Tootfinder

Chicago is in Cook County and now courts are safer.
"Cook County’s top judge signed an order barring ICE from arresting people at court."
"...bars the civil arrest of any “party, witness, or potential witness” while going to court proceedings. It includes arrests inside courthouses and in parking lots, surrounding sidewalks and entryways."

Cook County judge's order bars ICE from arresting people at court in Chicago area
Cook County’s top judge signed an order barring ICE from arresting people at court. The county includes Chicago, which has seen a federal immigration crackdown in recent months. Detaining residents outside courthouses has been a common tactic for federal agents, who have been stationed outside county courthouses for weeks, making arrests and drawing crowds of protesters. The order, which takes effect Wednesday, bars the civil arrest of any “party, witness, or potential witness” while goin…

“In a recent meeting where BBC studio executives again voiced problems, I realised my choices. Obedience and being quieter to remain making Monkey Cage, or resign and have the freedom to speak out against what I believe are injustices. I chose the latter. It broke my heart.”
– @…
Told you folks he’s a good egg :)

KnowRL: Teaching Language Models to Know What They Know
Sahil Kale, Devendra Singh Dhami
https://arxiv.org/abs/2510.11407 https://arxiv.org/pdf/2510.11407

KnowRL: Teaching Language Models to Know What They Know
Truly reliable AI requires more than simply scaling up knowledge; it demands the ability to know what it knows and when it does not. Yet recent research shows that even the best LLMs misjudge their own competence in more than one in five cases, making any response born of such internal uncertainty impossible to fully trust. Inspired by self-improvement reinforcement learning techniques that require minimal data, we present a simple but powerful framework KnowRL that strengthens a model's intern…

Revised comment on the paper titled "The Origin of Quantum Mechanical Statistics: Insights from Research on Human Language
Miko{\l}aj Sienicki, Krzysztof Sienicki
https://arxiv.org/abs/2512.07881 https://arxiv.org/pdf/2512.07881 https://arxiv.org/html/2512.07881
arXiv:2512.07881v1 Announce Type: new
Abstract: This short note comments on \citet{Aerts2024Origin}, which proposes that ranked word frequencies in texts should be read through the lens of Bose--Einstein (BE) statistics and even used to illuminate the origin of quantum statistics in physics. The core message here is modest: the paper offers an interesting analogy and an eye-catching fit, but several key steps mix physical claims with definitions and curve-fitting choices. We highlight three such points: (i) a normalization issue that is presented as "bosonic enhancement", (ii) an identification of rank with energy that makes the BE fit only weakly diagnostic of an underlying mechanism, and (iii) a baseline comparison that is too weak to support an ontological conclusion. We also briefly flag a few additional concerns (interpretation drift, parameter semantics, and reproducibility).
toXiv_bot_toot

Spectral Properties of Anomalous Microwave Emission in 144 Galactic Clouds
Roke Cepeda-Arroita, J. A. Rubi\~no-Mart\'in, R. T. G\'enova-Santos, C. Dickinson, S. E. Harper, F. Poidevin, M. W. Peel, R. Rebolo, D. Adak, A. Almeida, K. Aryan, R. B. Barreiro, F. J. Casas, J. M. Casas, J. Chluba, M. Fern\'andez-Torreiro, D. Herranz, G. A. Hoerning, Michael E. Jones, J. Leech, E. Mart\'inez-Gonz\'alez, T. J. Pearson, Angela C. Taylor, P. Vielva, R. A. Watson, Z. Zhang

Spectral Properties of Anomalous Microwave Emission in 144 Galactic Clouds
Anomalous Microwave Emission (AME) is a diffuse microwave component thought to arise from spinning dust grains, yet remains poorly understood. We analyze AME in 144 Galactic clouds by combining low-frequency maps from S-PASS (2.3 GHz), C-BASS (4.76 GHz), and QUIJOTE (10-20 GHz) with 21 ancillary maps. Using aperture photometry and parametric SED fitting via MCMC methods without informative priors, we measure AME emissivity, peak frequency, and spectral width. We achieve peak frequency constrain…

ELIGANT-TN -- ELI Gamma Above Neutron Threshold: The Thermal Neutron setup
P. -A. S\"oderstr\"om, D. L. Balabanski, M. Cuciuc, D. M. Filipescu, I. Gheorghe, A. Ku\c{s}o\u{g}lu, C. Matei, D. Testov, S. Aogaki, H. T. Aslani, L. Capponi, D. Choudhury, G. Ciocan, T. Glodariu, M. Krzysiek, V. Lelasseux, R. Roy, R. F. Andrei, M. Brezeanu, R. Corbu, A. Dhal, D. Iancu, D. Kahl, S. Ioannidis, K. KeunHwan, G. Lorusso, B. Mauyey, T. Petruse, G. V. Turturic\u{a}

ELIGANT-TN -- ELI Gamma Above Neutron Threshold: The Thermal Neutron setup
Here we present the thermal neutron counter from the ELI Gamma Above Neutron Threshold setup recently implemented at the Extreme Light Infrastructure - Nuclear Physics. The main goals of the detector array are presented, as well as the mechanical design, electronics, and the performance of the detectors. In addition, we have performed Geant4 and MCNP simulations monoenergetic neutrons and measurements with typical neutron sources. Finally, we show the performance of the array as a whole and out…

Single-Round Deterministic Quantum Anonymous Veto Using Bell States
Ravi Sangwan, Harishankar Mishra, Henry Sukumar, Gudapati Naresh Raghava
https://arxiv.org/abs/2509.15951 htt…

Single-Round Deterministic Quantum Anonymous Veto Using Bell States
Quantum Anonymous Veto (QAV) protocols enable secure and anonymous decision-making by allowing participants to detect the presence of a veto without revealing individual choices. While existing QAV schemes offer strong theoretical guarantees, they face significant limitations in practical implementation due to resource requirements, scalability issues, and the need for multipartite entanglement. In this work, we propose a novel deterministic QAV protocol that leverages only bipartite entangleme…

A Hypothesis-First Framework for Mechanistic Model Evaluation and Selection in Neuroimaging
Dominic Boutet, Sylvain Baillet
https://arxiv.org/abs/2509.16070 https://

A Hypothesis-First Framework for Mechanistic Model Evaluation and Selection in Neuroimaging
Neuroimaging provides rich measurements of brain structure and neural activity, but turning these data into mechanistic insight remains difficult. Statistical models quantify associations without much considerations for how they arise, whereas bio-realistic models directly embody candidate mechanisms but remain hard to deploy rigorously without specialized training. We present a framework that recasts modeling choices as testable mechanistic hypotheses and supplies a simple protocol for rejecti…