静息态功能脑网络差异指标分析及抑郁症分类应用

为了构建辅助诊断模型,以提高抑郁症诊断的准确率。在连续的阈值空间(8%~32%)内构建所有被试的功能脑网络并使用复杂网络理论对抑郁症患者的脑网络进行分析。通过设定阈值,根据统计显著性提取不同数量的节点属性与全局属性组合作为分类特征,并选择四种不同的分类算法进行分类研究,以得到构建一个准确率较高的模型。结果是SVM和神经网络算法在阈值P为0.05下,所建的模型的分类模型的准确率较高,分别达82.78%及81.36%,因此利用该方法所构建的诊断模型可以用于抑郁症的辅助临床诊断中。     

基于多尺度的稀疏脑功能超网络构建及多特征融合分类研究

脑功能超网络已成功应用于脑疾病的诊断。在之前的研究中,集中通过改变超边的方法来改善超网络的构建,忽略了不同尺度的节点定义对脑功能超网络拓扑的影响。考虑到该问题,提出了基于不同尺度的脑区划分来进行脑功能超网络的创建,从而分析其对脑功能超网络拓扑和分类性能的影响。具体来说,首先,基于自动解剖标记模板,利用聚类算法和随机动态种子点的方法对大脑进行细分割;其次,基于每种节点规模下所得的平均时间序列,利用LASSO方法分别进行脑功能超网络的构建;接着分别提取功能超网络的多组局部特征(节点度、最短路径长度、聚类系数),并利用非参数检验和基于相关的方法选取每种节点规模下的差异特征;最后,分别利用支持向量机构建分类模型。分类结果显示,随着节点规模的增大,所构建的脑功能超网络分类准确率增高,在节点尺度1 501下,准确率高达95.45%。同时,多尺度融合的分类准确率优于任一尺度下的分类准确率,这表明不同尺度的节点定义会影响脑功能超网络的拓扑,在未来的脑功能超网络研究中,除了关注超边的构建方法外,应更加关注大脑划分方案的选择,而且多种基于大脑划分的尺度融合特征可以补充更多的分类信息,提高抑郁症与正常人的分...     

静息态功能脑网络的基因基础以及分类研究

为了判断抑郁症患者组与健康对照组之间是否存在显著的基因型差异及基因型与疾病状态间是否存在显著交互效应,选择GSK-3β(Glycogen Synthase Kinase-3β)基因,通过功能脑网络指标进行统计分析,并利用统计显著性作为特征选择的依据,提取不同数量的节点属性作为分类特征。选择四种不同的分类算法进行分类研究,结果表明SVM和人工神经网络算法构建的分类模型正确率较高,疾病状态分类模型分别达到73.50%和70.87%,基因分类模型分别达到74.35%和76.66%。因此,基因对静息态功能脑网络存在着一定的影响,并且证明了脑网络的相关指标可以作为对基因与抑郁症疾病之间存在交互效应的判断依据。     

重度抑郁症患者脑功能网络的分类研究

为了构建辅助诊断模型,为抑郁症的诊断提供一个新的方法,以提高抑郁症诊断的准确率。在连续的阈值空间(8%～32%)内构建所有被试的功能脑网络并使用复杂网络理论对抑郁症患者的脑网络进行分析,力求提取出可以从各个维度来表征抑郁症患者的脑网络的特征值,采用不同的属性组合并使用SVM分类算法对所有被试进行分类研究,结果发现将全局属性与局部属性组合作为分类特征得到的分类正确率最高,因此该方法可以用于抑郁症的辅助诊断中。     

抑郁症复杂脑网络社团结构差异分析及分类研究

为比较抑郁症脑网络结构差异及实现患者自动识别,提出了基于功能脑网络社团结构特征的机器学习方法.利用静息态功能磁共振影像数据构建功能脑网络,利用基于“堆结构”的贪婪算法进行社团划分,从脑网络模块结构的角度分析正常人和抑郁症患者的差异,并将脑网络的模块指标用于机器学习方法.利用统计显著性为阈值以筛选特征,以判断不同特征数目对分类模型的性能影响.实验结果表明,神经网络算法在28个特征下(P＜0.05),分类正确率最高达90.50％.     

抑郁症患者功能脑网络属性特征分类研究

重度抑郁症是一种常见的心理障碍.功能影像研究表明,重度抑郁症与大脑许多区域的异常有关,这些区域包括海马、海马旁回、中央前回、尾状核等.复杂网络理论为脑网络拓扑结构的研究提供了一个有效的研究方法,但以往的研究方法主要集中在功能脑网络属性的统计分析上.通过对抑郁症患者和正常人的功能脑网络属性特征的分类对比研究,从机器学习的角度提出一种新的诊断抑郁症患者的方法.     

多模态网络融合在轻度认知障碍分类中的应用

针对早期轻度认知障碍（MCI）根据医学诊断认知量表评估极有可能无法判断的问题,提出了一种多模态网络融合的MCI辅助诊断分类方法。基于图论的复杂网络分析方法在神经影像领域的应用已得到广泛认可,但采用不同模态的成像技术研究脑部疾病对大脑网络拓扑结构属性的影响会产生不同结果。首先,使用弥散张量成像（DTI）与静息态功能磁共振成像（rs-fMRI）数据构建大脑结构和功能连接的融合网络。然后,融合网络的拓扑属性被施以单因素方差分析（ANOVA）,选择具有显著差异的属性作为分类特征。最后,利用支持向量机（SVM）留一法交叉验证对健康组和MCI组分类,估算准确率。实验结果表明,所提方法的分类结果准确率达到94.44%,相较单一模态数据法的分类结果有明显提高。所提方法诊断出的MCI患者在扣带回、颞上回以及额叶和顶叶部分区域等许多脑区表现出显著异常,与已有研究结果基本一致。     

基于多层次模板的脑功能网络特征选择及分类

基于单一脑图谱模板的功能连接网络中提取的特征表示不足以揭示患者组和正常对照组（NC）之间的复杂拓扑结构差异，而传统的基于多模板的功能脑网络定义多采用独立模板，缺乏模板间的关联，从而忽略了各模板构建的功能脑网络中潜在的拓扑关联信息。针对上述问题，提出了一种多层次脑图谱模板和一种使用关系诱导稀疏（RIS）特征选择模型的方法。首先定义了具有关联的多层次脑图谱模板，挖掘模板之间潜在关系和表征组间网络结构差异；然后用RIS特征选择模型进行参数优化，进而提取组间差异特征；最后利用支持向量机（SVM）方法构建分类模型，并应用于抑郁症患者的诊断。在山西大学第一医院抑郁症临床诊断数据库上的实验结果显示，基于多层次模板的功能脑网络通过使用具有RIS特征的选择方法取得了91.7%的分类准确率，相比传统多模板方法的准确率提高了3个百分点。     

基于独立成分分析功能连接的抑郁症分类研究*

已有的功能连接研究大多根据脑图谱构建全脑功能连接,但目前可选用的脑图谱种类有限且采用不同脑图谱的分析结果可能存在一定的差异。针对上述问题,利用独立成分分析方法研究了抑郁症辅助诊断问题。首先利用组独立成分分析提取独立成分并构建全脑功能连接网络,然后采用BoostFS(Boosting Feature Selection)方法进行特征选择,最后应用多元模式分析方法对20名抑郁症患者和21名健康被试进行分类。实验分类准确率达到95.12%,错分了一名抑郁症患者和一名健康被试。进一步分析表明,具有较强分辨能力的脑网络为感觉运动网络、默认网络和视觉网络,与已有基于脑图谱的研究结果基本一致,从而说明了基于独立成分分析方法的合理性,使其可能成为抑郁症辅助诊断的一种新方法。     

基于基因基础的静息态功能脑网络及分类研究

为了判断基因是否对抑郁症患者的功能脑网络存在影响,以及是否与抑郁症疾病状态之间存在相互效应,本实验选择了GSK-3β基因,利用功能脑网络的特性对其进行统计分析,再根据统计检验方法,构建分类模型.结果表明,SVM和神经网络在特征数目为25-30左右,分类模型的正确率比较高,疾病状态分别达到了77.30%和73.50%,基因型分别达到了77.35%及76.30%.由此可见,基因对静息态功能脑网络存在一定影响,也证明了基因与抑郁症疾病状态之间存在相互效应.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.     

Editorial: Special issue on recent advances in hybrid control theory and applications

正The demands of a rapidly advancing technology for faster and more accurate controllers have always had a strong influence on the progress of automatic control theory.In recent years control problems have been arising with increasing frequency in widely different areas,which cannot be addressed using conventional control techniques.The principal reason for this is the fact that a highly competitive economy is forcing systems to operate in regimes where     

Information for Authors

正Aim The Journals of Zhejiang University-SCIENCE(A/B/C)areedited by the international board of distinguished Chinese andforeign scientists,and are aimed to present the latest devel-opments and achievements in scientific research in China andoverseas to the world’s scientific circles,especially to stimulateand promote academic exchange between Chinese and for-eign scientists everywhere.     

Enhancing a leaf radiative transfer model to estimate concentrations and in vivo specific absorption coefficients of total carotenoids and chlorophylls a and b from single-needle reflectance and transmittance

The relative concentrations of different pigments within a leaf have significant physiological and spectral consequences. Photosynthesis, light use efficiency, mass and energy exchange, and stress response are dependent on relationships among an ensemble of pigments. This ensemble also determines the visible characteristics of a leaf, which can be measured remotely and used to quantify leaf biochemistry and structure. But current remote sensing approaches are limited in their ability to resolve individual pigments. This paper focuses on the incorporation of three pigments—chlorophyll a, chlorophyll b, and total carotenoids—into the LIBERTY leaf radiative transfer model to better understand relationships between leaf biochemical, biophysical, and spectral properties.Pinus ponderosa and Pinus jeffreyi needles were collected from three sites in the California Sierra Nevada. Hemispheric single-leaf visible reflectance and transmittance and concentrations of chlorophylls a and b and total carotenoids of fresh needles were measured. These data were input to the enhanced LIBERTY model to estimate optical and biochemical properties of pine needles. The enhanced model successfully estimated reflectance (RMSE = 0.0255, BIAS = 0.00477, RMS%E = 16.7%), had variable success estimating transmittance (RMSE = 0.0442, BIAS = 0.0294, RMS%E = 181%), and generated very good estimates of carotenoid concentrations (RMSE = 2.48 µg/cm², BIAS = 0.143 µg/cm², RMS%E = 20.4%), good estimates of chlorophyll a concentrations (RMSE = 10.7 µg/cm², BIAS = − 0.992 µg/cm², RMS%E = 21.1%), and fair estimates of chlorophyll b concentrations (RMSE = 7.49 µg/cm², BIAS = − 2.12 µg/cm², RMS%E = 43.7%). Overall root mean squared errors of reflectance, transmittance, and pigment concentration estimates were lower for the three-pigment model than for the single-pigment model. The algorithm to estimate three in vivo specific absorption coefficients is robust, although estimated values are distorted by inconsistencies in model biophysics. The capacity to invert the model from single-leaf reflectance and transmittance was added to the model so it could be coupled with vegetation canopy models to estimate canopy biochemistry from remotely sensed data.     

Mr. Twinn’s bombes

This article discusses the history and design of the special versions of the bombe key-finding machines used by Britain’s Government Code & Cypher School (GC&CS) during World War II to attack the Enigma traffic of the Abwehr (the German military intelligence service). These special bombes were based on the design of their more numerous counterparts used against the traffic of the German armed services, but differed from them in important ways that highlight the adaptability of the British bombe design, and the power and flexibility of the diagonal board. Also discussed are the changes in the Abwehr indicating system that drove the development of these machines, the ingenious ways in which they were used, and some related developments involving the bombes used by the U.S. Navy’s cryptanalytic unit (OP-20-G).