Tootfinder

PROTIP: filling your head with an incalculable amount of random bullshit is a shortcut to becoming funny! 😃

#JamesSloyan #CarolSiskind
Season 1 Episode 6 "The Sky is Falling"
#RandomBaywatch #lvdlpx

Space-time fractional stochastic partial differential equations driven by L\'evy white noise
Yuhui Guo, Jiang-Lun Wu
https://arxiv.org/abs/2506.12834 h…

Space-time fractional stochastic partial differential equations driven by Lévy white noise
This paper is concerned with the following space-time fractional stochastic nonlinear partial differential equation \begin{equation*} \left(\partial_t^β+\fracν{2}\left(-Δ\right)^{α/ 2}\right) u=I_{t}^γ\Big[ f(t,x,u)-\sum_{i=1}^{d} \frac{\partial}{\partial x_i} q_i(t,x,u)+ σ(t,x,u) F_{t,x}\Big] \end{equation*} for a random field $u(t,x):[0,\infty)\times\mathbb{R}^d \mapsto\mathbb{R}$, where $α>0, β\in(0,2), γ\ge0, ν>0, F_{t,x}$ is a Lévy space-time white noise, $I_{t}^γ$ stands for…

Completely randomly and for absolutely no reason at all...
Arrow, "(Feeling) Hot Hot Hot"
#80sMusic #Soca #UKweather

Faster Estimation of the Average Degree of a Graph Using Random Edges and Structural Queries
Lorenzo Beretta, Deeparnab Chakrabarty, C. Seshadhri
https://arxiv.org/abs/2507.06925 …

Faster Estimation of the Average Degree of a Graph Using Random Edges and Structural Queries
We revisit the problem of designing sublinear algorithms for estimating the average degree of an $n$-vertex graph. The standard access model for graphs allows for the following queries: sampling a uniform random vertex, the degree of a vertex, sampling a uniform random neighbor of a vertex, and ``pair queries'' which determine if a pair of vertices form an edge. In this model, original results [Goldreich-Ron, RSA 2008; Eden-Ron-Seshadhri, SIDMA 2019] on this problem prove that the complexity of…

Maximal intrinsic randomness of noisy quantum measurements
Fionnuala Curran, Morteza Moradi, Gabriel Senno, Magdalena Stobinska, Antonio Ac\'in
https://arxiv.org/abs/2506.22294

Maximal intrinsic randomness of noisy quantum measurements
Quantum physics exhibits an intrinsic and private form of randomness with no classical counterpart. Any setup for quantum randomness generation involves measurements acting on quantum states. In this work, we consider the following question: Given a quantum measurement, how much randomness can be generated from it? In real life, measurements are noisy and thus contain an additional, extrinsic form of randomness due to ignorance. This extrinsic randomness is not private since, in an adversarial …

ConStyX: Content Style Augmentation for Generalizable Medical Image Segmentation
Xi Chen, Zhiqiang Shen, Peng Cao, Jinzhu Yang, Osmar R. Zaiane
https://arxiv.org/abs/2506.10675

ConStyX: Content Style Augmentation for Generalizable Medical Image Segmentation
Medical images are usually collected from multiple domains, leading to domain shifts that impair the performance of medical image segmentation models. Domain Generalization (DG) aims to address this issue by training a robust model with strong generalizability. Recently, numerous domain randomization-based DG methods have been proposed. However, these methods suffer from the following limitations: 1) constrained efficiency of domain randomization due to their exclusive dependence on image style…

The Ex spent the night
#WendieMalick
Season 1 Episode 6 "The Sky is Falling"
#RandomBaywatch #lvdlpx

🇺🇦 #NowPlaying on BBCRadio3's #RoundMidnight
Poppy Daniels:
🎵 Feeling Better
#PoppyDaniels
https://open.spotify.com/track/1gZ3AzQq85TOyX5nTQgZlg

On Chaos in QFT
Jacob Sonnenschein, Nadav Shrayer
https://arxiv.org/abs/2506.10784 https://arxiv.org/pdf/2506.10784

On Chaos in QFT
In this note we explore the chaotic behavior of non-integrable QFTs and compare them to integrable ones. We choose as prototypes the double sine-Gordon and the sine-Gordon models. We analyze their discrete spectrum determined by a truncation method. We examine the map of the corresponding energy eigenvalues to the eigenvalues of the random matrix theory (RMT) Gaussian orthogonal ensemble (GOE). This is done by computing the following properties: (a) The distribution of the adjacent spacings and…