Tootfinder

Wind-fed Supermassive Black Hole Accretion by the Nuclear Star Cluster: the Case of M31*
Zhao Su, Zhiyuan Li, Zongnan Li
https://arxiv.org/abs/2506.04778 h…

Wind-fed Supermassive Black Hole Accretion by the Nuclear Star Cluster: the Case of M31*
The central supermassive black hole (SMBH) of the Andromeda galaxy, known as M31*, exhibits dim electromagnetic emission and is inferred to have an extremely low accretion rate for its remarkable mass ($\sim10^8~\rm~M_\odot$). In this work, we use three-dimensional hydrodynamical simulations to explore a previously untested scenario, in which M31* is fed by the collective stellar mass-loss from its surrounding nuclear star cluster, manifested as a famous eccentric disk of predominantly old stel…

With His Reordering of Global Trade, Trump Turns to the Untested (Ana Swanson/New York Times)
https://www.nytimes.com/2025/08/01/business/economy/trump-tariffs.html
http://www.memeorandum.com/250801/p76#a250801p76

Simulating Evolvability as a Learning Algorithm: Empirical Investigations on Distribution Sensitivity, Robustness, and Constraint Tradeoffs
Nicholas Fidalgo, Puyuan Ye
https://arxiv.org/abs/2507.18666 …

Simulating Evolvability as a Learning Algorithm: Empirical Investigations on Distribution Sensitivity, Robustness, and Constraint Tradeoffs
The theory of evolvability, introduced by Valiant (2009), formalizes evolution as a constrained learning algorithm operating without labeled examples or structural knowledge. While theoretical work has established the evolvability of specific function classes under idealized conditions, the framework remains largely untested empirically. In this paper, we implement a genetic algorithm that faithfully simulates Valiant's model and conduct extensive experiments across six Boolean function classes…

Predictive Analytics for Collaborators Answers, Code Quality, and Dropout on Stack Overflow
Elijah Zolduoarrati, Sherlock A. Licorish, Nigel Stanger
https://arxiv.org/abs/2506.18329

Predictive Analytics for Collaborators Answers, Code Quality, and Dropout on Stack Overflow
Previous studies that used data from Stack Overflow to develop predictive models often employed limited benchmarks of 3-5 models or adopted arbitrary selection methods. Despite being insightful, their limited scope suggests the need to benchmark more models to avoid overlooking untested algorithms. Our study evaluates 21 algorithms across three tasks: predicting the number of question a user is likely to answer, their code quality violations, and their dropout status. We employed normalisation,…

Hooray, parallel downloading of repository metadata was merged for #dnf! 🥳
https://github.com/rpm-software-management/dnf5/issues/307#issuecomment-2…

Statement from CND opposing Starmer/Miliband's #nuclear power plans, noting,
a) uranium is imported, b) Sizesell & SMRs are intested designs, c) the link to nuclear weapons of mass destruction.
"Government’s nuclear power roll out is about building up its weapons programme" - #CND…

Government’s nuclear power roll out is about building up its weapons programme - CND
The government’s announcement to pour billions of pounds into Sizewell C and roll out untested small modular reactor (SMR) nuclear plants is about building up its nuclear weapons programme rather than securing energy self-sufficiency.

Watermark in the Classroom: A Conformal Framework for Adaptive AI Usage Detection
Yangxinyu Xie, Xuyang Chen, Zhimei Ren, Weijie J. Su
https://arxiv.org/abs/2507.23113 https://

Watermark in the Classroom: A Conformal Framework for Adaptive AI Usage Detection
As artificial intelligence tools become ubiquitous in education, maintaining academic integrity while accommodating pedagogically beneficial AI assistance presents unprecedented challenges. Current AI detection systems fail to control false positive rates (FPR) and suffer from bias against minority student groups, prompting institutional suspensions of these technologies. Watermarking techniques offer statistical rigor through precise $p$-values but remain untested in educational contexts where…

Discovering the Higgsino at CTAO-North within the Decade
Shotaro Abe, Tomohiro Inada, Emmanuel Moulin, Nicholas L. Rodd, Benjamin R. Safdi, Weishuang Linda Xu
https://arxiv.org/abs/2506.08084

Discovering the Higgsino at CTAO-North within the Decade
We demonstrate that higgsino dark matter (DM) could be discovered within the next few years using the Cherenkov Telescope Array Observatory's soon-to-be-operational northern site (CTAO-North). A 1.1 TeV thermal higgsino is a highly motivated yet untested model of DM. Despite its strong theoretical motivation in supersymmetry and beyond, the higgsino is notoriously difficult to detect; it lies deep within the neutrino fog of direct detection experiments and could pose a challenge even for a futu…

This https://arxiv.org/abs/2503.02189 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

Adaptive Traffic Signal Control based on Multi-Agent Reinforcement Learning. Case Study on a simulated real-world corridor
Previous studies that have formulated multi-agent reinforcement learning (RL) algorithms for adaptive traffic signal control have primarily used value-based RL methods. However, recent literature has shown that policy-based methods may perform better in partially observable environments. Additionally, RL methods remain largely untested for real-world normally signal timing plans because of the simplifying assumptions common in the literature. The current study attempts to address these gaps and…

Recognizing Every Voice: Towards Inclusive ASR for Rural Bhojpuri Women
Sakshi Joshi, Eldho Ittan George, Tahir Javed, Kaushal Bhogale, Nikhil Narasimhan, Mitesh M. Khapra
https://arxiv.org/abs/2506.09653

Recognizing Every Voice: Towards Inclusive ASR for Rural Bhojpuri Women
Digital inclusion remains a challenge for marginalized communities, especially rural women in low-resource language regions like Bhojpuri. Voice-based access to agricultural services, financial transactions, government schemes, and healthcare is vital for their empowerment, yet existing ASR systems for this group remain largely untested. To address this gap, we create SRUTI ,a benchmark consisting of rural Bhojpuri women speakers. Evaluation of current ASR models on SRUTI shows poor performance…