Tootfinder

SakugaFlow: A Stagewise Illustration Framework Emulating the Human Drawing Process and Providing Interactive Tutoring for Novice Drawing Skills
Kazuki Kawamura, Jun Rekimoto
https://arxiv.org/abs/2506.08443

SakugaFlow: A Stagewise Illustration Framework Emulating the Human Drawing Process and Providing Interactive Tutoring for Novice Drawing Skills
While current AI illustration tools can generate high-quality images from text prompts, they rarely reveal the step-by-step procedure that human artists follow. We present SakugaFlow, a four-stage pipeline that pairs diffusion-based image generation with a large-language-model tutor. At each stage, novices receive real-time feedback on anatomy, perspective, and composition, revise any step non-linearly, and branch alternative versions. By exposing intermediate outputs and embedding pedagogical …

Laterally Excited Bulk Acoustic Wave (LBAW) X-Cut Lithium Niobate Resonators
Walter Gubinelli, Ryan Tetro, Pietro Simeoni, Luca Colombo, Matteo Rinaldi
https://arxiv.org/abs/2506.04433

Laterally Excited Bulk Acoustic Wave (LBAW) X-Cut Lithium Niobate Resonators
In this work, Laterally excited Bulk Acoustic Wave (LBAW) resonators on X-cut Lithium Niobate (LiNbO3) and, for the first time their higher-order overtones (LOBAW) are demonstrated by embedding interdigitated electrodes recessed into the piezoelectric thin film, allowing to exploit both S0 and SH0 vibrational modes. This recessed electrode architecture decouples the dispersion relation from film thickness, enabling lithographic tuning of resonance frequency and on-chip multi-frequency scaling o…

Educators' Perceptions of Large Language Models as Tutors: Comparing Human and AI Tutors in a Blind Text-only Setting
Sankalan Pal Chowdhury, Terry Jingchen Zhang, Donya Rooein, Dirk Hovy, Tanja K\"aser, Mrinmaya Sachan
https://arxiv.org/abs/2506.08702

Educators' Perceptions of Large Language Models as Tutors: Comparing Human and AI Tutors in a Blind Text-only Setting
The rapid development of Large Language Models (LLMs) opens up the possibility of using them as personal tutors. This has led to the development of several intelligent tutoring systems and learning assistants that use LLMs as back-ends with various degrees of engineering. In this study, we seek to compare human tutors with LLM tutors in terms of engagement, empathy, scaffolding, and conciseness. We ask human tutors to annotate and compare the performance of an LLM tutor with that of a human tut…