关键词： Medical imaging   Biomechanics   Artificial intelligence  

摘要： Machine learning with deep neural networks has demonstrated high performance for high dimensionality prediction tasks across multiple domains with sufficient sample data. Cardiovascular disease is a pertinent public health issue that has the potential to be better understood and addressed via deep learning approaches. In this work, we study machine learning approaches toward diagnosing various forms of cardiovascular disease and predicting its biomechanical behavior across multiple scales. We begin by training deep learning models for an initial classification objective in echocardiography, a ubiquitous imaging modality for cardiologists. For view classification, we are able to demonstrate physician-level performance. We then expand the work from a methods and clinical application perspective. We address the high cost of annotation in medical imaging by examining data-efficient supervised and semi-supervised algorithms. In addition, we expand our prediction tasks towards the ultimate goal of automated, accurate cardiovascular disease diagnosis by predicting left ventricular hypertrophy. To understand the nature of cardiovascular disease and develop treatments, a close look at the underlying biomechanics is important. For atherosclerosis, a leading cause of morbidity and mortality, we bridge finite element methods and machine learning to predict arterial tissue stress. Likewise for cytoskeletal proteins, which are the structural building blocks of human biology and influence cardiovascular health, we develop graph neural network algorithms to predict force response and conformational dynamics in calponin homology domains. Moreover, we hope to lay the groundwork to advance the intersection of machine learning, biomechanics, and cardiovascular disease.

关键词： Crisis   Event Detection   Text Mining   Classification   Ranking  

摘要： The use of social media is expanding significantly and can serve a variety of purposes. Over the last few years, users of social media have played an increasing role in the dissemination of emergency and disaster information. It is becoming more common for affected populations and other stakeholders to turn to Twitter to gather information about a crisis when decisions need to be made, and action is taken. However, social media platforms, especially on Twitter, presents some drawbacks when it comes to gathering information during disasters. These drawbacks include information overload, messages are written in an informal format, the presence of noise and irrelevant information. These factors make gathering accurate information online very challenging and confusing, which in turn may affect public, communities, and organizations to prepare for, respond to, and recover from disasters. To address these challenges, we present an integrated three parts (clustering-classification-ranking) framework, which helps users choose through the masses of Twitter data to find useful information. In the first part, we build standard machine learning models to automatically extract and identify topics present in a text and to derive hidden patterns exhibited by a dataset. Next part, we developed a binary and multi-class classification model of Twitter data to categorize each tweet as relevant or irrelevant and to further classify relevant tweets into four types of community engagement: reporting information, expressing negative engagement, expressing positive engagement, and asking for information. In the third part, we propose a binary classification model to categorize the collected tweets into high or low priority tweets. We present an evaluation of the effectiveness of detecting events using a variety of features derived from Twitter posts, namely: textual content, term frequency-inverse document frequency, Linguistic, sentiment, psychometric, temporal, and spatial. Our framework

关键词： Plant sciences   Bioinformatics   Computer science  

摘要： Biological systems contain data of high dimensions and magnitudes, including biochemistry, cellular patterning, transcriptomics, and genomics. Here I combined network analyses and machine learning, to identify developmental patterns that may be amenable to the improvement of drought tolerance. Cuticles comprise the hydrophobic lipid layer covering the aboveground plant body, and have long been a research focus into water conservation in plants. However, no prior studies have examined cuticle development across a temporal and spatial gradient in a crop plant. I used gene network analyses to correlate the biochemical/developmental gradient of cuticle components with the underlying transcriptomic transitions to identify the role of PHYTOCHROME B-mediated light signaling in cuticle development. Subsequent statistical and biochemical analysis revealed LIPID-TRANSFER PROTEINs as evolutionary novelties contributing to the emergence of cuticles in land plants. Additionally, combining the power of genome- and transcriptome-wide association studies (GWAS and TWAS), vesicular trafficking was implicated in the regulation of cuticular evaporation rate. Water loss through the leaf surface is also moderated by specialized cell types (bulliform cells) in maize. Bulliform cell ontogeny was investigated in the developing maize leaf, and a machine learning approach (convolutional neural networks) was employed to conduct high-throughput phenotyping of microscopic bulliform cell traits in 60,780 leaf epidermal glue-impression images. A subsequent GWAS analysis on bulliform cell column number and column width identified a set of gene candidates implicated to function in cell division and DNA methylation. Overall this dissertation demonstrates a multidisciplinary approach combining developmental biology, transcriptomics, quantitative genetics, machine learning, and statistical data analysis, toward a more holistic understanding of the mechanisms of water conservation in maize.

关键词： Statistics   Computer science  

摘要： We present contributions to the application areas of photography and social sciences using a statistical machine learning approach, as well as methodological work in dimension reduction techniques. We first present a machine-learning based photography project in which we analyzed patterns of rule break among high-quality photographs. While high-quality photographs generally adhere to high-level rules set forth in photography, some high-quality photographs purposefully bend the rules to achieve a greater effect. Our work in photography solidifies the patterns of conditions that allow for such bends in rules. To do so, we used a machine learning and image processing based approach to find and evaluate rule breaking photographs in order to discover the patterns that justify their rule breaking. Next, we present an analysis of whether gender bias exists in the field of statistics. Specifically, we quantify the differences in citation counts between men and women in the statistics field, using a sample of papers from the top statistics journals. We first identified the gender of the first author of each paper in our dataset. We also added important covariates such as job title and country of employment of the first author, in order to account for factors that may possibly affect or possibly explain any disparity in citation counts between men and women. Our analysis found that, controlling for covariates and replications, that there is no evidence to suggest that there is a citation disparity between men and women authors in statistics. We also developed a modification of the locally linear embedding (LLE) dimension reduction algorithm that is designed to handle additive noise. This new modification is termed LLEAN, short for locally linear embedding with additive noise, and has been shown to perform better in the presence of noise distortion. LLEAN seeks to recover the noiseless data from the noisy data by exploiting the relationship between local linearity and reconstr

关键词： Autonomous   Latency   Self-adaptive   Tactic  

摘要： From self-driving cars to self-adaptive websites, the world is increasingly becoming more reliant on autonomous systems. Similar to many other domains, the system's behavior is often determined by its requirements. For example, a self-adaptive web service is likely to have some maximum value that response time should not surpass. To maintain this requirement, the system uses tactics, which may include activating additional computing resources. In real-world environments, tactics will frequently experience volatility, known as tactic volatility. This can include unstable time required to execute the tactic or frequent fluctuations in the cost to execute the tactic. Unfortunately, current self-adaptive approaches do not account for tactic volatility in their decision-making processes, and merely assume that tactics have static attributes. To address the limitations in current processes, we propose a Tactic Volatility Aware (TVA) solution. Our approach focuses on providing a volatility aware solution that enables the system to properly maintain requirements. Specifically, TVA utilizes a Autoregressive Integrated Moving Average Model (ARIMA) to estimate potential future values for requirements, while also using a Multiple Regression Analysis (MRA) model to make predictions of tactic latency and tactic cost at runtime. This enables the system to both better estimate the true behavior of its tactics and it allows the system to properly maintain its requirements. Using data containing real-world volatility, we demonstrate the effectiveness of using TVA with both statistical analysis methods and self-adaptive experiments. In this work, we demonstrate (I) The negative impact of not accounting for tactic volatility (II) The benefits of a ARIMA-modeling approach in monitoring system requirements (III) The effectiveness of MRA in predicting tactic volatility (IV) The overall benefits of TVA to the self-adaptive process. This work also presents the first known publicly available

关键词： Computer science  

摘要： The use of big data analytics to uncover hidden knowledge, make better decisions, and support strategic planning is expanding as digital technology becomes more ubiquitous. Machine learning algorithms leverage digital data that is expected to reach zettabytes in volume over the next few years. However, poor data quality can affect the performance of even the most proficient of machine learners. Deceptive content and online misinformation can have a significant effect on the veracity of big data. The focus of this qualitative phenomenological research study was to assess the use of machine learning algorithms to evaluate the quality of big data so that organizations have a higher level of confidence in making strategic decisions. Researchers with expertise in automated deception detection were interviewed to gain insights on foundational elements of their machine learning algorithms and potential areas of improvement through the use of additional features. The study found that both common and contextual classification methodologies are important in machine learning algorithms to detect deception. Most research teams leveraged supervised machine learning but there was a growing interest in deep learning for future research due to the high overhead investment required to create machine learning features. The study revealed context dependency of features limited transferability of algorithms and there was a lack of consensus on the efficacy of personalized predictors to enhance algorithm efficiency.

关键词： 妊娠糖尿病   机器学习   分类算法   随机森林   XGBoost算法  

摘要： 近年来,随着人民生活水平的提高、孕育年龄推迟等因素,妊娠期糖尿病(GDM)也逐渐成为孕妇围产期普遍面临的问题。妊娠期糖尿孺显著的增加了不良孕育结局,危害孕妇和胎儿的身体健康,甚至提高了孕妇和胎儿成年后患有一型和二型糖尿病的风险,因此非常有必要建立妊娠糖尿病的预测模型,通过建模对孕妇妊娠糖尿病发病风险进行预测,发现高危人群,进而对糖尿病发病进行预报和预警。由于妊娠期糖尿病同其他类型的糖尿病类似,对于致病因素的研究目前认为和身体的体征、血清中的某些指标比如白细胞、HDLC等和体内的遗传因素有关,为此,本文在总结前人研究的基础上,采用在对处理分类问题上有较好的准确度的逻辑回归算法、决策树算法,以及可以显著提高基础分类器的准确度和泛化能力的随机森林算法和XGBoost算法,对一家基因检测公司1200个孕妇的数据进行建模,实验将样本集中1200条数据按照2∶1的比例分为训练集和测试集,在模型训练过程中观察了如惩罚项、最大数深度、弱学习器的最大迭代次数等相关参数的改变对准确度的影响,然后采用GridSearch方法对相关参数枚举计算准确度,选择准确度最大的参数作为模型的最优参数。同时,将训练好的模型在测试集上进行了测试,计算出混淆矩阵中各值和精确率、召回率、F1分数,以及画出各个模型的ROC曲线,通过比较各个模型F1分数和AUC值,最终选择随机森林模型为妊娠糖尿病预测的最优模型。最后输出了影响妊娠糖尿病的15个最重要的特征。

关键词： 脑机接口   运动想象   黎曼几何   迁移学习   流形学习  

摘要： 脑机接口通过供额外的信号通路,实现大脑直接控制外部设备,在残疾人功能辅助与康复等方面有着广阔的应用前景。在运动想象脑机接口中,受试者通过想象部分肢体的运动产生多种模式的脑电信号,系统分析受试者的脑电解析控制指令,实现对外部设备的控制,其中的核心技术是对运动想象脑电信号的解码。然而,脑电信号的低信噪比、非平稳性以及个体差异性是运动想象脑机接口从实验室走向商业应用的巨大障碍。因此,寻求高效的解码算法、降低训练成本是该领域长期的研究热点。近年来,有学者出使用黎曼几何的工具对运动想象脑电信号的协方差矩阵进行建模和分析,取得了良好的效果,为解码脑电信号供了新工具。在实际应用中,黎曼几何方法常常面临维度灾难的问题,往往需要设计降维算法。本文基于黎曼几何工具,出了一种新的解码运动想象脑电信号的算法,利用流形学习的等距映射降维算法对局部黎曼切空间投影的结果进行降维,然后使用局部线性嵌入算法获取全局坐标。该算法改善了直接使用黎曼切空间进行投影带来的边缘样本分布扭曲的问题,并实现了降维操作,在国际脑机接口竞赛数据上表现优异。同时,针对运动想象脑电信号非平稳性和个体差异大导致模型难以训练的问题,本文结合黎曼几何与降维方法,设计了一种新的迁移学习算法应用于运动想象脑机接口系统。该方法利用黎曼切空间投影法分别将不同受试者的样本集投影到相似的特征空间上,并拉直成向量,然后对所有样本使用多尺度放缩算法进行降维,并在低维欧氏空间上使用成熟的线性分类器进行训练和分类。实验证实,该算法在国际脑机接口竞赛数据集的表现优于现有黎曼几何方法。最后本文根据运动想象脑电信号在黎曼流形上的分布讨论了信号的非平稳性和个体差异特性对结果的影响。

关键词： 原油期货   价格波动   预测模型   机器学习  

摘要： 原油被誉为“工业的血液”，它不仅是我国国防、交通、工农业等产业的支柱性能源，也是一种宝贵的化工原料，对我国经济发展的意义重大。20世纪80年代以后，全球原油期货市场不断发展并逐渐成为原油现货市场的主要定价依据。2018年3月26日，中国原油期货正式在上海国际能源交易中心挂牌上市，这是我国期货市场上具有里程碑意义的事件，不仅标志着我国期货品种进一步完善和趋于国际化，更向我国在国际原油期货市场上取得定价权的目标迈出了一大步。　　原油期货的上市对投资者既是机遇更是巨大的挑战。面对瞬息万变的国内外期货市场，研究如何精确而即时地预测原油期货价格趋势以规避价格波动所带来的风险对于原油期货市场各方参与者来说至关重要。针对期货市场交易速度快、交易频率高和交易量大的特性，机器学习方法相较于传统市场分析方法和统计模型有着其更强的适用性。本文以此为切入点提出研究课题。文章首先介绍了期货市场背景、研究思路与框架、研究方法及国内外研究现状等内容;之后对研究所涉及到的概念与理论知识进行了阐述;在实证分析部分使用近一年我国原油期货5分钟高频数据，以涨跌幅为因变量，并以28个行情指标和技术指标为自变量进行建模;建模前首先对数据进行预处理，并分别使用小波方法进行降噪和Lasso方法进行变量筛选;之后分别使用决策树类方法（包括回归树、袋装技术、推进技术和随机森林）、支持向量机、神经网络等机器学习方法进行建模并对模型参数进行了优化;最后对建模结果从不同维度进行了比较研究。　　本文研究结果证明了使用机器学习方法对我国原油期货高频数据进行预测的可行性，且随机森林、支持向量机和神经网络方法均具有较好的预测能力，其中随机森林方法优于后两者。同时，自变量技术指标中的收盘价相对位置指标(CLV)和布林线相对位置指标(pctB)对预测结果的贡献程度最高，这与许多期货交易者利用行情指标绝对值进行未来价格涨跌判断的观念有一些差异，但也为市场参与者提供了量化交易相关研究和实践的新思路。

ISBN: (纸本)9787111622451