5,074 research outputs found
Magnitude and Sign Correlations in Heartbeat Fluctuations
We propose an approach for analyzing signals with long-range correlations by
decomposing the signal increment series into magnitude and sign series and
analyzing their scaling properties. We show that signals with identical
long-range correlations can exhibit different time organization for the
magnitude and sign. We find that the magnitude series relates to the nonlinear
properties of the original time series, while the sign series relates to the
linear properties. We apply our approach to the heartbeat interval series and
find that the magnitude series is long-range correlated, while the sign series
is anticorrelated and that both magnitude and sign series may have clinical
applications.Comment: 4 pages,late
Effect of nonstationarities on detrended fluctuation analysis
Detrended fluctuation analysis (DFA) is a scaling analysis method used to
quantify long-range power-law correlations in signals. Many physical and
biological signals are ``noisy'', heterogeneous and exhibit different types of
nonstationarities, which can affect the correlation properties of these
signals. We systematically study the effects of three types of
nonstationarities often encountered in real data. Specifically, we consider
nonstationary sequences formed in three ways: (i) stitching together segments
of data obtained from discontinuous experimental recordings, or removing some
noisy and unreliable parts from continuous recordings and stitching together
the remaining parts -- a ``cutting'' procedure commonly used in preparing data
prior to signal analysis; (ii) adding to a signal with known correlations a
tunable concentration of random outliers or spikes with different amplitude,
and (iii) generating a signal comprised of segments with different properties
-- e.g. different standard deviations or different correlation exponents. We
compare the difference between the scaling results obtained for stationary
correlated signals and correlated signals with these three types of
nonstationarities.Comment: 17 pages, 10 figures, corrected some typos, added one referenc
Variance fluctuations in nonstationary time series: a comparative study of music genres
An important problem in physics concerns the analysis of audio time series
generated by transduced acoustic phenomena. Here, we develop a new method to
quantify the scaling properties of the local variance of nonstationary time
series. We apply this technique to analyze audio signals obtained from selected
genres of music. We find quantitative differences in the correlation properties
of high art music, popular music, and dance music. We discuss the relevance of
these objective findings in relation to the subjective experience of music.Comment: 13 pages, 4 fig
Correlation Differences in Heartbeat Fluctuations During Rest and Exercise
We study the heartbeat activity of healthy individuals at rest and during
exercise. We focus on correlation properties of the intervals formed by
successive peaks in the pulse wave and find significant scaling differences
between rest and exercise. For exercise the interval series is anticorrelated
at short time scales and correlated at intermediate time scales, while for rest
we observe the opposite crossover pattern -- from strong correlations in the
short-time regime to weaker correlations at larger scales. We suggest a
physiologically motivated stochastic scenario to explain the scaling
differences between rest and exercise and the observed crossover patterns.Comment: 4 pages, 4 figure
Sirtuin 1 facilitates generation of induced pluripotent stem cells from mouse embryonic fibroblasts through the miR-34a and p53 pathways
published_or_final_versio
Computational network design from functional specifications
Connectivity and layout of underlying networks largely determine agent behavior and usage in many environments. For example, transportation networks determine the flow of traffic in a neighborhood, whereas building floorplans determine the flow of people in a workspace. Designing such networks from scratch is challenging as even local network changes can have large global effects. We investigate how to computationally create networks starting from only high-level functional specifications. Such specifications can be in the form of network density, travel time versus network length, traffic type, destination location, etc. We propose an integer programming-based approach that guarantees that the resultant networks are valid by fulfilling all the specified hard constraints and that they score favorably in terms of the objective function. We evaluate our algorithm in two different design settings, street layout and floorplans to demonstrate that diverse networks can emerge purely from high-level functional specifications
Single-particle-sensitive imaging of freely propagating ultracold atoms
We present a novel imaging system for ultracold quantum gases in expansion.
After release from a confining potential, atoms fall through a sheet of
resonant excitation laser light and the emitted fluorescence photons are imaged
onto an amplified CCD camera using a high numerical aperture optical system.
The imaging system reaches an extraordinary dynamic range, not attainable with
conventional absorption imaging. We demonstrate single-atom detection for
dilute atomic clouds with high efficiency where at the same time dense
Bose-Einstein condensates can be imaged without saturation or distortion. The
spatial resolution can reach the sampling limit as given by the 8 \mu m pixel
size in object space. Pulsed operation of the detector allows for slice images,
a first step toward a 3D tomography of the measured object. The scheme can
easily be implemented for any atomic species and all optical components are
situated outside the vacuum system. As a first application we perform
thermometry on rubidium Bose-Einstein condensates created on an atom chip.Comment: 24 pages, 10 figures. v2: as publishe
Modeling long-range cross-correlations in two-component ARFIMA and FIARCH processes
We investigate how simultaneously recorded long-range power-law correlated
multi-variate signals cross-correlate. To this end we introduce a two-component
ARFIMA stochastic process and a two-component FIARCH process to generate
coupled fractal signals with long-range power-law correlations which are at the
same time long-range cross-correlated. We study how the degree of
cross-correlations between these signals depends on the scaling exponents
characterizing the fractal correlations in each signal and on the coupling
between the signals. Our findings have relevance when studying parallel outputs
of multiple-component of physical, physiological and social systems.Comment: 8 pages, 5 figures, elsart.cl
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