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Titlebook: Research in Mathematics and Public Policy; Mary Lee,Aisha Najera Chesler Book 2020 The Author(s) and the Association for Women in Mathemat

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21#
發(fā)表于 2025-3-25 04:27:40 | 只看該作者
22#
發(fā)表于 2025-3-25 08:42:25 | 只看該作者
Book 2020f cybersecurity and climate change, two broad and impactful issues that benefit greatly from mathematical techniques. Each chapter in the book is a mathematical look into a specific research question related to one of these issues, an approach that offers the reader insight into the application of m
23#
發(fā)表于 2025-3-25 13:30:26 | 只看該作者
Exploring the Application of Machine Learning for Downscaling Climate Projections,s (here represented by ERA-Interim reanalyses) to create temperature predictions at specific locations in areas of complex terrain. The methods are trained on and validated by temperature readings from mesonet weather stations in Colorado. This work has implications for fire prevention and water resources management, among other applications.
24#
發(fā)表于 2025-3-25 15:52:35 | 只看該作者
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發(fā)表于 2025-3-25 22:42:35 | 只看該作者
Association for Women in Mathematics Serieshttp://image.papertrans.cn/r/image/827995.jpg
26#
發(fā)表于 2025-3-26 01:14:07 | 只看該作者
https://doi.org/10.1007/978-3-030-58748-2cybersecurity; climate change; public policy; cryptography; modeling weather phenomena; digital forensics
27#
發(fā)表于 2025-3-26 07:05:30 | 只看該作者
978-3-030-58750-5The Author(s) and the Association for Women in Mathematics 2020
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發(fā)表于 2025-3-26 09:19:28 | 只看該作者
29#
發(fā)表于 2025-3-26 13:13:29 | 只看該作者
Transparency Tools for Fairness in AI (Luskin),tools on three different publicly available datasets. We find that the tools are useful for understanding various dimensions of bias, and that in practice the algorithms are effective in starkly reducing a given observed bias when tested on new data.
30#
發(fā)表于 2025-3-26 20:50:15 | 只看該作者
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