewajko
Keyword(s):  
Time Scale ◽  
Characteristic Time ◽  
Good Description ◽  
Stochastic Approach ◽  
Light Curves ◽  
Characteristic Time Scale ◽  
Large Area ◽  
Power Spectral ◽  
Γ Ray ◽  
Intermediate Part

AbstractWe studied the γ-ray variability of 13 blazars observed with the Fermi Large Area Telescope (LAT). These blazars were among the brightest ones monitored during the first 4 years of the Fermi sky survey. We modelled their γ-ray light curves with the Ornstein-Uhlenbeck (OU) process or mixed OU process. The power spectral density (PSD) of the OU process is a zero-centered Lorentzian function, proportional to 1/fα with α changing at a characteristic time scale, τ0, from 0 (τ ≫ τ0) to 2 (τ ≪ τ0). The PSD of the mixed OU process has in addition an intermediate part with 0 < α < 2 between the long and short characteristic time scales. We show that the OU model provides a good description of the Fermi/LAT light curves of three blazars in our sample. For the first time we provide constraints on the characteristic γ-ray time scale of variability in two BL Lac sources, 3C 66A and PKS 2155-304. We find that the mixed OU process describes the light curves of the remaining 10 blazars better than the OU process. We infer that their Fermi/LAT PSD resemble power-law functions and constrain their PSD slopes.

Evaluating a Stream of Relational KNNQueries by a Knowledge Base

2015 ◽  
Vol 24 (02) ◽  
pp. 1550003
Author(s):  
Liang Zhu ◽  
Xin Song ◽  
Chunnian Liu
Keyword(s):  
Knowledge Base ◽  
Relational Databases ◽  
Main Memory ◽  
High Dimensional ◽  
Clustering Methods ◽  
The Past ◽  
Related Information ◽  
New Strategy ◽  
Low Dimensional ◽  
Better Than

In relational databases and their applications, there are opportunities for evaluating a stream of K NN queries submitted one by one at different times. For this issue, we propose a new method with learning-based techniques, region clustering methods and caching mechanisms. This method uses a knowledge base to store related information of some past K NN queries, groups the search regions of the past queries into larger regions, and retrieves the tuples from the larger regions. To answer a newly submitted query, our strategy tries to obtain a majority or all of the results from the previously retrieved tuples cached in main memory. Thus, this method seeks to minimize the response time by reducing the search region or avoiding the accesses to the underlying database. Meanwhile, our method remains effective for high-dimensional data. Extensive experiments are carried out to measure the performance of this new strategy and the results indicate that it is significantly better than the state-of-the-art naïve methods of evaluating a stream of K NN queries for both low-dimensional (2, 3 and 4) and high-dimensional (25, 50 and 104) data.

Variable oil and argumentative conflict in the Middle East

2020 ◽  
pp. 824
Author(s):  
Malik Daham Mata’ab
Keyword(s):  
Middle East ◽  
Past Century ◽  
Large Area ◽  
The Past ◽  
The World

Oil has formed since its discovery so far one of the main causes of global conflict, has occupied this energy map a large area of conflict the world over the past century, and certainly this matter will continue for the next period in our century..

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

scholarly journals A Nonlinear Maximum Correntropy Information Filter for High-Dimensional Neural Decoding

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 743
Author(s):  
Xi Liu ◽  
Shuhang Chen ◽  
Xiang Shen ◽  
Xiang Zhang ◽  
Yiwen Wang
Keyword(s):  
State Estimation ◽  
Measurement Model ◽  
High Dimensional ◽  
Neural Firing ◽  
The Neural Network ◽  
Information Filter ◽  
Critical Technology ◽  
Dimensional Measurements ◽  
Non Gaussian ◽  
Low Dimensional

Neural signal decoding is a critical technology in brain machine interface (BMI) to interpret movement intention from multi-neural activity collected from paralyzed patients. As a commonly-used decoding algorithm, the Kalman filter is often applied to derive the movement states from high-dimensional neural firing observation. However, its performance is limited and less effective for noisy nonlinear neural systems with high-dimensional measurements. In this paper, we propose a nonlinear maximum correntropy information filter, aiming at better state estimation in the filtering process for a noisy high-dimensional measurement system. We reconstruct the measurement model between the high-dimensional measurements and low-dimensional states using the neural network, and derive the state estimation using the correntropy criterion to cope with the non-Gaussian noise and eliminate large initial uncertainty. Moreover, analyses of convergence and robustness are given. The effectiveness of the proposed algorithm is evaluated by applying it on multiple segments of neural spiking data from two rats to interpret the movement states when the subjects perform a two-lever discrimination task. Our results demonstrate better and more robust state estimation performance when compared with other filters.

PSS Business Case Map: Supporting Idea Generation in PSS Design

2012 ◽  
Author(s):  
Fumiya Akasaka ◽  
Kazuki Fujita ◽  
Yoshiki Shimomura
Keyword(s):  
Idea Generation ◽  
Business Case ◽  
Literature Survey ◽  
The Self ◽  
High Dimensional ◽  
Self Organizing Map ◽  
Two Dimensional ◽  
Service Type ◽  
Business Cases ◽  
Low Dimensional

This paper proposes the PSS Business Case Map as a tool to support designers’ idea generation in PSS design. The map visualizes the similarities among PSS business cases in a two-dimensional diagram. To make the map, PSS business cases are first collected by conducting, for example, a literature survey. The collected business cases are then classified from multiple aspects that characterize each case such as its product type, service type, target customer, and so on. Based on the results of this classification, the similarities among the cases are calculated and visualized by using the Self-Organizing Map (SOM) technique. A SOM is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional) view from high-dimensional data. The visualization result is offered to designers in a form of a two-dimensional map, which is called the PSS Business Case Map. By using the map, designers can figure out the position of their current business and can acquire ideas for the servitization of their business.

scholarly journals Fermi LARGE AREA TELESCOPE DETECTION OF TWO VERY-HIGH-ENERGY ( E > 100 GeV) γ-RAY PHOTONS FROM THE z = 1.1 BLAZAR PKS 0426–380

2013 ◽  
Vol 777 (1) ◽  
pp. L18 ◽  
Author(s):  
Y. T. Tanaka ◽  
C. C. Cheung ◽  
Y. Inoue ◽  
Ł. Stawarz ◽  
M. Ajello ◽  
...  
Keyword(s):  
High Energy ◽  
Large Area ◽  
Very High Energy ◽  
Γ Ray ◽  
Very High

scholarly journals Perspectives of the high-dimensional dynamics of neural microcircuits from the point of view of low-dimensional readouts

Complexity ◽  
2003 ◽  
Vol 8 (4) ◽  
pp. 39-50 ◽  
Author(s):  
Stefan Häusler ◽  
Henry Markram ◽  
Wolfgang Maass
Keyword(s):  
Point Of View ◽  
High Dimensional ◽  
Low Dimensional

Improved interactive color visualization approach for hyperspectral images

2021 ◽  
pp. 147387162110481
Author(s):  
Haijun Yu ◽  
Shengyang Li
Keyword(s):  
Real Time ◽  
Hyperspectral Images ◽  
High Dimensional ◽  
Interactive Control ◽  
Output Image ◽  
Dr Method ◽  
The Rich ◽  
Low Dimensional ◽  
Color Visualization ◽  
Fusion Coefficient

Hyperspectral images (HSIs) have become increasingly prominent as they can maintain the subtle spectral differences of the imaged objects. Designing approaches and tools for analyzing HSIs presents a unique set of challenges due to their high-dimensional characteristics. An improved color visualization approach is proposed in this article to achieve communication between users and HSIs in the field of remote sensing. Under the real-time interactive control and color visualization, this approach can help users intuitively obtain the rich information hidden in original HSIs. Using the dimensionality reduction (DR) method based on band selection, high-dimensional HSIs are reduced to low-dimensional images. Through drop-down boxes, users can freely specify images that participate in the combination of RGB channels of the output image. Users can then interactively and independently set the fusion coefficient of each image within an interface based on concentric circles. At the same time, the output image will be calculated and visualized in real time, and the information it reflects will also be different. In this approach, channel combination and fusion coefficient setting are two independent processes, which allows users to interact more flexibly according to their needs. Furthermore, this approach is also applicable for interactive visualization of other types of multi-layer data.

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