Short Correlation Time Research Articles

Hypoxia evokes a sequence of raphe-pontomedullary network operations for inspiratory drive amplification and gasping.

Hypoxia can trigger a sequence of breathing-related behaviors, from augmentation to apneusis to apnea and gasping. Gasping is an autoresuscitative behavior that, via large tidal volumes and altered intrathoracic pressure, can enhance coronary perfusion, carotid blood flow, and sympathetic activity, and thereby coordinate cardiac and respiratory functions. We tested the hypotheses that hypoxia-evoked gasps are amplified through a disinhibitory microcircuit within the inspiratory neuron chain and that this drive is distributed via an efference copy mechanism. This generates coordinated gasplike discharges concurrently in other circuits of the raphe-pontomedullary respiratory network. Data were obtained from six decerebrate, vagotomized, neuromuscularly blocked, and artificially ventilated adult cats. Arterial blood pressure, phrenic nerve activity, end-tidal CO2, and other parameters were monitored. Hypoxia was produced by ventilation with a gas mixture of 5% O2 in nitrogen. Neuron spike trains were recorded at multiple pontomedullary sites simultaneously and evaluated for firing rate modulations and short-timescale correlations indicative of functional connectivity. Experimental perturbations evoked reconfiguration of raphe-pontomedullary circuits during initial augmentation, apneusis and augmented bursts, apnea, and gasping. Functional connectivity, altered firing rates, efference copy of gasp drive, and coordinated incremental blood pressure increases support a distributed brain stem network model for amplification and broadcasting of inspiratory drive during autoresuscitative gasping. Gasping begins with a reduction in inhibition by expiratory neurons and an initial loss of inspiratory drive during hypoxic apnea and culminates in autoresuscitative efforts. NEW & NOTEWORTHY Severe hypoxia evokes a sequence of breathing-related behaviors culminating in gasping. We report firing rate modulations and short-timescale correlations in spike trains recorded simultaneously in the raphe-pontomedullary respiratory network during hypoxia. Our findings support a disinhibitory microcircuit and a distributed efference copy mechanism for amplification of gasping. Coordinated increments in blood pressure lead to a model for autoresuscitative bootstrapping of peripheral chemoreceptor reflexes, breathing, and sympathetic activity, complementing and extending prior work.

Multifunction integrated lithium niobate photonic chip for photon pairs generation and manipulation.

We report on a unique photonic quantum source chip highly integrating four-stage photonic elements in a lithium niobate (LN) waveguide circuit platform, where an aperiodically poled LN (APPLN) electro-optic (EO) polarization mode converter (PMC) is sandwiched between two identical type-0 PPLN spontaneous parametric down-converters (SPDCs), followed by an EO phase controller (PC). These core nonlinear optic and EO building blocks on the chip are systematically characterized stage by stage to show its high performance as an integrated quantum source. The APPLN EO PMC, optimally constructed by a genetic algorithm, is characterized to have a broad bandwidth (>13 nm), benefiting an efficient control of broadband type-0 SPDC photon pairs featuring a short correlation time. We demonstrate an efficient conversion of the |VV› photon-pair state generated from the first PPLN SPDC stage to the |HH› state through the APPLN EO PMC stage over its operating bandwidth, a broadband or broadly tunable polarization-entangled state can thus be possibly produced via the superposition of the |VV› state generated from the other PPLN SPDC on the third stage of the chip. Such a state can be further manipulated into two of the Bell states if the relative phases between the two polarization states can be properly modulated through the EO PC on the fourth stage of the chip. Such a multifunction integrated quantum photonic source chip can be of high value to developing a compact, efficient, and high-speed quantum information processor.

Power system data-driven dispatch using improved scenario generation considering time-series correlations

Reinforcement learning (RL) is recently studied for realizing fast and adaptive power system dispatch under the increasing penetration of renewable energy. RL has the limitation of relying on samples for agent training, and the application in power systems often faces the difficulty of insufficient scenario samples. So, scenario generation is of great importance for the application of RL. However, most of the existing scenario generation methods cannot handle time-series correlation, especially the correlation over long time scales, when generating the scenario. To address this issue, this paper proposes an RL-based dispatch method which can generate power system operational scenarios with time-series correlation for the agent’s training. First, a time-generative adversarial network (GAN)-based scenario generation model is constructed, which generates system operational scenarios with long- and short-time scale time-series correlations. Next, the “N-1” security is ensured by simulating “N-1” branch contingencies in the agent’s training. Finally, the model is trained in parallel in an actual power system environment, and its effectiveness is verified by comparisons against benchmark methods.

A field theory approach to the statistical kinematic dynamo

Variations in the geomagnetic field occur on a vast range of time scales, from milliseconds to millions of years. The advent of satellite measurements has allowed for detailed studies of short timescale geomagnetic field behaviour, but understanding its long timescale evolution remains challenging due to the sparsity of the paleomagnetic record. This paper introduces a field theory framework for studying magnetic field generation as a result of stochastic fluid motions. Starting from a stochastic kinematic dynamo model (the Kazantsev kinematic model), we derive statistical properties of the magnetic field that may be compared to observations from the paleomagnetic record. The fluid velocity is taken to be a Kraichnan field with general covariance, which acts as a random forcing obeying Gaussian statistics. Using the Martin-Siggia-Rose-Janssen-de Dominicis formalism, we compute the average magnetic field response function for fluid velocities with short correlation time. From this we obtain an estimate for the turbulent contribution to the magnetic diffusivity, and find that it is consistent with results from mean-field dynamo theory. This framework presents much promise for studying the geomagnetic field in a stochastic context.

High-speed feedback based wavefront shaping for spatiotemporal enhancement of incoherent light through dynamic scattering media.

Feedback-based wavefront shaping is a promising and versatile technique for enhancing the contrast of a target signal for both coherent and incoherent light through a highly scattering medium. However, this technique can fail for a dynamic sample with a short correlation time. So far, most proposed methods for high-speed wavefront shaping can only directly enhance the intensity of coherent light but not incoherent light. Here we try to fill this gap to directly enhance incoherent light with high speed, such as fluorescence, which is essential in extending wavefront shaping to biomedical applications. For this purpose, we develop a technique based on a single acousto-optic deflector (AOD) with field-programmable gate array (FPGA) acceleration for spatiotemporal focusing within milliseconds. With the digital time gating of the feedback signal, spatiotemporal focusing of laser light with high contrast can be formed behind dynamic scattering media in milliseconds resulting in fluorescence enhancement. Furthermore, FPGA-based wavefront shaping is shown to effectively enhance fluorescence directly behind dynamic samples with short correlation times.

Quantum wake dynamics in Heisenberg antiferromagnetic chains

Traditional spectroscopy, by its very nature, characterizes physical system properties in the momentum and frequency domains. However, the most interesting and potentially practically useful quantum many-body effects emerge from local, short-time correlations. Here, using inelastic neutron scattering and methods of integrability, we experimentally observe and theoretically describe a local, coherent, long-lived, quasiperiodically oscillating magnetic state emerging out of the distillation of propagating excitations following a local quantum quench in a Heisenberg antiferromagnetic chain. This “quantum wake” displays similarities to Floquet states, discrete time crystals and nonlinear Luttinger liquids. We also show how this technique reveals the non-commutativity of spin operators, and is thus a model-agnostic measure of a magnetic system’s “quantumness.”

Oscillating gravity, non-singularity and mass quantization from Moffat stochastic gravity arguments

In Moffat stochastic gravity arguments, the spacetime geometry is assumed to be a fluctuating background and the gravitational constant is a control parameter due to the presence of a time-dependent Gaussian white noise In such a surrounding, both the singularities of gravitational collapse and the Big Bang have a zero probability of occurring. In this communication, we generalize Moffat’s arguments by adding a random temporal tiny variable for a smoothing purpose and creating a white Gaussian noise process with a short correlation time. The Universe accordingly is found to be non-singular and is dominated by an oscillating gravity. A connection with a quantum oscillator was established and analyzed. Surprisingly, the Hubble mass which emerges in extended supergravity may be quantized.

Transport Properties of Critical Sulfur Hexafluoride From Multiscale Analysis of Density Fluctuations

Density fluctuations near critical points have a wide range of sizes limited only by the boundaries of the enclosing container. How would a fluctuating image near the critical point look if we could break it into disjoint spatial scales, like decomposing white light into narrow-band, monochromatic waves? What are the scaling laws governing each spatial scale? How are the relaxation times of fluctuations at each spatial scale related to the dynamics of fluctuations in the original image? Fluctuations near the critical point of pure fluids lead to different patterns of phase separation, which has a significant influence on the materials’ properties. Due to the diverging compressibility of pure fluids near the critical temperature, the critical phase collapses under its weight on Earth. It limits both the spatial extent of fluctuations and their duration. In microgravity, the buoyancy and convection are suppressed, and the critical state can be observed much closer to the critical point for a more extended period. Local density fluctuations induce light intensity fluctuations (the so-called “critical opalescence”), which we recorded for a sulfur hexafluoride (SF6) sample near the critical point in microgravity using the ALI (Alice Like Instrumentation insert) of the DECLIC (Dispositif pour l’Etude de la Croissance et des Liquides Critiques) facility on the International Space Station (ISS). From the very short (approximately 173 s total recording) data set very near, within 200 μK, the critical temperature, we determined the effective diffusion coefficient for fluctuations of different sizes. For transient and non-stationary data recorded very near the critical point immediately after a thermal quench that steps through critical temperature, we separated fluctuations of various sizes from the original images using the Bidimensional Empirical Mode Decomposition (BEMD) technique. Orthogonal and stationary Intrinsic Mode Function (IMF) images were analyzed using the Fourier-based Dynamic Differential Microscopy (DDM) method to extract the correlation time of fluctuations. We found that a single power-law exponent represented each IMF’s structure factor. Additionally, each Intermediate Scattering Function (ISF) was determined by fluctuations’ unique relaxation time constant. We found that the correlation time of fluctuations increases with IMF’s order, which shows that small size fluctuations have the shortest correlation time. Estimating thermophysical properties from short data sets affected by transient phenomena is possible within the BEMD framework

Comparison of cluster algorithms for the bond-diluted Ising model.

Monte Carlo cluster algorithms are popular for their efficiency in studying the Ising model near its critical temperature. We might expect that this efficiency extends to the bond-diluted Ising model. We show, however, that this is not always the case by comparing how the correlation times τ_{w} and τ_{sw} of the Wolff and Swendsen-Wang cluster algorithms scale as a function of the system size L when applied to the two-dimensional bond-diluted Ising model. We demonstrate that the Wolff algorithm suffers from a much longer correlation time than in the pure Ising model, caused by isolated (groups of) spins which are infrequently visited by the algorithm. With a simple argument we prove that these cause the correlation time τ_{w} to be bounded from below by L^{z_{w}} with a dynamical exponent z_{w}=γ/ν≈1.75 for a bond concentration p<1. Furthermore, we numerically show that this lower bound is actually taken for several values of p in the range 0.5<p<1. Moreover, we show that the Swendsen-Wang algorithm does not suffer from the same problem. Consequently, it has a much shorter correlation time, shorter than in the pure Ising model even. Numerically at p=0.6, we find that its dynamical exponent is z_{sw}=0.09(4).

Fundamentals of turbulent flow spectrum imaging.

PurposeTo introduce a mathematical framework and in‐silico validation of turbulent flow spectrum imaging (TFSI) of stenotic flow using phase‐contrast MRI, evaluate systematic errors in quantitative turbulence parameter estimation, and propose a novel method for probing the Lagrangian velocity spectra of turbulent flows.Theory and MethodsThe spectral response of velocity‐encoding gradients is derived theoretically and linked to turbulence parameter estimation including the velocity autocorrelation function spectrum. Using a phase‐contrast MRI simulation framework, the encoding properties of bipolar gradient waveforms with identical first gradient moments but different duration are investigated on turbulent flow data of defined characteristics as derived from computational fluid dynamics. Based on theoretical insights, an approach using velocity‐compensated gradient waveforms is proposed to specifically probe desired ranges of the velocity autocorrelation function spectrum with increased accuracy.ResultsPractical velocity‐encoding gradients exhibit limited encoding power of typical turbulent flow spectra, resulting in up to 50% systematic underestimation of intravoxel SD values. Depending on the turbulence level in fluids, the error due to a single encoding gradient spectral response can vary by 20%. When using tailored velocity‐compensated gradients, improved quantification of the Lagrangian velocity spectrum on a voxel‐by‐voxel basis is achieved and used for quantitative correction of intravoxel SD values estimated with velocity‐encoding gradients.ConclusionTo address systematic underestimation of turbulence parameters using bipolar velocity‐encoding gradients in phase‐contrast MRI of stenotic flows with short correlation times, tailored velocity‐compensated gradients are proposed to improve quantitative mapping of turbulent blood flow characteristics.

300 attosecond response of acetylene in two-photon ionization/dissociation processes

By performing delay scans of two replicas of a few-pulse attosecond pulse train (APT), we investigated two-photon ionization/dissociation processes of C 2 H 2 . The correlation time of the C + yield is determined to be 300 as, which is, to the best of our knowledge, the shortest correlation time ever obtained in the delay-scan measurement of two APTs. We also extracted the contribution of the resonant excitation from the ground state to a specific excited state of C 2 H 2 + leading to C–C bond breaking by performing Fourier analyses of the yield and angular distribution of the C H + fragment ions, which are also generated within 300 as.

Amplitude-phase description of stochastic neural oscillators across the Hopf bifurcation

We derive a unified amplitude-phase decomposition for both noisy limit cycles and quasicycles; in the latter case, the oscillatory motion has no deterministic counterpart. We extend a previous amplitude-phase decomposition approach using the stochastic averaging method (SAM) for quasicycles by taking into account nonlinear terms up to order 3. We further take into account the case of coupled networks where each isolated network can be in a quasi- or noisy limit-cycle regime. The method is illustrated on two models which exhibit a deterministic supercritical Hopf bifurcation: the Stochastic Wilson-Cowan model of neural rhythms, and the Stochastic Stuart-Landau model in physics. At the level of a single oscillatory module, the amplitude process of each of these models decouples from the phase process to the lowest order, allowing a Fokker-Planck estimate of the amplitude probability density. The peak of this density captures well the transition between the two regimes. The model describes accurately the effect of Gaussian white noise as well as of correlated noise. Bursting epochs in the limit-cycle regime are in fact favored by noise with shorter correlation time or stronger intensity. Quasicycle and noisy limit-cycle dynamics are associated with, respectively, Rayleigh-type and Gaussian-like amplitude densities. This provides an additional tool to distinguish quasicycle from limit-cycle origins of bursty rhythms. The case of multiple oscillatory modules with excitatory all-to-all delayed coupling results in a system of stochastic coupled amplitude-phase equations that keeps all the biophysical parameters of the initial networks and again works across the Hopf bifurcation. The theory is illustrated for small heterogeneous networks of oscillatory modules. Numerical simulations of the amplitude-phase dynamics obtained through the SAM are in good agreement with those of the original oscillatory networks. In the deterministic and nearly identical oscillators limits, the stochastic Stuart-Landau model leads to the stochastic Kuramoto model of interacting phases. The approach can be tailored to networks with different frequency, topology, and stochastic inputs, thus providing a general and flexible framework to analyze noisy oscillations continuously across the underlying deterministic bifurcation.

Analytical model to determine the relevant parameters governing the transferred momentum to spherical indenters by laser-induced shock waves

Rapid material development is a new approach to reduce the time and cost intensity. It is based on small and simple sample geometries. Many conventional test methods such as hardness measurements or tensile tests are not suitable for this approach, because they either require a sample geometry adapted to the test method or they are time-consuming. Thus, research is being conducted on a novel impact-based method, which allows short time indentations and correlations with mechanical material properties such as hardness and tensile strength: With a high intensity pulsed TEA-CO2 laser, a shock wave is generated on top of a spherical indenter. The momentum of the shock wave pushes the indenter inside a sample. From the induced indentations, characteristic values such as indentation depth and indentation diameter are extracted, which correlate well with the material hardness and tensile strength. Still, the process dynamics are not fully understood. Thus, a model is developed to determine the main parameters that govern the interaction between laser-induced shock wave as well as indenter and accordingly, significantly affect the forming energy. The model is validated by experimental results. From the findings of the analytical model and the conducted experiments, fundamental correlations with respect to the available forming energy are determined. The main parameters influencing the available forming energy are pulse energy, pulse duration, laser spot area, density of the ambient medium and indenter mass.

Radial Diffusion of Planetary Radiation Belts’ Particles by Fluctuations with Finite Correlation Time

Abstract Radial diffusion in planetary radiation belts is a dominant transport mechanism resulting in the energization and losses of charged particles by large-scale electromagnetic fluctuations. In this study, we revisit the radial diffusion formalism by relaxing the assumption of zero correlation time in the spectrum of fluctuations responsible for the transport of charged particles. We derive a diffusion coefficient by assuming fluctuations that (1) are time homogeneous, (2) too small to trap the particles, and (3) can decorrelate on timescales comparable to the transit time of the particles. We demonstrate through self-similar solutions of the Fokker–Planck equation that autocorrelation time τ c much larger than the linear transit time/particle drift period τ L = Ω D − 1 results in characteristic time for transport independent of the drift frequency and faster than for short correlation time. In both instances, that is for short (τ L ≫ τ c ) and long (τ L ≪ τ c ) autocorrelation time, the diffusion of particles is subdiffusive since the variance of increments along the magnetic drift shells L*, scales as 〈ΔL*2〉 ∼ t s , with s &lt; 1. However, in the absence of sources and sinks, particle transport for both short and long autocorrelation times result in equilibrium distribution along L* with differences of less than 10% across lower magnetic drift shells. The main consequence of incorporating finite correlation time appears in intermediate times much longer than the drift period but before the distribution function reaches equilibrium and indicates the importance of quantifying observationally the spectral properties of fluctuations for the modeling of planetary radiation belts.

Broadband fiber-based entangled photon-pair source at telecom O-band.

In this Letter, we report a polarization-entangled photon-pair source based on type-II spontaneous parametric downconversion at telecom O-band in periodically poled silica fiber (PPSF). The photon-pair source exhibits more than 130 nm (∼24THz) emission bandwidth centered at 1306.6 nm. The broad emission spectrum results in a short biphoton correlation time, and we experimentally demonstrate a Hong-Ou-Mandel interference dip with a full width of 26.6 fs at half-maximum. Owing to the low birefringence of the PPSF, the biphotons generated from type-II SPDC are polarization-entangled over the entire emission bandwidth, with a measured fidelity to a maximally entangled state greater than 95.4%. The biphoton source provides the broadest bandwidth entangled biphotons at O-band to our knowledge.

Nanoconfinement Effects on the Kapitza Resistance at Water-CNT Interfaces.

The Kapitza resistance (Rk) at the water-carbon nanotube (CNT) interface, with water on the inside of the nanotube, was investigated using molecular dynamics simulations. We propose a new equilibrium molecular dynamics (EMD) method, also valid in the weak flow regime, to determine the Kapitza resistance in a cylindrical nanoconfinement system where nonequilibrium molecular dynamics (NEMD) methods are not suitable. The proposed method is independent of the correlation time compared to Green-Kubo-based methods, which only work in short correlation time intervals. Rk between the CNT and the confined water strongly depends on the diameter of the nanotube and is found to decrease with an increase in the CNT diameter, the opposite to what is reported in the literature when water is on the outside of the nanotube. Rk is furthermore found to converge to the planar graphene surface value as the number of water molecules per unit surface area approaches the value in the graphene surface and a higher overlap of the vibrational spectrum. A slight increase in Rk with the addition of the number of CNT walls was observed, whereas the chirality and flow do not have any impact.

A New Decorrelation Phase Covariance Model for Noise Reduction in Unwrapped Interferometric Phase Stacks

The accuracy of geophysical parameter estimation made with interferometric synthetic aperture radar (InSAR) time-series techniques can be improved with rapidly increasing available data volumes and with the development of noise covariance matrices applicable to joint analysis of networks of interferograms. In this article, we present a new decorrelation phase covariance model and discuss its role in noise reduction in unwrapped interferometric phase stacks. We demonstrate with an example in which we average unwrapped interferogram phase stacks that span over a transient event how a noise covariance model can aid in noise reduction. Our model suggests that, for rapidly decorrelating surfaces (i.e., surfaces with much shorter correlation time than SAR acquisition intervals), it is preferable to incorporate all available interferograms from long observation windows. For slowly decorrelating surfaces (i.e., surfaces with longer correlation time than SAR acquisition intervals), our model suggests that a small subset of interferometric pairs is sufficient. We validate our model and three existing models of decorrelation phase covariance matrices in both Cascadia, a region with heavy vegetation cover, and Death Valley, a desert region with C-band Sentinel-1 A observations. Our proposed model matches observations with the smallest average discrepancy between theory and observations.

Leveraging large-deviation statistics to decipher the stochastic properties of measured trajectories

Extensive time-series encoding the position of particles such as viruses, vesicles, or individual proteins are routinely garnered in single-particle tracking experiments or supercomputing studies. They contain vital clues on how viruses spread or drugs may be delivered in biological cells. Similar time-series are being recorded of stock values in financial markets and of climate data. Such time-series are most typically evaluated in terms of time-averaged mean-squared displacements (TAMSDs), which remain random variables for finite measurement times. Their statistical properties are different for different physical stochastic processes, thus allowing us to extract valuable information on the stochastic process itself. To exploit the full potential of the statistical information encoded in measured time-series we here propose an easy-to-implement and computationally inexpensive new methodology, based on deviations of the TAMSD from its ensemble average counterpart. Specifically, we use the upper bound of these deviations for Brownian motion (BM) to check the applicability of this approach to simulated and real data sets. By comparing the probability of deviations for different data sets, we demonstrate how the theoretical bound for BM reveals additional information about observed stochastic processes. We apply the large-deviation method to data sets of tracer beads tracked in aqueous solution, tracer beads measured in mucin hydrogels, and of geographic surface temperature anomalies. Our analysis shows how the large-deviation properties can be efficiently used as a simple yet effective routine test to reject the BM hypothesis and unveil relevant information on statistical properties such as ergodicity breaking and short-time correlations.

Renormalization of stochastic differential equations with multiplicative noise using effective potential methods

We present a method to renormalize stochastic differential equations subjected to multiplicative noise. The method is based on the widely used concept of effective potential in high-energy physics and has already been successfully applied to the renormalization of stochastic differential equations subjected to additive noise. We derive a general formula for the one-loop effective potential of a single ordinary stochastic differential equation (with arbitrary interaction terms) subjected to multiplicative Gaussian noise (provided the noise satisfies a certain normalization condition). To illustrate the usefulness (and limitations) of the method, we use the effective potential to renormalize a toy chemical model based on a simplified Gray-Scott reaction. In particular, we use it to compute the scale dependence of the toy model's parameters (in perturbation theory) when subjected to a Gaussian power-law noise with short time correlations.

The Global Analysis of the Ionospheric Correlation Time and Its Implications for Ionospheric Data Assimilation

AbstractIonospheric correlation time is an important parameter that contains information about the temporal variability, structures, and dynamics of the ionosphere. This parameter is also important in forecasting of the ionospheric state. Ionospheric data assimilation algorithms employing empirical background models, such as Ionospheric Data Assimilation Four‐Dimensional (IDA4D), apply Gauss‐Markov approximation for the propagation of temporal updates from one time stamp to the next. In this process, the relaxation parameter, or the correlation time, determines to what degree the projected state depends on the background model and analysis density. An ad hoc approach is usually applied to choose this user‐defined parameter. This paper focuses on the estimation of the ionospheric correlation time using high temporal resolution global ionospheric maps (GIMs). It is found that the correlation time changes significantly with latitude. The longest correlation time is observed in the equatorial region, whereas the shortest correlation time is observed at high latitudes and in the polar cap regions. The global distribution of the correlation time exhibits seasonal variation and depends on the solar flux conditions. The correlation time at the equatorial and mid‐latitude regions increases with increasing solar and geomagnetic activity, whereas the correlation time at high‐latitude regions decreases. The results of this study can be directly applied to improve ionospheric data assimilation models.