北京谱仪Ⅱ（BESⅡ）顶点探测器数据获取系统

北京谱仪（ＢＥＳⅡ）顶点探测器数据获取系统是北京谱仪数据获取系统的一部分，该子系统电子学采用快总线标准，本文描述了该系统的硬件结构和软件系统，软件包括快总线系统的微码软件和上层控制软件，该系统的死时间为１．５ｎｓ，对ＢＥＳⅡ系统总死时间的贡献小于０．５ｍｓ。     

Dominant negative alleles of SEC10 reveal distinct domains involved in secretion and morphogenesis in yeast

The accurate targeting of secretory vesicles to distinct sites on the plasma membrane is necessary to achieve polarized growth and to establish specialized domains at the surface of eukaryotic cells. Members of a protein complex required for exocytosis, the exocyst, have been localized to regions of active secretion in the budding yeast Saccharomyces cerevisiae where they may function to specify sites on the plasma membrane for vesicle docking and fusion. In this study we have addressed the function of one member of the exocyst complex, Sec10p. We have identified two functional domains of Sec10p that act in a dominant-negative manner to inhibit cell growth upon overexpression. Phenotypic and biochemical analysis of the dominant-negative mutants points to a bifunctional role for Sec10p. One domain, consisting of the amino-terminal two-thirds of Sec10p directly interacts with Sec15p, another exocyst component. Overexpression of this domain displaces the full-length Sec10 from the exocyst complex, resulting in a block in exocytosis and an accumulation of secretory vesicles. The carboxy-terminal domain of Sec10p does not interact with other members of the exocyst complex and expression of this domain does not cause a secretory defect. Rather, this mutant results in the formation of elongated cells, suggesting that the second domain of Sec10p is required for morphogenesis, perhaps regulating the reorientation of the secretory pathway from the tip of the emerging daughter cell toward the mother-daughter connection during cell cycle progression.     

层次式级联多网络模型及其性能

本文提出了一种层次式级联多网络结构，论述了其训练算法，并验证了该结构的收敛性。这种结构在处理高阶非线性分布数据的分类问题方面具有优良的性能，用北京谱仪事例过滤进行性能测试结果表明，其训练和泛化性能都优于单个ＢＰ网络。     

温度,压力对漂移室气体增益的影响

研究了温度及气体压力对漂移室（工作于正比区）气体放大倍数的影响，温度和气体压力的变化范围分别是５￣４０℃及９０￣１０５ｋＰａ。用实验数据拟合得到了函数关系并测量了几种饱和度下函数中的参数值。     

Performance analysis of the forgetting factor RLS algorithm

An analysis is given of the performance of the standard forgetting factor recursive least squares (RLS) algorithm when used for tracking time-varying linear regression models. Three basic results are obtained: (1) the ‘P-matrix’ in the algorithm remains bounded if and only if the (time-varying) covariance matrix of the regressors is uniformly non-singular; (2) if so, the parameter tracking error covariance matrix is of the order O(μ + γ²/μ), where μ = 1 - λ, λ is the forgetting factor and γ is a quantity reflecting the speed of the parameter variations; (3) this covariance matrix can be arbitrarily well approximated (for small enough μ) by an expression that is easy to compute.     

Authority updating: An expert authority evaluation algorithm considering post‐evaluation and power indices in social networks

In group assessment, the focus is on finding high‐authority experts to improve the reliability of assessment results. In this study, we propose an authority updating algorithm while considering the power and judgement reliability of an expert on the basis of social networks and post‐evaluations. A network power index is established and used to reflect the power of an expert while considering social networks. The measurement of the judgement reliability of an expert considers the post‐evaluation of the objects selected by experts, thereby more scientifically reflecting the reliability of experts. The analysis shows the following: although the social‐network structure influences the authority of experts, the influence weakens when the assessment group is a highly or even fully connected group; the network effect may increase the authority of some experts and reduce that of others, and it will weaken as the network connectivity increases; moreover, the judgement reliability and authority of an expert while considering post‐evaluation can encourage him/her to make fair assessments and strive to reduce his/her motivation and cognitive biases.     

Protect privacy of deep classification networks by exploiting their generative power

Machine Learning - Research showed that deep learning models are vulnerable to membership inference attacks, which aim to determine if an example is in the training set of the model. We propose a...     

FastUDP: a highly scalable user-level UDP framework in multi-core systems for fast packet I/O

The Journal of Supercomputing - Nowadays, many applications, e.g., network routers, distributed data process engines, firewall, need to transfer packets at linear rate. With the increasing data...     

Granular multiple kernel learning for identifying RNA-binding protein residues via integrating sequence and structure information

Neural Computing and Applications - RNA-binding proteins play an important role in the biological process. However, the traditional experiment technology to predict RNA-binding residues is...