arXivDaily arXiv每日学术速递 周一至周五更新

高校专区

Cornell University(康奈尔大学)

2026-04-21 至 2026-04-21 共收录 7
2507.17869 2026-04-21 eess.IV cs.CV cs.LG

Integrating Feature Selection and Machine Learning for Nitrogen Assessment in Grapevine Leaves using In-Field Hyperspectral Imaging

整合特征选择与机器学习用于葡萄叶氮含量评估的田间高光谱成像

Atif Bilal Asad, Achyut Paudel, Safal Kshetri, Chenchen Kang, Salik Ram Khanal, Nataliya Shcherbatyuk, Pierre Davadant, R. Paul Schreiner, Santosh Kalauni, Manoj Karkee, Markus Keller

机构 * organization= Center for Precision Automated Agricultural Systems , addressline= Washington State University , city= Prosser , postcode= 99350 , state= WA , country= USA organization= Biological \& Environmental Engineering Department , addressline= Cornell University , city= Ithaca , postcode= 14853 , state= NY , country= USA organization= Fruit Research Extension Center , addressline= The Penn State University , city= Biglerville , postcode= 17307 , state= PA , country= USA organization= School of Business Technology , addressline= Curry College , city= Milton , postcode= 02186 , state= MA , country= USA organization= Department of Viticulture Enology , addressline= Washington State University , city= Prosser , postcode= 99350 , state= WA , country= USA Genetic Improvement Research Unit (HCPGIRU) , city= Corvallis , postcode= 973300 , state= OR , country= USA organization= Mid-Columbia Agricultural Research Extension Center , addressline = Oregon State University , city= Corvallis , postcode= 97031 , state= OR , country= USA organization= Department of Plant Sciences , addressline = University of Tennessee, Institute of Agriculture , city= Knoxville , postcode= 37996 , state= TN , country= USA

AI总结 本文通过田间高光谱成像与机器学习整合,开发了特征选择框架以提高葡萄叶氮含量预测精度,验证了在不同生长阶段和品种间的方法有效性。

Comments Major Revision

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2604.16106 2026-04-21 cs.CY cs.AI

Reckoning with the Political Economy of AI: Avoiding Decoys in Pursuit of Accountability

审视人工智能的政治经济学:避免诱饵以实现问责

Janet Vertesi, danah boyd, Alex Taylor, Benjamin Shestakofsky

机构 * Sociology Department(社会学系) Princeton University(普林斯顿大学) Department of Communication(传播学系) Cornell University(康奈尔大学) Institute of Design Informatics(设计信息研究所) University of Edinburgh(爱丁堡大学) Department of Information Science(信息科学系)

AI总结 本文探讨人工智能项目中诱饵对权力与经济的影响,指出需识别诱饵的误导性并直接面对其物质政治经济,以促进更公平的AI发展。

Comments To be presented at ACM FAccT, Montréal, Canada, June 25 to June 28, 2026

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2512.10906 2026-04-21 math.OC cs.LG cs.SY eess.SY

Distributionally Robust Regret Optimal Control Under Moment-Based Ambiguity Sets

在基于矩的模糊集下实现分布鲁棒的后悔最优控制

Feras Al Taha, Eilyan Bitar

机构 * School of Electrical and Computer Engineering, Cornell University(康奈尔大学电气与计算机工程学院)

AI总结 本文研究了有限时间 horizon 的线性二次随机控制问题,设计了因果仿射控制策略以最小化模糊集内所有分布的最坏预期后悔,将其转化为可解的凸优化问题,并提出可扩展的投影子梯度方法进行计算。

Comments 24 pages, 4 figures, to appear in the Proceedings of the 8th Annual Learning for Dynamics & Control Conference

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2407.06048 2026-04-21 cs.CL cs.CV

Vision-Braille: A Curriculum Learning Toolkit and Braille-Chinese Corpus for Braille Translation

Vision-Braille: 一种课程学习工具包及用于盲文翻译的盲文-中文语料库

Alan Wu, Ye Yuan, Zhiping Xiao, Ming Zhang

机构 * State Key Laboratory for Multimedia Information Processing, School of Computer Science, PKU-Anker LLM Lab, Peking University(多媒体信息处理国家重点实验室,计算机学院,PKU-Anker LLM实验室,北京大学) College of Agriculture and Life Sciences, Cornell University(农业与生命科学学院,康奈尔大学) Computer Science Department, University of California at Los Angeles(计算机科学系,加州大学洛杉矶分校)

AI总结 本文提出Vision-Braille,首个可公开获取的端到端系统,用于将图像中提取的中文盲文翻译成书面中文。该系统解决有限标注资源和声调缺失问题,结合鲁棒的盲文OCR流程与针对序列到序列翻译微调的LLM。

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2502.02871 2026-04-21 cs.CL cs.AI

Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning

位置:多模态大语言模型可以显著推动科学推理

Yibo Yan, Shen Wang, Jiahao Huo, Jingheng Ye, Zhendong Chu, Xuming Hu, Philip S. Yu, Carla Gomes, Bart Selman, Qingsong Wen

机构 * Squirrel AI HKUST(GZ)(香港科技大学(广州)) HKUST(香港科技大学) Tsinghua University(清华大学) University of Illinois at Chicago(伊利诺伊大学香槟分校) Cornell University(康奈尔大学)

AI总结 本文探讨多模态大语言模型在科学推理中的应用,提出四阶段研究路线,指出当前模型在跨领域推理中的潜力与挑战,为实现通用人工智能提供新视角。

Comments Accepted by The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026, Findings)

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2604.16403 2026-04-21 cs.AI cs.CY

Computational Hermeneutics: Evaluating generative AI as a cultural technology

计算阐释学:评估生成式AI作为文化技术

Cody Kommers, Ruth Ahnert, Maria Antoniak, Emmanouil Benetos, Steve Benford, Mercedes Bunz, Baptiste Caramiaux, Shauna Concannon, Martin Disley, James Dobson, Yali Du, Edgar Duéñez-Guzmán, Kerry Francksen, Evelyn Gius, Jonathan W. Y. Gray, Ryan Heuser, Sarah Immel, Richard Jean So, Sang Leigh, Dalaki Livingston, Hoyt Long, Meredith Martin, Georgia Meyer, Daniela Mihai, Ashley Noel-Hirst, Kirsten Ostherr, Deven Parker, Yipeng Qin, Jessica Ratcliff, Emily Robinson, Karina Rodriguez, Adam Sobey, Ted Underwood, Aditya Vashistha, Matthew Wilkens, Youyou Wu, Yuan Zheng, Drew Hemment

机构 * The Alan Turing Institute(艾伦·图灵研究所) Queen Mary University of London(伦敦玛丽女王大学) University of Colorado(科罗拉多大学) University of Nottingham(诺丁汉大学) King’s College London(伦敦国王学院) Sorbonne Université(索邦大学) Durham University(杜伦大学) University of Edinburgh(爱丁堡大学) Dartmouth College(达特茅斯学院) Gibran AI(吉布兰人工智能) University of Coventry(科文特大学) Technische Universität Darmstadt(德累斯顿技术大学) University of Cambridge(剑桥大学) McGill University(麦吉尔大学) Cornell University(康奈尔大学) University of Utah(犹他大学) University of Chicago(芝加哥大学) Princeton University(普林斯顿大学) London School of Economics(伦敦经济学院) University of Southampton(南安普顿大学) Rice University(德克萨斯大学稻谷分校) University of Glasgow(格拉斯哥大学) Cardiff University(卡迪夫大学) University of Exeter(埃克塞特大学) University of Brighton(布里斯托尔大学) University of Illinois Urbana-Champaign(伊利诺伊大学厄巴纳-香槟分校) University College London(伦敦大学学院) University of Sheffield(谢菲尔德大学)

AI总结 本文提出计算阐释学框架,旨在通过阐释学理论评估生成式AI作为文化技术的含义生成与理解挑战。

Comments Published in Frontiers in Artificial Intelligence

Journal ref Front. Artif. Intell. 9:1753041

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2604.08626 2026-04-21 cs.CV

WildDet3D: Scaling Promptable 3D Detection in the Wild

WildDet3D: 在真实世界中扩展可提示的3D检测

Weikai Huang, Jieyu Zhang, Sijun Li, Taoyang Jia, Jiafei Duan, Yunqian Cheng, Jaemin Cho, Matthew Wallingford, Rustin Soraki, Chris Dongjoo Kim, Shuo Liu, Donovan Clay, Taira Anderson, Winson Han, Ali Farhadi, Bharath Hariharan, Zhongzheng Ren, Ranjay Krishna

机构 * Allen Institute for AI(人工智能研究院) University of Washington(华盛顿大学) Cornell University(康奈尔大学) Johns Hopkins University(约翰霍普金斯大学)

AI总结 本文提出WildDet3D,一种统一的几何感知架构,支持文本、点和框提示,并在推理时整合辅助深度信号。同时,构建了最大的开放3D检测数据集WildDet3D-Data,包含13.5K类别的100万张图像,在多个基准测试中取得新突破。

Comments code: https://github.com/allenai/WildDet3D website: https://allenai.github.io/WildDet3D/

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