Demonstrating Universal Scaling in Quench Dynamics of a Yukawa One-Component Plasma

The Yukawa one-component plasma (OCP) is a paradigm model for describing plasmas that contain one component of interest and one or more other components that can be treated as a neutralizing, screening background. In appropriately scaled units, interactions are characterized entirely by a screening parameter, $\kappa$. As a result, systems of similar $\kappa$ show the same dynamics, regardless of the underlying parameters (e.g., density and temperature). We demonstrate this behavior using ultracold neutral plasmas (UNP) created by photoionizing a cold ($T\le10$ mK) gas. The ions in UNP systems are well described by the Yukawa model, with the electrons providing the screening. Creation of the plasma through photoionization can be thought of as a rapid quench from $\kappa_{0}=\infty$ to a final $\kappa$ value set by the electron density and temperature. We demonstrate experimentally that the post-quench dynamics are universal in $\kappa$ over a factor of 30 in density and an order of magnitude in temperature. Results are compared with molecular dynamics simulations. We also demonstrate that features of the post-quench kinetic energy evolution, such as disorder-induced heating and kinetic-energy oscillations, can be used to determine the plasma density and the electron temperature.Comment: 10 pages, 12 figures, to be submitted to Physical Review

On the treatment of ℓ\ell-changing proton-hydrogen Rydberg atom collisions

Energy-conserving, angular momentum-changing collisions between protons and highly excited Rydberg hydrogen atoms are important for precise understanding of atomic recombination at the photon decoupling era, and the elemental abundance after primordial nucleosynthesis. Early approaches to $\ell$-changing collisions used perturbation theory for only dipole-allowed ($\Delta \ell=\pm 1$) transitions. An exact non-perturbative quantum mechanical treatment is possible, but it comes at computational cost for highly excited Rydberg states. In this note we show how to obtain a semi-classical limit that is accurate and simple, and develop further physical insights afforded by the non-perturbative quantum mechanical treatment

Theory and simulation of spectral line broadening by exoplanetary atmospheric haze

Atmospheric haze is the leading candidate for the flattening of expolanetary spectra, as it's also an important source of opacity in the atmospheres of solar system planets, satellites, and comets. Exoplanetary transmission spectra, which carry information about how the planetary atmospheres become opaque to stellar light in transit, show broad featureless absorption in the region of wavelengths corresponding to spectral lines of sodium, potassium and water. We develop a detailed atomistic model, describing interactions of atomic or molecular radiators with dust and atmospheric haze particulates. This model incorporates a realistic structure of haze particulates from small nano-size seed particles up to sub-micron irregularly shaped aggregates, accounting for both pairwise collisions between the radiator and haze perturbers, and quasi-static mean field shift of levels in haze environments. This formalism can explain large flattening of absorption and emission spectra in haze atmospheres and shows how the radiator - haze particle interaction affects the absorption spectral shape in the wings of spectral lines and near their centers. The theory can account for nearly all realistic structure, size and chemical composition of haze particulates and predict their influence on absorption and emission spectra in hazy environments. We illustrate the utility of the method by computing shift and broadening of the emission spectra of the sodium D line in an argon haze. The simplicity, elegance and generality of the proposed model should make it amenable to a broad community of users in astrophysics and chemistry.Comment: 16 pages, 4 figures, submitted to MNRA

A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction

In spite of its importance, passenger demand prediction is a highly challenging problem, because the demand is simultaneously influenced by the complex interactions among many spatial and temporal factors and other external factors such as weather. To address this problem, we propose a Spatio-TEmporal Fuzzy neural Network (STEF-Net) to accurately predict passenger demands incorporating the complex interactions of all known important factors. We design an end-to-end learning framework with different neural networks modeling different factors. Specifically, we propose to capture spatio-temporal feature interactions via a convolutional long short-term memory network and model external factors via a fuzzy neural network that handles data uncertainty significantly better than deterministic methods. To keep the temporal relations when fusing two networks and emphasize discriminative spatio-temporal feature interactions, we employ a novel feature fusion method with a convolution operation and an attention layer. As far as we know, our work is the first to fuse a deep recurrent neural network and a fuzzy neural network to model complex spatial-temporal feature interactions with additional uncertain input features for predictive learning. Experiments on a large-scale real-world dataset show that our model achieves more than 10% improvement over the state-of-the-art approaches.Comment: https://epubs.siam.org/doi/abs/10.1137/1.9781611975673.1

A Grand Old Church Rose in the East: The Church of God in Christ (COGIC) in East Texas

This essay traces the history of the Church of God in Christ and its early beginnings in East Texas

Extreme value laws for fractal intensity functions in dynamical systems: Minkowski analysis

Typically, in the dynamical theory of extremal events, the function that gauges the intensity of a phenomenon is assumed to be convex and maximal, or singular, at a single, or at most a finite collection of points in phase--space. In this paper we generalize this situation to fractal landscapes, i.e. intensity functions characterized by an uncountable set of singularities, located on a Cantor set. This reveals the dynamical r\^ole of classical quantities like the Minkowski dimension and content, whose definition we extend to account for singular continuous invariant measures. We also introduce the concept of extremely rare event, quantified by non--standard Minkowski constants and we study its consequences to extreme value statistics. Limit laws are derived from formal calculations and are verified by numerical experiments.Comment: 20 pages, 13 figure

Probing dynamical symmetry breaking using quantum-entangled photons

We present an input/output analysis of photon-correlation experiments whereby a quantum mechanically entangled bi-photon state interacts with a material sample placed in one arm of a Hong-Ou-Mandel (HOM) apparatus. We show that the output signal contains detailed information about subsequent entanglement with the microscopic quantum states in the sample. In particular, we apply the method to an ensemble of emitters interacting with a common photon mode within the open-system Dicke Model. Our results indicate considerable dynamical information concerning spontaneous symmetry breaking can be revealed with such an experimental system

Discrete structure of the brain rhythms

Neuronal activity in the brain generates synchronous oscillations of the Local Field Potential (LFP). The traditional analyses of the LFPs are based on decomposing the signal into simpler components, such as sinusoidal harmonics. However, a common drawback of such methods is that the decomposition primitives are usually presumed from the onset, which may bias our understanding of the signal's structure. Here, we introduce an alternative approach that allows an impartial, high resolution, hands-off decomposition of the brain waves into a small number of discrete, frequency-modulated oscillatory processes, which we call oscillons. In particular, we demonstrate that mouse hippocampal LFP contain a single oscillon that occupies the $\theta$-frequency band and a couple of $\gamma$-oscillons that correspond, respectively, to slow and fast $\gamma$-waves. Since the oscillons were identified empirically, they may represent the actual, physical structure of synchronous oscillations in neuronal ensembles, whereas Fourier-defined "brain waves" are nothing but poorly resolved oscillons.Comment: 17 pages, 9 figure

Matrix methods for Pad\'e approximation: numerical calculation of poles, zeros and residues

A representation of the Pad\'e approximation of the $Z$-transform of a signal as a resolvent of a tridiagonal matrix $J$ is given. Several formulas for the poles, zeros and residues of the Pad\'e approximation in terms of the matrix $J$ are proposed. Their numerical stability is tested and compared. Methods for computing forward and backward errors are presented