Tootfinder

Congress authorized the SAVE program to help states ensure eligibility for public benefits applications
-- not to block American citizens from voting.
With substantial new changes to the SAVE program,
there is even more reason for election officials to exercise caution when using the system and interpreting its results.
SAVE is a tool for verifying citizenship.
Like other large-scale data sources,
it has flaws, exacerbated by an aggressive overhaul.
T…

Homeland Security’s “SAVE” Program Exacerbates Risks to Voters
Bad data and a politicized overhaul could spread election fraud lies and disenfranchise voters.

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People are incorporating the UK Fuel Finder API into their Home Assistant setups.
https://github.com/philmale/UK-Fuel-Finder

GitHub - philmale/UK-Fuel-Finder: Maintain a database of UK fuel prices from the official UK Fuel Finder API, script supports querying the data and returning JSON responses suitable for Home Assistant command_line sensors and any other application that can ingest JSON.
Maintain a database of UK fuel prices from the official UK Fuel Finder API, script supports querying the data and returning JSON responses suitable for Home Assistant command_line sensors and any o...

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Welfarist Formulations for Diverse Similarity Search
Siddharth Barman, Nirjhar Das, Shivam Gupta, Kirankumar Shiragur
https://arxiv.org/abs/2602.08742 https://arxiv.org/pdf/2602.08742 https://arxiv.org/html/2602.08742
arXiv:2602.08742v1 Announce Type: new
Abstract: Nearest Neighbor Search (NNS) is a fundamental problem in data structures with wide-ranging applications, such as web search, recommendation systems, and, more recently, retrieval-augmented generations (RAG). In such recent applications, in addition to the relevance (similarity) of the returned neighbors, diversity among the neighbors is a central requirement. In this paper, we develop principled welfare-based formulations in NNS for realizing diversity across attributes. Our formulations are based on welfare functions -- from mathematical economics -- that satisfy central diversity (fairness) and relevance (economic efficiency) axioms. With a particular focus on Nash social welfare, we note that our welfare-based formulations provide objective functions that adaptively balance relevance and diversity in a query-dependent manner. Notably, such a balance was not present in the prior constraint-based approach, which forced a fixed level of diversity and optimized for relevance. In addition, our formulation provides a parametric way to control the trade-off between relevance and diversity, providing practitioners with flexibility to tailor search results to task-specific requirements. We develop efficient nearest neighbor algorithms with provable guarantees for the welfare-based objectives. Notably, our algorithm can be applied on top of any standard ANN method (i.e., use standard ANN method as a subroutine) to efficiently find neighbors that approximately maximize our welfare-based objectives. Experimental results demonstrate that our approach is practical and substantially improves diversity while maintaining high relevance of the retrieved neighbors.
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CALL FOR APPLICATIONS | 21st Singapore Graduate Forum on Southeast Asian Studies
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CALL FOR APPLICATIONS | 21st Singapore Graduate Forum on Southeast Asian Studies
https://ift.tt/K0xc4hV
Mining the Logs: Sources on Blue Humor URL …
via Input 4 RELCFP https://