We study the decay processes of $bar{B}^0 to J/psi bar{K}^{*0} K^0$ and $bar{B}^0 to J/psi f_1(1285)$ to analyse the $f_1(1285)$ resonance. By the calculation within chiral unitary approach where $f_1(1285)$ resonance is dynamically generated from th
e $K^*bar{K}-c.c.$ interaction, we find that the $bar{K}^{*0} K^0$ invariant mass distribution has a clear broad peak. Such broad peak has been understood as the signal of the $f_1(1285)$. Finally, we obtain a theoretical result $R_t=Gamma_{bar{B}^0 to J/psi bar{K}^{*0} K^0}/Gamma_{bar{B}^0 to J/psi f_1(1285)}$ which is expected to be compared with the experimental data.
Recently, Generative Adversarial Networks (GANs)} have been widely used for portrait image generation. However, in the latent space learned by GANs, different attributes, such as pose, shape, and texture style, are generally entangled, making the exp
licit control of specific attributes difficult. To address this issue, we propose a SofGAN image generator to decouple the latent space of portraits into two subspaces: a geometry space and a texture space. The latent codes sampled from the two subspaces are fed to two network branches separately, one to generate the 3D geometry of portraits with canonical pose, and the other to generate textures. The aligned 3D geometries also come with semantic part segmentation, encoded as a semantic occupancy field (SOF). The SOF allows the rendering of consistent 2D semantic segmentation maps at arbitrary views, which are then fused with the generated texture maps and stylized to a portrait photo using our semantic instance-wise (SIW) module. Through extensive experiments, we show that our system can generate high quality portrait images with independently controllable geometry and texture attributes. The method also generalizes well in various applications such as appearance-consistent facial animation and dynamic styling.
In this work, we demonstrate yet another approach to tackle the amodal segmentation problem. Specifically, we first introduce a new representation, namely a semantics-aware distance map (sem-dist map), to serve as our target for amodal segmentation i
nstead of the commonly used masks and heatmaps. The sem-dist map is a kind of level-set representation, of which the different regions of an object are placed into different levels on the map according to their visibility. It is a natural extension of masks and heatmaps, where modal, amodal segmentation, as well as depth order information, are all well-described. Then we also introduce a novel convolutional neural network (CNN) architecture, which we refer to as semantic layering network, to estimate sem-dist maps layer by layer, from the global-level to the instance-level, for all objects in an image. Extensive experiments on the COCOA and D2SA datasets have demonstrated that our framework can predict amodal segmentation, occlusion and depth order with state-of-the-art performance.
We further discuss the suggestion that the chemistry in a photon-dominated region is coupled to that in the UV-shielded region behind it by turbulent t ransport processes. In addition to transport time-scales, we discuss why MHD waves/turbulence will likely cause transport instead of prohibiting it.
Under the basic assumption that the observed turbulent motions in molecular clouds are Alfvenic waves or turbulence, we emphasize that the Doppler broadening of molecular line profiles directly measures the velocity amplitudes of the waves instead of
the Alfven velocity. Assuming an equipartition between the kinetic energy and the Alfvenic magnetic energy, we further propose the hypothesis that observed standard scaling laws in molecular clouds imply a roughly scale-independent fluctuating magnetic field, which might be understood as a result of strong wave-wave interactions and subsequent energy cascade. We predict that $sigma_{v}propto rho^{-0.5}$ is a more basic and robust relation in that it may approximately hold in any regions where the spatial energy density distribution is primarily determined by wave-wave interactions, including gravitationally unbound regions. We also discuss the fact that a scale-independent $sigma_{B}^{2}$ appears to contradict existing 1-D and 2-D computer simulations of MHD turbulence in molecular clouds.
It has been proposed recently that the small size and long lifetime of ultra-compact HII regions (UCHIIs) could be due to pressure confinement if the thermal pressure of the ambient gas is higher than previous estimates. We point out that confinement
by thermal pressure alone implies emission measures in excess of observed values. We show that turbulent pressure, inferred from observed non-thermal velocities, is sufficient to confine UC HIIs and explain their longevity. We predict an anti-correlation between the size of UCHIIs and the velocity dispersion of the ambient neutral gas, and show that it is consistent with existing observations.
