Medical data mining: insights from winning two competitions

Two major data mining competitions in 2008 presented challenges in medical domains: KDD Cup 2008, which concerned cancer detection from mammography data; and Informs Data Mining Challenge 2008, dealing with diagnosis of pneumonia based on patient information from hospital files. Our team won both of these competitions, and in this paper we share our lessons learned and insights. We emphasize the aspects that pertain to the general practice and methodology of medical data mining, rather than to the specifics of each modeling competition. We concentrate on three topics: information leakage, its effect on competitions and proof-of-concept projects; consideration of real-life model performance measures in model construction and evaluation; and relational learning approaches to medical data mining tasks.     

A survey of operating system support for persistent memory

Emerging persistent memory technologies, like PCM and 3D XPoint, offer numerous advantages, such as higher density, larger capacity, and better energy efficiency, compared with the DRAM. However, they also have some drawbacks, e.g., slower access speed, limited write endurance, and unbalanced read/write latency. Persistent memory technologies provide both great opportunities and challenges for operating systems. As a result, a large number of solutions have been proposed. With the increasing number and complexity of problems and approaches, we believe this is the right moment to investigate and analyze these works systematically.To this end, we perform a comprehensive and in-depth study on operating system support for persistent memory within three steps. First, we present an overview of how to build the operating system on persistent memory from three perspectives: system abstraction, crash consistency, and system reliability. Then, we classify the existing research works into three categories: storage stack, memory manager, and OS-bypassing library. For each category, we summarize the major research topics and discuss these topics deeply. Specifically, we present the challenges and opportunities in each topic, describe the contributions and limitations of proposed approaches, and compare these solutions in different dimensions. Finally, we also envision the future operating system based on this study.     

表面网格控制的快速毛发建模

针对现有角色毛发建模复杂耗时的问题,提出一种基于多边形表面网格的快速毛发建模方法.首先构建粗糙的表面网格来大致表示毛发的整体形态,随后计算控制毛发走向的参数,并基于此自动生成形状网格对应的空间毛发轨迹线来修正毛发走向,使结果在拟合用户指定的毛发形态的同时兼容现有的风格化方法.实验结果表明,该方法使用类似表面网格建模的方法即可完成高质量毛发的建模,在得到较好结果的同时大大降低了建模的难度与工作量,并增强对结果的可控性.另外,文中方法也可方便地与基于物理的运动模拟方法结合生成毛发动画.     

Recent advances in feature selection and its applications

Feature selection is one of the key problems for machine learning and data mining. In this review paper, a brief historical background of the field is given, followed by a selection of challenges which are of particular current interests, such as feature selection for high-dimensional small sample size data, large-scale data, and secure feature selection. Along with these challenges, some hot topics for feature selection have emerged, e.g., stable feature selection, multi-view feature selection, distributed feature selection, multi-label feature selection, online feature selection, and adversarial feature selection. Then, the recent advances of these topics are surveyed in this paper. For each topic, the existing problems are analyzed, and then, current solutions to these problems are presented and discussed. Besides the topics, some representative applications of feature selection are also introduced, such as applications in bioinformatics, social media, and multimedia retrieval.     

Knowledge discovery through directed probabilistic topic models: a survey

Graphical models have become the basic framework for topic based probabilistic modeling. Especially models with latent variables have proved to be effective in capturing hidden structures in the data. In this paper, we survey an important subclass Directed Probabilistic Topic Models (DPTMs) with soft clustering abilities and their applications for knowledge discovery in text corpora. From an unsupervised learning perspective, “topics are semantically related probabilistic clusters of words in text corpora; and the process for finding these topics is called topic modeling”. In topic modeling, a document consists of different hidden topics and the topic probabilities provide an explicit representation of a document to smooth data from the semantic level. It has been an active area of research during the last decade. Many models have been proposed for handling the problems of modeling text corpora with different characteristics, for applications such as document classification, hidden association finding, expert finding, community discovery and temporal trend analysis. We give basic concepts, advantages and disadvantages in a chronological order, existing models classification into different categories, their parameter estimation and inference making algorithms with models performance evaluation measures. We also discuss their applications, open challenges and future directions in this dynamic area of research.     

Contamination event diagnosis in drinking water networks: A review

Water distribution systems are susceptible to contamination events, which can occur due to naturally occurring events, accidents or even malicious attacks. When a contamination event occurs, dangerous substances infiltrating the network may be consumed thereby deteriorating the consumers’ health and possibly affecting the economy. Advances in sensor and actuator technologies are enabling water networks to become smarter and more resilient to these types of events. This paper provides a broad review of the theoretical, modeling, and computational developments in the area of contamination event diagnosis for water distribution systems. Research is segmented into three main tasks, summarized as “Preparedness”, “Event Detection and Isolation” and “Emergency Event Management”. The key research topics from each task are described within a unified systems-theoretic mathematical framework, and their open challenges are discussed.     

An Integrated System for Modeling,Animating and Rendering Hair

There are basically four problems to solve in order to produce realistic animated synthetic actors with hair: hair modeling and creation, hair motion, collision detection and hair rendering. This paper describes a complete methodology to solve these basic four problems. We present how hair styles may be designed with our Hair Styler module. Then we survey the animation model and emphasize a method of collision processing. Finally, we explain how hair may be rendered using an extension of a standard ray-tracing program. We also show applications of our synthetic actors with various hair styles and different styles of mustaches and beards.     

三维头发的建模方法研究

在现代的一些虚拟现实系统中,如模拟人体模型、游戏以及电影特技中需要对头发的真实感进行建模。虚拟现实的关键技术之一是对显示对象进行建模。因此,为解决由于头发数量庞大和其纤细的特性而使得头发图像生成过程缓慢复杂的问题,研究其相关技术具有重要价值。头发模拟的方法主要有：直接模拟、体积纹理模型、聚簇发束模型和宽松连接的粒子系统模型等。该文在现有的头发模拟方法分析的基础上,提出了一种基于粒子系统的头发模拟设计方法,从而在时间和计算开销上解决已有方法的不足。     

Soft medical robotics: clinical and biomedical applications,challenges, and future directions

Bioinspired soft robotics allow for safer clinical interactions with human patients but conventional, hard robots, which are often built with rigid materials and complex control systems, compromise tissue integrity, freedom of movement, conformability, and overall human bio-compatibility. Soft, compliant materials intrinsically reduce mechanical complexity, accommodate their usage environment, and provide great practical potential for medical device developments. Previous review papers have generally covered the topics of materials, manufacturing processes, actuator modeling and control, and current trends. Here, we focus on recent developments in soft robotic applications for the medical field including advances in cardiac devices, surgical robots, and soft rehabilitation and assistance devices. In medical applications, soft robotic devices not only expedite the evolution of minimally invasive surgery but also improve the bio-compatibility of rehabilitation and assistance devices. Here, we evaluate design requirements, mechanisms, achievements and challenges in these key areas. Of particular note, this paper concludes with a discussion on advances in 3D printing and adapting neural networks for modeling and control frameworks that have facilitated the development of faster and less expensive soft medical devices.     

Analyzing market performance via social media: a case study of a banking industry crisis

Analyzing market performance via social media has attracted a great deal of attention in the finance and machine-learning disciplines.However,the vast majority of research does not consider the enormous influence a crisis has on social media that further affects the relationship between social media and the stock market.This article aims to address these challenges by proposing a multistage dynamic analysis framework.In this framework,we use an authorship analysis technique and topic model method to identify stakeholder groups and topics related to a special firm.We analyze the activities of stakeholder groups and topics in different periods of a crisis to evaluate the crisis’s influence on various social media parameters.Then,we construct a stock regression model in each stage of crisis to analyze the relationships of changes among stakeholder groups/topics and stock behavior during a crisis.Finally,we discuss some interesting and significant results,which show that a crisis affects social media discussion topics and that different stakeholder groups/topics have distinct effects on stock market predictions during each stage of a crisis.     

Modeling, animating, and rendering complex scenes using volumetrictextures

Complex repetitive scenes containing forests, foliage, grass, hair, or fur, are challenging for common modeling and rendering tools. The amount of data, the tediousness of modeling and animation tasks, and the cost of realistic rendering have caused such kind of scene to see only limited use even in high-end productions. The author describes how the use of volumetric textures is well suited to such scenes. These primitives can greatly simplify modeling and animation tasks. More importantly, they can be very efficiently rendered using ray tracing with few aliasing artifacts. The main idea, initially introduced by Kajiya and Kay (1989), is to represent a pattern of 3D geometry in a reference volume, that is tiled over an underlying surface much like a regular 2D texture. In our contribution, the mapping is independent of the mesh subdivision, the pattern can contain any kind of shape, and it is prefiltered at different scales as for MIP-mapping. Although the model encoding is volumetric, the rendering method differs greatly from traditional volume rendering. A volumetric texture only exists in the neighborhood of a surface, and the repeated instances (called texels) of the reference volume are spatially deformed. Furthermore, each voxel of the reference volume contains a key feature which controls the reflectance function that represents aggregate intravoxel geometry. This allows for ray tracing of highly complex scenes with very few aliasing artifacts, using a single ray per pixel (for the part of the scene using the volumetric texture representation). The major technical considerations of our method lie in the ray-path determination and in the specification of the reflectance function     

基于LDA的软件代码主题摘要自动生成方法

理解软件代码的功能是软件复用的一个重要环节。基于主题建模技术的代码理解方法能够挖掘软件代码中潜在的主题,这些主题在一定程度上代表了软件代码所实现的功能。但是使用主题建模技术所挖掘出的代码主题有着语义模糊、难以理解的弊端。潜在狄利克雷分配(Latent Dirichlet Allocation,LDA)技术是一种比较常用的主题建模技术, 其在软件代码主题挖掘领域已取得了较好的结果,但同样存在上述问题。为此,需要为主题生成解释性文本描述。基于LDA的软件代码主题摘要自动生成方法除了利用主题建模技术对源代码生成主题之外,还利用文档、问答信息等包含软件系统功能描述的各类软件资源挖掘出代码主题的描述文本并提取摘要,从而能够更好地帮助开发人员理解软件的功能。     

Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions

The integration of optimization methodologies with computational analyses/simulations has a profound impact on the product design. Such integration, however, faces multiple challenges. The most eminent challenges arise from high-dimensionality of problems, computationally-expensive analysis/simulation, and unknown function properties (i.e., black-box functions). The merger of these three challenges severely aggravates the difficulty and becomes a major hurdle for design optimization. This paper provides a survey on related modeling and optimization strategies that may help to solve High-dimensional, Expensive (computationally), Black-box (HEB) problems. The survey screens out 207 references including multiple historical reviews on relevant subjects from more than 1,000 papers in a variety of disciplines. This survey has been performed in three areas: strategies tackling high-dimensionality of problems, model approximation techniques, and direct optimization strategies for computationally-expensive black-box functions and promising ideas behind non-gradient optimization algorithms. Major contributions in each area are discussed and presented in an organized manner. The survey exposes that direct modeling and optimization strategies to address HEB problems are scarce and sporadic, partially due to the difficulty of the problem itself. Moreover, it is revealed that current modeling research tends to focus on sampling and modeling techniques themselves and neglect studying and taking the advantages of characteristics of the underlying expensive functions. Based on the survey results, two promising approaches are identified to solve HEB problems. Directions for future research are also discussed.     

三维人脸建模与应用

三维建模是计算机视觉和计算机图形学领域中一个基本的问题，人脸借助其特有的普遍性和易用性成为众多先进三维建模算法的实验平台。但是由于人脸的复杂性、易变性，建立逼真的三维人脸模型成为众多研究者挑战的课题。本文分析了三维人脸建模的研究内容和研究难点，总结了1990年以来出现的最新方法和进展，从三维数据的获取、标准三维人脸建模、特定三维人脸建模及应用等三维建模的几个主要环节进行了分类综述，重点介绍了一些主要算法的基本思路和最新进展，并对该领域的研究热点和方向作了较为详细的分析。     

Gender classification by combining clothing, hair and facial component classifiers

In this paper, we propose a novel gender classification framework, which utilizes not only facial features, but also external information, i.e. hair and clothing. Instead of using the whole face, we consider five facial components: forehead, eyes, nose, mouth and chin. We also design feature extraction methods for hair and clothing; these features have seldom been used in previous work because of their large variability. For each type of feature, we train a single support vector machine classifier with probabilistic output. The outputs of these classifiers are combined using various strategies, namely fuzzy integral, maximal, sum, voting, and product rule. The major contributions of this paper are (1) investigating the gender discriminative ability of clothing information; (2) using facial components instead of the whole face to obtain higher robustness for occlusions and noise; (3) exploiting hair and clothing information to facilitate gender classification. Experimental results show that our proposed framework improves classification accuracy, even when images contain occlusions, noise, and illumination changes.     

面向微博主题的可视分析研究

随着微博的发展,其影响力日益增大,对微博主题内容进行分析具有重要的价值.主题模型技术能够从文本数据中提取主题,但是,由于微博文本短、随意性大、信息量小等特点,微博主题的分析具有一定的难度.提出了一个微博主题可视分析系统,利用多种互相关联的视图与丰富的交互手段,支持用户对主题模型结果进行分析与探索.系统结合了微博数据的特点,引入微博用户与时间因素,支持分析者从多角度对微博主题进行全面分析.系统支持用户在主题可视分析的基础上,通过交互操作对主题进行编辑,从而改进主题模型,提高模型的准确性和可靠性.案例分析结果表明,提出的系统可以有效地帮助用户分析微博主题和修正主题.     

Advanced issues in artificial intelligence and pattern recognition for intelligent surveillance system in smart home environment

During the last decades, many researchers in image processing and AI community have been focused on developing image and video analysis and understanding. However, despite their extensive efforts on these, there are still several significant challenges such as robust object segmentation and tracking, motion feature extraction, context modeling, and machine learning algorithms. Therefore, more advanced related issues should be taken into account. We have selected nine research papers whose topics are strongly related to the intelligent surveillance system in smart home environment.     

Book Review [review of Fuzzy Modeling and Fuzzy Control (Zhang, H. and Liu)]

This book presents a systematic framework targeting at fuzzy modeling and fuzzy control of nonlinear systems with uncertainties. The book is organized into three major parts incorporating 13 chapters. The first part contains four chapters focusing on the modeling of nonlinear dynamical systems by using fuzzy logic. The second part includes five chapters in which fuzzy inference and control techniques are addressed. The final part consists of four chapters, which covers several advanced topics in fuzzy control ranging from controller and filter design to chaotification of fuzzy systems and feedforward fuzzy control of nonlinear systems via Fourier integrals. One of the major contributions of this book is that it presents the latest original research developments in the field. It provides rich examples and references, too. It is a valuable resource for those researchers and practitioners interested in expanding their knowledge from fuzzy logic and applications to nonlinear dynamical systems.     

Virtual humans: thirty years of research, what next?

In this paper, we present research results and future challenges in creating realistic and believable Virtual Humans. To realize these modeling goals, real-time realistic representation is essential, but we also need interactive and perceptive Virtual Humans to populate the Virtual Worlds. Three levels of modeling should be considered to create these believable Virtual Humans: 1) realistic appearance modeling, 2) realistic, smooth and flexible motion modeling, and 3) realistic high-level behaviors modeling. At first, the issues of creating virtual humans with better skeleton and realistic deformable bodies are illustrated. To give a level of believable behavior, challenges are laid on generating on the fly flexible motion and complex behaviours of Virtual Humans inside their environments using a realistic perception of the environment. Interactivity and group behaviours are also important parameters to create believable Virtual Humans which have challenges in creating believable relationship between real and virtual humans based on emotion and personality, and simulating realistic and believable behaviors of groups and crowds. Finally, issues in generating realistic virtual clothed and haired people are presented.