V699

Documentation for GitLab Community Edition, GitLab Enterprise Edition, Omnibus GitLab, and GitLab Runner.

Identifying hit songs is notoriously difficult. Traditionally, song elements have been measured from large databases to identify the lyrical aspects of hits. We took a different methodological approach, measuring neurophysiologic responses to a set of songs provided by a streaming music service that identified hits and flops. We compared several statistical approaches to examine the predictive accuracy of each technique. A linear statistical model using two neural measures identified hits with 69% accuracy. Then, we created a synthetic set data and applied ensemble machine learning to capture inherent non-linearities in neural data. This model classified hit songs with 97% accuracy. Applying machine learning to the neural response to 1st min of songs accurately classified hits 82% of the time showing that the brain rapidly identifies hit music. Our results demonstrate that applying machine learning to neural data can substantially increase classification accuracy for difficult to predict market outcomes.

testing federation @flancian

#fediverse

President Joe Biden said on Monday the threat of Russian President Vladimir Putin using tactical nuclear weapons is "real", days after denouncing Russia's deployment of such weapons in Belarus. On Saturday, Biden called Putin's announcement that Russia had deployed its first tactical nuclear weapons to Belarus "absolutely irresponsible".

TypeScript 5.2 introduces 'using', a keyword that disposes anything with a Symbol.dispose function upon leaving scope, making resource management easier.

I feel like /m/tech is better than hacker news

#tech