RI: Small: Explicit and Implicit Regularity Perception

RI：小：显性和隐性规律性感知

• 批准号: 1909315
• 负责人: Yanxi Liu
• 金额: $ 50万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909315&HistoricalAwards=false
• 关键词: RI; Small; Explicit; Implicit; Regularity

This is an era of pervasive big data, streaming in from all sorts of sensors such as medical scanners, surveillance cameras, smart appliances and cellphones. Even equipped with the most powerful hardware and software for data processing, one may still get lost in this massive collection of noisy data and miss "seeing the forest for the trees". This project addresses a fundamental issue in the data sciences: what are we looking for in a seemingly structure-less complex data set? Based on proven mathematical theories about symmetry and inspired by human vision, the investigator will construct a roadmap for discovering all possible potential patterns in these datasets and provide computationally effective tools to discover such patterns even in their distorted and disguised forms. Such a relentless pattern-seeking algorithm will illustrate the power of a "treasure hunt" type search, promote automatic pattern discovery, and fill a gap in current practice of data analysis, thus advancing data science. The outcome from this project will be applicable to data sets from diverse fields, from biomedical domains such as drug discovery, gene mapping and human movement monitoring, to computer vision applications like driverless car navigation, urban scene understanding, and anomalous activity detection for surveillance. This research formulates a symmetry group theory-based regularity space and constructs a computational platform for pattern-seeking algorithms that are principle-driven and human perception-inspired. The transitions among zero states of the proposed regularity-space, guided by group theory and learned from human perception, will be governed by a seamless marriage with Bayesian probability theory. The two key ingredients of a pattern, minimum cells and inter-cell topology, will be explored extensively on large scale, multimodality, high dimensional real-world sensory data. The aims are to construct and validate computational tools facilitating effective unsupervised recurring pattern discovery, followed by recovering geometric transformations, and finally by quantitatively linking the full regularity-perception spectrum from regular, near-regular to irregular. The finiteness, uniqueness and completeness of this group theory-based regularity space will provide a sound computational framework to guide pattern seeking in general data science.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这是一个普遍存在的大数据的时代，来自各种传感器，例如医疗扫描仪，监视摄像头，智能电器和手机。即使配备了用于数据处理的最强大的硬件和软件，也可能会迷失在大量的嘈杂数据中，而错过了“看树木的森林”。该项目解决了数据科学中的一个基本问题：我们在看似无结构的复杂数据集中寻找什么？基于关于对称性的经过验证的数学理论，并受到人类视觉的启发，研究者将构建一个路线图，以发现这些数据集中所有可能的潜在模式，并提供计算上有效的工具，即使以其扭曲和伪装的形式发现了此类模式。这种无情的寻求模式的算法将说明“寻宝”类型搜索的力量，促进自动模式发现并填补当前数据分析实践的空白，从而进步数据科学。该项目的结果将适用于不同领域的数据集，从生物医学领域，例如药物发现，基因映射和人类运动监测，再到计算机视觉应用，例如无人驾驶汽车导航，城市场景的理解以及对监测的异常活动检测。这项研究制定了基于对称群体理论的规律性空间，并为寻求模式的算法构建了一个计算平台，该算法是原理驱动的，人类感知的启发。以群体理论为指导并从人类感知中学到的零状态的零状态之间的过渡将受贝叶斯概率理论的无缝婚姻的控制。 模式的两种关键成分，最小单元格和间间拓扑结构将在大规模，多模式，高维真实世界的感觉数据上进行广泛探索。目的是构建和验证计算工具促进有效的无监督重复的模式发现，然后恢复几何变换，最后是通过定量地将正常，近规则，近规则到不规则的正常性感知光谱链接起来。该基于理论的规律性空间的有限，独特性和完整性将提供一个合理的计算框架，以指导一般数据科学中的模式。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和评估来支持的。更广泛的影响审查标准。