Tootfinder

Lossless, Non-Volatile Post-Fabrication Trimming of PICs via On-Chip High-Temperature Annealing of Undercut Waveguides
Yating Wu, Haozhe Sun, Bo Xiong, Yalong Yv, Jiale Zhang, Zhaojie Zheng, Wei Ma, Tao Chu
https://arxiv.org/abs/2506.18633

Lossless, Non-Volatile Post-Fabrication Trimming of PICs via On-Chip High-Temperature Annealing of Undercut Waveguides
Limited by equipment precision, manufacturing deviations in waveguide width, etch depth, and layer thickness inevitably occur in photonic integrated circuits (PICs). These variations cause initial phase errors, compromising the reliability of phase-sensitive devices such as Mach-Zehnder Interferometers (MZI) and microring resonators. To overcome this, we report a nonvolatile, near-lossless post-trimming method utilizing sufficient high-temperature thermal treatment for undercut waveguides, repo…

Can AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation
Chenrui Ma, Zechang Sun, Tao Jing, Zheng Cai, Yuan-Sen Ting, Song Huang, Mingyu Li
https://arxiv.org/abs/2506.16233

Can AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation
Observational astronomy relies on visual feature identification to detect critical astrophysical phenomena. While machine learning (ML) increasingly automates this process, models often struggle with generalization in large-scale surveys due to the limited representativeness of labeled datasets -- whether from simulations or human annotation -- a challenge pronounced for rare yet scientifically valuable objects. To address this, we propose a conditional diffusion model to synthesize realistic g…

Doubly Smoothed Optimistic Gradients: A Universal Approach for Smooth Minimax Problems
Taoli Zheng, Anthony Man-Cho So, Jiajin Li
https://arxiv.org/abs/2506.07397

Doubly Smoothed Optimistic Gradients: A Universal Approach for Smooth Minimax Problems
Smooth minimax optimization problems play a central role in a wide range of applications, including machine learning, game theory, and operations research. However, existing algorithmic frameworks vary significantly depending on the problem structure -- whether it is convex-concave, nonconvex-concave, convex-nonconcave, or even nonconvex-nonconcave with additional regularity conditions. In particular, this diversity complicates the tuning of step-sizes since even verifying convexity (or concavi…