A scalable kernel-based semisupervised metric learning algorithm with out-of-sample generalization ability

In recent years, metric learning in the semisupervised setting has aroused a lot of research interest. One type of semisupervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale well with the data set size. In this letter, we propose a nonlinear metric learning method based on the kernel approach. By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.     

Macrophage Membrane-Coated Nanoparticles Sensitize Glioblastoma to Radiation by Suppressing Proneural–Mesenchymal Transformation in Glioma Stem Cells

Radiotherapy is identified as a crucial treatment for patients with glioblastoma, but recurrence is inevitable. The efficacy of radiotherapy is severely hampered partially due to the tumor evolution. Growing evidence suggests that proneural glioma stem cells can acquire mesenchymal features coupled with increased radioresistance. Thus, a better understanding of mechanisms underlying tumor subclonal evolution may develop new strategies. Herein, data highlighting a positive correlation between the accumulation of macrophage in the glioblastoma microenvironment after irradiation and mesenchymal transdifferentiation in glioblastoma are presented. Mechanistically, elevated production of inflammatory cytokines released by macrophages promotes mesenchymal transition in an NF-κB-dependent manner. Hence, rationally designed macrophage membrane-coated porous mesoporous silica nanoparticles (MMNs) in which therapeutic anti-NF-κB peptides are loaded for enhancing radiotherapy of glioblastoma are constructed. The combination of MMNs and fractionated irradiation results in the blockage of tumor evolution and therapy resistance in glioblastoma-bearing mice. Intriguingly, the macrophage invasion across the blood-brain barrier is inhibited competitively by MMNs, suggesting that these nanoparticles can fundamentally halt the evolution of radioresistant clones. Taken together, the biomimetic MMNs represent a promising strategy that prevents mesenchymal transition and improves therapeutic response to irradiation as well as overall survival in patients with glioblastoma.     

Research on the Mechanical Properties of Magnetorheological Damping and the Performance of Microprobe Test Process

Journal of Electronic Testing - In order to control the stable and controllable loading of the wafer level microprobe test bench, the magnetorheological (MR) damper used in the existing research...     

The effect of curing on the crystallization of poly(phenylene sulfide)

Summary Poly(phenylene sulfide) (PPS) was cured in the melt and solid states under air and/or nitrogen environments. Crystallization behavior of such cured materials as a function of cure time at 320°C and 255°C was studied via differential scanning calorimetry. It was found, on general, that while short-time curing leads to an increase in crystallization rate, prolonged curing leads to a decrease in both crystallinity and crystallization rate. In contrast to the enhanced crystallization rate caused by curing in nitrogen at the melt state, no significant change of crystallization rate is observed while curing is done in the solid state (in nitrogen).     

一种基于自适应量化嵌入器的隐写方法

感知质量和容量一直是图像隐写算法设计中难以折中的两个要素,如何取得更好的综合性能已吸引了很多研究者的兴趣.提出一种基于自适应量化嵌入器的隐写方法.这一方法通过改变某一载体信号所在子空间的索引来调制秘密信息,自适应特性体现在决定着子空间尺寸的分割码本的设计上.将这种隐写方法用于以2×2图像块为单元的数字图像隐写算法设计中,并将分割码本设计得具备HVS特性以取得较好的折中效果.实验表明,算法在感知质量和容量方面具有较好的综合性能.     

Group interaction analysis in dynamic context.

Computer understanding of human actions and interactions is one of the key research issues in human computing. In this regard, context plays an essential role in semantic understanding of human behavioral and social signals from sensor data. This paper put forward an event-based dynamic context model to address the problems of context awareness in the analysis of group interaction scenarios. Event-driven multilevel dynamic Bayesian network is correspondingly proposed to detect multilevel events, which underlies the context awareness mechanism. Online analysis can be achieved, which is superior over previous works. Experiments in our smart meeting room demonstrate the effectiveness of our approach.     

多级故障诊断系统

介绍了为提高故障诊断的正确率,将一混合故障诊断专家系统中的基于BP神经网络诊断模块、基于自组织神经网络诊断模块和基于小波分析诊断模块设计成多级诊断系统。对多级诊断系统的性能进行了分析,给出了多级诊断系统的拒绝输出策略。     

计算机维修模拟训练系统的设计与实现

探索了一种在多媒体环境下进行计算机及外部设备维修教学、训练的新方法 ,介绍了计算机维修模拟训练系统的设计与实现 ,给出了主要的设计思想、总体结构及关键技术。     

中药知母有效组分——知母皂甙元“滋阴”作用的研究

本工作以~(123)I-心得静结合分析法及环核苷酸RIA法观察了知母中提取得到的知母皂甙元对甲亢模型(模拟“阴虚”)βAR-cAMP系统的作和,结果表现,知母皂甙元明显降低甲亢小鼠脑β受体的RT值,显著降低甲亢小鼠β受体-cAMP系统的反应性,重现了知母水煎剂的下调作用。体外竞争抑制实验表明,知母皂甙元和知母皂甙元半琥珀酸都不能抑制~(125)I-心得静与β受体的结合。