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CIF:Small: Towards Information Content of Dynamic Structures

CIF:Small：走向动态结构的信息内容

基本信息

批准号：
2006440
负责人：
Wojciech Szpankowski
金额：
$ 50万
依托单位：
Purdue University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006440&HistoricalAwards=false
关键词：
CIF Small Towards Information Content

项目摘要

Brain networks, protein‐protein interactions, and social networks all build on basic ingredients of sets, trees, and graphs, yet a theory of information for such advanced dynamic data structures is lacking. Fundamental questions for such data structures include: What are the fundamental limits on storage and processing of structural and temporal information? What are the limits on the extraction of information from large biological databases? What are the fewest bits of an incomplete database needed to reliably answer a query? This project seeks to find answers to these questions and thus make headway in information theory for dynamic structures by finding efficient algorithms to appraise structural and temporal information – whether it is embodied in graphs of social networks, Markov fields of images, or protein tertiary structures.The specific goals of the project are as follows: 1) Finding fundamental limits of information content in a wide range of dynamic structures, including dynamic networks, Markov fields, and structurally complex channels such as Boltzmann’s channel; 2) Examining dynamic networks by: (a) Inferring underlying dynamic processes governing network evolution; (b) determining the minimum number of bits to describe dynamic networks; and (c) inferring spatial‐temporal properties such as node arrivals; 3) Understanding structural properties of large systems with local mutual dependencies, constraints, and interactions often described by Markov fields; 4) Analyzing flow of structural information over a noisy channel. Tools developed as part of the project will be used in areas such as characterization of biological and social networks, deciphering evolution of the human brain, development of a comprehensive formal model for misinformation, and designing a robust pervasive communications infrastructure.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.

大脑网络、蛋白质-蛋白质相互作用和社会网络都建立在集合、树和图的基本成分上，但缺乏这种高级动态数据结构的信息理论。这类数据结构的基本问题包括：结构信息和时间信息的存储和处理的基本限制是什么？从大型生物数据库中提取信息的限制是什么？对于一个不完整的数据库，可靠地回答查询所需的最少比特是多少？该项目旨在找到这些问题的答案，从而通过寻找有效的算法来评估结构和时间信息，从而在动态结构的信息论方面取得进展-无论是体现在社会网络图，图像的马尔可夫场还是蛋白质三级结构中。该项目的具体目标如下：1)在广泛的动态结构中发现信息内容的基本限制，包括动态网络、马尔可夫场和结构复杂的通道，如玻尔兹曼通道；2)通过以下方式检查动态网络：(a)推断控制网络演变的潜在动态过程；(b)确定描述动态网络的最小位数；(c)推断时空属性，如节点到达；3)理解具有局部相互依赖、约束和相互作用的大型系统的结构特性，这些特性通常由马尔可夫场描述；4)分析结构信息在噪声信道上的流动。作为该项目的一部分开发的工具将用于生物和社会网络的表征，破译人类大脑的进化，开发错误信息的综合正式模型，以及设计强大的普及通信基础设施等领域。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。