QCD and Relativistic O(αsv2)O(\alpha_{s}v^2) Corrections to Hadronic Decays of Spin-Singlet Heavy Quarkonia hc,hbh_c, h_b and ηb\eta_b

We calculate the annihilation decay widths of spin-singlet heavy quarkonia $h_c, h_b$ and $\eta_b$} into light hadrons with both QCD and relativistic corrections at order $O(\alpha_{s}v^{2})$ in nonrelativistic QCD. With appropriate estimates for the long-distance matrix elements by using the potential model and operator evolution method, we find that our predictions of these decay widths are consistent with recent experimental measurements. We also find that the $O(\alpha_{s}v^{2})$ corrections are small for $b\bar{b}$ states but substantial for $c\bar{c}$ states. In particular, the negative contribution of $O(\alpha_{s}v^{2})$ correction to the $h_{c}$ decay can lower the decay width, as compared with previous predictions without the $O(\alpha_{s}v^{2})$ correction, and thus result in a good agreement with the recent BESIII measurement.Comment: version published in PRD, 30 pages, 8 figures, more discussions on LDMEs adde

Study on the Rheological Properties and Constitutive Model of Shenzhen Mucky Soft Soil

In order to obtain the basic parameters of numerical analysis about the time-space effect of the deformation occurring in Shenzhen deep soft-soil foundation pit, a series of triaxial consolidated-undrained shear rheology tests on the peripheral mucky soft soil of a deep foundation pit support were performed under different confining pressures. The relations between the axial strain of the soil and time, as well as between the pore-water pressure of the soil and time, were achieved, meanwhile on the basis of analyzing the rheological properties of the soil, the relevant rheological models were built. Analysis results were proved that the rheology of Shenzhen mucky soft soil was generally viscous, elastic, and plastic, and had a low yield stress between 90 and 150 kPa. The increase in pore-water pressure made the rheological time effect of the mucky soft soil more remarkable. Thus, the drainage performance in practical engineering should be improved to its maximum possibility extent to decrease the soft-soil rheological deformation. Lastly, a six-component extended Burgers model was employed to fit the test results and the parameters of the model were determined. Findings showed that the extended Burgers model could satisfactorily simulate the various rheological stages of the mucky soft soil. The constitutive model and the determination of its parameters can be served as a foundation for the time-space effect analysis on the deformation of deep soft-soil foundation pits

Learning to Predict Diverse Human Motions from a Single Image via Mixture Density Networks

Human motion prediction, which plays a key role in computer vision, generally requires a past motion sequence as input. However, in real applications, a complete and correct past motion sequence can be too expensive to achieve. In this paper, we propose a novel approach to predicting future human motions from a much weaker condition, i.e., a single image, with mixture density networks (MDN) modeling. Contrary to most existing deep human motion prediction approaches, the multimodal nature of MDN enables the generation of diverse future motion hypotheses, which well compensates for the strong stochastic ambiguity aggregated by the single input and human motion uncertainty. In designing the loss function, we further introduce the energy-based formulation to flexibly impose prior losses over the learnable parameters of MDN to maintain motion coherence as well as improve the prediction accuracy by customizing the energy functions. Our trained model directly takes an image as input and generates multiple plausible motions that satisfy the given condition. Extensive experiments on two standard benchmark datasets demonstrate the effectiveness of our method in terms of prediction diversity and accuracy

Properties of Interstellar Medium In Infrared-Bright QSOs Probed by [O I] 63 μm and [C II] 158 μm Emission Lines

We present a study of the interstellar medium (ISM) in the host galaxies of nine QSOs at 0.1 1 IR-bright QSOs. One target, W0752+19, shows an additional broad velocity component (~720 km s^(−1)) and exceptionally strong [O I] 63 μm emission with L_([O I]63μm)/L_(FIR) of 10^(−2), an order of magnitude higher than the average value found among local (U)LIRGs. Combining with the analyses of the Sloan Digital Sky Survey optical spectra, we conclude that the [O I] 63 μm emission in these QSOs is unlikely excited by shocks. We infer that the broad [O I] 63 μm emission in W0752+19 could arise from the warm and dense ISM in the narrow-line region of the central active galactic nucleus. Another possible explanation is the existence of a dense gas outflow with n_H ~ 10^4 cm^(−3), where the corresponding broad [C II] emission is suppressed. Based on the far-IR [O I] and [C II] line ratios, we estimate constraints on the ISM density and UV radiation field intensity of n_H ≾ 10^(3.3) cm^(−3) and 10^3 10^4

BITS-Net: Blind Image Transparency Separation Network

This research presents a new approach for blind single-image transparency separation, a significant challenge in image processing. The proposed framework divides the task into two parallel processes: feature separation and image reconstruction. The feature separation task leverages two deep image prior (DIP) networks to recover two distinct layers. An exclusion loss and deep feature separation loss are used to decompose features. For the image reconstruction task, we minimize the difference between the mixed image and the re-mixed image while also incorporating a regularizer to impose natural priors on each layer. Our results indicate that our method performs comparably or outperforms state-of-the-art approaches when tested on various image datasets