Tootfinder

This https://arxiv.org/abs/2504.18299 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Dust-driven vortex cascades originating at water snow regions: A pathway to planetesimal formation
The origin of observed planetary systems, including our Solar System, as well as their diversity, is still an open question. Streaming instability (SI) is an important mechanism for the formation of gravitationally bound planetesimals, which can grow to form planetary embryos and eventually planets. Snow lines in a protoplanetary disk can assist this process, as they can form pressure maxima and promote both dust accumulation and growth. Since the sublimation of a volatile is gradual due to opa…

SSIMBaD: Sigma Scaling with SSIM-Guided Balanced Diffusion for AnimeFace Colorization
Junpyo Seo (Department of Computer Science, Seoul National University), Hanbin Koo (Department of Computer Science, Seoul National University), Jieun Yook (Department of Computer Science, Seoul National University), Byung-Ro Moon (Department of Computer Science, Seoul National University)
…

SSIMBaD: Sigma Scaling with SSIM-Guided Balanced Diffusion for AnimeFace Colorization
We propose a novel diffusion-based framework for automatic colorization of Anime-style facial sketches. Our method preserves the structural fidelity of the input sketch while effectively transferring stylistic attributes from a reference image. Unlike traditional approaches that rely on predefined noise schedules - which often compromise perceptual consistency -- our framework builds on continuous-time diffusion models and introduces SSIMBaD (Sigma Scaling with SSIM-Guided Balanced Diffusion). …

Leveraging AM and FM Rhythm Spectrograms for Dementia Classification and Assessment
Parismita Gogoi, Vishwanath Pratap Singh, Seema Khadirnaikar, Soma Siddhartha, Sishir Kalita, Jagabandhu Mishra, Md Sahidullah, Priyankoo Sarmah, S. R. M. Prasanna
https://arxiv.org/abs/2506.00861

Leveraging AM and FM Rhythm Spectrograms for Dementia Classification and Assessment
This study explores the potential of Rhythm Formant Analysis (RFA) to capture long-term temporal modulations in dementia speech. Specifically, we introduce RFA-derived rhythm spectrograms as novel features for dementia classification and regression tasks. We propose two methodologies: (1) handcrafted features derived from rhythm spectrograms, and (2) a data-driven fusion approach, integrating proposed RFA-derived rhythm spectrograms with vision transformer (ViT) for acoustic representations alo…