基于卷积神经网络的单目深度估计

针对利用深度学习方法对街道图像进行深度估计，提出采用语义分割的方法解决深度图出现边界模糊等问题；估计深度通过左右视角图生成视差图进行无监督的训练。在网络模型中添加语义分割层，采取多个空洞卷积并行的结构增加感受野，同时减少了图像下采样的次数，降低了由于下采样带来的信息损失，使得的结果更加准确。这也是在深度估计中首次与空洞卷积相结合增加准确率。通过对KITTI街道数据集进行训练，与现有结果相比，除了增加检测准确性，降低错误率之外，使得效果图中的物体更加清晰，并且在效果图中还保留了一些原模型中被忽视掉的细节信息，将原始图像更加完整的表现出来。     

基于条件信息卷积生成对抗网络的图像识别

传统的图像识别方法需要大量有标签样本进行训练，且模型训练难以达到稳定。针对这些问题，结合条件生成网络和信息最大化生成网络的结构优势建立了条件信息卷积生成网络（C-Info-DCGAN）。模型增加图像的类别信息和潜在信息作为输入数据，然后利用Q网络去更好地发挥类别信息和潜在信息对训练的引导作用，并且利用深度卷积网络来加强对图像特征的提取能力。实验结果表明，该方法能够加快模型训练收敛速度，并有效提高图像识别的准确率。     

Role of Extracellular Vesicles in Epithelial Ovarian Cancer: A Systematic Review

Extracellular vesicles (EVs) are a heterogeneous group of cell-derived submicron vesicles released under physiological or pathological conditions. EVs mediate the cellular crosstalk, thus contributing to defining the tumor microenvironment, including in epithelial ovarian cancer (EOC). The available literature investigating the role of EVs in EOC has been reviewed following PRISMA guidelines, focusing on the role of EVs in early disease diagnosis, metastatic spread, and the development of chemoresistance in EOC. Data were identified from searches of Medline, Current Contents, PubMed, and from references in relevant articles from 2010 to 1 April 2020. The research yielded 194 results. Of these, a total of 36 papers, 9 reviews, and 27 original types of research were retained and analyzed. The literature findings demonstrate that a panel of EV-derived circulating miRNAs may be useful for early diagnosis of EOC. Furthermore, it appears clear that EVs are involved in mediating two crucial processes for metastatic and chemoresistance development: the epithelial–mesenchymal transition, and tumor escape from the immune system response. Further studies, more focused on in vivo evidence, are urgently needed to clarify the role of EV assessment in the clinical management of EOC patients.     

The benefits of integrated eye tracking with airborne image recorders in the flight deck: A rejected landing case study

Aircraft accident investigation has played a pivotal role in improving the safety of aviation. Advances in recorder technology, specifically Cockpit Voice Recorders (CVRs) and Flight Data Recorders (FDR) have made a huge contribution to the understanding of occurrences for accident investigators. However, even these recorders have limitations such as the evidence they provide about pilots' situation awareness or behaviours. Supplementing audio and data recordings with video has been discussed for many years and whilst there continues to be debate among regulators, operators, manufacturers and pilot unions, the International Civil Aviation Organization (ICAO) has mandated Airborne Image Recorders (AIR) from 2023. The purpose of installing such systems is to provide evidence of crew operational behaviours in terms of both human-human and human-computer interactions (HCI) on the flight deck. Video alone is unlikely to provide sufficient evidence for investigators. This study examines the additional value that eye-tracking technology may provide through the case study of an accident involving an Airbus A330-300 aircraft which experienced a rejected landing. Currently, the investigation of such events, where crew interaction with automation is critical to their situation awareness, relies heavily on interview data. Such data may be unavailable (in the case of serious injury) or unreliable (based on hindsight bias). By integrating eye tracking technology into an AIR, accident investigators will potentially gain a better understanding of pilots’ visual scan patterns across flight deck instrumentation. This has implications for flight deck and procedural design as well as training and simulation.     

Convolutional neural network with adaptive inferential framework for skeleton-based action recognition

In the task of skeleton-based action recognition, CNN-based methods represent the skeleton data as a pseudo image for processing. However, it still remains as a critical issue of how to construct the pseudo image to model the spatial dependencies of the skeletal data. To address this issue, we propose a novel convolutional neural network with adaptive inferential framework (AIF-CNN) to exploit the dependencies among the skeleton joints. We particularly investigate several initialization strategies to make the AIF effective with each strategy introducing the different prior knowledge. Extensive experiments on the dataset of NTU RGB+D and Kinetics-Skeleton demonstrate that the performance is improved significantly by integrating the different prior information. The source code is available at: https://github.com/hhe-distance/AIF-CNN.     

内插螺旋立式上行管流场及传热性能的实验研究

通过粒子图像测速流场实验与传热实验相结合，研究了内插螺旋立式上行管的螺旋节距、丝径、中径比等结构参数在不同Re下对流场、阻力及传热性能的影响。结果表明，内插螺旋能够有效扰动和混合管内流体，使管内形成多个纵向旋涡的流体结构、增大管壁附近液体涡量，有利于强化传热。当Re相同时，管内平均流速v、Nu和综合换热性能PEC均随丝径增大而增大，随中径比减小而增大；随节距增大，3种参数均出现增大的趋势，节距大于20 mm后开始减小。管内流体的阻力f随丝径和节距增大而减小，随中径比增大而增大。综合比较，在较低Re时，节距p=20 mm、丝径e=1.6 mm、中径比D/d=0.75时综合传热效果最好。     

Study of the surfactant role in latex–aerogel systems by scanning transmission electron microscopy on aqueous suspensions

For insulation applications, boards thinner than 2 cm are under design with specific thermal conductivities lower than 15 mW m^?1 K^?1. This requires binding slightly hydrophobic aerogels which are highly nanoporous granular materials. To reach this step and ensure insulation board durability at the building scale, it is compulsory to design, characterise and analyse the microstructure at the nanoscale. It is indeed necessary to understand how the solid material is formed from a liquid suspension. This issue is addressed in this paper through wet‐STEM experiments carried out in an Environmental Scanning Electron Microscope (ESEM). Latex–surfactant binary blends and latex–surfactant–aerogel ternary systems are studied, with two different surfactants of very different chemical structures. Image analysis is used to distinguish the different components and get quantitative morphological parameters which describe the sample architecture. The evolution of such morphological parameters during water evaporation permits a good understanding of the role of the surfactant.