arXiv subjects

Donghyun Na

Publications and source records attributed to Donghyun Na.

5 recordsLinked to original sources

arXiv2025

Tunable, phase-locked hard X-ray pulse sequences generated by a free-electron laser

The ability to arbitrarily dial in amplitudes and phases enables the fundamental quantum state operations pioneered for microwaves and then infrared and visible wavelengths during the second half of the last century. Self-seeded X-ray free-electron lasers (FELs) routinely generate coherent, high-brightness, and ultrafast pulses for a wide range of experiments, but have so far not achieved a comparable level of amplitude and phase control. Here we report the first tunable phase-locked, ultra-fast hard X-ray (PHLUX) pulses by implementing a recently proposed method: A fresh-bunch self-seeded FEL, driven by an electron beam that was shaped with a slotted foil and a corrugated wakefield structure, generates coherent radiation that is intensity-modulated on the femtosecond time scale. We measure phase-locked (to within a shot-to-shot phase jitter corresponding to 0.1 attoseconds) pulse triplets with a photon energy of 9.7 keV, a pulse energy of several tens of microjoules, a freely tunable relative phase, and a pulse delay tunability between 4.5 and 11.9 fs. Such pulse sequences are suitable for a wide range of applications, including coherent spectroscopy, and have amplitudes sufficient to enable hard X-ray quantum optics experiments. More generally, these results represent an important step towards a hard X-ray arbitrary waveform generator.

physics.acc-ph↗

arXiv2024

SwitchLight: Co-design of Physics-driven Architecture and Pre-training Framework for Human Portrait Relighting

We introduce a co-designed approach for human portrait relighting that combines a physics-guided architecture with a pre-training framework. Drawing on the Cook-Torrance reflectance model, we have meticulously configured the architecture design to precisely simulate light-surface interactions. Furthermore, to overcome the limitation of scarce high-quality lightstage data, we have developed a self-supervised pre-training strategy. This novel combination of accurate physical modeling and expanded training dataset establishes a new benchmark in relighting realism.

cs.CV↗

arXiv2022

Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks

While tasks could come with varying the number of instances and classes in realistic settings, the existing meta-learning approaches for few-shot classification assume that the number of instances per task and class is fixed. Due to such restriction, they learn to equally utilize the meta-knowledge across all the tasks, even when the number of instances per task and class largely varies. Moreover, they do not consider distributional difference in unseen tasks, on which the meta-knowledge may have less usefulness depending on the task relatedness. To overcome these limitations, we propose a novel meta-learning model that adaptively balances the effect of the meta-learning and task-specific learning within each task. Through the learning of the balancing variables, we can decide whether to obtain a solution by relying on the meta-knowledge or task-specific learning. We formulate this objective into a Bayesian inference framework and tackle it using variational inference. We validate our Bayesian Task-Adaptive Meta-Learning (Bayesian TAML) on multiple realistic task- and class-imbalanced datasets, on which it significantly outperforms existing meta-learning approaches. Further ablation study confirms the effectiveness of each balancing component and the Bayesian learning framework.

cs.LG↗

arXiv2019

Operation of PAL-XFEL LLRF

PAL-XFEL (Pohang Accelerator Laboratory X-ray Free Electron Laser) started RF conditioning in October 2015 and has been operating reliably for ~ 4 years. The machine's LLRF and SSA systems contributed to the stable operation of PAL-XFEL with over 99% availability. The LLRF and SSA systems showed some problems in rare cases. The delay caused by the problem is very small, but PAL-XFEL can stop working. Some issues have been identified and resolved. We want to share the experience.

physics.acc-ph↗

arXiv2019

Learning to Generalize to Unseen Tasks with Bilevel Optimization

Recent metric-based meta-learning approaches, which learn a metric space that generalizes well over combinatorial number of different classification tasks sampled from a task distribution, have been shown to be effective for few-shot classification tasks of unseen classes. They are often trained with episodic training where they iteratively train a common metric space that reduces distance between the class representatives and instances belonging to each class, over large number of episodes with random classes. However, this training is limited in that while the main target is the generalization to the classification of unseen classes during training, there is no explicit consideration of generalization during meta-training phase. To tackle this issue, we propose a simple yet effective meta-learning framework for metricbased approaches, which we refer to as learning to generalize (L2G), that explicitly constrains the learning on a sampled classification task to reduce the classification error on a randomly sampled unseen classification task with a bilevel optimization scheme. This explicit learning aimed toward generalization allows the model to obtain a metric that separates well between unseen classes. We validate our L2G framework on mini-ImageNet and tiered-ImageNet datasets with two base meta-learning few-shot classification models, Prototypical Networks and Relation Networks. The results show that L2G significantly improves the performance of the two methods over episodic training. Further visualization shows that L2G obtains a metric space that clusters and separates unseen classes well.

cs.LG↗