Deep Multilabel Multilingual Document Learning for Cross-Lingual Document Retrieval

Cross-lingual document retrieval, which aims to take a query in one language to retrieve relevant documents in another, has attracted strong research interest in the last decades. Most studies on this task start with cross-lingual comparisons at the word level and then represent documents via word embeddings, which leads to insufficient structure information. In this work, the cross-lingual comparison at the document level is achieved through the cross-lingual semantic space. Our method, MDL (deep multilabel multilingual document learning), leverages a six-layer fully connected network to project cross-lingual documents into a shared semantic space. The semantic distances can be calculated when the cross-lingual documents are transformed into embeddings in semantic space. The supervision signals are automatically extracted from the data and then used to construct the semantic space via a linear classifier. The ambiguity of manual labels could be avoided and the multilabel supervision signals can be acquired instead of a single label. The representation of the semantic space is enriched by multilabel supervision signals, which improves the discriminative ability of the embeddings. The MDL is easy to extend to other fields since it does not depend on specific data. Furthermore, MDL is more efficient than the models training all languages jointly, since each language is trained individually. Experiments on Wikipedia data showed that the proposed method outperforms the state-of-the-art cross-lingual document retrieval methods.     

Harnessing Protein-Ligand Interaction Fingerprints to Predict New Scaffolds of RIPK1 Inhibitors

Necroptosis has emerged as an exciting target in oncological, inflammatory, neurodegenerative, and autoimmune diseases, in addition to acute ischemic injuries. It is known to play a role in innate immune response, as well as in antiviral cellular response. Here we devised a concerted in silico and experimental framework to identify novel RIPK1 inhibitors, a key necroptosis factor. We propose the first in silico model for the prediction of new RIPK1 inhibitor scaffolds by combining docking and machine learning methodologies. Through the data analysis of patterns in docking results, we derived two rules, where rule #1 consisted of a four-residue signature filter, and rule #2 consisted of a six-residue similarity filter based on docking calculations. These were used in consensus with a machine learning QSAR model from data collated from ChEMBL, the literature, in patents, and from PubChem data. The models allowed for good prediction of actives of >90, 92, and 96.4% precision, respectively. As a proof-of-concept, we selected 50 compounds from the ChemBridge database, using a consensus of both molecular docking and machine learning methods, and tested them in a phenotypic necroptosis assay and a biochemical RIPK1 inhibition assay. A total of 7 of the 47 tested compounds demonstrated around 20–25% inhibition of RIPK1’s kinase activity but, more importantly, these compounds were discovered to occupy new areas of chemical space. Although no strong actives were found, they could be candidates for further optimization, particularly because they have new scaffolds. In conclusion, this screening method may prove valuable for future screening efforts as it allows for the exploration of new areas of the chemical space in a very fast and inexpensive manner, therefore providing efficient starting points amenable to further hit-optimization campaigns.     

掺镱双包层光纤放大器分布泵浦方式下的优化算法

 利用平均反转率迭代算法计算掺镱双包层光纤放大器分布泵浦方式下的稳态速率方程组,并采用遗传算法对分布泵浦功率的大小和每段光纤长度同时进行优化。评估函数中引入了每段光纤中最高温度的标准方差,以确保每段光纤中的最高工作温度是相同的。通过优化,7段泵浦的最高温度和标准方差分别为126.34 ℃和1.95 ℃ ,8段泵浦条件下的最高温度和标准方差分别为119.76 ℃和2.12 ℃。而未经优化的7段泵浦的最高温度和标准方差分别为147.12 ℃和21.37 ℃,8段泵浦条件下的最高温度和标准方差分别为139.95 ℃和20.83 ℃。计算结果表明：泵浦方式的优化降低了最高温度和标准方差,实现了光纤中温度分布的均匀性,并且通过增加泵浦段数可以进一步降低最高温度和平坦温度分布。     

高频地波雷达下的多目标跟踪数据处理

根据高频地波雷达监测海上低速移动目标的要求及该类雷达的特点,提出了一种与高频地波雷达目标跟踪相适应的多目标跟踪数据处理方案。在该方案中对BTC(bayes tracking confirm)方法与BTT(bayes tracking termination)方法进行了改进,解决了上述两种方法在高频地波雷达实际应用中存在的问题。提出了贝叶斯准则与次数判定相结合的跟踪起始和终结方法。模拟结果表明,该方案能够稳定、有效地实现高频地波雷达密集回波下的多目标跟踪。     

一种超高耐久混凝土——梯度结构混凝土

分析超高耐久混凝土的研究现状,提出了一种高耐久、低成本的超高耐久混凝土--梯度结构混凝土(Gradient Structural Concrete,简称GSC),采用电量法和氯离子扩散系数法来评价梯度结构混凝土的抗氯离子渗透性能,最后进行梯度结构混凝土的寿命预测与成本分析.结果表明,高性能混凝土(High Performance Concrete,简称HPC)、无细观界面过渡区水泥基复合材料(Meso-interfacial transition zone-free cement-based materials,简称 MIF)和梯度结构混凝土MIF-HPC的6 h导电量和氯离子扩散系数的排列顺序均为:HPC>MIF-HPC>MIF,其抗氯离子渗透性能排列顺序为:MIF>MIF-HPC>HPC.梯度结构混凝土的成本会有所增加,但由于其氯离子渗透性能大幅降低,其使用寿命得到大幅延长,使得其年损耗费用大幅下降.     

3,4-噻吩二羧酸、1,10-邻菲罗啉与稀土配合物的荧光性质及对硝基苯的检测

3,4-噻吩二羧酸(3,4-H₂tdc),1,10-邻菲罗啉(phen)和稀土硝酸盐经水热法合成三种配合物Ln₂(Htdc)₂(tdc)₂(phen)₂(H₂O)₄(Ln=Eu 1, Gd 2, Tb 3),并用X-射线单晶衍射分析方法测定了配合物1-3晶体结构,配合物1-3为双核分子。每个金属离子周围有2个3,4-tdc,1个3,4-Htdc,1个phen和2个配位水分子,配位数为9。配合物1和3在紫外灯下显强红光和绿光,其荧光发射光谱,在619和545 nm出现最大发射峰,分别对应于Eu(Ⅲ)离子的5D₀→7F₂和Tb(Ⅲ)离子的5D₄→7F₅跃迁。配合物2在425 nm观察到来自基于配体的π*→π最大发射峰。不同溶剂分子对配合物1荧光有不同程度的影响,基于荧光猝灭机理,配合物1具有选择性检测硝基苯污染物的能力。     

129Xe30+与Au作用激发的Au M-X射线 与Xe L-X射线

探测了动能为1.0——7.0 MeV的¹²⁹Xe³⁰⁺ 入射Au表面产生的X射线谱. 实验结果表明, 入射离子动能较高时, 不仅激发出很强的Au的M-X射线, 还激发出了Xe的L-X射线, 且X射线产额与入射离子动能有强相关性. 分析了X射线产额与入射离子动能的关系.     

Charge state effect on the K-shell ionization of iron by xenon ions near the Bohr velocity

Fe K-shell ionization cross sections induced by 2.4–6.0 MeV Xe20+are measured and compared with different binaryencounter-approximation(BEA)models.The results indicate that the BEA model corrected both by the Coulomb repulsion and by the effective nuclear charge(Zeff)agrees well with the experimental data.Comparison of Fe K-shell X-ray emission induced by 5 MeV xenon ions with different initial charge states(20+,22+,26+,30+)verifies the applicability of the effective nuclear charge(Zeff)correction for the BEA model.It is found that Zeffcorrection is reasonable to describe direct ionization induced by xenon ions with no initial M-shell vacancies.However,when the M shell is opened,the Zeffcorrected BEA model is unable to explain the inner-shell ionization,and the electron transfer by molecular-orbital promotion should be considered.     

氰化给体中分子间相互作用促进的全小分子光伏混合物的超快电子转移

氰基取代被认为是优化全小分子有机太阳能电池性能的可行方法. 然而,氰基取代对太阳能电池中电荷产生动力学的影响仍未得到探索. 本文光谱研究表明,在全小分子太阳能电池中,氰化给体中增强的分子间电荷转移相互作用会显著促进共混物中的电子转移. 实验发现,在氰基取代给体中,分子间相互作用引起的离域激发,在混合物中会进行超快电子转移. 相比之下,在没有氰基取代的给体中剩余的局域激发态,并没有积极参与电荷分离. 此发现很好地解释了为何氰化取代给体的共混物器件的性能会得到提升,表明可以通过调控分子间相互作用、来优化全小分子器件性能.     

Compound buried layer SOI high voltage device with a step buried oxide

A silicon-on-insulator (SOI) high performance lateral double-diffusion metal oxide semiconductor (LDMOS) on a compound buried layer (CBL) with a step buried oxide (SBO CBL SOI) is proposed.The step buried oxide locates holes in the top interface of the upper buried oxide (UBO) layer.Furthermore,holes with high density are collected in the interface between the polysilicon layer and the lower buried oxide (LBO) layer.Consequently,the electric fields in both the thin LBO and the thick UBO are enhanced by these holes,leading to an improved breakdown voltage.The breakdown voltage of the SBO CBL SOI LDMOS increases to 847 V from the 477 V of a conventional SOI with the same thicknesses of SOI layer and the buried oxide layer.Moreover,SBO CBL SOI can also reduce the self-heating effect.