predForm-Site: Formylation site prediction by incorporating multiple features and resolving data imbalance

Formylation is one of the newly discovered post-translational modifications in lysine residue which is responsible for different kinds of diseases. In this work, a novel predictor, named predForm-Site, has been developed to predict formylation sites with higher accuracy. We have integrated multiple sequence features for developing a more informative representation of formylation sites. Moreover, decision function of the underlying classifier have been optimized on skewed formylation dataset during prediction model training for prediction quality improvement. On the dataset used by LFPred and Formator predictor, predForm-Site achieved 99.5% sensitivity, 99.8% specificity and 99.8% overall accuracy with AUC of 0.999 in the jackknife test. In the independent test, it has also achieved more than 97% sensitivity and 99% specificity. Similarly, in benchmarking with recent method CKSAAP_FormSite, the proposed predictor significantly outperformed in all the measures, particularly sensitivity by around 20%, specificity by nearly 30% and overall accuracy by more than 22%. These experimental results show that the proposed predForm-Site can be used as a complementary tool for the fast exploration of formylation sites. For convenience of the scientific community, predForm-Site has been deployed as an online tool, accessible at http://103.99.176.239:8080/predForm-Site.     

A combination of modified particle swarm optimization algorithm and support vector machine for gene selection and tumor classification

In the analysis of gene expression profiles, the number of tissue samples with genes expression levels available is usually small compared with the number of genes. This can lead either to possible overfitting or even to a complete failure in analysis of microarray data. The selection of genes that are really indicative of the tissue classification concerned is becoming one of the key steps in microarray studies. In the present paper, we have combined the modified discrete particle swarm optimization (PSO) and support vector machines (SVM) for tumor classification. The modified discrete PSO is applied to select genes, while SVM is used as the classifier or the evaluator. The proposed approach is used to the microarray data of 22 normal and 40 colon tumor tissues and showed good prediction performance. It has been demonstrated that the modified PSO is a useful tool for gene selection and mining high dimension data.     

基于卡诺机的大规模MIMO通信容量建模及性能分析

信息本质上以能量和熵的形式在通信系统中流动,类似于传统热机中的能量流动过程。结合热力学卡诺机模型和经典香农信息论理论,建立了一个广义的热力学(multiple input multiple output, MIMO)通信系统模型,对大规模天线通信场景下使用前向纠错码的信道容量进行分析。利用通信系统中存在的自由度和熵的概念,推导了热力学模型下的广义信道容量,并对信道容量与噪声自由度及编码开销的关系进行验证仿真。仿真结果表明,所提出的热力学信道容量与香农信道容量是一致的。     

Tractability frontiers in probabilistic team semantics and existential second-order logic over the reals

Probabilistic team semantics is a framework for logical analysis of probabilistic dependencies. Our focus is on the axiomatizability, complexity, and expressivity of probabilistic inclusion logic and its extensions. We identify a natural fragment of existential second-order logic with additive real arithmetic that captures exactly the expressivity of probabilistic inclusion logic. We furthermore relate these formalisms to linear programming, and doing so obtain PTIME data complexity for the logics. Moreover, on finite structures, we show that the full existential second-order logic with additive real arithmetic can only express NP properties. Lastly, we present a sound and complete axiomatization for probabilistic inclusion logic at the atomic level.     

Single machine group scheduling with ordered criteria

The single machine group scheduling problem is considered. Jobs are classified into several groups on the basis of group technology, i.e. jobs of the same group have to be processed jointly. A machine set-up time independent of the group sequence is needed between each two consecutive groups. A schedule specifies the sequence of groups and the sequence of jobs in each group. The quality of a schedule is measured by the criteriaF ₁, ...,F _m ordered by their relative importance. The objective is to minimize the least important criterionF _m subject to the schedule being optimal with respect to the more important criterionF _m–1 which is minimized on the set of schedules minimizing criterionF _m–2 and so on. The most important criterion isF ₁, which is minimized on the set of all feasible schedules. An approach to solve this multicriterion problem in polynomial time is presented if functionsF ₁, ...,F _m have special properties. The total weighted completion time and the total weighted exponential time are the examples of functionsF ₁, ...,F _m–1 and the maximum cost is an example of functionF _m for which our approach can be applied.The research of the authors was partially supported by a KBN Grant No. 3 P 406 003 05, the Fundamental Research Fund of Belarus, Project N 60-242, and the Deutsche Forschungsgemeinschaft, Project Schema, respectively. The paper was completed while the first author was visiting the University of Melbourne.     

人机系统中作业域仿真研究

该文讨论了人机系统中人机表现功能及其关系。采用人体运动仿真确定最大作业空间,以保证操纵的合理性与可靠性。论述了立式镗庄的结构布局设计,采用按黄金分割比的造型比例,达到秸比例调和视觉效果,动画仿真模拟了立式镗庄的操纵过程。     

基于实例的工艺知识获取模型

介绍了耦合神经网络的工艺设计实例推理系统的实现方法论,提出了一种基于神经网络的工艺设计实例推理索引模型．与现存大多数实例推理系统不同,该方法用神经网络实现实例的动态分类和索引．实例层次分类的三层结构和基于特征的聚类模板概念,为实现基于符号处理的实例推理求解模式向基于神经计算的模式识别求解模式映射提供了条件．提出了基于实例的工艺知识获取模型,采用新实例的入库操作实现工艺知识的隐式获取,从而使知识获取得以简化．神经网络的自适应、自学习能力将减少系统的日常维护工作．基于实例的系统可望解决知识获取的难题．     

同步测功机扩大容量和应用范围的探讨

本文导出了在发电和电动工况下的转矩—转速曲线,论述了电动运行下的起动方法,并尝试采用Ｙ－Δ混合绕组以改善发电运行下的电势波形．结构简单,制造方便是这种电机的特点     

日中机器翻译中的可保留歧义关系

着重阐述了日中机器翻译中可保留歧义关系（ＲＳＡ）这一新概念及其处 理手法．所谓可保留歧义关系是指即使不消除原输入文中授受构造上的歧义 性也能生成反映原文意思的译文关系．具体而言,就是研究由并列助词［］、 连体助词［の］和名词构成的名词短语及由用言连体形构成的句子中授受构 造上的可保留歧义性;并在此基础上提出了一个新的翻译方法．从技术论文 的２ ９１９个句子中抽出了含有上述可保留歧义关系类型的句子８０６个进行了 翻译实验,正确率为９７．７％．从而确认了所提方法的有效性．