基于改进残差特征蒸馏的轻量级超分辨率网络

基于深度学习的图像超分辨率算法通常采用递归的方式或参数共享的策略来减少网络参数，这将增加网络的深度，使得运行网络花费大量的时间，从而很难将模型部署到现实生活中。为了解决上述问题，本文设计一种轻量级超分辨率网络，对中间特征的关联性及重要性进行学习，且在重建部分结合高分辨率图像的特征信息。首先，引入层间注意力模块，通过考虑层与层之间的相关性，自适应地分配重要层次特征的权重。其次，使用增强重建模块提取高分辨率图像中更精细的特征信息，以此得到更加清晰的重建图片。通过大量的对比实验表明，本文设计的网络与其他轻量级模型相比，有更小的网络参数量，并且在重建精度和视觉效果上都有一定的提升。     

Superconducting Quantum Metamaterials from High Pressure Melt Infiltration of Metals into Block Copolymer Double Gyroid Derived Ceramic Templates

Mesoscale order can lead to emergent properties including phononic bandgaps or topologically protected states. Block copolymers offer a route to mesoscale periodic architectures, but their use as structure directing agents for metallic materials has not been fully realized. A versatile approach to mesostructured metals via bulk block copolymer self-assembly derived ceramic templates, is demonstrated. Molten indium is infiltrated into mesoporous, double gyroidal silicon nitride templates under high pressure to yield bulk, 3D periodic nanocomposites as free-standing monoliths which exhibit emergent quantum-scale phenomena. Vortices are artificially introduced when double gyroidal indium metal behaves as a type II superconductor, with evidence of strong pinning centers arrayed on the order of the double gyroid lattice size. Sample behavior is reproducible over months, showing high stability. High pressure infiltration of bulk block copolymer self-assembly based ceramic templates is an enabling tool for studying high-quality metals with previously inaccessible architectures, and paves the way for the emerging field of block-copolymer derived quantum metamaterials.     

铝业废阴极炭块资源化利用技术研究

为实现铝业废阴极炭块在钢铁冶金流程中资源化利用，通过深入研究废阴极炭块的相关物性，结合炼钢工艺对炭素及氟化物的物质需求，明确了废阴极炭块的无害化资源利用原理，工业试验结果表明废阴极炭块在炼钢转炉内可较好地实现无害化资源利用。     

导管架平台上部组块双船拆除

为研究复杂海域环境中海洋平台双船拆除方法的安全性与可操作性,采用水动力数值计算与水池模型试验相结合的方法进行研究。将双船水动力耦合计算与水池模型试验进行对比,在相对理想海况下,结合与实际工况接近的规则波作用,进行驳船水动力性能变化与运动响应的幅值分析。在受力分析方面,对甲板耦合装置进行改良设计,对比试验数据与数值计算数据,得到受力在合理范围内的结论。研究内容为实际施工的合理性提出一定的指导性意见。     

Saliency4ASD: Challenge,dataset and tools for visual attention modeling for autism spectrum disorder

The recent studies showing that gaze features can be useful in the identification of Autism Spectrum Disorder (ASD), have opened a new domain where Visual Attention (VA) modeling could be of great help. In this sense, this paper presents a report of the Grand Challenge “Saliency4ASD: Visual attention modeling for Autism Spectrum Disorder”, organized at IEEE ICME’19, aiming at supporting the research on VA modeling towards this healthcare societal challenge. In particular, this paper describes the workflow, obtained results, and datasets and tools that were used within this activity, in order to help on the development and evaluation of two types of VA models: (1) to predict saliency maps that fit gaze behavior of people with ASD, and (2) to identify individuals with ASD from typical development.     

地表移动带范围内建构筑物安全可靠性分析

地表建构筑物位于在生产矿山地表开采移动范围内，如果按照一般圈定地表移动范围原则判断建构筑物的安全可靠性，结果是不安全或者预留保安矿柱可保证安全。为分析地表移动带范围内建构筑物安全可靠性，采用FLAC3D软件建立数值模拟模型，对矿区地表移动进行分析研究，并结合建构筑物的破坏等级评判标准，可以更加合理地判断地表移动范围内建构筑物的安全可靠性，对存在相似条件的矿山具有一定的借鉴意义。     

Functional organisation of the endomembrane network in the digestive gland of the Venus flytrap: revisiting an old story with a new microscopy toolbox

Up-to-date imaging approaches were used to address the spatiotemporal organisation of the endomembrane system in secretory cells of Dionaea muscipula. Different ‘slice and view’ methodologies were performed on resin-embedded samples to finally achieve a 3D reconstruction of the cell architecture, using ultrastructural tomography, array tomography, serial block face-scanning electron microscopy (SBF-SEM), correlation, and volume rendering at the light microscopy level. Observations of cryo-fixed samples by high-pressure freezing revealed changes of the endomembrane system that occur after trap activation and prey digestion. They provide evidence for an original strategy that adapts the secretory machinery to a specific and unique case of stimulated exocytosis in plant cells. A first secretion peak is part of a rapid response to deliver digestive fluids to the cell surface, which delivers the needed stock of digestive materials ‘on site’. The second peak of activity could then be associated with the reconstruction of the Golgi apparatus (GA), endoplasmic reticulum (ER) and vacuolar machinery, in order to prepare for a subsequent round of prey capture. Tubular continuum between ER and Golgi stacks observed on ZIO-impregnated tissues may correspond to an efficient transfer mechanism for lipids and/or proteins, especially for use in rapidly resetting the molecular GA machinery. The occurrence of one vacuolar continuum may permit continuous adjustment of cell homeostasy. The subcellular features of the secretory cells of Dionaea muscipula outline key innovations in the organisation of plant cell compartmentalisation that are used to cope with specific cell needs such as the full use of the GA as a protein factory, and the ability to create protein reservoirs in the periplasmic space. Shape-derived forces of the pleiomorphic vacuole may act as signals to accompany the sorting and entering flows of the cell.     

采动覆岩卸荷膨胀累积效应

地下煤层开采引起的岩层运动是一系列安全和环境问题的根源,研究采动岩层运动规律是安全、高效、绿色开采的重要基础。通过对岩层运动过程的研究,揭示了采动覆岩卸荷膨胀累积效应及其对岩层运动规律的影响机制。研究表明:采动覆岩经历了卸荷膨胀与再压实的动态过程。受关键层结构控制,上覆岩层由下向上成组破断运动,关键层破断前,阻断了上覆载荷向下方岩层的传递,导致其因卸荷而产生膨胀,包括碎胀与弹性膨胀。随着关键层破断高度增加,覆岩卸荷高度同步增大,因卸荷而膨胀的岩层总厚度不断增大;同时卸荷煤岩也受到已破断关键层载荷的压实作用,从而造成覆岩卸荷膨胀总量的不断变化。将这种覆岩卸荷膨胀总量随覆岩卸荷高度动态变化的现象定义为采动覆岩卸荷膨胀累积效应,进而建立了理论模型,并通过淮北海孜煤矿巨厚火成岩下采煤覆岩裂隙实测进行了验证。结果表明,采动覆岩卸荷膨胀累积效应对采动岩层运动规律产生了重要影响,如影响覆岩关键层下离层量,影响覆岩关键层贯通破断的高度,影响不同开采条件的地表下沉系数等。采动覆岩卸荷膨胀累积效应改变了对离层存在形式的传统认识,该效应的存在导致关键层下最大离层量一般小于采高的10%,覆岩可注浆充填空间并非传统认识上的“离层区”,而主要是注浆充填压力“压实”作用下将覆岩卸荷累积膨胀所转化出的那部分空间,该发现指导了覆岩隔离注浆充填绿色开采技术的创新研发及其在建筑物压煤开采中的成功实践。     

碳酸盐岩裂缝性储层常规测井评价方法

哈萨克斯坦K油田石炭系KT-Ⅱ段储层为开阔台地相中低孔、特低渗灰岩储层，裂缝对改善储层的渗流能力至关重要，前人研究的利用常规测井资料评价碳酸盐岩储层裂缝的方法由于受裂缝产状、饱和度、泥浆侵入深浅等多方面因素的影响，存在确定性差、容易误判的缺陷。基于研究区目的层段有限的取心和电成像测井资料，结合孔隙型储层和裂缝-孔隙型储层测井响应特征，提出了利用补偿中子确定基块岩石电阻率与基块声波时差，再比较二者与深侧向电阻率、声波时差的差异特征，进而综合识别储层不同产状裂缝发育段，最后建立了裂缝孔隙度、次生孔隙度、裂缝渗透率、总渗透率等储层参数的测井解释模型，实现了利用常规测井资料对碳酸盐岩油藏储层的裂缝识别及参数定量评价。该方法应用的测井综合评价成果与取心物性分析、生产动态情况能够更好地匹配，为该类油藏的合理高效开发提供了依据。     

Densely connected convolutional network block based autoencoder for panorama map compression

As a novel virtual reality (VR) format, panorama maps are attracting increasing attention, while the compression of panorama images is still a concern. In this paper, a densely connected convolutional network block (dense block) based autoencoder is proposed to compress panorama maps. In the proposed autoencoder, dense blocks are specially designed to reuse feature maps and reduce redundancy of features. Meanwhile, a loss function, which imports a position-dependent weight item for each pixel, is proposed to train and adjust network parameters, in order to make the autoencoder fit to properties of panorama maps. Based on the proposed autoencoder and the weighted loss function, a greedy block-wise training scheme is also designed to avoid gradient vanishing problem and speed up training. During training process, the autoencoder is divided into several sub-nets. After each sub-net is trained separately, the whole network is fine-tuned to achieve the best performance. Experimental results demonstrate that the proposed autoencoder, compared with JPEG, saves up to 79.69 % bit rates, and obtains 7.27dB gain in BD-WS-PSNR or 0.0789 gain in BD-WS-SSIM. The proposed autoencoder also outperforms JPEG 2000, HEVC and VVC in both BD-WS-PSNR and BD-WS-SSIM. Meanwhile, subjective results show that the proposed autoencoder can recover details of panorama images, and reconstruct maps with high visual quality.