Collaborative Research: CIF: Small: A New Paradigm for Distributed Information Processing, Simulation and Inference in Networks: The Promise of Law of Small Numbers

合作研究：CIF：小：网络中分布式信息处理、模拟和推理的新范式：小数定律的承诺

基本信息

批准号：
2132815
负责人：
Sandeep Sadanandarao
金额：
$ 25万
依托单位：
Regents of the University of Michigan - Ann Arbor
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：
2021-10-01 至 2025-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2132815&HistoricalAwards=false
关键词：
Collaborative Research CIF Small New

项目摘要

The ability to access, process, and store distributed data in a reliable, efficient, and secure manner has become indispensable in everyday lives. A variety of emerging applications such as augmented reality, autonomous vehicles, and cloud computing heavily rely on handling large amounts of distributed information. The pandemic has further accentuated the global need for technologies that enable communication, collaboration, education and scientific discourse whilst maintaining physical distance, and this has increased awareness of the critical nature of the communication network infrastructure. The exponentially increasing demands for faster data processing and higher communication rates pose new challenges. This project addresses these challenges by developing novel approaches and techniques for distributed information processing, randomness generation, data storage and transmission, and inference. The project will tightly integrate research with a significant education and outreach program consisting of two focus areas: (i) Training students in interdisciplinary research, and (ii) Broadly disseminating research outcomes in the forms of new curricular development and student involvement. A concerted effort will be made to broaden the participation of women and under-represented minority students in the project. The project is based on two research thrusts that are expected to provide a deeper understanding of the fundamental laws that govern the processing of information. In the first thrust, a new framework is developed based on two conceptual innovations: (i) A characterization of the fundamental memory structure of information processing functions using a novel notion of dependency spectrum, and (ii) Development of a new law of small numbers, which describes a fundamental interplay between the dependency spectrum and distributed cooperation. In particular, the project uncovers a trade-off between the correlation-preserving ability of distributed information-processing functions --- which is necessary for distributed cooperation --- and their ability to efficiently perform individual information-processing tasks. The second thrust addresses two application scenarios. (i) Building upon the concept of dependency spectrum, novel techniques are developed for distributed data compression, and transmission of information in interference and broadcast networks. (ii) The fundamental limits and practical design of distributed randomness generation algorithms are derived. These innovations lead to significant improvements over the state of the art both in terms of characterizations of asymptotic performance limits and constructive practical algorithms.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.

以可靠、高效和安全的方式访问、处理和存储分布式数据的能力已成为日常生活中不可或缺的一部分。增强现实、自动驾驶汽车和云计算等各种新兴应用严重依赖于处理大量分布式信息。此次疫情进一步凸显了全球对能够在保持物理距离的同时实现通信、协作、教育和科学讨论的技术的需求，这提高了人们对通信网络基础设施重要性的认识。对更快的数据处理和更高的通信速率的需求呈指数级增长，带来了新的挑战。该项目通过开发分布式信息处理、随机性生成、数据存储和传输以及推理的新方法和技术来解决这些挑战。该项目将把研究与重要的教育和推广计划紧密结合起来，该计划包括两个重点领域：(i) 培训学生进行跨学科研究，(ii) 以新课程开发和学生参与的形式广泛传播研究成果。我们将共同努力扩大女性和代表性不足的少数族裔学生对该项目的参与。该项目基于两个研究主旨，预计将加深对信息处理基本规律的理解。在第一个推力中，基于两个概念创新开发了一个新框架：（i）使用依赖谱的新概念来表征信息处理功能的基本记忆结构，以及（ii）开发新的小数定律，它描述了依赖谱和分布式合作之间的基本相互作用。特别是，该项目揭示了分布式信息处理功能的相关性保留能力（这是分布式合作所必需的）与它们有效执行单个信息处理任务的能力之间的权衡。第二个要点涉及两个应用场景。 (i) 基于依赖频谱的概念，开发了用于分布式数据压缩以及干扰和广播网络中的信息传输的新技术。 (ii) 推导了分布式随机生成算法的基本限制和实际设计。这些创新在渐近性能极限的表征和建设性的实用算法方面都比现有技术有了显着改进。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进行评估，被认为值得支持标准。