Graphs with many valencies and few eigenvalues

Dom de Caen posed the question whether connected graphs with three distinct eigenvalues have at most three distinct valencies. We do not answer this question, but instead construct connected graphs with four and five distinct eigenvalues and arbitrarily many distinct valencies. The graphs with four distinct eigenvalues come from regular two-graphs. As a side result, we characterize the disconnected graphs and the graphs with three distinct eigenvalues in the switching class of a regular two-graph

Parametric survey of longitudinal prominence oscillation simulations

It is found that both microflare-sized impulsive heating at one leg of the loop and a suddenly imposed velocity perturbation can propel the prominence to oscillate along the magnetic dip. An extensive parameter survey results in a scaling law, showing that the period of the oscillation, which weakly depends on the length and height of the prominence, and the amplitude of the perturbations, scales with $\sqrt{R/g_\odot}$, where $R$ represents the curvature radius of the dip, and $g_\odot$ is the gravitational acceleration of the Sun. This is consistent with the linear theory of a pendulum, which implies that the field-aligned component of gravity is the main restoring force for the prominence longitudinal oscillations, as confirmed by the force analysis. However, the gas pressure gradient becomes non-negligible for short prominences. The oscillation damps with time in the presence of non-adiabatic processes. Compared to heat conduction, the radiative cooling is the dominant factor leading to the damping. A scaling law for the damping timescale is derived, i.e., $\tau\sim l^{1.63} D^{0.66}w^{-1.21}v_{0}^{-0.30}$, showing strong dependence on the prominence length $l$, the geometry of the magnetic dip (characterized by the depth $D$ and the width $w$), and the velocity perturbation amplitude $v_0$. The larger the amplitude, the faster the oscillation damps. It is also found that mass drainage significantly reduces the damping timescale when the perturbation is too strong.Comment: 17 PAGES, 8FIGURE

Adversarial Sparse-View CBCT Artifact Reduction

We present an effective post-processing method to reduce the artifacts from sparsely reconstructed cone-beam CT (CBCT) images. The proposed method is based on the state-of-the-art, image-to-image generative models with a perceptual loss as regulation. Unlike the traditional CT artifact-reduction approaches, our method is trained in an adversarial fashion that yields more perceptually realistic outputs while preserving the anatomical structures. To address the streak artifacts that are inherently local and appear across various scales, we further propose a novel discriminator architecture based on feature pyramid networks and a differentially modulated focus map to induce the adversarial training. Our experimental results show that the proposed method can greatly correct the cone-beam artifacts from clinical CBCT images reconstructed using 1/3 projections, and outperforms strong baseline methods both quantitatively and qualitatively

The effect of 3He impurities on the nonclassical response to oscillation of solid 4He

We have investigated the influence of impurities on the possible supersolid transition by systematically enriching isotopically-pure 4He (< 1 ppb of 3He) with 3He. The onset of nonclassical rotational inertia is broadened and shifts monotonically to higher temperature with increasing 3He concentration, suggesting that the phenomenon is correlated to the condensation of 3He atoms onto the dislocation network in solid 4He.Comment: 4 page

A Random Effect Bayesian Neural Network (RE-BNN) for travel mode choice analysis across multiple regions

Travel mode choice modelling plays a critical role in predicting passengers’ travel demand and planning local transportation systems. Researchers commonly adopt classical Random Utility Models to analyse individual decision-making based on the utility theory. Recently, with an increasing interest in applying Machine Learning techniques, a number of studies have used these methods for modelling travel mode preferences for their excellent predictive power. However, none of these studies proposes machine learning models that investigate the regional heterogeneity of travel behaviours. To address this gap, this study develops a Random Effect-Bayesian Neural Network (RE-BNN) framework to predict and explain travel mode choice across multiple regions by combining the Random Effect (RE) model and the Bayesian Neural Networks (BNN). The results show that this model outperforms the plain Deep Neural Network (DNN) regarding prediction accuracy and is more robust across different datasets. In addition, in terms of interpretation, the capability of RE-BNN to learn the travel behaviours across regions has been demonstrated by offset utilities, choice probability functions and local travel mode shares

Test of the τ-model of Bose–Einstein correlations and reconstruction of the source function in hadronic Z-boson decay at LEP

Bose–Einstein correlations of pairs of identical charged pions produced in hadronic Z decays are analyzed in terms of various parametrizations. A good description is achieved using a Lévy stable distribution in conjunction with a model where a particle’s momentum is correlated with its space–time point of production, the τ-model. Using this description and the measured rapidity and transverse momentum distributions, the space–time evolution of particle emission in two-jet events is reconstructed. However, the elongation of the particle emission region previously observed is not accommodated in the τ-model, and this is investigated using an ad hoc modification