Assessing the effect of lens mass model in cosmological application with updated galaxy-scale strong gravitational lensing sample

By comparing the dynamical and lensing masses of early-type lens galaxies, one can constrain both the cosmological parameters and the density profiles of galaxies. We explore the constraining power on cosmological parameters and the effect of the lens mass model in this method with 161 galaxy-scale strong lensing systems, which is currently the largest sample with both high resolution imaging and stellar dynamical data. We assume a power-law mass model for the lenses, and consider three different parameterizations for $\gamma$ (i.e., the slope of the total mass density profile) to include the effect of the dependence of $\gamma$ on redshift and surface mass density. When treating $\delta$ (i.e., the slope of the luminosity density profile) as a universal parameter for all lens galaxies, we find the limits on the cosmological parameter $\Omega_m$ are quite weak and biased, and also heavily dependent on the lens mass model in the scenarios of parameterizing $\gamma$ with three different forms. When treating $\delta$ as an observable for each lens, the unbiased estimate of $\Omega_m$ can be obtained only in the scenario of including the dependence of $\gamma$ on both the redshift and the surface mass density, that is $\Omega_m = 0.381^{+0.185}_{-0.154}$ at 68\% confidence level in the framework of a flat $\Lambda$CDM model. We conclude that the significant dependencies of $\gamma$ on both the redshift and the surface mass density, as well as the intrinsic scatter of $\delta$ among the lenses, need to be properly taken into account in this method.Comment: Accepted for publication in MNRAS; 17 pages, 5 figures, 2 table

Geometry-Aware Face Completion and Editing

Face completion is a challenging generation task because it requires generating visually pleasing new pixels that are semantically consistent with the unmasked face region. This paper proposes a geometry-aware Face Completion and Editing NETwork (FCENet) by systematically studying facial geometry from the unmasked region. Firstly, a facial geometry estimator is learned to estimate facial landmark heatmaps and parsing maps from the unmasked face image. Then, an encoder-decoder structure generator serves to complete a face image and disentangle its mask areas conditioned on both the masked face image and the estimated facial geometry images. Besides, since low-rank property exists in manually labeled masks, a low-rank regularization term is imposed on the disentangled masks, enforcing our completion network to manage occlusion area with various shape and size. Furthermore, our network can generate diverse results from the same masked input by modifying estimated facial geometry, which provides a flexible mean to edit the completed face appearance. Extensive experimental results qualitatively and quantitatively demonstrate that our network is able to generate visually pleasing face completion results and edit face attributes as well

DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion

Denosing diffusion model, as a generative model, has received a lot of attention in the field of image generation recently, thanks to its powerful generation capability. However, diffusion models have not yet received sufficient research in the field of image fusion. In this article, we introduce diffusion model to the image fusion field, treating the image fusion task as image-to-image translation and designing two different conditional injection modulation modules (i.e., style transfer modulation and wavelet modulation) to inject coarse-grained style information and fine-grained high-frequency and low-frequency information into the diffusion UNet, thereby generating fused images. In addition, we also discussed the residual learning and the selection of training objectives of the diffusion model in the image fusion task. Extensive experimental results based on quantitative and qualitative assessments compared with benchmarks demonstrates state-of-the-art results and good generalization performance in image fusion tasks. Finally, it is hoped that our method can inspire other works and gain insight into this field to better apply the diffusion model to image fusion tasks. Code shall be released for better reproducibility

Potential of Geo-neutrino Measurements at JUNO

The flux of geoneutrinos at any point on the Earth is a function of the abundance and distribution of radioactive elements within our planet. This flux has been successfully detected by the 1-kt KamLAND and 0.3-kt Borexino detectors with these measurements being limited by their low statistics. The planned 20-kt JUNO detector will provide an exciting opportunity to obtain a high statistics measurement, which will provide data to address several questions of geological importance. This paper presents the JUNO detector design concept, the expected geo-neutrino signal and corresponding backgrounds. The precision level of geo-neutrino measurements at JUNO is obtained with the standard least-squares method. The potential of the Th/U ratio and mantle measurements is also discussed.Comment: 8 pages, 6 figures, an additional author added, final version to appear in Chin. Phys.

HEAVY TRUCK COLLISION WITH BRIDGE PIERS

Based on bridge failure data compiled by the New York State Department of Transportation, collision, both caused by vessels and vehicles, is the second leading cause of bridge failures after hydraulic. The current AASHTO-LRFD (2017) specification recommends designing a bridge pier vulnerable to vehicular impacts for an equivalent static force of 2,670-kN (600 kips) applied in a horizontal plane at a distance of 1.5 m (5.0 feet) above the ground level. The vast majority of research studies on vehicular collision with bridge piers have been carried out with single-unit trucks, which are typically classified as medium-duty vehicles weighing about 89 kN (20,000 lb). Yet, collision events that involve severe bridge damage are generally caused by heavy-duty trucks, generally tractor-semitrailers weighing 360 kN (80,000 lb). The handful of tests that were conducted to study heavy truck collision employed rigid piers, which means that the deformation and failure mechanisms of the piers were neglected. This study proposed a performance-based approach for designing a bridge pier subject to impact by a tractor-semitrailer weighing up to 360 kN (80,000 lb) based on a computational investigation. Validated, high-fidelity finite element simulations of collisions between tractor-semitrailers and reinforced concrete bridge piers have been carried out to investigate the demands imposed upon, and damage modes of, concrete piers. Through extensive numerical simulation of heavy vehicle (tractor-semitrailer) impacts on piers, the impact force time histories were simplified in the form of analytical triangular pulse functions. The parameters of these functions were derived through numerical regression based on the simulation results. A performance-based approach that relates demands (in terms of the applied force time histories) and capacity (in terms of acceptable shear distortion and plastic rotation) was proposed for the design of bridge piers vulnerable to heavy vehicle impact. Since many collision failures have been observed to be dominated by shear failure, the proposed performance-based approach used capacity-design concepts from earthquake engineering to mitigate collapse by minimizing shear distortion of piers impacted by heavy vehicles. Simulation results in this study have shown that the capacity design method can significantly reduce the shear distortion in the piers when subject to heavy truck impact. The risk of pier collapse in a given impact event was also evaluated based on Monte Carlo simulations, and a risk-based design framework was proposed in this study. The proposed risk-based design approach can serve as a powerful tool for the bridge owners to leverage the capacity of bridge piers and the risk of bridge damage caused by the impact event