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Energetic X-ray radiations emitted from various accretion systems are widely considered to be produced by Comptonization in the hot corona. The corona and its interaction with the disc play an essential role in the evolution of the system and are potentially responsible for many observed features. However many intrinsic properties of the corona are still poorly understood, especially for the geometrical configurations. The traditional spectral fitting method is not powerful enough to distinguish various configurations. In this paper we intent to investigate the possible configurations by modeling the polarization properties of X-ray radiations. The geometries of the corona include the slab, sphere and cylinder. The simulations are implemented through the publicly available code, LEMON, which can deal with the polarized radiative transfer and different electron distributions readily. The results demonstrate clearly that the observed polarizations are dependent on the geometry of the corona heavily. The slab-like corona produces the highest polarization degrees, the following are the cylinder and sphere. One of the interesting things is that the polarization degrees first increase gradually and then decrease with the increase of photon energy. For slab geometry there exists a zero point where the polarization vanishes and the polarization angle rotates for $90^\circ$. These results may potentially be verified by the upcoming missions for polarized X-ray observations, such as $IXPE$ and $eXTP$., Comment: 29 pages, 12 figures, RAA accepted. The source code can be downloaded from \url{https://bitbucket.org/yangxiaolinsc/corona_geometry/src/main/}

In this paper, we proposed a new Monte Carlo radiative transport (MCRT) scheme, which is based completely on the Neumann series solution of Fredholm integral equation. This scheme indicates that the essence of MCRT is the calculation of infinite terms of multiple integrals in Neumann solution simultaneously. Under this perspective we redescribed MCRT procedure systematically, in which the main work amounts to choose an associated probability distribution function (PDF) for a set of random variables and the corresponding unbiased estimation functions. We can select a relatively optimal estimation procedure that has a lower variance from an infinite possible choices, such as the term by term estimation. In this scheme, MCRT can be regarded as a pure problem of integral evaluation, rather than as the tracing of random walking photons. Keeping this in mind, one can avert some subtle intuitive mistakes. In addition the $\delta$-functions in these integrals can be eliminated in advance by integrating them out directly. This fact together with the optimal chosen random variables can remarkably improve the Monte Carlo (MC) computational efficiency and accuracy, especially in systems with axial or spherical symmetry. An MCRT code, Lemon (Linear Integral Equations' Monte Carlo Solver Based on the Neumann solution), has been developed completely based on this scheme. Finally, we intend to verify the validation of Lemon, a suite of test problems mainly restricted to flat spacetime have been reproduced and the corresponding results are illustrated in detail., Comment: 37 pages, 28 figures. The code can be download from: https://github.com/yangxiaolinyn/Lemon (or https://bitbucket.org/yangxiaolinsc/lemonsourcecode/src/main/) and https://doi.org/10.5281/zenodo.4686355. Comments are welcome

The impact of large atmospheric disturbances on deep benthic communities is not well known quantitatively. Observations are scarce but may reveal specific processes leading to turbulent disturbances. Here, we present high-resolution deep-ocean observations to study potential turbulent mixing by large atmospheric disturbances. We deployed an array of 100-Hz sampling-rate geophysical broadband Ocean Bottom Seismometers (OBSs) on the seafloor. Within the footprint of this array we also deployed an oceanographic 1-Hz sampling-rate vertical temperature sensor string covering the water phase between 7 and 207 m above the seafloor at about 3000 m depth off eastern Taiwan between June 2017 and April 2018. All instruments registered Category 4 cyclone Typhoon Talims passage northeast of the array one day ahead of the cyclones closest approach when the cyclones eye was more than 1,000 km away. For 10 days, a group of near-inertial motions appeared most clearly in temperature. The registration reflects the importance of barotropic response to cyclones and the propagation of inertio-gravity waves in weak density stratification. In addition to internal tides, these waves drove turbulent overturns larger than 200 m that were concurrently registered by OBSs. The turbulent signals were neither due to seismic activity nor to ocean-surface wave action. Cyclones can generate not only microseisms and earth hums, as well as turbulence in the water column, producing additional ground motions. Quantified turbulence processes may help constrain models on sediment resuspension and its effect on deep-sea benthic life., Comment: 39 pages, 11 figures

In order to learn object segmentation models in videos, conventional methods require a large amount of pixel-wise ground truth annotations. However, collecting such supervised data is time-consuming and labor-intensive. In this paper, we exploit existing annotations in source images and transfer such visual information to segment videos with unseen object categories. Without using any annotations in the target video, we propose a method to jointly mine useful segments and learn feature representations that better adapt to the target frames. The entire process is decomposed into two tasks: 1) solving a submodular function for selecting object-like segments, and 2) learning a CNN model with a transferable module for adapting seen categories in the source domain to the unseen target video. We present an iterative update scheme between two tasks to self-learn the final solution for object segmentation. Experimental results on numerous benchmark datasets show that the proposed method performs favorably against the state-of-the-art algorithms., Comment: Accepted in ACCV'18 (oral). Code is available at https://github.com/wenz116/TransferSeg

This study investigated conditions for optimizing college students' analogical problem solving. Students given pictorial (not verbal) representations of solutions to source problems were better problem solvers, as were students given a longer time. Students who derived personal solution principles to source problems were not superior to students given solution principles. (SM)