CIF: Small: Adaptive Information: Sequential Sensing and Active Learning Theory, Methods and Applications

CIF：小型：自适应信息：顺序感知和主动学习理论、方法和应用

• 批准号: 1218189
• 负责人: Robert Nowak
• 金额: $ 44.31万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218189&HistoricalAwards=false
• 关键词: CIF; Small; Adaptive; Information; Sequential

Complex systems, such as living cells and the Internet, involve interactions and associations between large numbers of individual components. Specific examples include interactions between proteins in biological cells and connections between users in communication networks. Developing theory and methods to model and analyze large-scale networked systems is a grand challenge for the 21st century. For instance, new mathematical methods will lead to better models of biological systems and thus improvements in disease prevention. Measuring the signals of such systems is a crucial step in developing good models, and a major hurdle is that it is often impractical or impossible to measure all variables in large systems. This project addresses the hurdle by developing adaptive methods that automatically adjust the measurement process by using information gleaned from previously collected data in order to focus and optimize the gathering of new information. Ultimately, these methods will dramatically accelerate the pace of discovery in science and engineering.The main theme of the project is an investigation of the role of adaptive measurement, sensing and experimentation in large complex systems of many variables. Adaptive methods are sequential procedures that optimize the selection of the next measurements or experiments based on previously gathered data. The research involves the development of a general theory for adaptive measurement that is applicable to various domains of engineering and science. The main goals are to mathematically characterize and quantify the advantages of adaptive measurements relative to non-adaptive methods and to design optimal adaptive measurement procedures. The investigation also explores the potential of adaptive methods by studying specific problems and applications in biology, national security, and human-computer interaction.

复杂的系统，例如活细胞和互联网，涉及大量各个组件之间的相互作用和关联。具体示例包括生物细胞中蛋白质与通信网络中用户之间的连接之间的相互作用。开发建模和分析大型网络系统的理论和方法是21世纪的巨大挑战。例如，新的数学方法将导致更好的生物系统模型，从而改善疾病预防。测量此类系统的信号是开发良好模型的关键步骤，一个主要的障碍是，在大型系统中测量所有变量通常是不切实际或不可能的。该项目通过开发自适应方法来解决障碍，该方法通过使用先前收集的数据收集的信息自动调整测量过程，以便集中并优化新信息的收集。最终，这些方法将大大加速科学和工程学中发现的速度。该项目的主题是对许多变量的大型复杂系统中适应性测量，感应和实验的作用进行研究。自适应方法是基于先前收集的数据优化下一个测量或实验的选择的顺序程序。该研究涉及开发一种适用于工程和科学领域的自适应测量理论。主要目标是数学表征和量化相对于非自适应方法的自适应测量的优势，并设计最佳的自适应测量程序。该调查还通过研究生物学，国家安全和人类计算机相互作用中的特定问题和应用来探讨自适应方法的潜力。