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In this paper, we study the size effects on the phonon transmission across material interfaces using the atomistic Green's function method. Layered Si and Ge or Ge-like structures are modeled with a variety of confined sizes in both transverse and longitudinal directions. The dynamical equation of the lattice vibration (phonon waves) is solved using the Green's function method and the phonon transmission is calculated through the obtained Green's function. Phonon transmission across a single interface of semi-infinite Si and Ge materials is studied first for the validation of the methodology. We show that phonon transmission across an interface can be tuned by changing the mass ratio of the two materials. Multi-layered superlattice-like structures with longitudinal size confinement are then studied. Frequency-dependent phonon transmission as a function of both the number of periods and the period thickness is reported. A converged phonon transmission after ten periods is observed due to the formation of phonon minibands. Frequency-dependent phonon transmission with transverse size confinement is also studied for the interface of Si and Ge nanowire-like structures. The phonon confinement induces new dips and peaks of phonon transmission when compared with the results for a bulk interface. With increasing size in the transverse direction, the phonon transmission approaches that of a bulk Si/Ge interface.

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Two-stage methods have dominated Human-Object Interaction (HOI) detection for several years. Recently, one-stage HOI detection methods have become popular. In this paper, we aim to explore the essential pros and cons of two-stage and one-stage methods. With this as the goal, we find that conventional two-stage methods mainly suffer from positioning positive interactive human-object pairs, while one-stage methods are challenging to make an appropriate trade-off on multi-task learning, i.e., object detection, and interaction classification. Therefore, a core problem is how to take the essence and discard the dregs from the conventional two types of methods. To this end, we propose a novel one-stage framework with disentangling human-object detection and interaction classification in a cascade manner. In detail, we first design a human-object pair generator based on a state-of-the-art one-stage HOI detector by removing the interaction classification module or head and then design a relatively isolated interaction classifier to classify each human-object pair. Two cascade decoders in our proposed framework can focus on one specific task, detection or interaction classification. In terms of the specific implementation, we adopt a transformer-based HOI detector as our base model. The newly introduced disentangling paradigm outperforms existing methods by a large margin, with a significant relative mAP gain of 9.32% on HICO-Det.

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The mathematical models in reservoir simulation are usually discretized into large linear equations, and solving them needs lots of time. Taking into account the mathematical characteristics of the black oil model, a multilevel preconditioning solution method is designed to deal with the algebraic equations in reservoir numerical simulation. Takes into account some of the properties of pressure, saturation, and implicit well variables in flow model, the multilevel preconditioner is comprised of several different iterative methods, such as algebraic multigrid method, Incomplete LU factorization, Gauss-Seidel iteration with downwind ordering and crosswind blocks and et al. The efficiency and robustness of multilevel preconditioner is proved by a million-cell benchmark problem and a real-world matured reservoir with high heterogeneity, high water-cut, geological faults, and complex well scheduling. The numerical results indicate that the proposed method is not only robust with respect to the heterogeneity, anisotropy, and number of wells but also efficient method that can solve large Jacobian system in reservoir simulation quickly and precisely.

Underwater images always suffer from low contrast and inaccurate colors due to scattering and absorption by particles when the target light propagates through turbid water. In this paper, we first found that a lot of intensity space is occupied by fewer pixels, called ‘tails’, on both sides of the histograms for the red, green and blue channels of the image. Based on this histogram attenuation prior and taking account of the advantage of a polarization filter we proposed an effective polarimetric recovery method to enhance the underwater image quality, which includes a specially designed histogram processing method, named ‘cut-tail histogram stretching’. This processing overcomes the limitation of traditional histogram-based methods and can further improve the restoration performance. The experimental results corresponding to underwater scenes with different turbidities and colors show that the proposed method can simultaneously enhance the image contrast and reduce the color distortion to some extent, and thus realize clear underwater vision.