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The ground state equilibrium properties of copper-gold alloys have been explored with the state of art random phase approximation (RPA). Our estimated lattice constants agree with the experiment within a mean absolute percentage error…
We investigate the dynamics of an inertial chiral active Brownian particle in the presence of a harmonic confinement. Through numerical simulation, we observe that when the harmonic frequency becomes comparable to the chiral frequency, the…
In phase retrieval, the goal is to recover a signal $\mathbf{x}\in\mathbb{C}^N$ from the magnitudes of linear measurements $\mathbf{Ax}\in\mathbb{C}^M$. While recent theory has established that $M\approx 4N$ intensity measurements are…
We propose a novel sparse spectrum approximation of Gaussian process (GP) tailored for Bayesian optimization. Whilst the current sparse spectrum methods provide desired approximations for regression problems, it is observed that this…
How do statistical dependencies in measurement noise influence high-dimensional inference? To answer this, we study the paradigmatic spiked matrix model of principal components analysis (PCA), where a rank-one matrix is corrupted by…
We employ the recently introduced Geometric Phase Propagator Approach (GPPA) [Phys. Rev. A85, 062110 (2012)] to develop an improved perturbative scheme for the calculation of life times in driven quantum systems. This incorporates a…
This paper presents a study of electron spin dynamics in bulk GaAs at low temperatures for elevated optical excitation conditions. Our time-resolved Faraday rotation measurements yield sub-nanosecond electron spin dephasing-times over a…
We explore the coexistence region in the vicinity of the Mott critical end point employing a compressible cell spin-$1/2$ Ising-like model. We analyze the case for the spin-liquid candidate $\kappa$-(BEDT-TTF)$_2$Cu$_2$(CN)$_3$, where close…
A time-domain representation of chromatographic peak shapes is presented as an analytic expression designed for high computational efficiency, which can be used for direct time-domain peak fitting with parameters that represent physical…
Given a single observation from a Gaussian distribution with unknown mean $\theta$, we design computationally efficient procedures that can approximately generate an observation from a different target distribution $Q_{\theta}$ uniformly…
We report on the spin waves and crystal field excitations in single crystal LiFePO$_4$ by inelastic neutron scattering over a wide range of temperatures, below and above the antiferromagnetic transition of this system. In particular, we…
We consider the problem of approximate sampling from the finite volume Gibbs measure with a general pair interaction. We exhibit a parallel dynamics (Probabilistic Cellular Automaton) which efficiently implements the sampling. In this…
The detection of periodic signals in irregularly-sampled time series is a problem commonly encountered in astronomy. Traditional tools used for periodic searches, such as the periodogram, have poorly defined statistical properties under…
We study statistics of the gaps in Random Average Process (RAP) on a ring with particles hopping symmetrically, except one tracer particle which could be driven. These particles hop either to the left or to the right by a random fraction…
Oblique propagation of the spin-electron acoustic waves in degenerate magnetized plasmas is considered in terms of quantum kinetics with the separate spin evolution, where the spin-up electrons and the spin-down electrons are considered as…
Gravitational wave (GW) searches using pulsar timing arrays (PTAs) are assumed to be limited by the typical average observational cadence of $1/(2~{\rm weeks})$ for a single pulsar to GW frequencies $\lesssim 4\times 10^{-7}$ Hz. We show…
We study two families of excitable cellular automata known as the Greenberg-Hastings Model (GHM) and the Cyclic Cellular Automaton (CCA). Each family consists of local deterministic oscillating lattice dynamics, with parallel discrete-time…
This paper develops a novel algorithm, termed \emph{SPARse Truncated Amplitude flow} (SPARTA), to reconstruct a sparse signal from a small number of magnitude-only measurements. It deals with what is also known as sparse phase retrieval…
Adaptive learning is necessary for non-stationary environments where the learning machine needs to forget past data distribution. Efficient algorithms require a compact model update to not grow in computational burden with the incoming data…
This paper presents a novel approach for approximate integration over the uncertainty of noise and signal variances in Gaussian process (GP) regression. Our efficient and straightforward approach can also be applied to integration over…