Visual Analysis of Missing Values in Longitudinal Cohort Study Data

Attrition or dropout is the most severe missingness problem in longitudinal cohort study data where some participants do not show up for follow-up examinations. Dropouts result in biased data and cause the reduction of 1ata set size. Moreover, they limit the power of statistical analysis and the validity of study findings. Visualization can play a strong role in analysing and displaying the missingness patterns. In this work, we present VIVID, a framework for the v isual analysis of mi ssing v alues i n cohort study d ata. VIVID is inspired by discussions with epidemiologists and adds visual components to their current statistics-based approaches. VIVID provides functions for exploration, imputation and validity check of imputations. The main focus of this paper is multiple imputation to fix the missing data.     

Toward The New Age Data Warehouse

The data warehouse architecture of the future moves beyond performance optimization and query flexibility to provide an interactive, intelligent cyberwarehouse based on supra-real-time computing.     

DASS Good: Explainable Data Mining of Spatial Cohort Data

Developing applicable clinical machine learning models is a difficult task when the data includes spatial information, for example, radiation dose distributions across adjacent organs at risk. We describe the co-design of a modeling system, DASS, to support the hybrid human-machine development and validation of predictive models for estimating long-term toxicities related to radiotherapy doses in head and neck cancer patients. Developed in collaboration with domain experts in oncology and data mining, DASS incorporates human-in-the-loop visual steering, spatial data, and explainable AI to augment domain knowledge with automatic data mining. We demonstrate DASS with the development of two practical clinical stratification models and report feedback from domain experts. Finally, we describe the design lessons learned from this collaborative experience.     

Dawning Nebulae: A PetaFLOPS Supercomputer with a Heterogeneous Structure

Dawning Nebulae is a heterogeneous system composed of 9280 multi-core x86 CPUs and 4640 NVIDIA Fermi GPUs. With a Linpack performance of 1.271 petaFLOPS, it was ranked the second in the TOP500 List released in June 2010. In this paper, key issues in the system design of Dawning Nebulae are introduced. System tuning methodologies aiming at petaFLOPS Linpack result are presented, including algorithmic optimization and communication improvement. The design of its file I/O subsystem, including HVFS and the underlying DCFS3, is also described. Performance evaluations show that the Linpack efficiency of each node reaches 69.89%, and 1024-node aggregate read and write bandwidths exceed 100 GB/s and 70GB/s respectively. The success of Dawning Nebulae has demonstrated the viability of CPU/GPU heterogeneous structure for future designs of supercomputers.     

Editorial: Internetware of the Age

While the infrastructure of computing shifting from isolated computers to Internet-linked resources, the software industry transfers its focus from computing products to dependable services. That''s one of the reasons why pervasive computing, grid computing, service computing and cloud computing are introduced on the open and dynamic Internet-linked platform. The communication abilities it introduced enable various resources exchanging and sharing freely, and participating in the community in the social network. There are three layers in such a platform: computing infrastructure, software services and information web. The underline layer, computing infrastructure, provides computing and communication facilities, and the surface layer, information web, is full of information recombination and consuming. The layer of software services serves as a factory to process constantly emerging and heterogeneous dynamic information using distributed, autonomous, and evolvable computing facilities. Therefore, services provided by such software systems should be adaptive, situational, trustworthy, autonomous and etc. To achieve flexible objectives, these services can be coordinated in various styles such as integration, cooperation, orchestration and etc. A portmanteau term \Internetware" is used     

Toward a Comprehensive Framework for the Spatiotemporal Statistical Analysis of Longitudinal Shape Data

This paper proposes an original approach for the statistical analysis of longitudinal shape data. The proposed method allows the characterization of typical growth patterns and subject-specific shape changes in repeated time-series observations of several subjects. This can be seen as the extension of usual longitudinal statistics of scalar measurements to high-dimensional shape or image data. The method is based on the estimation of continuous subject-specific growth trajectories and the comparison of such temporal shape changes across subjects. Differences between growth trajectories are decomposed into morphological deformations, which account for shape changes independent of the time, and time warps, which account for different rates of shape changes over time. Given a longitudinal shape data set, we estimate a mean growth scenario representative of the population, and the variations of this scenario both in terms of shape changes and in terms of change in growth speed. Then, intrinsic statistics are derived in the space of spatiotemporal deformations, which characterize the typical variations in shape and in growth speed within the studied population. They can be used to detect systematic developmental delays across subjects. In the context of neuroscience, we apply this method to analyze the differences in the growth of the hippocampus in children diagnosed with autism, developmental delays and in controls. Result suggest that group differences may be better characterized by a different speed of maturation rather than shape differences at a given age. In the context of anthropology, we assess the differences in the typical growth of the endocranium between chimpanzees and bonobos. We take advantage of this study to show the robustness of the method with respect to change of parameters and perturbation of the age estimates.     

论大数据时代信息安全的新特点与新要求

大数据时代信息安全具有新的特点,同时,也提出新的挑战,需要制定出新的应对策略,以符合时代发展和技术发展的新要求。大数据时代信息的安全呈现综合性、规模性、跨越性、广泛性和隐蔽性的特点。针对这些特点,提出以下要求:第一,提升信息安全意识;第二,创新信息安全保障体系;第三,深化信息安全技术改革。     

一种新的数据质量模型的研究

给出了一个软件系统中数据质量评估和优化的模型CLDQ,该模型的特点是：作用于软件生命周期的全进程;数据质量的评估是可计算的。     

Likelihood and Pseudo-likelihood Methods for Semiparametric Joint Models for a Primary Endpoint and Longitudinal Data

Inference on the association between a primary endpoint and features of longitudinal profiles of a continuous response is of central interest in medical and public health research. Joint models that represent the association through shared dependence of the primary and longitudinal data on random effects are increasingly popular; however, existing inferential methods may be inefficient or sensitive to assumptions on the random effects distribution. We consider a semiparametric joint model that makes only mild assumptions on this distribution and develop likelihood-based inference on the association and distribution, which offers improved performance relative to existing methods that is insensitive to the true random effects distribution. Moreover, the estimated distribution can reveal interesting population features, as we demonstrate for a study of the association between longitudinal hormone levels and bone status in peri-menopausal women.     

VisualNeuro: A Hypothesis Formation and Reasoning Application for Multi-Variate Brain Cohort Study Data

We present an application, and its development process, for interactive visual analysis of brain imaging data and clinical measurements. The application targets neuroscientists interested in understanding the correlations between active brain regions and physiological or psychological factors. The application has been developed in a participatory design process and has subsequently been released as the free software ‘VisualNeuro’. From initial observations of the neuroscientists' workflow, we concluded that while existing tools provide powerful analysis options, they lack effective interactive exploration requiring the use of many tools side by side. Consequently, our application has been designed to simplify the workflow combining statistical analysis with interactive visual exploration. The resulting environment comprises parallel coordinates for effective overview and selection, Welch's t-test to filter out brain regions with statistically significant differences and multiple visualizations for comparison between brain regions and clinical parameters. These exploration concepts enable neuroscientists to interactively explore the complex bidirectional interplay between clinical and brain measurements and easily compare different patient groups. A qualitative user study has been performed with three neuroscientists from different domains. The study shows that the developed environment supports simultaneous analysis of more parameters, provides rapid pathways to insights and is an effective tool for hypothesis formation.     

Editorial Note: Machine Learning for Multimodal Data Understanding

Multimedia Tools and Applications -