Convergence study and optimal weight functions of an explicit particle method for the incompressible Navier--Stokes equations

To increase the reliability of simulations by particle methods for incompressible viscous flow problems, convergence studies and improvements of accuracy are considered for a fully explicit particle method for incompressible Navier--Stokes equations. The explicit particle method is based on a penalty problem, which converges theoretically to the incompressible Navier--Stokes equations, and is discretized in space by generalized approximate operators defined as a wider class of approximate operators than those of the smoothed particle hydrodynamics (SPH) and moving particle semi-implicit (MPS) methods. By considering an analytical derivation of the explicit particle method and truncation error estimates of the generalized approximate operators, sufficient conditions of convergence are conjectured.Under these conditions, the convergence of the explicit particle method is confirmed by numerically comparing errors between exact and approximate solutions. Moreover, by focusing on the truncation errors of the generalized approximate operators, an optimal weight function is derived by reducing the truncation errors over general particle distributions. The effectiveness of the generalized approximate operators with the optimal weight functions is confirmed using numerical results of truncation errors and driven cavity flow. As an application for flow problems with free surface effects, the explicit particle method is applied to a dam break flow.Comment: 27 pages, 13 figure

Anomalous time delays and quantum weak measurements in optical micro-resonators

We study inelastic resonant scattering of a Gaussian wave packet with the parameters close to a zero of the complex scattering coefficient. We demonstrate, both theoretically and experimentally, that such near-zero scattering can result in anomalously-large time delays and frequency shifts of the scattered wave packet. Furthermore, we reveal a close analogy of these anomalous shifts with the spatial and angular Goos-H\"anchen optical beam shifts, which are amplified via quantum weak measurements. However, in contrast to other beam-shift and weak-measurement systems, we deal with a one-dimensional scalar wave without any intrinsic degrees of freedom. It is the non-Hermitian nature of the system that produces its rich and non-trivial behaviour. Our results are generic for any scattering problem, either quantum or classical. As an example, we consider the transmission of an optical pulse through a nano-fiber with a side-coupled toroidal micro-resonator. The zero of the transmission coefficient corresponds to the critical coupling conditions. Experimental measurements of the time delays near the critical-coupling parameters verify our weak-measurement theory and demonstrate amplification of the time delay from the typical inverse resonator linewidth scale to the pulse duration scale.Comment: 14 pages, 5 figure

Boosting up quantum key distribution by learning statistics of practical single photon sources

We propose a simple quantum-key-distribution (QKD) scheme for practical single photon sources (SPSs), which works even with a moderate suppression of the second-order correlation $g^{(2)}$ of the source. The scheme utilizes a passive preparation of a decoy state by monitoring a fraction of the signal via an additional beam splitter and a detector at the sender's side to monitor photon number splitting attacks. We show that the achievable distance increases with the precision with which the sub-Poissonian tendency is confirmed in higher photon number distribution of the source, rather than with actual suppression of the multi-photon emission events. We present an example of the secure key generation rate in the case of a poor SPS with $g^{(2)} = 0.19$, in which no secure key is produced with the conventional QKD scheme, and show that learning the photon-number distribution up to several numbers is sufficient for achieving almost the same achievable distance as that of an ideal SPS.Comment: 11 pages, 3 figures; published version in New J. Phy

Avoiding spurious feedback loops in the reconstruction of gene regulatory networks with dynamic bayesian networks

Feedback loops and recurrent structures are essential to the regulation and stable control of complex biological systems. The application of dynamic as opposed to static Bayesian networks is promising in that, in principle, these feedback loops can be learned. However, we show that the widely applied BGe score is susceptible to learning spurious feedback loops, which are a consequence of non-linear regulation and autocorrelation in the data. We propose a non-linear generalisation of the BGe model, based on a mixture model, and demonstrate that this approach successfully represses spurious feedback loops