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1. State of the Climate in 2022

   https://doi.org/10.1175/2023BAMSStateoftheClimate.1  

   Blunden, J; Boyer, T; Bartow-Gillies, E (September 2023, Bulletin of the American Meteorological Society)

   Abstract Editors note: For easy download the posted pdf of the State of the Climate in 2022 is a low-resolution file. A high-resolution copy of the report is available by clickinghere. Please be patient as it may take a few minutes for the high-resolution file to download. 

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2. State of the Climate in 2023

   https://doi.org/10.1175/2024BAMSStateoftheClimate.1  

   Blunden, J; Boyer, T (August 2024, Bulletin of the American Meteorological Society)

   Abstract Editors note: For easy download the posted pdf of the State of the Climate in 2023 is a low-resolution file. A high-resolution copy of the report is available by clickinghere. Please be patient as it may take a few minutes for the high-resolution file to download. 

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3. BiasedMNIST

   https://doi.org/10.35009/CFCCIS-AY07  

   Shrestha, Robik; Kafle, Kushal; Kanan, Christopher (January 2022, European Conference on Computer Vision (ECCV))

   Dataset download link: https://drive.google.com/file/d/1_77AKsY5MoYpDnXgNkjWi9n2_mfQBW-F/view 

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4. The Capacity of Private Information Retrieval from Decentralized Uncoded Caching Databases

   https://doi.org/10.3390/info10120372  

   Wei, Yi-Peng; Arasli, Batuhan; Banawan, Karim; Ulukus, Sennur (December 2019, Information)

   null (Ed.)

   We consider the private information retrieval (PIR) problem from decentralized uncoded caching databases. There are two phases in our problem setting, a caching phase, and a retrieval phase. In the caching phase, a data center containing all the K files, where each file is of size L bits, and several databases with storage size constraint μ K L bits exist in the system. Each database independently chooses μ K L bits out of the total K L bits from the data center to cache through the same probability distribution in a decentralized manner. In the retrieval phase, a user (retriever) accesses N databases in addition to the data center, and wishes to retrieve a desired file privately. We characterize the optimal normalized download cost to be D * = ∑ n = 1 N + 1 N n - 1 μ n - 1 ( 1 - μ ) N + 1 - n 1 + 1 n + ⋯ + 1 n K - 1 . We show that uniform and random caching scheme which is originally proposed for decentralized coded caching by Maddah-Ali and Niesen, along with Sun and Jafar retrieval scheme which is originally proposed for PIR from replicated databases surprisingly results in the lowest normalized download cost. This is the decentralized counterpart of the recent result of Attia, Kumar, and Tandon for the centralized case. The converse proof contains several ingredients such as interference lower bound, induction lemma, replacing queries and answering string random variables with the content of distributed databases, the nature of decentralized uncoded caching databases, and bit marginalization of joint caching distributions. 

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5. Source code: Basins modulate signatures of river salinization

   https://doi.org/10.5281/zenodo.11130548  

   Platt, Lindsay; Dugan, Hilary (May 2024, Zenodo)

   This resource contains source code and select data products behind the following Master's Thesis: Platt, L. (2024). Basins modulate signatures of river salinization (Master's thesis). University of Wisconsin-Madison, Freshwater and Marine Sciences. The source code represents an R-based data processing and modeling pipeline written using the R package "targets". Some of the folders in the source code zipfile are intentionally left empty (except for a hidden file ".placeholder") in order for the code repository to be setup with the required folder structure. To execute this code, download the zip folder, unzip, and open the salt-modeling-data.Rproj file. Then, reference the instructions in the README.md file for installing packages, building the pipeline, and examining the results. Newer versions of this repository may be updated in GitHub at github.com/lindsayplatt/salt-modeling-data. In addition to the source code, this resource contains three data files containing intermediate products of the pipeline. The first two represent data prepared for the random forest modeling. Data download and processing were completed in pipeline phases 1 - 5, and the random forest modeling was completed in phase 6 (see source code).  site_attributes.csv which contains the USGS gage site numbers and their associated basin attributes site_classifications.csv which contains the classification of a site for both episodic signatures ("Episodic" or "Not episodic") and baseflow salinization signatures ("positive", "none", "negative", or NA). Note that an NA in the baseflow classification column means that the site did not meet minimum data requirements for calculating a trend and was not used in the random forest model for baseflow salinization. site_attribute_details.csv contains a table of each attribute shorthand used as column names in site_attributes.csv and their names, units, description, and data source. 

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