Unified Framework for Robust Estimation of Brain Networks From fMRI Using Temporal and Spatial Correlation Analyses

There is a rapidly growing interest in the neuroimaging field to use functional magnetic resonance imaging (fMRI) to explore brain networks, i.e., how regions of the brain communicate with one another. This paper presents a general and novel statistical framework for robust and more complete estimation of brain functional connectivity from fMRI based on correlation analyses and hypothesis testing. In addition to the ability of examining the correlations with each individual seed as in the standard and existing methods, the proposed framework can detect functional interactions by simultaneously examining multiseed correlations via multiple correlation coefficients. Spatially structured noise in fMRI is also taken into account during the identification of functional interconnection networks through noncentral $F$ hypothesis tests. The associated issues for the multiple testing and the effective degrees-of-freedom are considered as well. Furthermore, partial multiple correlations are introduced and formulated to measure any additional task-induced but not stimulus-locked relation over brain regions so that we can take the analysis of functional connectivity closer to the characterization of direct functional interactions of the brain. Evaluation for accuracy and advantages, and comparisons of the new approaches in the presented general framework are performed using both realistic synthetic data and in vivo fMRI data.      

Quantitative statistical assessment of conditional models for synthetic aperture radar

Many applications of object recognition in the presence of pose uncertainty rely on statistical models-conditioned on pose-for observations. The image statistics of three-dimensional (3-D) objects are often assumed to belong to a family of distributions with unknown model parameters that vary with one or more continuous-valued pose parameters. Many methods for statistical model assessment, for example the tests of Kolmogorov-Smirnov and K. Pearson, require that all model parameters be fully specified or that sample sizes be large. Assessing pose-dependent models from a finite number of observations over a variety of poses can violate these requirements. However, a large number of small samples, corresponding to unique combinations of object, pose, and pixel location, are often available. We develop methods for model testing which assume a large number of small samples and apply them to the comparison of three models for synthetic aperture radar images of 3-D objects with varying pose. Each model is directly related to the Gaussian distribution and is assessed both in terms of goodness-of-fit and underlying model assumptions, such as independence, known mean, and homoscedasticity. Test results are presented in terms of the functional relationship between a given significance level and the percentage of samples that wold fail a test at that level.     

Multivariate statistical methods for modeling and analysis of wafer probe test data

Probe testing following wafer fabrication can produce extremely large amounts of data, which is often used to inspect a final product to determine if the product meets specifications. This data can be further utilized in studying the effects of the wafer fabrication process on the quality or yield of the wafers. Relationships among the parameters may provide valuable process information that can improve future production. This paper compares many methods of using the probe test data to determine the cause of low yield wafers. The methods discussed include two classes of traditional multivariate statistical methods, clustering and principal component methods and regression-based methods. These traditional methods are compared to a classification and regression tree (CART) method. The results for each method are presented. CART adequately fits the data and provides a "recipe" for avoiding low yield wafers and because CART is distribution-free there are no assumptions about the distributional properties of the data. CART is strongly recommended for analyzing wafer probe data.     

Local versus global decisions in image recognition

This paper discusses the ways of finding consistency between the well-known statistical statement that "guessing destroys information" and the practically obvious advantage of hierarchical decisions. Certain nonstatistical sources of recognition errors are indicated, the influence of these sources increasing with the size of the image parts on which the first-stage discrete decisions are taken. The rejection criterion is examined from the statistical point of view and the necessity of mathematical models for all images to be rejected is demonstrated. The analysis of the possibilities for developing models of both images to be recognized and to be rejected leads to the conclusion that image recognition should be realized by hierarchical systems. An example of a working hierarchical recognition system for interpretation of handmade drawings is described.     

汉字识别研究的回顾

本文回顾了汉字识别研究的历史。根据模仿人类视觉模型，基于文字图像的统计模式识别方法是文字识别取得瞩目进展的基础。模式识别信息熵理论揭示了模式分类的信息过程和理论极限，本文讨论了从汉字图像中提取特征以及文字识别分类器设计和学习的各种方法。介绍了文本识别必须解决的文字切分，版面分析、理解和重构，及提高识别性能等重点问题，最后，总结了文字识别研究的重要进展和对今后的展望。     

Locating performance problems in massively parallel executions

Performance problems in asynchronous massively parallel programs are often the result of unforeseen and complex asynchronous interactions between autonomous processing elements. Then performance problems are not inefficiencies in source code, but gaps in the algorithm designer's understanding of a complex physical system. The analyst forms hypotheses about the probable causes or possible improvements, and verifies these hypotheses by modifying the program and testing it again. These hypotheses can be formed by a variety of methods, from simple and mostly fruitful techniques for suggesting possible source code improvements to the difficult, indirect, and possibly futile activity of visualizing execution. The author describes a visualization system for massively parallel execution data and shows how drawbacks in other analysis methods sometimes make visualization necessary despite its difficulty     

Statistical fingerprint‐based intrusion detection system (SF‐IDS)

Intrusion detection systems (IDS) are systems aimed at analyzing and detecting security problems. The IDS may be structured into misuse and anomaly detection. The former are often signature/rule IDS that detect malicious software by inspecting the content of packets or files looking for a “signature” labeling malware. They are often very efficient, but their drawback stands in the weakness of the information to check (eg, the signature), which may be quickly dated, and in the computation time because each packet or file needs to be inspected. The IDS based on anomaly detection and, in particular, on statistical analysis have been originated to bypass the mentioned problems. Instead of inspecting packets, each traffic flow is observed so getting a statistical characterization, which represents the fingerprint of the flow. This paper introduces a statistical analysis based intrusion detection system, which, after extracting the statistical fingerprint, uses machine learning classifiers to decide whether a flow is affected by malware or not. A large set of tests is presented. The obtained results allow selecting the best classifiers and show the performance of a decision maker that exploits the decisions of a bank of classifiers acting in parallel.     

Defect detection in multilayer ceramic capacitors

Cracks in Multilayer Capacitors are often latent defects, which are not recognized in production, but can cause substantial problems in field. Therefore it is important to find possibilities to detect those candidates before delivering electronic equipment.In this work, cracked capacitors were characterized by electrical parameter testing and by piezoelectric spectroscopy. As a new method, sound emission spectroscopy was employed as indicator for latent defects and correlated with electrical data and physical analysis. The results show that sound emission used on a statistical basis and piezoelectric response might be effective to screen latent defects in electronic control units.     

情感识别中的迁移学习问题综述

情感识别是实现自然人机交互的必要过程。然而,情感数据高昂的采集和标注成本成为了限制情感识别研究发展的一大瓶颈。在无标注或有限标注的场景下,利用知识的跨领域或跨任务迁移提升情感识别效果的问题值得探索。本文对情感识别中的迁移学习问题进行了梳理和分析。首先,将迁移学习问题划分为针对领域差异和针对任务差异的两大部分,并进一步将每部分问题细分为多种不同的情况。随后,基于情感识别领域的研究现状,分别总结不同情况下的现有工作。在目标领域训练资源匮乏的情况下,可以利用其他带标注的数据集作为源领域训练模型,并对齐不同领域下的特征分布,或将特征映射到域间共享的空间。考虑到情感标签所提供的监督信息往往较为有限,为了进一步提升模型的识别效果,可以引入其他相关任务进行联合训练,或将预训练模型、外部知识库提供的先验语义知识迁移到情感识别任务中。最后,讨论了情感识别领域中未来需要得到更多关注和探索的迁移学习问题,旨在为研究者带来新的启发。     

硅片Map图信息的提取和利用

分析了硅片Ｍａｐ图所提供的生产成品率和各类不合格芯片的位置分布信息，讨论了利用硅片之间Ｏｖｅｒｌａｐ法（重叠法）和硅片上Ｗｉｎｄｏｗ法（窗口法）对Ｍａｐ图进行的统计。着重讨论了：按硅片中不合格芯片密度的显著差异划分边缘区及中心区；不合格芯片局部聚集现象的定量表示；随机性强的不合格芯片的统计分布；有关信息由相应Ｃ语言软件自动提取，与Ｍａｐ图计算机测试进行联用，可用于生产监控、影响成品率因素分析和工艺缺陷的深入研究。     

Discriminative semantic region selection for fine-grained recognition

Performances of fine-grained recognition have been greatly improved thanks to the fast developments of deep convolutional neural networks (DCNN). DCNN methods often treat each image region equally. Besides, researchers often rely on visual information for classification. To solve these problems, we propose a novel discriminative semantic region selection method for fine-grained recognition (DSRS). We first select a few image regions and then use the pre-trained DCNN models to predict their semantic correlations with corresponding classes. We use both visual and semantic representations to represent image regions. The visual and semantic representations are then linearly combined for joint representation. The combination parameters are determined by considering both semantic distinctiveness and spatial-semantic correlations. We use the joint representations for classifier training. A testing image can be classified by obtaining the visual and semantic representations and encoded for joint representation and classification. Experiments on several publicly available datasets demonstrate the proposed method's superiority.     

Nonlinear Regularized Wiener Filtering With Kernels: Application in Denoising MEG Data Corrupted by ECG

Magnetoencephalographic and electroencephalographic recordings are often contaminated by artifacts such as eye movements, blinks, and cardiac or muscle activity. These artifacts, whose amplitude may exceed that of brain signals, may severely interfere with the detection and analysis of events of interest. In this paper, we consider a nonlinear approach for cardiac artifacts removal from magnetoencephalographic data, based on Wiener filtering. In recent works, nonlinear Wiener filtering based on reproducing kernel Hilbert spaces and the kernel trick has been proposed. However, the filter parameters are determined by the resolution of a linear system which may be ill conditioned. To deal with this problem, we introduce three kernel methods that provide powerful tools for solving ill-conditioned problems, namely, kernel principal component analysis, kernel partial least squares, and kernel ridge regression. A common feature of these methods is that they regularize the solution by assuming an appropriate prior on the class of possible solutions. We avoid the use of QRS-synchronous averaging techniques, which may induce distortions in brain signals if artifacts are not well detected. Moreover, our approach shows the nonlinear relation between magnetoencephalographic and electrocardiographic signals     

Spatio-temporal fMRI analysis using Markov random fields

Functional magnetic resonance images (fMRI's) provide high-resolution datasets which allow researchers to obtain accurate delineation and sensitive detection of activation areas involved in cognitive processes. To preserve the resolution of this noninvasive technique, refined methods are required in the analysis of the data. In this paper, we first discuss the widely used methods based on a statistical parameter map (SPM) analysis exposing the different shortcomings of this approach when considering high-resolution data. First, the often used Gaussian filtering results in a blurring effect and in delocalization of the activated area. Secondly, the SPM approach only considers false alarms due to noise but not rejections of activated voxels. We propose to embed the fMRI analysis problem into a Bayesian framework consisting of two steps: i) data restoration and ii) data analysis. We, therefore, propose two Markov random fields (MRF's) to solve these two problems. Results on three protocols (visual, motor and word recognition) are shown for two SPM approaches and compared with the proposed MRF approach.     

An approach to cardiac arrhythmia analysis using hidden Markov models

This paper describes a new approach to ECG arrhythmia analysis based on "hidden Markov modeling" (HMM), a technique successfully used since the mid-1970's to model speech waveforms for automatic speech recognition. Many ventricular arrhythmias can be classified by detecting and analyzing QRS complexes and determining R-R intervals. Classification of supraventricular arrhythmias, however, often requires detection of the P wave in addition to the QRS complex. The hidden Markov modeling approach combines structural and statistical knowledge of the ECG signal in a single parametric model. Model parameters are estimated from training data using an iterative, maximum likelihood reestimation algorithm. Initial results suggest that this approach may provide improved supraventricular arrhythmia analysis through accurate representation of the entire beat including the P wave.     

基于参数化统计模型的雷达HRRP目标识别方法综述

现代战争日趋信息化和智能化,雷达自动目标识别技术(RATR)在国家安全防卫和战略预警等军事应用方面发挥着更加重要的作用。高分辨距离像(HRRP)反映了目标散射点沿雷达视线方向的分布情况,包含了目标丰富的结构信息,对目标识别十分有价值,已成为RATR领域的研究热点。参数化统计建模旨在构建参数化数学模型表征观测数据的分布特性,是估计数据概率分布和挖掘数据隐含信息的重要手段。基于参数化统计模型的雷达HRRP目标识别就是在对HRRP参数化统计建模的基础上,直接利用估计的概率分布进行统计识别或将获取的隐含信息输入分类器进行识别。由于模型具有可融入一定的先验知识、扩展灵活、提供待求参数的不确定性评价以及能结合贝叶斯理论实现自动定阶等优势,基于参数化统计模型的HRRP识别方法整体识别性能优于其他方法,是目前HRRP识别的重点研究方向。该文从浅层和深层参数化统计建模两方面,对近15年的雷达HRRP目标识别方法进行了归纳总结,并分析了各类方法的特点和存在的问题,最后对基于HRRP参数化统计建模的雷达目标识别发展方向进行了展望。      

A hierarchical algorithm for MR brain image parcellation

We introduce an algorithm for segmenting brain magnetic resonance (MR) images into anatomical compartments such as the major tissue classes and neuro-anatomical structures of the gray matter. The algorithm is guided by prior information represented within a tree structure. The tree mirrors the hierarchy of anatomical structures and the subtrees correspond to limited segmentation problems. The solution to each problem is estimated via a conventional classifier. Our algorithm can be adapted to a wide range of segmentation problems by modifying the tree structure or replacing the classifier. We evaluate the performance of our new segmentation approach by revisiting a previously published statistical group comparison between first-episode schizophrenia patients, first-episode affective psychosis patients, and comparison subjects. The original study is based on 50 MR volumes in which an expert identified the brain tissue classes as well as the superior temporal gyrus, amygdala, and hippocampus. We generate analogous segmentations using our new method and repeat the statistical group comparison. The results of our analysis are similar to the original findings, except for one structure (the left superior temporal gyrus) in which a trend-level statistical significance (p = 0.07) was observed instead of statistical significance.     

Statistical methods for visual defect metrology

Automated systems are used to inspect unpatterned and product wafers for particulates and other defects. Wafer defect count and defect density statistics are used as process control parameters, but are known to be deceptive in the presence of defect clustering. An improvement path using novel visual defect metrology statistical analyses is proposed. Quadrat analysis, nested analysis of variance, and principal component analysis use data available currently. Spatial point pattern statistics and spatial pattern recognition require special algorithms. Future process control systems made possible by these statistical analyses are discussed     

基于高光谱图像的协同分层波谱识别--以兰州、榆林地区为例

提出协同分层波谱识别法,分别从兰州、榆林市Hyperion高光谱图像上识别9种目标地类,并与SVM监督分类对比。针对Hyperion图像波谱识别的4个难点:光谱信息高保真融合、敏感谱段提取、"椒盐效应"去除、消除"同物异谱"现象导致的误判,协同应用WP-GS融合、导数变换、4尺度面向对象分割和多谱段SAM解决上述难点,并基于Hyperion导数变换图像分析波谱变化特征、提取敏感谱段、从4个尺度层依次识别9种目标地类,然后根据目视评判和定量评价,与综合使用Gram-Schmidt光谱锐化融合/Savitzky-Golay卷积滤波/PCA变换的SVM监督分类结果比较识别精度。实验结果表明WP-GS融合的光谱保真效果优于Gram-Schmidt光谱锐化;4尺度面向对象分割抑制"椒盐效应"的效果优于Savitzky-Golay卷积滤波、移动均值滤波;多谱段SAM利用导数波谱特征能够消除因照度不同对同一类别地物的误判。采用协同分层波谱识别法,兰州市Hyperion图像波谱识别的总体精度、Kappa系数分别为89.52%、0.852,较SVM分类分别提高18.68%和17.52%;榆林市Hyperion图像识别地物的总体精度、Kappa系数分别为91.12%、0.873,较SVM分类分别提高17.80%和16.89%。协同分层波谱识别法应用多种技术一体化解决Hyperion图像应用难点,有效利用导数波谱变化特征提取目标敏感谱段,在复杂环境下识别目标地类的能力优于SVM监督分类。     

Total variation regularization for fMRI-based prediction of behavior

While medical imaging typically provides massive amounts of data, the extraction of relevant information for predictive diagnosis remains a difficult challenge. Functional magnetic resonance imaging (fMRI) data, that provide an indirect measure of task-related or spontaneous neuronal activity, are classically analyzed in a mass-univariate procedure yielding statistical parametric maps. This analysis framework disregards some important principles of brain organization: population coding, distributed and overlapping representations. Multivariate pattern analysis, i.e., the prediction of behavioral variables from brain activation patterns better captures this structure. To cope with the high dimensionality of the data, the learning method has to be regularized. However, the spatial structure of the image is not taken into account in standard regularization methods, so that the extracted features are often hard to interpret. More informative and interpretable results can be obtained with the l(1) norm of the image gradient, also known as its total variation (TV), as regularization. We apply for the first time this method to fMRI data, and show that TV regularization is well suited to the purpose of brain mapping while being a powerful tool for brain decoding. Moreover, this article presents the first use of TV regularization for classification.     

基于插桩技术的动态测试研究与实现

为了动态进行白盒、黑盒测试,设计实现了基于源代码插桩的动态测试工具,该工具包含了源代码的预处理方法、插桩库设计、插桩策略以及统计分析等内容。通过对源代码的语法、词法分析,对其插桩能获取最高的准确度,并且设计在函数执行,结束之前统一将桩信息写入桩文件中,减少了大量的I/O操作。最终,通过测试用例的执行获得了覆盖率、执行时间、复杂度等测试数据,正确地得到了测试用例优劣性的指标。