Tootfinder

A Story About Bypassing Air Canada's In-flight Wifi Network Restrictions
https://ramsayleung.github.io/en/post/2025/a_story_about_bypassing_air_canadas_in-flight_network_restrictions/

Search for a photon peak from keV-scale dark matter annihilation with NuSTAR: Constraints on $\langle \sigma v \rangle$ after 11 years of observations
E. I. Zakharov, V. V. Barinov, D. S. Gorbunov, R. A. Krivonos, A. A. Mukhin
https://arxiv.org/abs/2509.08506

Search for a photon peak from keV-scale dark matter annihilation with NuSTAR: Constraints on $\langle σv \rangle$ after 11 years of observations
We report new constraints on the velocity-independent annihilation cross section $\langle σv \rangle$ of keV-scale dark matter particles based on 11 years of observations with the NuSTAR X-ray telescope. Using the unfocused stray light mode of the instrument, which provides a wide field-of-view and a stable instrumental background, we perform a sensitive search for photon signatures from dark matter annihilation in the Galactic halo. We model the resulting diffuse X-ray spectrum over the 3-20 …

🐮 Warming climate—not overgrazing—is biggest threat to rangelands, study suggests
https://phys.org/news/2025-09-climate-overgrazing-biggest-threat-rangelands.html

Warming climate—not overgrazing—is biggest threat to rangelands, study suggests
More than half of Earth's terrestrial surface is rangeland—vast open areas of native vegetation, suitable for grazing. These areas feed 50% of the world's livestock and support the livelihoods of more than 2 billion people. The continental U.S. is about one-third rangeland.

Learning stabilizer structure of quantum states
Srinivasan Arunachalam, Arkopal Dutt
https://arxiv.org/abs/2510.05890 https://arxiv.org/pdf/2510.05890

Learning stabilizer structure of quantum states
We consider the task of learning a structured stabilizer decomposition of an arbitrary $n$-qubit quantum state $|ψ\rangle$: for $\varepsilon > 0$, output a state $|ϕ\rangle$ with stabilizer-rank $\textsf{poly}(1/\varepsilon)$ such that $|ψ\rangle=|ϕ\rangle+|ϕ'\rangle$ where $|ϕ'\rangle$ has stabilizer fidelity $< \varepsilon$. We firstly show the existence of such decompositions using the recently established inverse theorem for the Gowers-$3$ norm of states …

Towards an application of fourth-order shear statistics I. The information content of $\langle M_\mathrm{ap}^4 \rangle $
Elena Silvestre-Rosello, Lucas Porth, Peter Schneider, Laila Linke, Jonas Krueger, Sebastian Grandis, Jonathan Oel
https://arxiv.org/abs/2509.07973

Towards an application of fourth-order shear statistics I. The information content of $\langle M_\mathrm{ap}^4 \rangle $
Higher-order shear statistics contain part of the non-Gaussian information of the projected matter field and therefore can provide additional constraints on the cosmological parameters when combined with second-order statistics. We aim to provide the theoretical framework for studying shear four-point correlation functions (4PCF) using fourth-order aperture statistics and develop a numerical integration pipeline to compute them. Finally, we forecast the information content of fourth-order apert…

Trace Minimization and Roots in ${\rm PSL}(2,\mathbb{R})$
Martin Kreuzer, Anja Moldenhauer, Gerhard Rosenberger
https://arxiv.org/abs/2508.06185 https://ar…

Trace Minimization and Roots in ${\rm PSL}(2,\mathbb{R})$
Suppose that $A,B \in {\rm PSL}(2,\mathbb{R})$ generate a discrete and free group of rank 2, and let $m,n\ge 1$. We consider subgroups $\langle R,S\rangle$ of ${\rm PSL}(2,\mathbb{R})$ generated by roots of $A$ and $B$, i.e., by elements such that $R^m=A$ and $S^n=B$. Depending on whether the commutator trace $τ={\rm tr}([A,B])$ is larger or smaller than 2, we describe necessary and sufficient conditions for $\langle R,S\rangle$ to be discrete and free of rank 2. For $τ\le -2$, this can be ch…

Michael Pratt, GirlsDoPorn Ringleader, Sentenced to 27 Years in Prison https://www.404media.co/michael-pratt-girlsdoporn-ringleader-sentenced/

Blockchain-Enabled Federated Learning
Murtaza Rangwala, Venugopal K R, Rajkumar Buyya
https://arxiv.org/abs/2508.06406 https://arxiv.org/pdf/2508.06406

Blockchain-Enabled Federated Learning
Blockchain-enabled federated learning (BCFL) addresses fundamental challenges of trust, privacy, and coordination in collaborative AI systems. This chapter provides comprehensive architectural analysis of BCFL systems through a systematic four-dimensional taxonomy examining coordination structures, consensus mechanisms, storage architectures, and trust models. We analyze design patterns from blockchain-verified centralized coordination to fully decentralized peer-to-peer networks, evaluating tr…

On the Quantum Equivalence between $S|LWE\rangle$ and $ISIS$
Andr\'e Chailloux, Paul Hermouet
https://arxiv.org/abs/2510.06097 https://arxiv.org/pdf/25…

On the Quantum Equivalence between $S|LWE\rangle$ and $ISIS$
Chen, Liu, and Zhandry [CLZ22] introduced the problems $S|LWE\rangle$ and $C|LWE\rangle$ as quantum analogues of the Learning with Errors problem, designed to construct quantum algorithms for the Inhomogeneous Short Integer Solution ($ISIS$) problem. Several later works have used this framework for constructing new quantum algorithms in specific cases. However, the general relation between all these problems is still unknown. In this paper, we investigate the equivalence between $S
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Variational Quantum Circuits in Offline Contextual Bandit Problems
Lukas Schulte, Daniel Hein, Steffen Udluft, Thomas A. Runkler
https://arxiv.org/abs/2509.07633 https://…

Variational Quantum Circuits in Offline Contextual Bandit Problems
This paper explores the application of variational quantum circuits (VQCs) for solving offline contextual bandit problems in industrial optimization tasks. Using the Industrial Benchmark (IB) environment, we evaluate the performance of quantum regression models against classical models. Our findings demonstrate that quantum models can effectively fit complex reward functions, identify optimal configurations via particle swarm optimization (PSO), and generalize well in noisy and sparse datasets.…