LEAPS-MPS: Uncovering and Exploiting Multiscale Structures in Big Data Using Diffusion-Based Representation and Optimal Sampling

LEAPS-MPS：使用基于扩散的表示和最佳采样来发现和利用大数据中的多尺度结构

• 批准号: 2137649
• 负责人: Karamatou Yacoubou Djima
• 金额: $ 15.08万
• 依托单位: Amherst College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2137649&HistoricalAwards=false
• 关键词: LEAPS; MPS; Uncovering; Exploiting; Multiscale

Consumers of information desire efficient methods for processing data, and scientists are interested in understanding patterns and structures in data to explain why various phenomena occur. The focus of emerging big data analytics research has shifted to alternative data structures, especially (combinatorial) graphs, and networks. Although graphs and networks require mathematically complex algorithms, they have built-in links that highlight important relationships between nodes and make them simpler to interpret in applications. This project introduces new theories and algorithms to address aspects of graph data challenges that have impacts on diverse segments of society including imaging tools used by medical professionals to diagnose and treat diseases, and on applications ranging from the regulatory networks describing the interactions between genes, RNA, and proteins in the brain, to hardware and sensor technology. The methods explored expand the family of harmonic analysis algorithms that produced tools such as the wavelet-based ones for images used in commercial applications and sampling theorems for telegraph communication. The project also involves training and mentoring undergraduate students, particularly students from underrepresented groups with less exposure to careers in science and technology. The intent is to motivate and prepare them for careers in advanced mathematical sciences and increase the global competitiveness of the US-based STEM workforce. Other activities include promoting equity and inclusion in STEM fields through public lectures and coordinated outreach activities. Applied harmonic analysis tools will play a central role in the two-year project. The primary research objectives include: (i) enhancing the quality of multiscale representations of data signals on graphs and networks, (ii) the optimization of sampling strategies for high-dimensional, non-bandlimited signals, and (iii) the design of compression algorithms to demonstrate the potential for datasets on graphs. The analysis combines frame theory, diffusion geometry, and directional multiscale modeling; novel numerical techniques will be developed using numerical linear algebra and computational harmonic analysis.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.

信息的消费者需要有效的方法来处理数据，科学家对理解数据中的模式和结构感兴趣，以解释为什么会发生各种现象。新兴的大数据分析研究的重点已经转移到替代数据结构，特别是（组合）图和网络。虽然图形和网络需要数学上复杂的算法，但它们具有内置的链接，可以突出节点之间的重要关系，并使它们在应用程序中更容易解释。该项目引入了新的理论和算法，以解决图形数据挑战的各个方面，这些挑战对社会的各个领域产生了影响，包括医疗专业人员用于诊断和治疗疾病的成像工具，以及从描述基因，RNA和蛋白质之间相互作用的监管网络到硬件和传感器技术的应用。探讨的方法扩展了谐波分析算法的家庭，产生的工具，如基于小波的商业应用和电报通信的采样定理中使用的图像。该项目还涉及培训和指导本科生，特别是来自代表性不足群体的学生，他们较少接触科学和技术职业。其目的是激励和准备他们的职业生涯在先进的数学科学和提高美国的STEM劳动力的全球竞争力。其他活动包括通过公开讲座和协调外联活动促进STEM领域的公平和包容。应用谐波分析工具将在这个为期两年的项目中发挥核心作用。主要研究目标包括：（i）提高图形和网络上数据信号的多尺度表示的质量，（ii）高维非带限信号的采样策略的优化，以及（iii）压缩算法的设计，以展示图形数据集的潜力。该分析结合了框架理论、扩散几何和定向多尺度建模;将使用数值线性代数和计算谐波分析开发新的数值技术。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。