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.

化石叶是古代植物生命中最丰富的记录，数以百万计的标本包含在化石叶子周围的博物馆收藏中，是古代植物生命中最丰富的记录，世界各地的博物馆收藏中都包含数百万个标本，每年都有更多的发现。然而，仅靠叶子化石目前提供了有关区域和全球植物群落演变的有限信息，因为单个植物物种的单个叶子特征可能会差异很大，并且对每种叶子化石的详细，耗时的检查可能仍无法与其真正的生物家庭联系起来。该项目通过两种方式解决了问题。首先是开发虚拟古生物助理（VPA），这是一种人工智能工具，它将使用机器学习技术快速分析叶子特征，以将单个化石分配给植物家庭和订单。 VPA以及更传统的古植物方法将被用来解释目前在东南亚存在的令人难以置信的热带雨林的起源。这些植物群落在大陆运动的重大运动时期发展，并与冈瓦纳，印度次大陆和欧亚大陆的前超大陆建立了联系。确定导致这些热带森林组装的进化和生物地理途径将有助于将这一重要的自然资源保存为地区人口繁殖。 VPA将在Internet和移动平台上免费提供，使世界各地的古植物学家能够将发现远远超出该项目。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基础，同时促进新的国际伙伴关系和学生机会。该项目解决了古生物，化石叶鉴定和东南亚（马拉西亚）雨林的化石历史上最困难的两个挑战。解码叶化石的生物学亲和力对于改善植物进化，生物地理和古气候的知识具有核心意义。该项目将在现存和化石叶子的图像数据库上使用深度学习，以开发第一个应用程序（虚拟古生物助理，VPA），以对叶子化石的计算机辅助识别来植物家庭和订单。东南亚的活植物植物植物由植物谱系的惊人复杂并置，这些植物谱系是从不同来源到达后的多样化的，包括冈瓦纳（包括冈瓦纳（在巴塔哥尼亚和澳大利亚要研究的化石），印度板块（印度和巴基斯坦）和欧亚大陆（欧亚大陆，中国，印第安纳州，马来西亚岛上的玛拉群岛）。但是，由于许多来源领域的古植物数据有限，多样化的生物地理成分仍然很少理解。许多被广泛引用的假设从化石中弱佐证。古植物和机器视觉将协调以揭示化石植物的身份，将其与地质时间尺度相关联，并重新释放马雷西亚的植物历史。新的古植物数据的涌入将检验有关来自不同来源领域对东南亚雨林植物群的相对贡献的基本假设。该奖项反映了NSF的法定任务，并认为值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估。

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