复超球面上奇异积分方程的正则化

<正> 在一维奇异积分方程论中,复变函数的 Cauchy 型积分起着十分重要的作用;但在研究高维奇异积分方程时,利用多复变数 Cauchy 型积分作为工具者,至今尚少(参看[2]—[6]).本文是用复超球的 Cauchy 型积分边界性质,处理复超球面上含 Cauchy 核、B 核与 h 核的奇异积分方程的正则化问题.     

植物种子风传播核的概率模型

种子风传播过程是植被地理分布及其对环境变化响应的关键.植物种子风传播核是种子风传播能力的重要特征.建立种子传播核模型对准确预测植被地理分布十分关键.基于风速的对数正态分布和维布尔分布模式,推导出了植物种子风传播核的两种概率模型,并指出了计算步骤.同时,以6种蒲公英为例,分析了不同植物在不同地点和不同月份种子风传播核特征.算例表明,不同植物、不同地点和不同时间种子风传播核的差异,并且种子风传播强度在风速为对数正态分布下比维布尔分布下要高,传播核强度较高最大传播距离在维布尔分布下比对数正态分布下要远.种子风传播模型研究未来需要考虑近地面层空气动力学参数和风速分布其它模式.     

管理层的讨论与分析语调对企业未来业绩表现的影响研究

本文选择年报披露中管理层的讨论与分析(Management’s Discussion and Analysis,MD&A)作为研究对象,以其语调为文本特征,提出年报MD&A语调与公司未来业绩表现相关性分析的假设,在实证分析中,选择我国2007年至2017年间A股上市公司逾20000份年报的MD&A文本数据,运用字典法构建财经领域的专用情感词典,并使用异质性面板回归模型分73个证监会二级行业深入探讨了年报MD&A语调对公司未来业绩的影响机制和相关关系。研究发现,整体来看年报MD&A语调对企业未来业绩表现存在很强的影响,即年报MD&A语调对公司未来经营状况具有信息增量作用,分行业来看,这种信息增量作用不仅显著存在,而且各行业的大小存在差异,即年报MD&A语调的信息增量具有异质性;同时,语调信息量的多少受公司信息不对称的影响,即信息不对称程度在年报MD&A语调与企业未来业绩表现之间发挥了调节效应,信息对外越不透明的公司,普遍来说其年报语调信息量越大。     

Hardy核积分算子的单权不等式

单位圆盘上Hardy空间再生核所诱导积分算子在函数空间算子理论的研究中发挥了重要作用.本文利用现代调和分析中的离散化技巧和二进模型建立它对应的单权不等式,引入和研究与之相关的R2,2-权.通过圆盘上Carleson盒子型极大算子M12得到了R2,2-权的算子论特征,系统讨论M12的p-q有界性.     

脉冲星PSR2111+46平均脉冲分析和谱特性

采用Gauss拟合分离方法对脉冲星PSR2111 + 46的平均脉冲轮廓进行分析研究. 这颗脉冲星有408, 610, 925, 1408, 1640, 4850 MHz 6个频段的信噪比均很高的观测资料, 提供了一个非常好的多频分析的样本. 通过分析发现PSR2111 + 46在6个频率上都是良好的5成分结构. 这一结果支持Rankin提出的脉冲星辐射区“核-双锥”模型. 在成分分离的基础上进一步讨论了核、内锥和外锥的几何结构、辐射束半径、辐射区的高度以及辐射区的对称性. 考察了核和内、外锥成分辐射的谱特性, 发现核成分的谱确实比内、外锥的要陡.     

基于支持向量回归和核岭回归对血糖值预测的对比分析

为了对比支持向量回归(SVR)和核岭回归(KRR)预测血糖值的效果,本文选择人工智能辅助糖尿病遗传风险的相关数据进行实证分析.首先对数据进行预处理,将处理后的数据导入Python.其次,为了使SVR和KRR的对比结果具有客观性,使用了三种有代表性的核方法(线性核函数,径向基核函数和sigmod核函数).然后,在训练集上采用网格搜索自动调参分别建立SVR和KRR的最优模型,对血糖值进行预测.最后,在测试集上对比分析SVR和KRR预测的均方误差(MSE)和拟合时间等指标.结果表明:均方误差(MSE)都小于0.006,且KRR的MSE比SVR的小0.0002,KRR的预测精度比SVR更高;而SVR的预测时间比KRR的少0.803秒,SVR的预测效率比KRR好.     

数学基本技能测试结果的性别差异分析

性别与数学学习的关系,几十年来颇受国内外数学教育界的关注;１９９３年,国际数学教育委员会（ＩＣＭＩ）还专门召开过“性别与数学教育”国际研讨会,提出讨论的问题包括产生数学上性别差异的三大因素,数学性别差异的证据和改变性别差异的措施等;似乎一致认为数学学习上存在着性别差异;也有的甚至提出,差异随着年级的升高（例如中学阶段）逐渐增大,特别是高认知水平上的数学学习,男生的优势更加明显;［１］在国内,近十几年对此也有一些探讨;据笔者见到的资料,其中代表性的研究主要有两类,一类是全国范围的研究,一类是地区性…     

拍卖市场上不同经验类型竞价者竞价行为研究

研究公共价值拍卖市场上不同经验类型竞价者的竞价行为.根据拍卖经验的不同可以把竞价人分成两类:天真者和理性者.通过建立"天真-理性"的竞价模型,分析了天真者与理性者最优出价策略,得到了在价值信号均匀分布情况下,天真者的事前期望支付大于理性者的事前期望支付;并得到了天真者的进化比例.     

相对论性核与乳胶(Ag)碰撞中灰粒子的系统研究 *

用描写相对论性核 核碰撞的LUCIAE模型和相应的事件产生器系统地研究了 1 4.6 ,6 0和 2 0 0AGeV的O核、2 0 0AGeV的Si和S核以及 1 1 .6AGeV的Au核与乳胶 (Ag)碰撞中灰粒子产生的平均多重数、多重数分布以及角分布 3个物理量同入射能量、射弹质量及碰撞中心度间的关系 ,还研究了再散射在灰粒子产生机制中的作用 .LUCIAE的这些研究结果与相应的EMU0 1乳胶实验结果都相一致 .     

小黑麦离体花药中花粉细胞的分裂和分化

如何提高由花粉诱导单倍体植株的成功率,是当前单倍体育种工作中存在的首要问题。研究花粉在离体条件下的分裂和分化,对其机理有更多的了解,就有可能采取某种措施提高单倍体植株的诱导成功率。 在人工培养基上,小黑麦(Triticale)花药中单核花粉的第一次分裂经常是形成形态上相似而生理上已有很大分化的两个子核。两个子核的进一步分裂采取了不同的方式,而且在核酸代谢上也有明显的差异。它们在单倍体愈伤组织的建成上起了不同的作用,对分化、静止和衰退的愈伤组织细胞的核酸及线粒体动态进行了初步的分析。     

Speaker identification using nonlinear dynamical features

This paper reports results obtained in a speaker identification system which combines commonly used feature parameters, such as LP-derived cepstral coefficients and pitch, with nonlinear dynamic features, namely, fractal dimension, entropy and largest Lyapunov exponent, extracted from every window of 30 ms of speech, applied every 10 ms. The corpus used in the tests is composed of 37 different speakers, and the best results are obtained when the nonlinear dynamic features are included, suggesting that the information added with these features was not present in the others so far.     

Sentences in the language of reasoning

In the present paper we investigate a few grammatical aspects of texts in which a reasoning has been expressed. We are mainly interested in text units (called sentences) that are constituent parts of a reasoning.

Firstly we discuss the full conceptual framework reflected in the reasoning: its under lying form. Next we divide sentences into two main classes: fundamental and informative sentences. Each of these main classes will thereupon be subdivided. In classifying we are guided by the manner in which sentences function in a reasoning. The nature of each class is elucidated by a discussion and examples.

We subsequently deal with compound sentences and sentence groups. Finally we analyse two texts according to the grammatical classification principles explained in the paper.     

Direct product decompositions of structures and theories

In this article we present new methods in the theory of direct product decompositions of arbitrary structures.These methods are based on works of the author on the logic of product structures and on certain product-theoretic operations on sentences. In addition, some results included here rely on certain combinatorial properties and in such questions we apply an extension of König Infinity Lemma, also due to the author.Presented by R. McKenzie.     

Simple and efficient classification scheme based on specific vocabulary

Assuming a binomial distribution for word occurrence, we propose computing a standardized Z score to define the specific vocabulary of a subset compared to that of the entire corpus. This approach is applied to weight terms (character n-gram, word, stem, lemma or sequence of them) which characterize a document. We then show how these Z score values can be used to derive a simple and efficient categorization scheme. To evaluate this proposition and demonstrate its effectiveness, we develop two experiments. First, the system must categorize speeches given by B. Obama as being either electoral or presidential speech. In a second experiment, sentences are extracted from these speeches and then categorized under the headings electoral or presidential. Based on these evaluations, the proposed classification scheme tends to perform better than a support vector machine model for both experiments, on the one hand, and on the other, shows a better performance level than a Na?ve Bayes classifier on the first test and a slightly lower performance on the second (10-fold cross validation).     

Wavelet-based cepstrum calculation

In this paper we present a new wavelet-based algorithm for low-cost computation of the cepstrum. It can be used for real time precise pitch determination in automatic speech and speaker recognition systems. Many wavelet families are examined to determine the one that works best. The results confirm the efficacy and accuracy of the proposed technique for pitch extraction.     

Statistical methods in multi-speaker automatic speech recognition

Automatic speech recognition and understanding (ASR) plays an important role in the framework of man-machine communication. Substantial industrial developments are at present in progress in this area. However, after 40 years or so of efforts several fundamental questions remain open. This paper is concerned with a comparative study of four different methods for multi-speaker word recognition: (i) clustering of acoustic templates, (ii) comparison with a finite state automaton, (iii) dynamic programming and vector quantization, (iv) stochastic Markov sources. In order to make things comparable, the four methods were tested with the same material made up of the ten digits (0 to 9) pronounced four times by 60 different speakers (30 males and 30 females). We will distinguish in our experiments between multi-speaker systems (capable of recognizing words pronounced by speakers that have been used during the training phase of the system) and speaker-independent systems (capable of recognizing words pronounced by speakers totally unknown to the system). Half of the corpus (15 male and 15 female) were used for training, and the remaining part for test.     

Lambek vs. Lambek: Functorial vector space semantics and string diagrams for Lambek calculus

The Distributional Compositional Categorical (DisCoCat) model is a mathematical framework that provides compositional semantics for meanings of natural language sentences. It consists of a computational procedure for constructing meanings of sentences, given their grammatical structure in terms of compositional type-logic, and given the empirically derived meanings of their words. For the particular case that the meaning of words is modelled within a distributional vector space model, its experimental predictions, derived from real large scale data, have outperformed other empirically validated methods that could build vectors for a full sentence. This success can be attributed to a conceptually motivated mathematical underpinning, something which the other methods lack, by integrating qualitative compositional type-logic and quantitative modelling of meaning within a category-theoretic mathematical framework. The type-logic used in the DisCoCat model is Lambek?s pregroup grammar. Pregroup types form a posetal compact closed category, which can be passed, in a functorial manner, on to the compact closed structure of vector spaces, linear maps and tensor product. The diagrammatic versions of the equational reasoning in compact closed categories can be interpreted as the flow of word meanings within sentences. Pregroups simplify Lambek?s previous type-logic, the Lambek calculus. The latter and its extensions have been extensively used to formalise and reason about various linguistic phenomena. Hence, the apparent reliance of the DisCoCat on pregroups has been seen as a shortcoming. This paper addresses this concern, by pointing out that one may as well realise a functorial passage from the original type-logic of Lambek, a monoidal bi-closed category, to vector spaces, or to any other model of meaning organised within a monoidal bi-closed category. The corresponding string diagram calculus, due to Baez and Stay, now depicts the flow of word meanings, and also reflects the structure of the parse trees of the Lambek calculus.     

一种基于二部图的试题知识点标注方法

针对在线教育中试题知识点自动标注问题,本文提出了一种基于二部图的试题知识点标注方法。首先,为了合理划分知识点粒度,本文构建了知识点知识图谱来融合知识点;其次,基于教材等语料抽取知识点与特征词二部图和试题与特征词二部图,并利用TF-IDF公式计算知识点与特征词、试题与特征词之间的边权,以构建试题-知识点二部图模型;再次,提出词频加权的相似性度量方法计算试题和知识点之间的相似度,将相似度最高的知识点作为试题知识点标签。最后,以某在线教育平台提供的高中历史试题为实验数据集进行数值实验,实验结果表明该方法的效果显著优于朴素贝叶斯、K最近邻、随机森林以及支持向量机。     

Can belief structures improve our understanding of safety climate survey data?

Questionnaire-based surveys are a standard method used for assessing the safety climate within an organization. However, their analysis – in particular data aggregation – poses several challenges, among which are subjective judgment, incompleteness and uncertainty. This paper explores the use of approaches based on belief structures for aggregating data from safety climate questionnaires. Data relevant to this study were collected through a questionnaire administered to the employees of a nuclear research centre. The results show that, while belief structures may offer a promising way to represent data collected from questionnaires, the existing aggregation methods are not always adequate. Averaging schemes applied to belief structures seem the most suited – among the methods investigated – in the specific problem context analyzed. The analysis of the survey data shows the limitations of quantitative approaches for safety culture assessment and the need to always complement these with in-depth qualitative analysis.     

Iterative Denoising

One problem in many fields is knowledge discovery in heterogeneous, high-dimensional data. As an example, in text mining an analyst often wishes to identify meaningful, implicit, and previously unknown information in an unstructured corpus. Lack of metadata and the complexities of document space make this task difficult. We describe Iterative Denoising, a methodology for knowledge discovery in large heterogeneous datasets that allows a user to visualize and to discover potentially meaningful relationships and structures. In addition, we demonstrate the features of this methodology in the analysis of a heterogeneous Science News corpus.