量子核判别分析算法

核判别分析法通过核函数扩展了线性判别分析对非线性数据的处理能力,成为模式识别领域中一个重要的分支。然而,随着数据的指数增长,经典核判别分析算法在提取特征时会消耗大量计算资源。针对这一问题,利用量子叠加性和并行性提出了一种量子核判别分析算法。首先,借助量子随机存储器技术与控制旋转操作构造需要的类间矩阵和类内矩阵所对应的密度算子;然后,融入线性方程的求解思路并行获取特征态。理论分析表明,所提算法与经典算法相比具有指数级加速。

Acupuncture enhances brain function in patients with mild cognitive impairment: evidence from a functional-near infrared spectroscopy study

Mild cognitive impairment(MCI)is a precursor to Alzheimer’s disease.It is imperative to develop a proper treatment for this neurological disease in the aging society.This observational study investigated the effects of acupuncture therapy on MCI patients.Eleven healthy individuals and eleven MCI patients were recruited for this study.Oxy-and deoxy-hemoglobin signals in the prefrontal cortex during working-memory tasks were monitored using functional near-infrared spectroscopy.Before acupuncture treatment,working-memory experiments were conducted for healthy control(HC)and MCI groups(MCI-0),followed by 24 sessions of acupuncture for the MCI group.The acupuncture sessions were initially carried out for 6 weeks(two sessions per week),after which experiments were performed again on the MCI group(MCI-1).This was followed by another set of acupuncture sessions that also lasted for 6 weeks,after which the experiments were repeated on the MCI group(MCI-2).Statistical analyses of the signals and classifications based on activation maps as well as temporal features were performed.The highest classification accuracies obtained using binary connectivity maps were 85.7%HC vs.MCI-0,69.5%HC vs.MCI-1,and 61.69%HC vs.MCI-2.The classification accuracies using the temporal features mean from 5 seconds to 28 seconds and maximum(i.e,max(5:28 seconds))values were 60.6%HC vs.MCI-0,56.9%HC vs.MCI-1,and 56.4%HC vs.MCI-2.The results reveal that there was a change in the temporal characteristics of the hemodynamic response of MCI patients due to acupuncture.This was reflected by a reduction in the classification accuracy after the therapy,indicating that the patients’brain responses improved and became comparable to those of healthy subjects.A similar trend was reflected in the classification using the image feature.These results indicate that acupuncture can be used for the treatment of MCI patients.

SPEECH EMOTION RECOGNITION USING MODIFIED QUADRATIC DISCRIMINATION FUNCTION

Quadratic Discrimination Function （QDF） is commonly used in speech emotion recognition, which proceeds on the premise that the input data is normal distribution. In this paper, we propose a transformation to normalize the emotional features, emotion recognition. Features based on prosody then derivate a Modified QDF （MQDF） to speech and voice quality are extracted and Principal Component Analysis Neural Network （PCANN） is used to reduce dimension of the feature vectors. The results show that voice quality features are effective supplement for recognition, and the method in this paper could improve the recognition ratio effectively.

Statistical Classification Using the Maximum Function

The maximum of k numerical functions defined on , , by , ??is used here in Statistical classification. Previously, it has been used in Statistical Discrimination [1] and in Clustering [2]. We present first some theoretical results on this function, and then its application in classification using a computer program we have developed. This approach leads to clear decisions, even in cases where the extension to several classes of Fisher’s linear discriminant function fails to be effective.

Functional Data Analysis of Spectroscopic Data with Application to Classification of Colon Polyps

In this study, two functional logistic regression models with functional principal component basis (FPCA) and functional partial least squares basis (FPLS) have been developed to distinguish precancerous adenomatous polyps from hyperplastic polyps for the purpose of classification and interpretation. The classification performances of the two functional models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The results indicated that classification abilities of FPCA and FPLS models outperformed those of the PCDA and PLSDA models by using a small number of functional basis components. With substantial reduction in model complexity and improvement of classification accuracy, it is particularly helpful for interpretation of the complex spectral features related to precancerous colon polyps.

Functional Analysis of Chemometric Data

The objective of this paper is to present a review of different calibration and classification methods for functional data in the context of chemometric applications. In chemometric, it is usual to measure certain parameters in terms of a set of spectrometric curves that are observed in a finite set of points (functional data). Although the predictor variable is clearly functional, this problem is usually solved by using multivariate calibration techniques that consider it as a finite set of variables associated with the observed points (wavelengths or times). But these explicative variables are highly correlated and it is therefore more informative to reconstruct first the true functional form of the predictor curves. Although it has been published in several articles related to the implementation of functional data analysis techniques in chemometric, their power to solve real problems is not yet well known. Because of this the extension of multivariate calibration techniques (linear regression, principal component regression and partial least squares) and classification methods (linear discriminant analysis and logistic regression) to the functional domain and some relevant chemometric applications are reviewed in this paper.

Predictive Models for Functional MRI Data

In this study, we analyze brain activity data describing functional magnetic resonance imaging (MRI) imaging of 820 subjects with each subject being scanned at 4 different times. This multiple scanning gives us an opportunity to observe the consistency of imaging characteristics within the subjects as compared to the variability across the subjects. The most consistent characteristics are then used for the purpose of predicting subjects’ traits. We concentrate on four predictive methods (Regression, Logistic Regression, Linear Discriminant Analysis and Random Forest) in order to predict subjects’ traits such as gender and age based on the brain activities observed between brain regions. Those predictions are done based on the adjusted communication activity among the brain regions, as assessed from 4 scans of each subject. Due to a large number of such communications among the 116 brain regions, we performed a preliminary selection of the most promising pairs of brain regions. Logistic Regression performed best in classifying the subject gender based on communication activity among the brain regions. The accuracy rate was 85.6 percent for an AIC step-wise selected Logistic Regression model. On the other hand, the Logistic Regression model maintaining the entire set of ranked predictor was capable of getting an 87.7 percent accuracy rate. It is interesting to point out that the model with the AIC selected features was better classifying males, whereas the complete ranked model was better classifying females. The Random Forest technique performed best for prediction of age (grouped within five categories as provided by the original data) with 48.8 percent accuracy rate. Any set of predictors between 200 and 1600 was presenting similar rates of accuracy.

DISCRIMINATIVE ANALYSIS OF FUNCTIONAL NEAR-INFRARED SPECTROSCOPY SIGNALS FOR DEVELOPMENT OF NEUROIMAGING BIOMARKERS OF ELDERLY DEPRESSION

Functional near-infrared spectroscopy(fNIRS)is a neuroimaging technology which is suitable for psychiatric patients.Several fNIRS studies have found abnormal brain activations during cognitive tasks in elderly depression.In this paper,we proposed a discriminative model of multivariate pattern classification based on fNIRS signals to distinguish elderly depressed patients from healthy controls.This model used the brain activation patterns during a verbal fluency task as features of classification.Then Pseudo-Fisher Linear Discriminant Analysis was performed on the feature space to generate discriminative model.Using leave-one-out(LOO)cross-validation,our results showed a correct classification rate of 88%.The discriminative model showed its ability to identify people with elderly depression and suggested that fNIRS may be an efficient clinical tool for diagnosis of depression.This study may provide the first step for the development of neuroimaging biomarkers based on fNIRS in psychiatric disorders.

基于炎性细胞因子构建的Fisher判别函数对抑郁障碍诊断的辅助作用

背景目前,基于症状学的抑郁障碍诊断方式受临床医生经验的主观因素影响较大。寻找更加准确可靠的定量诊断方法是当前亟待解决的问题。目的探讨基于炎性细胞因子构建的Fisher判别函数在抑郁障碍诊断中的价值,为抑郁障碍的诊断提供参考。方法选取2020年4月—11月在河北省精神卫生中心住院治疗的、符合《精神障碍诊断与统计手册(第5版)》(DSM-5)抑郁障碍诊断标准的136例患者为研究组,同期招募与研究组年龄和性别相匹配的67例健康被试为对照组。使用酶联免疫吸附试验(ELISA)检测受试者血清炎性细胞因子水平。采用Fisher判别模型对研究组与对照组存在统计学差异的炎性细胞因子建立判别函数并进行验证。结果研究组促炎细胞因子白细胞介素1β(IL-1β)、白细胞介素6(IL-6)、干扰素γ(INF-γ)、肿瘤坏死因子α(TNF-α)水平均高于对照组,差异均有统计学意义(U=9.115、5.239、4.431、5.428,P均<0.01);研究组抗炎细胞因子白细胞介素4(IL-4)、白细胞介素10(IL-10)、白细胞介素13(IL-13)水平均低于对照组,差异均有统计学意义(U=7.398、7.331、7.614,P均<0.01)。Fisher判别函数回代性检验正确判别率为89.66%,交叉验证正确判别率为88.67%。结论本文构建的Fisher判别函数对抑郁障碍的诊断可能具有较好的辅助作用。

基于扎根理论及G1-CRITIC-距离函数判法-VIKOR的铁路建设项目安全管理标准化评价

为促进安全管理标准化管理模式顺利实施,铁路建设项目安全管理迈向高标准高水平,开展铁路建设项目安全管理标准化评价研究。首先,以标准化管理为理论基础,根据质性研究方法-扎根理论对数据进行编码处理,建立以制度管理标准化、人员配备标准化、物料管理标准化、环境管理标准化、现场作业标准化、过程控制标准化为6个核心范畴和32个主范畴的铁路建设项目安全管理标准化评价指标体系。其次,借助劳伦茨曲线的基尼系数改进CRITIC方法,提出“G1-CRITIC-距离函数判别法”模型,计算属性权重。最后,采用VIKOR算法对案例项目进行实证分析,基于安全管理标准化值U_(R)评估标准化管理水平。结果表明:各案例项目的安全管理标准化水平不同,U_(R)值具有显著差异,P_(1)~P_(5)各项目的U_R值分别为0.047 6、0.148 3、0.451 9、0.971 5、0.648(其中P_(4)属于“高水平”;P_(3)和P_(5)属于“中水平”;P_(1)和P_(2)属于“低水平”)。通过折衷系数扰动下的敏感性分析,综合考虑群体效用和个体妥协,对不同等级的建设项目(P_(1)~P_(5))分别进行讨论,并提出标准化建设的决策建议。研究结果与各案例项目实际安全管理标准化程度相符,为深入推进铁路建设项目标准化管理工作、促进建设项目各项安全管理行为规范运作和管理体系的持续改进提供参考依据。

基于挖掘算法的用户大数据周期智能推荐仿真

随着互联网技术的快速发展,用户数量日益增长,社交网络平台对周期性智能推荐的需求也日益增加。为了解决当前智能推荐算法准确率低、推荐速度慢等问题,提出了一种基于挖掘算法的用户大数据周期智能推荐算法。算法首先采用协同推荐算法对用户历史行为进行分析,并通过数据相似性衡量智能推荐的效果,使用Top-N算法优化推荐过程,达到周期智能推荐的目的;然后采用基于神经网络的挖掘算法对智能推荐算法进行优化,挖掘长时数据关系的同时保持短时数据之间的非线性;最后通过引入灰色均衡算法对相似度计算优化,从而缩短推荐时间。实验结果表明,所提算法在相似度计算准确度方面提升7%,推荐精确度提升6%,召回率提升8%,有效地提高了数据周期智能推荐的精读和效率,提高了个性化服务的质量。

基于阻力规律视角的水流能态区辨识

高、低水流能态区形成条件不同,其识别对于预测动床阻力具有重要意义。基于量纲一阻力方程推导了一个新的高、低水流能态区判别函数,包含悬浮数和水流作用数2个量纲一参数,提出了能态转变角和能态转移路径的概念,从流动阻力角度重新审视了水流能态区分问题。通过国内外4176组水槽和野外实测数据对该函数进行率定,确定了高、低能态区的临界方程。研究结果表明:该方法低能态区预测精度约为91%,高能态区约为85%,综合精度约为89%;在细沙河流中,随着水温的降低,更易发生水流低能态向高能态的转变。本文建立的判别函数不仅可高精度识别水流能态区,还可分析水温对能态区过渡的影响。

基于Fisher判别方法计算断层破裂面积的研究

实时计算并快速得到破坏性地震断层破裂分布范围可及时向潜在破坏区发出预警信息,为大震预警提供重要依据。本研究基于我国发生的5个重大破坏性地震的强震观测数据,利用Fisher线性判别方法,拟合出能够有效将台站记录进行分类的线性判别函数。应用该方法对2008年汶川地震断层破裂面积进行模拟计算,持续更新震后5~74s断层破裂面积,反演得到的断层破裂分布区域与野外地质调查结果基本一致。通过对实际震例测试分析表明,该方法反演的结果能够较准确地覆盖断层破裂区域,震后快速计算的断层破裂分布区域能够为地震预警及后续的烈度速报产出结果进行实时修正。

基于临床检验指标建立肺腺癌患者浸润程度判别模型

目的·利用肿瘤标志物、凝血功能指标、血常规指标与生化指标等临床易得检验指标,建立非小细胞肺腺癌患者浸润程度的多因素判别模型。方法·回顾性选取2022年上海交通大学医学院附属胸科医院收治的肺腺癌患者,通过多因素Logistic回归分析筛选预测肺腺癌患者浸润程度的独立影响因素并建立回归模型,同时引入人工智能算法构建判别模型,采用受试者工作特征曲线下面积(area under curve,AUC)评价模型对肺腺癌患者浸润程度的判别能力。结果·共纳入肺腺癌患者202例,分为浸润前病变组(59例)以及浸润性病变组(143例)。多因素Logistic回归分析结果显示,尿素、嗜碱性粒细胞百分比、白蛋白浓度是预测肺腺癌患者浸润程度的独立影响因素(均P<0.05)。预测模型表达式为P=e^(X)/(1+e^(X)),其中X=(0.534×尿素浓度)+(1.527×嗜碱性粒细胞百分比)−(1.916×白蛋白浓度)+6.373。机器学习结果显示,纳入尿素、纤维蛋白原、白蛋白浓度、嗜碱性粒细胞百分比、前白蛋白、癌胚抗原(carcino embryonic antigen,CEA)6个指标时模型判别性能最佳。通过DeLong检验比较8种机器学习算法(分别基于岭回归、最小绝对收缩和选择算子、神经网络、随机森林、k近邻、支持向量机、决策树和自适应增强算法)的判别性能,选择AUC最高的岭回归算法,预测模型AUC为0.744(95%CI 0.656~0.832),敏感度为70.8%,特异度为70.2%。结论·使用尿素、纤维蛋白原、白蛋白浓度、嗜碱性粒细胞百分比、前白蛋白、CEA这6个指标建立综合判别模型,可有效预测肺腺癌患者肿瘤浸润程度,有望为临床肺腺癌分级判别和辅助治疗提供更精确的指导。

脑卒中患者跌倒风险的相关因素研究

目的 探索影响脑卒中患者跌倒风险的相关因素,预测跌倒风险等级。方法 回顾性分析2022年7月至2024年1月在华东医院就诊的脑卒中患者64例,记录患者就诊时的人口学资料性别、年龄、身高、体质量、卒中类型、病程,功能指标世界卫生组织残疾评定量表2.0 (WHODAS 2.0)、Fugl-Meyer评定量表(FMA)、功能性前伸测试(FRT)、多方向伸展测试(MDRT)、蒙特利尔认知评估量表(MoCA),行走指标计时起立行走试验等。以脑卒中患者跌倒风险等级为因变量,先采用单因素分析,再采用判别分析对脑卒中患者跌倒风险因素进行观察。结果 纳入患者平均年龄约66岁,男性多于女性,脑梗死患者多于脑出血患者,平均病程(4.50±6.02)个月,跌倒风险等级轻度、中度、重度的脑卒中患者分别为19例、26例、19例。单因素分析显示,不同跌倒风险等级脑卒中患者间WHODAS 2.0、FMA、FMA-上肢部分(FMA-UE)、FMA-下肢部分(FMA-LE)、FRT、MDRT-向前(MDRT-F)、MDRT-向右(MDRT-R)、MoCA评分有显著性差异(F> 2.277, P <0.05)。判别分析显示,不同跌倒风险等级患者的功能参数方程不同,采用回顾法验证Fisher判别函数、Bayes判别函数正确率分别为75%、78.1%,误判率分别为25%、21.9%。结论 活动参与能力、上下肢运动功能、向前向右方向的稳定极限及认知功能影响脑卒中患者跌倒风险等级,通过功能指标建立判别函数可对跌倒风险等级进行预测。

N次幂下S-凸函数和其性质研究

以凸函数、S-凸函数、平方凸函数、平方S-凸函数的定义和性质为研究基础,根据从平方凸函数到平方S-凸函数的演变推进过程,采用了凸函数概念的代数不等式形式,给出了N次幂下S-凸函数的概念,并对其判别定理和性质进行了讨论,定义并证明了N次幂下S-凸函数的Jensen型不等式。研究表明,N次幂下S-凸函数包含了凸函数、平方凸函数、平方S-凸函数的性质,是凸函数的一种广义凸函数的推广,开辟了全新的推广途径。

匿名大数据访问风险精准监测与仿真

大数据已经成为一种经济资产,其包含大量的信息数据,服务器一旦遭到侵袭,就可能导致大量用户私人信息泄露。为实现大数据的安全共享与利用,提出基于深度对抗学习的匿名大数据访问风险监测研究。从主体、客体和环境三方面分析访问风险因素,主要包括访问时间、权限、数据敏感性、网络延时等因素;利用生成器和判别器生成深度对抗学习网络,将风险因素相关数据作为网络输入,提取风险特征;利用信息熵算法计算风险值,设定风险阈值,建立判别函数,利用该函数即可实现匿名大数据访问风险监测。实验结果表明,所提方法具备较强的特征学习能力,避免了监测过程中系统吞吐量过高,且监测结果准确。

基于条件GAN的复杂动作图像轮廓智能捕捉研究

针对复杂动作本身的高动态性和多样性,传统的图像处理方法难以准确捕捉其轮廓,文中研究基于条件GAN的复杂动作图像轮廓智能捕捉方法,精准了解动作执行情况。该方法利用像素覆盖分割模型来分割原始复杂动作图像,获取复杂动作目标图像,将其作为约束条件输入生成器,经过编解码器处理后输出虚假复杂动作图像轮廓生成结果,判别器将生成器输出的虚假轮廓和真实复杂动作图像轮廓作为输入,在损失函数作用下进行真假判别,并采用反向传输的方式对生成器和判别器的参数进行迭代更新,实现最佳复杂动作图像轮廓智能捕捉。结果显示:该方法可以有效捕捉不同类型的复杂动作图像轮廓,通过消融实验证明,改进后的条件GAN模型可显著提升捕捉到的图像轮廓的平滑度和形状保持性,为捕捉复杂动作图像轮廓提供了一种新方法。

基于改进CycleGAN的粉笔字书写风格迁移研究

针对采用循环一致性生成对抗网络(Cycle-consistent generative adversarial network,CycleGAN)进行粉笔字书写风格迁移时,生成的字体存在笔画缺失和模糊等问题,本研究提出改进CycleGAN的粉笔字生成算法。在原始CycleGAN基础上融合自注意力机制来提取粉笔字书法字体的风格特征,并在自注意力机制中使用最大池化和缩放点积来进一步提升模型捕获汉字全局特征的能力。使用相对鉴别生成对抗损失函数改进原网络中的损失函数,以引入先验知识并增强判别器的能力。实验结果表明,使用改进后的模型在学习粉笔字书写风格后生成的字体笔画更加完整,细节更加清晰。

基于文本特征融合的双流生成对抗修复网络

为解决深度学习技术存在特征挖掘不充分、语义表达不完整等问题,消除修复图像存在伪影或模糊纹理等现象,本文构建了上下文特征融合的双流生成对抗修复网络,以实现重建、感知与风格损失的补偿,从而使修复后的图像实现全局一致性。该网络采用融入注意力机制的U-Net作为主干网络,充分提取图像结构和纹理特征。采用上下文本特征融合网络充分挖掘图像高级语义及特征信息的上下文关系,实现空洞区域的结构及纹理特征的填充与精细修复。采用结构与纹理双流鉴别器来估计纹理和结构的特征并统计信息来区分真实图像和生成图像。采用基于语义的联合损失函数以增强修复图像在语义上的真实性。将本文算法与对比算法中表现最好的CTSDG算法在CelebA和Places2数据集上进行对比,其中PSNR与SSIM值在CelebA上分别提升2.74 dB和5.80%,FID下降4.02;PSNR与SSIM值在Place2上分别提升4.15 dB和3.33%,FID下降2.33。因此,改进的图像修复方法的客观评价指标更优,能够更加有效地修复破损图像的结构和纹理信息,使得图像修复的性能更佳。