7,654 research outputs found
Nonlinear time-series analysis of Hyperion's lightcurves
Hyperion is a satellite of Saturn that was predicted to remain in a chaotic
rotational state. This was confirmed to some extent by Voyager 2 and Cassini
series of images and some ground-based photometric observations. The aim of
this aticle is to explore conditions for potential observations to meet in
order to estimate a maximal Lyapunov Exponent (mLE), which being positive is an
indicator of chaos and allows to characterise it quantitatively. Lightcurves
existing in literature as well as numerical simulations are examined using
standard tools of theory of chaos. It is found that existing datasets are too
short and undersampled to detect a positive mLE, although its presence is not
rejected. Analysis of simulated lightcurves leads to an assertion that
observations from one site should be performed over a year-long period to
detect a positive mLE, if present, in a reliable way. Another approach would be
to use 2---3 telescopes spread over the world to have observations distributed
more uniformly. This may be achieved without disrupting other observational
projects being conducted. The necessity of time-series to be stationary is
highly stressed.Comment: 34 pages, 12 figures, 4 tables; v2 after referee report; matches the
version accepted in Astrophysics and Space Scienc
Multi-epoch, multi-frequency VLBI study of the parsec-scale jet in the blazar 3C 66A
We present the observational results of the Gamma-ray blazar, 3C 66A, at 2.3,
8.4, and 22 GHz at 4 epochs during 2004-05 with the VLBA. The resulting images
show an overall core-jet structure extending roughly to the south with two
intermediate breaks occurring in the region near the core. By model-fitting to
the visibility data, the northmost component, which is also the brightest, is
identified as the core according to its relatively flat spectrum and its
compactness. As combined with some previous results to investigate the proper
motions of the jet components, it is found the kinematics of 3C 66A is quite
complicated with components of inward and outward, subluminal and superluminal
motions all detected in the radio structure. The superluminal motions indicate
strong Doppler boosting exists in the jet. The apparent inward motions of the
innermost components last for at least 10 years and could not be caused by
new-born components. The possible reason could be non-stationarity of the core
due to opacity change.Comment: 24 pages, 4 figure
Active visual search in non-stationary scenes: coping with temporal variability and uncertainty
Objective. State-of-the-art experiments for studying neural processes underlying visual cognition often constrain sensory inputs (e.g., static images) and our behavior (e.g., fixed eye-gaze, long eye fixations), isolating or simplifying the interaction of neural processes. Motivated by the non-stationarity of our natural visual environment, we investigated the electroencephalography (EEG) correlates of visual recognition while participants overtly performed visual search in non-stationary scenes. We hypothesized that visual effects (such as those typically used in human–computer interfaces) may increase temporal uncertainty (with reference to fixation onset) of cognition-related EEG activity in an active search task and therefore require novel techniques for single-trial detection.
Approach. We addressed fixation-related EEG activity in an active search task with respect to stimulus-appearance styles and dynamics. Alongside popping-up stimuli, our experimental study embraces two composite appearance styles based on fading-in, enlarging, and motion effects. Additionally, we explored whether the knowledge obtained in the pop-up experimental setting can be exploited to boost the EEG-based intention-decoding performance when facing transitional changes of visual content.
Main results. The results confirmed our initial hypothesis that the dynamic of visual content can increase temporal uncertainty of the cognition-related EEG activity in active search with respect to fixation onset. This temporal uncertainty challenges the pivotal aim to keep the decoding performance constant irrespective of visual effects. Importantly, the proposed approach for EEG decoding based on knowledge transfer between the different experimental settings gave a promising performance. Significance. Our study demonstrates that the non-stationarity of visual scenes is an important factor in the evolution of cognitive processes, as well as in the dynamic of ocular behavior (i.e., dwell time and fixation duration) in an active search task. In addition, our method to improve single-trial detection performance in this adverse scenario is an important step in making brain–computer interfacing technology available for human–computer interaction applications.EC/FP7/611570/EU/Symbiotic Mind Computer Interaction for Information Seeking/MindSeeBMBF, 01GQ0850, Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktio
25 Years of Self-Organized Criticality: Numerical Detection Methods
The detection and characterization of self-organized criticality (SOC), in
both real and simulated data, has undergone many significant revisions over the
past 25 years. The explosive advances in the many numerical methods available
for detecting, discriminating, and ultimately testing, SOC have played a
critical role in developing our understanding of how systems experience and
exhibit SOC. In this article, methods of detecting SOC are reviewed; from
correlations to complexity to critical quantities. A description of the basic
autocorrelation method leads into a detailed analysis of application-oriented
methods developed in the last 25 years. In the second half of this manuscript
space-based, time-based and spatial-temporal methods are reviewed and the
prevalence of power laws in nature is described, with an emphasis on event
detection and characterization. The search for numerical methods to clearly and
unambiguously detect SOC in data often leads us outside the comfort zone of our
own disciplines - the answers to these questions are often obtained by studying
the advances made in other fields of study. In addition, numerical detection
methods often provide the optimum link between simulations and experiments in
scientific research. We seek to explore this boundary where the rubber meets
the road, to review this expanding field of research of numerical detection of
SOC systems over the past 25 years, and to iterate forwards so as to provide
some foresight and guidance into developing breakthroughs in this subject over
the next quarter of a century.Comment: Space Science Review series on SO
Dynamic reconfiguration of human brain networks during learning
Human learning is a complex phenomenon requiring flexibility to adapt
existing brain function and precision in selecting new neurophysiological
activities to drive desired behavior. These two attributes -- flexibility and
selection -- must operate over multiple temporal scales as performance of a
skill changes from being slow and challenging to being fast and automatic. Such
selective adaptability is naturally provided by modular structure, which plays
a critical role in evolution, development, and optimal network function. Using
functional connectivity measurements of brain activity acquired from initial
training through mastery of a simple motor skill, we explore the role of
modularity in human learning by identifying dynamic changes of modular
organization spanning multiple temporal scales. Our results indicate that
flexibility, which we measure by the allegiance of nodes to modules, in one
experimental session predicts the relative amount of learning in a future
session. We also develop a general statistical framework for the identification
of modular architectures in evolving systems, which is broadly applicable to
disciplines where network adaptability is crucial to the understanding of
system performance.Comment: Main Text: 19 pages, 4 figures Supplementary Materials: 34 pages, 4
figures, 3 table
Geometric deep learning: going beyond Euclidean data
Many scientific fields study data with an underlying structure that is a
non-Euclidean space. Some examples include social networks in computational
social sciences, sensor networks in communications, functional networks in
brain imaging, regulatory networks in genetics, and meshed surfaces in computer
graphics. In many applications, such geometric data are large and complex (in
the case of social networks, on the scale of billions), and are natural targets
for machine learning techniques. In particular, we would like to use deep
neural networks, which have recently proven to be powerful tools for a broad
range of problems from computer vision, natural language processing, and audio
analysis. However, these tools have been most successful on data with an
underlying Euclidean or grid-like structure, and in cases where the invariances
of these structures are built into networks used to model them. Geometric deep
learning is an umbrella term for emerging techniques attempting to generalize
(structured) deep neural models to non-Euclidean domains such as graphs and
manifolds. The purpose of this paper is to overview different examples of
geometric deep learning problems and present available solutions, key
difficulties, applications, and future research directions in this nascent
field
- …