Collaborative Research: Origins of Southeast Asian Rainforests from Paleobotany and Machine Learning

合作研究：古植物学和机器学习的东南亚雨林起源

• 批准号: 1925481
• 负责人: Thomas Serre
• 金额: $ 66.5万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1925481&HistoricalAwards=false
• 关键词: Collaborative; Research; Origins; Southeast; Asian

Fossil leaves are the most abundant record of ancient plant life and millions of specimens are contained in museum collections around Fossil leaves are the most abundant record of ancient plant life, and millions of specimens are contained in museum collections around the world, with more discoveries every year. Nevertheless, leaf fossils alone currently provide limited information about the evolution of regional and global plant communities because individual leaf characteristics from a single plant species can vary widely, and detailed, time-consuming examination of each leaf fossil might still not connect it to its true biological family. This project addresses the problem in two ways. First will be the development of the Virtual Paleobotany Assistant (VPA), an artificial intelligence tool that will use machine learning techniques to rapidly analyze leaf characteristics to assign individual fossils to plant families and orders. The VPA, together with more traditional methods of paleobotany, will then be used to interpret the origins of the incredibly diverse tropical rain forests that now exist in Southeast Asia. These plant communities evolved during times of major continental movements and have connections to the former supercontinent of Gondwana, the Indian subcontinent, and Eurasia. Ascertaining the evolutionary and biogeographic pathways that led to the assembly of these tropical forests will help in preserving this important natural resource as the regional human population burgeons. The VPA will be made freely available on the internet and mobile platforms, enabling paleobotanists around the world to make discoveries far beyond this project. The unique collaboration between paleontologists and machine-learning experts will create extremely fertile ground for interdisciplinary advances, while catalyzing new international partnerships and student opportunities. The project addresses two of the most difficult challenges in paleobotany, fossil leaf identification and the fossil history of Southeast Asian (Malesian) rainforests. Decoding the biological affinities of leaf fossils holds central significance for the improved knowledge of plant evolution, biogeography, and paleoclimate. This project will use deep learning on image databases of extant and fossil leaves to develop the first application (the Virtual Paleobotany Assistant, VPA) for computer-assisted identifications of leaf fossils to plant families and orders. The living floras of Southeast Asia are composed of a stunningly complex juxtaposition of plant lineages that diversified after arriving from disparate sources, including Gondwana (fossils to be studied in Patagonia and Australia), the Indian Plate (India and Pakistan), and Eurasia (South China, Indochina, Malay Archipelago). However, the diverse biogeographic components remain poorly understood due to limited paleobotanical data in many of the source areas. Many widely cited hypotheses are weakly corroborated from fossils; paleobotany and machine vision will coordinate to reveal the identities of fossil plants, correlate them to the geologic time scale, and re-interpret Malesia's floristic history. The influx of new paleobotanical data will test fundamental hypotheses about the relative contributions to Southeast Asian rainforest floras from different source areas.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.

化石叶子是古代植物生命最丰富的记录，各地博物馆收藏了数百万个标本化石叶子是古代植物生命最丰富的记录，世界各地博物馆收藏了数百万个标本，每年都有更多的发现。尽管如此，叶化石本身目前提供的关于区域和全球植物群落进化的信息有限，因为单个植物物种的个体叶特征可能差异很大，对每个叶化石进行详细而耗时的检查可能仍然无法将其与其真正的生物家族联系起来。这个项目从两个方面解决这个问题。首先是开发虚拟古植物学助手（VPA），这是一种人工智能工具，将使用机器学习技术快速分析叶子特征，将个体化石分配给植物家族和顺序。VPA与更传统的古植物学方法一起，将被用来解释现在存在于东南亚的令人难以置信的多样化热带雨林的起源。这些植物群落在主要的大陆运动时期进化，并与前冈瓦纳超大陆，印度次大陆和欧亚大陆有联系。确定导致这些热带森林聚集的进化和地理学途径将有助于保护这一重要的自然资源，因为该地区的人口激增。VPA将在互联网和移动的平台上免费提供，使世界各地的古植物学家能够做出远远超出本项目的发现。古生物学家和机器学习专家之间的独特合作将为跨学科的进步创造极其肥沃的土壤，同时促进新的国际合作伙伴关系和学生机会。该项目解决了古植物学中两个最困难的挑战，即化石叶鉴定和东南亚（马来群岛）雨林的化石历史。解读叶化石的生物亲缘关系对于提高植物进化、植物地理学和古气候的知识具有重要意义。该项目将利用对现存和化石叶子图像数据库的深度学习，开发第一个应用程序（虚拟古植物学助手，VPA），用于计算机辅助识别植物科和目的叶子化石。东南亚现存的植物群是由植物谱系的惊人复杂并置组成的，这些植物谱系在来自不同的来源后变得多样化，包括冈瓦纳大陆（巴塔哥尼亚和澳大利亚的化石），印度板块（印度和巴基斯坦）和欧亚大陆（华南，印度支那，马来群岛）。然而，由于在许多来源地区的古植物学数据有限，多样的植物地理成分仍然知之甚少。许多被广泛引用的假说都无法从化石中得到有力的证实;古植物学和机器视觉将协同揭示植物化石的身份，将它们与地质年代相关联，并重新解释Malibu的植物区系历史。新的古植物学数据的涌入将检验有关不同来源地区对东南亚雨林植物区系的相对贡献的基本假设。该奖项反映了NSF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。

项目成果

Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex

• DOI: 10.48550/arxiv.2306.03779
• 发表时间: 2023-06
• 期刊: ArXiv
• 作者: Drew Linsley;I. F. Rodriguez;Thomas Fel;Michael Arcaro;Saloni Sharma;M. Livingstone;Thomas Serre

Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines?

• DOI: 10.48550/arxiv.2301.11722
• 发表时间: 2023-01
• 作者: Victor Boutin;Thomas Fel;Lakshya Singhal;Rishav Mukherji;Akash Nagaraj;Julien Colin;Thomas Serre

A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimation

• DOI: 10.48550/arxiv.2306.07304
• 发表时间: 2023-06
• 期刊: ArXiv
• 作者: Thomas Fel;Victor Boutin;Mazda Moayeri;Rémi Cadène;Louis Béthune;L'eo And'eol;Mathieu Chalvidal;Thomas Serre

Learning Functional Transduction

• DOI: 10.48550/arxiv.2302.00328
• 发表时间: 2023-02
• 期刊: ArXiv
• 作者: Mathieu Chalvidal;Thomas Serre;Rufin VanRullen

Unlocking Feature Visualization for Deeper Networks with MAgnitude Constrained Optimization

• DOI: 10.48550/arxiv.2306.06805
• 发表时间: 2023-06
• 期刊: ArXiv
• 作者: Thomas Fel;Thibaut Boissin;Victor Boutin;Agustin Picard;Paul Novello;Julien Colin;Drew Linsley;Tom Rousseau;Rémi Cadène;L. Gardes;Thomas Serre