清华大学生命科学学院，创建于1926年的生物系，是中国近代最早开展生物学教育和科学研究的基地之一，曾为中国培养了一大批知名的生物学家。在中国科学院生命科学和医学部及中国工程院医药卫生学部的院士中，有40余位曾在清华大学学习或工作过。1952年全国院系大调整后，清华大学生物系被并入其他院校，生命科学在清华园曾一度处于空白状态。历经32年，清华大学于1984年恢复生物系，并更名为生物科学与技术系。著名神经生物学家蒲慕明教授受聘担任第一任系主任，赵南明教授任常务副系主任。2009年，为了适应生命科学快速发展的需要，清华大学成立生命科学学院（简称生命学院），施一公教授任首任院长。
Cryo-Electron Tomography (cryo-ET) is a new 3D imaging technique with unprecedented potential for resolving submicron structural detail. Existing volume visualization methods, however, cannot cope with its very low signal-to-noise ratio. In order to design more powerful transfer functions, we propose to leverage soft segmentation as an explicit component of visualization for noisy volumes. Our technical realization is based on semi-supervised learning where we combine the advantages of two segmentation algorithms. A first weak segmentation algorithm provides good results for propagating sparse user provided labels to other voxels in the same volume. This weak segmentation algorithm is used to generate dense pseudo labels. A second powerful deep-learning based segmentation algorithm can learn from these pseudo labels to generalize the segmentation to other unseen volumes, a task that the weak segmentation algorithm fails at completely. The proposed volume visualization uses the deep-learning based segmentation as a component for segmentation-aware transfer function design. Appropriate ramp parameters can be suggested automatically through histogram analysis. Finally, our visualization uses gradient-free ambient occlusion shading to further suppress visual presence of noise, and to give structural detail desired prominence. The cryo-ET data studied throughout our technical experiments is based on the highest-quality tilted series of intact SARS-CoV-2 virions. Our technique shows the high impact in target sciences for visual data analysis of very noisy volumes that cannot be visualized with existing techniques.
A lot of research attentions have been paid to image emotion analysis in recent years. Meanwhile, as convolutional neural networks (CNNs) have made great successful in computer vision, many researchers start to employ CNN to discriminate image emotions. However, the training procedure of CNNs depends on sufficient labeled data. Therefore, a CNN is hard to perform well in an image domain with scant labeled information. In this paper, we propose a deep transfer learning method for image emotion analysis. The method can leverage rich emotion knowledge from a source domain to the target domain. Our method reduces both marginal and joint domain distribution discrepancies at fully-connected layers. Through this way, we can effectively extract more transferable features and advance the performance of CNNs on poor-label emotion-image domains.
