Tootfinder

The Origami flip graph of the $2\times n$ Miura-ori
Lumi Christensen, Thomas C. Hull, Emma O'Neil, Valentina Pappano, Natalya Ter-Saakov, Kacey Yang
https://arxiv.org/abs/2506.19700

The Origami flip graph of the $2\times n$ Miura-ori
Given an origami crease pattern $C=(V,E)$, a straight-line planar graph embedded in a region of $\mathbb{R}^2$, we assign each crease to be either a mountain crease (which bends convexly) or a valley crease (which bends concavely), creating a mountain-valley (MV) assignment $μ:E\to\{-1,1\}$. An MV assignment $μ$ is locally valid if the faces around each vertex in $C$ can be folded flat under $μ$. In this paper, we investigate locally valid MV assignments of the Miura-ori, $M_{m,n}$, an $m\ti…

Discovery and Localization of the Swift-Observed FRB 20241228A in a Star-forming Host Galaxy
Alice P. Curtin, Shion Andrew, Sunil Simha, Alice Cai, Kenzie Nimmo, Shami Chatterjee, Amanda M. Cook, Fengqiu Adam Dong, Yuxin Dong, Tarraneh Eftekhari, Wen-fai Fong, Emmanuel Fonseca, Jason W. T. Hessels, Ronniy C. Joseph, Victoria Kaspi, Calvin Leung, Robert Main, Kiyoshi W. Masui, Ryan Mckinven, Daniele Michilli, Mason Ng, Ayush Pandhi, Aaron B. Pearlman, Ziggy Pleunis, Mawson W. Sammons, K…

Discovery and Localization of the Swift-Observed FRB 20241228A in a Star-forming Host Galaxy
On 2024 December 28, CHIME/FRB detected the thus-far non-repeating FRB 20241228A with a real-time signal-to-noise ratio of $>50$. Approximately 112~s later, the X-ray Telescope onboard the Neil Gehrels Swift Observatory was on source, the fastest follow-up to-date of a non-repeating FRB (Tohuvavohu et al. in prep.). Using CHIME/FRB and two of the three CHIME/FRB Outriggers, we obtained a Very Long Baseline Interferometry localization for FRB 20241228A with a 1$σ$ confidence ellipse of 11$^{\pr…

Overcoming Data Scarcity in Scanning Tunnelling Microscopy Image Segmentation
Nikola L. Kolev, Max Trouton, Filippo Federici Canova, Geoff Thornton, David Z. Gao, Neil J. Curson, Taylor J. Z. Stock
https://arxiv.org/abs/2506.01678

Overcoming Data Scarcity in Scanning Tunnelling Microscopy Image Segmentation
Scanning tunnelling microscopy (STM) is a powerful technique for imaging surfaces with atomic resolution, providing insight into physical and chemical processes at the level of single atoms and molecules. A regular task of STM image analysis is the identification and labelling of features of interest against a uniform background. Performing this manually is a labour-intensive task, requiring significant human effort. To reduce this burden, we propose an automated approach to the segmentation of…