Semantic guided knowledge graph for large-scale zero-shot learning

Zero-shot learning has received growing attention, which aims to improve generalization to unseen concepts. The key challenge in zero-shot tasks is to precisely model the relationship between seen and unseen classes. Most existing zero-shot learning methods capture inter-class relationships via a shared embedding space, leading to inadequate use of relationships and poor performance. Recently, knowledge graph-based methods have emerged as a new trend of zero-shot learning. These methods use a knowledge graph to accurately model the inter-class relationships. However, the currently dominant method for zero-shot learning directly extracts the fixed connection from off-the-shelf WordNet, which will inherit the inherent noise in WordNet. In this paper, we propose a novel method that adopts class-level semantic information as a guidance to construct a new semantic guided knowledge graph (SG-KG), which can correct the errors in the existing knowledge graph and accurately model the inter-class relationships. Specifically, our method includes two main steps: noise suppression and semantic enhancement. Noise suppression is used to eliminate noise edges in the knowledge graph, and semantic enhancement is used to connect two classes with strong relations. To promote high efficient information propagation among classes, we develop a novel multi-granularity fusion network (MGFN) that integrates discriminative information from multiple GCN branches. Extensive experiments on the large-scale ImageNet-21K dataset and AWA2 dataset demonstrate that our method consistently surpasses existing methods and achieves a new state-of-the-art result.     

Unsupervised learning of multi-task deep variational model

We propose a general deep variational model (reduced version, full version as well as the extension) via a comprehensive fusion approach in this paper. It is able to realize various image tasks in a completely unsupervised way without learning from samples. Technically, it can properly incorporate the CNN based deep image prior (DIP) architecture into the classic variational image processing models. The minimization problem solving strategy is transformed from iteratively minimizing the sub-problem for each variable to automatically minimizing the loss function by learning the generator network parameters. The proposed deep variational (DV) model contributes to the high order image edition and applications such as image restoration, inpainting, decomposition and texture segmentation. Experiments conducted have demonstrated significant advantages of the proposed deep variational model in comparison with several powerful techniques including variational methods and deep learning approaches.     

Learning to align organizational design and data

The relationship between organizational learning and organizational design is explored. In particular, we examine the information processing aspects of organizational learning as they apply to a two-valued decision making task and the relation of such aspects to organizational structure. Our primary contribution is to extend Carley's (1992) model of this process. The original model assumes that all data input into the decision making processes are of equal importance or weight in determining the correct overall organizational decision. The extension described here allows for the more natural situation of non-uniform weights of evidence. Further extensions to the model are also discussed. Such organizational learning performance measures provide an interesting framework for analyzing the recent trend towards flatter organizational structures. This research suggests that flatter structures are not always better, but rather that data environment, ultimate performance goals, and relative need for speed in learning can be used to form a contingency model for choosing organizational structure.     

Dimer asymmetry in superoxide dismutase studied by molecular dynamics simulation

Summary Molecular dynamics (MD) simulations of 100 ps have been carried out to study the active-site behaviour of the Cu,Zn superoxide dismutase dimer (SOD) in water. The active site of each subunit was monitored during the whole simulation by calculating the distances between functional residues and the catalytic copper. The results indicate that charge orientation is maintained at each active site but the solvent accessibility varies. Analysis of the MD simulation, carried out by using the atomic displacement covariance matrix, has shown a different intra-subunit correlation pattern for the two monomers and the presence of inter-subunit correlations. The MD simulation presented here indicates an asymmetry in the two active sites and different dynamic behaviour of the two SOD subunits.     

烯烃氢甲酰化催化剂活性物种的原位1H NMR研究

The rhodium-phosphine complex catalyst Rh(CO)(acac)(PPh3)(Ⅰ) for 1-hexene hydroformylation was studied under the following reaction conditions: CO/H2=1(mole rate), pressure 1.0 MPa, temperature 25-120℃, by using the pressurized in-situ 1H NMR technique. Experimental results indicated that the formation of a rhodium hydride complex from (Ⅰ) began at room temperature and its amount increased with increasing of reaction temperature. This intermediate complex began to decompose at 100℃ and disapeared completely at 120℃. The intensity change of the proton signal was parallel to catalytical activity in hydroformylation of olefins. Under pure CO pressure the proton signal of Ph-H bond was not observed. There was a 0.2 ppm difference in proton chemical shifts of Rh-H bond under pure H2 pressure and under H2+CO pressure. The results showed that the rhodium-hydride carbonyl complex is the active intermediate in the industrial hydroformylation process.     

烯烃聚合数学模拟研究与现状

综述了近年来国内外在烯烃聚合微观形态和宏观反应器数学模拟方面取得的研究成果，指出了烯烃聚合过程数学模拟的发展方向。     

构建网络学习资源提高民族高校有机化学课程教学质量

High-quality online course materials were reconstructed through feedback from undergraduate students and online data analysis regarding constructed learning materials on the Chao Xing Learning Platform. They were used for studying organic chemistry courses online and offline in university for nationalities. This model, based on student-centered teaching, can effectively transform students from passively accepting knowledge to actively learning and internalizing knowledge. This strategy can also enhance the students' learning initiative and the effects of learning, and hopefully be helpful to the universities in multi-ethnic areas in developing fundamental disciplined construction of organic chemistry and other courses.     

功能高分子活性材料硫酸根离子选择电极的研制

利用辐射方法制备硫酸根离子选择电极活性材料,至今未见报导,我们首次采用辐射接枝方法制备了以疏水性高分子为骨架的带有SO~-活性基团的功能高分子活性材料,研制了硫酸根离子选择电极。结果表明,该电极具有内阻低,响应快,稳定性较好的特点,且其功能曲线的线性范围为10~(-1)～10~(-4)MSO_4~-,适宜的pH范围为4～9。     

磷肥中有效磷的示波滴定

根据Ｐｂ２＋能与ＰＯ＿４￣（３－）定量形成沉淀，过量的Ｐｂ２＋在ＮａＣｌＯ３－六次甲基四胺底液示波图上有敏锐切口的性质，报告了磷肥中有效磷含量测定的示波滴定新方法。该法与通用方法相比较，具有操作简便、测定快速、终点直观的优点。     

Unraveling the mystery of efficacy in Chinese medicine formula: New approaches and technologies for research on pharmacodynamic substances

Traditional Chinese medicine (TCM) is the key to unlock treasures of Chinese civilization. TCM and its compound play a beneficial role in medical activities to cure diseases, especially in major public health events such as novel coronavirus epidemics across the globe. The chemical composition in Chinese medicine formula is complex and diverse, but their effective substances resemble “mystery boxes”. Revealing their active ingredients and their mechanisms of action has become focal point and difficulty of research for herbalists. Although the existing research methods are numerous and constantly updated iteratively, there is remain a lack of prospective reviews. Hence, this paper provides a comprehensive account of existing new approaches and technologies based on previous studies with an in vitro to in vivo perspective. In addition, the bottlenecks of studies on Chinese medicine formula effective substances are also revealed. Especially, we look ahead to new perspectives, technologies and applications for its future development. This work reviews based on new perspectives to open horizons for the future research. Consequently, herbal compounding pharmaceutical substances study should carry on the essence of TCM while pursuing innovations in the field.