Hackers made death threats against this security researcher. In April 2024, a mysterious someone using the online handles "Waifu" and "Judische" began posting death threats on Telegram and Discord channels aimed at a cybersecurity researcher named Allison Nixon. These anonymous personas targeted Nixon because she had become a formidable threat: As chief research officer at the cyber investigations firm Unit 221B, named after Sherlock Holmes's apartment, she had built a career tracking cybercriminals and helping get them arrested. Though she'd done this work for more than a decade, Nixon couldn't understand why the person behind the accounts was suddenly threatening her. And although she had taken an interest in the Waifu persona in years past for crimes he boasted about committing, he hadn't been on her radar for a while when the threats began, because she was tracking other targets. Now Nixon resolved to unmask Waifu/Judische and others responsible for the death threats--and take them down for crimes they admitted to committing.

As recommended by the NeurIPS dataset and benchmark track, we documented QM1B and intended uses through the Datasheets for Datasets framework [1]. The goal of dataset datasheets as outlined by [1] is to provide a standardized process for documentating datasets. The authors of [1] present a list of carefully selected questions which dataset authors should answer. We hope our answers to these questions will facilitate better communication between us (the dataset creators) and future users of QM1B. For what purpose was the dataset created? Prior gaussian-based Density Functional Theory (DFT) datasets contained fewer than 20 million training examples.

Pleaseprovideadescription.531 We want to provide an open-source large-scale music dataset for the research com-532 munity. Such large datasets do not yet exist in this domain, and we believetheyare533 neededtodemocratize innovationinmusicresearch andML-assisted musiccreation.534

However, existing music datasets suffer from limited size, accessibility, and lack of audio resources.

A.1 Deriving the Optimum of the KL-Constrained Reward Maximization Objective In this appendix, we will derive Eq. 4. Analogously to Eq. 3, we optimize the following objective: max

Membership inference arises in these contexts as a potential auditing method for detecting unauthorized data usage.