New individual titles of interest to physics, math, and astronomy:
Machine Learning for Small Bodies in the Solar System
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Mathematical and Computational Methods for Complex Social Systems
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Practis: Precalculus Review and Calculus Topics in Sync
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One-Dimensional Ergodic Schrödinger Operators: II. Specific Classes
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Machine Learning for Physics and Astronomy
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Introduction to Lie Algebras: Finite and Infinite Dimension
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Relativity and Cosmology - From First Principles to Interpretation
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From Representation Theory to Mathematical Physics and Back
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We now have access to American Journal of Mathematics, 1996 to current.
The content is hosted by Johns Hopkins University Press, and is on the Project Muse platform.
To access directly, visit this link: https://muse.jhu.edu/journal/5
(Off-campus access, please use this proxied link.
To access older content, 1878 to present, please visit JSTOR: https://www.jstor.org/journal/amerjmath (off-campus access, please use this proxied link). (Note there is a 72-month embargo on JSTOR. Therefore, to access content from 72 months ago to current, please access using the Project Muse link above).
The Caltech Library has subscribed to a new research tool called Keenious. Keenious makes recommendations for further reading based either on an existing text or PDF documents using a variety of techniques, including a language model to incorporate semantics of the texts and topics. Keenious is a very different type of search tool than you might be used to. With Keenious, you use your current document as an input into the search, compared to using just keywords as done in traditional search engines. Access the Keenious Help Center.
(Sign in with your Caltech email address if off-campus, which will authenticate via Access credentials).
This article provides a brief overview of how Keenious works.
This article describes some of the main features of the tool.
The video on this page provides a very useful orientation in about 7 minutes.
Here are some libguides:
This article, in the Journal of eScience Librarianship, discusses the implementation of Keenious at Carnegie Mellon University, including "the nature of AI-based tools when compared with traditional library resources, including ethical questions surrounding data privacy, algorithmic transparency, and the impact on the research process."
Keenious offers introductory webinars for users with no experience.
